Reviewerly/exHarmony huggingface dataset:
Preliminary Investigation of the Integrated Evaluation Model based on Random Forest Method

With the deepening of reforms, as the closing year of the “Modern Vocational Education System Construction Plan (2014-2020)”, it has witnessed the development of vocational education. Its development trend is “only specialization” and “light basic courses”. Currently, the random forest method is very popular. This method forms a “forest” by building many decision trees, and votes on multiple trees to make decisions. This method can improve the classification accuracy of new samples effectively and it is more suitable for the research proposed. Random forest evaluation model has three functions: formative, diagnostic and summative. This paper establishes a comprehensive evaluation model for basic courses to make the evaluation more standardized, comprehensive and operability. And this evaluation system can supervise all stages of teaching.
A Global Deep Reranking Model for Semantic Role Classification

The current approaches to semantic role classification usually first define a representation vector for a candidate role and feed the vector into a deep neural network to perform classification. The representation vector contains some lexicalization features like word embeddings, lemmar embeddings. From linguistics, the semantic role frame of a sentence is a joint structure with strong dependencies between arguments which is not considered in current deep SRL systems. Therefore, this paper proposes a global deep reranking model to exploit these strong dependencies. The evaluation experiments on the CoNLL 2009 shared tasks show that our system can outperforms a strong local system significantly that does not consider role dependency relations.
HyperEA: Hyperbolic Entity Alignment between Knowledge Graphs

Entity alignment aims to identify same entities in different Knowledge Graphs (KGs). Recent studies have shown that hierarchical structures widely exist in KGs, and we observe that efficiently capturing hierarchical structures in KGs can benefit the entity alignment task. However, existing entity alignment methods usually have no ability to capture hierarchical structures of entities. To bridge this gap, in this paper we study the entity alignment problem from the perspective of hyperbolic space, and propose a novel hyperbolic entity alignment framework (HyperEA) which captures hierarchical structures of entities to improve the accuracy of the entity alignment task. We also conduct extensive experiments over the benchmark dataset to demonstrate the superiority of HyperEA compared with other state-of-the-art methods.
Information - Education Milieu: Methodology of Forming Knowledge Content and Practice Oriented Training of IT-disciplines

The article herein considers the innovative methodology of forming the knowledge content, based on ontological model. The basic methodology concepts are project-oriented technology of CDIO training and graduate's competence model, and implementation tool is information - education milieu. Information - education milieu allows implement adaptive, in compliance with production requirements, knowledge trend planning and knowledge content forming both for specialty degree programs disciplines and individual training trajectory, using for those aims the smart-contract.For the discipline Technologies of processing the distributed applications there have been shown examples of using the technologies on modeling the knowledge, connected with designing the real time systems of parallel and distributed applications, where as a project there has been used the banking system.The methodology's software implementation has been fulfilled in the form of a web-application and currently it is going through approbation at the chair Computer and software engineering of Turan university.
Digital reality: Choosing the future

We present an analysis of reports, round table discussions, approaches presented at the IV International Conference “Designing the Future and Horizons of Digital Reality”. The focus of the conference participants was the analysis of the results, risks and prospects for the development of the computer reality of the world and the Belarus-Russia Union State from the position of an interdisciplinary synthesis of knowledge at the intersection of philosophy, mathematics, computer science, sociology and a number of other disciplines. At one time, the Nobel Prize laureate academician Zh.I. Alferov said that it is in Russia and Belarus that the potential in the field of information and telecommunication (IT) technologies is greatest among the post-Soviet countries. And it is these technologies that should become the basis of our scientific and technical breakthrough. This foresight is the leitmotif of this conference. Many innovations in our countries are perceived with the imperative: “New is good”. But it’s not always the case. Our world is not linear. It is characterized by bifurcation points, in one of which it is now. Discoveries, technologies, inventions at this point can determine which branch of further development will be chosen. Therefore, a broad interdisciplinary analysis of the strategic risks of the development of computer reality is required. The choice that is now being made must be conscious and responsible. This circle of problems is also in the center of attention of the conference participants.
Linearizing Computing the Power Set with OpenMP

This paper presents an alternative to computing the power set in a sequential computing environment. The algorithms to compute the power set run concurrently on the Stampede2 supercomputer using OpenMP.Given a set of size n, we partition the binomial coefficient function into n-1 tasks. The method gives each task various cardinalities to the power set to compute. We will present a load balance algorithm that computes the maximum number of sets for each cardinality in the power set for each task.Using the load balance algorithm; the partition of the binomial coefficient function; and the algorithms to compute the power set; we will show that n-1 tasks can compute the power set of the given set of size n phenomenally efficiently using OpenMP. With increasing input sizes n, the OpenMP algorithm run-times have a non-exponential function.
Blockchain: Application Domains, Research Issues and Challenges

Blockchain technologies offer an innovative approach to data storage, transaction execution, process automation, and confidence-building in an open and distributed environment. A wide range of blockchain-based applications have been developed and deployed since its inception, and the convergence of blockchain with other state-of-the-art technologies has been on the rise. However, the issues and challenges associated with it are also rising with the advancement of blockchain technologies and their applications. This paper provides an overview of blockchain and reviews the typical domains of blockchain application discussing the open issues and challenges. Besides, this article reviews the recent advances in addressing different blockchain issues and points out the research directions that help to develop technological innovations for future blockchain systems.
Introduction of elements of distance technologies in the educational process

In the context of the development of the digital economy, it is necessary to introduce elements of distance education technologies into the educational process. This will make the learning process continuous. Distance technologies allow you to master programs of higher and secondary education (especially in correspondence and full-time forms) on the job. The introduction of distance learning technologies in the educational process increases students ‘ interest in the subject being studied. In modern conditions, the development of online courses is relevant. To increase the interest of students in online courses, it is recommended to involve representatives of employers to record lectures on specific topics within the online course. An important role is played by the introduction of elements of remote technologies in the organization of current and intermediate certification.The article is written on the basis of the author’s own pedagogical experience. Systematizes the directions of introduction of distance educational technologies that can be used both for the organization of the educational process and for all types of control of students knowledge. The article dis-cusses the advantages and disadvantages of distance learning, as well as the possibility of using elements of distance learning technologies in the implementation based correspondence courses that will be relevant in the implementation of federal state educational standard 3++ (including professional standards).Examples of experience in organizing the educational process with elements of distance learning are given.
Gesture Recognition of Indian Sign Language

In today’s world, many people are not as fortunate as us. They suffer from speaking and hearing disabilities that make daily tasks very difficult to perform. This paper mainly deals with people suffering from the loss of hearing and cannot speak. In this study, we have introduced a recognition and classification technique which can detect hand gestures used by the deaf and dumb community, which is then translated to an equivalent English word. The vision-based system is built using two machine learning algorithms, namely convolution neural network (CNN) and recurrent neural network (RNN). The video sequences are not directly fed into the model instead those videos are first converted into frames and then fed into the model. Further, each frame is directed into CNN, the exact features are extracted, trained on those features and stored in a model file, these model files are fed into RNN for further feature extraction, and the model is trained on those features. The model can convert the sign language to text with an accuracy of 96%. To make the system as user-friendly as possible, we have provided a feature where the general public can record the video of the person doing the hand gesture and use it to make the equivalent English word prediction.
IOT-Based Traffic Signal Violation and Over Speed Recognition

Internet of Things (IoT) uses Unique Identifiers (UIDs) assigned on every device to communicate between objects equipped with sensors and digital devices around the world through the Internet. Faster Internet speeds and wider bandwidths allow more opportunities for inventions and marketability opportunities. The world is quickly urbanizing. This has brought about a complex increase in the number of vehicles on city streets causing engendering traffic violations to turn out to be progressively basic these days. This causes extreme devastation of property and more accidents that may endanger the lives of the individuals. To take care of the disturbing issue and prevent such incomprehensible results, criminal traffic offense identification frameworks are required. It must be realized in real time as the authorities track the streets constantly. Subsequently, traffic authorities will not just be quiet in actualizing safe streets precisely, but also efficiently; as the traffic violations will be detected faster than humans. This work proposes a useful approach for monitoring and detection of traffic rules violations like traffic signal jumping (red signal) and over speeding of the vehicles.
Dual-mode Integrity Measurement System Based on Virtualized TEE

Abstract The Trusted Execution Environment (TEE) is mainly used to protect sensitive information and ensure information security. TEE has been used in the system structure of various mobile devices. With the development of TEE, attack methods against TEE gradually appear. The virtualized TEE architecture proposed by existing research can solve some security problems caused by TEE architecture, but there are still some deficiencies. To enhance the TEE architecture, a virtualized TEE-based kernel integrity measurement scheme is proposed, and identity-based and neural network-based integrity measurements are used in different scenarios. The experimental results show that the designed dual-mode integrity measurement system can effectively detect attacks against different kernel objects and has little impact on system performance, thus achieving security enhancements for virtualized TEE.
Dealing with the Challenge of Business Analyst Skills Mismatch in the Fourth Industrial Revolution

This paper describes the skills mismatch within the Business Analysis profession. The Business Analysis profession emerged in the early 90s and is still considered a new field in the information systems domain which has not been extensively researched. However, the advent of the Fourth Industrial Revolution has put the future skills of the profession into question. This paper uses the Business Analysis Competency Model to develop a proposed Skills Mismatch framework to understand the skills mismatch within the Business Analysis profession. The Business Analysis Competency Model highlights technical skills, business skills, analysis techniques, interpersonal skills, and the Business Analysis Methodology. The gap identified in the literature is that there are limited studies in the digital age that have tracked the changes in required skills over time. This paper will be useful to the Business Analysis Body of Knowledge by proposing a framework for addressing a possible Fourth Industrial Revolution induced skills mismatch.
Fast Implementation of Genetic Algorithm Based on Software/Hardware Co-design Method

Abstract Genetic algorithm (GA) has been widely used since it was proposed. However, due to the complexity of the algorithm itself, the fact that the actual problems have huge data and scale, and that it needs to meet certain real-time requirements, the acceleration of genetic algorithm has become a hot spot in both academia and industry field. Previous work have been done on the acceleration of GA, which primarily focuses on the software aspect, while the existing work in hardware aspect lacks adaptability for different industrial scenarios. Therefore, this paper puts forward a novel architecture based on software/hardware co-design method, in which the software produces random numbers, and the hardware, taking full account of the characteristics of sequential circuit, is implemented in a hierarchical pipeline architecture. This architecture accelerates the algorithm while providing certain flexibility. This paper completes the simulation on MATLAB and Vivado, and the total running time is 987.4.u. Compared with the pure software implementation, the speed is increased by 73.9 times.
Artistic Natural Images Generation Using Neural Style Transfer

Rendering an image in a different form of style is a difficult task in image processing. And the representation of an image is very important as an image contains a lot of information. To represent the image uniquely, the Neural Style Transfer technique plays an important role. Neural Style Transfer refers to a technique of generating new images in the style of another image and generates new images as if they are oil painted images. In this paper, we have modified different types of natural images of Bangladesh with the help of Neural Style Transfer. It is a technique that is used to blend the content image and style image in an optimized way and gives a creative output image that is very interesting and attractive. We have transformed the input image for minimizing its content distance and style distance with the help of the content image and style image using the VGG19 network model.
Art, science, time

The main forms of time and models of their association are considered. A criterion for the symmetry of the triple balance of time as a degree of harmony in the system is proposed. Examples of degenerate, deformed, and symmetric balances are given. Art is designated as the most important tool for restoring the balance of symmetry in various fields of knowledge.
Dynamics of Terminology in the Field of Information Networks: “Grid”

The article analyses dynamics and lines of terminology development in the field of information networks at the present stage in the net - web - grid chain. The purpose of the study is to shed light on the reasons for yet another active introduction of new terms in this subject area. The study is novel in that it is the first to consider the formation dynamics of new terms that make up an extensive terminology pool. The authors have considered the process of the emergence, usage and expansion of the terminological corpus based on the lexeme ‘grid’. As a result, a forecast is made for expansion of applicable scope and differentiation of terms. In particular, the authors have predicted the tendency toward expected substitution of the term “web” for “grid” by users.
E-Learning transformation during the covid-19 pandemic among faculty members at princess nourah bint abdul rahman university

espanolSe busca arrojar luz sobre los problemas de la transicion al e-learning durante la pandemia de Covid-19 entre los colaboradores de la facultad de la Universidad Princess Nourah Bint Abdul Rahman, a traves de un enfoque descriptivo y analitico. La herramienta fue representada con la ayuda de un cuestionario organizado por el investigador dividido en 3 ejes, concluyendo que los problemas organizacionales como problemas netos de lentitud, problemas de descarga, falla de conversacion y sus obstaculos, tienen la necesidad de articular actividades, criterios de evaluacion y preguntas acordes con las especificaciones y pruebas de la educacion a distancia. EnglishIt seeks to shed light on the problems of the transition to e-learning during the Covid-19 pandemic among the collaborators of the Princess Nourah Bint Abdul Rahman University faculty, through a descriptive and analytical approach. The tool was represented with the help of a questionnaire organized by the researcher divided into 3 axes, concluding that organizational problems such as problems of slowness, download problems, conversation failure and their obstacles, have the need to articulate activities, criteria of evaluation and questions according to the specifications and tests of distance
HyCoNN: Hybrid Cooperative Neural Networks for Personalized News Discussion Recommendation

Many online news platforms provide comment sections for reader discussions below articles. While users of these platforms often read comments, only a minority of them regularly write comments. To encourage and foster more frequent engagement, we study the task of personalized recommendation of reader discussions to users. We present a neural network model that jointly learns embeddings for users and comments encoding general properties. Based on explicit and implicit user feedback, we sample relevant and irrelevant reader discussions to build a representative training dataset. We compare to several baselines and state-of-the-art approaches in an evaluation on two datasets from The Guardian and Daily Mail. Experimental results show that the recommendations of our approach are superior in terms of precision and recall. Further, the learned user embeddings are of general applicability because they preserve the similarity of users who share interests in similar topics.
Efficient Noninteractive Outsourcing of Large-Scale QR and LU Factorizations

QR and LU factorizations are two basic mathematical methods for decomposition and dimensionality reduction of large-scale matrices. However, they are too complicated to be executed for a limited client because of big data. Outsourcing computation allows a client to delegate the tasks to a cloud server with powerful resources and therefore greatly reduces the client’s computation cost. However, the previous methods of QR and LU outsourcing factorizations need multiple interactions between the client and cloud server or have low accuracy and efficiency in large-scale matrix applications. In this paper, we propose a noninteractive and efficient outsourcing algorithm of large-scale QR and LU factorizations. The proposed scheme is based on the specific perturbation method including a series of consecutive and sparse matrices, which can be used to protect the original matrix and obtain the results of factorizations. The generation and inversion of sparse matrix has small workloads on the client’s side, and the communication cost is also small since the client does not need to interact with the cloud server in the outsourcing algorithms. Moreover, the client can verify the outsourcing result with a probability of approximated to 1. The experimental results manifest that as for the client, the proposed algorithms reduce the computational overhead of direct computation successfully, and it is most efficient compare with the previous ones.
Aid Grants vs. Technical Cooperation Grants: Implications for Inclusive Growth in Sub-Saharan Africa, 1984-2018

This study investigates the effects of aid grants on inclusive growth in 37 Sub-Saharan African countries for the period 1984-2018. Grant aid is decomposed into aid grants and technical cooperation grants. Two inclusive growth indicators are used namely: gross domestic product (GDP) per capita growth and unemployment rate. The dynamic panel autoregressive distributed lag (ARDL) approach which is employed comprises three different estimators; the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE). The Hausman diagnostics were used to assess the efficiency and consistency of the estimators. Based on the PMG estimator, our findings show that aid grants and technical cooperation grants exert a positive influence on GDP per capita growth in the long-run. However, while the observed influence of aid grants is found to be significant, technical cooperation grants display insignificant effects. In the short run, however, the PMG estimates show that aid grants and technical cooperation grants have negative and insignificant effects on GDP per capita growth. On the other hand, results based the DFE estimators reveal that neither of the aid grants has influenced the unemployment rate positively in the short-run. However, whereas aid grants contribute significantly to the reduction of the unemployment rate in the long run, technical cooperation grants do not. This study complements the attendant literature by assessing how aid grants versus technical cooperation grants affect inclusive growth. The findings are relevant to international policy coordination for the attainment of sustainable development goals.
Algorithms for Probabilistic and Stochastic Subsequential Failure Transducers

This paper introduces a framework for building probabilistic models with subsequential failure transducers. We first show how various types of subsequential transducers commonly used in natural language processing are represented by probabilistic and conditional probabilistic subsequential failure transducers. Afterwards we introduce efficient algorithms for composition of conditional probabilistic subsequential transducers with probabilistic subsequential failure transducers and weight pushing (canonization) of probabilistic subsequential failure transducers. Those algorithms are applicable to many tasks for representing probabilistic models with subsequential failure transducers. One such task is the construction of the $$HCLG$$ weighted transducer used in speech recognition which we describe in detail. At the end, empirical results and comparison between the presented $$HCLG$$ failure weighted transducer and the standard $$HCLG$$ weighted transducer constructions are shown.