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Saturday, November 7, 2015
Am I Alcoholic Self Test
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Rochester holds anonymous gun buyback
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Nacional wins 1-0 at Guimaraes in Portugal
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Teen goalie excels as AC Milan draws Atalanta 0-0 at home
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I have a new follower on Twitter
TropicalEntrepreneur
Tropical Entrepreneur Is The Daily Podcast For Location Independent Entrepreneurs. Get your free top 10 resource guide here: http://t.co/WsXhF4pD6E
On iTunes! Subscribe below:
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Following: 6630 - Followers: 7526
November 07, 2015 at 05:00PM via Twitter http://twitter.com/JoshTropical
[FD] Google AdWords API client libraries - XML eXternal Entity Injection (XXE)
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[FD] Google AdWords API PHP client library <= 6.2.0 Arbitrary PHP Code Execution
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Chelsea loses 3rd straight EPL game, Man U beats West Brom
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Bayern, Guardiola to discuss future plans in winter break
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Oleg Sukhorukov / tags / anonymous
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Bologna beats Verona 2-0 in Serie A relegation fight
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[FD] Broken, Abandoned, and Forgotten Code, Part 14
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Man United wins to close gap on EPL leaders
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Ibrahimovic scores twice as leader PSG thrashes Toulouse 5-0
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Napout: New president not enough to solve FIFA crisis
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Southampton beats Sunderland 1-0 in Premier League
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Alcacer, Parejo lead Valencia to 5-1 win at Celta in Spain
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Hackers have Hacked into US Arrest Records Database
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What is Threat Intelligence and How It Helps to Identify Security Threats
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I have a new follower on Twitter
Thumbtack Technology
We design and build high-speed high-volume systems.
New York City, NY
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Following: 2339 - Followers: 1433
November 07, 2015 at 12:40PM via Twitter http://twitter.com/thumbtacktech
Newcastle claims narrow 1-0 win over Bournemouth in EPL
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I have a new follower on Twitter
gutBetittet
Ich wohne im Pott im Ruhrgebiet in NRW und ich nehme alles ganz locker auf freche Sprüche reagiere ich ebenso mit nem Spruch!
Gelsenkirchen
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November 07, 2015 at 10:16AM via Twitter http://twitter.com/gutBetittet
Former Chelsea manager Bobby Campbell dies at 78
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Hiroshima beats Gamba Osaka 2-0 in J-League's 2nd stage
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Western Sydney beats Newcastle in key A-League match
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Unraveling NGC 3169
Solar Wind Strips the Martian Atmosphere
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Solar Wind and Mars Bow Shock
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Friday, November 6, 2015
Women's soccer's W-League folds
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Anonymous's KKK Hack
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Rennes ends 7-game winless slump with 2-0 win away to Angers
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[FD] SQLiteManager 1.2.4: Multiple XSS
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[FD] TheHostingTool 1.2.6: Multiple XSS
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[FD] TheHostingTool 1.2.6: Multiple SQL Injection
Berlin wins 3-1 in Hannover in Bundesliga on Kalou hat trick
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[FD] TheHostingTool 1.2.6: Code Execution
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[FD] Quick.Cart 6.6: Multiple XSS
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[FD] Quick.Cart 6.6: CSRF
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[FD] CubeCart 6.0.7: XSS
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[FD] Supercali Event Calendar 1.0.8: XSS
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Orioles Video: Tim Kurkjian says despite making qualifying offers to Chris Davis, Matt Wieters, Baltimore may lose both (ESPN)
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[FD] Supercali Event Calendar 1.0.8: CSRF
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[FD] MyWebSQL 3.6: CSRF
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[FD] MiniBB 3.1.1: XSS
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Re: [FD] eBay Magento <= 1.9.2.1 XML eXternal Entity Injection (XXE) on PHP FPM
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[FD] New release: UFONet v0.6 - "Galactic OFFensive!"
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Parents Anonymous Inc
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Anonymous Integrity Violation Report
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Anonymous Unhoods 1000 KKK Members
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Nagbe, Miazga on US roster for World Cup qualifiers
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Lawyer: Blatter in hospital for checkup but is 'fine'
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Lawyer: Blatter in hospital for checkup but is 'fine'
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Brazil adds Gabriel Paulista, Douglas Santos for qualifiers
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Striker Mario Gomez recalled to German national team
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Carli Lloyd, Lionel Messi nominated for FIFA goal of year
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Anonymous Reveals Full List Of Alleged KKK Members
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Portugal gives Ronaldo a rest, calls up newcomers for games
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Leaders Sydney FC, Brisbane in scoreless draw in A-League
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Diego Costa recalled for Spain's national team
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David Beckham plays soccer with Nepalese children
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Bob Bradley leaves Norway yearning for a bigger job
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ISS Daily Summary Report – 11/5/15
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FBI Deputy Director's Email Hacked by Teenager Who Hacked CIA Chief
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Anonymous has just outed 1000 Klu Klux Klan members
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ProtonMail Paid Hackers $6000 Ransom in Bitcoin to Stop DDoS Attacks
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Anonymous Group Leaks Identities of 1000 KKK Members
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Football Federation Australia, union approve new contract
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NGC 1333: Stellar Nursery in Perseus
Thursday, November 5, 2015
Computing sets of graded attribute implications with witnessed non-redundancy. (arXiv:1511.01640v1 [cs.AI])
In this paper we extend our previous results on sets of graded attribute implications with witnessed non-redundancy. We assume finite residuated lattices as structures of truth degrees and use arbitrary idempotent truth-stressing linguistic hedges as parameters which influence the semantics of graded attribute implications. In this setting, we introduce algorithm which transforms any set of graded attribute implications into an equivalent non-redundant set of graded attribute implications with saturated consequents whose non-redundancy is witnessed by antecedents of the formulas. As a consequence, we solve the open problem regarding the existence of general systems of pseudo-intents which appear in formal concept analysis of object-attribute data with graded attributes and linguistic hedges. Furthermore, we show a polynomial-time procedure for determining bases given by general systems of pseudo-intents from sets of graded attribute implications which are complete in data.
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Adaptive information-theoretic bounded rational decision-making with parametric priors. (arXiv:1511.01710v1 [cs.AI])
Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded rationality ranging from optimality principles to heuristics. Here we take an information-theoretic approach to bounded rationality, where information-processing costs are measured by the relative entropy between a posterior decision strategy and a given fixed prior strategy. In the case of multiple environments, it can be shown that there is an optimal prior rendering the bounded rationality problem equivalent to the rate distortion problem for lossy compression in information theory. Accordingly, the optimal prior and posterior strategies can be computed by the well-known Blahut-Arimoto algorithm which requires the computation of partition sums over all possible outcomes and cannot be applied straightforwardly to continuous problems. Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory. Importantly, the update rule avoids typical infeasible operations such as the computation of partition sums. We show in simulations a proof of concept for discrete action and environment domains. This approach is not only interesting as a generic computational method, but might also provide a more realistic model of human decision-making processes occurring on a fast and a slow time scale.
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Symmetry-invariant optimization in deep networks. (arXiv:1511.01754v1 [cs.LG])
Recent works have highlighted scale invariance or symmetry that is present in the weight space of a typical deep network and the adverse effect that it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that these and other commonly used deep networks, such as those which use a max-pooling and sub-sampling layer, possess more complex forms of symmetry arising from scaling based reparameterization of the network weights. We then propose two symmetry-invariant gradient based weight updates for stochastic gradient descent based learning. Our empirical evidence based on the MNIST dataset shows that these updates improve the test performance without sacrificing the computational efficiency of the weight updates. We also show the results of training with one of the proposed weight updates on an image segmentation problem.
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Thoughts on Massively Scalable Gaussian Processes. (arXiv:1511.01870v1 [cs.LG])
We introduce a framework and early results for massively scalable Gaussian processes (MSGP), significantly extending the KISS-GP approach of Wilson and Nickisch (2015). The MSGP framework enables the use of Gaussian processes (GPs) on billions of datapoints, without requiring distributed inference, or severe assumptions. In particular, MSGP reduces the standard $O(n^3)$ complexity of GP learning and inference to $O(n)$, and the standard $O(n^2)$ complexity per test point prediction to $O(1)$. MSGP involves 1) decomposing covariance matrices as Kronecker products of Toeplitz matrices approximated by circulant matrices. This multi-level circulant approximation allows one to unify the orthogonal computational benefits of fast Kronecker and Toeplitz approaches, and is significantly faster than either approach in isolation; 2) local kernel interpolation and inducing points to allow for arbitrarily located data inputs, and $O(1)$ test time predictions; 3) exploiting block-Toeplitz Toeplitz-block structure (BTTB), which enables fast inference and learning when multidimensional Kronecker structure is not present; and 4) projections of the input space to flexibly model correlated inputs and high dimensional data. The ability to handle many ($m \approx n$) inducing points allows for near-exact accuracy and large scale kernel learning.
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My Reflections on the First Man vs. Machine No-Limit Texas Hold 'em Competition. (arXiv:1510.08578v1 [cs.GT] CROSS LISTED)
The first ever human vs. computer no-limit Texas hold 'em competition took place from April 24-May 8, 2015 at River's Casino in Pittsburgh, PA. In this article I present my thoughts on the competition design, agent architecture, and lessons learned.
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Add anonymous access token token.
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Arsenal look to return to winning ways against rival Spurs
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Allow anonymous posting for one particular forum, but not all?
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Replace lambda with anonymous class across all project?
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FIFA hopeful Ali takes diplomatic path to presidential goal
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Fans clash with police ahead of Ajax-Fenerbahce match
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France coach Deschamps leaves out Valbuena amid Benzema case
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Palestine's World Cup qualifiers to be played in Jordan
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English club Huddersfield appoints David Wagner as coach
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Rome derby to be a damp squib in the stands as fans protest
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"Areas" menu link visible to anonymous users
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inspirehep/inspire-next
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ISS Daily Summary Report – 11/4/15
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Bayern hosts Stuttgart, Dortmund faces Schalke in Bundesliga
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France striker Karim Benzema charged in sex tape case
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[FD] SEC Consult SA-20151105-0 :: Insecure default configuration in Ubiquiti Networks products
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Madrid and Barcelona vie for the lead ahead the 'clasico'
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Anonymous set for KKK 'unmasking'
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FIFA World Rankings List
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Belgium moves to No. 1 in FIFA rankings for 1st time
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Benzema faces possible charges in sex tape case
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The Great Orion Nebula M42
Wednesday, November 4, 2015
Phoenix cancer patient's medical flight paid for by anonymous donor
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Learning in Auctions: Regret is Hard, Envy is Easy. (arXiv:1511.01411v1 [cs.GT])
We show that there are no polynomial-time no-regret learning algorithms for simultaneous second price auctions (SiSPAs), unless $RP\supseteq NP$, even when the bidders are unit-demand. We prove this by establishing a specific result about SiSPAs and a generic statement about online learning.
We complement this result by proposing a novel solution concept of learning in auctions, termed "no-envy learning". This notion is founded on Walrasian equilibrium, and we show that it is both efficiently computable and it results in approximate efficiency in SiSPAs, even for bidders from the broad class of XOS valuations (assuming demand oracle access to the valuations) or coverage valuations (even without demand oracles). Our result can be viewed as the first constant approximation for welfare maximization in combinatorial auctions with XOS valuations, where both the designer and the agents are computationally bounded. Our positive result for XOS valuations is based on a new class of Follow-The-Perturbed-Leader algorithms and an analysis framework for general online learning problems, which generalizes the existing framework of (Kalai and Vempala 2005) beyond linear utilities. Our results provide a positive counterpart to recent negative results on adversarial online learning via best-response oracles (Hazan and Korren 2015). We show that these results are of interest even outside auction settings, such as in security games of (Balcan et al. 2015). Our efficient learning result for coverage valuations is based on a novel use of convex rounding (Dughmi et al. 2011) and a reduction to online convex optimization.
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On the Tightness of LP Relaxations for Structured Prediction. (arXiv:1511.01419v1 [stat.ML])
Structured prediction applications often involve complex inference problems that require the use of approximate methods. Approximations based on linear programming (LP) relaxations have proved particularly successful in this setting, with both theoretical and empirical support. Despite the general intractability of inference, it has been observed that in many real-world applications the LP relaxation is often tight. In this work we propose a theoretical explanation to this striking observation. In particular, we show that learning with LP relaxed inference encourages tightness of training instances. We complement this result with a generalization bound showing that tightness generalizes from train to test data.
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Chelsea and Roma score late, stay alive in Champions League
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Barcelona midfielder Rakitic injures leg muscle
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Zenit reaches CL knockout phase with 2-0 win at Lyon
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I have a new follower on Twitter
Murray Newlands
Columnist @entrepreneur @forbes @HuffingtonPost
San Francisco - USA
https://t.co/B0qZTDEBTr
Following: 242969 - Followers: 298901
November 04, 2015 at 05:34PM via Twitter http://twitter.com/MurrayNewlands
Porto beats Maccabi Tel-Aviv in Champions League
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Pardo leads Olympiakos to 2-1 win over Dinamo Zagreb
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Gent beats Valencia 1-0 to stay alive in Champions League
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Bayern hammers Arsenal 5-1 in Champions League
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Neymar, Suarez score as Barcelona beats BATE 3-0 in CL
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Nantes wins 2-1 at Nice to go 7th in French league
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[FD] [KIS-2015-10] Piwik <= 2.14.3 (DisplayTopKeywords) PHP Object Injection Vulnerability
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[FD] [KIS-2015-09] Piwik <= 2.14.3 (viewDataTable) Autoloaded File Inclusion Vulnerability
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[FD] [KIS-2015-08] ATutor <= 2.2 (edit_marks.php) PHP Code Injection Vulnerability
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[FD] [KIS-2015-07] ATutor <= 2.2 (popuphelp.php) Reflected Cross-Site Scripting Vulnerability
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[FD] [KIS-2015-06] ATutor <= 2.2 (confirm.php) Session Variable Overloading Vulnerability
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[FD] [KIS-2015-05] ATutor <= 2.2 (Custom Course Icon) Unrestricted File Upload Vulnerability
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Prosecutors confirm Beckenbauer not target of tax probe
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ISS Daily Summary Report – 11/3/15
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France striker Karim Benzema arrested in blackmail case
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Sepp Blatter latest celebrity to be burned in effigy
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Fourth, a 16-year-old Hacker, Arrested over TalkTalk Hack
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Researcher releases Free Hacking Tool that Can Steal all Your Secrets from Password Manager
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nodejs/node
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FIFA rules Palestinians cannot host 2 World Cup qualifiers
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Seeking Venus under the Spitzkoppe Arch
Tuesday, November 3, 2015
The Murky Ethics of Doxing: Anonymous VS the KKK
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Galaxy-X: A Novel Approach for Multi-class Classification in an Open Universe. (arXiv:1511.00725v1 [cs.LG])
Classification is a fundamental task in machine learning and artificial intelligence. Existing classification methods are designed to classify unknown instances within a set of previously known classes that are seen in training. Such classification takes the form of prediction within a closed-set. However, a more realistic scenario that fits the ground truth of real world applications is to consider the possibility of encountering instances that do not belong to any of the classes that are seen in training, $i.e.$, an open-set classification. In such situation, existing closed-set classification methods will assign a training label to these instances resulting in a misclassification. In this paper, we introduce Galaxy-X, a novel multi-class classification method for open-set problem. For each class of the training set, Galaxy-X creates a minimum bounding hyper-sphere that encompasses the distribution of the class by enclosing all of its instances. In such manner, our method is able to distinguish instances resembling previously seen classes from those that are of unseen classes. Experimental results on benchmark datasets show the efficiency of our approach in classifying novel instances from known as well as unknown classes. We also introduce a novel evaluation procedure to adequately evaluate open-set classification.
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A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning. (arXiv:1511.00787v1 [cs.AI])
Path planning is one of the most vital elements of mobile robotics, providing the agent with a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost. D* is widely used for its dynamic replanning capabilities. Path planning optimization typically looks to minimize the distance traversed from start to goal, but many mobile robot applications call for additional path planning objectives, presenting a multiobjective optimization (MOO) problem. Common search algorithms, e.g. A* and D*, are not well suited for MOO problems, yielding suboptimal results. The search algorithm presented in this paper is designed for optimal MOO path planning. The algorithm incorporates Pareto optimality into D*, and is thus named D*-PO. Non-dominated solution paths are guaranteed by calculating the Pareto front at each search step. Simulations were run to model a planetary exploration rover in a Mars environment, with five path costs. The results show the new, Pareto optimal D*-PO outperforms the traditional A* and D* algorithms for MOO path planning.
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SAT as a game. (arXiv:1511.00813v1 [cs.CC])
We propose a funny representation of SAT. While the primary interest is to present propositional satisfiability in a playful way for pedagogical purposes, it could also inspire new search heuristics.
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Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments. (arXiv:1511.00840v1 [cs.AI])
Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm's best performance.
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SWISH: SWI-Prolog for Sharing. (arXiv:1511.00915v1 [cs.PL])
Recently, we see a new type of interfaces for programmers based on web technology. For example, JSFiddle, IPython Notebook and R-studio. Web technology enables cloud-based solutions, embedding in tutorial web pages, atractive rendering of results, web-scale cooperative development, etc. This article describes SWISH, a web front-end for Prolog. A public website exposes SWI-Prolog using SWISH, which is used to run small Prolog programs for demonstration, experimentation and education. We connected SWISH to the ClioPatria semantic web toolkit, where it allows for collaborative development of programs and queries related to a dataset as well as performing maintenance tasks on the running server and we embedded SWISH in the Learn Prolog Now! online Prolog book.
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Lowering the learning curve for declarative programming: a Python API for the IDP system. (arXiv:1511.00916v1 [cs.PL])
Programmers may be hesitant to use declarative systems, because of the associated learning curve. In this paper, we present an API that integrates the IDP Knowledge Base system into the Python programming language. IDP is a state-of-the-art logical system, which uses SAT, SMT, Logic Programming and Answer Set Programming technology. Python is currently one of the most widely used (teaching) languages for programming. The first goal of our API is to allow a Python programmer to use the declarative power of IDP, without needing to learn any new syntax or semantics. The second goal is allow IDP to be added to/removed from an existing code base with minimal changes.
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A web-based IDE for IDP. (arXiv:1511.00920v1 [cs.PL])
IDP is a knowledge base system based on first order logic. It is finding its way to a larger public but is still facing practical challenges. Adoption of new languages requires a newcomer-friendly way for users to interact with it. Both an online presence to try to convince potential users to download the system and offline availability to develop larger applications are essential. We developed an IDE which can serve both purposes through the use of web technology. It enables us to provide the user with a modern IDE with relatively little effort.
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Bound Your Models! How to Make OWL an ASP Modeling Language. (arXiv:1511.00924v1 [cs.PL])
To exploit the Web Ontology Language OWL as an answer set programming (ASP) language, we introduce the notion of bounded model semantics, as an intuitive and computationally advantageous alternative to its classical semantics. We show that a translation into ASP allows for solving a wide range of bounded-model reasoning tasks, including satisfiability and axiom entailment but also novel ones such as model extraction and enumeration. Ultimately, our work facilitates harnessing advanced semantic web modeling environments for the logic programming community through an "off-label use" of OWL.
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Visualising interactive inferences with IDPD3. (arXiv:1511.00928v1 [cs.PL])
A large part of the use of knowledge base systems is the interpretation of the output by the end-users and the interaction with these users. Even during the development process visualisations can be a great help to the developer. We created IDPD3 as a library to visualise models of logic theories. IDPD3 is a new version of $ID^{P}_{Draw}$ and adds support for visualised interactive simulations.
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Understanding symmetries in deep networks. (arXiv:1511.01029v1 [cs.LG])
Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that a commonly used deep network, which uses convolution, batch normalization, reLU, max-pooling, and sub-sampling pipeline, possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights. We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold. Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold. Our empirical evidence based on the MNIST dataset shows that the proposed updates improve the test performance beyond what is achieved with batch normalization and without sacrificing the computational efficiency of the weight updates.
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