Reviewerly/exHarmony huggingface dataset:
On the Construction of E-Learning Platform for Translation Instruction Featured with Knowledge Systems

In light of the advanced international conception for e-learning translation instruction and the development of professional translation education in China, it is proposed that an e-learning platform conceived with knowledge systems be constructed. The new platform aims at utilizing advantages and characteristics of the online instruction, and at the same time categorizing instructive resources leading to knowledge service systems. This advantageous e-learning environment is featured with a collection of electronic resources of translation technology, instructive resources and interactive communication forums.
Blind noise estimation and denoising filter for recovery of microquake signals

In this study, a data-driven linear filtering method is proposed to recover microseismic signals from noisy data/observations. The proposed method is based on the statistics of the background noise and the observation, which are directly extracted from the recorded data, obviating prior statistical knowledge of the microseismic source signal. The proposed method does not depend on any specific underlying noise statistics; therefore, it works for any type of noise, e.g. uncorrelated (random/white Gaussian), temporally correlated and spatially correlated noises. Consequently, the proposed method is suitable for microquake data sets that are recorded in contrastive noise environments. A mathematical analysis is presented to interpret the proposed method in two different ways. Furthermore, a number of practical concerns are discussed and their corresponding solutions are introduced. Finally, the proposed scheme is evaluated using both field and synthetic data sets and the experimental results show a reasonable and robust performance.
The Principles Established by the Recommendation CM/Rec(2017)5 on Standards for E-voting Applied to Other Channels of Remote Voting

E-voting is highly suspicious to many citizens and institutions. Past pilot implementations ended before Supreme Courts and mostly not in favour of e-voting. Beside these political and legal battles regarding e-voting, postal voting seems to be commonly accepted and not in question. Motivated by a landmark ruling of the Austrian Constitutional Court in 2016, which led to the revocation of the run-off elections result due to irregularities with postal voting, this paper analyses whether current postal voting regulations and standards in Germany comply to the principles established by the latest Council of Europe (CoE) recommendation on standards for e-voting. Both voting channels are channels for remote voting, hence principles established for one channel must, in the view of the author, also be fully applicable for the other channel. This paper applies the standards set by the recommendation to e-voting to the more commonly used remote voting channel postal voting and concludes that most of these standards cannot be met.
Information security model. Case study higher education institution

Abstract The objective of this project is to design an information security model applicable to higher education institutions that allows effective control of their processes. The development of the project starts with the characterization of the different existing processes in the higher education institutions of the Norte de Santander, Colombia, it is compared with the standards or good practices of security of the existing information, which allows structuring the elements that make up the model of information security for higher education institutions and, finally, the validation of the model designed in a higher education institution.
Exploiting Label Interdependencies in Multi-label Classification

Multi-label classification problems very often concern multidimensional datasets, thus, performance of the method is problematic in many cases. Exploiting label dependencies may ameliorate classification results. In the paper, new effective problem transformation method which uses label interdependencies is introduced. Experiments conducted on several benchmarking datasets showed the good performance of the presented technique, regarding six evaluation metrics, including the most restricting Classification Accuracy and confirmed by statistical inference. The obtained results are compared with those obtained by the most popular problem transformation methods.
Index Matrices as a Cost Optimization Tool of Resource Provisioning in Uncertain Cloud Computing Environment

The main objective in cloud computingTraneva, Velichka environment is efficient allocation of its resources in order to get optimal overall cost (time) for doing tasks. In today’s market environment, the time or costs for doing tasks are notTranev, Stoyan exact numbers, often provided data about their values may be incomplete. The intuitionistic fuzzy sets are developed to deal with these weaknesses of conventional crisp and fuzzy sets. The problem for allocating resources in uncerain cloud environment efficiently in order to get optimal cost is an intuitionistic fuzzy assignment problem. In the paper a new typeAtanassova, Vassia of three-dimensional assignment problem (3D-IFAP) is formulated, in which the costs of assigning tasks to resources are intuitionistic fuzzy pairs and the priority of tasks is known in advance. We propose intuitionistic fuzzy Hungarian algorithm, based on the concepts of index matrices and intuitionistic fuzzy logic, to optimally solve this problem.
Research on Equipment Automated Inspection and Acceptance Framework Based on Ontology Modeling

The traditional way of inspection and acceptance based on paper specifications and manual review have been unable to meet the practical demands of digital quality supervision and high-precision sophisticated weapon quality monitoring. Therefore, this paper presents an automated inspection framework for equipment based on ontology modeling. By modeling the key elements in the inspection and acceptance and express the semantics of the specification rules, it can automatically make qualification judgment on inspection results, thus enhance general efficiency of the inspection and acceptance. Finally, simulation experiments on the protégé platform show the validity of the framework.
An Ontology-Based Deep Learning Approach for Knowledge Graph Completion with Fresh Entities

This paper introduces a new initialization method for knowledge graph (KG) embedding that can leverage ontological information in knowledge graph completion problems, such as link classification and link prediction. Although the initialization method is general and applicable to different KG embedding approaches in the literature, such as TransE or RESCAL, this paper experiments with deep learning and specifically with the neural tensor network (NTN) model. The experimental results show that the proposed method can improve link classification for a given relation by up to 15%. In a second contribution, the proposed method allows for addressing a problem not studied in the literature and introduced here as “KG completion with fresh entities”. This is the use of KG embeddings for KG completion when one or several of the entities in a triple (head, relation, tail) has not been observed in the training phase.
Fuzzy Clustering and Fuzzy Co-clustering

Fuzzy co-clustering is a fundamental technique for summarizing the structural characteristics of cooccurrence information. In this chapter, following the brief introduction of fuzzy c-Means (FCM) clustering, FCM-induced fuzzy co-clustering model is reviewed with illustrative examples.
FPGALedger: FPGA based Decentralized Ledger for Enterprise Applications

Decentralized ledger has been found in many enterprise applications, such as cross-border transaction processing and supply chain management that multiple companies involved. While enjoying all the benefits of the decentralized ledger technology, there is a burden for each party to maintain its own IT infrastructure to run the decentralized ledger node. Therefore, the concept of blockchain-as-a-service has been proposed where these nodes are kept in the cloud. Blockchain-as-a-service greatly reduces the burden to adopt the decentralized ledger technology but also introduces a new security threat. To mitigate these concerns, we propose an FPGA based decentralized ledger for enterprise applications, which leverages security features of FPGA to prevent unauthorized access to data stored on the ledger without affecting all the desirable features of the decentralized ledger. We also implement the major components and evaluate their cost/performance to demonstrate the practicality.
User Relationship Privacy Protection on Trajectory Data

Various mobile devices facilitate the users’ life, but the issues are brought into privacy focus by the individuals. This paper aims at the protection of intimate relationships among users. We consider the intimacy of user relationships based on similar sub-trajectories between the users. Then, we propose a $$k_{mn}$$ -anonymity protection model. We generalize from two aspects: location and time. The first is location generalization. The range that user pass within the time that the location point stays is the generalization region, and the corresponding location point in the region’s trajectory is represented by the generalization region. When the location generalization is not enough to satisfy the $$k_{mn}$$ -anonymity, then we use time generalization. Finally, the performance of our algorithm is evaluated by the experiment and the validity of our algorithm is verified.
Towards Low Space Complexity Design of Gaussian Normal Basis Multiplication

Normal basis is hardware-friendly to perform squaring operations over binary fields. It is very attractive in some applications, like elliptic curve cryptography over Koblitz curves. In this paper, a new algorithm is proposed to reduce the space complexity of Gaussian Normal Basis (GNB) multiplier over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">40</sup> 9). As far as we know, by applying this method, the number of XOR gates needed for a bit-level SIPO GNB multiplier over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">409</sup> ) can be minimized. Also, it is a general methodology suitable for all binary fields that involve type-4 Gaussian Normal Basis.
Patella – Atlas Based Segmentation

The main aim of this research is to present an automated image segmentation methodology being used in the field of medicine. The paper shows the automatic extraction of patella in the knee joint based on atlas segmentation. This method allows to build a feature vector that automates and streamlines known segmentation methods (e.g. fuzzy c-means (FCM), fuzzy connectedness (FC)). The described methodology has been implemented in MATLAB and tested on clinical computed tomography (CT) slices of the knee joint in transverse plane.
Determination of GLE of Solar Energetic Particles by Means of Spectral Analysis

Using three nonstationary solar series, the solar flare index (FS), the sunspots index (SS), and the solar flux (F10.7) index, we apply the Morlet wavelet analysis to determine the most dominant harmonics of solar activity, 1.73, 3.27, 4.9, 10.4, and 11 yr. The periodicities obtained are processed by the fuzzy logic method, which allows us to reproduce the occurrence dates of ground level enhancements (GLE), since 1942–2006, which we use as a training baseline of these spectral techniques to determine the occurrence of solar particle enhancements in solar cycles. Then, the result of fuzzy logic is extended to periods later than the training period so as to cover the end of cycle 24 and the beginning of cycle 25. In addition to the forecastable aspect of this work, the obtained results are of high interest in view of the recent controversy that has arisen in relation to the occurrence of small GLE (namely sub-GLE), during cycle 24.
A computational approach for spider web‐inspired fabrication of string art

Abstract Creating objects with threads is commonly referred to as string art. It is typically a manual, tedious work reserved for skilled artists. In this paper, we investigate how to automatically fabricate string art pieces from one single continuous thread in such a way that it looks like an input image. The proposed system consists of a thread connection optimization algorithm and a custom‐made fabrication machine. It allows casual users to create their own personalized string art pieces in a fully automatic manner. Quantitative and qualitative evaluations demonstrated our system can create visually appealing results.
General Non-parametric Learning Procedure for Tracking Concept Drift

The problems of learning in non-stationary situations has rarely been a subject of studies even in a parametric case. Historically the first papers on learning in non-stationary environments where occasionally published in the sixties and seventies. The proper tool for solving such a type of problems seemed to be the dynamic stochastic approximation technique [1, 2] as an extension of the Robbins-Monro [3] procedure for the non-stationary case. The traditional procedure of stochastic approximation was also used [4, 5] with a good effect for tracking the changing regression function root.
Knowledge Management and its Impact on Organizational Performance: Evidence from Pakistan

The aim of this research is to investigate the impact of knowledge management on organizational performance with the moderating role of transformational and Participative leadership behavior in telecom sector of Punjab, Pakistan. How leaders motivate to their employees and how they enhance the performance of organizations by using their knowledge. Research approach was deductive. Postivism research paradiagm was used to measure the study. Simple random sampling technique was used to collect data from employees as well as managers of telecom sector. Sample size was 280 employees of telecome sector. This research is significant for academically and practically in public and policy making. How an organization can enhance to their performance, effectively when long-term commitment, trust, successful socialization of employees and organizational citizenship behavior take place in the organization.
Edge Detection in Low-Quality Medical Images Based on Augmented Lagrangian Method and B-Spline

Medical images are very important for the treatment process of specialists because of containing a lot of important information in patient’s body. The edge detection is a prepared process for object boundaries. Therefore, the edge detection in medical images is very important and necessary with low-quality medical images. Other previous methods must sacrifice time to have the accurate results. In this paper, we proposed a new method to detect edges in low-quality medical images which have impurities by using augmented Lagrangian method and B-spline. Our method improves the ability for smoothing and detecting faster.
Design of Real-Time Transaction Monitoring System for Blockchain Abnormality Detection

Due to the recent popularity of Bitcoin, interest in the blockchain which is the basic technology of Bitcoin, has also increased. Blockchain is a distributed ledger technology that stores transaction information that occurs in P2P (Peer-to-Peer) networks on the ledger of all nodes in a different way than centralized method and verifies whether they are stored correctly. Blockchain features integrity, anonymity, and security. Bitcoin as well as various cryptocurrency such as Ethereum, Ripple and Bitcoin cash and blockchain based technologies are being developed using blockchain technology. However, there is a problem with illegal transactions by exploiting the anonymity of the blockchain. In this paper, we propose a system for detecting and tracking illegal transactions by collecting and analyzing information in a blockchain network and introducing the design of a stable storage system among the whole system.
SILKNOWViz: Spatio-Temporal Data Ontology Viewer

Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods.
DEVELOPING RESEARCH COMPETENCE OF MASTER’S PROGRAMME STUDENTS BY MEANS OF PROFESSIONAL FOREIGN LANGUAGE

University graduates should be ready for various types of professional activity, including the research one; accordingly, they should possess appropriate applied research skills. The paper considers the research competence formation of Master’s programme students by means of a foreign language, provides an analytical survey of foreign scientific sources and suggests an approach, which allows the students to integrate the findings of foreign researchers into their own promising research projects.
A Data-Driven Approach for Tracking Human Litter in Modern Cities

In the recent years, human litter, such as food waste, diapers, construction materials, used motor oil, hypodermic needles, etc, is causing growing problems for the environment and quality of life in modern cities. Data about this waste has a significant importance in the field of environmental sciences due to its important use cases that span saving marine life, reducing the risk from natural hazards, community cleaning efforts, etc. In addition, such litter spreads several diseases in urban areas with high populations such as undeveloped neighborhoods in large modern cities. In this paper, we introduce a data-driven approach that enables environmental scientists and organizations to track, manage, and model human litter data at a large scale through smart technologies. We make a major on-going effort to collect and maintain this data worldwide from different sources through a community of environmental scientists and partner organizations. With the increasing volume of collected datasets, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, we provide a scalable data management and visualization framework that digests datasets from different sources, with different formats, in a scalable backend that cleans, integrates, and unifies them in a structured form. On top of this backend, frontend applications are built to visualize litter data at multiple spatial levels, from continents and oceans to street level, to enable new opportunities for both environmental scientists and organizations to track, model, and clean up litter data. The framework is currently managing thirty real datasets and provide different interfaces for different kinds of users.
Niblack Algorithm Modification Using Maximum-Minimum (Max-Min) Intensity Approaches on Low Contrast Document Images

In recent decades, detection or segmentation has been one of the major interesting research subjects due to the analysis of the information. However, most of the historical document has degraded and low contrast problem. Recently, many binarization methods were proposed in order to segment the text region from the background region in the low-quality image. In this paper, an improved binarization method was inspired by Niblack method was presented. The modification focuses to find the optimum threshold value by using the Maximum-Minimum intensity technique. The main target is to reduce the unwanted detection image and increase the resultant performance compared to the original Niblack method. The proposed method was applied to the document images from H-DIBCO 2012 and H-DIBCO 2014 dataset. The results of the numerical simulation indicate that the target was achieved by the F-Measure by F-measure (58.706), PSNR (10.778) and Accuracy (86.876). This finding will give a new benchmark to other researchers to propose an advance binarization method.
Extreme Learning Machine for Multi-label Classification

Extreme Learning Machine (ELM) has drawn more and more attention in the machine learning fields because of its fast training speed and good generalization ability. In this paper, we employ ELM algorithm to deal with multi-label classification problems. The essence of the proposed algorithm is to convert the multi-label classification problem into some single-label classifications, and fully considers the relationship among different labels. The hidden nodes of ELM algorithm for one label classification come from three parts. Some of them are initialized nodes after PCA dimensionality reduction. And we design a backup pool to select appropriate hidden nodes. In addition, the nodes of the previous label classification would flow into the classification of the current label. In the simulation part, three famous databases demonstrate the satisfied classification accuracy of the proposed method.
Mine Detonating Sphere-Bot

The inspiration for this project came from the armadillo. This creature can curl itself into a ball, protecting its vulnerable parts with plates made of bone acting as armor. While the armadillo moves using its legs, this robot always remains in a spherical shape, and moves using wheels inside a sphere. It is theorized a robot of this design could be used in demining. The sphere rolls over a mine, detonates it, then survives the impact and can move on to detonate further mines. The robot makes use of soft materials rather than hard plates in order to survive impacts. This paper describes the design of the robot including consideration of different materials, different wheel shapes and spherical casing design. Simulations and compression testing were used to find the optimal designs. In addition, a hill climb test determines the robot’s ability to traverse sloped environments. Finally, a drop test shows its ability to survive impacts.
Agent Navigation via Harmonic Potentials with Half-Sweep Kaudd Successive Over Relaxation (HSKSOR) Method

Navigation problems of an agent in a virtual environment had been widely studied by many researchers. This paper presents a combination method based on Half-Sweep (HS) iteration and Kaudd Successive Over Relaxation (KSOR) to solve agent navigation problem in a simulated virtual environment. The proposed method, namely Half-Sweep Kaudd Successive Over Relaxation (HSKSOR), is used to obtain the solutions of Laplace's equation known as harmonic functions which then be applied in the search algorithm to generate collision-free path for an agent to navigate from a specified start point to a target point. The results found that the HSKSOR offers faster computational approach with less computational time compared to the existing iterative methods.