Reviewerly/exHarmony huggingface dataset:
Universal consistency of twin support vector machines

Abstract A classification problem aims at constructing a best classifier with the smallest risk. When the sample size approaches infinity, the learning algorithms for a classification problem are characterized by an asymptotical property, i.e., universal consistency. It plays a crucial role in measuring the construction of classification rules. A universal consistent algorithm ensures that the larger the sample size of the algorithm is, the more accurately the distribution of the samples could be reconstructed. Support vector machines (SVMs) are regarded as one of the most important models in binary classification problems. How to effectively extend SVMs to twin support vector machines (TWSVMs) so as to improve performance of classification has gained increasing interest in many research areas recently. Many variants for TWSVMs have been proposed and used in practice. Thus in this paper, we focus on the universal consistency of TWSVMs in a binary classification setting. We first give a general framework for TWSVM classifiers that unifies most of the variants of TWSVMs for binary classification problems. Based on it, we then investigate the universal consistency of TWSVMs. To do this, we give some useful definitions of risk, Bayes risk and universal consistency for TWSVMs. Theoretical results indicate that universal consistency is valid for various TWSVM classifiers under some certain conditions, including covering number, localized covering number and stability. For applications of our general framework, several variants of TWSVMs are considered.
Performance Evaluation of Parallel International Data Encryption Algorithm on IMAN1 Super Computer

Distributed security is an evolving sub-domain of information and network security. Security applications play a serious role when data exchanging, different volumes of data should be transferred from one site to another safely and at high speed. In this paper, the parallel International Data Encryption Algorithm (IDEA) which is one of the security applications is implemented and evaluated in terms of running time, speedup, and efficiency. The parallel IDEA has been implemented using message passing interface (MPI) library, and the results have been conducted using IMAN1 Supercomputer, where a set of simulation runs carried out on different data sizes to define the best number of processor which can be used to manipulate these data sizes and to build a visualization about the processor number that can be used while the size of data increased. The experimental results show a good performance by reducing the running time, and increasing speed up of encryption and decryption processes for parallel IDEA when the number of processors ranges from 2 to 8 with achieved efficiency 97% to 83% respectively.
Enhance Content Selection for Multi-Document Summarization with Entailment Relation

Automatic text summarization is one of the common tasks in natural language processing. The main task is to generate a shorter version based on the original text and maintain relevant information. This paper studies multi-document summarization (MDS) that applies to news articles. MDS has two significant issues which are information overlap and information difference among multiple articles. Existing models mostly deal with MDS from the perspective of single document summarization (SDS). The models do not consider the relation between sentences in multiple news articles. Our proposed method deals with the issue and consists of two models. The sentence selector model selects representative sentences based on the entailment relation in different articles. The content is related to the event of the article extracted through the algorithm. The summary generator model generates a final summary to ensure that the summary contains no redundancy and maintains vital information. Experiment results show that our proposed model has effectively improved in the evaluation results. The main contribution of our approach is to use the entailment relation to obtain key content in multiple articles. Adding semantic comprehension can identify salient information clearly and improve the accuracy of MDS.
ModelRecycling: Predicting user’s interest with connected predictive models

Many different services acquire feedback from users, and many predictive models of users interests have been made for recommending items by using data acquired from different services. Here, although we would be able to get deep insights about a user's interest by connecting predictive models, interactions among services and potential synergies of their predictive models have been neglected. Moreover, in the era of big data, it should be relatively easy to develop a recommendation system that works across services by connecting predictive models and using existing technologies. However, the effect and conditions under which such the recommendation system works effectively have not been analyzed in detail.We have developed ModelRecycling FrameWork (MRFW) as an implementation of our idea of connecting predictive models to make recommendations across services. We experimentally evaluated the performance of MRFW by using an online questionnaire survey. The results show that MRFW significantly outperforms predictive models that make recommendations without considering the interaction between services or synergies of predictive models. We also clarified the conditions under which MRFW works effectively by comparing its performance with that of the individual models on which it is based in order to make use of the results of analysis for designing the method that connects predictive models.
MLANE: Meta-Learning Based Adaptive Network Embedding

Most existing random walk based network embedding methods often follow only one of two principles, homophily or structural equivalence. In real world networks, however, nodes exhibit a mixture of homophily and structural equivalence, which requires adaptive network embedding that can adaptively preserve both homophily and structural equivalence for different nodes in different down-stream analysis tasks. In this paper, we propose a novel method called Meta-Learning based Adaptive Network Embedding (MLANE), which can learn adaptive sampling strategy for different nodes in different tasks by incorporating sampling strategy learning with embedding learning into one optimization problem that can be solved via an end-to-end meta-learning framework. In extensive experiments on real datasets, MLANE shows significant performance improvements over the baselines. The source code of MLANE and the datasets used in experiments and all the hyperparameter settings for baselines are available at https://github.com/7733com/MLANE.
Jensen–Shannon Divergence-Based <i>k</i>-Medoids Clustering

Several conventional clustering methods use the squared L 2 -norm as the dissimilarity. The squared L 2 -norm is calculated from only the object coordinates and obtains a linear cluster boundary. To extract meaningful cluster partitions from a set of massive objects, it is necessary to obtain cluster partitions that consisting of complex cluster boundaries. In this study, a JS-divergence-based k -medoids (JSKMdd) is proposed. In the proposed method, JS-divergence, which is calculated from the object distribution, is considered as the dissimilarity. The object distribution is estimated from kernel density estimation to calculate the dissimilarity based on both the object coordinates and their neighbors. Numerical experiments were conducted using five artificial datasets to verify the effectiveness of the proposed method. In the numerical experiments, the proposed method was compared with the k -means clustering, k -medoids clustering, and spectral clustering. The results show that the proposed method yields better results in terms of clustering performance than other conventional methods.
ScEd-ALS as ubiquitous learning in science

Abstract The use of technology in education can be carried out through various telematics networks. The use of hypermedia or web (e-learning) and computing on mobile allows the subject matter to be distributed to students quickly, not limited by space and time. Independent learning is possible to occur. This paper aims to inform the framework of curriculum development at the Science Education Adaptive Learning System (ScEd-ALS) version of the smartphone that supports Ubiquitous learning. The research method used is development research. The research instruments included: observation sheets, checklist sheets, and VARK questionnaire. The results of the study: The ScEd-ALS curriculum development framework that accommodates the VARK Flemming learning style preferences of students. This curriculum framework is the foundation for the development of the smartphone version of the ScEd-ALS media prototype. This research was validated by material experts and media experts and was declared worthy of being tested. The curriculum development framework in this media has provided the right teaching materials, and it is easier to create a new environment that adapts to the learning style preferences of students.
Quality of distance learning: analysis of contradictions and prospects for development

The article reviews the scientific literature on the quality of distance learning. The forced temporary transfer of the educational process to a distance form is considered as an unplanned event to assess the quality of education, which allows us to assess the effectiveness of the management system in education for all participants in educational relations, as well as to determine the directions of modernization and development of the education system as a whole. The use of digital technologies in the educational process helps the teacher to more fully build interaction with students, improve their methodological and organizational skills and competencies, use visual formats for presenting various information, which, in turn, contributes to the development of cognitive activity of schoolchildren and students. Educational interaction in the framework of distance learning has a special specificity and special qualities that place increased demands on the professional competence and pedagogical qualifications of teachers, as well as on the personal and professional competencies of all subjects of the distance educational process. Distance learning now allows interaction between the trainers and trainees, but not fully appropriate to ensure the achievement of learning objectives, learning content of the subject and control assessment of knowledge, skills, competencies. Distance learning technologies should complement (and this is their significant functionality and potential), and not replace the already established traditional face-to-face forms of educational interaction, models and methods of teaching.
Personal Digital Life Coach for Physical Therapy

The functional tests are essential to test the functionality of different types of people, and specialty for older adults. The primary purpose of this paper is to create a method for the automatic measurement of the results of the different functional tests. These are the Heel-rise Test, Functional Reach Test, Timed Up and Go Test, Ten Meter Walk Test, Eight hop test, Up-down hop test, Side hop test, Single hop test, Chair Stand Test, Arm Curl Test, and Chair Sit and Reach test. The use of sensors may increase the accuracy of the measurements of these tests. These tests may identify several diseases, and it will be subject to further research in the future.
Locating Creativity in Differing Approaches to Musical Robotics

The field of musical robotics presents an interesting case study of the intersection between creativity and robotics. While the potential for machines to express creativity represents an important issue in the field of robotics and AI, this subject is especially relevant in the case of machines that replicate human activities that are traditionally associated with creativity, such as music making. There are several different approaches that fall under the broad category of musical robotics, and creativity is expressed differently based on the design and goals of each approach. By exploring elements of anthropomorphic form, capacity for sonic nuance, control, and musical output, this article evaluates the locus of creativity in six of the most prominent approaches to musical robots, including: 1) nonspecialized anthropomorphic robots that can play musical instruments, 2) specialized anthropomorphic robots that model the physical actions of human musicians, 3) semi-anthropomorphic robotic musicians, 4) non-anthropomorphic robotic instruments, 5) cooperative musical robots, and 6) individual actuators used for their own sound production capabilities.
Performance Evaluation of Learning Based Frameworks for Devanagari Character Recognition

Advancement of technology in the field of Character Recognition have emerged as the key technology which has helped the entire industrial space in many ways including digitization of hand written manuscripts, postal automation, text to speech conversion to help the blind. Today character recognition has emerged and is available for several languages, through which we are able to recognize character in any particular time, however for some languages for which this technology haven't developed much and needs an improvement in the direction. With development of technologies like Artificial Intelligence, Convolutional Neural Network(CNN) and other Deep Learning Techniques there is a tremendous improvement in performance of Character Recognition system. This paper work presents a comparative evaluation of different machine learning approaches such as Gaussian Naive Bayes, Decision Tree, K-Nearest Neighbor(KNN) Classifier and a deep learning based CNN approach using simple architecture for Devanagari character recognition. The proposed deep learning based CNN model outperformed all other algorithms in terms of accuracy.
Word embedding for job market spatial representation: tracking changes and predicting skills demand

What will the job market of the future look like? What jobs will be popular, and which skills will they require? Modeling the temporal progression of the job market, as represented by job ads, may help us answer this question. This paper represents a first step in this direction. In order to build a spatial representation of job market that allows to track changes in skills' demand, authors are training models to classify job tasks. Different natural language processing and classification approaches were compared, including term frequency - inverse document frequency, principal components analysis, word2vec, GloVe, fastText and BERT models, and feedforward neural networks, support vector machines, and bidirectional long short term memory recurrent neural networks. BERT obtained the best accuracy results with 52% for 94 classes and 65% for 22 classes.
E-hypertext Media Topic Model with Automatic Label Assignment

This article deals with the principles of automatic label assignment for e-hypertext markup. We’ve identified 40 topics that are characteristic of hypertext media, after that, we used an ensemble of two graph-based methods using outer sources for candidate labels generation: candidate labels extraction from Yandex search engine (Labels-Yandex); candidate labels extraction from Wikipedia by operations on word vector representations in Explicit Semantic Analysis (ESA). The results of the algorithms are label’s triplets for each topic, after which we carried out a two-step evaluation procedure of the algorithms’ results: at the first stage, two experts assessed the triplet’s relevance to the topic on a 3-value scale (non-conformity to the topic/partial compliance to the topic/full compliance to the topic), second, we carried out evaluation of single labels by 10 assessors who were asked to mark each label by weights «0» – a label doesn’t match a topic; «1» – a label matches a topic. Our experiments show that in most cases Labels-Yandex algorithm predicts correct labels but frequently relates the topic to a label that is relevant to the current moment, but not to a set of keywords, while Labels-ESA works out labels with generalized content. Thus, a combination of these methods will make it possible to markup e-hypertext topics and create a semantic network theory of e-hypertext.
Multi-task Learning Network based on Attention for Aspect-Based Sentiment Analysis

Abstract Aspect-based sentiment analysis is to analyse the sentiment polarity of the aspect in a specific context. The previous methods mostly use the working mode of pipeline model, and internally use Recurrent Neural Network to predict the sentiment polarity of the aspect. This way clearly defines the order of aspect extraction and aspect-level sentiment classification, which will lead to the problem of error transmission. And traditional models have great limitations. In this paper, we propose a multi-task learning network based on attention (MNA), which uses shared word embedding to transmit to downstream tasks, allowing extraction and classification tasks to be processed simultaneously. We design a network architecture based on attention mechanism, and use the improved multi-head attention mechanism to transfer information from extracted tasks to classified tasks. Experimental results on three benchmark datasets show the effectiveness of MNA model.
Risk Prediction in E-commerce Mobile Payment Based on PSO-SVM

The security of e-commerce mobile payment is a key issue that needs to be paid attention to when using mobile payment. Improvement of the security performance of e-commerce mobile payment will help promote its use. In order to avoid the risks of e-commerce mobile payment and provide timely warnings, a modeling method using support vector machines and particle swarm algorithm to model and analyze e-commerce mobile payment risks is proposed in this paper. On the basis of ensuring the safety of communication and completing secure wireless transactions, PSO-SVM is used to establis-h a sophisticated risk prediction mechanism to promote the further expansion of the application scope of e-commerce mo-bile payment technology.
A safe architecture for authorisation grant in healthcare ecosystems

Recent regulations for security and privacy of personal data request new approaches in data access and retrieval processes. In the context of healthcare, the regulations point two types of consent to access sensitive personal medical data: explicit and implicit consent. This paper focus on explicit consent cases and presents an architecture where the patient grants on-demand access to private health records. The architecture is supported by a Discretionary Access Control model suited for cross-domain and cloud environments. Each resource belongs to a patient that has the power to grant or deny any access rights to users or groups of users. All the process is designed to be secure, from the authentication of the physician in the terminal until the communications between entities, passing by the physician's terminal check by the patient. Furthermore, the security methods are discussed and evaluated. Finally, it is presented a summary of the developed work and the plans to apply in the future work.
A New Efficient Quantum Digital Signature Scheme for Multi-bit Messages

Quantum digital signatures (QDS) is a cryptography primitive based on quantum mechanics, and has the same role as the classical digital signature. Many novel QDS protocols have been proposed, which can guarantee the information-theoretic security of the signature for a single bit against forging and denying. Recently, T.Y. Wang et al. first proposed a QDS scheme satisfying multi-bit security which based on arbitrary single-bit signature scheme. However, their coding scheme requires $$2n+4$$ signature keys to sign a classical n-bit message. In this paper, we propose a more efficient protocol for signing multi-bit message. We need about $$1.5n+7$$ signature keys for a n-bit message.
RETRACTED ARTICLE: Energy and CPU utilization of cloud virtual machine resource allocation using dynamic heuristic mitigate migration algorithm

Cloud computing systems face new challenges every day due to the large and complex structure of system clusters and the data processed by the systems. The ability to obtain resources flexibly is considered to be the main reason for adopting cloud computing systems. Resiliency primarily supports facility growth and shrinks virtual resources according to the needs of dynamic cloud consumers. The cloud infrastructure promises to provide efficient resource utilization for data centers. The CPU is one of the many resources a client can use in a cloud environment. However, ensuring resource allocation flexibility is one of the major challenges in cloud environments. Develop a Dynamic Heuristic Mitigate Migration (DHMM) to increase the utilization of resources that are effectively allocated between virtual machines via cloud servers. In this proposed method DHMM focuses the storage migration and CPU utilization approaches to improve the cloud performance. In order to evaluate the performance, the proposed algorithm is compared to the prior art. Experimental results show that the proposed framework proves that it has been found very effective to improve resource utilization in a cloud computing environment.
Investigation of the stability of solutions of systems of ordinary differential equations

An apparatus of partial and external derived numbers is proposed, which makes it possible to investigate the behavior of a function of several variables, without requiring its differentiability, but using only information about partial derived numbers, reducing restrictions on the degree of smoothness of the functions studied. The use of the apparatus of external derived numbers makes it possible to reduce the restrictions on the degree of smoothness of manifolds when studying the question of the integrability of the field of hyperplanes.The proposed method can be used to obtain the necessary or sufficient conditions for the stability of solutions of systems of differential equations. Using the apparatus of partial and external derived numbers, it can be shown that the investigation of the stability of solutions of a system of differential equations can be reduced to an investigation of the solvability of a system of equations of a special form.
Fast computational depth segmentation using orthogonal fringe patterns without pattern sequence changing

The recently proposed omnidirectional depth segmentation method (ODSM) has advantages over traditional depth segmentation in terms of robustness and computational costs. However, this method uses at least six fringe patterns and changes their sequences multiple times to perform depth segmentation, which limits its segmentation speed and increases computational complexity. This paper proposes a fast computational depth segmentation (FCDS) method in which only five patterns are used for object segmentation at different depths into isolated regions without the requirement of pattern sequence changing. Phase singularity points are fully utilized due to their significance as depth segmentation markers to extract segmenting lines used for depth determination. Meanwhile, a modified Fourier transform algorithm (MFTA) is introduced to calculate the wrapped phase sequences, which uses two groups of orthogonal phase-shifting fringe patterns and a DC component pattern (five in total). The segmenting lines along orthogonal directions can be extracted with the FCDS method without changing the fringe sequences, which not only solves the problem of phase insensitivity but reduces the calculation costs. Besides, the problem of mis-segmentation is solved with an optimization algorithm for depth segmenting lines and successfully segments objects with abrupt depth changes. The simulation results demonstrate the effectiveness and precision of the proposed method. The experimental results prove the success of the proposed method for segmenting objects of similar color with a segmentation speed that is up to a 120% increase relative to previous methods.