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Highlights • A discretization approach based on mutual information concept was proposed. • This research aims to extract RUARs in accordance with discretization result. • A large dataset with about 12,000 medical data records is used to generate RUARs. • Our MID method provides a high coverage ratio of RUARs. • Graph-based visualization is used to represent RUARs clearly and easily. ABSTRACT In knowledge discovery studies, association rules mining has been extensively studied to discover hidden knowledge and relationships among set of items in a transactional dataset. Most research on association rule mining focuses on discovering frequent patterns based on the most frequent items occurring in the dataset. However, the process of extracting rare rules has received less attention. In medical dataset studies, the discovery of rare association rules (RARs) is more challenging, because it could likely be used to obtain more potentially rare and unusual knowledge for physicians, beside frequent association rules. Hence, the aim of this paper is to discover non-frequent or rare-unusual association rules (RUARs) from a stroke medical dataset to provide potential meaningful knowledge to the user domain. A discretization method needs to be performed as the data preprocessing step before generating rules. To the best of our knowledge, fewer studies have focused on the role of discretization results to support the extraction of a better amount and quality of RUARs, particularly for medical datasets. In addition, the extracted RUARs is expected to provide potential new unusual insights on stroke risk patterns. This paper applies mutual information measure to discretize a stroke examination dataset collected from a medical center in Taiwan. The interval merging method was proposed to simplify the discrete form and enrich the quality of generated rules. Towards the end, rare association rules, with relatively low support, were generated by employing the Apriori-Rare method accordingly. In addition, a filtering process was applied to the content of the rule itemsets to discover the expected set of RUARs for physicians. Furthermore, the extracted RUARs was analyzed based on the relative risk values toward the occurrence of stroke. Results indicated that the mutual information discretization outperformed the traditional discretization methods in terms of how the discretization scheme can support the extraction of RUARs with a better quantity and quality measurements for further analysis purpose in medical point of view. Moreover, the proposed method had a relatively higher number of RUARs. The knowledge of unusual rule patterns from rare association rules might provide potential new and unusual insights for medical pratitioners and increase the awareness of stroke examination results. [ABSTRACT FROM AUTHOR]

After digitally designing components of vehicles, a design team creates a virtual manufacturing environment that resembles actual manufacturing facilities. During this digital pre-assembly process, a review team examines each component, and records its problems and requirements in part verification reports. Once these reports are delivered to specific design team responsible for each part, the design team can make appropriate adjustments to their designs. This digital pre-assembly process can evaluate and prevent flaws in design prior to actual manufacturing, improving production quality and reducing manufacturing cost. As these reports are written in free text form, they, however, are not fully utilized for understanding problems arising from the design process. This paper proposes a method of applying text mining techniques on verification reports to extract insights for quality improvement. In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights. The proposed method is applied on approximately 140,000 reports, and is validated through the quality of the answers obtained for the questions posed by the domain experts. The proposed method successfully extracts useful information from the text database, and provides intuitive graphical interface, thereby satisfying the need of the domain experts. This paper proposes a systematic framework of transforming huge amount of raw text data into intuitive visualization. Through this framework, meaningful knowledge can be extracted, analyzed and shared to improve the quality of the products. Main contribution of our paper is that it proposes a framework for knowledge extraction from pre-assembly process. Not only does it systematically arrange the data, but it also combines various data sources and creates a knowledge system to improve efficiency of the design process. [ABSTRACT FROM AUTHOR]

Automatic linguistic description of the available data about complex phenomena is a challenging task that is receiving the attention of data scientists in recent years. As an evolution of previous research results, there is a need of creating new linguistic computational models that allow us dealing with more complex phenomena and more complex descriptions of a growing amount of heterogeneous and real-time data. This paper contributes to this field by presenting three new ways of describing added-value information automatically extracted from data. Also, we extend previous computational models by including a description of the reliability of the available input data. Namely, we face this challenge by using a new implementation of the concept of Z-number proposed by Zadeh. We demonstrate the possibilities of the proposed extension with a practical application. The application generates automatic linguistic reports about the deforestation evolution in the Amazon region, e.g., “The deforestation last month was high. Because of the cloudiness, the reliability of this information is moderate”. Additionally, we evaluate the quality of the generated linguistic descriptions through fuzzy rating scale-based questionnaires. Moreover, we have also made a comparative study between reports generated with and without the new contributions introduced in this paper. The results show that the new types of computational perceptions introduced in this paper are ready to help data scientists to automatically generate good quality reports. [ABSTRACT FROM AUTHOR]

The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules. [ABSTRACT FROM AUTHOR]

Clustering is an important field for making data meaningful at various applications such as processing satellite images, extracting information from financial data or even processing data in social sciences. This paper presents a new clustering approach called Gaussian Density Distance (GDD) clustering algorithm based on distance and density properties of sample space. The novel part of the method is to find best possible clusters without any prior information and parameters. Another novel part of the algorithm is that it forms clusters very close to human clustering perception when executed on two dimensional data. GDD has some similarities with today’s most popular clustering algorithms; however, it uses both Gaussian kernel and distances to form clusters according to data density and shape. Since GDD does not require any special parameters prior to run, resulting clusters do not change at different runs. During the study, an experimental framework is designed for analysis of the proposed clustering algorithm and its evaluation, based on clustering performance for some characteristic data sets. The algorithm is extensively tested using several synthetic data sets and some of the selected results are presented in the paper. Comparative study outcomes produced by other well-known clustering algorithms are also discussed in the paper. [ABSTRACT FROM AUTHOR]

In recent years, semantic network is applied to more and more research areas, such as Information Science areas. Differing from traditional users’ recommendations, the tweets’ recommendation in a micro-blog network has two crucial differences. One is high authority users or one’s special friends usually play a very active role in tweet-oriented recommendation. Micro-blog user will put the users his/her very interested into “special attention” group, and the topics discussed more in “special attention” group are more likely to be the user interested topic. The other is that users hope to obtain more relevant tweets about what he/she is interested in. Thus, this paper uses the k-cores analysis method to extract topics that users pay attention to, and employs the method of factor analysis to analyze index, and to extract the tweet heat factor and user authority factor. Besides, this paper intends to use the method of RS and linear regression to determine the parameters for balancing the value of the tweet heat factor and user authority factor. Finally, this paper manages to establish a timely personalized recommendation model based on semantic network for SINA tweets. According to the experimental results, the proposed method in this paper can effectively solve problems existing in micro-blog tweets in a personalized and timely recommendation way. [ABSTRACT FROM AUTHOR]

Abstract: A large number of items are described, and subsequently bought and sold every day in auction marketplaces across the web. The amount of information and the number of available items makes finding what to buy as well as describing an item to sell, a challenge for the participants. In this paper we consider two functions of electronic marketplaces. First, we address the recommendation of related items for users browsing the items offered in a marketplace. Second, in order to support potential sellers we propose the recommendation of relevant items and terms which can be used to describe an item to be sold in the marketplace. The contribution of this paper lies in the proposal of an innovative system that exploits the hidden topics of unstructured information found in the e-marketplace in order to support these functions. We propose a three-step process in which a probabilistic topic modelling approach is used in order to uncover latent topics that provide the basis for item and term similarity calculation for the corresponding recommendations. We present the design of our system and perform evaluations of the quality of the extracted topics as well as of the recommender system using real life scenarios using data extracted from a widely used auction marketplace. The evaluations demonstrate the perceived usefulness of our approach. [Copyright &y& Elsevier]

Abstract: Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reliable than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electro-mechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation. [Copyright &y& Elsevier]

Abstract: Organizations need diverse information systems to deal with the increasing requirements in information storage and processing, yielding the creation of information islands and therefore an intrinsic difficulty to obtain a global view. Being able to provide such an unified view of the -likely heterogeneous-information available in an organization is a goal that provides added-value to the information systems and has been subject of intense research. In this paper we present an extension of a solution named Searchy, an agent-based mediator system specialized in data extraction and Integration. Through the use of a set of wrappers, it integrates information from arbitrary sources and semantically translates them according to a mediated scheme. Searchy is actually a domain-independent wrapper container that ease wrapper development, providing, for example, semantic mapping. The extension of Searchy proposed in this paper introduces an evolutionary wrapper that is able to evolve wrappers using regular expressions. To achieve this, a Genetic Algorithm (GA) is used to learn a regex able to extract a set of positive samples while rejects a set of negative samples. [Copyright &y& Elsevier]

Abstract: This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms. [Copyright &y& Elsevier]

Abstract: The detection of non-technical losses (NTLs), in most papers, commonly deals with the utilization of the registered consumption for each customer; besides, some researchers used the economic activity, the active/reactive ratio and the contract power. Currently, utility company databases store enormous amounts of information on both installations and customers: consumption, technical information on the measure equipment, documentation, inspections results, commentaries of inspectors, etc. In this paper, an integrated expert system (IES) for the analysis and classification of all the available useful information of the customer is presented. Customer classification identifies the presence of an NTL and the problem type. This IES include several modules: text mining module for analysis of inspector commentaries and extraction of additional information on the customer, data mining module to draw up the rules that determine the customer estimate consumption, and the Rule Based Expert System module to analyze each customer using the results of the text and data mining modules. This IES is used with real data extracted from Endesa company databases. Endesa is the most important power distribution company in Spain, and one of the most significant companies of Europe. This IES is used in the test phase by human experts in the Endesa company. In this phase, the IES is used as a Decision Support System (DSS), as it contains another module which provides a report with additional information about the customer and a summarized result that the inspectors can use to reach a decision. [Copyright &y& Elsevier]

Opinion target extraction or aspect extraction is the most important subtask of the aspect-based sentiment analysis. This task focuses on the identification of the targets of user's opinions or sentiments from online reviews. In the recent years, syntactic patterns-based approaches have performed quite well and produced significant improvement in the aspect extraction task. However, these approaches are heavily dependent on the dependency parsers which produced syntactic relations following the grammatical rules and language constraints. In contemporary, users do not give much importance to these rules and constraints while expressing their opinions about particular product and neither reviewer websites restrict users to do so. This makes syntactic patterns-based approaches vulnerable. Therefore, in this paper, we are proposing a two-fold rules-based model (TF-RBM) which uses rules defined on the basis of sequential patterns mined from customer reviews. The first fold extracts aspects associated with domain independent opinions and the second fold extracts aspects associated with domain dependent opinions. We have also applied frequency- and similarity-based approaches to improve the aspect extraction accuracy of the proposed model. Our experimental evaluation has shown better results as compared with the state-of-the-art and most recent approaches. [ABSTRACT FROM AUTHOR]

Finger-vein verification has drawn increasing attention because it is highly secured and private biometric in practical applications. However, as the imaging environment is affected by many factors, the captured image contains not only the vein pattern but also the noise and irregular shadowing which can decrease the verification accuracy. To address this problem, in this paper, we proposed a new finger-vein extraction approach which detects the valley-like structures using the curvatures in Radon space. Firstly, given a pixel, we obtain eight patches centered on it by rotating a window along eight different orientations and project the resulting patches into Radon space using the Radon transform. Secondly, the vein patches create prominent valleys in Radon space. The vein patterns are enhanced according to the curvature values of the valleys. Finally, the vein network is extracted from the enhancing image by a binarization scheme and matched for personal verification. The experimental results on both contacted and contactless finger-vein databases illustrate that our approach can significantly improve the accuracy of the finger-vein verification system. [ABSTRACT FROM AUTHOR]

Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA . While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval. [ABSTRACT FROM AUTHOR]

Cluster analysis, or clustering, refers to the analysis of the structural organization of a data set. This analysis is performed by grouping together objects of the data that are more similar among themselves than to objects of different groups. The sampled data may be described by numerical features or by a symbolic representation, known as categorical features. These features often require a transformation into numerical data in order to be properly handled by clustering algorithms. The transformation usually assigns a weight for each feature calculated by a measure of importance (i.e., frequency, mutual information). A problem with the weight assignment is that the values are calculated with respect to the whole set of objects and features. This may pose as a problem when a subset of the features have a higher degree of importance to a subset of objects but a lower degree with another subset. One way to deal with such problem is to measure the importance of each subset of features only with respect to a subset of objects. This is known as co-clustering that, similarly to clustering, is the task of finding a subset of objects and features that presents a higher similarity among themselves than to other subsets of objects and features. As one might notice, this task has a higher complexity than the traditional clustering and, if not properly dealt with, may present an scalability issue. In this paper we propose a novel co-clustering technique, called HBLCoClust, with the objective of extracting a set of co-clusters from a categorical data set, without the guarantees of an enumerative algorithm, but with the compromise of scalability. This is done by using a probabilistic clustering algorithm, named Locality Sensitive Hashing, together with the enumerative algorithm named InClose. The experimental results are competitive when applied to labeled categorical data sets and text corpora. Additionally, it is shown that the extracted co-clusters can be of practical use to expert systems such as Recommender Systems and Topic Extraction. [ABSTRACT FROM AUTHOR]

New security threats are generated with the coming of information industrial 5.0 represented by artificial intelligence and Industrial Internet of Things (IIoT). One of the most significant concerns in IIoT is information security which refers to the protection of the privacy of image owner. To this end, a new reversible data hiding in encrypted image (RDHEI) method with high capacity for IIoT security is proposed in this paper. On image owner side, image contents are protected by a block level image encryption method while the correlations of adjacent pixels inside the same block are not damaged. On receiver side, to realize reversibility, all encrypted pixel blocks are classified into overflow blocks and non-overflow blocks and only non-overflow blocks are used to carry additional data. Then, some secret data, which is used to manage encrypted images, is embedded into the largest and smallest pixels in non-overflow blocks by utilizing pixel-value-ordering and histogram shifting techniques. On the receiver side, original image and secret data can be recovered perfectly. Moreover, according the availability of decryption key and data hiding key, image recovery and data extraction are separably executed. Experimental results demonstrate that significant improvement in embedding capacity is achieved without destroying reversibility. In this way, the proposed method outperforms several state-of-the-art methods and it is quite suitable for IIoT. • Compared with previous methods that use a pixel block or dozens of pixels to carry one bit, more data bits can be embedded into each non-overlapped 2 × 2 sized block. In this way higher capaicty is achieved. • Since only scrambling is adopted for image encryption, the pixel value order of pixels in a block is maintained and this is conducive to vacating the embedding room. • Performance improvement is achieved under the condition of realizing real reversibility. Therefore, the proposed method is more suitable for IIoT scenarios with very high requirement on image quality. • Separability of image decryption and data extraction. Receiver with only the decryption key can restore the cover image while receiver with only the data-hiding key can losslessly extract the hidden data. [ABSTRACT FROM AUTHOR]

Sensor network technology is becoming more widespread and sophisticated, and devices with many sensors, such as smartphones and sensor nodes, have been used extensively. Since these devices have more easily accumulated various kinds of micrometeorological data, such as temperature, humidity, and wind speed, an enormous amount of micrometeorological data has been accumulated. In recent years, it has been expected that such an enormous amount of data, called big data, will produce novel knowledge and value. Accordingly, many current applications have used data mining technology or machine learning to exploit big data. However, micrometeorological data has a complicated correlation among different features, and its characteristics change variously with time. Therefore, it is difficult to predict micrometeorological data accurately with low computational complexity even if state-of-the-art machine learning algorithms are used. In this paper, we propose a new methodology for predicting micrometeorological data, sliding window-based support vector regression (SW-SVR) that involves a novel combination of support vector regression (SVR) and ensemble learning. To represent complicated micrometeorological data easily, SW-SVR builds several SVRs specialized for each representative data group in various natural environments, such as different seasons and climates, and changes weights to aggregate the SVRs dynamically depending on the characteristics of test data. In our experiment, we predicted the temperature after 1 h and 6 h by using large-scale micrometeorological data in Tokyo. As a result, regardless of testing periods, training periods, and prediction horizons, the prediction performance of SW-SVR was always greater than or equal to other general methods such as SVR, random forest, and gradient boosting. At the same time, SW-SVR reduced the building time remarkably compared with those of complicated models that have high prediction performance. [ABSTRACT FROM AUTHOR]

Building detection from aerial images has many applications in fields like urban planning, real-estate management, and disaster relief. In the last two decades, a large variety of methods on automatic building detection have been proposed in the remote sensing literature. Many of these approaches make use of local features to classify each pixel or segment to an object label, therefore involving an extra step to fuse pixelwise decisions. This paper presents a generic framework that exploits recent advances in image segmentation and region descriptors extraction for the automatic and accurate detection of buildings on aerial orthophotos. The proposed solution is supervised in the sense that appearances of buildings are learnt from examples. For the first time in the context of building detection, we use the matrix covariance descriptor, which proves to be very informative and compact. Moreover, we introduce a principled evaluation that allows selecting the best pair segmentation algorithm-region descriptor for the task of building detection. Finally, we provide a performance evaluation at pixel level using different classifiers. This evaluation is conducted over 200 buildings using different segmentation algorithms and descriptors. The performance analysis quantifies the quality of both the image segmentation and the descriptor used. The proposed approach presents several advantages in terms of scalability, suitability and simplicity with respect to the existing methods. Furthermore, the proposed scheme (detection chain and evaluation) can be deployed for detecting multiple object categories that are present in images and can be used by intelligent systems requiring scene perception and parsing such as intelligent unmanned aerial vehicle navigation and automatic 3D city modeling. [ABSTRACT FROM AUTHOR]

Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous or that the features of this data set can be mapped to an n-dimensional space of independent dimensions when there is a correlation among their features. In this paper, a new verification system based on the Best-Worst Ant System (BWAS) is presented to overcome previous weaknesses. The experimental results show an accuracy improvement of 7.5% over current solutions. [ABSTRACT FROM AUTHOR]

Frequent itemset mining allows us to find hidden, important information from large databases. Moreover, processing incremental databases in the itemset mining area has become more essential because a huge amount of data has been accumulated continually in a variety of application fields and users want to obtain mining results from such incremental data in more efficient ways. One of the major problems in incremental itemset mining is that the corresponding mining results can be very large-scale according to threshold settings and data volumes. In addition, it is considerably hard to analyze all of them and find meaningful information. Furthermore, not all of the mining results become actually important information. In this paper, to solve these problems, we propose an algorithm for mining weighted maximal frequent itemsets from incremental databases. By scanning a given incremental database only once, the proposed algorithm can not only conduct its mining operations suitable for the incremental environment but also extract a smaller number of important itemsets compared to previous approaches. The proposed method also has an effect on expert and intelligent systems since it can automatically provide more meaningful pattern results reflecting characteristics of given incremental databases and threshold settings, which can help users analyze the given data more easily. Our comprehensive experimental results show that the proposed algorithm is more efficient and scalable than previous state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for automatic query-reply in social network. Information extraction and query-oriented summarization method are applied here to reply people’s query. There are few effective and commonly used methods on filtering the redundancy and noise of the raw data, which results in the poor quality of the reply. Due to the characteristics of social network messages, we pay more attention to reducing the noise and eliminating the redundancy of the messages to ensure the quality of the final reply. First, we propose an information extraction method to extract high quality information from social network messages, which is based on time-frequency transformation. Second, query-oriented text summarization is implemented to generate a brief and concise summary as the final reply, which is based on the scoring, ranking and selection of sentences of high quality social network messages produced by previous step. Experimental results show that the research is effective in filtering the redundancy and noise of social network messages, the final query-reply results outperform other commonly used methods’ results in both automatic evaluation and manual evaluation. Through our approach, noise and redundancy of social network messages are effectively filtered. Certainly, our method improves the quality of the reply for people’s query. [ABSTRACT FROM AUTHOR]

Capturing the compositional process from words to documents is a key challenge in natural language processing and information retrieval. Extractive style query-oriented multi-document summarization generates a summary by extracting a proper set of sentences from multiple documents based on pre-given query. This paper proposes a novel document summarization framework based on deep learning model, which has been shown outstanding extraction ability in many real-world applications. The framework consists of three parts: concepts extraction, summary generation, and reconstruction validation. A new query-oriented extraction technique is proposed to extract information distributed in multiple documents. Then, the whole deep architecture is fine-tuned by minimizing the information loss in reconstruction validation. According to the concepts extracted from deep architecture layer by layer, dynamic programming is used to seek most informative set of sentences for the summary. Experiment on three benchmark datasets (DUC 2005, 2006, and 2007) assess and confirm the effectiveness of the proposed framework and algorithms. Experiment results show that the proposed method outperforms state-of-the-art extractive summarization approaches. Moreover, we also provide the statistical analysis of query words based on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform. There exists underlying relationships from topic words to the content which can contribute to summarization task. [ABSTRACT FROM AUTHOR]

A multi-label classification based approach for sentiment analysis is proposed in this paper. To the best of our knowledge, this work is the first to propose to use multi-label classification for sentiment classification of microblogs. The proposed prototype has three main components, text segmentation, feature extraction, and multi-label classification. Raw segmented words and sentiment features based on the three different sentiment dictionaries, Dalian University of Technology Sentiment Dictionary, National Taiwan University Sentiment Dictionary and HowNet Dictionary, are the features and the bag of words is the feature representation. A detailed empirical study of different multi-label classification methods on sentiment classification is conducted to compare their classification performances. Specifically, total 11 state of the art multi-label classification methods are compared on two microblog datasets and 8 evaluation metrics are used. The effects of the three sentiment dictionaries for multi-label classification are empirically studied and compared, which, to the best of our knowledge, have not been performed. The performed empirical comparisons show that Dalian University of Technology Sentiment Dictionary has the best performance among the three different sentiment dictionaries. [ABSTRACT FROM AUTHOR]

In this paper we present a novel approach to semi supervised classification based on a weight transmission model. Our research is motivated by rule extraction from transactional data, where a transaction consists of a collection of items, each of which is assigned a weight denoting its importance in relation to other items in the collection. The assignment of weight to items enables the end user to guide the rule extraction process to generate rules involving high impact items, thus enhancing the knowledge discovery process. Most previous research to weight assignment has used domain specific information to assign weights to items. We propose a model, XWeightTransmitter, that relaxes the assumption that domain information is available for all items. XWeightTransmitter interpolates the unknown weights from a known subset of weights and is an extension of the WeightTransmitter approach. Our experimentation shows that XWeightTransmitter outperforms a previously established weight transmission model known as WeightTransmitter, producing higher Recall and Precision in inferring unknown weights while incurring lower execution overheads. Although the research setting is weighted association rule mining the methods developed are equally applicable to the supervised classification context where class labels are not known for all instances, a typical scenario in many data mining applications. [ABSTRACT FROM AUTHOR]

This paper presents a new image feature descriptor, namely directional local motif XoR patterns (DLMXoRPs) for image retrieval application. The proposed motif representation is entirely different from existing motif. The DLMXoRP presents a novel technique for the calculation of motif using 1 × 3 grids. The proposed motif (1 × 3) representation is having a flexible structure; hence it can able to extract all directional information. This flexibility is not present in the existing (2 × 2) motif. Further, the XoR operation is performed on the transformed new motif images which are not present in the literature (local binary patterns (LBP) and motif co-occurrence matrix (MCM)). To elevate the benefits of DLMXoRP, we compare it with the Motif XoR pattern (MXoR) which is calculated by applying the XoR operation on existing transformed motif image. The performance of the proposed method is tested by conducting three experiments on Corel-5000, Corel-10000 and MIT-VisTex databases. The results after investigation show a significant improvement in terms of average retrieval precision (ARP) and average retrieval rate (ARR) as compared to the state-of-the-art techniques for image retrieval. [ABSTRACT FROM AUTHOR]

Highlights: [•] In this paper, we propose a novel data structure for frequent itemset mining. [•] Based on Nodeset, we present an efficient mining algorithm named FIN. [•] Experiment results show that FIN outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Highlights: [•] Keywords can reflect the theme of an article. [•] Keywords retrieved from the full text is a time-consuming task. [•] Blog Connect is provided to collect the keywords queried by readers. [•] The user behaviors for an article are considered in the scheme. [•] This paper confirms that queried keywords can act as a quick path to an article. [Copyright &y& Elsevier]

Abstract: The Qualitative Rectilinear Projection Calculus (QRPC), a new representation model based on planar trajectories, is presented in this work for describing qualitatively motion patterns. Several models have been developed for this purpose and by comparing them with our proposal we show that the proposed model results in an intuitive approach for representing, in any context, the kinematical behavior of two objects in motion on the plane through the possible relationships among the rectilinear projection of their trajectories. The paper is centered on the formal definition of the set of geometric relations in terms of the front–back and left–right dichotomies, and how by the composition of these relations, it can be possible enumerate an exhaustive set of qualitative states and the possible transitions among them. The complete iconic representation of the set of qualitative states defines the Conceptual Neighborhood Graph of the QRPC model. In order to illustrate how this qualitative representation can be used to analyze and describe the relative motion of two objects, some examples extracted from the traffic engineering field have been studied. [Copyright &y& Elsevier]

Abstract: An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a system. An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port system with inputs and outputs. Therefore, a vapor compression system can be modeled using an ANN in a mapping configuration. That is, some parameters from the compression system can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting. This paper presents a new method to model a variable speed vapor compression system. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a vapor compression system, concluding that the model can predict the energetic performance of a variable speed vapor compression system. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the vapor compression system. [Copyright &y& Elsevier]

Abstract: In this paper, a new multi-document summarization framework which combines rhetorical roles and corpus-based semantic analysis is proposed. The approach is able to capture the semantic and rhetorical relationships between sentences so as to combine them to produce coherent summaries. Experiments were conducted on datasets extracted from web-based news using standard evaluation methods. Results show the promise of our proposed model as compared to state-of-the-art approaches. [Copyright &y& Elsevier]

Abstract: Several studies have been carried out on augmented reality (AR)-based environments that deal with user interfaces for manipulating and interacting with virtual objects aimed at improving immersive feeling and natural interaction. Most of these studies have utilized AR paddles or AR cubes for interactions. However, these interactions overly constrain the users in their ability to directly manipulate AR objects and are limited in providing natural feeling in the user interface. This paper presents a novel approach to natural and intuitive interactions through a direct hand touchable interface in various AR-based user experiences. It combines markerless augmented reality with a depth camera to effectively detect multiple hand touches in an AR space. Furthermore, to simplify hand touch recognition, the point cloud generated by Kinect is analyzed and filtered out. The proposed approach can easily trigger AR interactions, and allows users to experience more intuitive and natural sensations and provides much control efficiency in diverse AR environments. Furthermore, it can easily solve the occlusion problem of the hand and arm region inherent in conventional AR approaches through the analysis of the extracted point cloud. We present the effectiveness and advantages of the proposed approach by demonstrating several implementation results such as interactive AR car design and touchable AR pamphlet. We also present an analysis of a usability study to compare the proposed approach with other well-known AR interactions. [Copyright &y& Elsevier]

Abstract: It is commonly acknowledged that ever-increasing video archives should be conveniently indexed with the conveyed semantic information to facilitate later video retrieval. Domain-independent semantic video indexing is usually carried out through manual means which is too time-consuming and labor-intensive to be employed in practical settings. On the other hand, fully automated approaches are usually proposed for very specialized domains such as team sports videos. In this paper, we propose a generic text-based semi-automatic system for off-line semantic indexing and retrieval of news videos, since video texts such as speech transcripts stand as a plausible source of semantic information. The proposed system has a pipelined flow of execution where the sole manual intervention takes place during text extraction, yet it could execute in fully automated mode in case the associated video text is already available or a convenient text extractor is available to be incorporated into the system. At the core of the system is an information extraction component – a named entity recognizer – which extracts representative semantic information from the video texts. Based on the proposed generic system, a novel semantic annotation and retrieval system for Turkish is designed, implemented, and evaluated on two distinct news video data sets. By equipping it with the necessary components, the ultimate system is also turned into a multilingual video retrieval system and executed on a video data set in English, thereby facilitating multilingual semantic video retrieval. [Copyright &y& Elsevier]

Abstract: The amount of scientific papers in the Molecular Biology field has experienced an enormous growth in the last years, prompting the need of developing automatic Information Extraction (IE) systems. This work is a first step towards the ontology-based domain-independent generalization of a system that identifies Escherichia coli regulatory networks. First, a domain ontology based on the RegulonDB database was designed and populated. After that, the steps of the existing IE system were generalized to use the knowledge contained in the ontology, so that it could be potentially applied to other domains. The resulting system has been tested both with abstract and full articles that describe regulatory interactions for E. coli, obtaining satisfactory results. [Copyright &y& Elsevier]

Abstract: Due to its ability to handle nonlinear problems, artificial neural networks are applied in several areas of science. However, the human elements are unable to assimilate the knowledge kept in those networks, since such knowledge is implicitly represented by their connections and the respective numerical weights. In recent formal concept analysis, through the FCANN method, it has demonstrated a powerful methodology for extracting knowledge from neural networks. However, depending on the settings used or the number of the neural network variables, the number of formal concepts and consequently of rules extracted from the network can make the process of knowledge and learning extraction impossible. Thus, this paper addresses the application of the JBOS approach to extracted reduced knowledge from the formal contexts extracted by FCANN from the neural network. Thus, providing a small number of formal concepts and rules for the final user, without losing the ability to understand the process learned by the network. [Copyright &y& Elsevier]

Abstract: Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90. [Copyright &y& Elsevier]

Abstract: Robust and effective optic disc detection is a necessary processing component in automatic retinal screening systems. In this paper, optic disc localization is achieved by a novel illumination correction operation, and contour segmentation is completed by a supervised gradient vector flow snake (SGVF snake) model. Conventional GVF snake is not sufficient to segment contour due to vessel occlusion and fuzzy disc boundaries. In view of this reason, the SGVF snake is extended in each time of deformation iteration, so that the contour points can be classified and updated according to their corresponding feature information. The classification relies on the feature vector extraction and the statistical information generated from training images. This approach is evaluated by means of two publicly available databases, Digital Retinal Images for Vessel Extraction (DRIVE) database and Structured Analysis of the Retina (STARE) database, of color retinal images. The experimental results show that the overall performance is with 95% correct optic disc localization from the two databases and 91% disc boundaries are correctly segmented by the SGVF snake algorithm. [Copyright &y& Elsevier]

Abstract: Geographical information systems are commonly used for a variety of purposes. Many of them make use of a large database of geographical data, the correctness of which strongly influences the reliability of the system. In this paper, we present an approach to quality maintenance that is based on automatic discovery of non-perfect regularities in the data. The underlying idea is that exceptions to these regularities (‘outliers’) are considered probable errors in the data, to be investigated by a human expert. A case study shows how the tool can be used for extracting valuable knowledge about outliers in real-world geographical data, in an adaptive manner to the evolving data model supporting it. While the tool aims specifically at geographical information systems, the underlying approach is more broadly applicable for quality maintenance in data-rich intelligent systems. [Copyright &y& Elsevier]

Abstract: Video texture, a novel type of medium, can produce a new video with a continuously varying stream of images from a recorded video. A classic approach to generate video textures is to apply principal components analysis (PCA) for dimensionality reduction (i.e. extraction of frame signatures) and autoregressive (AR) process for prediction purposes. In this paper we investigate the use of other dimensionality reduction techniques to generate accurate video textures. Based on our experiments, the quality of video textures can be improved further. We also propose a new approach for generating video textures using probabilistic principal components analysis (PPCA) and Gaussian process dynamical model (GPDM) to synthesize video textures which contain frames that never appeared before and with similar motions as original videos. Furthermore, we propose two ways of generating online video textures by applying the incremental Isomap and incremental Spatio-temporal Isomap (IST-Isomap). Both approaches can produce good online video texture results. In particular, IST-Isomap, that we propose, is more suitable for sparse video data (e.g. cartoon). [Copyright &y& Elsevier]

Abstract: This paper presents a new knowledge-based system for extracting and identifying text-lines from various real-life mixed text/graphics compound document images. The proposed system first decomposes the document image into distinct object planes to separate homogeneous objects, including textual regions of interest, non-text objects such as graphics and pictures, and background textures. A knowledge-based text extraction and identification method obtains the text-lines with different characteristics in each plane. The proposed system offers high flexibility and expandability by merely updating new rules to cope with various types of real-life complex document images. Experimental and comparative results prove the effectiveness of the proposed knowledge-based system and its advantages in extracting text-lines with a large variety of illumination levels, sizes, and font styles from various types of mixed and overlapping text/graphics complex compound document images. [Copyright &y& Elsevier]

Abstract: Resource allocation is of great importance for business process management. In business process execution, a set of rules that specify resource allocation is always implied. Although many approaches have been offered to support resource allocation, they are not sufficient to derive interesting resource allocation rules which ensure that each activity is performed by suitable resource. Hence, this paper introduces an association rule mining based approach to mine interesting resource allocation rules from event log. The idea is to concern the ordered correlations between items in event log, and then to present two efficient algorithms to mine real “interesting” rules. The event log of radiology CT-scan examination process provided by the Chinese Huzhou hospital is used to verify the proposed approach. The evaluation results showed that the proposed approach not only is able to extract the rules more efficient and much faster, but also can discover more important resource allocation rules. [Copyright &y& Elsevier]

Abstract: In this paper, an efficient algorithm is presented to solve flexible flow-shop problems using fuzzy approach. The goal is to minimize the total job tardiness. We assume parallel machines with different operation times. In this algorithm, parameters like “due date” and “operation time” follow a triangular fuzzy number. We used data mining technique as a facilitator to help in finding a better solution in such combined optimization problems. Therefore, using a combination of genetic algorithm and an attribute-deductive tool such as data mining, a near optimal solution can be achieved. According to the structure of the presented algorithm, all of the feasible solutions for the flexible flow-shop problem are considered as a database. Via data mining and attribute-driven deduction algorithm, hidden relationships among reserved solutions in the database are extracted. Then, genetic algorithm can use them to seek an optimum solution. Since there are inherited properties in the solutions provided by genetic algorithm, future generation should have the same behavioral models more than preliminary ones. Data mining can significantly improve the performance of the genetic algorithm through analysis of near-optimal scheduling programs and exploration of hidden relationships among pre-reached solutions. [Copyright &y& Elsevier]

Abstract: In surgery simulation, the extracted tissue data can be operated repeatedly in a Virtual-reality (VR) system which provides a good alternative to classical training method. Fully automated segmentation techniques cannot guarantee the efficiency and precision in general case. This paper describes a user interactive segmentation method: given a labeled 2D image plane in Multi-Planar Reformation (MPR), the rest tissues are segmented automatically by a cellular automaton in multi-scale domain. Labels image generated in higher level Gaussian pyramid can be extended to lower level ones according to selected resolution. An edge indicator function is also set to avoid over-segmentation in Laplacian pyramid. The evolution can be observed and guided with volume rendering results. The proposed method shows the merits of higher precision, real time response in GPU framework and few interactions are required. [Copyright &y& Elsevier]

Abstract: Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly. [Copyright &y& Elsevier]

Abstract: This paper presents an improved genetic algorithm (IGA) by which the optimal feature subset can be selected effectively and efficiently from a multi-character feature set (MCFS). IGA adopts segmented chromosome management scheme to implement local management of chromosome. This scheme encodes a solution with an entire binary chromosome; but logically, it divides the chromosome into several segments according to the number of feature groups in MCFS for local management. A segmented crossover operator and a segmented mutation operator are employed to operate on these segments to avoid invalid chromosomes. The probability of crossover and mutation are adjusted dynamically according to the generation number and the fitness value. As a result, IGA obtains strong searching ability at the beginning of the evolution and achieves accelerated convergence along the evolution. IGA is tested using features extracted from cotton foreign fiber objects, and compared with the Simple Genetic Algorithm (SGA) under the same condition. The results show that IGA receives improved searching ability and convergence speed compared with SGA. The optimal feature subset selected by the IGA has much smaller size than that of the SGA. This is very important for the online classification of foreign fibers. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a method for the detection and classification of defects in weld radiographs is presented. The method has been applied for detecting and discriminating discontinuities in the weld images that may correspond to false alarms or defects such as worm holes, porosity, linear slag inclusion, gas pores, lack of fusion or crack. A set of 43 descriptors corresponding to texture measurements and geometrical features is extracted for each segmented object and given as input to a classifier. The classifier is trained to classify each of the objects it into one of the defect classes or characterize it as non-defect. Three fold cross validation was utilized and experimental results are reported for three different classifiers (Support Vector Machine, Neural Network, k-NN). [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a method to extract knowledge from a set of surveyors’ reports is proposed. Many surveyors from shipping companies are working in shipyards to improve the quality and to meet requirements of the ship owner. At present, the reports from surveyors are used only for managing the status of problems of the product. The proposed method here has three steps. First, the description field of the reports which contains information about some trouble is summarized in a pair of a component name and a trouble name in the failure database; this is made possible through text-processing technology. Second, synonymous component and trouble names are consolidated into normalized terms using synonym ontology to prepare this information for statistical analysis. The synonym ontology is developed prior to this procedure. The system evaluates frequent trouble records by classifying the records using component and process ontology. In the last step, the designers extract knowledge from the information generated by the system. The results illustrate common design problems, and the method is proved to be useful. [Copyright &y& Elsevier]

Abstract: As the Internet has been the virtual place where citizens are united and their opinions are promptly shifted into the action, two way communications between the government sector and the citizen have been more important among activities of e-Government. Hence, Anti-corruption and Civil Rights Commission (ACRC) in the Republic of Korea has constructed the online petition portal system named e-People. In addition, the nation’s Open Innovation through e-People has gained increasing attention. That is because e-People can be applied for the virtual space where citizens participate in improving the national law and policy by simply filing petitions to e-People as the voice of the nation. However, currently there are problems and challenging issues to be solved until e-People can function as the virtual space for the nation’s Open Innovation based on petitions collected from citizens. First, there is no objective and systematic method for analyzing a large number of petitions filed to e-People without a lot of manual works of petition inspectors. Second, e-People is required to forecast the trend of petitions filed to e-People more accurately and quickly than petition inspectors for making a better decision on the national law and policy strategy. Therefore, in this paper, we propose the framework of applying text and data mining techniques not only to analyze a large number of petitions filed to e-People but also to predict the trend of petitions. In detail, we apply text mining techniques to unstructured data of petitions to elicit keywords from petitions and identify groups of petitions with the elicited keywords. Moreover, we apply data mining techniques to structured data of the identified petition groups on purpose to forecast the trend of petitions. Our approach based on applying text and data mining techniques decreases time-consuming manual works on reading and classifying a large number of petitions, and contributes to increasing accuracy in evaluating the trend of petitions. Eventually, it helps petition inspectors to give more attention on detecting and tracking important groups of petitions that possibly grow as nationwide problems. Further, the petitions ordered by their petition groups’ trend values can be used as the baseline for making a better decision on the national law and policy strategy. [Copyright &y& Elsevier]

Abstract: In the recent era of increasing volume crimes, crime prevention is now one of the most important global issues, along with the great concern of strengthening public security. Government and community officials are making an all-out effort to improve the effectiveness of crime prevention. Numerous investigations addressing this problem have generally employed disciplines of behavior science and statistics. Recently, the data mining approach has been shown to be a proactive decision-support tool in predicting and preventing crime. However its effectiveness is often limited due to different natures of crime data, such as linguistic crime data evolving over time. In this paper, we propose a framework of intelligent decision-support model based on a fuzzy self-organizing map (FSOM) network to detect and analyze crime trend patterns from temporal crime activity data. In addition, a rule extraction algorithm is employed to uncover hidden causal-effect knowledge and reveal the shift around effect. In contrast to most present crime related studies, we target a non-Western real-world case, i.e. the National Police Agency (NPA) in Taiwan. The resultant model can support police managers in assessing more appropriate law enforcement strategies, as well as improving the use of police duty deployment for crime prevention. [Copyright &y& Elsevier]

Abstract: Principal component analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets, while kernel PCA (KPCA) is the application of PCA in a kernel-defined feature space. For standard PCA and KPCA, if the size of dataset is large, it will need a very large memory to store kernel matrix and a lot of time to calculate eigenvalues and corresponding eigenvectors. The aim of this paper is to learn linear and nonlinear principal components by using a few partial data points and determine which data points can be used. To verify the performance of the proposed approaches, a series of experiments on artificial datasets and UCI benchmark datasets are accomplished. Simulation results demonstrate that the proposed approaches can compete with or outperform the standard PCA and KPCA in generalization ability but with much less memory and time consuming. [Copyright &y& Elsevier]

Abstract: The study and development of transportation systems have been a focus of attention in recent years, with many research efforts directed in particular at modelling traffic behaviour from both macroscopic and microscopic points of views. Although many statistical regression models of road traffic relationships have been formulated, they have proven to be unsuitable due to multiple and ill-defined traffic characteristics. Alternative methods such as neural networks have thus been sought but, despite some promising results, their design remains problematic and implementation is equally difficult. Another salient issue is that the opaqueness of trained networks prevents understanding the underlying models. Hybrid neuro-fuzzy rule-based systems, which combine the complementary capabilities of both neural networks and fuzzy logic, constitute a more promising technique for modelling traffic flow. This paper describes the application of a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network using Truth-Value-Restriction method (POPFNN-TVR) for modelling traffic behaviour. This approach has been shown to perform better on such problems than similar architectures. The results obtained highlight the capability of POPFNN-TVR in fuzzy knowledge extraction for modelling inter-lane relationships in a highway traffic stream, as well as in generalizing from sample data, as compared to traditional feed-forward neural networks using back-propagation learning. The model thus obtained automatically can be understood, analysed, and readily applied for transportation planning. [Copyright &y& Elsevier]