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Saturday, December 26, 2015
Chaconne in A major, Schrank II/38/38 (Anonymous)
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1 Today 14:22:29
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I have a new follower on Twitter
Hans-Christian Preym
Co-founder of https://t.co/njQbUbrRgI Country manager at Ebbon-Dacs Deutschland GmbH and owner/MD of Universal Business Development GmbH
Salzburg Area | Austria
Following: 767 - Followers: 1124
December 26, 2015 at 05:03AM via Twitter http://twitter.com/HCPreymann
Friday, December 25, 2015
I have a new follower on Twitter
OMI
We assist businesses in engaging clients across Customer Lifecycles. #Marketing #Sales #CRM
Atlanta, GA
http://t.co/tYZMwofOLH
Following: 4508 - Followers: 5261
December 25, 2015 at 05:46AM via Twitter http://twitter.com/OMI4U
I have a new follower on Twitter
Big Cloud
Big thinking recruiters, specialising in Big Data, Data Science & Machine Learning. #BigData #DataScience #MachineLearning #InternetofThings #Analytics
World via Manchester
https://t.co/d1QRXoiGaM
Following: 2415 - Followers: 6166
December 25, 2015 at 05:31AM via Twitter http://twitter.com/BigCloudTeam
Star Colors and Pinyon Pine
Thursday, December 24, 2015
Representation and Coding of Signal Geometry. (arXiv:1512.07636v1 [cs.IT])
Approaches to signal representation and coding theory have traditionally focused on how to best represent signals using parsimonious representations that incur the lowest possible distortion. Classical examples include linear and non-linear approximations, sparse representations, and rate-distortion theory. Very often, however, the goal of processing is to extract specific information from the signal, and the distortion should be measured on the extracted information. The corresponding representation should, therefore, represent that information as parsimoniously as possible, without necessarily accurately representing the signal itself.
In this paper, we examine the problem of encoding signals such that sufficient information is preserved about their pairwise distances and their inner products. For that goal, we consider randomized embeddings as an encoding mechanism and provide a framework to analyze their performance. We also demonstrate that it is possible to design the embedding such that it represents different ranges of distances with different precision. These embeddings also allow the computation of kernel inner products with control on their inner product-preserving properties. Our results provide a broad framework to design and analyze embeddins, and generalize existing results in this area, such as random Fourier kernels and universal embeddings.
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The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms. (arXiv:1512.07650v1 [stat.ML])
We consider the Max $K$-Armed Bandit problem, where a learning agent is faced with several stochastic arms, each a source of i.i.d. rewards of unknown distribution. At each time step the agent chooses an arm, and observes the reward of the obtained sample. Each sample is considered here as a separate item with the reward designating its value, and the goal is to find an item with the highest possible value. Our basic assumption is a known lower bound on the {\em tail function} of the reward distributions. Under the PAC framework, we provide a lower bound on the sample complexity of any $(\epsilon,\delta)$-correct algorithm, and propose an algorithm that attains this bound up to logarithmic factors. We analyze the robustness of the proposed algorithm and in addition, we compare the performance of this algorithm to the variant in which the arms are not distinguishable by the agent and are chosen randomly at each stage. Interestingly, when the maximal rewards of the arms happen to be similar, the latter approach may provide better performance.
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Reinforcement Learning in Large Discrete Action Spaces. (arXiv:1512.07679v1 [cs.AI])
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, industrial plants and language models are only some of the many real-world tasks involving large numbers of discrete actions for which current methods can be difficult or even impossible to apply.
An ability to generalize over the set of actions as well as sub-linear complexity relative to the size of the set are both necessary to handle such tasks. Current approaches are not able to provide both of these, which motivates the work in this paper. Our proposed approach leverages prior information about the actions to embed them in a continuous space upon which it can generalize. Additionally, approximate nearest-neighbor methods allow for logarithmic-time lookup complexity relative to the number of actions, which is necessary for time-wise tractable training. This combined approach allows reinforcement learning methods to be applied to large-scale learning problems previously intractable with current methods. We demonstrate our algorithm's abilities on a series of tasks having up to one million actions.
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Measuring pattern retention in anonymized data -- where one measure is not enough. (arXiv:1512.07721v1 [cs.AI])
In this paper, we explore how modifying data to preserve privacy affects the quality of the patterns discoverable in the data. For any analysis of modified data to be worth doing, the data must be as close to the original as possible. Therein lies a problem -- how does one make sure that modified data still contains the information it had before modification? This question is not the same as asking if an accurate classifier can be built from the modified data. Often in the literature, the prediction accuracy of a classifier made from modified (anonymized) data is used as evidence that the data is similar to the original. We demonstrate that this is not the case, and we propose a new methodology for measuring the retention of the patterns that existed in the original data. We then use our methodology to design three measures that can be easily implemented, each measuring aspects of the data that no pre-existing techniques can measure. These measures do not negate the usefulness of prediction accuracy or other measures -- they are complementary to them, and support our argument that one measure is almost never enough.
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RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles. (arXiv:1512.07734v1 [cs.AI])
Recently, several large-scale RDF knowledge bases have been built and applied in many knowledge-based applications. To further increase the number of facts in RDF knowledge bases, logic rules can be used to predict new facts based on the existing ones. Therefore, how to automatically learn reliable rules from large-scale knowledge bases becomes increasingly important. In this paper, we propose a novel rule learning approach named RDF2Rules for RDF knowledge bases. RDF2Rules first mines frequent predicate cycles (FPCs), a kind of interesting frequent patterns in knowledge bases, and then generates rules from the mined FPCs. Because each FPC can produce multiple rules, and effective pruning strategy is used in the process of mining FPCs, RDF2Rules works very efficiently. Another advantage of RDF2Rules is that it uses the entity type information when generates and evaluates rules, which makes the learned rules more accurate. Experiments show that our approach outperforms the compared approach in terms of both efficiency and accuracy.
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Distinguishing cause from effect using observational data: methods and benchmarks. (arXiv:1412.3773v3 [cs.LG] UPDATED)
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.
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The Information-theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition. (arXiv:1501.04242v5 [cs.AI] UPDATED)
We survey concepts at the frontier of research connecting artificial, animal and human cognition to computation and information processing---from the Turing test to Searle's Chinese Room argument, from Integrated Information Theory to computational and algorithmic complexity. We start by arguing that passing the Turing test is a trivial computational problem and that its pragmatic difficulty sheds light on the computational nature of the human mind more than it does on the challenge of artificial intelligence. We then review our proposed algorithmic information-theoretic measures for quantifying and characterizing cognition in various forms. These are capable of accounting for known biases in human behavior, thus vindicating a computational algorithmic view of cognition as first suggested by Turing, but this time rooted in the concept of algorithmic probability, which in turn is based on computational universality while being independent of computational model, and which has the virtue of being predictive and testable as a model theory of cognitive behavior.
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Online Context-Dependent Clustering in Recommendations based on Exploration-Exploitation Algorithms. (arXiv:1502.03473v4 [cs.LG] UPDATED)
We investigate two context-dependent clustering techniques for content recommendation based on exploration-exploitation strategies in contextual multiarmed bandit settings. Our algorithms dynamically group users based on the items under consideration and, possibly, group items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on extensive real-world datasets, showing scalability and increased prediction performance over state-of-the-art methods for clustering bandits. For one of the two algorithms we also give a regret analysis within a standard linear stochastic noise setting.
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jobs from Anonymous
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[FD] Nordex Control 2 (NC2) SCADA V16 and prior versions - XSS
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ISS Daily Summary Report – 12/23/15
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Want WhatsApp Free Video Calling? This Leaked Screenshot Reveals Upcoming Feature
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India temporarily Bans Facebook's Free Internet Service
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Ocean City, MD's surf is at least 6.53ft high
Ocean City, MD Summary
At 2:00 AM, surf min of 6.53ft. At 8:00 AM, surf min of 5.42ft. At 2:00 PM, surf min of 4.12ft. At 8:00 PM, surf min of 3.2ft.
Surf maximum: 7.53ft (2.3m)
Surf minimum: 6.53ft (1.99m)
Tide height: 0.45ft (0.14m)
Wind direction: E
Wind speed: 20.51 KTS
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Hyatt Hotel Says Payment Systems Hacked with Credit-Card Stealing Malware
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Geminid Meteors over Xinglong Observatory
Wednesday, December 23, 2015
Two Denton Churches Receive Anonymous Threats
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The ERA of FOLE: Foundation. (arXiv:1512.07430v1 [cs.DB])
This paper discusses the representation of ontologies in the first-order logical environment FOLE (Kent 2013). An ontology defines the primitives with which to model the knowledge resources for a community of discourse (Gruber 2009). These primitives, consisting of classes, relationships and properties, are represented by the entity-relationship-attribute ERA data model (Chen 1976). An ontology uses formal axioms to constrain the interpretation of these primitives. In short, an ontology specifies a logical theory. This paper is the first in a series of three papers that provide a rigorous mathematical representation for the ERA data model in particular, and ontologies in general, within the first-order logical environment FOLE. The first two papers show how FOLE represents the formalism and semantics of (many-sorted) first-order logic in a classification form corresponding to ideas discussed in the Information Flow Framework (IFF). In particular, this first paper provides a foundation that connects elements of the ERA data model with components of the first-order logical environment FOLE, and the second paper provides a superstructure that extends FOLE to the formalisms of first-order logic. The third paper defines an interpretation of FOLE in terms of the transformational passage, first described in (Kent 2013), from the classification form of first-order logic to an equivalent interpretation form, thereby defining the formalism and semantics of first-order logical/relational database systems (Kent 2011). The FOLE representation follows a conceptual structures approach, that is completely compatible with formal concept analysis (Ganter and Wille 1999) and information flow (Barwise and Seligman 1997).
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Selecting the top-quality item through crowd scoring. (arXiv:1512.07487v1 [cs.AI])
We investigate crowdsourcing algorithms for finding the top-quality item within a large collection of objects with unknown intrinsic quality values. This is an important problem with many relevant applications, for example in networked recommendation systems. The core of the algorithms is that objects are distributed to crowd workers, who return a noisy evaluation. All received evaluations are then combined, to identify the top-quality object. We first present a simple probabilistic model for the system under investigation. Then, we devise and study a class of efficient adaptive algorithms to assign in an effective way objects to workers. We compare the performance of several algorithms, which correspond to different choices of the design parameters/metrics. We finally compare our approach based on scoring object qualities against traditional proposals based on comparisons and tournaments.
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Randomized Social Choice Functions Under Metric Preferences. (arXiv:1512.07590v1 [cs.AI])
We determine the quality of randomized social choice mechanisms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric. We assume that these costs are unknown to the mechanisms (and possibly even to the agents themselves), which means we cannot simply select the optimal alternative, i.e. the alternative that minimizes the total agent cost (or median agent cost). However, we do assume that the agents know their ordinal preferences that are induced by the metric space. We examine randomized social choice functions that require only this ordinal information and select an alternative that is good in expectation with respect to the costs from the metric. To quantify how good a randomized social choice function is, we bound the distortion, which is the worst-case ratio between expected cost of the alternative selected and the cost of the optimal alternative. We provide new distortion bounds for a variety of randomized mechanisms, for both general metrics and for important special cases. Our results show a sizable improvement in distortion over deterministic mechanisms.
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Putting Things in Context: Community-specific Embedding Projections for Sentiment Analysis. (arXiv:1511.06052v2 [cs.CL] UPDATED)
Variation in language is ubiquitous, and is particularly evident in newer forms of writing such as social media. Fortunately, variation is not random, but is usually linked to social factors. By exploiting linguistic homophily --- the tendency of socially linked individuals to use language similarly --- it is possible to build models that are more robust to variation. In this paper, we focus on social network communities, which make it possible to generalize sociolinguistic properties from authors in the training set to authors in the test sets, without requiring demographic author metadata. We detect communities via standard graph clustering algorithms, and then exploit these communities by learning community-specific projections of word embeddings. These projections capture shifts in word meaning in different social groups; by modeling them, we are able to improve the overall accuracy of Twitter sentiment analysis by a significant margin over competitive prior work.
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Anonymous donor leaves check for $50000 at nativity scene
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Hacking group Anonymous declares 'cyber war' on Turkey for
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Dirtyphonics
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I have a new follower on Twitter
Daniel Barth-Jones
#HIPAA Health Data #Privacy De-identification, HIV/Inf Dis/Computnl/Digital Epidemiology, Vaccinology, #DataScience, #BigData, #GIS, UrbanScience, Public Policy
Columbia University, NYC
http://t.co/rBga3M1HPc
Following: 6445 - Followers: 5890
December 23, 2015 at 01:36PM via Twitter http://twitter.com/dbarthjones
[FD] esoTalk 1.0.0g4: XSS
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[FD] CouchCMS 1.4.5: Code Execution
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[FD] CouchCMS 1.4.5: XSS & Open Redirect
- href="%3C?php%20echo%20k_create_link(array('status',%20'pg'));%20?%3E">t('all'); ?> |
- href="%3C?php%20echo%20k_create_link(array('status',%20'pg'));%20?%3E&status=0">t('unapproved'); ?> |
- href="%3C?php%20echo%20k_create_link(array('status',%20'pg'));%20?%3E&status=1">t('approved'); ?> (of '.$page_title.')'; } ?>
| t('view'); ?> | t('edit'); ?> | t('delete'); ?>
Open Redirect The filter which checks if a user supplied redirect value leads to external pages can be bypassed by an attacker. Proof of Concept (Only works for logged in victims or after login): http://localhost/CouchCMS-1.4.5/couch/login.php?redirect=//google.com Code: /couch/auth/auth.php function redirect( $dest ){ global $FUNCS, $DB; // sanity checks $dest = $FUNCS->sanitize_url( trim($dest) ); if( !strlen($dest) ){ $dest = ( $this->user->access_level < K_ACCESS_LEVEL_ADMIN ) ? K_SITE_URL : K_ADMIN_URL . K_ADMIN_PAGE; } elseif( strpos(strtolower($dest), 'http')===0 ){ if( strpos($dest, K_SITE_URL)!==0 ){ // we don't allow redirects external to our site $dest = K_SITE_URL; } } $DB->commit( 1 ); header( "Location: ".$dest ); die(); } 4. Solution To mitigate this issue please upgrade at least to version 1.4.7: http://ift.tt/1Psz3AY Please note that a newer version might already be available. 5. Report Timeline 11/17/2015 Informed Vendor about Issue 11/18/2015 Vendor sends fixes for confirmation 11/20/2015 Verified fixes 11/24/2015 Vendor releases fix 12/21/2015 Disclosed to public Blog Reference: http://ift.tt/1JuUficSource: Gmail -> IFTTT-> Blogger
[FD] Grawlix 1.0.3: Code Execution
[FD] Grawlix 1.0.3: CSRF
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[FD] Grawlix 1.0.3: XSS
$_GET|';print_r($_GET);echo '|'; XSS 3 The edit_id parameter of the site.nav-edit.ajax.php is vulnerable to XSS. Proof of Concept: http://localhost/grawlix-1.0.3/_admin/site.nav-edit.ajax.php?edit_id=">Code: _admin/site.nav-edit.ajax.php $edit_id = $_GET['edit_id']; [...] $modal->value($edit_id); _admin/lib/GrlxForm.php $this->value ? $value = ' value="'.$this->value.'"' : null; XSS 4 When viewing the book overview, the start_sort_order parameter is vulnerable to XSS. Proof of Concept: http://localhost/grawlix-1.0.3/_admin/book.view.php?delete_page_id=1&start_sort_order=" onmouseover="alert(1) Code: _admin/book.view.php $delete_link->query("delete_page_id=$val[id]&start_sort_order=$start_sort_order"); XSS 5 (limited) In two scripts, the page_id value is put into a hidden input element without encoding quotes. It may be possible to execute JavaScript via a style element in older browsers. Proof of Concept: http://localhost/grawlix-1.0.3/_admin/sttc.xml-edit.php?msg=created&page_id=" style="STYLE http://localhost/grawlix-1.0.3/_admin/book.page-edit.php?page_id=" style="STYLE 4. Solution This issue was not fixed by the vendor. 5. Report Timeline 11/17/2015 Informed Vendor about Issue (no reply) 12/10/2015 Reminded Vendor of Disclosure Date (no reply) 12/21/2015 Disclosed to public Blog Reference: http://ift.tt/1NCqGiG
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[FD] Arastta 1.1.5: SQL Injection
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[FD] Arastta 1.1.5: XSS
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[FD] PhpSocial v2.0.0304: CSRF
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[FD] PhpSocial v2.0.0304: XSS
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ISS Daily Summary Report – 12/22/15
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Re: [FD] Wordpress Content Text Slider on Post 6.8 - Persistent Vulnerability
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Re: [FD] Symfony CMS 2.6.3 – Multiple Cross-Site Scripting Vulnerability
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I have a new follower on Twitter
Football Fans
World's first fan-run pro sports franchise. Fans decide everything from mascot to in-game play calls via proprietary technology. Let's do this.
Dallas
https://t.co/TVVRuzQgIx
Following: 3487 - Followers: 2210
December 23, 2015 at 09:22AM via Twitter http://twitter.com/ProFootball_Fan
Anonymous declares cyber war on Turkey
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Anonymous declares cyber war on Turkish government websites, citing Ankara's support of Islamic ...
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130 Celebrities' Email Accounts Hacked; Hacker Stole Movie Scripts and Sex Tapes
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Kim Dotcom loses Fight Against Extradition to the US
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NASA Images Show Human Fingerprint on Global Air Quality - Release Materials
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Tuesday, December 22, 2015
Ravens: G Marshal Yanda and P Sam Koch selected to Pro Bowl (ESPN)
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Addressing Complex and Subjective Product-Related Queries with Customer Reviews. (arXiv:1512.06863v1 [cs.IR])
Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in the overhead compartment of a 747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer such questions we must either wade through huge volumes of consumer reviews hoping to find one that is relevant, or otherwise pose our question directly to the community via a Q/A system.
In this paper we hope to fuse these two paradigms: given a large volume of previously answered queries about products, we hope to automatically learn whether a review of a product is relevant to a given query. We formulate this as a machine learning problem using a mixture-of-experts-type framework---here each review is an `expert' that gets to vote on the response to a particular query; simultaneously we learn a relevance function such that `relevant' reviews are those that vote correctly. At test time this learned relevance function allows us to surface reviews that are relevant to new queries on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million questions (and answers) and 13 million reviews. We show quantitatively that it is effective at addressing both binary and open-ended queries, and qualitatively that it surfaces reviews that human evaluators consider to be relevant.
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Restricted Predicates for Hypothetical Datalog. (arXiv:1512.06945v1 [cs.DB])
Hypothetical Datalog is based on an intuitionistic semantics rather than on a classical logic semantics, and embedded implications are allowed in rule bodies. While the usual implication (i.e., the neck of a Horn clause) stands for inferring facts, an embedded implication plays the role of assuming its premise for deriving its consequence. A former work introduced both a formal framework and a goal-oriented tabled implementation, allowing negation in rule bodies. While in that work positive assumptions for both facts and rules can occur in the premise, negative assumptions are not allowed. In this work, we cover this subject by introducing a new concept: a restricted predicate, which allows negative assumptions by pruning the usual semantics of a predicate. This new setting has been implemented in the deductive system DES.
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On the Differential Privacy of Bayesian Inference. (arXiv:1512.06992v1 [cs.AI])
We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and Bayesian linear regression illustrate the application of our mechanisms.
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Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences. (arXiv:1512.07048v1 [cs.AI])
Our world is filled with both beautiful and brainy people, but how often does a Nobel Prize winner also wins a beauty pageant? Let us assume that someone who is both very beautiful and very smart is more rare than what we would expect from the combination of the number of beautiful and brainy people. Of course there will still always be some individuals that defy this stereotype; these beautiful brainy people are exactly the class of anomaly we focus on in this paper. They do not posses rare qualities, but it is the unexpected combination of factors that makes them stand out.
In this paper we define the above described class of anomaly and propose a method to quickly identify them in transaction data. Further, as we take a pattern set based approach, our method readily explains why a transaction is anomalous. The effectiveness of our method is thoroughly verified with a wide range of experiments on both real world and synthetic data.
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Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns. (arXiv:1512.07056v1 [cs.AI])
We study how to obtain concise descriptions of discrete multivariate sequential data in terms of rich multivariate sequential patterns that can capture potentially highly interesting (cor)relations between sequences. To this end we allow our pattern language to span over the alphabets (domains) of all sequences, allow patterns to overlap temporally, and allow for gaps in their occurrences. We formalise our goal by the Minimum Description Length principle, by which our objective is to discover the set of patterns that provides the most succinct description of the data. To discover good pattern sets, we introduce Ditto, an efficient algorithm to approximate the ideal result. We support our claim with a set of experiments on both synthetic and real data.
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SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation. (arXiv:1512.07143v1 [cs.AI])
While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods.
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Heuristic algorithms for finding distribution reducts in probabilistic rough set model. (arXiv:1512.07162v1 [cs.AI])
Attribute reduction is one of the most important topics in rough set theory. Heuristic attribute reduction algorithms have been presented to solve the attribute reduction problem. It is generally known that fitness functions play a key role in developing heuristic attribute reduction algorithms. The monotonicity of fitness functions can guarantee the validity of heuristic attribute reduction algorithms. In probabilistic rough set model, distribution reducts can ensure the decision rules derived from the reducts are compatible with those derived from the original decision table. However, there are few studies on developing heuristic attribute reduction algorithms for finding distribution reducts. This is partly due to the fact that there are no monotonic fitness functions that are used to design heuristic attribute reduction algorithms in probabilistic rough set model. The main objective of this paper is to develop heuristic attribute reduction algorithms for finding distribution reducts in probabilistic rough set model. For one thing, two monotonic fitness functions are constructed, from which equivalence definitions of distribution reducts can be obtained. For another, two modified monotonic fitness functions are proposed to evaluate the significance of attributes more effectively. On this basis, two heuristic attribute reduction algorithms for finding distribution reducts are developed based on addition-deletion method and deletion method. In particular, the monotonicity of fitness functions guarantees the rationality of the proposed heuristic attribute reduction algorithms. Results of experimental analysis are included to quantify the effectiveness of the proposed fitness functions and distribution reducts.
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Diffusion Methods for Classification with Pairwise Relationships. (arXiv:1505.06072v3 [cs.AI] UPDATED)
We define two algorithms for propagating information in classification problems with pairwise relationships. The algorithms are based on contraction maps and are related to non-linear diffusion and random walks on graphs. The approach is also related to message passing algorithms, including belief propagation and mean field methods. The algorithms we describe are guaranteed to converge on graphs with arbitrary topology. Moreover they always converge to a unique fixed point, independent of initialization. We prove that the fixed points of the algorithms under consideration define lower-bounds on the energy function and the max-marginals of a Markov random field. The theoretical results also illustrate a relationship between message passing algorithms and value iteration for an infinite horizon Markov decision process. We illustrate the practical application of the algorithms under study with numerical experiments in image restoration, stereo depth estimation and binary classification on a grid.
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Black-Box Policy Search with Probabilistic Programs. (arXiv:1507.04635v3 [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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Action-Conditional Video Prediction using Deep Networks in Atari Games. (arXiv:1507.08750v2 [cs.LG] UPDATED)
Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are high-dimensional in size, can involve tens of objects with one or more objects being controlled by the actions directly and many other objects being influenced indirectly, can involve entry and departure of objects, and can involve deep partial observability. We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Experimental results show that the proposed architectures are able to generate visually-realistic frames that are also useful for control over approximately 100-step action-conditional futures in some games. To the best of our knowledge, this paper is the first to make and evaluate long-term predictions on high-dimensional video conditioned by control inputs.
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Ocean City, MD's surf is at least 5.42ft high
Ocean City, MD Summary
At 2:00 AM, surf min of 3.08ft. At 8:00 AM, surf min of 4.72ft. At 2:00 PM, surf min of 5.42ft. At 8:00 PM, surf min of 4.12ft.
Surf maximum: 6.43ft (1.96m)
Surf minimum: 5.42ft (1.65m)
Tide height: 0.55ft (0.17m)
Wind direction: ESE
Wind speed: 22.12 KTS
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Ravens: Despite a loss, Baltimore (4-10) inches up a spot to 28th in Week 16 NFL Power Rankings; open for full rankings (ESPN)
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Orioles Buzz: Baltimore reportedly keeping eye on free agent SP Mat Latos; 64-55, 3.51 ERA in 7-year career (ESPN)
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Encrypted Email Servers Seized by German Authorities After School Bomb Threats
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[FD] SIPROTEC 4 and SIPROTEC Compact FAQ #5
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I have a new follower on Twitter
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Alcoholics Anonymous
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SiteSell Inc.
Helping entrepreneurs take whatever their passions are and move them from ideas to income. Social, marketing, web design, blogging. Lovers of freedom.
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