Reviewerly/exHarmony huggingface dataset:
A correspondence between belief function combination and knowledge base merging

In intelligent systems, belief function is a common approach to modelling uncertain and imperfect information obtained constantly while knowledge bases are used to encapsulate multiple static information items. In the literature, many different approaches have been proposed for combining belief functions (resp. merging knowledge bases) when there are multiple belief functions (resp. knowledge bases) generated from different sources depicting the same issue of interest. However, the connection between belief function combination and knowledge base merging is not adequately explored. In this paper, we aim to study the correspondence between the two lines of research. By introducing a numerical characteristic function for each knowledge base, we show that there is one to one correspondence between a set of combination rules of belief functions and merging methods of weighted knowledge bases.
School Week of High Technologies and Technical Entrepreneurship

For the modern school, the task of organizing the design and research activities of schoolchildren, connected with modern science and high technologies, is very actual. This is difficult to do without the interaction of the school with scientists and representatives of high-tech businesses. It is not easy to integrate such cooperation into the educational program of a secondary school. This chapter describes the experience of the School Week of High Technologies as an integrated model of the organization of the school's educational space. The chapter discusses the general logic of the preparation and conduct of such educational events as cycles of educational research, projects, conferences, problem lectures, excursions to high-tech production (including in the format of business games), meetings with leading specialists and enterprise management, and business games. Possible difficulties, educational results, social and pedagogical effects of realization of such forms of cooperation of the school, scientific organizations, and business partners are analyzed.
One-Pile Nim games with arbitrarily many random players

Krawec (2015) developed a method of analyzing n -player games where a subset of players plays randomly. In this paper, we use this method to analyze a class of combinatorial games “One-Pile Nim with n players”, denoted by OPNim( n ), which allows arbitrarily many players to play randomly. We analyze the general structure of random players. The game values of OPNim( n ) are determined for the general structure, assuming that a particular alliance matrix A 0 defined by Krawec is adopted. We next try to answer the question: Fix a structure of random players, does there exist an alliance matrix A ′ different from A 0 such that the game values under A ′ are the same with that under A 0 ? We find a class of alliance matrices A s which give this question a positive answer for any structure of random players.
Automatic Weapon Detection in Social Media Image Data using a Two-Pass Convolutional Neural Network

Police analysts are faced with a deluge of data when monitoring the activities in specific areas of social networks and other internet data sources. Image recognition can help to prioritize the reading and subsequent analysis. The paper presents a case study for weapon detection in image data that has the potential to reduce the workload of the analyst by a factor of 200.
Location-Based Collaborative Filtering for Web Service Recommendation

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.
Semi-Automatic Training Set Construction for Supervised Sentiment Analysis in Polarized Contexts

Standard sentiment analysis techniques rely either on sets of rules based on semantic and affective information or in supervised machine learning approaches whose quality heavily depends on the size and significance of a training set of pre-labeled text samples. In many situations, this labeling needs to be performed by hand, potentially limiting the size of the training set. In order to address this issue, in this work we propose a methodology to retrieve text samples from Twitter and automatically label them. We then apply this methodology to several Twitter conversations and assess the quality of the produced training sets. Additionally, we also tackle the situation in which the base rates of positive and negative sentiment samples in the training and test sets are biased with respect to the system in which the classifier is intended to be applied. The results presented in this respect hold relevance beyond this particular application.
Digital Image Analysis in Clinical and Experimental Pathology

Conventional pathology using a light microscope is rapidly shifting towards digital integration. Digital imaging plays an increasing role in clinical diagnosis, biomedical research, and continuing medical education. Currently, pathology platforms are composed of clinical and molecular pathologists and engineers with the sole intention of investigating cellular and molecular basis of human health through applied research in disease aetiology, pathogenesis, diagnosis, and treatment. Molecular diagnosis using technical advances and the application of specific biomarkers in clinical practice are the two main pillars of modern personalized medicine especially in oncology. Thus, it has become evident that accredited clinical and molecular pathology laboratories using digital imaging and advanced technologies can make the most of diagnostic and specific biomarker analyses as well as incorporating other key aspects of translational research and data analysis.
Japanese Contributions to the Development of Alternative Test Methods

There are two organisations in Japan that have been instrumental in promoting the development of alternatives to test methods that use animals: the Japanese Society for Alternatives to Animal Experiments (JSAAE) and the Japanese Center for the Validation of Alternative Methods (JaCVAM). The JSAAE is a scientific organisation that promotes international acceptance of the replacement, reduction and refinement of animal use (the Three Rs) as a guiding principle in scientific testing. The Society's members are science professionals, who are active in the research, development and surveillance of alternative methods, and who make an effort to educate other professionals, as well as to enhance public awareness of alternatives to animal testing. For more than a quarter of a century, the JSAAE has built a reputation, both in Japan and around the world, for sponsoring significant professional activities, including the 2007 6th World Congress on Alternatives and Animal Use in the Life Sciences in Tokyo, and the 2016 Asian Congress on Alternatives and Animal Use in the Life Sciences in Karatsu and Saga. In contrast to the JSAAE, JaCVAM is a public agency that was established in 2005 as part of the Biological Safety Research Center (BSRC) at the National Institute of Health Sciences (NIHS). JaCVAM's stated mission is to promote the use of alternative methods to animal testing in regulatory studies, while meeting the responsibility of the BSRC to ensure the protection of the public by assessing the safety of chemicals and other materials, as stipulated in NIHS regulations. JaCVAM plays an active role in the approval process for permission to manufacture and sell pharmaceutical and other products, as well as in revising standards for cosmetic products. JaCVAM also participates in International Cooperation on Alternative Test Methods (ICATM), under which it has organised a number of international validation studies that have led to the issuance of nine test guidelines and one guidance document from the Organisation for Economic Cooperation and Development (OECD).
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This paper presents design of T - Direct codes over GF ( 2 N ) with increased users. The class of T - Direct codes are an extension to the class of linear codes with complementary duals (LCD codes) and can be effectively used over a multiple access channel environment. In this paper, a construction procedure that constructs an n 3 - Direct code from an n - Direct code is described. An n n - Direct codes are also constructed. Further, a recursive construction procedure is presented that constructs n 3 m - Direct codes for m ≥ 0 . Finally, T T - Direct codes with constituent codes having variable code rates are obtained. The advantage of these constructions is that it increases the number of constituent codes (users) of an existing T - Direct code, thereby supporting more users in a multi-user environment.
HANDS18: Methods, Techniques and Applications for Hand Observation

This report outlines the proceedings of the Fourth International Workshop on Observing and Understanding Hands in Action (HANDS 2018). The fourth instantiation of this workshop attracted significant interest from both academia and the industry. The program of the workshop included regular papers that are published as the workshop’s proceedings, extended abstracts, invited posters, and invited talks. Topics of the submitted works and invited talks and posters included novel methods for hand pose estimation from RGB, depth, or skeletal data, datasets for special cases and real-world applications, and techniques for hand motion re-targeting and hand gesture recognition. The invited speakers are leaders in their respective areas of specialization, coming from both industry and academia. The main conclusions that can be drawn are the turn of the community towards RGB data and the maturation of some methods and techniques, which in turn has led to increasing interest for real-world applications.
On Methods of the Verification and Elaboration of Development Programs for Agricultural Territories

Nowadays, the methods of program-targeted management for the development of various socio-economic systems of complex structure, such as agricultural areas, have become ubiquitous. Therefore, the current tasks at hand are the verification of already created development programs and the development of “proper” programs for the development of such systems, by analogy with the verification and development of proper computer programs through advanced disciplines in theoretical programming. In this paper, in order to solve the problem of the verification of development programs for agricultural territories, a structural scheme of the program is first constructed, through which an axiomatic theory is created using the Hoare’s algorithmic logic system. The main problem in the construction of the axiomatic theory is the development of the axioms of the theory that reflect preconditions and effects of the implementation of meaningful actions indicated in the text of the development program. The verification of the development program corresponds to the provability check of some Hoare triplet, according to the initial and target conditions of the program. For the task of elaborating proper development programs, we describe the mechanism for constructing a domain model using the PDDL family description languages. The description of a specific model is purely declarative in nature and consists of descriptions of predicates and actions of the chosen subject area. In this paper, using the described model with the help of intelligent planners, including temporal planners such as OPTIC, we show how to automatically build solutions to the targets of development programs. Based on expert knowledge and industry standards, a model of an agricultural territory is constructed, a brief description of which is given in this work. The conducted experiments showed the effectiveness of the proposed approach for the development of proper development programs.
Adaptive Learning Machine

Based on the source separation models in Chapter 2, this chapter further introduces a series of machine learning algorithms which are developed to carry out different adaptive learning solutions to different separation problems. The learning algorithms range from information-theoretic learning to Bayesian learning, sparse learning, online learning, discriminative learning and deep learning. The fundamentals of approximate inference based on variational inference and Gibbs sampling inference are surveyed. The information theory is incorporated to construct learning objective for optimization of a source separation model. Probabilistic representations of mixed signals, demixed signals, basis vectors and the other latent variables fulfill the uncertainty modeling for finding a regularized source separation model. Model regularization is further explored by imposing sparsity in learning representation. Sequential learning is introduced to implement an online source separation to meet the changing environments. Discriminative learning enforces the discrimination in learning objective so as to improve the separation quality for source signals. Deep learning carries out deep abstraction and hierarchical representation for signal decomposition. The approximate inference provides the basics to implement these learning algorithms for source separation.
Off-line and on-line algorithms for closed string factorization

A string X=X[1..n], n>1, is said to be closed if it has a nonempty proper prefix that is also a suffix, but that otherwise occurs nowhere else in X; for n=1, every X is closed. Closed strings were introduced by Fici in [1] as objects of combinatorial interest. Recently Badkobeh et al. [2] described a variety of algorithms to factor a given string into closed factors. In particular, they studied the Longest Closed Factorization (LCF) problem, which greedily computes the decomposition X=X1X2⋯Xk, where X1 is the longest closed prefix of X, X2 the longest closed prefix of X with prefix X1 removed, and so on. In this paper we present an O(log⁡n) amortized per character algorithm to compute LCF on-line, where n is the length of the string. We also introduce the Minimum Closed Factorization (MCF) problem, which identifies the minimum number of closed factors that cover X. We first describe an off-line O(nlog2⁡n)-time algorithm to compute MCF(X), then we present an on-line algorithm for the same problem. In fact, we show that MCF(X) can be computed in O(Llog⁡n) time from MCF(X′), where X′=X[1..n−1], and L is the largest integer such that the suffix X[n−L+1..n] is a substring of X′.
A Tool for Creating Community Knowledge Objects

International organizations and government agencies have developed and collected a wealth of knowledge resources relevant to poor communities; however, the people who need these resources most often do not know these materials exist or are unable to access or understand them. Electronic sources of knowledge materials and means of communication are rarely integrated with traditional methods of knowledge delivery. This chapter addresses the issue of knowledge sharing with poor communities and presents a software tool for developing multimedia knowledge materials suitable for people with little or no formal education. A multimedia editor uses a data structure composed of multimedia objects (texts, images, video and audio clips) to generate the knowledge browser. Local specialists with a basic knowledge of computing can modify and customize how the knowledge is presented by adding new materials relevant to the local environment.
Noise Reduction Method of Adaptive Wiener Filtering for Ocean Remote Sensing Image

Tang, C., 2018. Noise reduction method of adaptive wiener filtering for ocean remote sensing image. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 651–655. Coconut Creek (Florida), I SSN 0749-0208.Currently, when the denoising method is used for noise reduction and filtering process on marine remote sensing image with complex background, there is the low calculation speed, and the effect of noise reduction is poor. Therefore, a method for noise reduction of adaptive Wiener filtering of marine remote sensing image was proposed. Based on representational ability of frequency phase information for features of ocean remote sensing image, the phase consistency was introduced to extract multi-scale gradient of ocean remote sensing image. Wiener filter was used for adaptive Wiener filter process based on the multi-scale gradient characteristics of remote sensing image. Experimental result shows that the proposed method can solve the fuzzy boundary of marine remote sensing image well.
Natural Image Quality Assessment Based on Visual Biological Cognitive Mechanism

With the focus of the main problems in no-reference natural image quality assessment (NR-IQA), the researchers propose a more universal, efficient and integrated resolution based on visual biological cognitive mechanism. First, the authors bring up an inspiring visual cognitive computing model (IVCCM) on the basis of visual heuristic principles. Second, the authors put forward an asymmetric generalized gaussian mixture distribution model (AGGMD), and the model can describe the probability distribution density of the images more precisely. Third, the authors extract the quality-aware multiscale local invariant features (QAMLIF) statistic and perceptive from natural images and form quality-aware uniform features descriptors (QAUFD) based on clustering and encoding the visual quality features. Fourth, the authors build topic semantic model and realize the resolution with Bayesian inference with IVCCM, AGGDM and QAUFD to implement NR-IQA. Theoretical research and experimental results show that the proposed resolution perform better with biological cognitive mechanism.
SWFQA Semantic Web Based Framework for Question Answering

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.
Integrating the importance levels of friends into trust-based ant-colony recommender systems

Purpose This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems. Design/methodology/approach To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater. Findings The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage. Originality/value Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.
A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies

Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible to check the validity of the discovered rules in terms of support and confidence rule validity measures without re-applying association rule mining algorithms. The authors conducted several preliminary experiments to test the framework using the Titanic dataset by identifying the association rules after pre- and post-constraints are applied. The results have shown that the framework can be practically applied for rule pruning and discovering novel association rules.