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Saturday, December 19, 2015
Crunch time for Santa Anonymous toy drive
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Six-day Match!
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Anonymous at NALIP
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Djangoholics Anonymous Tickets
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5 Colorado Springs nonprofits receive anonymous donations totaling nearly $250K
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Herbig Haro 24
Friday, December 18, 2015
Local Woman To Receive Kidney From Anonymous Donor
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Anonymous Donor Hides $50k Check in Nonprofit's Nativity Scene
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When creating node as anonymous
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Ocean City, MD's surf is at least 5.12ft high
Ocean City, MD Summary
At 2:00 AM, surf min of 2.11ft. At 8:00 AM, surf min of 2.61ft. At 2:00 PM, surf min of 3.01ft. At 8:00 PM, surf min of 5.12ft.
Surf maximum: 5.91ft (1.8m)
Surf minimum: 5.12ft (1.56m)
Tide height: 2.05ft (0.63m)
Wind direction: SSW
Wind speed: 12.44 KTS
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Ocean City, MD's surf is at least 6.02ft high
Ocean City, MD Summary
At 2:00 AM, surf min of 6.02ft. At 8:00 AM, surf min of 5.42ft. At 2:00 PM, surf min of 4.82ft. At 8:00 PM, surf min of 3.9ft.
Surf maximum: 7.03ft (2.14m)
Surf minimum: 6.02ft (1.84m)
Tide height: 0.58ft (0.18m)
Wind direction: SSW
Wind speed: 12.77 KTS
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[FD] KL-001-2015-008 : Dell Pre-Boot Authentication Driver Uncontrolled Write to Arbitrary Address
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[FD] KL-001-2015-007 : Seagate GoFlex Satellite Remote Telnet Default Password
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[FD] Samsung softap weak random generated password
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ISS Daily Summary Report – 12/17/15
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Juniper Firewalls with ScreenOS Backdoored Since 2012
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Shocking! Instagram HACKED! Researcher hacked into Instagram Server and Admin Panel
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Geminids of the South
Thursday, December 17, 2015
Comments not showing for anonymous on SOME pages
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I have a new follower on Twitter
John Scotland
I help entrepreneurs make dynamic video content. Filmmaker, Trainer. Give me a camera and slice of pizza and I'm happy! #brandyouvideo #video #VideoProduction
Liverpool, England
http://t.co/aW8iCgHf69
Following: 351 - Followers: 562
December 17, 2015 at 09:37PM via Twitter http://twitter.com/johnscotlandtv
Signal Representations on Graphs: Tools and Applications. (arXiv:1512.05406v1 [cs.AI])
We present a framework for representing and modeling data on graphs. Based on this framework, we study three typical classes of graph signals: smooth graph signals, piecewise-constant graph signals, and piecewise-smooth graph signals. For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties. We then study how such graph dictionary works in two standard tasks: approximation and sampling followed with recovery, both from theoretical as well as algorithmic perspectives. Finally, for each class, we present a case study of a real-world problem by using the proposed methodology.
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Differential Evolution with Event-Triggered Impulsive Control Scheme. (arXiv:1512.05449v1 [cs.NE])
Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas. In this paper, an event-triggered impulsive control scheme (ETI) is introduced to improve the performance of DE. Impulsive control, the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). By introducing impulsive control and ETM into DE, we hope to change the search performance of the population in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the impulsive control operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of impulsive control, two types of impulses are presented within the framework of DE in this paper: stabilizing impulses and destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated. In addition, the proposed ETI is flexible to be incorporated into several state-of-the-art DE variants. Experimental results over the CEC 2014 benchmark functions exhibit that the developed scheme is simple yet effective, which significantly improves the performance of the considered DE algorithms.
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Continuous online sequence learning with an unsupervised neural network model. (arXiv:1512.05463v1 [cs.NE])
The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, a recent study proposed hierarchical temporal memory (HTM) sequence memory as a theoretical framework for sequence learning in the cortex. In this paper, we analyze properties of HTM sequence memory and apply it to various sequence learning and prediction problems. We show the model is able to continuously learn a large number of variable-order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory and other sequence learning algorithms, including the autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM), on sequence prediction problems with both artificial and real-world data. The HTM model not only achieves comparable or better accuracy than state-of-the-art algorithms, but also exhibits a set of properties that is critical for sequence learning. These properties include continuous online learning, the ability to handle multiple predictions and branching sequences, robustness to sensor noise and fault tolerance, and good performance without task-specific hyper-parameters tuning. Therefore the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem, but is also applicable to a wide range of real-world problems such as discrete and continuous sequence prediction, anomaly detection, and sequence classification.
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Unsupervised Feature Construction for Improving Data Representation and Semantics. (arXiv:1512.05467v1 [cs.AI])
Feature-based format is the main data representation format used by machine learning algorithms. When the features do not properly describe the initial data, performance starts to degrade. Some algorithms address this problem by internally changing the representation space, but the newly-constructed features are rarely comprehensible. We seek to construct, in an unsupervised way, new features that are more appropriate for describing a given dataset and, at the same time, comprehensible for a human user. We propose two algorithms that construct the new features as conjunctions of the initial primitive features or their negations. The generated feature sets have reduced correlations between features and succeed in catching some of the hidden relations between individuals in a dataset. For example, a feature like $sky \wedge \neg building \wedge panorama$ would be true for non-urban images and is more informative than simple features expressing the presence or the absence of an object. The notion of Pareto optimality is used to evaluate feature sets and to obtain a balance between total correlation and the complexity of the resulted feature set. Statistical hypothesis testing is used in order to automatically determine the values of the parameters used for constructing a data-dependent feature set. We experimentally show that our approaches achieve the construction of informative feature sets for multiple datasets.
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Deep Active Object Recognition by Joint Label and Action Prediction. (arXiv:1512.05484v1 [cs.AI])
An active object recognition system has the advantage of being able to act in the environment to capture images that are more suited for training and that lead to better performance at test time. In this paper, we propose a deep convolutional neural network for active object recognition that simultaneously predicts the object label, and selects the next action to perform on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time.
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Blind, Greedy, and Random: Ordinal Approximation Algorithms for Graph Problems. (arXiv:1512.05504v1 [cs.GT])
We study Matching, Clustering, and related problems in a partial information setting, where the agents' true utilities are hidden, and the algorithm only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph of agent utilities, but the algorithm receives as input only a preference ordering for each agent induced by the underlying weights. Against this backdrop, we design algorithms to approximate the true optimum solution with respect to the hidden weights. Perhaps surprisingly, such algorithms are possible for many important problems, as we show using our framework based on greedy and random techniques. Our framework yields a 1.6-approximation algorithm for the maximum weighted matching problem, a 2-approximation for the problem of clustering agents into equal sized partitions, a 4-approximation algorithm for Densest $k$-subgraph, and a 1.88-approximation algorithm for Max TSP as long as the hidden weights constitute a metric. Our results are the first non-trivial ordinal approximation algorithms for such problems, and indicate that in many situations, we can design robust algorithms even when we are agnostic to the precise agent utilities.
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An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments. (arXiv:1512.05509v1 [cs.NE])
This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network architectures: Long Short-Term Memory, Gated Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of several thousands candidate architectures. A variant of fitted Q iteration, based on Advantage values instead of Q values, is also explored. The results show that GRU performs significantly better than LSTM and MUT1 for most of the problems considered, requiring less training episodes and less CPU time before learning a very good policy. Advantage learning also tends to produce better results.
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A thermodynamical approach towards multi-criteria decision making (MCDM). (arXiv:1512.05569v1 [cs.AI])
In multi-criteria decision making (MCDM) problems, ratings are assigned to the alternatives on different criteria by the expert group. In this paper, we propose a thermodynamically consistent model for MCDM using the analogies for thermodynamical indicators - energy, exergy and entropy. The most commonly used method for analysing MCDM problem is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The conventional TOPSIS method uses a measure similar to that of energy for the ranking of alternatives. We demonstrate that the ranking of the alternatives is more meaningful if we use exergy in place of energy. The use of exergy is superior due to the inclusion of a factor accounting for the quality of the ratings by the expert group. The unevenness in the ratings by the experts is measured by entropy. The procedure for the calculation of the thermodynamical indicators is explained in both crisp and fuzzy environment. Finally, two case studies are carried out to demonstrate effectiveness of the proposed model.
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Probabilistic Programming with Gaussian Process Memoization. (arXiv:1512.05665v1 [cs.LG])
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can be difficult to apply; all but the simplest classification or regression applications require specification and inference over complex covariance functions that do not admit simple analytical posteriors. This paper shows how to embed Gaussian processes in any higher-order probabilistic programming language, using an idiom based on memoization, and demonstrates its utility by implementing and extending classic and state-of-the-art GP applications. The interface to Gaussian processes, called gpmem, takes an arbitrary real-valued computational process as input and returns a statistical emulator that automatically improve as the original process is invoked and its input-output behavior is recorded. The flexibility of gpmem is illustrated via three applications: (i) robust GP regression with hierarchical hyper-parameter learning, (ii) discovering symbolic expressions from time-series data by fully Bayesian structure learning over kernels generated by a stochastic grammar, and (iii) a bandit formulation of Bayesian optimization with automatic inference and action selection. All applications share a single 50-line Python library and require fewer than 20 lines of probabilistic code each.
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A Survey of Available Corpora for Building Data-Driven Dialogue Systems. (arXiv:1512.05742v1 [cs.CL])
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets and how they can be used to learn diverse dialogue strategies. We also describe other potential uses of these datasets, such as methods for transfer learning between datasets and the use of external knowledge, and discuss appropriate choice of evaluation metrics for the learning objective.
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Specifying and Staging Mixed-Initiative Dialogs with Program Generation and Transformation. (arXiv:1108.0476v4 [cs.PL] UPDATED)
Specifying and implementing flexible human-computer dialogs, such as those used in kiosks and smart phone apps, is challenging because of the numerous and varied directions in which each user might steer a dialog. The objective of this research is to improve dialog specification and implementation. To do so we enriched a notation based on concepts from programming languages, especially partial evaluation, for specifying a variety of unsolicited reporting, mixed-initiative dialogs in a concise representation that serves as a design for dialog implementation. We also built a dialog mining system that extracts a specification in this notation from requirements. To demonstrate that such a specification provides a design for dialog implementation, we built a system that automatically generates an implementation of the dialog, called a stager, from it. These two components constitute a dialog modeling toolkit that automates dialog specification and implementation. These results provide a proof of concept and demonstrate the study of dialog specification and implementation from a programming languages perspective. The ubiquity of dialogs in domains such as travel, education, and health care combined with the demand for smart phone apps provide a landscape for further investigation of these results.
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Anonymous: ESA Hack Was All Us
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anonymous emails
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Replies to discussion posts result in an additional 'anonymous' reply
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Anonymous Donor Leaves $50K Check At Texas Nativity Scene
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WOW! Anonymous Do-Gooders Cover $500K-Worth of Layaways at Walmart!
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Anonymous 'Santa B' pays $79000 for layaway bills
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I have a new follower on Twitter
Kay Lighting
We're making the world brighter by celebrating the art of light.
Conshohocken, PA
http://t.co/Wrn7FvtbFJ
Following: 10044 - Followers: 9488
December 17, 2015 at 01:06PM via Twitter http://twitter.com/KayLighting
Orioles: OF Hyun-soo Kim agrees to 2-year, $7 million deal; owns career .406 on-base percentage - Eddie Matz, reports (ESPN)
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New Pics Added to the Blog Gallery (December 17, 2015)
New Pics Added to the Blog Gallery! (December 17, 2015)
Click link below to visit gallery now!
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ISS Daily Summary Report – 12/16/15
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19-Year-Old Teen Steals $150,000 by Hacking into Airline's Website
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Bad Santa! Microsoft Offers — 'Upgrade now' or 'Upgrade tonight' to Push Windows 10
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You can Hack into a Linux Computer just by pressing 'Backspace' 28 times
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Hackers Plan to Ruin Christmas Eve for Millions of PlayStation and Xbox Live Gamers
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The Horsehead Nebula
Monthly burned area from the Global Fire Emissions Database (GFED)
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Wednesday, December 16, 2015
Conditions for Normative Decision Making at the Fire Ground. (arXiv:1512.04976v1 [cs.AI])
We discuss the changes in an attitude to decision making at the fire ground. The changes are driven by the recent technological shift. The emerging new approaches in sensing and data processing (under common umbrella of Cyber-Physical Systems) allow for leveling off the gap, between humans and machines, in perception of the fire ground. Furthermore, results from descriptive decision theory question the rationality of human choices. This creates the need for searching and testing new approaches for decision making during emergency. We propose the framework that addresses this need. The primary feature of the framework are possibilities for incorporation of normative and prescriptive approaches to decision making. The framework also allows for comparison of the performance of decisions, between human and machine.
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BayesDB: A probabilistic programming system for querying the probable implications of data. (arXiv:1512.05006v1 [cs.AI])
Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality? This paper describes BayesDB, a probabilistic programming platform that aims to enable users to query the probable implications of their data as directly as SQL databases enable them to query the data itself. This paper focuses on four aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data analysis, that answers queries by averaging over an implicit space of probabilistic models; (ii) techniques for implementing BQL using a broad class of multivariate probabilistic models; (iii) a semi-parametric Bayesian model-builder that auomatically builds ensembles of factorial mixture models to serve as baselines; and (iv) MML, a "meta-modeling" language for imposing qualitative constraints on the model-builder and combining baseline models with custom algorithmic and statistical models that can be implemented in external software. BayesDB is illustrated using three applications: cleaning and exploring a public database of Earth satellites; assessing the evidence for temporal dependence between macroeconomic indicators; and analyzing a salary survey.
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Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory. (arXiv:1512.05245v1 [cs.NE])
Reverse engineering the brain is proving difficult, perhaps impossible. While many believe that this is just a matter of time and effort, a different approach might help. Here, we describe a very simple idea which explains the power of the brain as well as its structure, exploiting complex dynamics rather than abstracting it away. Just as a Turing Machine is a Universal Digital Computer operating in a world of symbols, we propose that the brain is a Universal Dynamical Systems Modeller, evolved bottom-up (itself using nested networks of interconnected, self-organised dynamical systems) to prosper in a world of dynamical systems.
Recent progress in Applied Mathematics has produced startling evidence of what happens when abstract Dynamical Systems interact. Key latent information describing system A can be extracted by system B from very simple signals, and signals can be used by one system to control and manipulate others. Using these facts, we show how a region of the neocortex uses its dynamics to intrinsically "compute" about the external and internal world.
Building on an existing "static" model of cortical computation (Hawkins' Hierarchical Temporal Memory - HTM), we describe how a region of neocortex can be viewed as a network of components which together form a Dynamical Systems modelling module, connected via sensory and motor pathways to the external world, and forming part of a larger dynamical network in the brain.
Empirical modelling and simulations of Dynamical HTM are possible with simple extensions and combinations of currently existing open source software. We list a number of relevant projects.
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Solving stable matching problems using answer set programming. (arXiv:1512.05247v1 [cs.AI])
Since the introduction of the stable marriage problem (SMP) by Gale and Shapley (1962), several variants and extensions have been investigated. While this variety is useful to widen the application potential, each variant requires a new algorithm for finding the stable matchings. To address this issue, we propose an encoding of the SMP using answer set programming (ASP), which can straightforwardly be adapted and extended to suit the needs of specific applications. The use of ASP also means that we can take advantage of highly efficient off-the-shelf solvers. To illustrate the flexibility of our approach, we show how our ASP encoding naturally allows us to select optimal stable matchings, i.e. matchings that are optimal according to some user-specified criterion. To the best of our knowledge, our encoding offers the first exact implementation to find sex-equal, minimum regret, egalitarian or maximum cardinality stable matchings for SMP instances in which individuals may designate unacceptable partners and ties between preferences are allowed.
This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
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Feature Representation for ICU Mortality. (arXiv:1512.05294v1 [cs.AI])
Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom evaluated quantitatively. This study compares the performance of different representations of clinical event data from MIMIC II in a logistic regression model to predict 36-hour ICU mortality. The most common representations are linear (normalized counts) and binary (yes/no). These, along with a new representation termed "hill", are compared using both L1 and L2 regularization. Results indicate that the introduced "hill" representation outperforms both the binary and linear representations; the hill representation thus has the potential to improve existing models of ICU mortality.
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Computing rational decisions in extensive games with limited foresight. (arXiv:1502.03683v4 [cs.AI] UPDATED)
We introduce a class of extensive form games where players might not be able to foresee the possible consequences of their decisions and form a model of their opponents which they exploit to achieve a more profitable outcome. We improve upon existing models of games with limited foresight, endowing players with the ability of higher-order reasoning and proposing a novel solution concept to address intuitions coming from real game play. We analyse the resulting equilibria, devising an effective procedure to compute them.
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I have a new follower on Twitter
D. Ho
Software Engineer for @Microsoft
London
http://t.co/CkKdykNxBP
Following: 1000 - Followers: 2376
December 16, 2015 at 05:01PM via Twitter http://twitter.com/ComethTheNerd
[FD] User man Local Root Exploit/Linux Kernel setgid Directory Privilege Escalation/PAM Owner Check Weakness
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[FD] Two bytes change and you have a zero day
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[FD] libnsbmp: heap overflow (CVE-2015-7508) and out-of-bounds read (CVE-2015-7507)
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[FD] libnsgif: stack overflow (CVE-2015-7505) and out-of-bounds read (CVE-2015-7506)
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[FD] Back to 28: Grub2 Authentication Bypass 0-Day [CVE-2015-8370]
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[FD] #BadWinmail: The "Enterprise Killer" Attack Vector in Microsoft Outlook
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[FD] ERPSCAN Research Advisory [ERPSCAN-15-022] SAP NetWeaver 7.4 - XSS
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[FD] [ERPSCAN-15-021] SAP NetWeaver 7.4 - SQL Injection vulnerability
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[FD] [CFP] Speak About Your Cyberwar at PHDays VI
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TRIPLE Your Impact Today!
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'LIKE SANTA': Anonymous man pays off $106G in shoppers' layaways at 2 Ohio Walmart
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[FD] OLE DB Provider for Oracle multiple DLL side loading vulnerabilities
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[FD] Shockwave Flash Object DLL side loading vulnerability
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[FD] Shutdown UX DLL side loading vulnerability
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than 300 children receive bikes from anonymous donors
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ISS Daily Summary Report – 12/15/15
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Cyber bullying concerns over anonymous parent-proof phone app
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Fit notes: plans for collecting anonymous data in England
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N3XT — Advanced CHIP that Could Make Your Computer 1000 Times Faster
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Module to display no of authenticated and anonymous users visiting the site
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British Intelligence Open-Sources its Large-Scale Graph Database Software
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Top 10 — 2016 New Year's Resolution for Cyber Security Professionals
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Colorful Arcs over Buenos Aires
Global Rainfall-Triggered Landslides from 2007 through 2015
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Tuesday, December 15, 2015
Colorado College gets anonymous $8.5 million donation to advance innovation program
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Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease. (arXiv:1512.04652v1 [cs.AI])
Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features. Feature selection is the problem of selecting informative features among all features. Materials and Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We obtained an accuracy of 80.63% using reduced 11 feature set. The proposed method compare to the accuracy of 5 machine learning feature selections. The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks
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From One Point to A Manifold: Orbit Models for Knowledge Graph Embedding. (arXiv:1512.04792v1 [cs.AI])
Knowledge graph embedding aims at offering a numerical paradigm for knowledge representation by translating the entities and relations into continuous vector space. This paper studies the problem of unsatisfactory precise knowledge embedding and attributes a new issue to this problem that \textbf{\textit{inaccuracy of truth characterization}}, indicating that existing methods could not express the true facts in a fine degree. To alleviate this issue, we propose the orbit-based embedding model, \textbf{OrbitE}. The new model is a well-posed algebraic system that expands the position of golden triples from one point in current models to a manifold. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines, especially for precise prediction.
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Increasing the Action Gap: New Operators for Reinforcement Learning. (arXiv:1512.04860v1 [cs.AI])
This paper introduces new optimality-preserving operators on Q-functions. We first describe an operator for tabular representations, the consistent Bellman operator, which incorporates a notion of local policy consistency. We show that this local consistency leads to an increase in the action gap at each state; increasing this gap, we argue, mitigates the undesirable effects of approximation and estimation errors on the induced greedy policies. This operator can also be applied to discretized continuous space and time problems, and we provide empirical results evidencing superior performance in this context. Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator. As corollaries we provide a proof of optimality for Baird's advantage learning algorithm and derive other gap-increasing operators with interesting properties. We conclude with an empirical study on 60 Atari 2600 games illustrating the strong potential of these new operators.
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Anonymous Targets Donald Trump's Trump Tower Website
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Pluto: From Mountains to Plains
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