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Saturday, July 9, 2016
Dallas police placed on high alert after anonymous threat following attack
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Dallas Police Heighten Security After Anonymous Threat
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Dallas shootings: Police receive anonymous threat against officers, security heightened
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URGENT – Dallas Police HQ on Lockdown After Anonymous Threat
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Dallas Police Heighten Security After Anonymous Threat
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Dallas police say department received anonymous threat across city
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Orioles Video: Mark Trumbo sends his 28th HR of the season into the center field seats in 3-2 win over the Angels (ESPN)
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Anonymous user 6b676b
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Another CEO Hacked... It's Twitter CEO Jack Dorsey!
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Anonymous users have access to restricted entities
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Snowden says It's a 'Dark Day for Russia' after Putin Signs Anti-Terror Law
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Permanent Shadows on Ceres
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Friday, July 8, 2016
Antwerp researchers identify anonymous internet culprits
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Facebook Messenger adds End-to-End Encryption (Optional) for Secret Conversations
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Ravens LB Elvis Dumervil, who has made Pro Bowl each of last two seasons, joins SportsCenter; watch live in the ESPN App (ESPN)
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ISS Daily Summary Report – 07/07/16
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Over 1000 Wendy's Restaurants Hit by Credit Card Hackers
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Hackers Can Steal Your ATM PIN from Your Smartwatch Or Fitness Tracker
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[FD] BMW ConnectedDrive - (Update) VIN Session Vulnerability
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[FD] BMW - (Token) Client Side Cross Site Scripting Vulnerability
Ihr neues Passwort für Mein BMW.
Legen Sie hier Ihr neues Passwort fest.
I have a new follower on Twitter
Dialog Group
Dialog combines high-level strategy and top-notch execution: strategy at the speed of digital. 512.697.9425
Austin, TX
https://t.co/q7LSSKfJbx
Following: 1997 - Followers: 2097
July 08, 2016 at 04:00AM via Twitter http://twitter.com/DialogGroup
Gauteng Legislature on outcome of investigations on anonymous letter – allegations of corruption ...
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The Altiplano Night
Anonymous Donor Helps the Unity Center
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Thursday, July 7, 2016
Not working for anonymous users
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Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising. (arXiv:1607.01869v1 [cs.IR])
Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.
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Mapping Data to Ontologies with Exceptions Using Answer Set Programming. (arXiv:1607.02018v1 [cs.AI])
In ontology-based data access, databases are connected to an ontology via mappings from queries over the database to queries over the ontology. In this paper, we consider mappings from relational databases to first-order ontologies, and define an ASP-based framework for GLAV mappings with queries over the ontology in the mapping rule bodies. We show that this type of mappings can be used to express constraints and exceptions, as well as being a powerful mechanism for succinctly representing OBDA mappings. We give an algorithm for brave reasoning in this setting, and show that this problem has either the same data complexity as ASP (NP- complete), or it is at least as hard as the complexity of checking entailment for the ontology queries. Furthermore, we show that for ontologies with UCQ-rewritable queries there exists a natural reduction from mapping programs to \exists-ASP, an extension of ASP with existential variables that itself admits a natural reduction to ASP.
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Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity. (arXiv:1607.02061v1 [cs.CL])
Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, which causes the loss of important information for word expectations, such as word order and interrelations. In this paper, we present a DSM which addresses the issue by defining verb contexts as joint dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts are more efficient than single dependencies, even with a relatively small amount of training data.
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Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization. (arXiv:1307.4847v4 [cs.LG] UPDATED)
We consider the problem of reinforcement learning over episodes of a finite-horizon deterministic system and as a solution propose optimistic constraint propagation (OCP), an algorithm designed to synthesize efficient exploration and value function generalization. We establish that when the true value function lies within a given hypothesis class, OCP selects optimal actions over all but at most K episodes, where K is the eluder dimension of the given hypothesis class. We establish further efficiency and asymptotic performance guarantees that apply even if the true value function does not lie in the given hypothesis class, for the special case where the hypothesis class is the span of pre-specified indicator functions over disjoint sets. We also discuss the computational complexity of OCP and present computational results involving two illustrative examples.
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Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge. (arXiv:1606.04422v2 [cs.AI] UPDATED)
We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's tensorflow primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge completion.
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Orioles: Mark Trumbo (26 HR) will face Dodgers rookie Corey Seager (17 HR) at Home Run Derby on Monday (ESPN)
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Orioles: OF Mark Trumbo says he will participate in Monday's Home Run Derby in San Diego; leads MLB with 26 HRs (ESPN)
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Ravens: Steve Smith against Broncos CB Aqib Talib ranks as one of the NFL's top 10 feuds - Kevin Seifert (ESPN)
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Flaw Allows Attackers to Remotely Hack BMW's In-Car Infotainment Systems
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VFX Breakdown of “Anonymous”
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I have a new follower on Twitter
Emely Dishman
Hiker | Educator and believer in learning from hands-on experiences in Maker education | Love romance books
Following: 1130 - Followers: 202
July 07, 2016 at 01:35PM via Twitter http://twitter.com/Emely2213
[FD] [KIS-2016-11] IPS Community Suite <= 4.1.12.3 Autoloaded PHP Code Injection Vulnerability
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[FD] CODEBLUE.JP - Conference in Tokyo Calling for Papers by Aug.10
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[FD] Acer Portal Android Application - MITM SSL Certificate Vulnerability (CVE-2016-5648)
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[FD] Zero-day flaw lets hackers tamper with your car through BMW portal
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ISS Daily Summary Report – 07/06/16
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Bulgaria passes Law that mandates Government Software must be Open Source
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Antivirus firm Avast to Buy its rival AVG for $1.3 Billion
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Facebook launches OpenCellular — An open-source Wireless Access Platform
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Arp 286: Trio in Virgo
Wednesday, July 6, 2016
I have a new follower on Twitter
J.D. Wyborny
Bringing Big Data Solutions to the Masses
Winterville, NC
https://t.co/NMGziRpmmd
Following: 2997 - Followers: 2948
July 06, 2016 at 09:52PM via Twitter http://twitter.com/Exceligentbiz
Orioles Video: Jonathan Schoop's bloop 2-run double in 14th inning snaps tie and leads to 6-4 victory over the Dodgers (ESPN)
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Mixed Strategy for Constrained Stochastic Optimal Control. (arXiv:1607.01478v1 [cs.RO])
Choosing control inputs randomly can result in a reduced expected cost in optimal control problems with stochastic constraints, such as stochastic model predictive control (SMPC). We consider a controller with initial randomization, meaning that the controller randomly chooses from K+1 control sequences at the beginning (called K-randimization).It is known that, for a finite-state, finite-action Markov Decision Process (MDP) with K constraints, K-randimization is sufficient to achieve the minimum cost. We found that the same result holds for stochastic optimal control problems with continuous state and action spaces.Furthermore, we show the randomization of control input can result in reduced cost when the optimization problem is nonconvex, and the cost reduction is equal to the duality gap. We then provide the necessary and sufficient conditions for the optimality of a randomized solution, and develop an efficient solution method based on dual optimization. Furthermore, in a special case with K=1 such as a joint chance-constrained problem, the dual optimization can be solved even more efficiently by root finding. Finally, we test the theories and demonstrate the solution method on multiple practical problems ranging from path planning to the planning of entry, descent, and landing (EDL) for future Mars missions.
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Towards Self-explanatory Ontology Visualization with Contextual Verbalization. (arXiv:1607.01490v1 [cs.AI])
Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) elements, displaying controlled natural language (CNL) explanations of OWL axioms corresponding to the selected visual notation elements. Thus, the domain experts will benefit from both the high-level overview provided by the graphical notation and the detailed textual explanations of particular elements in the diagram.
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Lattice Structure of Variable Precision Rough Sets. (arXiv:1607.01634v1 [cs.AI])
The main purpose of this paper is to study the lattice structure of variable precision rough sets. The notion of variation in precision of rough sets have been further extended to variable precision rough set with variable classification error and its algebraic properties are also studied.
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A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features. (arXiv:1607.01690v1 [cs.LG])
The Tree Augmented Naive Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies. In this work, we propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN) algorithm, which considers removing the hierarchical redundancy during the classifier learning process, when coping with data containing hierarchically structured features. The experiments showed that HRE-TAN obtains significantly better predictive performance than the conventional Tree Augmented Naive Bayes classifier, and enhanced the robustness against imbalanced class distributions, in aging-related gene datasets with Gene Ontology terms used as features.
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Deep CORAL: Correlation Alignment for Deep Domain Adaptation. (arXiv:1607.01719v1 [cs.CV])
Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupervised adaptation. CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation. Here, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). Experiments on standard benchmark datasets show state-of-the-art performance.
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Cost-Optimal Algorithms for Planning with Procedural Control Knowledge. (arXiv:1607.01729v1 [cs.AI])
There is an impressive body of work on developing heuristics and other reasoning algorithms to guide search in optimal and anytime planning algorithms for classical planning. However, very little effort has been directed towards developing analogous techniques to guide search towards high-quality solutions in hierarchical planning formalisms like HTN planning, which allows using additional domain-specific procedural control knowledge. In lieu of such techniques, this control knowledge often needs to provide the necessary search guidance to the planning algorithm, which imposes a substantial burden on the domain author and can yield brittle or error-prone domain models. We address this gap by extending recent work on a new hierarchical goal-based planning formalism called Hierarchical Goal Network (HGN) Planning to develop the Hierarchically-Optimal Goal Decomposition Planner (HOpGDP), an HGN planning algorithm that computes hierarchically-optimal plans. HOpGDP is guided by $h_{HL}$, a new HGN planning heuristic that extends existing admissible landmark-based heuristics from classical planning to compute admissible cost estimates for HGN planning problems. Our experimental evaluation across three benchmark planning domains shows that HOpGDP compares favorably to both optimal classical planners due to its ability to use domain-specific procedural knowledge, and a blind-search version of HOpGDP due to the search guidance provided by $h_{HL}$.
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Rolling Horizon Coevolutionary Planning for Two-Player Video Games. (arXiv:1607.01730v1 [cs.AI])
This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI. The approach is to extend recent work on rolling horizon evolutionary planning, which has been shown to work well for single-player games, to two (or in principle many) player games. To select an action the algorithm co-evolves two (or in the general case N) populations, one for each player, where each individual is a sequence of actions for the respective player. The fitness of each individual is evaluated by playing it against a selection of action-sequences from the opposing population. When choosing an action to take in the game, the first action is chosen from the fittest member of the population for that player. The new algorithm is compared with a number of general video game AI algorithms on three variations of a two-player space battle game, with promising results.
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Distributed Constraint Optimization Problems and Applications: A Survey. (arXiv:1602.06347v2 [cs.AI] UPDATED)
The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.
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Audio Event Detection using Weakly Labeled Data. (arXiv:1605.02401v3 [cs.SD] UPDATED)
Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.
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Conduct anonymous ph interview -questions provided
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No Views Results for Anonymous User
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Anonymous user reaction to the entity
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[FD] GNU Wget < 1.18 Arbitrary File Upload
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Lambda (anonymous/first class procedures) and custom reporters
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[FD] RS232-NET Converter (JTC-200) - Multiple vulnerabilities
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[FD] CIMA DocuClass ECM - Multiple Vulnerabilities
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[FD] CVE-2016-4979: HTTPD webserver - X509 Client certificate based authentication can be bypassed when HTTP/2 is used [vs]
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[FD] Putty (beta 0.67) DLL Hijacking Vulnerability
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Re: [FD] Samsung SW Update - Insecure ACLs on SW Update Service Directory - EoP Vulnerability
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ISS Daily Summary Report – 07/05/16
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Oops! TP-Link forgets to Renew and Loses its Domains Used to Configure Router Settings
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[FD] Micron CMS v5.3 - (cat_id) SQL Injection Vulnerability
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[FD] Teampass 2.1.26 - Authenticated File Upload Vulnerability
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[FD] IBM BlueMix Cloud - (API) Persistent Web Vulnerability
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anonymous instance
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Orioles Video: Manny Machado blasts a towering go-ahead three-run home run in the 5th inning of 4-1 win over Dodgers (ESPN)
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The Colorful Clouds of Rho Ophiuchi
Tuesday, July 5, 2016
Anonymous posting
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Application of Statistical Relational Learning to Hybrid Recommendation Systems. (arXiv:1607.01050v1 [cs.AI])
Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current model-based hybrid recommendation systems typically require extensive feature engineering to construct a user profile. Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches. However, due to the large scale of the data used in real world recommendation systems, little research exists on applying SRL models to hybrid recommendation systems, and essentially none of that research has been applied on real big-data-scale systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system. Furthermore, in order to satisfy a common requirement in recommendation systems (i.e. that false positives are more undesirable and therefore penalized more harshly than false negatives), our approach can also allow tuning the trade-off between the precision and recall of the system in a principled way. Our experimental results demonstrate the efficiency of our proposed approach as well as its improved performance on recommendation precision.
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Click Carving: Segmenting Objects in Video with Point Clicks. (arXiv:1607.01115v1 [cs.CV])
We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest. Whereas conventional interactive pipelines take the user's initialization as a starting point, we show the value in the system taking the lead even in initialization. In particular, for a given video frame, the system precomputes a ranked list of thousands of possible segmentation hypotheses (also referred to as object region proposals) using image and motion cues. Then, the user looks at the top ranked proposals, and clicks on the object boundary to carve away erroneous ones. This process iterates (typically 2-3 times), and each time the system revises the top ranked proposal set, until the user is satisfied with a resulting segmentation mask. Finally, the mask is propagated across the video to produce a spatio-temporal object tube. On three challenging datasets, we provide extensive comparisons with both existing work and simpler alternative methods. In all, the proposed Click Carving approach strikes an excellent balance of accuracy and human effort. It outperforms all similarly fast methods, and is competitive or better than those requiring 2 to 12 times the effort.
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Optimal control for a robotic exploration, pick-up and delivery problem. (arXiv:1607.01202v1 [cs.SY])
This paper addresses an optimal control problem for a robot that has to find and collect a finite number of objects and move them to a depot in minimum time. The robot has fourth-order dynamics that change instantaneously at any pick-up or drop-off of an object. The objects are modeled by point masses with a-priori unknown locations in a bounded two-dimensional space that may contain unknown obstacles. For this hybrid system, an Optimal Control Problem (OCP) is approximately solved by a receding horizon scheme, where the derived lower bound for the cost-to-go is evaluated for the worst and for a probabilistic case, assuming a uniform distribution of the objects. First, a time-driven approximate solution based on time and position space discretization and mixed integer programming is presented. Due to the high computational cost of this solution, an alternative event-driven approximate approach based on a suitable motion parameterization and gradient-based optimization is proposed. The solutions are compared in a numerical example, suggesting that the latter approach offers a significant computational advantage while yielding similar qualitative results compared to the former. The methods are particularly relevant for various robotic applications like automated cleaning, search and rescue, harvesting or manufacturing.
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An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers. (arXiv:1607.01254v1 [cs.AI])
In this paper, a novel multi-attribute decision making (MADM) methodology is presented for evaluation and selection of the most suitable candidate for a software company which is heading to hire a system analysis engineer based on few attributes in fuzzy environment. A novel systematic assessment methodology is proposed by integrating trapezoidal interval type-2 fuzzy numbers (TrIT2FNs) based MABAC (Multi-Attributive Border Approximation area Comparison). Type-2 fuzzy sets involve more uncertainties than type-1 fuzzy sets. They provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real world. TrIT2FNs- based MABAC evaluates the candidates considered for the job based on some attributes whose weights/priorities are fixed by a group of experts. The proposed model is validated through an well-known example and results are compared with two other MADM methods.
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Temporal Topic Analysis with Endogenous and Exogenous Processes. (arXiv:1607.01274v1 [cs.CL])
We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com.
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Can mobile usage predict illiteracy in a developing country?. (arXiv:1607.01337v1 [cs.AI])
The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geographical mapping of illiteracy is crucial to know where the illiterate people are, and where to put in resources. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than 1 trillion dollars each year. These results potentially enable costeffective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale
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One-Shot Session Recommendation Systems with Combinatorial Items. (arXiv:1607.01381v1 [stat.ML])
In recent years, content recommendation systems in large websites (or \emph{content providers}) capture an increased focus. While the type of content varies, e.g.\ movies, articles, music, advertisements, etc., the high level problem remains the same. Based on knowledge obtained so far on the user, recommend the most desired content. In this paper we present a method to handle the well known user-cold-start problem in recommendation systems. In this scenario, a recommendation system encounters a new user and the objective is to present items as relevant as possible with the hope of keeping the user's session as long as possible. We formulate an optimization problem aimed to maximize the length of this initial session, as this is believed to be the key to have the user come back and perhaps register to the system. In particular, our model captures the fact that a single round with low quality recommendation is likely to terminate the session. In such a case, we do not proceed to the next round as the user leaves the system, possibly never to seen again. We denote this phenomenon a \emph{One-Shot Session}. Our optimization problem is formulated as an MDP where the action space is of a combinatorial nature as we recommend in each round, multiple items. This huge action space presents a computational challenge making the straightforward solution intractable. We analyze the structure of the MDP to prove monotone and submodular like properties that allow a computationally efficient solution via a method denoted by \emph{Greedy Value Iteration} (G-VI).
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Compliant Conditions for Polynomial Time Approximation of Operator Counts. (arXiv:1605.07989v2 [cs.AI] UPDATED)
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains. The contribution of this abstract is threefold, we (1) propose an efficient closed form approximation to the operator count heuristic using the Lagrangian dual; (2) leverage compressed sensing techniques to obtain an integer approximation for operator counts in polynomial time; and (3) discuss the relationship of the proposed formulation to existing heuristics and investigate properties of domains where such approaches appear to be useful.
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Cooperative Inverse Reinforcement Learning. (arXiv:1606.03137v2 [cs.AI] UPDATED)
For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partial-information game with two agents, human and robot; both are rewarded according to the human's reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.
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