Reviewerly/exHarmony huggingface dataset:
Evaluating the effectiveness of Farsi-English query production using ontology: a case of scientometric ontology

Purpose This paper aims to discover the effectiveness of Farsi-English query using ontology. Design/methodology/approach The present study is quasi-experimental. The sample consisted of 60 students and graduate and doctoral staff from Shiraz University and the Regional Center for Science and Technology. A researcher-made questionnaire was used to assess the level of English language proficiency of users, background knowledge and their level of satisfaction with search results before and after using ontology. Each user also evaluated the relevance of the top ten results on the Google search engine results page before and after using ontology. Findings The findings showed that the level of complexity of the task, the use of ontology, the interactive effect of the level of complexity of the task with the domain knowledge of the users, and the interactive effect of the level of complexity of the task with ontology, influence the effectiveness of retrieval results from the users' point of view. The results of the present study also showed that the level of complexity of the task, the use of ontology, and the interactive effect of the level of complexity of the task and the use of ontology, affect the level of user satisfaction. Originality/value The results of this research are significant in both theoretical and practical aspects. Theoretically, given the lack of research in which the interactive effect of the use of ontology has examined the level of complexity of tasks and domain knowledge of users, the present study can be considered as an attempt to improve information retrieval systems. From a practical point of view, the results of this research will help researchers and designers of information retrieval systems to understand that the use of ontologies can be used to retrieve information and improve the query and assess the needs of users and their satisfaction in this field, and ultimately, making the information retrieval process more effective.
University reform and autonomy: S.M. Solovyov’s apologia

The article looks at the problem insufficiently explored within Russian historiography, namely the administrative and managerial activities carried out by the 19th-century Russian historian S.M. Solovyov. This research aims to study the theoretical and practical contribution made by Solovyov to the development and implementation of the basic principles within the system of higher education in Russia. The article shows his achievements, potential, and the difficulties he faced in the course of solution of the organizational and managerial tasks. An important result of the research, which reflects its novelty, is a comprehensive exploration of the institutional source materials. This approach allows the authors to understand and restore the history of the development of Russian universities at the stage of implementation of the 1863 Charter, which was characterized by unconventional situations in the relations between the bodies of power and the university professorial corporation. The work also describes Solovyov’s strategy and tactics as an administrator in the context of violations of the University Charter of 1863 by the government circles. The authors make conclusions about Solovyov’s moral qualities, who, as a true historian, while holding a senior administrative position, understood his duty to his country as protection of the interests of science and the principles of university autonomy and serving education.
Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping

This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports. Given training observations are assumed as a sample from a high-dimensional space with an embedding low-dimensional manifold. An approximating function consisting of adaptable built-in parameters is optimized subject to given training observations by the proposed learning process, and verified for transformation of novel testing observations to images in the low-dimensional output space. Optimized internal representations sketch graph-organized supports of distributed data clusters and their representative images in the output space. On the basis, the approximating function is able to operate for testing without reserving original massive training observations. The neural approximating model contains multiple modules. Each activates a non-zero output for mapping in response to an input inside its correspondent local support. Graph-organized data supports have lateral interconnections for representing neighboring relations, inferring the minimal path between centroids of any two data supports, and proposing distance constraints for mapping all centroids to images in the output space. Following the distance-preserving principle, this work proposes Levenberg-Marquardt learning for optimizing images of centroids in the output space subject to given distance constraints, and further develops local embedding constraints for mapping during execution phase. Numerical simulations show the proposed neural approximation effective and reliable for nonlinear dimensionality reduction mapping.
Verifying the Causes of Adversarial Examples

The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial examples falls behind studies on attacks and defenses. In this paper, we present a collection of potential causes of adversarial examples and verify (or partially verify) them through carefully -designed controlled experiments. The major causes of adversarial examples include model linearity, one-sum constraint, and geometry of the categories. To control the effect of those causes, multiple techniques are applied such as L2 normalization, replacement of loss functions, construction of reference datasets, and novel models using multi-layer perceptron probabilistic neural networks (MLP-PNN) and density estimation (DE). Our experiment results show that geometric factors tend to be more direct causes and statistical factors magnify the phenomenon, especially for assigning high prediction confidence. We believe this paper will inspire more studies to rigorously investigate the root causes of adversarial examples, which in turn provide useful guidance on designing more robust models.
Optimized Text Classification Using Correlated Based Improved Genetic Algorithm

Text Classification (TC) is one of the basic processes in many Information Retrieval systems. Still, the performance of TC is a subject of improvement, and many approaches were proposes to achieve this aim. This work proposes an Improved Genetic algorithm (IGA) inspired by Genetic Engineering to enhance TC performance. In IGA, chromosome generation process were re-designed to diminish the effect of correlated genes. The Support Vector Machine (SVM) classifier were utilized based on the “Sport Text” popular dataset to evaluate the proposed approach. Empirical classification results were improved using IGA as compared to normal GA optimization. The proposed Improved Genetic Algorithm (IGA) improved the correct rates of TC by 1.39% in average.
Dynamic Key-Value Gated Recurrent Network for Knowledge Tracing

Knowledge tracing model is one of the important research fields to realize personalized intelligent education. The focus of the model is to trace students’ knowledge mastery from the records of students’ answering performance. However, the existing knowledge tracing models don’t consider the problems of students’ knowledge application ability and forgetting rules. Based on the external memory mechanism of Dynamic Key-Value Memory Networks (DKVMN), this paper proposes a knowledge tracing model based on a dynamic key-value gated recurrent network (DKVGRU), which designs a knowledge update network inspired the idea of Gated Recurrent Unit (GRU). DKVGRU calculates the proportion of concepts students apply and measures the degree of forgetting of learned concepts, which traces the knowledge state of each concept well. In this paper, the area under the receiver operating characteristic curve (AUC) of the prediction result is used as an evaluation indicator. The performance of DKVGRU is higher than DKVMN on four public datasets.
Remote Monitoring Framework for Elderly Care Home Centers in UAE

In an effort to improve the quality of life of its residents, elderly care centers in the UAE are exploring the use of remote monitoring techniques as a viable and cost-effective option. Currently, elderlies within the elderly home care center might be in different places unmonitored. Moreover, at night, the number of caregivers or attending nurses is usually limited where at times making the continuous monitoring of all the residents a very difficult if not impossible task. Elderlies would be highly susceptible to emergencies such as wandering off the care home center, falling and possibly abnormal changes in their vital signs. Thus there is critical need for continuous and reliable monitoring of residents in these centers preferably with realtime built-in alerting mechanism in case of emergencies. This work proposes a framework that enables the data collection from wearable sensor devices and customizes the elderly monitoring based on defined rules for each individual. Moreover, the work provides a dashboard for caregivers to visually monitor all elderlies' conditions and activities in the care center. This project is carried out in cooperation with one of the largest center in Dubai, UAE. It is validated by a prototype implementation.
A modular spectrum sensing node for Resources-Oriented Radio Monitoring

The existing radio monitoring practices suffer from two critical limitations: 1) current practices have been limited to isolated measuring and are unable to integrate data from different spectrum sensors; 2) these practices still require human operators to interpret signals, which prevent automated and intelligent processing. To address these limitations, we present a novel design of a spectrum sensing node based on the Representational State Transfer (REST) architecture. The spectrum sensing node exploits the REST architecture to integrate resources (data) into the web and to make it effortless to collect and share resources by exposing resources as service-oriented Web APIs. In addition, the spectrum sensing node introduces standardization and automation to radio monitoring. We outline the procedures of spectrum analysis and signal analysis for radio monitoring. Furthermore, to automate spectrum sensing node functionalities, we implement spectrum analysis and signal analysis using machine learning methods, which reliably extract and learn intrinsic features of complex data. The spectrum sensing node has been packaged and deployed in Honghe Hani and Yi Autonomous Prefecture of China for radio spectrum management. We describe and discuss our implemented prototype of the spectrum sensing node to demonstrate its practical advantages.
Student Compliance Intention Model for Continued Usage of E-Learning in University

Continued usage of e-learning is important and has been a major challenge. The problem continued usage of e-learning is a lack of student participation, unwillingness to learn, lack of motivation, lack of awareness, behavioral habits and cultural resistance. There are many studies proposed models for continued usage of e-learning. However, the previous models have not conducted research from the point of view of student compliance. If there is no student intention to comply with the rules of using e-learning, then continuing use of e-learning is very unlikely. Compliance with regulation can change culture. Compliance can also be used to ensure continued use in a system, such as the continued usage of an Enterprise System and continued usage of mobile social network service. So, this study proposes student compliance intention model for continued usage of e-learning. The result of this study is the proposed model that will help developer, university, and policy maker to develop e-learning application.
High-Frequency Data Link System Architecture Design based on SysML Modeling Language

Abstract This article is to show a new type of aircraft system design method, which decomposes the civil aircraft high-frequency data link system architecture into functional architecture, logical architecture and physical architecture, and describes the hierarchical relationship between the function, logic and physics of the system through the SysML model language.This article shows the method what converts the traditional high-frequency data link system design work based on experience and documents into model-based architecture design work.It uses the association and traceability of the model to ensure the integrity of the information in the design process and achieve the purpose of fast and convenient system design.
Test Data Generation Method Based on Siamese Network in Face Recognition System

With the development of AI and big data technology, machine learning system has been widely used in various fields and achieved satisfactory results. However, due to the increasing complexity of machine learning system, there are inevitably a variety of unpredictable problems, errors or failures in machine learning system, which cause fatal damage to the quality and reliability of machine learning system. In order to test machine learning system better, automatic test data generation is the key technology. An improved test data generation method based on GAN and siamese network in face recognition system is proposed. The method focuses on discriminator module composed of siamese network which can better measure the similarity between real and fake images. The results show that the proposed test data generation algorithm can generate images that are very close to the real samples. The test data of machine learning system is enhanced.
Forming universal competencies in the course of teaching a foreign language at a nonlinguistic university

The labor market modernization and the transition to the digital economy have led to a revision of the requirements for a contemporary specialist, who, in addition to professional competencies, possesses a broad range of universal skills and personal qualities. The development of leadership skills can become a universal pedagogical basis for implementing the concept of continuous nationally oriented independent education at all stages starting from the preparation of applicants, secondary vocational education, bachelor’s and master’s degree programs, to postgraduate studies, including advanced training and additional education. The study aimed to identify efficient educational technologies aimed at forming universal competencies in the framework of disciplines “Foreign Language” and “Business Foreign Language” at various stages of training, both in academic and extracurricular activities. The basis for identifying efficient educational technologies was theoretical methods (literature analysis (active teaching methods, and mixed learning technologies), documentation analysis (Federal State Education Standard, FSES 3+++)), as well as empirical methods (observation, study, and generalization). The practical significance of the work consists in the presented model of forming universal competencies, which can be considered as a component of the professional education of a future graduate of a nonlinguistic university. The universal competencies formed within the framework of the disciplines “Foreign Language” and “Business Foreign Language” will allow the graduate to become a competitive specialist in the international labor market.
The history of blockchain technology and cryptocurrency market development

Subject. The article addresses the cryptocurrency market that originates from the creation of the world's first cryptocurrency, i.e. Bitcoin. It overviews the blockchain technology and mining procedures for the digital currency. Objectives. The purpose of the study is to evaluate trends in the cryptocurrency market within the historical aspects, perform a profound analysis of digital assets terminology, including the concept of forks, tokens and altcoins, investigate changes in the Bitcoin and alternative cryptocurrency market by analyzing the Bitcoin Dominance Index, which has suffered significant fluctuations over the past five years. Methods. The study rests on the systems analysis of conceptual framework, the design of classification criteria, the functional, structural, statistical, graphical, and economic analysis. Results. The findings enabled to identify growth drivers for world’s cryptocurrency trading platforms. The growth primarily relates to the progressive development of the cryptocurrency market infrastructure. Other factors include the extended usage of stablecoins on the world’s biggest crypto exchanges. Conclusions. The paper provides a detailed consideration of changes in the cryptocurrency market development, which are divided into three main stages. The cryptocurrency community appreciates blockchain projects that have introduced new technologies. The most popular projects are implemented in the banking sector or used in the interaction between the cryptocurrency industry and traditional fiat currencies.
Safety of quantified ABoxes w.r.t. singleton ε <i>L</i> policies

In recent work, we have shown how to compute compliant anonymizations of quantified ABoxes w.r.t. εL policies. In this setting, quantified ABoxes can be used to publish information about individuals, some of which are anonymized. The policy is given by concepts of the Description Logic (DL) εL, and compliance means that one cannot derive from the ABox that some non-anonymized individual is an instance of a policy concept. If one assumes that a possible attacker could have additional knowledge about some of the involved non-anonymized individuals, then compliance with a policy is not sufficient. One wants to ensure that the quantified ABox is safe in the sense that none of the secret instance information is revealed, even if the attacker has additional compliant knowledge. In the present paper, we show that safety can be decided in polynomial time, and that the unique optimal safe anonymization of a non-safe quantified ABox can be computed in exponential time, provided that the policy consists of a single εL concept.
Application of laser displacement measurement system in thermal test of aircraft

Abstract At present, there is no research on nanosecond displacement measurement technology for spacecraft thermal test in China. Firstly, the principle of laser displacement sensor measurement is introduced. A system of hardware and software design and implementation was designed. Finally, a set of test system is built to verify the application of laser displacement meter in the spacecraft vacuum thermal test displacement measurement system. The experimental results and data analysis show that the laser displacement meter can meet the nanosecond displacement measurement requirements of spacecraft in vacuum and low temperature environment.
CBI: A Data Access Control System Based on Cloud and Blockchain Integration

The proliferation of mobile smartphones and the Internet has enabled people to stay online, gradually changing how people live and work. Particularly during epidemic isolation, people are more likely to communicate online via the Internet. Limited by the storage capacity and network bandwidth of mobile terminals, users are more likely to use the cloud to store and share data. However, data stored by centralized cloud services in a semi-trusted environment is at risk of being compromised, and most systems use cryptographic access control for the data, which failed to achieve fine-grained access control. In this paper, we propose a data access control system based on cloud and blockchain integration (CBI), which takes the decentralized, tamper-proof characteristics of blockchain and combines the advantages of cloud storage to design a novel integrated architecture, and designed a CBI-CP-ABE algorithm to implement attribute-based encryption for fine-grained access control. Experiments show that our proposed solution has similar performance to traditional data access control systems, and users have higher control and management rights over their data in the cloud so that they can store and share data more securely and keep track of their data usage.
Re-Imagining data analytics software development

Creation of data analytics pipeline is a tedious task. The algorithm search space for creating a suitable solution for a given goal in a given constrained infrastructure is generally very large. The exploratory work to choose the best possible solution is an effort-, time- and intellect-intensive process. The current industry practice largely relies on the domain experts for this work. To improve a domain expert’s productivity, we propose a model- and rule-based system to automate the process of creation of data analytics pipeline. The proposed system provides a mechanism to specify domain knowledge in the form of an object model and a set of rules defined over it. Recommendations are given to choose suitable algorithm/s for carrying out various data analytics tasks based on the problem context. On successful creation of the pipeline, the system generates pipeline code. Moreover, the system also generates a trace data to help in cognitive knowledge upgrade. We discuss the approach using case study of sensor data-based health monitoring system and showcase its efficacy and lesson learnt.
Electronic Speckle Pattern Interferometry (ESPI)

Speckle metrology includes techniques such as speckle photography, photography-based speckle interferometry, electronic speckle pattern interferometry (ESPI) and speckle shearography. ESPI, developed from speckle pattern interferometry (SPI), is a whole field deformation measurement technique that uses laser speckles as the carrier of deformation. The temporal phase-shift method extracts phase from multiple images recorded at different times with a known phase shift, while the spatial phase-shift method extracts phase from a single image. The main applications of ESPI are for deformation measurements, including out-of-plane, in-plane and three-dimensional deformation measurements, for vibration measurement, and for non-destructive testing. The time-Averaged and stroboscopic-illumination methods are usually utilized for harmonic vibration measurement, while double-pulse spatial phase-shift ESPI is used for deformation measurement under a transient loading or an impact loading. The time-averaged technique can be used to monitor the spatial distribution of the vibration mode and can also be used to detect subsurface defects and structural properties of the object.
How To Transport A Graphics Card Safely

There are many reasons why losing your graphics card or getting it destroyed now is not the best thing for you, one of these reasons is that you will ...