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Saturday, August 6, 2016
Cantata a voce sola di Contraldo con Flauto (Anonymous)
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MONUMENTO SIMÓN BOLÍVAR [Material gráfico] | Anonymous
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Ravens: Despite Justin Tucker's claim he'd try 84.5-yd FG, ST coordinator Jerry Rosburg says \"we have no plans for such\" (ESPN)
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On This Day 25-years Ago, The World's First Website Went Online
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Iran Bans Pokémon GO — It's My Way or the Highway!
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I have a new follower on Twitter
John R. Patterson
Customer Service Guru, Leadership Consultant, Office Construction & Relocation Expert, Best-selling Author, In-demand Speaker, Husband and Father of 5.
Atlanta, Georgia
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Following: 5270 - Followers: 7481
August 06, 2016 at 02:26AM via Twitter http://twitter.com/johnrpatt
I have a new follower on Twitter
Susan Cork
WordPress Designer and Front-End Developer.
https://t.co/GZvmvxh4vd
Following: 3307 - Followers: 3608
August 06, 2016 at 12:52AM via Twitter http://twitter.com/susancork
Apollo 15 Panorama
Orioles Video: Pedro Alvarez crushes two 400-plus foot HR in 7-5 win over White Sox; has hit 6 HR in his last 5 games (ESPN)
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Friday, August 5, 2016
Cantata Non lasciarmi o bella speme (Anonymous)
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I have a new follower on Twitter
McGuire Space llc
Following: 1 - Followers: 1
August 05, 2016 at 09:21PM via Twitter http://twitter.com/McGuireSpace
[FD] [SYSS-2016-063] VMware ESXi 6 - Improper Input Validation (CWE-20)
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Re: [FD] Multiple remote vulnerabilities (RCE, bof) in Nuuo NVR and NETGEAR Surveillance
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[FD] K2 (Joomla! Extension) < 2.7.1 - Reflected Cross Site Scripting
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[FD] CVE-2016-6527 Possible Privilege Escalation in telecom of Samsung Mobile Phone
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CyberTipline Allows Anonymous Online Tips
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[FD] CVE-2016-6526 Possible Privilege Escalation in telecom of Samsung Mobile Phone
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[FD] [SYSS-2016-065] NASdeluxe NDL-2400r: OS Command Injection
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[FD] D-Link NAS, DNS Series: Stored XSS via Unauthenticated SMB
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[FD] Kaspersky Safe Browser iOS Application - MITM SSL Certificate Vulnerability (CVE-2016-6231)
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Improvers Anonymous
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I have a new follower on Twitter
GrowthHIT
Growth on Demand. Hire an Elite Growth Team in 90 Seconds on https://t.co/l5dJik0NEK. #SEO #GrowthHacking #CRO #Startup #Launch #EmailMarketing #PPC
New York, NY
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Following: 958 - Followers: 1782
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Webgrow UK
I'm A Family Man Who Loves Travel And Helping E-Commerce Owners Get More Sales & Conversions: Magento, PrestaShop and Woocommerce https://t.co/2HAu0koCk1
Aylesbury, UK
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Following: 3792 - Followers: 4349
August 05, 2016 at 03:36PM via Twitter http://twitter.com/webgrowuk
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Kaymu
Kaymu is the #1 Online Shopping Community in Emerging Countries http://t.co/KYG3M5QCIC #Africa #Asia #Europe #Ecommerce #Marketplace
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Following: 866 - Followers: 7626
August 05, 2016 at 03:36PM via Twitter http://twitter.com/KaymuGlobal
Ravens: Marshall Yanda is the top-rated guard in Madden NFL 17 with a 97 overall, 5 points ahead of Cowboys' Zack Martin (ESPN)
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[FD] Ecwid Ecommerce Shopping Cart WordPress Plugin unauthenticated PHP Object injection vulnerability
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[FD] Cross-Site Scripting in Store Locator Plus for WordPress
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[FD] DLL side loading vulnerability in VMware Host Guest Client Redirector
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Allow anonymous event submitters to edit their own event
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ISS Daily Summary Report – 08/04/2016
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This ATM Hack Allows Crooks to Steal Money From Chip-and-Pin Cards
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Torrentz.eu Shuts Down Forever! End of Biggest Torrent Search Engine
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Hack Apple & Get Paid up to $200,000 Bug Bounty Reward
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[FD] FortiCloud - (Reports Summary) Multiple Persistent Vulnerabilities
[FD] Subrion v4.0.5 CMS - SQL Injection Vulnerability
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[FD] Typesettercms v5.0.1 - (Delete Files) CSRF Vulnerability
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Mediasphere
Online solutions for businesses of all sizes. #Induction #Training #CPD #Certification #PerformanceManagement #RiskManagement #HRTech #LMS
Gold Coast, Australia
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Following: 1533 - Followers: 1524
August 05, 2016 at 03:05AM via Twitter http://twitter.com/mediasphere_au
M63: Sunflower Galaxy Wide Field
Thursday, August 4, 2016
Paraconsistency and Word Puzzles. (arXiv:1608.01338v1 [cs.AI])
Word puzzles and the problem of their representations in logic languages have received considerable attention in the last decade (Ponnuru et al. 2004; Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is the problem of generating such representations directly from natural language (NL) or controlled natural language (CNL). An interesting variation of this problem, and to the best of our knowledge, scarcely explored variation in this context, is when the input information is inconsistent. In such situations, the existing encodings of word puzzles produce inconsistent representations and break down. In this paper, we bring the well-known type of paraconsistent logics, called Annotated Predicate Calculus (APC) (Kifer and Lozinskii 1992), to bear on the problem. We introduce a new kind of non-monotonic semantics for APC, called consistency preferred stable models and argue that it makes APC into a suitable platform for dealing with inconsistency in word puzzles and, more generally, in NL sentences. We also devise a number of general principles to help the user choose among the different representations of NL sentences, which might seem equivalent but, in fact, behave differently when inconsistent information is taken into account. These principles can be incorporated into existing CNL translators, such as Attempto Controlled English (ACE) (Fuchs et al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC with the consistency preferred stable model semantics can be equivalently embedded in ASP with preferences over stable models, and we use this embedding to implement this version of APC in Clingo (Gebser et al. 2011) and its Asprin add-on (Brewka et al. 2015).
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Interacting Conceptual Spaces. (arXiv:1608.01402v1 [cs.AI])
We propose applying the categorical compositional scheme of [6] to conceptual space models of cognition. In order to do this we introduce the category of convex relations as a new setting for categorical compositional semantics, emphasizing the convex structure important to conceptual space applications. We show how conceptual spaces for composite types such as adjectives and verbs can be constructed. We illustrate this new model on detailed examples.
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Quantifier Scope in Categorical Compositional Distributional Semantics. (arXiv:1608.01404v1 [cs.CL])
In previous work with J. Hedges, we formalised a generalised quantifiers theory of natural language in categorical compositional distributional semantics with the help of bialgebras. In this paper, we show how quantifier scope ambiguity can be represented in that setting and how this representation can be generalised to branching quantifiers.
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Stable Models for Infinitary Formulas with Extensional Atoms. (arXiv:1608.01603v1 [cs.LO])
The definition of stable models for propositional formulas with infinite conjunctions and disjunctions can be used to describe the semantics of answer set programming languages. In this note, we enhance that definition by introducing a distinction between intensional and extensional atoms. The symmetric splitting theorem for first-order formulas is then extended to infinitary formulas and used to reason about infinitary definitions. This note is under consideration for publication in Theory and Practice of Logic Programming.
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Query Answering in Resource-Based Answer Set Semantics. (arXiv:1608.01604v1 [cs.AI])
In recent work we defined resource-based answer set semantics, which is an extension to answer set semantics stemming from the study of its relationship with linear logic. In fact, the name of the new semantics comes from the fact that in the linear-logic formulation every literal (including negative ones) were considered as a resource. In this paper, we propose a query-answering procedure reminiscent of Prolog for answer set programs under this extended semantics as an extension of XSB-resolution for logic programs with negation. We prove formal properties of the proposed procedure.
Under consideration for acceptance in TPLP.
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Deploying learning materials to game content for serious education game development: A case study. (arXiv:1608.01611v1 [cs.AI])
The ultimate goals of serious education games (SEG) are to facilitate learning and maximizing enjoyment during playing SEGs. In SEG development, there are normally two spaces to be taken into account: knowledge space regarding learning materials and content space regarding games to be used to convey learning materials. How to deploy the learning materials seamlessly and effectively into game content becomes one of the most challenging problems in SEG development. Unlike previous work where experts in education have to be used heavily, we proposed a novel approach that works toward minimizing the efforts of education experts in mapping learning materials to content space. For a proof-of-concept, we apply the proposed approach in developing an SEG game, named \emph{Chem Dungeon}, as a case study in order to demonstrate the effectiveness of our proposed approach. This SEG game has been tested with a number of users, and the user survey suggests our method works reasonably well.
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Black-Box Policy Search with Probabilistic Programs. (arXiv:1507.04635v4 [stat.ML] UPDATED)
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box variational methods which generalize to probabilistic program inference. We present case studies in the Canadian traveler problem, Rock Sample, and a benchmark for optimal diagnosis inspired by Guess Who. Each study illustrates how programs can efficiently represent policies using moderate numbers of parameters.
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier. (arXiv:1602.04938v2 [cs.LG] UPDATED)
Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.
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Coordination in Categorical Compositional Distributional Semantics. (arXiv:1606.01515v2 [cs.CL] UPDATED)
An open problem with categorical compositional distributional semantics is the representation of words that are considered semantically vacuous from a distributional perspective, such as determiners, prepositions, relative pronouns or coordinators. This paper deals with the topic of coordination between identical syntactic types, which accounts for the majority of coordination cases in language. By exploiting the compact closed structure of the underlying category and Frobenius operators canonically induced over the fixed basis of finite-dimensional vector spaces, we provide a morphism as representation of a coordinator tensor, and we show how it lifts from atomic types to compound types. Linguistic intuitions are provided, and the importance of the Frobenius operators as an addition to the compact closed setting with regard to language is discussed.
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Ravens: Justin Tucker thinks he can make field goal from 84.5 yards "if the situation is prime"; NFL record is 64 yards (ESPN)
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Bekanntmachung
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[FD] Stored Cross-Site Scripting vulnerability in Count per Day WordPress Plugin
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[FD] Cross-Site Scripting in Count per Day WordPress Plugin
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[FD] Cross-Site Scripting in FormBuilder WordPress Plugin
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[FD] Cross-Site Scripting vulnerability in Events Made Easy WordPress plugin
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Ravens: DE Bronson Kaufusi, a 2016 3rd-round pick, will miss season after breaking his ankle in Thursday's practice (ESPN)
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I have a new follower on Twitter
Gagliano Alexandre
innovateur-entrepreneur-marketeur- digitalizer@CX@Oracle
France&Spain
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Following: 4255 - Followers: 2973
August 04, 2016 at 11:20AM via Twitter http://twitter.com/alex_gagliano
ISS Daily Summary Report – 08/03/2016
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Lambda (anonymous/first class procedures) and custom reporters
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[FD] FortiManager (Series) - (Bookmark) Persistent Vulnerability
Name | Type | Location | Description | |
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Bookmarks | ||||
"> <"<[PERSISTENT SCRIPT CODE EXECUTION!]" | ||||
"><"<[PERSISTENT SCRIPT CODE EXECUTION!]>" | SSH | benjamin-km | [PERSISTENT SCRIPT CODE EXECUTION!] | |
Name | <"<[PERSISTENT SCRIPT CODE EXECUTION!]" maxlength=35 > | |||
Type | ||||
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Description | <"<[PERSISTENT SCRIPT CODE EXECUTION!]>" maxlength=128> | |||
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[FD] FortiAnalyzer & FortiManager - Client Side Cross Site Scripting Web Vulnerability
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Prev - Next doesn't work for Anonymous user
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I have a new follower on Twitter
Sachin Ghai
technology creator, adopter... current interests BigData and hadoop sphere...
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Following: 844 - Followers: 675
August 04, 2016 at 01:38AM via Twitter http://twitter.com/sachinghai
Behold the Universe
anonymous-sums
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Wednesday, August 3, 2016
Crime Stoppers Gets $2000 to Support Anonymous Tip-Line
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Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science. (arXiv:1608.01018v1 [cs.CL])
This volume contains the Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science (SLPCS 2016), which was held on the 11th of June at the University of Strathclyde, Glasgow, and was co-located with Quantum Physics and Logic (QPL 2016). Exploiting the common ground provided by the concept of a vector space, the workshop brought together researchers working at the intersection of Natural Language Processing (NLP), cognitive science, and physics, offering them an appropriate forum for presenting their uniquely motivated work and ideas. The interplay between these three disciplines inspired theoretically motivated approaches to the understanding of how word meanings interact with each other in sentences and discourse, how diagrammatic reasoning depicts and simplifies this interaction, how language models are determined by input from the world, and how word and sentence meanings interact logically. This first edition of the workshop consisted of three invited talks from distinguished speakers (Hans Briegel, Peter G\"ardenfors, Dominic Widdows) and eight presentations of selected contributed papers. Each submission was refereed by at least three members of the Programme Committee, who delivered detailed and insightful comments and suggestions.
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Empirical Evaluation of Real World Tournaments. (arXiv:1608.01039v1 [cs.GT])
Computational Social Choice (ComSoc) is a rapidly developing field at the intersection of computer science, economics, social choice, and political science. The study of tournaments is fundamental to ComSoc and many results have been published about tournament solution sets and reasoning in tournaments. Theoretical results in ComSoc tend to be worst case and tell us little about performance in practice. To this end we detail some experiments on tournaments using real wold data from soccer and tennis. We make three main contributions to the understanding of tournaments using real world data from English Premier League, the German Bundesliga, and the ATP World Tour: (1) we find that the NP-hard question of finding a seeding for which a given team can win a tournament is easily solvable in real world instances, (2) using detailed and principled methodology from statistical physics we show that our real world data obeys a log-normal distribution; and (3) leveraging our log-normal distribution result and using robust statistical methods, we show that the popular Condorcet Random (CR) tournament model does not generate realistic tournament data.
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Generation of Near-Optimal Solutions Using ILP-Guided Sampling. (arXiv:1608.01093v1 [cs.AI])
Our interest in this paper is in optimisation problems that are intractable to solve by direct numerical optimisation, but nevertheless have significant amounts of relevant domain-specific knowledge. The category of heuristic search techniques known as estimation of distribution algorithms (EDAs) seek to incrementally sample from probability distributions in which optimal (or near-optimal) solutions have increasingly higher probabilities. Can we use domain knowledge to assist the estimation of these distributions? To answer this in the affirmative, we need: (a) a general-purpose technique for the incorporation of domain knowledge when constructing models for optimal values; and (b) a way of using these models to generate new data samples. Here we investigate a combination of the use of Inductive Logic Programming (ILP) for (a), and standard logic-programming machinery to generate new samples for (b). Specifically, on each iteration of distribution estimation, an ILP engine is used to construct a model for good solutions. The resulting theory is then used to guide the generation of new data instances, which are now restricted to those derivable using the ILP model in conjunction with the background knowledge). We demonstrate the approach on two optimisation problems (predicting optimal depth-of-win for the KRK endgame, and job-shop scheduling). Our results are promising: (a) On each iteration of distribution estimation, samples obtained with an ILP theory have a substantially greater proportion of good solutions than samples without a theory; and (b) On termination of distribution estimation, samples obtained with an ILP theory contain more near-optimal samples than samples without a theory. Taken together, these results suggest that the use of ILP-constructed theories could be a useful technique for incorporating complex domain-knowledge into estimation distribution procedures.
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Autonomous Grounding of Visual Field Experience through Sensorimotor Prediction. (arXiv:1608.01127v1 [cs.RO])
In a developmental framework, autonomous robots need to explore the world and learn how to interact with it. Without an a priori model of the system, this opens the challenging problem of having robots master their interface with the world: how to perceive their environment using their sensors, and how to act in it using their motors. The sensorimotor approach of perception claims that a naive agent can learn to master this interface by capturing regularities in the way its actions transform its sensory inputs. In this paper, we apply such an approach to the discovery and mastery of the visual field associated with a visual sensor. A computational model is formalized and applied to a simulated system to illustrate the approach.
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A Novel Approach for Data-Driven Automatic Site Recommendation and Selection. (arXiv:1608.01212v1 [cs.AI])
This paper presents a novel, generic, and automatic method for data-driven site selection. Site selection is one of the most crucial and important decisions made by any company. Such a decision depends on various factors of sites, including socio-economic, geographical, ecological, as well as specific requirements of companies. The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data. The presented method for site selection is robust, efficient, scalable, and is capable of handling challenges emerging in Big Data. To assess the effectiveness of the presented method, it is evaluated on real data (collected from Federal Statistical Office of Germany) of around 200 influencing factors which are considered by economists for site selection of Supermarkets in Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big overlap (86.4 \%) between the sites of existing supermarkets and the sites recommended by the presented method. In addition, the method also recommends many sites (328) for supermarket where a store should be opened.
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Learning to Rank for Synthesizing Planning Heuristics. (arXiv:1608.01302v1 [cs.AI])
We investigate learning heuristics for domain-specific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the resulting planner's performance than mean squared error. Thus, we instead frame learning a heuristic as a learning to rank problem which we solve using a RankSVM formulation. Additionally, we introduce new methods for computing features that capture temporal interactions in an approximate plan. Our experiments on recent International Planning Competition problems show that the RankSVM learned heuristics outperform both the original heuristics and heuristics learned through ordinary regression.
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In Love With a Robot: the Dawn of Machine-To-Machine Marketing. (arXiv:1302.4475v3 [cs.AI] UPDATED)
The article looks at mass market artificial intelligence tools in the context of their ever-growing sophistication, availability and market penetration. The subject is especially relevant today for these exact reasons - if a few years ago AI was the subject of high tech research and science fiction novels, today, we increasingly rely on cloud robotics to cater to our daily needs - to trade stock, predict weather, manage diaries, find friends and buy presents online.
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A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments. (arXiv:1404.2314v2 [cs.AI] UPDATED)
We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as score-performance matching. We point out the importance of temporal information, and propose a hidden Markov model which describes it explicitly and represents ornaments with several state types. Following a review of the indeterminacies, they are carefully incorporated into the model through its topology and parameters, and the state construction for quite general polyphonic scores is explained in detail. By analyzing piano performance data, we find significant overlaps in inter-onset-interval distributions of chordal notes, ornaments, and inter-chord events, and the data is used to determine details of the model. The model is applied for score following and offline score-performance matching, yielding highly accurate matching for performances with many ornaments and relatively frequent errors, repeats, and skips.
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Probabilistic Reasoning via Deep Learning: Neural Association Models. (arXiv:1603.07704v2 [cs.AI] UPDATED)
In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated. The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained. In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relation-modulated neural nets (RMNN), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi-relational knowledge bases and commonsense reasoning. Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples. To further prove the effectiveness of the proposed models, in this work, we have applied NAMs to solving challenging Winograd Schema (WS) problems. Experiments conducted on a set of WS problems prove that the proposed models have the potential for commonsense reasoning.
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[FD] Cross-Site Scripting in WordPress Landing Pages Plugin
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[FD] Cross-Site Scripting in Activity Log WordPress Plugin
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[FD] Cross-Site Scripting vulnerability in search function Activity Log WordPress Plugin
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4 Flaws hit HTTP/2 Protocol that could allow Hackers to Disrupt Servers
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SportsCenter Video: Buster Olney says P Dylan Bundy (7 K, 1 H on Tuesday) has "ability to put the Orioles over the top" (ESPN)
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Ravens: John Harbaugh says recently waived RB Trent Richardson "definitely on our radar" to rejoin the team in future (ESPN)
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Ravens: RB Terrance West sits No. 3 on depth chart, but has been the No. 1 playmaker in camp so far - Jamison Hensley (ESPN)
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I have a new follower on Twitter
Netuitive
Full stack #monitoring powered by machine learning & #analytics for cloud infrastructure, apps & services #devops #AWS Free trial at https://t.co/TtaSrnw9m3
Reston, Va.
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Following: 1622 - Followers: 1820
August 03, 2016 at 09:40AM via Twitter http://twitter.com/Netuitive
ISS Daily Summary Report – 08/02/2016
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