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Saturday, January 7, 2017
Anonymous – Operation Awake The Masses 2017
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Craig Green reveals the meaning of his anonymous travellers
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[FD] YSTS 11th Edition - CFP
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[FD] Trango Altum AC600 Default root Login
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Friday, January 6, 2017
I have a new follower on Twitter
rohit
Ex @Microsoft design prototyper, now working in FinTech. Likes: UX design, open-source, JavaScript, chicken and burritos.
London, UK
https://t.co/Iy3NyhNITe
Following: 1283 - Followers: 7813
January 06, 2017 at 07:49PM via Twitter http://twitter.com/romiem
Am I the only one who thinks anonymous functions are harder to read? I usually try to avoid using ...
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Get Smart About Hallucinogens
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I have a new follower on Twitter
Vindicia
The True Leader in Enterprise-Class Subscription Billing. Join us on the official home for products, news and payment industry intelligence.
Redwood City, CA
https://t.co/wsNV96nh1d
Following: 8741 - Followers: 11324
January 06, 2017 at 06:34PM via Twitter http://twitter.com/Vindicia
Microsoft/TypeScript
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MLB: Mariners acquire former All-Star P Yovani Gallardo and cash considerations from Orioles for OF Seth Smith (ESPN)
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Ravens: Justin Tucker named unanimous selection to 2016 AP All-Pro team; Chiefs PR Tyreek Hill only other unanimous pick (ESPN)
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FTC Sues D-Link Over Failure to Secure Its Routers and IP Cameras from Hackers
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Ravens Image: Steve Smith Sr. writes retirement letter to Roger Goodell, says he'll "no longer be antagonizing" DBs (ESPN)
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ISS Daily Summary Report – 1/5/2017
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Netgear launches Bug Bounty Program for Hacker; Offering up to $15,000 in Rewards
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COOs reject anonymous resumes -- and get their minds blown when the people behind those ...
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Send order data to MailChimp after an anonymous user enters their email address
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Re: [FD] Persisted Cross-Site Scripting (XSS) in Confluence Jira Software
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Re: [FD] Persisted Cross-Site Scripting (XSS) in Confluence Jira Software
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Get Smart About Marijuana
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KillDisk Ransomware Targets Linux; Demands $250,000 Ransom, But Won't Decrypt Files
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Best VPN Services for 2017 — Get Up to 91% Discount On Lifetime Subscriptions
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Thursday, January 5, 2017
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making. (arXiv:1701.01302v1 [cs.AI])
Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine's policy will prioritize each player's interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player's own beliefs in evaluating how well an action will serve that player's utility function, and (2) shift the relative priority it assigns to each player's expected utilities over time, by a factor proportional to how well that player's beliefs predict the machine's inputs. Observation (2) represents a substantial divergence from na\"{i}ve linear utility aggregation (as in Harsanyi's utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.
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Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks. (arXiv:1701.01329v1 [cs.NE])
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target.
Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.
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NeuroRule: A Connectionist Approach to Data Mining. (arXiv:1701.01358v1 [cs.AI])
Classification, which involves finding rules that partition a given data set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based symbolic learning methods. The connectionist approach based on neural networks has been thought not well suited for data mining. One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans. This paper examines this issue. With our newly developed algorithms, rules which are similar to, or more concise than those generated by the symbolic methods can be extracted from the neural networks. The data mining process using neural networks with the emphasis on rule extraction is described. Experimental results and comparison with previously published works are presented.
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Applications of Algorithmic Probability to the Philosophy of Mind. (arXiv:1404.1718v8 [cs.AI] UPDATED)
This paper presents formulae that can solve various seemingly hopeless philosophical conundrums. We discuss the simulation argument, teleportation, mind-uploading, the rationality of utilitarianism, and the ethics of exploiting artificial general intelligence. Our approach arises from combining the essential ideas of formalisms such as algorithmic probability, the universal intelligence measure, space-time-embedded intelligence, and Hutter's observer localization. We argue that such universal models can yield the ultimate solutions, but a novel research direction would be required in order to find computationally efficient approximations thereof.
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Combining Existential Rules and Transitivity: Next Steps. (arXiv:1504.07443v2 [cs.AI] UPDATED)
We consider existential rules (aka Datalog+) as a formalism for specifying ontologies. In recent years, many classes of existential rules have been exhibited for which conjunctive query (CQ) entailment is decidable. However, most of these classes cannot express transitivity of binary relations, a frequently used modelling construct. In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules.
First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely aGRD (acyclic graph of rule dependencies), which clarifies the landscape of `finite expansion sets' of rules.
Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-Lite-R) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules.
Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment.
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Ravens: Steve Smith missed $1M incentive because offense didn't improve from 2015 - Schefter; team needed 187 more yds (ESPN)
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ISS Daily Summary Report – 1/3/2017
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FBI Hacked, Again! Hacker Leaks Data After Agency Failed to Patch Its Site
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I have a new follower on Twitter
Smargasy Inc.
Smargasy is your one-stop shop for all your software-related needs.
Cape Coral, FL, USA
http://t.co/XrYtvMdA
Following: 1923 - Followers: 2153
January 05, 2017 at 05:39AM via Twitter http://twitter.com/smargasy
I have a new follower on Twitter
Evercurrent.io
Know when updates are available on @drupal & @meteorjs, never miss another update.
United States
https://t.co/mQCyFmHXyY
Following: 970 - Followers: 1011
January 05, 2017 at 05:39AM via Twitter http://twitter.com/evercurrentio
This Ransomware Unlocks Your Files For Free If You Read CyberSecurity Articles
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I have a new follower on Twitter
Inclusive Wicca Shop
Making beautiful things to raise funds for Gendered Intelligence. Inclusive Wicca welcomes LGBTQIA, BIPOC, & disabled people. https://t.co/Gzj04sBKn3
Oxford, England
https://t.co/UvhlxXYMjg
Following: 492 - Followers: 43
January 05, 2017 at 03:09AM via Twitter http://twitter.com/incWiccaShop
Clouds of Andromeda
Wednesday, January 4, 2017
I have a new follower on Twitter
Envision
#Envision is a cloud-based #BI platform to manage #data Architected for an optimized self-service user experience. Speeds up #data #visualization & #analysis
Worldwide
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Following: 2857 - Followers: 3287
January 04, 2017 at 10:59PM via Twitter http://twitter.com/envisionbi
Anonymous Henchmen
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Fuzzy finite element model updating using metaheuristic optimization algorithms. (arXiv:1701.00833v1 [cs.AI])
In this paper, a non-probabilistic method based on fuzzy logic is used to update finite element models (FEMs). Model updating techniques use the measured data to improve the accuracy of numerical models of structures. However, the measured data are contaminated with experimental noise and the models are inaccurate due to randomness in the parameters. This kind of aleatory uncertainty is irreducible, and may decrease the accuracy of the finite element model updating process. However, uncertainty quantification methods can be used to identify the uncertainty in the updating parameters. In this paper, the uncertainties associated with the modal parameters are defined as fuzzy membership functions, while the model updating procedure is defined as an optimization problem at each {\alpha}-cut level. To determine the membership functions of the updated parameters, an objective function is defined and minimized using two metaheuristic optimization algorithms: ant colony optimization (ACO) and particle swarm optimization (PSO). A structural example is used to investigate the accuracy of the fuzzy model updating strategy using the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy finite element model updating are compared with the Bayesian model updating results.
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A K-fold Method for Baseline Estimation in Policy Gradient Algorithms. (arXiv:1701.00867v1 [cs.AI])
The high variance issue in unbiased policy-gradient methods such as VPG and REINFORCE is typically mitigated by adding a baseline. However, the baseline fitting itself suffers from the underfitting or the overfitting problem. In this paper, we develop a K-fold method for baseline estimation in policy gradient algorithms. The parameter K is the baseline estimation hyperparameter that can adjust the bias-variance trade-off in the baseline estimates. We demonstrate the usefulness of our approach via two state-of-the-art policy gradient algorithms on three MuJoCo locomotive control tasks.
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On the Usability of Probably Approximately Correct Implication Bases. (arXiv:1701.00877v1 [cs.LO])
We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use-cases. To this end, we quantitatively examine the behavior of probably approximately correct implication bases on artificial and real-world data sets and compare their precision and recall with respect to their corresponding exact implication bases. Using a small example, we also provide qualitative insight that implications from probably approximately correct bases can still represent meaningful knowledge from a given data set.
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Stochastic Planning and Lifted Inference. (arXiv:1701.01048v1 [cs.AI])
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorithmic efforts to use abstraction in stochastic systems. Over the years, these ideas evolved into two distinct lines of research, each supported by a rich literature. Lifted probabilistic inference focused on efficient arithmetic operations on template-based graphical models under a finite domain assumption while symbolic dynamic programming focused on supporting sequential decision-making in rich quantified logical action models and on open domain reasoning. Given their common motivation but different focal points, both lines of research have yielded highly complementary innovations. In this chapter, we aim to help close the gap between these two research areas by providing an overview of lifted stochastic planning from the perspective of probabilistic inference, showing strong connections to other chapters in this book. This also allows us to define Generalized Lifted Inference as a paradigm that unifies these areas and elucidates open problems for future research that can benefit both lifted inference and stochastic planning.
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Fitted Learning: Models with Awareness of their Limits. (arXiv:1609.02226v3 [cs.AI] UPDATED)
Though deep learning has pushed the boundaries of classification forward, in recent years hints of the limits of standard classification have begun to emerge. Problems such as fooling, adding new classes over time, and the need to retrain learning models only for small changes to the original problem all point to a potential shortcoming in the classic classification regime, where a comprehensive a priori knowledge of the possible classes or concepts is critical. Without such knowledge, classifiers misjudge the limits of their knowledge and overgeneralization therefore becomes a serious obstacle to consistent performance. In response to these challenges, this paper extends the classic regime by reframing classification instead with the assumption that concepts present in the training set are only a sample of the hypothetical final set of concepts. To bring learning models into this new paradigm, a novel elaboration of standard architectures called the competitive overcomplete output layer (COOL) neural network is introduced. Experiments demonstrate the effectiveness of COOL by applying it to fooling, separable concept learning, one-class neural networks, and standard classification benchmarks. The results suggest that, unlike conventional classifiers, the amount of generalization in COOL networks can be tuned to match the problem.
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The London Guide and Stranger's Safeguard
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[FD] Stop User Enumeration does not stop user enumeration (WordPress plugin)
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Re: [FD] Persisted Cross-Site Scripting (XSS) in Confluence Jira Software
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Ravens: G John Urschel makes Forbes' "30 under 30" list in field of science; currently pursuing PHD at MIT (ESPN)
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Superheroes Anonymous
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FTC sets $25,000 Prize for Automatic IoT Patch Management Solution
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Clouds of Andromeda
Pandora Close up at Saturn
ISS Daily Summary Report – 1/3/2017
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Someone Hijacking Unsecured MongoDB Databases for Ransom
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Re: [FD] 0-day: QNAP NAS Devices suffer of heap overflow
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Re: [FD] 0-day: QNAP NAS Devices suffer of heap overflow
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[FD] Persisted Cross-Site Scripting (XSS) in Confluence Jira Software
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[FD] Advisories Unsafe Dll in Audacity, telegram and Akamai
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[FD] CINtruder v0.3 released...
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[FD] Zend Framework / zend-mail < 2.4.11 Remote Code Execution (CVE-2016-10034)
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Tuesday, January 3, 2017
I have a new follower on Twitter
MW 🇺🇸
What I if told you... You just read that wrong. If I follow you, follow back please! ..................... By the way, who eats a sandwich without Miracle Whip?
Minnesota, USA
Following: 3020 - Followers: 3001
January 03, 2017 at 11:39PM via Twitter http://twitter.com/Wrede93
Okay Anonymous, Now Is The Time To Help Us Stop Trump
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Truthful Facility Location with Additive Errors. (arXiv:1701.00529v1 [cs.GT])
We address the problem of locating facilities on the $[0,1]$ interval based on reports from strategic agents. The cost of each agent is her distance to the closest facility, and the global objective is to minimize either the maximum cost of an agent or the social cost.
As opposed to the extensive literature on facility location which considers the multiplicative error, we focus on minimizing the worst-case additive error. Minimizing the additive error incentivizes mechanisms to adapt to the size of the instance. I.e., mechanisms can sacrifice little efficiency in small instances (location profiles in which all agents are relatively close to one another), in order to gain more [absolute] efficiency in large instances. We argue that this measure is better suited for many manifestations of the facility location problem in various domains.
We present tight bounds for mechanisms locating a single facility in both deterministic and randomized cases. We further provide several extensions for locating multiple facilities.
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Knowledge Engineering for Hybrid Deductive Databases. (arXiv:1701.00622v1 [cs.DB])
Modern knowledge base systems frequently need to combine a collection of databases in different formats: e.g., relational databases, XML databases, rule bases, ontologies, etc. In the deductive database system DDBASE, we can manage these different formats of knowledge and reason about them. Even the file systems on different computers can be part of the knowledge base. Often, it is necessary to handle different versions of a knowledge base. E.g., we might want to find out common parts or differences of two versions of a relational database.
We will examine the use of abstractions of rule bases by predicate dependency and rule predicate graphs. Also the proof trees of derived atoms can help to compare different versions of a rule base. Moreover, it might be possible to have derivations joining rules with other formalisms of knowledge representation.
Ontologies have shown their benefits in many applications of intelligent systems, and there have been many proposals for rule languages compatible with the semantic web stack, e.g., SWRL, the semantic web rule language. Recently, ontologies are used in hybrid systems for specifying the provenance of the different components.
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Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs. (arXiv:1701.00642v1 [cs.AI])
In this paper, we tackle the problem of risk-averse route planning in a transportation network with time-dependent and stochastic costs. To solve this problem, we propose an adaptation of the A* algorithm that accommodates any risk measure or decision criterion that is monotonic with first-order stochastic dominance. We also present a case study of our algorithm on the Manhattan, NYC, transportation network.
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From Preference-Based to Multiobjective Sequential Decision-Making. (arXiv:1701.00646v1 [cs.AI])
In this paper, we present a link between preference-based and multiobjective sequential decision-making. While transforming a multiobjective problem to a preference-based one is quite natural, the other direction is a bit less obvious. We present how this transformation (from preference-based to multiobjective) can be done under the classic condition that preferences over histories can be represented by additively decomposable utilities and that the decision criterion to evaluate policies in a state is based on expectation. This link yields a new source of multiobjective sequential decision-making problems (i.e., when reward values are unknown) and justifies the use of solving methods developed in one setting in the other one.
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A pre-semantics for counterfactual conditionals and similar logics. (arXiv:1701.00696v1 [cs.AI])
The elegant Stalnaker/Lewis semantics for counterfactual conditonals works with distances between models. But human beings certainly have no tables of models and distances in their head. We begin here an investigation using a more realistic picture, based on findings in neuroscience. We call it a pre-semantics, as its meaning is not a description of the world, but of the brain, whose structure is (partly) determined by the world it reasons about.
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Simulated Tornado Optimization. (arXiv:1701.00736v1 [math.OC])
We propose a swarm-based optimization algorithm inspired by air currents of a tornado. Two main air currents - spiral and updraft - are mimicked. Spiral motion is designed for exploration of new search areas and updraft movements is deployed for exploitation of a promising candidate solution. Assignment of just one search direction to each particle at each iteration, leads to low computational complexity of the proposed algorithm respect to the conventional algorithms. Regardless of the step size parameters, the only parameter of the proposed algorithm, called tornado diameter, can be efficiently adjusted by randomization. Numerical results over six different benchmark cost functions indicate comparable and, in some cases, better performance of the proposed algorithm respect to some other metaheuristics.
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How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation. (arXiv:1603.08023v2 [cs.CL] UPDATED)
We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future development of better automatic evaluation metrics for dialogue systems.
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Poset-based Triangle: An Improved Alternative for Bilattice-based Triangle. (arXiv:1609.05616v2 [cs.AI] UPDATED)
Bilattice-based triangle provides elegant algebraic structure for reasoning with vague and uncertain information. But the truth and knowledge ordering of intervals in bilattice-based triangle can not deal with nonmonotonic reasoning and are not always intuitive. In this work, we construct an alternative algebraic structure, namely poset-based triangle and we provide with proper logical connectives for this. It as an enhancement of the bilattice-based triangle to handle nonmonotonicity in logical reasoning.
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Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence. (arXiv:1611.03218v3 [cs.AI] UPDATED)
Learning your first language is an incredible feat and not easily duplicated. Doing this using nothing but a few pictureless books, a corpus, would likely be impossible even for humans. As an alternative we propose to use situated interactions between agents as a driving force for communication, and the framework of Deep Recurrent Q-Networks (DRQN) for learning a common language grounded in the provided environment. We task the agents with interactive image search in the form of the game Guess Who?. The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication. Our experiments show that it is possible to learn this task using DRQN and even more importantly that the words the agents use correspond to physical attributes present in the images that make up the agents environment.
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Staying Anonymous Online
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Ravens will retain OC Marty Mornhinweg, DC Dean Pees and special teams coach Jerry Rosburg (ESPN)
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Ravens Video: Steve Smith Sr. explains about walking away from the game on his own terms and things he won't miss (ESPN)
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Ravens WR Steve Smith Sr. joins Mike and Mike, addresses recent retirement; listen live in the ESPN App (ESPN)
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ISS Daily Summary Report – 12/30/2016
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ISS Daily Summary Report – 12/29/2016
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Anonymous Hackers Deface Victoria's Human Rights Commission Website
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Critical Updates — RCE Flaws Found in SwiftMailer, PhpMailer and ZendMail
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Cantates et arias italiennes pour voix de basse (Anonymous)
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BHIM App — How to Send & Receive Money with UPI
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I have a new follower on Twitter
Stephanie Diamond
Started at AOL in '94 and watched the world change and change...Author of Content Marketing Strategies For Dummies and 8+ other marketing books
New York
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Following: 24053 - Followers: 29565
January 03, 2017 at 12:55AM via Twitter http://twitter.com/diamondsf
I have a new follower on Twitter
Sonny Vu
Philomath, dad, husband, believer. Have a thing for #languages #hardtech #brands.
Ho Chi Minh, Vietnam
Following: 478 - Followers: 27505
January 03, 2017 at 12:44AM via Twitter http://twitter.com/sonnyvu
Monday, January 2, 2017
Hello @anonymous … we have been part of the revolution for nearly 50 years.
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I have a new follower on Twitter
David Reske
Founder and CEO @Nowspeed, Internet Marketing Veteran, Social Media, SEO, PPC, Website Optimization, Father, Sailor, Traveler
Boston, MA
http://t.co/ML1tCmkzoC
Following: 21899 - Followers: 24108
January 02, 2017 at 10:18PM via Twitter http://twitter.com/davidreske
I have a new follower on Twitter
Chris Kirksey
CEO @directionllc with a passion for life, family, friends, motivation, success, helping businesses w/ SEO, Web Design & Digital Marketing. Believer & Army Vet
Augusta, GA
https://t.co/28qXJazD21
Following: 1292 - Followers: 1553
January 02, 2017 at 10:18PM via Twitter http://twitter.com/SuperChrisK
I have a new follower on Twitter
Lisa Danforth
Business Strategist & Coach for Women Entrepreneurs. I teach women how to earn more & work less through mindset, business strategy and sustainable action plan.
Vermont
https://t.co/uc62j9Jkj9
Following: 1429 - Followers: 1743
January 02, 2017 at 09:53PM via Twitter http://twitter.com/lisadanforth1
Anonymous even Braveheart helped us
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I have a new follower on Twitter
Marius
From Paris, interested in AI and future in general
Paris
Following: 334 - Followers: 43
January 02, 2017 at 09:18PM via Twitter http://twitter.com/marius5141
Digital Advertising Traffic Operation: Machine Learning for Process Discovery. (arXiv:1701.00001v1 [cs.CY])
In a Web Advertising Traffic Operation it's necessary to manage the day-to-day trafficking, pacing and optimization of digital and paid social campaigns. The data analyst on Traffic Operation can not only quickly provide answers but also speaks the language of the Process Manager and visually displays the discovered process problems. In order to solve a growing number of complaints in the customer service process, the weaknesses in the process itself must be identified and communicated to the department. With the help of Process Mining for the CRM data it is possible to identify unwanted loops and delays in the process. With this paper we propose a process discovery based on Machine Learning technique to automatically discover variations and detect at first glance what the problem is, and undertake corrective measures.
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Non-Negative Matrix Factorization Test Cases. (arXiv:1701.00016v1 [math.NA])
Non-negative matrix factorization (NMF) is a prob- lem with many applications, ranging from facial recognition to document clustering. However, due to the variety of algorithms that solve NMF, the randomness involved in these algorithms, and the somewhat subjective nature of the problem, there is no clear "correct answer" to any particular NMF problem, and as a result, it can be hard to test new algorithms. This paper suggests some test cases for NMF algorithms derived from matrices with enumerable exact non-negative factorizations and perturbations of these matrices. Three algorithms using widely divergent approaches to NMF all give similar solutions over these test cases, suggesting that these test cases could be used as test cases for implementations of these existing NMF algorithms as well as potentially new NMF algorithms. This paper also describes how the proposed test cases could be used in practice.
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Learning Weighted Association Rules in Human Phenotype Ontology. (arXiv:1701.00077v1 [q-bio.QM])
The Human Phenotype Ontology (HPO) is a structured repository of concepts (HPO Terms) that are associated to one or more diseases. The process of association is referred to as annotation. The relevance and the specificity of both HPO terms and annotations are evaluated by a measure defined as Information Content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of Association Rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents HPO-Miner (Human Phenotype Ontology-based Weighted Association Rules) a methodology for extracting Weighted Association Rules. HPO-Miner can extract relevant rules from a biological point of view. A case study on using of HPO-Miner on publicly available HPO annotation datasets is used to demonstrate the effectiveness of our methodology.
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RNN-based Encoder-decoder Approach with Word Frequency Estimation. (arXiv:1701.00138v1 [cs.CL])
This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.
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Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control. (arXiv:1701.00178v1 [math.OC])
Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori they offer convergence guarantees as well as bounds around the predictions. Considering a more general setting that builds on Hoelder continuity relative to pseudo-metrics, we propose an online method for estimating the Hoelder constant online from function value observations that possibly are corrupted by bounded observational errors. Utilising this to compute adaptive parameters within a kinky inference rule gives rise to a nonparametric machine learning method, for which we establish strong universal approximation guarantees. That is, we show that our prediction rule can learn any continuous function in the limit of increasingly dense data to within a worst-case error bound that depends on the level of observational uncertainty. We apply our method in the context of nonparametric model-reference adaptive control (MRAC). Across a range of simulated aircraft roll-dynamics and performance metrics our approach outperforms recently proposed alternatives that were based on Gaussian processes and RBF-neural networks. For discrete-time systems, we provide stability guarantees for our learning-based controllers both for the batch and the online learning setting.
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STRIPS Planning in Infinite Domains. (arXiv:1701.00287v1 [cs.AI])
Many practical planning applications involve continuous quantities with non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which supports infinite streams of objects and static predicates and provide two algorithms, which reduce the original problem to a sequence of finite-domain planning problems. The representation and algorithms are entirely domain independent. We demonstrate them on simple illustrative domains, and then on a high-dimensional, continuous robotic task and motion planning problem.
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An affective computational model for machine consciousness. (arXiv:1701.00349v1 [cs.AI])
In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations. Moreover, affective computing attributes that involve emotions, behavior and personality have not been the focus of models of consciousness as they lacked motivation for deployment in software applications and robots. The affective attributes are important factors for the future of machine consciousness with the rise of technologies that can assist humans. Personality and affection hence can give an additional flavor for the computational model of consciousness in humanoid robotics. Recent advances in areas of machine learning with a focus on deep learning can further help in developing aspects of machine consciousness in areas that can better replicate human sensory perceptions such as speech recognition and vision. With such advancements, one encounters further challenges in developing models that can synchronize different aspects of affective computing. In this paper, we review some existing models of consciousnesses and present an affective computational model that would enable the human touch and feel for robotic systems.
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