Extractive style query oriented multi document summariza tion generates the summary by extracting a proper set of sentences from multiple documents based on the pre given query. This paper proposes a novel multi document summa rization framework via deep learning model. This uniform framework consists of three parts: concepts extraction, summary generation, and reconstruction validation, which work together to achieve the largest coverage of the docu ments content. A new query oriented extraction technique is proposed to concentrate distributed information to hidden units layer by layer. Then, the whole deep architecture is fi ne tuned by minimizing the information loss of reconstruc tion validation. According to the concentrated information, dynamic programming is used to seek most informative set of sentences as the summary. Experiments on three bench mark datasets demonstrate the effectiveness of the proposed framework and algorithms.

Modeling visual saliency map of an image provides important information for image semantic understanding in many applications. Most existing computational visual saliency models follow a bottom-up framework that generates independent saliency map in each selected visual feature space and combines them in a proper way. Two big challenges to be addressed explicitly in these methods are (1) which features should be extracted for all pixels of the input image and (2) how to dynamically determine importance of the saliency map generated in each feature space. In order to address these problems, we present a novel saliency map computational model based on tensor decomposition and reconstruction. Tensor representation and analysis not only explicitly represent image's color values but also imply two important relationships inherent to color image. One is reflecting spatial correlations between pixels and the other one is representing interplay between color channels. Therefore, saliency map generator based on the proposed model can adaptively find the most suitable features and their combinational coefficients for each pixel. Experiments on a synthetic image set and a real image set show that our method is superior or comparable to other prevailing saliency map models.

In maintaining consistencies, such as GAC*, FDGAC* and weak EDGAC*, for global cost functions, Weighted CSP (WCSP) solvers rely on the projection and extension operations, which entail the computation of the cost functions' minima.  Tractability of this minimum computation is essential for efficient execution. Since projections/extensions modify the cost functions, an important issue is tractable projection-safety , concerning whether minimum cost computation remains tractable after projections/extensions. In this paper, we prove that tractable projection-safety is always possible for projections/extensions to/from the nullary cost function ( W 0 ), and always impossible for projections/extensions to/from n -ary cost functions for n > = 2.  When n = 1, the answer is indefinite.  We give a simple negative example, while Lee and Leung's flow-based projection-safe cost functions are also tractable projection-safe. We propose polynomially decomposable cost functions, which are amenable to tractable minimum computation.  We further prove that the polynomial decomposability property is unaffected by projections/extensionsto/from unary cost functions.  Thus, polynomially decomposable cost functions are tractable projection-safe.  We show that the SOFT_AMONG, SOFT_REGULAR, SOFT_GRAMMAR and MAX_WEIGHT/MIN_WEIGHT are polynomially decomposable.  They are embedded in a WCSP solver for extensive experiments to confirm the feasibility and efficiency of our proposal.

Local Search is one of the fundamental approaches to combinatorial optimization and it is used throughout AI. Several local search algorithms are based on searching the k -exchange neighborhood. This is the set of solutions that can be obtained from the current solution by exchanging at most k elements. As a rule of thumb, the larger k is, the better are the chances of finding an improved solution. However, for inputs of size n, a naive brute-force search of the k-exchange neighborhood requires n (O( k )) time, which is not practical even for very small values of k. Fellows et al. (IJCAI 2009) studied whether this brute-force search is avoidable and gave positive and negative answers for several combinatorial problems. They used the notion of local search in a strict sense. That is, an improved solution needs to be found in the k-exchange neighborhood even if a global optimum can be found efficiently. In this paper we consider a natural relaxation of local search, called permissive local search (Marx and Schlotter, IWPEC 2009) and investigate whether it enhances the domain of tractable inputs. We exemplify this approach on a fundamental combinatorial problem, Vertex Cover. More precisely, we show that for a class of inputs, finding an optimum is hard, strict local search is hard, but permissive local search is tractable. We carry out this investigation in the framework of parameterized complexity.

Modelling the density of an infectious disease in space and time is a task generally carried out separately from the diagnosis of that disease in individuals. These two inference problems are complementary, however: diagnosis of disease can be done more accurately if prior information from a spatial risk model is employed, and in turn a disease density model can benefit from the incorporation of rich symptomatic information rather than simple counts of presumed cases of infection. We propose a unifying framework for both of these tasks, and illustrate it with the case of malaria. To do this we first introduce a state space model of malaria spread, and secondly a computer vision based system for detecting plasmodium in microscopical blood smear images, which can be run on location-aware mobile devices. We demonstrate the tractability of combining both elements and the improvement in accuracy this brings about.

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically modeled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maximum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natural and social science include applying artificial intelligence, cloud computing, and multiplayer online games.

Illegal extraction of forest resources is fought, in many developing countries, by patrols that try to make this activity less profitable, using the threat of confiscation. With a limited budget, officials will try to distribute the patrols throughout the forest intelligently, in order to most effectively limit extraction. Prior work in forest economics has formalized this as a Stackelberg game, one very different in character from the discrete Stackelberg problem settings previously studied in the multiagent literature. Specifically, the leader wishes to minimize the distance by which a profit-maximizing extractor will trespass into the forest---or to maximize the radius of the remaining ``pristine'' forest area. The follower's cost-benefit analysis of potential trespass distances is affected by the likelihood of being caught and suffering confiscation. In this paper, we give a near-optimal patrol allocation algorithm and a 1/2-approximation algorithm, the latter of which is more efficient and yields simpler, more practical patrol allocations. Our simulations indicate that these algorithms substantially outperform existing heuristic allocations.

The recent popularization of social web services has made them one of the primary uses of the World Wide Web. An important concept in social web services is social actions such as making connections and communicating with others and adding annotations to web resources. Predicting social actions would improve many fundamental web applications, such as recommendations and web searches. One remarkable characteristic of social actions is that they involve multiple and heterogeneous objects such as users, documents, keywords, and locations. However, the high-dimensional property of such multinomial relations poses one fundamental challenge, that is, predicting multinomial relations with only a limited amount of data. In this paper, we propose a new multinomial relation prediction method, which is robust to data sparsity. We transform each instance of a multinomial relation into a set of binomial relations between the objects and the multinomial relation of the involved objects. We then apply an extension of a low-dimensional embedding technique to these binomial relations, which results in a generalized eigenvalue problem guaranteeing global optimal solutions. We also incorporate attribute information as side information to address the “cold start” problem in multinomial relation prediction. Experiments with various real-world social web service datasets demonstrate that the proposed method is more robust against data sparseness as compared to several existing methods, which can only find sub-optimal solutions.

During broadcast events such as the Superbowl, the U.S. Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the topics covered by the event and the tweets; the other is to segment the event. So far these problems have been viewed separately and studied in isolation. In this work, we argue that these problems are in fact inter-dependent and should be addressed together. We develop a joint Bayesian model that performs topic modeling and event segmentation in one unified framework. We evaluate the proposed model both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.

This article introduces the benefits of using computer as a tool for foreign language teaching and learning. It describes the effect of using Natural Language Processing (NLP) tools for learning Arabic. The technique explored in this particular case is the employment of pedagogically indexed corpora. This text-based method provides the teacher the advantage of building activities based on texts adapted to a particular pedagogical situation. This paper also presents ARAC: a Platform dedicated to language educators allowing them to create activities within their own pedagogical area of interest.