Latest YouTube Video
Saturday, May 14, 2016
Orioles Video: Matt Wieters launches the 3-2 pitch into the stands, brings in go-ahead runs in 9-3 win over Tigers (ESPN)
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anonymous-sums
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facebook anonymous proxy
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anonymous op citibank
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Legal Tech Aid
Web design that resonates and communicates your expertise and high standards. Featured Resource: https://t.co/m3UNpuvDPz
United States
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Following: 2788 - Followers: 1202
May 14, 2016 at 03:42PM via Twitter http://twitter.com/Legal_TechAid
Sannidhi receives an anonymous phone call
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Microsoft removes its controversial Windows 10 Wi-Fi Sense Password Sharing Feature
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Zoom in to MMS and Magnetopause Reconnection
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MMS Fly Along with Magnetopause Reconnection
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Data Tour of MMS and Magnetopause Reconnection
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Corestream
Offering payroll deductions for the expanding voluntary benefits marketplace; Human Resources advocates; Insurance specialists, technology driven
New York, NY
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Following: 3354 - Followers: 3751
May 14, 2016 at 12:18AM via Twitter http://twitter.com/CorestreamBen
ISS and Mercury Too
Friday, May 13, 2016
Orioles Video: Adam Jones homers in 1-0 win vs. Tigers after being robbed of a HR by Darren O'Day in batting practice (ESPN)
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Two Firearms Recovered From Boston Neighborhoods Thanks to Anonymous Tips From ...
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Writing Festival
Daily Writing Festival Deadlines: Scripts, Poems, Novels Go to http://t.co/bC1DeenwwY and submit your story. FULL FEEDBACK. Performed at festival.
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May 13, 2016 at 07:58PM via Twitter http://twitter.com/WritingFestival
General Manager - Fast Food
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Content Assets
We create relevant, engaging content that serve the information needs of business-to-business buyers.
London
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Following: 10175 - Followers: 11696
May 13, 2016 at 06:14PM via Twitter http://twitter.com/ContentAssets
Ravens sign CB Jerraud Powers; started 82 of the 87 games he has played in 7 seasons with Colts and Cardinals (ESPN)
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Follow up discussion to: "Who Pays Writers? Anonymous Freelance Writer Rates Reported"
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[FD] runAV mod_security Remote Command Execution
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Orioles' Manny Machado, 23, often overshadowed by Mike Trout, Bryce Harper, but his numbers just as good - Stats & Info (ESPN)
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Re: [FD] Skype Phishing Attack
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[FD] Webshell for IIS
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Ravens: 6th-rd pick Keenan Reynolds given permission to play this season, Secretary of the Navy tells \"Dan Patrick Show\" (ESPN)
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Unique Anonymous Submissions
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ISS Daily Summary Report – 05/12/16
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The Pirate Bay loses its Main Domain Name in Court Battle
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Second Bank hit by Malware attack similar to $81 Million Bangladesh Heist
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Mozilla asks Court to disclose Firefox Exploit used by FBI to hack Tor users
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A Transit of Mercury
Thursday, May 12, 2016
Orioles Video: Jonathan Schoop's 2-run triple in 7th inning caps rally from 5-0 down in victory over Tigers (ESPN)
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Anonymous takes 9 central banks offline – unleashes massive assault on
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Learning Representations for Counterfactual Inference. (arXiv:1605.03661v1 [stat.ML])
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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Optimizing human-interpretable dialog management policy using Genetic Algorithm. (arXiv:1605.03915v1 [cs.HC])
Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry. Approaches based on reinforcement learning have been proved to be effective. However, the numerical representation of dialog policy is human-incomprehensible and difficult for dialog system designers to verify or modify, which limits its practical application. In this paper we propose a novel framework for optimizing dialog policies specified in domain language using genetic algorithm. The human-interpretable representation of policy makes the method suitable for practical employment. We present learning algorithms using user simulation and real human-machine dialogs respectively.Empirical experimental results are given to show the effectiveness of the proposed approach.
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The AGI Containment Problem. (arXiv:1604.00545v2 [cs.AI] UPDATED)
There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem - the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed.
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Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like. (arXiv:1604.04660v2 [cs.AI] UPDATED)
The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane's performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial *general* intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A *task theory* would enable addressing tasks at the *class* level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.
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The SP theory of intelligence and the representation and processing of knowledge in the brain. (arXiv:1604.05535v2 [cs.AI] UPDATED)
The "SP theory of intelligence", with its realisation in the "SP computer model", aims to simplify and integrate observations and concepts across AI-related fields, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realised in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory -- "SP-neural" -- is a tentative and partial model for the representation and processing of knowledge in the brain. In the SP theory (apart from SP-neural), all kinds of knowledge are represented with "patterns", where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a "pattern" is realised as an array of neurons called a "pattern assembly", similar to Hebb's concept of a "cell assembly" but with important differences. Central to the processing of information in the SP system is the powerful concept of "multiple alignment", borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning -- significantly different from the "Hebbian" kinds of learning -- is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. Short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. The paper discusses several associated issues, with relevant empirical evidence.
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[FD] CakePHP Framework <= 3.2.4 IP Spoofing Vulnerability
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[FD] Skype Phishing Attack
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Allow anonymous users to publish content
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Contextors
Perfecting the Way Computers Understand Natural Language
Tel Aviv
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Secret Kernel Backdoor found in China-made Popular ARM Processors
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ISS Daily Summary Report – 05/11/16
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Hacker reports Vulnerability in Mr. Robot Season 2 Website
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Wednesday, May 11, 2016
I have a new follower on Twitter
X Ford
Want to build muscle. With these products? check out our site to get 2 free bottles
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Exaptive
Our mission is more data-driven aha moments, and we believe interoperability, modularity, and community make them happen. Tweets are on data and software dev.
Cambridge, MA
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May 11, 2016 at 10:09PM via Twitter http://twitter.com/exaptive
Fox News Resorts To Anonymous Sources In Attempt To Revive Debunked Benghazi Claim
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An urgent call for action: Stop anonymous shell companies from fleecing American tax payers
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Brain Scans Suggest Prayer Helps Alcoholics Anonymous Members Reduce Cravings
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A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies. (arXiv:1605.03269v1 [cs.RO])
Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception. A novel recurrent neural network called recurrent neural network with parametric bias units (RNNPB) runs in three modes, constructing a two-level emotion regulated learning model, was further applied to testify this theory in two different cases.
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Learning Bounded Treewidth Bayesian Networks with Thousands of Variables. (arXiv:1605.03392v1 [cs.AI])
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. We propose a novel algorithm for this task, able to scale to large domains and large treewidths. Our novel approach consistently outperforms the state of the art on data sets with up to ten thousand variables.
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Concept based Attention. (arXiv:1605.03416v1 [cs.AI])
Attention endows animals an ability to concentrate on the most relevant information among a deluge of distractors at any given time, either through volitionally 'top-down' biasing, or driven by automatically 'bottom-up' saliency of stimuli, in favour of advantageous competition in neural modulations for information processing. Nevertheless, instead of being limited to perceive simple features, human and other advanced animals adaptively learn the world into categories and abstract concepts from experiences, imparting the world meanings. This thesis suggests that the high-level cognitive ability of human is more likely driven by attention basing on abstract perceptions, which is defined as concept based attention (CbA).
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Deep Neural Networks Under Stress. (arXiv:1605.03498v1 [cs.CV])
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression rate. By introducing perturbations to image descriptions extracted from a deep convolutional neural network, we change their precision and number of dimensions, measuring how it affects the final score. We show that deep features are more robust to these disturbances when compared to classical approaches, achieving a compression rate of 98.4%, while losing only 0.88% of their original score for Pascal VOC 2007.
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Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide for practical applications. (arXiv:1605.03506v1 [cs.AI])
Important advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification models from simultaneously fulfilling every adequacy property that has been defined. Besides, the complexity of model definitions and adequacy properties makes very difficult for real users to understand the particularities of the different models that have been presented. In this work we will present several criteria conceived to help in the process of selecting the most adequate Quantifier Fuzzification Mechanisms for specific practical applications. In addition, some of the best known well-behaved models will be compared against this list of criteria. Based on this analysis, some guidance to choose fuzzy quantification models for practical applications will be provided.
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Collaborative Filtering Bandits. (arXiv:1502.03473v6 [cs.LG] UPDATED)
Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.
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Ultimate Intelligence Part II: Physical Measure and Complexity of Intelligence. (arXiv:1504.03303v2 [cs.AI] UPDATED)
We continue our analysis of volume and energy measures that are appropriate for quantifying inductive inference systems. We extend logical depth and conceptual jump size measures in AIT to stochastic problems, and physical measures that involve volume and energy. We introduce a graphical model of computational complexity that we believe to be appropriate for intelligent machines. We show several asymptotic relations between energy, logical depth and volume of computation for inductive inference. In particular, we arrive at a "black-hole equation" of inductive inference, which relates energy, volume, space, and algorithmic information for an optimal inductive inference solution. We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to the physical limits of intelligent computation in our universe.
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Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning. (arXiv:1510.08906v3 [stat.ML] UPDATED)
Recently, there has been significant progress in understanding reinforcement learning in discounted infinite-horizon Markov decision processes (MDPs) by deriving tight sample complexity bounds. However, in many real-world applications, an interactive learning agent operates for a fixed or bounded period of time, for example tutoring students for exams or handling customer service requests. Such scenarios can often be better treated as episodic fixed-horizon MDPs, for which only looser bounds on the sample complexity exist. A natural notion of sample complexity in this setting is the number of episodes required to guarantee a certain performance with high probability (PAC guarantee). In this paper, we derive an upper PAC bound $\tilde O(\frac{|\mathcal S|^2 |\mathcal A| H^2}{\epsilon^2} \ln\frac 1 \delta)$ and a lower PAC bound $\tilde \Omega(\frac{|\mathcal S| |\mathcal A| H^2}{\epsilon^2} \ln \frac 1 {\delta + c})$ that match up to log-terms and an additional linear dependency on the number of states $|\mathcal S|$. The lower bound is the first of its kind for this setting. Our upper bound leverages Bernstein's inequality to improve on previous bounds for episodic finite-horizon MDPs which have a time-horizon dependency of at least $H^3$.
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Stochastic Shortest Path with Energy Constraints in POMDPs. (arXiv:1602.07565v2 [cs.AI] UPDATED)
We consider partially observable Markov decision processes (POMDPs) with a set of target states and positive integer costs associated with every transition. The traditional optimization objective (stochastic shortest path) asks to minimize the expected total cost until the target set is reached. We extend the traditional framework of POMDPs to model energy consumption, which represents a hard constraint. The energy levels may increase and decrease with transitions, and the hard constraint requires that the energy level must remain positive in all steps till the target is reached. First, we present a novel algorithm for solving POMDPs with energy levels, developing on existing POMDP solvers and using RTDP as its main method. Our second contribution is related to policy representation. For larger POMDP instances the policies computed by existing solvers are too large to be understandable. We present an automated procedure based on machine learning techniques that automatically extracts important decisions of the policy allowing us to compute succinct human readable policies. Finally, we show experimentally that our algorithm performs well and computes succinct policies on a number of POMDP instances from the literature that were naturally enhanced with energy levels.
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Orioles Video: Chris Davis and Mark Trumbo club back-to-back jacks for the second straight game in 9-2 win over Twins (ESPN)
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Anonymous Samba share on Raspberry Pi3 and Samba 4.2
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CT4G anonymous
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Ravens receive offseason grade of C-plus; added real talent at WR but cap situation is a mess - Bill Barnwell (ESPN)
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Should An Anonymous Donor Be Able To Save A Public School?
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Anonymous users are unable to finish a quiz
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Wyden Probes Into Anonymous Shell Companies with Ties to Mossack Fonseca in Nevada and ...
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Orioles Podcast: Executive Dan Duquette tells Baseball Tonight he wants to keep Manny Machado for \"depth of his career\" (ESPN)
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ISS Daily Summary Report – 05/10/16
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Pornhub Launches Bug Bounty Program; Offering Reward up to $25,000
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Facebook Open Sources its Capture the Flag (CTF) Platform
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WhatsApp launches Desktop Software for Windows and Mac Users
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British Hacker Wins Legal Battle Over Encryption Keys
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Report a Crime
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Saturn and Mars visit Milky Way Star Clouds
Orioles Video: Adam Jones rips a go-ahead two-run single to left field in the 9th inning in 5-3 win over Twins (ESPN)
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Tuesday, May 10, 2016
Can lottery winners stay anonymous
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[FD] BulletProof Security 53.3 - Security Advisory - Multiple XSS Vulnerabilities
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Centurysoft
CenturySoft transforms data investments into actionable business results through the implementation of #BigData, Artificial Intelligence & #Analytics solutions.
Pune, Maharashtra
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Anonymous wait time encountered in DB2 object store DB
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Sycamore Camp Out
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Unethical Research: How to Create a Malevolent Artificial Intelligence. (arXiv:1605.02817v1 [cs.AI])
Cybersecurity research involves publishing papers about malicious exploits as much as publishing information on how to design tools to protect cyber-infrastructure. It is this information exchange between ethical hackers and security experts, which results in a well-balanced cyber-ecosystem. In the blooming domain of AI Safety Engineering, hundreds of papers have been published on different proposals geared at the creation of a safe machine, yet nothing, to our knowledge, has been published on how to design a malevolent machine. Availability of such information would be of great value particularly to computer scientists, mathematicians, and others who have an interest in AI safety, and who are attempting to avoid the spontaneous emergence or the deliberate creation of a dangerous AI, which can negatively affect human activities and in the worst case cause the complete obliteration of the human species. This paper provides some general guidelines for the creation of a Malevolent Artificial Intelligence (MAI).
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Function-Described Graphs for Structural Pattern Recognition. (arXiv:1605.02929v1 [cs.AI])
We present in this article the model Function-described graph (FDG), which is a type of compact representation of a set of attributed graphs (AGs) that borrow from Random Graphs the capability of probabilistic modelling of structural and attribute information. We define the FDGs, their features and two distance measures between AGs (unclassified patterns) and FDGs (models or classes) and we also explain an efficient matching algorithm. Two applications of FDGs are presented: in the former, FDGs are used for modelling and matching 3D-objects described by multiple views, whereas in the latter, they are used for representing and recognising human faces, described also by several views.
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Consciousness is Pattern Recognition. (arXiv:1605.03009v1 [cs.AI])
This is a nontechnical phenomenological proof that pattern-recognition and consciousness are the same activity, with some speculation about the importance of this. Since Husserl, many philosophers have accepted that consciousness consists of a stream of logical connections between an ego and external objects. These connections are called "intentions." Pattern recognition systems are achievable technical artifacts. The proof links a respected philosophical theory of consciousness with technical art, and may therefore enable a theoretically-grounded form of artificial intelligence called a "synthetic intentionality," a being able to synthesize, generalize, select and repeat a stream of intentions. If the pattern recognition is reflexive, able to operate on the stream of intentions, and flexible, able to find new types of connections, perhaps by evolutionary programming, an SI may be a particularly strong form of AI. The article then addresses some conventional problems: Searles' Chinese room, and how an SI could "understand" "meanings" and "be creative."
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Self-Modification of Policy and Utility Function in Rational Agents. (arXiv:1605.03142v1 [cs.AI])
Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify -- for example by changing its own source code. As we continue to create more and more intelligent agents, chances increase that they will learn about this ability. The question is: will they want to use it? For example, highly intelligent systems may find ways to change their goals to something more easily achievable, thereby `escaping' the control of their designers. In an important paper, Omohundro (2008) argued that goal preservation is a fundamental drive of any intelligent system, since a goal is more likely to be achieved if future versions of the agent strive towards the same goal. In this paper, we formalise this argument in general reinforcement learning, and explore situations where it fails. Our conclusion is that the self-modification possibility is harmless if and only if the value function of the agent anticipates the consequences of self-modifications and use the current utility function when evaluating the future.
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Avoiding Wireheading with Value Reinforcement Learning. (arXiv:1605.03143v1 [cs.AI])
How can we design good goals for arbitrarily intelligent agents? Reinforcement learning (RL) is a natural approach. Unfortunately, RL does not work well for generally intelligent agents, as RL agents are incentivised to shortcut the reward sensor for maximum reward -- the so-called wireheading problem. In this paper we suggest an alternative to RL called value reinforcement learning (VRL). In VRL, agents use the reward signal to learn a utility function. The VRL setup allows us to remove the incentive to wirehead by placing a constraint on the agent's actions. The constraint is defined in terms of the agent's belief distributions, and does not require an explicit specification of which actions constitute wireheading.
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Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases. (arXiv:1505.04746v2 [cs.DB] UPDATED)
Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.
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Matt Remuzzi
CapForge = Small Business #Bookkeeping. I'm also an #Author, #Startup Pro, #Business Consultant, World Traveler, #Blogger & SUP Surfer! http://t.co/wEzjVESQM3
CA, USA
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Paygration
Paygration is a Top 5 Intuit Premier Reseller, and expert in Payment integration for solutions such as QuickBooks, Sage, FreshBooks, Xero and more!
Scottsdale, Arizona
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Following: 473 - Followers: 486
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Anonymous PGA pro rips comparisons of young guns to Tiger Woods
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[FD] CFP: Passwords 2016, Ruhr-University Bochum, Germany, Dec 5-7
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[FD] Intuit QuickBooks 2007 - 2016 Arbitrary Code Execution
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anonymous web proxy java enable
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States where lottery winners can remain anonymous
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Top 4 Data Breaches reported in last 24 Hours
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Orioles Video: Did you know 3B Manny Machado is slugging an MLB-best .882 on fastballs this season? (ESPN)
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[FD] Nfdump Nfcapd 1.6.14 Multiple Vulnerabilities
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I have a new follower on Twitter
Sheila J Davis
#Wife of 1 | #Mother of 5 | #ChangingLives of #Entrepreneurs #LovingMyLife as an #Entrepreneur #BuildBizFaster #BuildBizBigger #BuildBizBetter Contact me!
Utah, USA
http://t.co/pdIEQvyg8U
Following: 6681 - Followers: 7656
May 10, 2016 at 07:59AM via Twitter http://twitter.com/sheila_j_davis
Hacker Arrested after Exposing Flaws in Elections Site
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ISS Daily Summary Report – 05/09/16
from ISS On-Orbit Status Report http://ift.tt/1YjrWeN
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FCC takes initiative to Speed Up Mobile Security Updates
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Theme (Bartik) not available for anonymous user when EU Cookie Compiance is installed
from Google Alert - anonymous http://ift.tt/1WmCHje
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[FD] Stanford University - Multiple SQL Injection Vulnerabilities
- November 30, 2002 /
- 5.5.47-0+deb7u1-log
- dasianlangdEALC@www02.stanford.edu
- 4: am — 5: am
- 7
8
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[FD] Trend Micro Direct Pass - Filter Bypass & Cross Site Scripting Vulnerability
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