Ravens: Breshad Perriman suffered partial left knee ACL tear - Schefter, Hensley; may need surgery, will get 2nd opinion (ESPN)

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NSA wants to Exploit Internet of Things and Biomedical Devices

The cyber attack vectors available to hackers will continue to grow as the Internet of Things (IoTs) become more commonplace, making valuable data accessible through an ever-widening selection of entry points. Although it's not the hackers alone, the NSA is also behind the Internet of Things. We already know the United States National Security Agency's (NSA) power to spy on American as well

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Anonymous EP

The second helping of the toxic elixir we call The Stagma comes in the form of the 2-track debut EP from Fallen. ANONYMOUS is a story of rebellion, ...

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Anonymous bidder pays $3.46 million for Warren Buffett lunch

June 11, 2016. By Jonathan Stempel and Joseph Ax. (Reuters) – An anonymous bidder agreed at auction to pay $3,456,789 to eat lunch with Warren ...

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Anonymous bidder wins $3456789 lunch with Warren Buffett

The winning bid for an annual charity lunch with the Oracle of Omaha came in from an anonymous bidder at $3,456,789 -- the same record set in 2012 ...

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Anonymous to Thomas Jefferson, April 18, 1803

Title: Anonymous to Thomas Jefferson, April 18, 1803; Created / Published: 1803-04-18; Genre: Correspondence; Call Number/Physical Location ...

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Lichess.org

Anonymous, 4+8, Amistosa. Anonymous, 6+0, Amistosa. Anonymous, 10+0, Amistosa. Anonymous, 11+7, Amistosa. Anonymous, 5+2, Amistosa.

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NGC 6888: The Crescent Nebula

NGC 6888, also known as the Crescent Nebula, is a cosmic bubble about 25 light-years across, blown by winds from its central, bright, massive star. This sharp telescopic portrait uses narrow band image data that isolates light from hydrogen and oxygen atoms in the wind-blown nebula. The oxygen atoms produce the blue-green hue that seems to enshroud the detailed folds and filaments. Visible within the nebula, NGC 6888's central star is classified as a Wolf-Rayet star (WR 136). The star is shedding its outer envelope in a strong stellar wind, ejecting the equivalent of the Sun's mass every 10,000 years. The nebula's complex structures are likely the result of this strong wind interacting with material ejected in an earlier phase. Burning fuel at a prodigious rate and near the end of its stellar life this star should ultimately go out with a bang in a spectacular supernova explosion. Found in the nebula rich constellation Cygnus, NGC 6888 is about 5,000 light-years away. via NASA http://ift.tt/1tgw3hK

Orioles select NC State 1B Preson Palmeiro in MLB draft's 7th round (No. 211); son of Rafael Palmeiro (569 career HRs) (ESPN)

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Anonymous Hamburger Society

It's big, it's saucy and we love it as much as we love PASTA. #ahs #comingsoon — http://ift.tt/1XewAgS

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anonymous user see login form when post a video

I enabled the permission for anonymous user to : 1: Use the media browser 2: Add media from remote services But when anonymous user click the ...

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Concerto for Flute, 2 Violins and Basso (Anonymous)

Work Title, Concerto para Flauta, dos Violines y Bajo. Alternative Title, Concerto for Flute, 2 Violins and Continuo ...

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I have a new follower on Twitter

Tomasz Motyl
Chief Innovation Officer at @AliorBankSA, @AliorLab. #innovation, #banking, #FinTech, #DisruptiveInnovation https://t.co/3wXPpn5qpo
Poland, Krakow
https://t.co/TI69ClDOlc
Following: 2621 - Followers: 2702

June 10, 2016 at 02:09AM via Twitter http://twitter.com/tomekmot

Pluto at Night

The night side of Pluto spans this shadowy scene. The spacebased view with the Sun behind the distant world was captured by New Horizons last July. The spacecraft was at a range of over 21,000 kilometers, about 19 minutes after its closest approach. A denizen of the Kuiper Belt in dramatic silhouette, the image also reveals Pluto's tenuous, surprisingly complex layers of hazy atmosphere. The crescent twilight landscape near the top of the frame includes southern areas of nitrogen ice plains informally known as Sputnik Planum and rugged mountains of water-ice in the Norgay Montes. via NASA http://ift.tt/1OeuUkE

Anonymous user issues uploading files since the release of Drupal 7.43

http://ift.tt/1R1ZQ2t. The issue appears to be that because we set the new file status to '0', Drupal cuts off anonymous ...

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I have a new follower on Twitter

Tri-Force Consulting
IT Experts with Excellence, Integrity and Customization, Three forces above the competition

http://t.co/Am3ZVMhK5U
Following: 1360 - Followers: 1382

June 09, 2016 at 11:25PM via Twitter http://twitter.com/triforceinc

Orioles Video: Chris Davis belts game-tying HR, adds sac fly in 9th inning of 6-5 victory over the Blue Jays (ESPN)

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MLB Draft: Orioles select Illinois RHP Cody Sedlock No. 27 overall; full player info in the ESPN App (ESPN)

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A Modified Vortex Search Algorithm for Numerical Function Optimization. (arXiv:1606.02710v1 [cs.AI])

The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.

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Theoretical Robopsychology: Samu Has Learned Turing Machines. (arXiv:1606.02767v1 [cs.AI])

From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing machines. Alan Turing had given an algorithm (aka the Turing Machine) to describe algorithms. If it has been applied to describe itself then this brings us to Turing's notion of the universal machine. In the present paper, we investigate algorithms to write algorithms. From a pedagogy point of view, this way of writing programs can be considered as a combination of learning by listening and learning by doing due to it is based on applying agent technology and machine learning. As the main result we introduce the problem of learning and then we show that it cannot easily be handled in reality therefore it is reasonable to use machine learning algorithm for learning Turing machines.

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Face valuing: Training user interfaces with facial expressions and reinforcement learning. (arXiv:1606.02807v1 [cs.HC])

An important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn about their human users' intentions and adapt to their preferences. In most current research, a user has conveyed preferences to a machine using explicit corrective or instructive feedback; explicit feedback imposes a cognitive load on the user and is expensive in terms of human effort. The primary objective of the current work is to demonstrate that a learning agent can reduce the amount of explicit feedback required for adapting to the user's preferences pertaining to a task by learning to perceive a value of its behavior from the human user, particularly from the user's facial expressions---we call this face valuing. We empirically evaluate face valuing on a grip selection task. Our preliminary results suggest that an agent can quickly adapt to a user's changing preferences with minimal explicit feedback by learning a value function that maps facial features extracted from a camera image to expected future reward. We believe that an agent learning to perceive a value from the body language of its human user is complementary to existing interactive machine learning approaches and will help in creating successful human-machine interactive applications.

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Arbitrage-Free Combinatorial Market Making via Integer Programming. (arXiv:1606.02825v1 [cs.GT])

We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs. Although the problem of arbitrage-free pricing, while maintaining a bound on the subsidy provided by the market maker, is #P-hard in the worst case, we posit that the typical case might be amenable to modern integer programming (IP) solvers. At the crux of our method is the Frank-Wolfe (conditional gradient) algorithm which is used to implement a Bregman projection aligned with the market maker's cost function, using an IP solver as an oracle. We demonstrate the tractability and improved accuracy of our approach on real-world prediction market data from combinatorial bets placed on the 2010 NCAA Men's Division I Basketball Tournament, where the outcome space is of size $2^{63}$. To our knowledge, this is the first implementation and empirical evaluation of an arbitrage-free combinatorial prediction market on this scale.

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e-Commerce product classification: our participation at cDiscount 2015 challenge. (arXiv:1606.02854v1 [cs.LG])

This report describes our participation in the cDiscount 2015 challenge where the goal was to classify product items in a predefined taxonomy of products. Our best submission yielded an accuracy score of 64.20\% in the private part of the leaderboard and we were ranked 10th out of 175 participating teams. We followed a text classification approach employing mainly linear models. The final solution was a weighted voting system which combined a variety of trained models.

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A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task. (arXiv:1606.02858v1 [cs.CL])

Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 72.4% and 75.8% on these two datasets, exceeding current state-of-the-art results by over 5% and approaching what we believe is the ceiling for performance on this task.

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Understanding User Instructions by Utilizing Open Knowledge for Service Robots. (arXiv:1606.02877v1 [cs.RO])

Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., `take', `get', `put'). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with robots' primitive actions. To address this, we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans. Specifically, we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot's action model. To verify and evaluate our approach, we implemented a prototype system using knowledge from several open resources. Experiments on our system confirmed the correctness and efficiency of our algorithms. Notably, our system has been deployed in the KeJia robot, which participated the annual RoboCup@Home competitions in the past three years and achieved encouragingly high scores in the benchmark tests.

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A Cognitive Architecture for the Implementation of Emotions in Computing Systems. (arXiv:1606.02899v1 [cs.AI])

In this paper we present a new neurobiologically-inspired affective cognitive architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture). The objective of NEUCOGAR is the identification of a mapping from the influence of serotonin, dopamine and noradrenaline to the computing processes based on Von Neuman's architecture, in order to implement affective phenomena which can operate on the Turing's machine model. As basis of the modeling we use and extend the L\"ovheim Cube of Emotion with parameters of the Von Neumann architecture. Validation is conducted via simulation on a computing system of dopamine neuromodulation and its effects on the Cortex. In the experimental phase of the project, the increase of computing power and storage redistribution due to emotion stimulus modulated by the dopamine system, confirmed the soundness of the model.

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Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs). (arXiv:1606.02979v1 [cs.CL])

Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two types of patterns are complementary. In this paper, we propose a generative topic embedding model to combine the two types of patterns. In our model, topics are represented by embedding vectors, and are shared across documents. The probability of each word is influenced by both its local context and its topic. A variational inference method yields the topic embeddings as well as the topic mixing proportions for each document. Jointly they represent the document in a low-dimensional continuous space. In two document classification tasks, our method performs better than eight existing methods, with fewer features. In addition, we illustrate with an example that our method can generate coherent topics even based on only one document.

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MuFuRU: The Multi-Function Recurrent Unit. (arXiv:1606.03002v1 [cs.NE])

Recurrent neural networks such as the GRU and LSTM found wide adoption in natural language processing and achieve state-of-the-art results for many tasks. These models are characterized by a memory state that can be written to and read from by applying gated composition operations to the current input and the previous state. However, they only cover a small subset of potentially useful compositions. We propose Multi-Function Recurrent Units (MuFuRUs) that allow for arbitrary differentiable functions as composition operations. Furthermore, MuFuRUs allow for an input- and state-dependent choice of these composition operations that is learned. Our experiments demonstrate that the additional functionality helps in different sequence modeling tasks, including the evaluation of propositional logic formulae, language modeling and sentiment analysis.

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The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems. (arXiv:1606.03044v1 [cs.SD])

Building systems that possess the sensitivity and intelligence to identify and describe high-level attributes in music audio signals continues to be an elusive goal, but one that surely has broad and deep implications for a wide variety of applications. Hundreds of papers have so far been published toward this goal, and great progress appears to have been made. Some systems produce remarkable accuracies at recognising high-level semantic concepts, such as music style, genre and mood. However, it might be that these numbers do not mean what they seem. In this paper, we take a state-of-the-art music content analysis system and investigate what causes it to achieve exceptionally high performance in a benchmark music audio dataset. We dissect the system to understand its operation, determine its sensitivities and limitations, and predict the kinds of knowledge it could and could not possess about music. We perform a series of experiments to illuminate what the system has actually learned to do, and to what extent it is performing the intended music listening task. Our results demonstrate how the initial manifestation of music intelligence in this state-of-the-art can be deceptive. Our work provides constructive directions toward developing music content analysis systems that can address the music information and creation needs of real-world users.

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Context-Aware Bandits. (arXiv:1510.03164v3 [cs.LG] UPDATED)

We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects. CAB can be easily deployed in a real-world recommendation system, where multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering augmented by exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We give a theoretical analysis in the standard stochastic multi-armed bandits setting. We show the efficiency of our approach on production and real-world datasets, demonstrate the scalability, and, more importantly, the significant increased prediction performance against several state-of-the-art methods.

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Building and Measuring Privacy-Preserving Predictive Blacklists. (arXiv:1512.04114v3 [cs.CR] UPDATED)

Collaborative approaches to network defense are increasingly used to predict attacks as well as to speed up their detection. For instance, with highly predictive blacklisting, one aims to forecast attack sources based on alerts contributed by multiple organizations. While collaboration helps discover groups of correlated attacks targeting similar victims, it also raises important privacy concerns.

To address this challenge, we introduce a novel privacy-friendly system whereby organizations are clustered together based on the similarity of their logs, without disclosing them in the clear. Entities in the same cluster only share relevant logs and build more accurate blacklists. At the same time, we investigate how to measure the effect of collaboration on prediction and find that the state-of-the-art (non privacy-preserving) system actually achieves lower accuracy than if organizations predicted based on local alerts only. Our experiments shed light on how to improve the quality of predictions by optimizing information shared across organizations, showing that our privacy-friendly methods markedly outperform non private tools both in terms of precision and recall.

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Decoy Bandits Dueling on a Poset. (arXiv:1602.02706v2 [cs.LG] UPDATED)

We adress the problem of dueling bandits defined on partially ordered sets, or posets. In this setting, arms may not be comparable, and there may be several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms of any poset even when pairs of comparable arms cannot be distinguished from pairs of incomparable arms, with a set of minimal assumptions. This algorithm relies on the concept of decoys, which stems from social psychology. For the easier case where the incomparability information may be accessible, we propose a second algorithm, SlicingBandits, which takes advantage of this information and achieves a very significant gain of performance compared to UnchainedBandits. We provide theoretical guarantees and experimental evaluation for both algorithms.

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Filter based Taxonomy Modification for Improving Hierarchical Classification. (arXiv:1603.00772v2 [cs.AI] UPDATED)

Large scale classification of data organized as a hierarchy of classes has received significant attention in the literature. Top-Down (TD) Hierarchical Classification (HC), which exploits the hierarchical structure during the learning process is an effective method for dealing with problems at scale due to its computational benefits. However, its accuracy suffers due to error propagation i.e., prediction errors made at higher levels in the hierarchy cannot be corrected at lower levels. One of the main reasons behind errors at the higher levels is the presence of inconsistent nodes and links that are introduced due to the arbitrary process of creating these hierarchies by domain experts. In this paper, we propose two efficient data driven filter based approaches for hierarchical structure modification: (i) Flattening (local and global) approach that identifies and removes inconsistent nodes present within the hierarchy and (ii) Rewiring approach modifies parent-child relationships to improve the classification performance of learned models. Our extensive empirical evaluation of the proposed approaches on several image and text datasets shows improved performance over competing approaches.

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Generating Natural Questions About an Image. (arXiv:1603.06059v3 [cs.CL] UPDATED)

There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have focused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image. In this paper, we introduce the novel task of Visual Question Generation (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, with considerably more abstract training data than provided to state-of-the-art captioning systems thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics. Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision & language.

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On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. (arXiv:1603.07294v2 [cs.LG] UPDATED)

Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample (OPS) approach elegantly provides privacy "for free," it is data inefficient in the sense of asymptotic relative efficiency (ARE). We show that a simple alternative based on the Laplace mechanism, the workhorse of differential privacy, is as asymptotically efficient as non-private posterior inference, under general assumptions. This technique also has practical advantages including efficient use of the privacy budget for MCMC. We demonstrate the practicality of our approach on a time-series analysis of sensitive military records from the Afghanistan and Iraq wars disclosed by the Wikileaks organization.

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Audio Event Detection using Weakly Labeled Data. (arXiv:1605.02401v2 [cs.SD] UPDATED)

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for online multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data based on a Multiple Instance Learning (MIL) framework. We first show that audio event detection using weak data can be formulated as an MIL problem. We then suggest two frameworks for solving multiple-instance learning, one based on neural networks, and the second on support vector machines. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Our proposed framework can not only successfully detect events in a recording but can also provide temporal locations of events in the recording. This is interesting as these information were never known in the first place for weakly labeled data.

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Unsupervised Learning for Physical Interaction through Video Prediction. (arXiv:1605.07157v3 [cs.LG] UPDATED)

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 50,000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot's future actions amounts to learning a "visual imagination" of different futures based on different courses of action. Our experiments show that our proposed method not only produces more accurate video predictions, but also more accurately predicts object motion, when compared to prior methods.

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MLB: Orioles 3B Manny Machado suspended 4 games, fined $2,500 by league for role in bench-clearing brawl - reports (ESPN)

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Atixnet Anonymous Edit

This module add a new field type, 'Anonymous Edit Link', that you can add on a content type to manage token buy content for any user to edit or delete ...

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ISS Daily Summary Report – 06/08/16

Cognition Test Battery Operations: The crew completed pre-test questions and a battery of cognitive tests with performance feedback. Individualized Real-Time Neurocognitive Assessment Toolkit for Space Flight Fatigue (Cognition) is a battery of tests that measure how spaceflight-related physical changes, such as microgravity and lack of sleep, can affect cognitive performance. Cognition includes ten brief computerized tests that cover a wide range of cognitive functions, and provides immediate feedback on current and past test results. The software allows for real-time measurement of cognitive performance while in space.   Human Research Program (HRP) Blood and Urine Collection: The crew collected blood and urine samples and stowed them into Minus Eighty-degree Freezer for ISS (MELF).  These sample collections will be used to support the following HRP investigations: Biochem Profile tests blood and urine samples obtained from astronauts before, during and after spaceflight. Specific proteins and chemicals in the samples are used as biomarkers, or indicators of health. Post-flight analysis yields a database of samples and test results which scientists can use to study the effects of spaceflight on the body. Repository is a storage bank to maintain biological specimens over extended periods of time and under well-controlled conditions. Biosamples are archived for use as a resource for future space flight related research. Cardio Ox determines whether biological markers of oxidative and inflammatory stress are elevated during and after space flight and whether this results in an increased, long-term risk of atherosclerosis in astronauts.   Packed Bed Reactor Experiment (PBRE) Lab Video Setup: The crew set up a video Camcorder for over-the-shoulder view of the Microgravity Science Glovebox (MSG) high definition (HD) Monitor for the ground to view the water separator. The PBRE will be used to study the behavior of gases and liquids when they flow simultaneously through a column filled with fixed porous media. The porous media or “packing” can be made of different shapes and materials and are used widely in chemical engineering to enhance the contact between two immiscible fluid phases (e.g., liquid-gas, water-oil). Packed columns can serve as reactors, scrubbers, strippers, etc. in systems where efficient interphase contact is desired, both on Earth and in space.   Marrow Setup: In preparation for the Marrow Breath and Ambient Air Sample operations planned for tomorrow, today the crew prepared and temp stowed the Marrow air sample collection hardware in the crew quarters. The hardware will be used to take breath as well as ambient air samples immediately after crew wake tomorrow. The Marrow investigation studies the effect of microgravity on human bone marrow. It is believed that microgravity, like long-duration bed rest on Earth, has a negative effect on the bone marrow and the blood cells that are produced in the marrow. The extent of this effect, and its recovery, are of interest to space research and healthcare providers on Earth.   Dose Tracker: The crew completed entries for medication tracking. This investigation documents the medication usage of crew members before and during their missions by capturing data regarding medication use during spaceflight, including side effect qualities, frequencies and severities. The data is expected to either support or counter anecdotal evidence of medication ineffectiveness during flight and unusual side effects experienced during flight. It is also expected that specific, near-real-time questioning about symptom relief and side effects will provide the data required to establish whether spaceflight-associated alterations in pharmacokinetics (PK) or pharmacodynamics (PD) is occurring during missions.   Fine Motor Skills: A series of interactive tasks on a touchscreen tablet were completed for the Fine Motor Skills investigation.  This investigation is the first fine motor skills study to measure long-term microgravity exposure, different phases of microgravity adaptation, and sensorimotor recovery after returning to Earth gravity.   Bigelow Expandable Aerospace Module (BEAM) Sensor Installation: Sensor installation Parts A and B were completed yesterday. Today the crew assembled and tested the Distributed Impact Detection System (DIDS) Kits C and D, then assembled and affixed Wireless Temperature system (WTS) Kits C and D. They also deployed six Radiation Area Monitors (RAMs) throughout BEAM and photo documented each deployment location.   ISS Reboost: At 9:00 AM CDT today, the ISS performed a reboost using 63P R&D thrusters which set up for 45S landing and 47S 34-orbit rendezvous. Delta-V was 0.45 meters/second with a burn duration of 3 minutes, 59 seconds.   Node 1/ Airlock VAP modification:  The Node 1/ Airlock Vacuum Access Panel which was installed last week failed an 8 hour leak check overnight.  The crew checked connections and the leak check is being repeated tonight.   Crew Departure Preparations: The 45S crew packed personal items to be returned, and trashed unwanted items in preparation for return to Earth on June 18. They also had a conference with Russian specialists.   Today’s Planned Activities All activities were completed unless otherwise noted. KORREKTSIYA. Logging in fluids and food (medications) intake / r/g 2448 FINEMOTR. Assistance during the Experiment Filling in (Separation) of EDV (KOV) for Elektron or EDV-SV DOSETRK. Data Entry HRF. Urine Samples Collection ARED Footplate Partial Fold BEAM. Hatch Removal and Ingress Filling in Section 2 of DC1 Progress 431 Supply System with Nitrogen Preparation of Hardware for Return and Disposal by Soyuz 719 / r/g 2374, 2446 Air Samples Collection with [АК-1М] Samler during BEAM Ingress  r/g 2442, 2467 HRF. Samples Insertion into MELFI СОЖ Maintenance VCA1 Camcorder Setup Gathering Tools to Install VDPU Harness BEAM. Sensors and Internal Cables Assembly. Part С Rotation of ETC Rack Down On MCC Go Collection of Atmospheric Moisture Condensate [КАВ] from [СРВ-К2М] r/g 2477 Support of the Rotation of ETC Rack Down Installation of VDPU Harness Acoustic Dosimeters Installation, Day 2 Closing of the Shutters of Windows #6, 8, 9, 12, 13,14 r/g 6965 BIOCARD. Experiment Operations r/g 1907 Closing of the Shutters of the USOS Window Leak Check of Soyuz 719 Sokol Spacesuits BEAM. Sensors and Internal Cables Assembly. Part D Preparation for the Replacement of Unit 800А of SM Battery On MCC Go Urine Transfer from ЕДВ-У into Rodnik БВ1 Tank of […]

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Microsoft creates its own FreeBSD VM Image for Azure Cloud Computing Platform

This year, Microsoft impressed the world with 'Microsoft loves Linux' announcements, like developing a custom Linux-based OS for running Azure Cloud Switch, selecting Ubuntu as the operating system for its Cloud-based Big Data services and bringing the popular Bash shell to Windows 10. Now, the next big news for open-source community: Microsoft has released its own custom distribution of

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Warning! 32 Million Twitter Passwords May Have Been Hacked and Leaked

The world came to know about massive data breaches in some of the most popular social media websites including LinkedIn, MySpace, Tumblr, Fling, and VK.com when an unknown Russian hacker published the data dumps for sale on the underground black marketplace. However, these are only data breaches that have been publicly disclosed by the hacker. I wonder how much more stolen data sets this

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ANONYMOUS

Report Content. Your report has been submitted. Close. Home · Explore · CodeBug; ANONYMOUS. ANONYMOUS. Remix; Share; 0; 0. × ...

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The Horsehead Nebula in Infrared from Hubble

While drifting through the cosmos, a magnificent interstellar dust cloud became sculpted by stellar winds and radiation to assume a recognizable shape. Fittingly named the Horsehead Nebula, it is embedded in the vast and complex Orion Nebula (M42). A potentially rewarding but difficult object to view personally with a small telescope, the above gorgeously detailed image was taken in 2013 in infrared light by the orbiting Hubble Space Telescope in honor of the 23rd anniversary of Hubble's launch. The dark molecular cloud, roughly 1,500 light years distant, is cataloged as Barnard 33 and is seen above primarily because it is backlit by the nearby massive star Sigma Orionis. The Horsehead Nebula will slowly shift its apparent shape over the next few million years and will eventually be destroyed by the high energy starlight. via NASA http://ift.tt/1WEJ1T7

Orioles Video: Chris Tillman strikes out 9 over 7.1 innings in 4-0 victory over Royals to complete sweep (ESPN)

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All-Europe Young People in Alcoholics Anonymous Convention 2016

All Events › All-Europe Young People in Alcoholics Anonymous Convention 2016. All-Europe Young People in Alcoholics Anonymous Convention ...

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Active Long Term Memory Networks. (arXiv:1606.02355v1 [cs.LG])

Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially. This paper introduces the Active Long Term Memory Networks (A-LTM), a model of sequential multi-task deep learning that is able to maintain previously learned association between sensory input and behavioral output while acquiring knew knowledge. A-LTM exploits the non-convex nature of deep neural networks and actively maintains knowledge of previously learned, inactive tasks using a distillation loss. Distortions of the learned input-output map are penalized but hidden layers are free to transverse towards new local optima that are more favorable for the multi-task objective. We re-frame the McClelland's seminal Hippocampal theory with respect to Catastrophic Inference (CI) behavior exhibited by modern deep architectures trained with back-propagation and inhomogeneous sampling of latent factors across epochs. We present empirical results of non-trivial CI during continual learning in Deep Linear Networks trained on the same task, in Convolutional Neural Networks when the task shifts from predicting semantic to graphical factors and during domain adaptation from simple to complex environments. We present results of the A-LTM model's ability to maintain viewpoint recognition learned in the highly controlled iLab-20M dataset with 10 object categories and 88 camera viewpoints, while adapting to the unstructured domain of Imagenet with 1,000 object categories.

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SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks. (arXiv:1606.02378v1 [cs.LG])

We introduce SE3-Nets, which are deep networks designed to model rigid body motion from raw point cloud data. Based only on pairs of depth images along with an action vector and point wise data associations, SE3-Nets learn to segment effected object parts and predict their motion resulting from the applied force. Rather than learning point wise flow vectors, SE3-Nets predict SE3 transformations for different parts of the scene. Using simulated depth data of a table top scene and a robot manipulator, we show that the structure underlying SE3-Nets enables them to generate a far more consistent prediction of object motion than traditional flow based networks.

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Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation. (arXiv:1606.02382v1 [cs.CV])

Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN framework initially designed for electron microscope image segmentation. We hope that by sharing our volumetric vasculature datasets, we will inspire other researchers to experiment with vasculature dataset and improve the used network architectures.

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Deep Successor Reinforcement Learning. (arXiv:1606.02396v1 [stat.ML])

Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternative, called Successor Representations (SR), which decomposes the value function into two components -- a reward predictor and a successor map. The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards. The value function of a state can be computed as the inner product between the successor map and the reward weights. In this paper, we present DSR, which generalizes SR within an end-to-end deep reinforcement learning framework. DSR has several appealing properties including: increased sensitivity to distal reward changes due to factorization of reward and world dynamics, and the ability to extract bottleneck states (subgoals) given successor maps trained under a random policy. We show the efficacy of our approach on two diverse environments given raw pixel observations -- simple grid-world domains (MazeBase) and the Doom game engine.

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Structured Convolution Matrices for Energy-efficient Deep learning. (arXiv:1606.02407v1 [cs.NE])

We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrices and achieve state-of-the-art trade-off between energy efficiency and classification accuracy for well-known image recognition tasks. We also put forward a novel method to train binary convolutional networks by utilising an existing connection between noisy-rectified linear units and binary activations.

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Learning Language Games through Interaction. (arXiv:1606.02447v1 [cs.CL])

We introduce a new language learning setting relevant to building adaptive natural language interfaces. It is inspired by Wittgenstein's language games: a human wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., removing all red blocks). The computer initially knows nothing about language and therefore must learn it from scratch through interaction, while the human adapts to the computer's capabilities. We created a game in a blocks world and collected interactions from 100 people playing it. First, we analyze the humans' strategies, showing that using compositionality and avoiding synonyms correlates positively with task performance. Second, we compare computer strategies, showing how to quickly learn a semantic parsing model from scratch, and that modeling pragmatics further accelerates learning for successful players.

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Exploring Implicit Human Responses to Robot Mistakes in a Learning from Demonstration Task. (arXiv:1606.02485v1 [cs.RO])

As robots enter human environments, they will be expected to accomplish a tremendous range of tasks. It is not feasible for robot designers to pre-program these behaviors or know them in advance, so one way to address this is through end-user programming, such as via learning from demonstration (LfD). While significant work has been done on the mechanics of enabling robot learning from human teachers, one unexplored aspect is enabling mutual feedback between both the human teacher and robot during the learning process, i.e., implicit learning. In this paper, we explore one aspect of this mutual understanding, grounding sequences, where both a human and robot provide non-verbal feedback to signify their mutual understanding during interaction. We conducted a study where people taught an autonomous humanoid robot a dance, and performed gesture analysis to measure people's responses to the robot during correct and incorrect demonstrations.

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Symbolic Music Data Version 1.0. (arXiv:1606.02542v1 [cs.SD])

In this document, we introduce a new dataset designed for training machine learning models of symbolic music data. Five datasets are provided, one of which is from a newly collected corpus of 20K midi files. We describe our preprocessing and cleaning pipeline, which includes the exclusion of a number of files based on scores from a previously developed probabilistic machine learning model. We also define training, testing and validation splits for the new dataset, based on a clustering scheme which we also describe. Some simple histograms are included.

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DISCO Nets: DISsimilarity COefficient Networks. (arXiv:1606.02556v1 [cs.CV])

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.

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Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning. (arXiv:1606.02560v1 [cs.AI])

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.

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DialPort: Connecting the Spoken Dialog Research Community to Real User Data. (arXiv:1606.02562v1 [cs.AI])

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users. We introduce a distributed, multi-modal, multi-agent prototype dialog framework that affords easy integration with various remote resources, ranging from end-to-end dialog systems to external knowledge APIs. To date, the DialPort portal has successfully connected to the multi-domain spoken dialog system at Cambridge University, the NOAA (National Oceanic and Atmospheric Administration) weather API and the Yelp API.

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Convolution by Evolution: Differentiable Pattern Producing Networks. (arXiv:1606.02580v1 [cs.NE])

In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution.

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The Dark Side of Ethical Robots. (arXiv:1606.02583v1 [cs.RO])

Concerns over the risks associated with advances in Artificial Intelligence have prompted calls for greater efforts toward robust and beneficial AI, including machine ethics. Recently, roboticists have responded by initiating the development of so-called ethical robots. These robots would, ideally, evaluate the consequences of their actions and morally justify their choices. This emerging field promises to develop extensively over the next years. However, in this paper, we point out an inherent limitation of the emerging field of ethical robots. We show that building ethical robots also necessarily facilitates the construction of unethical robots. In three experiments, we show that it is remarkably easy to modify an ethical robot so that it behaves competitively, or even aggressively. The reason for this is that the specific AI, required to make an ethical robot, can always be exploited to make unethical robots. Hence, the development of ethical robots will not guarantee the responsible deployment of AI. While advocating for ethical robots, we conclude that preventing the misuse of robots is beyond the scope of engineering, and requires instead governance frameworks underpinned by legislation. Without this, the development of ethical robots will serve to increase the risks of robotic malpractice instead of diminishing it.

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Simplified Boardgames. (arXiv:1606.02645v1 [cs.AI])

We formalize Simplified Boardgames language, which describes a subclass of arbitrary board games. The language structure is based on the regular expressions, which makes the rules easily machine-processable while keeping the rules concise and fairly human-readable.

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Safe and Efficient Off-Policy Reinforcement Learning. (arXiv:1606.02647v1 [cs.LG])

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) low variance; (2) safety, as it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) efficiency, as it makes the best use of samples collected from near on-policy behaviour policies. We analyse the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. To our knowledge, this is the first return-based off-policy control algorithm converging a.s. to $Q^*$ without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q($\lambda$), which was still an open problem. We illustrate the benefits of Retrace($\lambda$) on a standard suite of Atari 2600 games.

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Deep Reinforcement Learning with a Natural Language Action Space. (arXiv:1511.04636v5 [cs.AI] UPDATED)

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to approximate the Q-function in reinforcement learning. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.

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Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning. (arXiv:1511.06728v2 [cs.CV] UPDATED)

We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.

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Iterative Hierarchical Optimization for Misspecified Problems (IHOMP). (arXiv:1602.03348v2 [cs.LG] UPDATED)

For complex, high-dimensional Markov Decision Processes (MDPs), it may be necessary to represent the policy with function approximation. A problem is misspecified whenever, the representation cannot express any policy with acceptable performance. We introduce IHOMP : an approach for solving misspecified problems. IHOMP iteratively learns a set of context specialized options and combines these options to solve an otherwise misspecified problem. Our main contribution is proving that IHOMP enjoys theoretical convergence guarantees. In addition, we extend IHOMP to exploit Option Interruption (OI) enabling it to decide where the learned options can be reused. Our experiments demonstrate that IHOMP can find near-optimal solutions to otherwise misspecified problems and that OI can further improve the solutions.

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Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. (arXiv:1602.07032v2 [cs.AI] UPDATED)

In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure. This plays a crucial role in the design of educational software, leading to a trade-off between teaching new material and reviewing what has already been taught. A common way to balance this trade-off is spaced repetition, which uses periodic review of content to improve long-term retention. Though spaced repetition is widely used in practice, e.g., in electronic flashcard software, there is little formal understanding of the design of these systems. Our paper addresses this gap in three ways. First, we mine log data from spaced repetition software to establish the functional dependence of retention on reinforcement and delay. Second, we use this memory model to develop a stochastic model for spaced repetition systems. We propose a queueing network model of the Leitner system for reviewing flashcards, along with a heuristic approximation that admits a tractable optimization problem for review scheduling. Finally, we empirically evaluate our queueing model through a Mechanical Turk experiment, verifying a key qualitative prediction of our model: the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions.

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Incorporating Copying Mechanism in Sequence-to-Sequence Learning. (arXiv:1603.06393v3 [cs.CL] UPDATED)

We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or even long phrases in conversation. The challenge with regard to copying in Seq2Seq is that new machinery is needed to decide when to perform the operation. In this paper, we incorporate copying into neural network-based Seq2Seq learning and propose a new model called CopyNet with encoder-decoder structure. CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Our empirical study on both synthetic data sets and real world data sets demonstrates the efficacy of CopyNet. For example, CopyNet can outperform regular RNN-based model with remarkable margins on text summarization tasks.

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Classifying Options for Deep Reinforcement Learning. (arXiv:1604.08153v2 [cs.LG] UPDATED)

In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities. We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.

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Learning Representations for Counterfactual Inference. (arXiv:1605.03661v2 [stat.ML] UPDATED)

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.

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Centenary receives $2 million anonymous estate gift

Centenary receives $2 million anonymous estate gift. (June 8, 2016). SHREVEPORT, LA — Centenary College has received a generous $2 million ...

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Ravens: Timmy Jernigan says he's \\"going to be the bigger person\\" in dustup with Warren Sapp over switching to his No. 99 (ESPN)

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Hino de Coroação de Nossa Senhora do Rosário (Anonymous)

Composer, Anonymous. Key, F major. Language, Portuguese. Piece Style, Early 20th century. Instrumentation, Voice and Organ ...

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I have a new follower on Twitter

Joel Comm
Keynote Speaker, NY Times Best-Selling author, Futurist and Social Influencer - For Sales/PR/Promo https://t.co/qcHBn8dS9x #DoGoodStuff - Life in Beta
Northern Colorado
https://t.co/9VVUt1FbMs
Following: 413374 - Followers: 490094

June 08, 2016 at 01:26PM via Twitter http://twitter.com/joelcomm

Hosting Migrate JS throws anonymous function error

When running a migrate task a JS error appears in the console warning about anonymous functions. The contents of hosting_migrate.js is not ...

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[FD] SQL Injection Vulnerabilities found in European Commisssion & European Parliament

Press|News: (SecurityWeek) Researchers have discovered several SQL injection vulnerabilities in the websites of the European Parliament and the European Commission — both hosted on the official domain of the European Union (europa.eu). URL: http://ift.tt/1XEn735

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Stanford rape victim's statement on remaining anonymous illustrates her fortitude, general heroism.

Throughout her case against her rapist Brock Turner, the now 23-year-old victim has managed to keep her identity hidden. After you read her ...

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μTorrent Forum Hacked; Change your Password Immediately

If you are a torrent lover and have registered on μTorrent community forum website, then you may have had your personal details compromised, along with your hashed passwords. The μTorrent team has announced that its community forums have been hacked, which exposed private information of hundreds of thousands of its users. As of now, μTorrent is the most visited BitTorrent client around the

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I have a new follower on Twitter

Charles Boicey
Chief Innovation Officer advancing healthcare with an affordable cloud, multi-tenant, Healthcare Analytics Ecosystem utilizing supported open source technology
Jacksonville Beach
https://t.co/8GOcQOlOSt
Following: 2236 - Followers: 2700

June 08, 2016 at 08:14AM via Twitter http://twitter.com/N2InformaticsRN

How to Run Android on your iPhone using this 3D Printed Phone Case

Hackers and geeks always tinkered with their devices, regardless of their operating system, and are always behind the ways to run Android on iPhone, iOS on Android phones, or Dual-Boot iOS and Android together in a single device. Though there are many solutions available on the Internet to solve these queries, but recently a hardware hacker has demonstrated a new way to run Android OS

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ISS Daily Summary Report – 06/07/16

Autonomous Mission Operations (AMO) Run 2: Following the successful completion of the first AMO experiment run last week, today the crew executed the second of three crew initiated experiment runs by using onboard AMO software to complete the autonomous deactivation and activation of an EXPRESS Rack (ER) 7 at Lab P2.  The AMO investigation tests advanced software and operational concepts to determine how crew members on the ISS can automate spacecraft systems with less involvement from the ground support staff.   Multi-Omics: The crew collected saliva samples and inserted them into a Box Module in Minus Eighty-degree Freezer for ISS (MELFI). The Multi-Omics analysis of human microbial-metabolic cross-talk in the space ecosystem evaluates the impacts of space environment and prebiotics on astronauts’ immune function by combining the data obtained from the measurements of changes in the gut microbiological composition, metabolites profiles, and the immune system.   Columbus Endcone Space Acceleration Measurement System (SAMS) Sensor Removal and Relocation:  The crew removed the SAMS Sensor Enclosure (SE) and the SAMS Adapter from Columbus Starboard Endcone and re-routed the SAMS cable from ER 3 to the European Physiology Module (EPM). SAMS is an ongoing study of the small forces (vibrations and accelerations) on the ISS resulting from the operation of hardware, crew activities, docking and maneuvering. Results generalize the types of vibrations affecting vibration-sensitive experiments. Investigators seek to better understand the vibration environment on the ISS.   Bigelow Expandable Aerospace Module (BEAM) Sensor Installation:  The Crew successfully completed Day 1 of BEAM sensor suite installation (impact and temperature sensors) as well as a tap test and photo documentation of the sensor and cable configuration.  Sensor installation will continue tomorrow.   Urine Processor Assembly (UPA): The UPA was shut down today due to erratic conductivity of the urine distillate. High conductivity levels caused the UPA to reprocess for an extended period of time as the Wastewater Storage Assembly Tank (WSTA) was approaching full.  The process run was terminated and the Waste and Hygiene Compartment (WHC) has been configured to internal EDV.  To expedite continued use of the UPA, the current concentration cycle will be ended early and both the Advanced Recycle Filter Tank Assembly (ARFTA) and the WSTA contents will be drained.  WHC will then be configured back to UPA to attempt a clean process cycle.   Mobile Servicing System (MSS) Operations: Overnight, Robotics Ground Controllers powered up the Mobile Servicing System (MSS) and maneuvered the Space Station Remote Manipulator System (SSRMS) and Special Purpose Dexterous Manipulator (SPDM) Arm 2 for rotating the Enhanced On-Orbit Replaceable Unit (ORU) / Tool Platform (EOTP) to its nominal stow position to stow the SPDM on Mobile Base System (MBS) Power Data Grapple Fixture 2 (PDGF2). Before grasping the EOTP drive fixture, checkouts of the Orbit Replaceable Unit (ORU) Tool Changeout Mechanism 2 (OTCM2) mechanisms (gripper, advancer, umbilical, torquer) were performed to assess potential impacts of the high loads event on Friday when trying to extract the P12B-A Remote Power Control Module (RPCM). Each mechanism passed the test and data is being reviewed by engineering teams.   Japanese Experiment Module (JEM) Stowage Frame Installation Part 3: The crew continued assembly and installation of the JEM Stowage Frame.  Once fully installed, the frame will increase JEM stowage capability by 12 Cargo Transfer Bag Equivalents (CTBE).   On-Board Training (OBT) Cygnus Departure: In preparation for OA-6 unberth and release planned for next Tuesday, June 14, the crew reviewed required departure steps.   Today’s Planned Activities All activities were completed unless otherwise noted. MO- saliva sampling MO- placement of samples into МELFI MO- equipment stow MO- questionnaire fill out On MCC GO БМП Ф2 absorption cartridge regeneration (start) KORREKTSIA. Registration of food, liquids (medicaments) acceptance  r/g 2448 Daily planning conference (S-band) Acoustic Dosimeters Setup – Day  1 Synchronize RS photo cameras with on-board time / r/g 1594 Fold ARED JEMAL – depress BEAM – remove hatch cover and ingress Air sampling with АК-1М sampler during ingress into BEAM module  r/g 2442 T2 – monthly inspection JAXA – install frame for cargo stowage BEAM – install handrails in the module Recharge Samsung tablet in Soyuz ТК №719 – start BIOCARD. Experiment ops r/g 1907 BIOCARD. Experimetn assist / r/g 1907 BEAM – install sensors and internal cables – part A Preparation of items to be returned and disposed of in Soyuz ТК №719 r/g 2374, 2446 On MCC GO ISS oxygen repress from the 1st section of СрПК Progress Recharge Samsung tablets in Soyuz ТК №720 – start JAXA – install stop for cargo stowage frame SOZh maintenance On MCC GO ISS oxygen repress from the 1st section of СрПК Progress WHC – replace urine receptacle and filter-insert Recharge Samsung tablet in Soyuz ТК №719 – term Photo and video imagery of life at the ISS / r/g 2000 JEMAL – check completion of depress Preventive maintenance program (CMS) – T2 treadmill BEAM – install sensors and internal cables – part В Recharge Samsung tablets in Soyuz ТК №720 – term IDENTIFIKATSIA. Copy ИМУ-Ц measurement results to a laptop r/g 1589 JEMAL – confirm end of ventilation in JEM WRS – water fill from EDV for processing COL-SSD- read instruction Closeout ops and egress BEAM, close Node 3 Aft hatch Tighten QD bolts between DC1 and Progress ТКГ №431 KORREKTSIA. Registration of food, liquids (medicaments) acceptance r/g 2448 AMO2 – deactivate ER7 Put ARED into working position Stowage operations prior to undock of Soyuz ТК №719 r/g 2374, 2446 AMO2 – status check Old data removal from RSE-Med laptop/ r/g 2463 WRS water sample analysis Tighten QD bolts between SM aggregate compartment АО and Progress и ТКГ №432 ASTROPI. Preparation for connection to ISS LAN Dust filter cartridge R&R in SM COL – relocate cargo from the kit to a vacant place COL. Retrieve and reposition the sensor URAGAN. Observation and imagery with НА ВСС equipment / r/g 2462 Relocate cargo into COL-STBD_ENDCONE AFT-DECK zone VIZIR. Experiment run with СКПФУ equipment r/g 2461 AMO2 – activate ER7 INTERACTION-2. Experiment run / r/g […]

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Anonymous hacktivists attack world's banks, London Stock Exchange

The online hacktivist group, Anonymous reportedly shut down the London Stock Exchange (LSE) website last week for more than two hours as part of ...

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University Pays Hackers $20,000 to get back its Ransomware Infected Files

What's the worst that could happen when a Ransomware malware hits University? Last month, the IT department of the University from where I have done my graduation called me for helping them get rid of a Ransomware infection that locked down all its student's results just a day before the announcement. Unfortunately, there was no decrypter available for that specific ransomware sample, but

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Night on Venus in Infrared from Orbiting Akatsuki

Why is Venus so different from Earth? To help find out, Japan launched the robotic Akatsuki spacecraft which entered orbit around Venus late last year after an unplanned five-year adventure around the inner Solar System. Even though Akatsuki has passed its original planned lifetime, the spacecraft and its instruments are operating so well that much of its original mission has been reinstated. In the featured image taken by Akatsuki late last month, Venus was captured in infrared light showing a surprising amount of atmospheric structure on its night side. The vertical orange terminator stripe between night and day is so wide because of light is so diffused by Venus' thick atmosphere. Also known as the Venus Climate Orbiter, Akatsuki has cameras and instruments that will investigate unknowns about the planet, including whether volcanoes are still active, whether lightning occurs in the dense atmosphere, and why wind speeds greatly exceed the planet's rotation speed. via NASA http://ift.tt/1WD9PDc

Orioles Video: Manny Machado says \"I don't regret anything\" after getting hit, charging mound; \"99 (mph) is no joke\" (ESPN)

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Stanford Victim Remains Anonymous

It said she has chosen to remain anonymous because, quote, “for now, I am every woman.” Last week, the young woman read an emotional statement ...

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Orioles Video: Buck Showalter not upset Manny Machado charged mound; \"I don't like when my guys are put in harm's way\" (ESPN)

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I have a new follower on Twitter

OptimalPlus
The leader in Manufacturing Intelligence for semiconductors and electronics

http://t.co/o4HhEan88Z
Following: 3158 - Followers: 3313

June 07, 2016 at 10:34PM via Twitter http://twitter.com/OptimalPlus

Preliminaries of a Space Situational Awareness Ontology. (arXiv:1606.01924v1 [cs.AI])

Space situational awareness (SSA) is vital for international safety and security, and the future of space travel. By improving SSA data-sharing we improve global SSA. Computational ontology should provide one means toward that goal. This paper develops the ontology of the SSA domain and takes steps in the creation of a Space Situational Awareness Ontology. It follows (Rovetto, 2015) and outlines objectives, requirements and desiderata; and describes the SSA domain and discipline of ontology. The purposes of the SSA ontology are to explore the potential for ontology development and engineering to (i) represent SSA data, general knowledge, and domain objects, (ii) clearly annotate and express the meaning of that orbital, near-earth and deep-space da-ta, and (iii) foster SSA data sharing among SSA actors and space object catalogs. By improving global SSA via actionable data- and knowledge-exchange, we can achieve the broader goals (and motivations) of (iv) advancing our capacity for planetary defense from near- or deep-space ob-ects, and (v) improving spaceflight safety for future generations.

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Consistency and Trust in Peer Data Exchange Systems. (arXiv:1606.01930v1 [cs.DB])

We propose and investigate a semantics for "peer data exchange systems" where different peers are related by data exchange constraints and trust relationships. These two elements plus the data at the peers' sites and their local integrity constraints are made compatible via a semantics that characterizes sets of "solution instances" for the peers. They are the intended -possibly virtual- instances for a peer that are obtained through a data repair semantics that we introduce and investigate. The semantically correct answers from a peer to a query, the so-called "peer consistent answers", are defined as those answers that are invariant under all its different solution instances. We show that solution instances can be specified as the models of logic programs with a stable model semantics. The repair semantics is based on null values as used in SQL databases, and is also of independent interest for repairs of single databases with respect to integrity constraints.

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Assisted Energy Management in Smart Microgrids. (arXiv:1606.01949v1 [cs.AI])

Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service interruption is an opposite objective to the one of service availability. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.

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Human vs. Computer Go: Review and Prospect. (arXiv:1606.02032v1 [cs.AI])

The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.

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Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system. (arXiv:1606.02041v1 [cs.AI])

Prior to seeking professional medical care it is increasingly common for patients to use online resources such as automated symptom checkers. Many such systems attempt to provide a differential diagnosis based on the symptoms elucidated from the user, which may lead to anxiety if life or limb-threatening conditions are part of the list, a phenomenon termed 'cyberchondria' [1]. Systems that provide advice on where to seek help, rather than a diagnosis, are equally popular, and in our view provide the most useful information. In this technical report we describe how such a triage system can be modelled computationally, how medical insights can be translated into triage flows, and how such systems can be validated and tested. We present babylon check, our commercially deployed automated triage system, as a case study, and illustrate its performance in a large, semi-naturalistic deployment study.

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Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions. (arXiv:1606.02096v1 [cs.AI])

We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.

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Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning. (arXiv:1606.02193v1 [cs.NI])

Monitoring Wireless Sensor Networks (WSNs) are composed by sensor nodes that report temperature, relative humidity and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration, because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The main goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73% the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.

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Multi-resource defensive strategies for patrolling games with alarm systems. (arXiv:1606.02221v1 [cs.AI])

Security Games employ game theoretical tools to derive resource allocation strategies in security domains. Recent works considered the presence of alarm systems, even suffering various forms of uncertainty, and showed that disregarding alarm signals may lead to arbitrarily bad strategies. The central problem with an alarm system, unexplored in other Security Games, is finding the best strategy to respond to alarm signals for each mobile defensive resource. The literature provides results for the basic single-resource case, showing that even in that case the problem is computationally hard. In this paper, we focus on the challenging problem of designing algorithms scaling with multiple resources. First, we focus on finding the minimum number of resources assuring non-null protection to every target. Then, we deal with the computation of multi-resource strategies with different degrees of coordination among resources. For each considered problem, we provide a computational analysis and propose algorithmic methods.

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Emotional Intensity analysis in Bipolar subjects. (arXiv:1606.02231v1 [cs.AI])

The massive availability of digital repositories of human thought opens radical novel way of studying the human mind. Natural language processing tools and computational models have evolved such that many mental conditions are predicted by analysing speech. Transcription of interviews and discourses are analyzed using syntactic, grammatical or sentiment analysis to infer the mental state. Here we set to investigate if classification of Bipolar and control subjects is possible. We develop the Emotion Intensity Index based on the Dictionary of Affect, and find that subjects categories are distinguishable. Using classical classification techniques we get more than 75\% of labeling performance. These results sumed to previous studies show that current automated speech analysis is capable of identifying altered mental states towards a quantitative psychiatry.

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A Formal Calculus for International Relations Computation and Evaluation. (arXiv:1606.02239v1 [cs.CR])

This publication presents a relation computation or calculus for international relations using a mathematical modeling. It examined trust for international relations and its calculus, which related to Bayesian inference, Dempster-Shafer theory and subjective logic. Based on an observation in the literature, we found no literature discussing the calculus method for the international relations. To bridge this research gap, we propose a relation algebra method for international relations computation. The proposed method will allow a relation computation which is previously subjective and incomputable. We also present three international relations as case studies to demonstrate the proposed method is a real-world scenario. The method will deliver the relation computation for the international relations that to support decision makers in a government such as foreign ministry, defense ministry, presidential or prime minister office. The Department of Defense (DoD) may use our method to determine a nation that can be identified as a friendly, neutral or hostile nation.

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Adaptive Skills, Adaptive Partitions (ASAP). (arXiv:1602.03351v2 [cs.LG] UPDATED)

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up to lifelong learning agents. The ASAP framework can also solve related new tasks simply by adapting where it applies its existing learned skills. We prove that ASAP converges to a local optimum under natural conditions. Finally, our experimental results, which include a RoboCup domain, demonstrate the ability of ASAP to learn where to reuse skills as well as solve multiple tasks with considerably less experience than solving each task from scratch.

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A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents. (arXiv:1604.03655v5 [cs.DS] UPDATED)

We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from the agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a bounded and discrete envy-free protocol. We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents.

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Distributed Clustering of Linear Bandits in Peer to Peer Networks. (arXiv:1604.07706v3 [cs.LG] UPDATED)

We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume that all the peers are solving the same linear bandit problem, and prove that our algorithm achieves the optimal asymptotic regret rate of any centralised algorithm that can instantly communicate information between the peers. For the second, we assume that there are clusters of peers solving the same bandit problem within each cluster, and we prove that our algorithm discovers these clusters, while achieving the optimal asymptotic regret rate within each one. Through experiments on several real-world datasets, we demonstrate the performance of proposed algorithms compared to the state-of-the-art.

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Plagiarism Detection in Polyphonic Music using Monaural Signal Separation. (arXiv:1503.00022v1 [cs.SD] CROSS LISTED)

Given the large number of new musical tracks released each year, automated approaches to plagiarism detection are essential to help us track potential violations of copyright. Most current approaches to plagiarism detection are based on musical similarity measures, which typically ignore the issue of polyphony in music. We present a novel feature space for audio derived from compositional modelling techniques, commonly used in signal separation, that provides a mechanism to account for polyphony without incurring an inordinate amount of computational overhead. We employ this feature representation in conjunction with traditional audio feature representations in a classification framework which uses an ensemble of distance features to characterize pairs of songs as being plagiarized or not. Our experiments on a database of about 3000 musical track pairs show that the new feature space characterization produces significant improvements over standard baselines.

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Ravens Image: Joe Flacco, Dennis Pitta, Justin Tucker and others gather to watch Monday's episode of \"The Bachelorette\" (ESPN)

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[FD] Microsoft Education - Code Execution Vulnerability

Document Title: =============== Microsoft Education - Code Execution Vulnerability References (Source): ==================== http://ift.tt/22LytRS MSRC Case: 32314 TRK: 0001002809 Research Article: http://ift.tt/1VKjN4L Release Date: ============= 2016-06-07 Vulnerability Laboratory ID (VL-ID): ==================================== 1669 Common Vulnerability Scoring System: ==================================== 8.6 Product & Service Introduction: =============================== Our mission is creating immersive and inclusive experiences that inspire lifelong learning, stimulating development of essential life skills and supporting educators in guiding and nurturing student passions. We empower students and educators to create and share in entirely new ways, to teach and learn through exploration, to adapt to individual learning needs, so they can make, design, invent and build with technology. (Copy of the Vendor Homepage: http://ift.tt/1K3NRUM ) Abstract Advisory Information: ============================== The Vulnerability Laboratory Core Research Team discovered a code execution and cross site scripting vulnerability in the microsoft education online service web-application. Vulnerability Disclosure Timeline: ================================== 2016-06-07: Public Disclosure (Vulnerability Laboratory) Discovery Status: ================= Published Affected Product(s): ==================== Microsoft Corp. Product: Education - Online Service (Web-Application) 2016 Q1 Exploitation Technique: ======================= Remote Severity Level: =============== High Technical Details & Description: ================================ Multiple persistent cross site scripting web vulnerabilities and a code execution bug has been discovered in the microsoft education web-application. The persistent web vulnerability allows remote attacker to inject own malicious script codes to the application-side of the affected module or function. The code execution vulnerability allows remote attacker to execute arbitrary codes to compromise the affected web-server, dbms or computer system. Several stored cross site scripting web vulnerabilities are located in a pretty vulnerable domain: education.microsoft.com It seems like they are all caused by the same bad secured input screen, as you will see in the proof of concepts that I prepared. For the stored XSS I used simple img tags, and one javascript attribute in a href as seen in the proof of concept which I provided. I also found a remote code execution vulnerability which seems to be caused by bad upload validation. I checked this for the video module, and was able to upload PHP files, which I could then locate on the server by viewing the source of the video module. I did check the image module for the same vulnerability, and was able to upload arbitrary files, but was not able to locate the executable PHP file. Proof of Concept (PoC): ======================= In the video below I show both the RCE and cross site scripting vulnerabilities. https://www.youtube.com/watch?v=CrrTENuNSks In this video, I show only the RCE vulnerability https://www.youtube.com/watch?v=mE8olRDYRCs Note: If any more information is needed, I will be happy to provide it! Security Risk: ============== The security risk of the code execution and cross site scripting vulnerabilities in the microsoft education service are estimated as high. (CVSS 8.6) Credits & Authors: ================== Kieran Claessens - (http://ift.tt/1NfhwHx) [http://ift.tt/1TTATIB] Disclaimer & Information: ========================= The information provided in this advisory is provided as it is without any warranty. Vulnerability Lab disclaims all warranties, either expressed or implied, including the warranties of merchantability and capability for a particular purpose. Vulnerability-Lab or its suppliers are not liable in any case of damage, including direct, indirect, incidental, consequential loss of business profits or special damages, even if Vulnerability-Lab or its suppliers have been advised of the possibility of such damages. Some states do not allow the exclusion or limitation of liability for consequential or incidental damages so the foregoing limitation may not apply. We do not approve or encourage anybody to break any licenses, policies, deface websites, hack into databases or trade with stolen data. Domains: http://ift.tt/1jnqRwA - www.vuln-lab.com - http://ift.tt/1kouTut Contact: admin@vulnerability-lab.com - research@vulnerability-lab.com - admin@evolution-sec.com Section: magazine.vulnerability-lab.com - http://ift.tt/1zNuo47 - http://ift.tt/1wo6y8x Social: twitter.com/vuln_lab - http://ift.tt/1kouSqa - http://youtube.com/user/vulnerability0lab Feeds: http://ift.tt/1iS1DH0 - http://ift.tt/1kouSqh - http://ift.tt/1kouTKS Programs: http://ift.tt/1iS1GCs - http://ift.tt/1iS1FyF - http://ift.tt/1oSBx0A Any modified copy or reproduction, including partially usages, of this file requires authorization from Vulnerability Laboratory. Permission to electronically redistribute this alert in its unmodified form is granted. All other rights, including the use of other media, are reserved by Vulnerability-Lab Research Team or its suppliers. All pictures, texts, advisories, source code, videos and other information on this website is trademark of vulnerability-lab team & the specific authors or managers. To record, list, modify, use or edit our material contact (admin@ or research@vulnerability-lab.com) to get a ask permission. Copyright © 2016 | Vulnerability Laboratory - [Evolution Security GmbH]™

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[FD] Wordpress Levo-Slideshow 2.3 - Arbitrary File Upload Vulnerability

Document Title: =============== Wordpress Levo-Slideshow 2.3 - Arbitrary File Upload Vulnerability References (Source): ==================== http://ift.tt/1Yck1C5 Release Date: ============= 2016-06-07 Vulnerability Laboratory ID (VL-ID): ==================================== 1854 Common Vulnerability Scoring System: ==================================== 7.5 Product & Service Introduction: =============================== Make sure you have a Levo slideshow a very effective technique to display unlimited number of product images within a single box and just takes only few minutes to accomplish without getting too much into coding. No WP slider plugin has become as wide-spread and as popular recently as this particular free WP Levo slider, offering a marvelous method for displaying a lot of content in such a minimal space, and to mention also a great way to highlight your best and most popular product images or articles in an enhanced way. And on top off all that, this indispensable, yet smooth and free WP slider plugin is incorporated with amazing set of features including a colossal space set aside to main flash image, a miniature sized image, image reflection option, description box with title, navigation arrows, auto-play/pause button, auto play timer into your WP powered websites or blogs. (Copy of the Homepage: http://ift.tt/1TTzRfK ) Abstract Advisory Information: ============================== An independent Vulnerability Laboratory Researcher discovered a arbitrary file upload vulnerability in the Wordpress Levo-Slideshow v2.3 plugin. Vulnerability Disclosure Timeline: ================================== 2016-06-07: Public Disclosure (Aaditya Purani) Discovery Status: ================= Published Affected Product(s): ==================== Go Responsive (Themes) Product: Levo-Slideshow - Wordpress Plugin (Web-Application) 2.3 Exploitation Technique: ======================= Remote Severity Level: =============== High Technical Details & Description: ================================ An arbitrary file upload web vulnerability has been discovered in the official Levo-Slideshow v2.3 wordpress plugin web-application. The vulnerability allows remote attackers to upload files via POST method with multiple extensions to unauthorized access them on application-side of the service. The vulnerability is located in the "admin.php" file when processing to upload via the admin.php file own malicious context or webshells. After the upload the remote attackers are able to access the file with one extension and exchange it with the another one to execute for example php codes. The security risk of the vulnerability is estimated as high with a cvss (common vulnerability scoring system) count of 7.5. Exploitation of the arbitrary file upload vulnerability requires no user interaction and an unprivileged application user account. Successful exploitation of the vulnerability results in unauthorized file access via arbitrary file upload exploitation. Vulnerable Application(s): [+] Wordpress Levo-Slideshow v2.3 Vulnerable Module(s): [+] /wordpress/wp-admin/ Vulnerable File(s): [+] admin.php Affected Module(s): [+] /uploads/levoslideshow/[ALBUM_NUMBER]_uploadfolder/big/ Proof of Concept (PoC): ======================= The arbitrary file upload web vulnerability can be exploited by remote attackers without user interaction and with an uprivileged application user account. For security demonstration or to reproduce the vulnerability follow the provided information and steps below to continue. Manual steps to reproduce the vulnerability ... 1. Login as an unprivileged user, which has no privilege of even uploading a plugin 2. Go to http://ift.tt/1X8Maui 3. If any Gallery exists than don't create and go to "Category Management" 4. Click on "Add New", Upload any .png / ,jpg image from your PC and intercept the request 5. After Intercepting the request while upload, Send request to Repeater 6. Change filename = image.png.php and in $POST image data add your PHP Backdoor between image chunk . It should look like this http://ift.tt/1TTzstM 7. Forward the request and go to http://ift.tt/1X8MtVW] to access your shell 8. Successful reproduce of the vulnerability! PoC: Exploitation $GET: http://ift.tt/1TTzfa2

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[FD] Wordpress Levo-Slideshow v2.3 - Persistent Vulnerability

Document Title: =============== Wordpress Levo-Slideshow v2.3 - Persistent Vulnerability References (Source): ==================== http://ift.tt/28fmYq3 Release Date: ============= 2016-06-06 Vulnerability Laboratory ID (VL-ID): ==================================== 1855 Common Vulnerability Scoring System: ==================================== 2.5 Product & Service Introduction: =============================== Make sure you have a Levo slideshow a very effective technique to display unlimited number of product images within a single box and just takes only few minutes to accomplish without getting too much into coding. No WP slider plugin has become as wide-spread and as popular recently as this particular free WP Levo slider, offering a marvelous method for displaying a lot of content in such a minimal space, and to mention also a great way to highlight your best and most popular product images or articles in an enhanced way. And on top off all that, this indispensable, yet smooth and free WP slider plugin is incorporated with amazing set of features including a colossal space set aside to main flash image, a miniature sized image, image reflection option, description box with title, navigation arrows, auto-play/pause button, auto play timer into your WP powered websites or blogs. (Copy of the Homepage: http://ift.tt/1TTzRfK ) Abstract Advisory Information: ============================== An independent Vulnerability Laboratory Researcher discovered a persistent cross site scripting vulnerability in the official Levo-Slideshow v2.3 wordpress plugin. Vulnerability Disclosure Timeline: ================================== 2016-06-06: Public Disclosure (Aaditya Purani) Discovery Status: ================= Published Affected Product(s): ==================== Go Responsive (Themes) Product: Levo-Slideshow - Wordpress Plugin (Web-Application) 2.3 Exploitation Technique: ======================= Remote Severity Level: =============== Low Technical Details & Description: ================================ A persistent cross site scripting web vulnerabilities has been discovered in the official Levo-Slideshow v2.3 wordpress plugin. The client-side validation issue allows remote attacker to inject client-side script codes to compromise browser to application requests. The vulnerability is located in the `image_description`parameter of the `Lenvo Image Upload` module. Remote attackers are able to inject client-side script code. The `image_description` value is wrong encoded and not filtered by the regular validation. The attack vector of the issue is client-side and the request method to execute is GET. The security risk of the client-side vulnerabilities is estimated as medium with a cvss (common vulnerability scoring system) count of 2.5. Exploitation of the security vulnerability requires no a privileged web-application user account and low user interaction. Successful exploitation of the vulnerabilities results in session hijacking, non-persistent phishing, non-persistent external redirects, non-persistent load of malicious script codes or non-persistent web module context manipulation. Request Method(s): [+] POST Vulnerable Input(s): [+] Image Title [+] Image Description Affected Module(s): [+] Edit Post Proof of Concept (PoC): ======================= The persistent cross site scripting vulnerability can be exploited by remote attackers without user interaction and with an uprivileged application user account. For security demonstration or to reproduce the vulnerability follow the provided information and steps below to continue. PoC: Payload t" onmouseover=alert(document.domain); a=' Manual steps to reproduce the vulnerability ... 1. Go to Levo Slideshow and click to Manage Images 2. Add an Image Include to the Image Title input and script code payload enter the payload as follows Note: t" onmouseover=alert(document.domain); a=' 3. Save it and Reload the Page and Game over. :)

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