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This paper proposes a research on cross-media intelligent perception and retrieval analysis technology based on deep learning education in view of the complex learning knowledge environment of artificial intelligence of traditional cross-media retrieval technology, which is unable to obtain retrieval information timely and accurately. Based on the cross-media theory, this paper analyzes the pre-processing of cross-media data, transforms from a single media form such as text, voice, image, and video to a cross-media integration covering network space and physical space, and designs a cross-media intelligent perception platform system. With multi-core method of typical correlation analysis algorithm, this paper develops a new joint learning framework based on the joint feature selection and subspace learning cross-modal retrieval. What's more, the algorithm is tested experimentally. The results show that the retrieval analysis technology is significantly better than the traditional media retrieval technology, which can effectively identify text semantics and visual image classification, and can better maintains the relevance of data content and the consistency of semantic information, which has important reference value for cross-media applications. [ABSTRACT FROM AUTHOR]

Quantum Private Information Retrieval (QPIR) allows a user (Alice) to retrieve a database item from the database owned by the database holder (Bob) in such a way that Alice can query only the database item she wants, but cannot get other items, and Bob does not know which item Alice queries. However, the real quantum channel between Alice and Bob is noisy, and the noise may result in not only the chance that Alice obtains a false item, but also that both parties may cheat by camouflaging themselves with noise. In this paper, we use the decoherence-free subspace (DFS) for QPIR, which we call DFS-QPIR. The DFS-QPIR protocol removes the effect of the channel noise on the errors in the retrieved item so that two parties cannot cheat by replacing the noisy channel with a noiseless one. It can work over a collective noisy channel while retaining high reliability, database security and user privacy simultaneously. While only the collective unitary noise is taken into account, the proposed DFS-QPIR protocol can be straightforwardly extended to more general collective noise channel. [ABSTRACT FROM AUTHOR]

This paper addresses the challenging task of word spotting in Arabic handwritten documents. We proposed a novel feature that we called Spatial Distribution of Ink at Keypoints (SDIK). The proposed feature captures the characteristics of Arabic handwriting concentrated at endpoints and branch points. SDIK feature quantizes the spatial repartition of ink pixels in the neighborhoods of keypoints. The resulting SDIK features are very fast to match, we take this advantage to match a query word with lines images rather than words images. By this matching mechanism, we overcome the hard task of segmenting an Arabic document into words. The method proposed in this study is tested on historical Arabic document with IBN SINA dataset and on modern handwriting with IFN/ENIT database. The obtained results are great of interest for retrieving query words in an Arabic document. [ABSTRACT FROM AUTHOR]

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models. [ABSTRACT FROM AUTHOR]

Indexing video by the concept is one of the most appropriate solutions for such problems. It is based on an association between a concept and its corresponding visual sound, or textual features. This kind of association is not a trivial task. It requires knowledge about the concept and its context. In this paper, we investigate a new concept detection approach to improve the performance of content-based multimedia documents retrieval systems. To achieve this goal, we are going to tackle the problem from different plans and make four contributions at various stages of the indexing process. We propose a new method for multimodal indexation based on (i) a new weakly supervised semi-automatic method based on the genetic algorithm (ii) the detection of concepts from the text in the videos (iii) the enrichment of the basic concepts thanks to the usage of our method DCM. Subsequently, the semantic and enriched concepts allow a better multimodal indexation and the construction of an ontology. Finally, the different contributions are tested and evaluated on a large dataset (TRECVID 2015). [ABSTRACT FROM AUTHOR]

We are fully aware that an ordered semigroup has a very close relation with theoretical computer science, especially with the theory of pattern recognition, decision processes, artificial intelligence, information retrieval and so on. We are eager to introduce the new concepts of hybrid interior ideals and hybrid simple in an ordered semigroup in this paper. We discuss characteristic hybrid structures using ideals and interior ideals, and characterize ordered semigroup in terms of different hybrid ideal structures. Further, we establish the equivalent condition for an ordered semigroup to be simple. [ABSTRACT FROM AUTHOR]

Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively. [ABSTRACT FROM AUTHOR]

Many tasks of natural language processing such as information retrieval, intelligent question answering, and machine translation require the calculation of sentence similarity. The traditional calculation methods used in the past could not solve semantic understanding problems well. First, the model structure based on Siamese lack of interaction between sentences; second, it has matching problem which contains lacking position information and only using partial matching factor based on the matching model. In this paper, a combination of word and word's dependence is proposed to calculate the sentence similarity. This combination can extract the word features and word's dependency features. To extract more matching features, a bi-directional multi-interaction matching sequence model is proposed by using word2vec and dependency2vec. This model obtains matching features by convolving and pooling the word-granularity (word vector, dependency vector) interaction sequences in two directions. Next, the model aggregates the bi-direction matching features. The paper evaluates the model on two tasks: paraphrase identification and natural language inference. The experimental results show that the combination of word and word's dependence can enhance the ability of extracting matching features between two sentences. The results also show that the model with dependency can achieve higher accuracy than these models without using dependency. [ABSTRACT FROM AUTHOR]

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image's basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles 0 ∘ , 4 5 ∘ , 9 0 ∘ , and 1 3 5 ∘ . Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing. [ABSTRACT FROM AUTHOR]

Detecting the interaction between humans and objects in images is a critical problem for obtaining a deeper understanding of the visual relationship in a scene and also a critical technology in many practical applications, such as augmented reality, video surveillance and information retrieval. Be that as it may, due to the fine-grained actions and objects in the real scene and the coexistence of multiple interactions in one scene, the problem is far from being solved. This paper differs from prior approaches, which focused only on the features of instances, by proposing a method that utilizes a four-stream CNNs network for human-object interaction (HOI) detection. More detailed visual features, spatial features and pose features from human-object pairs are extracted to solve the challenging task of detection in images. Specially, the core idea is that the region where people interact with objects contains important identifying cues for specific action classes, and the detailed cues can be fused to facilitate HOI recognition. Experiments on two large-scale HOI public benchmarks, V-COCO and HICO-DET, are carried out and the results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The existing image retrieval methods generally require at least one complete image as a query sample. From the practical point of view, a user may not have an image sample in hand for query. Instead, partial information from multiple image samples would be available. This paper therefore attempts to deal with this problem by presenting a novel framework that allows a user to make an image query composed of several partial information extracted from multiple image samples via Boolean operations (i.e. AND, OR and NOT). Based on the request from the query, a Descriptor Cluster Label Table (DCLT) is designed to efficiently find out the result of Boolean operations on partial information. Experiments show the promising result of the proposed framework on commodity query and criminal investigation, respectively, although it is essentially applicable to different scenarios as well by changing descriptors. [ABSTRACT FROM AUTHOR]

Steganography has become one of the most significant techniques to conceal secret data in media files. This paper proposes a novel automated methodology of achieving two levels of security for videos, which comprise encryption and steganography techniques. The methodology enhances the security level of secret data without affecting the accuracy and capacity of the videos. In the first level, the secret data is encrypted based on Advanced Encryption Standard (AES) algorithm using Java language, which renders the data unreadable. In the second level, the encrypted data is concealed in the video frames (images) using FPGA hardware implementation that renders the data invisible. The steganographic technique used in this work is the least significant bit (LSB) method; a 1–1–0 LSB scheme is used to maintain significantly high frame imperceptibility. The video frames used as cover files are selected randomly by the randomization scheme developed in this work. The randomization method scatters the data throughout the video frames rendering the retrieval of the data in its original order, without a proper key, a challenging task. The experimental results of concealment of secret data in video frames are presented in this paper and compared with those of similar approaches. The performance in terms of area, power dissipation, and peak signal-to-noise ratio (PSNR) of the proposed method outperformed traditional approaches. Furthermore, it is demonstrated that the proposed method is capable of automatically embedding and extracting the secret data at two levels of security on video frames, with a 57.1 dB average PSNR. [ABSTRACT FROM AUTHOR]

With the emerge of advanced technologies such as high-resolution cameras and computational power, it seems to ease to built a better dermatological diagnostic system. However, the identification of skin disease is still a challenging problem with the origination of various skin diseases. In this paper, we proposed a new fusion architecture — CBI + R to support the diagnosis in multiple skin diseases. The architecture combines Content-Based Image Retrieval (CBIR) and Case-Based Reasoning (CBR) technology together to facilitate medical diagnosis. CBIR is to retrieve digital dermoscopy images from a data repository using the shape, texture and color features. Along with these features, CBR is incorporated which contains symptoms, case history and treatment plan of the disease. Experiments on a set of 1210 images yielded an accuracy of 98.2%. This was a superior retrieval and diagnosis performance in comparison with the state-of-the-art works. [ABSTRACT FROM AUTHOR]

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%. [ABSTRACT FROM AUTHOR]

Recommender systems' evaluation is usually based on predictive accuracy and information retrieval metrics, with better scores meaning recommendations are of higher quality. However, new algorithms are constantly developed and the comparison of results of algorithms within an evaluation framework is difficult since different settings are used in the design and implementation of experiments. In this paper, we propose a guidelines-based approach that can be followed to reproduce experiments and results within an evaluation framework. We have evaluated our approach using a real dataset, and well-known recommendation algorithms and metrics; to show that it can be difficult to reproduce results if certain settings are missing, thus resulting in more evaluation cycles required to identify the optimal settings. [ABSTRACT FROM AUTHOR]

Thousands of criminal events are reported in newspapers and social networks every day. They describe violent acts that include actors, places, times, causes and any information concerning them. Verbal and nominal phrases are used to characterize and expose criminal events, which employ an important variety of natural language structures in the newspapers. In addition, causes, times and spaces of criminal events, use linguistic phrases to represent them in text. All of them need to be extracted as a pattern recognition process in order to extract criminal events from text and the information that concerns them. The extracted events, as a knowledge base, are very useful for information retrieval tasks. Therefore, this paper presents an approach based on pattern recognition in order to extract criminal events from Spanish text, by populating and enriching an ontology model. Ontology population and enrichment involve the instantiation of criminal events and their cause relationships. An evaluation process is carried out with a set of manually tagged newspapers with categories of specific events, and shows promising results. [ABSTRACT FROM AUTHOR]

Graph-Based Data Mining (GBDM) is an emerging research topic nowadays, for the retrieval of the essential information from the graph database. There exist many algorithms that find frequent patterns in a given graph database. One such algorithm, GASTON uses support based on frequency to discover frequent patterns. The discovery phase in the Gaston algorithm is time-consuming, and the pages captured the interest of the users are ignored by the existing GASTON algorithm. This paper proposes an algorithm, Weighted-Gaston (W-Gaston) algorithm, by modifying the existing Gaston algorithm. Here, four interesting measures are developed based on the frequency, entropy, and the page duration, for the retrieval of the interesting sub-graphs. The proposed interesting measures include four types of support: (1) Support based on the page duration (W-Support), (2) Support based on the entropy (E-Support), (3) Support based on the page duration and the entropy (WE-Support), and (4) Support based on the frequency, page duration, and the entropy (FWE-Support). The simulation of the proposed work is done using the MSNBC and the weblog databases. The experimental results show that the proposed algorithm performed well as compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Most of the documents use fingerprint impression for authentication. Property related documents, bank checks, application forms, etc., are the examples of such documents. Fingerprint-based document image retrieval system aims to provide a solution for searching and browsing of such digitized documents. The major challenges in implementing fingerprint-based document image retrieval are an efficient method for fingerprint detection and an effective feature extraction method. In this work, we propose a method for automatic detection of a fingerprint from given query document image employing Discrete Wavelet Transform (DWT)-based features and SVM classifier. In this paper, we also propose and investigate two feature extraction schemes, DWT and Stationary Wavelet Transform (SWT)-based Local Binary Pattern (LBP) features for fingerprint-based document image retrieval. The standardized Euclidean distance is employed for matching and ranking of the documents. Proposed method is tested on a database of 1200 document images and is also compared with current state-of-art. The proposed scheme provided 98.87% of detection accuracy and 73.08% of Mean Average Precision (MAP) for document image retrieval. [ABSTRACT FROM AUTHOR]

Various problems are just rising with regard to mining in massive datasets, among which finding similar documents can be pinpointed. The Shingling method converts this problem to a set-based problem. Some of existing methods have used min-hashing to compress the results already driven from the shingling method and then have exploited LSH method to find candidate pairs for similarity search from all pairs of documents. In this paper, an apriori-based method is proposed for finding similar documents based on frequent itemset mining approach. To this end, the apriori algorithm is modified and is customized for similarity search problem. Modeling the similarity search problem as a frequent pattern mining problem, using a modified version of apriori, and dynamic selection the minimum support threshold are the most important advantages of the proposed method, which lead to its appropriate execution time and high quality results. The proposed method finds similar documents in less time than the combined method and MCVM method because it generates fewer candidate pairs for finding similar documents. Furthermore, experimental results show the high quality of the answers of the proposed methods. [ABSTRACT FROM AUTHOR]

In problems involving control of financial processes, it is usually complicated to quantify exactly the state variables. It could be expensive to acquire the exact value of a given state, even if it may be physically possible to do so. In such cases it may be interesting to support the decision-making process on inaccurate information pertaining to the system state. In addition, for modeling real-world application, it is necessary to compute the values of the parameters of the environment (transition probabilities and observation probabilities) and the reward functions, which are typically, hand-tuned by experts in the field until it has acquired a satisfactory value. This results in an undesired process. To address these shortcomings, this paper provides a new Reinforcement Learning (RL) framework for computing the mean-variance customer portfolio with transaction costs in controllable Partially Observable Markov Decision Processes (POMDPs). The solution is restricted to finite state, action, observation sets and average reward problems. For solving this problem, a controller/actor-critic architecture is proposed, which balance the difficult tasks of exploitation and exploration of the environment. The architecture consists of three modules: controller, fast-tracked portfolio learning and an actor-critic module. Each module involves the design of a convergent Temporal Difference (TD) learning algorithm. We employ three different learning rules to estimate the real values of: (a) the transition matrices π ^ (j | i k) (t) , (b) the rewards J ^ (i j k) (t) and (c) the resources destined for carrying out a promotion ζ ^ (i ^ | k ^) (t). We present a proof for the estimated transition matrix rule π ^ (j | i k) (t) and showing that it converges when t → ∞. For solving the optimization programming problem we extend the c-variable method for partially observable Markov chains. The c-variable is conceptualized as joint strategy given by the product of the control policy, the observation kernel Q(y|s) and the stationary distribution vector. A major advantage of this procedure is that it can be implemented efficiently for real settings in controllable POMDP. A numerical example illustrates the results of the proposed method. [ABSTRACT FROM AUTHOR]

The simplest building blocks for quantum computations are the qubit-qubit quantum channels. In this paper, we analyze the structure of these channels via their Choi representation. The restriction of a quantum channel to the space of classical states (i.e. probability distributions) is called the underlying classical channel. The structure of quantum channels over a fixed classical channel is studied, the volume of the general and unital qubit channels with respect to the Lebesgue measure is computed and explicit formulas are presented for the distribution of the volume of quantum channels over given classical channels. We study the state transformation under uniformly random quantum channels. If one applies a uniformly random quantum channel (general or unital) to a given qubit state, the distribution of the resulted quantum states is presented. [ABSTRACT FROM AUTHOR]

Genomic data nowadays is playing a vital role in number of fields such as personalized medicine, forensic, drug discovery, sequence alignment and agriculture, etc. With the advancements and reduction in the cost of next-generation sequencing (NGS) technology, these data are growing exponentially. NGS data are being generated more rapidly than they could be significantly analyzed. Thus, there is much scope for developing novel data compression algorithms to facilitate data analysis along with data transfer and storage directly. An innovative compression technique is proposed here to address the problem of transmission and storage of large NGS data. This paper presents a lossless non-reference-based FastQ file compression approach, segregating the data into three different streams and then applying appropriate and efficient compression algorithms on each. Experiments show that the proposed approach (WBFQC) outperforms other state-of-the-art approaches for compressing NGS data in terms of compression ratio (CR), and compression and decompression time. It also has random access capability over compressed genomic data. An open source FastQ compression tool is also provided here (http://www.algorithm-skg.com/wbfqc/home.html). [ABSTRACT FROM AUTHOR]

Current image retrieval techniques are mainly based on text or visual contents. However, both text-based and contents-based methods lack the capability of utilizing human intuition and KANSEI (impression). In this paper, we proposed an impression-based image retrieval method in order to realize the image retrieval according to our impression presented by impression keywords. We first propose a generic and specific impressions estimation method based on machine learning and then apply it to impression-based clothing fabric image retrieval. We use a semantic differential (SD) method to measure the user’s impressions such as brightness and warmth while they view a cloth fabric image. We also extract both global and local features of cloth fabric images such as color and texture using computer vision techniques. Then we use support vector regression to model the mapping functions between the generic impression (or specific impression) and image features. The learnt mapping functions are used to estimate the generic and specific impressions of cloth fabric images. The retrieval is done by comparing the query impression with the estimated impression of images in the database. [ABSTRACT FROM AUTHOR]

An efficient and secure platform is needed for data storage and transmission, specially for the multimedia content like audio, image and video. In this paper, a new methodology is proposed for the secured medical image compression. Initially, Bandelet transform is applied to the input medical image to obtain the coefficients. Using unequal error protection (UEP) method, the obtained coefficients are prioritized. These coefficients are encrypted using data encryption standard (DES) and will be encoded using listless set partitioning embedded block (Listless SPECK) technique. For the performance evaluation, peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM), image quality index (IQI), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE) are taken. From the experimental results and performance evaluation, it is shown that the proposed approach is producing good results. [ABSTRACT FROM AUTHOR]

Traditional biological neural networks lack the capability of reflecting variable synaptic weights when simulating associative memory of human brains. In this paper, we investigate the existence and exponential stability of a novel memristive multidirectional associative memory neural networks (MAMNNs) model, which includes the time-varying delays. In the proposed approach, the time-varying delays are set to be bounded, and it is not necessary for their derivative to be differentiable. With removal of certain conditions, less conservative results are generated. Sufficient criteria guaranteeing the stability of the memristive MAMNNs are derived based on the Lyapunov function and some inequality techniques. To illustrate the performance of the proposed criteria, a procedure is designed to realize information storage. Meanwhile, the effectiveness of the proposed theories is validated with numerical experiments. [ABSTRACT FROM AUTHOR]

This paper introduces a novel descriptor technique denoted as Contour-Point Signature (CPS) useful to find correspondences of points selected from the outer contours of two arbitrary shapes, and to establish a relationship to map an ordered sequence of contour points from one shape to another. The proposal has proved to be invariant, to translation, scaling and rotation, it also induces a measure which is proved to be non-negative, unique, symmetric and identity-preserving. Experimental tests were performed in shape detection under noise, with image retrieval from a MPEG-7 database and letter recognition. Numerical results show that the proposal is robust for noise perturbation, as well as, having adequate accuracy and hit rate, even with coarse tuning for its parameters. This makes the method attractive to a wide range of applications. [ABSTRACT FROM AUTHOR]

Quantum computing and quantum communication have become the most popular research topic. Nitrogen-vacancy (NV) centers in diamond have been shown the great advantage of implementing quantum information processing. The generation of entanglement between NV centers represents a fundamental prerequisite for all quantum information technologies. In this paper, we propose a scheme to realize the high-fidelity storage and extraction of quantum entanglement information based on the NV centers at room temperature. We store the entangled information of a pair of entangled photons in the Bell state into the nuclear spins of two NV centers, which can make these two NV centers entangled. And then we illuminate how to extract the entangled information from NV centers to prepare on-demand entangled states for optical quantum information processing. The strategy of engineering entanglement demonstrated here maybe pave the way towards a NV center-based quantum network. [ABSTRACT FROM AUTHOR]

Online opinions play an important role for customers and companies because of the increasing use they do to make purchase and business decisions. A consequence of that is the growing tendency to post fake reviews in order to change purchase decisions and opinions about products and services. Therefore, it is really important to filter out deceptive comments from the retrieved opinions. In this paper we propose the character n-grams in tokens, an efficient and effective variant of the traditional character n-grams model, which we use to obtain a low dimensionality representation of opinions. A Support Vector Machines classifier was used to evaluate our proposal on available corpora with reviews of hotels, doctors and restaurants. In order to study the performance of our model, we make experiments with intra and cross-domain cases. The aim of the latter experiment is to evaluate our approach in a realistic cross-domain scenario where deceptive opinions are available in a domain but not in another one. After comparing our method with state-of-the-art ones we may conclude that using character n-grams in tokens allows to obtain competitive results with a low dimensionality representation. [ABSTRACT FROM AUTHOR]

One step towards breaking down barriers between citizens and politicians is to help people identify those politicians who share their concerns. This paper is set in the field of expert finding and is based on the automatic construction of politicians' profiles from their speeches on parliamentary committees. These committee-based profiles are treated as documents and are indexed by an information retrieval system. Given a query representing a citizen's concern, a profile ranking is then obtained. In the final step, the different results for each candidate are combined in order to obtain the final politician ranking. We explore the use of classic combination strategies for this purpose and present a new approach that improves state-of-the-art performance and which is more stable under different conditions. We also introduce a two-stage model where the identification of a broader concept (such as the committee) is used to improve the final politician ranking. [ABSTRACT FROM AUTHOR]

User-generated content such as comments or tweets (also called by social information) following a Web document provides additional information for enriching the content of an event mentioned in sentences. This paper presents a framework named SoSVMRank, which integrates the user-generated content of a Web document to generate a highquality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which comments or tweets are exploited to support sentences in a mutual reinforcement fashion. To model sentence-comment (or tweet) relation, a set of local and social features are proposed. After ranking, top m ranked sentences and comments (or tweets) are selected as the summarization. To validate the efficiency of our framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results indicate that: (i) our new features improve the summary performance of the framework in term of ROUGE-scores compared to state-of-the-art baselines and (ii) the integration of user-generated content benefits single-document summarization. [ABSTRACT FROM AUTHOR]

Traditional cryptography considers the security of cryptosystems when the attackers have no access to the secret key. However, due to the imperfect implementation of cryptosystems, the attackers are able to obtain partial secret state of the systems via side-channel attacks, which are not considered in the traditional security notions of cryptographic primitives, including digital signature, and thus break their security. Leakage-resilient cryptography was then proposed to solve the problem. Recently, Wang et al. showed that any signature scheme can be transformed to a strongly unforgeable one in the leakage setting. However, their transformation requires to change the key pair of the scheme. In this paper, we present a key-modification-free solution in both the bounded leakage model and the auxiliary input model. Specifically, we propose a black-box construction of strongly unforgeable signature scheme in the leakage setting, and show that if the underlying building blocks are leakage-resilient, so is the resulting scheme. [ABSTRACT FROM AUTHOR]

In the era of big data, cloud storages become prevalent while security issues on outsourced data emerge. Without the physical control of the data owners, the outsourced data faces more risks. Provable Data Possession (PDP) is a probabilistic technique for checking whether data is correctly stored in remote servers without retrieving the entire data. For many previous PDP schemes, correctly choosing public keys for data owners relies on the security of Public Key Infrastructure (PKI), which is resource consuming. In this paper, we propose an Identity-Based Batch Provable Data Possession (ID-BPDP) scheme to eliminate the certificate management. Meanwhile, to the best of our knowledge, it is the first identity-based provable data possession scheme supporting batch verification for multiple owners and multiple clouds simultaneously to reduce computation cost greatly. We prove that our scheme is correct and secure based on bilinear pairings and the hardness assumption of Computational Diffie-Hellman problem, and also give detailed analyses to show that our scheme is able to verify the integrity of data efficiently. [ABSTRACT FROM AUTHOR]

In the last decades, several research areas experienced key improvements due to the appearance of numerous tools made available to the scientific community. For instance, Moses plays an important role in recent developments in machine translation and Lucene is, with no doubt, a widespread tool in information retrieval. The existence of these sys-tems allows an easy development of baselines and, therefore, researchers can focus on improving preliminary results, instead of spending time in developing software from scratch. In addition, the existence of appropriate test collections leads to a straightfor-ward comparison of systems and of their specific components In this paper we describe Just.Ask, a multi-pronged approach to open-domain ques-tion answering. Just.Ask combines rule- with machine learning-based components and implements several state-of-the-art strategies in question answering. Also, it has a flex-ible architecture that allows for further extensions. Moreover, in this paper we report a detailed evaluation of each one of Just.Ask components. The evaluation is split into two parts: in the first one, we use a manually built test collection -- the GoldWebQA -- that intends to evaluate Just.Ask performance when the information source in use is the Web, without having to deal with its constant changes; in the second one, we use a set of questions gathered from the TREC evaluation forum, having a closed text collection, lo-cally indexed and stored, as information source. Therefore, this paper contributes with a benchmark for research on question answering, since both Just.Ask and the GoldWebQA corpus are freely available for the scientific community. [ABSTRACT FROM AUTHOR]

This paper proposes a score-based indexing technique for biometric databases. A fixed-length index is computed for each image in the database by calculating its match scores against a preselected set of representative images. Further, an efficient storage mechanism (i.e. index space) is developed to arrange the biometric images like traditional database records so that a rapid search is possible. During identification, the retrieval technique finds a list of similar candidates for the query image from the database using voting scheme. Finally, to identify the genuine match from the retrieved similar candidates, we perform a one-one match between the query and each similar candidate using modified Hausdorff distance measure. Experimental results on different databases show a significant performance improvement in terms of response time and identification accuracy compared to the existing indexing methods. [ABSTRACT FROM AUTHOR]

Computing over large platforms calls for the ability to maintain distributed structures at large scale. Among the many different structures proposed in this context, the prefix tree structure has been identified as an adequate one for indexing and retrieving information. One weakness of using such a distributed structure stands in its poor native fault tolerance, leading to the use of preventive costly mechanisms such as replication. Self-stabilization is a suitable approach to design reliable solutions for dynamic systems, and was recently enhanced with new models to be able to deal with large scale dynamic platforms. A self-stabilizing system is guaranteed to reach a correct configuration, whatever its initial state is. Following this path, it is becoming possible to make distributed structures self-stabilizing at large scale. In this paper, we focus on making tries self-stabilizing over such platforms, and propose a self-stabilizing maintenance algorithm for a prefix tree using a message passing model. The proof of self-stabilization is provided, and simulation results are given, to better capture its performances. Still based on simulations, we provide evidences that the protocol, beyond its capacity to repair the structure, can significantly improve the system's availability, even when the system is not yet stabilized. [ABSTRACT FROM AUTHOR]

Events represent a crucial source of information in any text; dates and proper names (people, locations, organizations, products, diseases, etc.) combine to describe a certain event. In this paper, our focus is on these events, on how we find them in the text, extract them, organize them, save them in a database, and use them to answer any question about them. In our system, each event has three main types of identifying factors: keywords that indicate that an interesting event has occurred, dates that specify the time of the event, and proper nouns that specify the people, organizations, locations, products, etc., that are involved in the event. In this paper, we describe a system that uses these keys to find events, classify them, and save them in a database along with the identifier of the document that mentions that event. The retrieval process uses this information to provide the user with menus to form queries about the events; it then executes those queries, and finds the related documents. [ABSTRACT FROM AUTHOR]

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely, hierarchical support vector machine (SVM) and k-nearest neighbor ( k-NN) that are applied to a LSHTC task. Our experimental results show that the latter outperforms the former for the LSHTC task, which is quite different from the existing results for normal text categorization tasks. In addition, this paper compares different similarity measures and ranking strategies in k-NN framework for LSHTC task. From our empirical study, conclusions can be drawn that k-NN is more appropriate for the LSHTC task than hierarchical SVM. BM25 outperforms other similarity measures and ListWeak gains a better performance than other ranking strategies. Our empirical results also indicate that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors. [ABSTRACT FROM AUTHOR]

This paper describes an information system that was architected to enable front end distributed intelligence in a real manufacturing environment. By distributing intelligence to the most robust components of the system, data can be preserved at the cost of very real-time information. In order for the system as a whole to function this way, each software component was fitted with watchdog agent and granted system level access to the operating system so that it could detect and repair itself and other tasks in the event of a failure. During the repair process, the appropriate data collection processes were granted a level of autonomy that preserved data integrity. The paper is quite practical and designed as a case study rather than a theoretical research paper, although the techniques and principles applied are the result of much study. [ABSTRACT FROM AUTHOR]

Aiming at content-based image retrieval (CBIR) in fractal domain, this paper puts forward a fast fractal encoding method to extract image features, which is based on a novel non-searching and adaptive quadtree division. As a result, it enhances fractal coding speed sharply, only needs 0.0485 seconds on average for a 256 × 256 image and is approximately 70 times faster than algorithm in addition to good reconstructed image quality. Furthermore, this paper improves image matching algorithm, consequently enhancing the accuracy of query results. In addition, we present a method to further accelerate image retrieval based on the analysis to fractal codes distance and number. Experimental results show that our proposed method is performs highly in retrieval speed and feasible in retrieval accuracy. [ABSTRACT FROM AUTHOR]

The paper presents a preliminary investigation of potential methods for extracting semantic views of text contents under the form of structured sets of words, which go beyond standard statistical indexing. The aim is to build kinds of fuzzily weighted structured images of semantic contents. A preliminary step consists in identifying the different types of relations (is-a, part-of, related-to, synonymy, domain, glossary relations) that exist between the words of a text, using some general ontology such as WordNet. Then taking advantage of these relations, different types of fuzzy clusters of words can be built. Moreover, apart from its frequency of occurrence, the importance of a word may be also evaluated through some estimate of its specificity. A degree of "centrality" is also computed for each word in a cluster. The size of the clusters, the frequency, the specificity and the centrality of their words are indications that enable us to build a fuzzy set of sets of words that progressively "emerge" from a text, as being representative of its contents. The ideas advocated in the paper and their potential usefulness are illustrated on a running example and on two experiments. It is expected that obtaining a better representation of the semantic contents of texts may help in particular to give indications of what the text is about to a potential reader. [ABSTRACT FROM AUTHOR]

Fresh information is important for real business. In order to realize fresh information retrieval, we need not only to collect documents in a short time, but also to rank the results in the suitable order. However, conventional ranking methods are not suited for fresh information retrieval because they ignore temporal value of information. So, we have proposed the novel ranking method FTF·IDF for fresh information retrieval. FTF·IDF extends TF·IDF by means of using FTF (Fresh Term Frequency) instead of TF (Term Frequency). FTF differs from TF because FTF decreases as time goes. The speed of decreasing FTF is determined by the dumping factor. The dumping factor is sensitive against small changes of documents. So, we use a threshold to ignore such small changes. In some papers we published, we detect the optimal threshold manually. In this paper, we proposed an adaptive calculating method in order to detect threshold automatically. In this method, the optimal value is determined by iterating to test generated thresholds. In this paper, we describe our method and its evaluation. [ABSTRACT FROM AUTHOR]

This paper briefly describes Toshiba Knowledge Media Laboratory's recent research efforts for effective information retrieval and access. Firstly, I will mention the main research topics that are being tackled by our information access group, including document retrieval, speech-input/multimedia question answering, and evaluation metrics. Secondly, I will focus on the problem of cross-language information retrieval and access, and describe a system called BRIDJE (Bi-directional Retriever/Information Distiller for Japanese and English), which achieved many gold-medal performances at the recent NTCIR (a.k.a. "Asian TREC") workshop. Finally, I will conclude the paper by mentioning some unsolved problems and suggesting possible directions for future Information Access research. [ABSTRACT FROM AUTHOR]

A stem in Arabic is a root verb form combined with derivational morphemes but with affixes removed. One part of this paper is to repeat the Mixed Stemming experiment, that chooses the use of a word, a stem, or a root for a query term based upon which form has the highest average inverse document frequency value, using more documents and queries. A consistency result has been obtained. The root with weighting method was the superior. The mixed stemming improved binary weighting search results in all cases but did not increase performance over weighted stems or roots. N-gram matching is widely used for term conflation when searching the World Wide Web for something without knowing whether it is stored as singular or plural, as a compound or in the form of several words, in old or new spelling or possibly in wrong orthography. Part two of this paper reports a comparison of Stemming and N-gram matching. The Stem-NGram-Stem Search outperforms (at recall 0.4 and above) the Digrams method when both the queries and documents words are not stemmed. It also suggests that the (Stem-NGram-Stem) Search approach outperforms (at recall 0.4 and above) the Trigrams method (Stem-NGram-Word) when the queries words are stemmed and documents words are not stemmed. The results also suggest that the Trigrams method (Stem-NGram-Word) outperforms the Digrams method when both the queries and documents words are not stemmed. All of the proposed N-gram methods outperform the Word, Stem, and Root index methods for Binary Weighting scheme. [ABSTRACT FROM AUTHOR]

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.[sup 4] In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance. [ABSTRACT FROM AUTHOR]

Memory-based learning schemes are characterized by their memorization of observed events. A memory-based learning scheme either memorizes the collected data directly or reorganizes such information and stores it distributively into a tabular memory. For the tabular type, the system requires a training process to determine the contents of the associative memory. This training process helps filter out zero-mean noise. Since the stored data are associated to pre-assigned input locations, memory management and data retrieval are easier and more efficient. Despite these merits, a drawback of tabular schemes is the difficulty in applying it to high-dimensional problems due to the curse of dimensionality. As the input dimensionality increases, the number of quantized elements in the input space increases at an exponential rate and that causes a huge demand of memory. In this paper, a dynamic tabular structure is proposed for possible relaxation of such a demand. The memorized data are organized as part of a k-d tree. Nodes in the tree, called vertices, correspond to some regularly assigned input points. Memory resource is allocated only at locations where it is needed. Being able to easily compute the vertex positions helps reduce the searching cost in data retrieval. In addition, the learning process is able to expand the tree structure into one covering the problem domain. With memory allocated based on demand, memory consumption becomes closely related to task complexity instead of input dimensionality, and is minimized. Algorithms for structure construction and training are presented in this paper. Simulation results demonstrate that the memory can be efficiently utilized. The developed scheme offers a solution for high-dimensional learning problems with a manageable size of effective input domain. [ABSTRACT FROM AUTHOR]

In a big data era, there are and will be more demands and higher requirements for data organization and management in the storage system. Previous studies on semantic relations of blocks have set an essential theoretical basis in storage systems. Revealing semantic relationship of blocks has great scientific values and indicates a promising prospect in the application. However, only qualitative description of block correlations has been obtained by traditional ways, and few researches have been done on unifying and quantizing the blocks semantic relationships. In this paper, an uniform design-aided gene expression programming (UGEP) method has been proposed to address this issue. After analyzing historical access streams, a quantitative description system on semantic relationship of blocks is established. The semantic relationship of blocks can also be used in prefetching in storage system, which means the next access block can be calculated by the prefetching model. Simulation tests in four real system traces indicate that compared with sequential prefetching and no prefetching model, the quantitative semantic relationships model based on UGEP can highly improve the performance of the storage system. The experimental results show that in Cello-92 cases, compared with no prefetching method and sequential prefetching method, prefetching based on UGEP can reduce average I/O response time by 23.7% and 17.9%, and hit ratio is also increased by 16% and 8% as well. [ABSTRACT FROM AUTHOR]

Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at data in the past would be a valuable tool to perform complex calculations. In this paper we present the first implementation of a chemical delay line providing information storage that can reliably capture information over an extended period of time. The delay line is capable of parallel operations in a single instruction, multiple data (SIMD) fashion. Using mass action and Michaelis-Menten kinetics, we describe the chemical delay line implementation featuring enzymes acting as a means to reduce copy errors. We also discuss how information is randomly accessible from any element on the delay line. Our work shows how the chemical delay line retains and provides a value from a previous cycle. The system's modularity allows for integration with existing chemical systems. We exemplify the delay line capabilities by integration with a threshold asymmetric signal perceptron to demonstrate how it learns all 14 linearly separable binary functions over a sliding window of size two. The delay line has applications in biomedical diagnosis and treatment, such as smart drug delivery. [ABSTRACT FROM AUTHOR]

We propose an ontology alignment framework with two core features: the use of background knowledge and the ability to handle vagueness in the matching process and the resulting concept alignments. The procedure is based on the use of a generic reference vocabulary, which is used for fuzzifying the ontologies to be matched. The choice of this vocabulary is problem-dependent in general, although Wikipedia represents a general-purpose source of knowledge that can be used in many cases, and even allows cross language matchings. In the first step of our approach, each domain concept is represented as a fuzzy set of reference concepts. In the next step, the fuzzified domain concepts are matched to one another, resulting in fuzzy descriptions of the matches of the original concepts. Based on these concept matches, we propose an algorithm that produces a merged fuzzy ontology that captures what is common to the source ontologies. The paper describes experiments in the domain of multimedia by using ontologies containing tagged images, as well as an evaluation of the approach in an information retrieval setting. The undertaken fuzzy approach has been compared to a classical crisp alignment by the help of a ground truth that was created based on human judgment. [ABSTRACT FROM AUTHOR]

Combining multiple information retrieval (IR) systems has been shown to improve performance over individual systems. However, it remains a challenging problem to determine when and how a set of individual systems should to be combined. In this paper, we investigate these issues using combinatorial fusion analysis and five data sets provide by TREC 2, 3, 4, 5, and 6. In particular, we compare the performance of combining six IR systems selected by random choice vs. by performance measurement from these five TREC data sets. Two experiments are conducted, which include: (1) combination of two systems and their performance outcome in terms of performance ratio and cognitive diversity, and (2) combinatorial fusion of t-systems, t = 2 to 6, using both score and rank combinations and exploration of the effect of diversity on the performance outcome. It is demonstrated in both experiments that combination of two or more systems improves the performance more significantly when the systems are selected by performance evaluation than those selected by random choice. Our work provides a distinctive method of system selection for the combination of multiple retrieval systems. [ABSTRACT FROM AUTHOR]

In a case-based reasoning system, adaptation is a complicated task since it requires domain-specific knowledge, which is generally difficult to define. To acquire such knowl-edge, we propose a semi-automatic approach based on Formal Concept Analysis (FCA) techniques. We use Logical Concept Analysis (LCA), a generalization of FCA, to extract adaptation conditions that enhance the retrieval and adaptation processes. In this paper, we present this approach, that has been implemented in COBRA, our ontology-based CBR platform, and applied to the diagnosis of gas sensor failures [ABSTRACT FROM AUTHOR]