Reviewerly/exHarmony huggingface dataset:
Strategic Management of Worker-Computer Interaction

ABSTRACT Global threats, such as COVID-19, lead to the necessity to develop techniques that allow people who have to be isolated from each other to operate at full capacity, to produce goods, to provide services, to purchase goods and services, to study, to communicate, to have fun etc. Information and communication technology is a cornerstone for this. However, the effective usage of digital technologies depends on the competences of their users that is Worker- Computer interaction. Therefore, the article is devoted to the strategic management of the worker-computer interaction based on the digital potential of employment that is developed through the interaction of digital competence of an employee and digital components of a workplace. By conducting an extensive literature review about definitions and frameworks of digital competences presented by the European Parliament, the Organisation for Economic Co- operation and Development, the European Commission (DigComp), the National Research Council, the World Economic Forum, Japan Telework Association, the Skills for Employment Global Public-Private Knowledge Sharing Platform, the author broadens the scope of the concept of the digital potential of employment and gives the enhanced definitions of digital competence of an employee and digital components of a workplace. The author also scrutinizes the factors that influence the development of digital competences of an employee and digital components of a workplace, and suggests approaches to regulating and strategizing the digital potential of employment which can help to increase the effectiveness of the usage of digital technologies, labour productivity, human capital impact and to minimize the risks from the consequences of such a threat as coronavirus as well. Keywords strategic management, worker-computer interaction, the digital potential of employment, digital competence of an employee, digital (information) components of a workplace. ABOUT THE AUTHOR/S Irina V. Novikova novikovaiv5@gmail.com Doctor of Economics, Professor of the Economic and Financial Strategy Department at the Moscow School of Economics of Lomonosov Moscow State University, leading researcher of the Center for Strategic Studies at the Institute of Complex Systems Mathematical Research, Lomonosov Moscow State University New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s).
Iteration and nontermination

Iteration in programming-language semantics requires a more sophisticated approach than the other constructs we have seen so far: once loops are introduced, we are forced to describe the meaning of a loop’s failing to terminate, in the most extreme case for example WHILE TRUE DO SKIP.
Greek within the Global FrameNet Initiative: Challenges and Conclusions so far

Large coverage lexical resources that bear deep linguistic information have always been considered useful for many natural language processing (NLP) applications including Machine Translation (MT). In this respect, Frame-based resources have been developed for many languages following Frame Semantics and the Berkeley FrameNet project. However, to a great extent, all those efforts have been kept fragmented. Consequentially, the Global FrameNet initiative has been conceived of as a joint effort to bring together FrameNets in different languages. The proposed paper is aimed at describing ongoing work towards developing the Greek (EL) counterpart of the Global FrameNet and our efforts to contribute to the Shared Annotation Task. In the paper, we will elaborate on the annotation methodology employed, the current status and progress made so far, as well as the problems raised during annotation.
Deep Network for Network Intrusion with Concept Drift

A deep learning approach has been proposed for the classification of cluster instances as being intrusive or not intrusive. Mini-batch Adam optimizer was used due to a large number of hidden layers in the model. Massive amounts of data accumulated for training prevented the model from overfitting. After extensive testing of data with various algorithms, it was found that deep learning model with Adam optimizer outperformed others.
Parsisanj: a semi-automatic component-based approach towards search engine evaluation.

Accessing to required data on the internet is wide via search engines in the last two decades owing to the huge amount of available data and the high rate of new data is generating daily. Accordingly, search engines are encouraged to make the most valuable existing data on the web searchable. Knowing how to handle a large amount of data in each step of a search engines' procedure from crawling to indexing and ranking is just one of the challenges that a professional search engine should solve. Moreover, it should also have the best practices in handling users' traffics, state-of-the-art natural language processing tools, and should also address many other challenges on the edge of science and technology. As a result, evaluating these systems is too challenging due to the level of internal complexity they have, and is crucial for finding the improvement path of the existing system. Therefore, an evaluation procedure is a normal subsystem of a search engine that has the role of building its roadmap. Recently, several countries have developed national search engine programs to build an infrastructure to provide special services based on their needs on the available data of their language on the web. This research is conducted accordingly to enlighten the advancement path of two Iranian national search engines: Yooz and Parsijoo in comparison with two international ones, Google and Bing. Unlike related work, it is a semi-automatic method to evaluate the search engines at the first pace. Eventually, we obtained some interesting results which based on them the component-based improvement roadmap of national search engines could be illustrated concretely.
Komparasi Algoritma SVM Dan SVM Berbasis PSO Dalam Menganalisa Kinerja Guru SMAN 3 Selong

Teachers have an important role in printing quality students, so that there is a need for good performance. teacher performance is assessed from how the duties and responsibilities are carried out as educators. SMAN 3 selong is one of the educational institutions in East Lombok Regency, in measuring the performance of the headmaster of SMAN 3 Selong, usually looking at attendance data and the number of lesson hours, this causes the school principal to assess the teacher's performance is not effective so it needs to be built a system that can help school principals assess teacher performance. The purpose of this study is to analyze the performance of teachers of SMAN 3 Selong so that the performance criteria of teachers who teach well and are good enough can be known. To help the process of analyzing teacher performance accurately, a method of data mining is needed by applying a support vector machine (SVM) algorithm based on particle swarm optimization (PSO). Based on the results of the experiments conducted using the PSO-based SVM (support vector machine) method (particle swarm optimization), by training 55 data records, 29 teachers were expected to be good performing teachers, 25 teachers with fairly good performance and 1 teacher It is estimated that it is quite good but has a good performance, with an accuracy of 98.33%. DOI : 10.29408/jit.v2i2.1446
Cloud Computing Service Models: Traditional and User-Centric Approaches

Cloud computing is the use of computer hardware or virtual advances to shape a shared infrastructure that provides a user with many online value-added services. Understanding cloud services is important when choosing a suitable business arrangement that balances the required capabilities with reduced hardware costs, configuration and maintenance. The three prevailing service models in cloud computing are software, infrastructure and platform. Infrastructure as a service (IaaS) is the utilization of servers to provide utility services, for example, services for clients. Platform as a service (PaaS) suppliers give admittance to APIs that allow supporters of applications without installing or designing the advancement condition. Software as a service (SaaS) provides subscribed clients with access to software located on the cloud and not on the client's device. Mobile devices are becoming the main players in the service today. Classic client and server-based mobile models cannot meet the developing needs of mobile clients for a variety of good services. The cloud allows mobiles to download complex mobile apps that are not feasible on mobile devices alone. Cloud computing has overcome many challenges to prove its many advantages for organizations, such as the association between heterogeneous networks and working gatherings. Nevertheless challenges remain, and security, privacy and cloud integrity are crucial. Future models should enhance the characteristics of the current model without endangering it. This chapter reviews security threats to the cloud and provides an overview of mobile cloud computing services and key performance models.
Target Tracking Problem Based on Visual Image

With the rapid development of society, people began to pay attention to target recognition and target tracking. However, there is no suitable way to achieve the desired results. In order to solve this problem, this paper proposes a target tracking algorithm based on visual image. This paper extracts the covariance features in the visual image to construct the corresponding kernel function, then adds the kernel partial least squares to construct the framework in the modeling, and finally learns the low-dimensional discriminant features. The dimension discrimination feature is closer to the real situation of the data. At the end, the algorithm model of the article uses two different levels to replace the new method strategy and finally achieves a more effective target tracking algorithm. In the traditional target tracking algorithm, a single image block is used to construct a target or a background model. In this paper, the most basic individuals of visual images are extracted, and the covariance features in the visual images are extracted. The kernel functions are constructed between the covariance features. Finally, the kernel partial least squares regression model is used to achieve the target prediction. In this paper, the sparse MOSSE filter is studied, and the non-dense constraints are added to the MOSSE filter, and a new optimization function is finally obtained.
HUBUNGAN PENGETAHUAN DAN SIKAP SISWA TENTANG PERILAKU HIDUP BERSIH DAN SEHAT DENGAN UPAYA PENCEGAHAN DIARE DI SD NEGERI 060880 MEDAN

Knowledge and attitude become the basis for someone to behave. In elementary school students, many morbidity rates are caused by unhealthy behavior, one of the diseases that arise is diarrhea. The high rate of diarrhea morbidity in children in Medan is due to the knowledge and attitudes in implementing clean and healthy living habits. This study aims at analyzing the relationship between knowledge and attitudes of students about clean and healthy living behaviors with the prevention of diarrhea at SD Negeri 060880 Medan. This type of research is descriptive correlation. The population in this study is all students in grades IV and V, amounting to 59 people. The samples are 59 people and the sampling technique used is the total sample. Data analysis was done by using the chi-square test with α <0.05. Based on the results of the study, the p-value for the knowledge variable is p = 0.002, meaning that there is a relationship between knowledge and efforts to prevent diarrhea and the p-value for the attitude variable p = 0.003, meaning that there is a relationship between attitude and efforts to prevent diarrhea. This means that knowledge and attitudes play an important role in underlying students' behavior in terms of preventing diarrhea. Schools are expected to improve the health status of students by carrying out Students Activities Unit, properly using existing facilities, and increasing number of posters about washing hands properly. Students are expected to further increase their willingness, awareness, and ability to live healthy so that PHBS can be increased
РЕАЛІЗАЦІЯ СИСТЕМНО-ДІЯЛЬНІСНОГО ПІДХОДУ НА УРОКАХ МАТЕМАТИКИ

The article features of realization of systemic-activity approach in mathematics lessons in elementary school. The principles of this approach: the principle of activity continuity, a holistic view of the world, minimal, psychological comfort, variability and creativity. The activity is a process of solving problems in life a man who excites her to certain actions to achieve a specific goal. The main reason is the theory of gradual formation of mental actions by P. Galperin and N. Talyzina. The attention is focused on algorithmization of the process of teaching mathematics of primary school students. It is noted that the algorithm is clear, understandable description of a specific sequence of operations that will lead to obtaining a certain result. Shows the characteristics of the verbal (verbal) and iconic (graphical) algorithms for performing mathematical tasks. The methods and forms of operational control of development of a particular algorithm: mathematical simulators, pages, Champions, racing and the like. Key words: learning content, activity, activities, activity approach, mathematical education, mental activity motivation, algorithm.
Information Retrieval Approach based on Recursive Query Shifting

In this paper, we introduce an information retrieval approach based on a Recursive Query Shifting. The idea is coming from the observation that precision value, in Precision/Recall curves, begins very high before downgrading and so no matter what the considered parameters: feature, matching measure, threshold, etc. In other words, the first results are commonly better than the other results coming later. Considering then the first returned result, recursively, as a query seems to contribute very well for improving the system accuracy. The idea is adopted firstly in the case of content based image retrieval and generalized for the case of documentary retrieval. The simple specificity is that the first returned image is considered as the new query, in the case of image retrieval, while the new query is extracted from the first returned document, in the case of text retrieval, for the reason that there is no documentary retrieval system with an entire document as a query. The proposed approach falls in the purview of mechanisms for pseudo relevance feedback with long-term learning helping to improve the interrogation protocol. It consists to reformulate the original submitted query recursively as an attempt for being closer to the user requirement as well as to the other collection elements. The approach is materialized into two algorithms namely QRM1 and QRM2. The experiments conducted on returned Google Scholar results as a text collection and COREL-1K images benchmark yield very promising results.
Design and Implementation of Meteorological Equipment Management System

In order to meet the requirements of information and office automation, a set of meteorological technical equipment management system is developed, which can be used to directly query, modify, add and browse various instruments and equipment data in the warehouse. The establishment of the system has improved the efficiency and reliability of meteorological technology and equipment management.
The Influencing Factors of Reading Ability and Its Test Types

Reading is the most influential aspect in the language learning, which can foster the improvement of listening, speaking, writing and translation to some extent. It is a complex input and output process of cognition and needs a variety of basic knowledge and ability, which is the fundamental skill to learn other information. It contains the vocabulary, phrase, text and its comprehension so that it involves visual sense, auditory sense, memory and faculty of understanding. English reading plays a vital proportion in the most part of exams and in acquring and dealing with information in daily information.
LibGuides: Using Primo, the Library catalog, to Find Books & Articles: Search by Citation and ILL Request tabs

About the Library's tool for searching print and online collections for books, articles and more.
Mapping of gold-related alteration minerals and linear structures using ASTER data in the Giyani Greenstone Belt, Limpopo Province, South Africa

This paper focuses on the mapping of alteration minerals and linear structures associated with gold mineralization in the Giyani Greenstone Belt (GGB). Spectral Information Divergence (SID) and Linear Spectral Unmixing (LSU) classification algorithms were applied to the ASTER data using image extracted endmember spectra which match the spectral profiles of predominant alteration minerals (biotite and calcite) related to gold mineralization in the GGB. Directional convolution filters were also applied to the ASTER data to extract structural lineaments that may be related to gold mineralization. In addition, Fry analysis of known gold occurrences in the GGB was conducted to determine the major distribution patterns along which gold mineralization have occurred. LSU and SID results show several pixels classified as alteration minerals corresponding to known gold occurrences. SID results also show minerals overlapping into the gneissic rock, which is a rare host of gold mineralization in the study area. On the other hand, LSU results show high abundances of calcite and biotite generally confined within the mafic-ultramafic greenstone rocks, which have been hypothesized as the source of gold-mineralized fluids in the GGB. In general, LSU show distinct broad regions classified as alteration minerals corresponding to known gold occurrences that have been previously reported to host these alteration minerals. Directional convolution filters enabled the extraction of predominately NE-SW oriented linear structures, including the major shear zone which is associated with gold mineralization in the GGB. Fry analysis revealed three major distribution patterns: N-S, NE-SW, ENE-WSW along which gold mineralization have occurred. These patterns correspond to major lineaments associated with gold mineralization reported in the previous structural studies of the GGB.
Adaptive Online Multi-modal Hashing via Hadamard Matrix.

Hashing plays an important role in information retrieval, due to its low storage and high speed of processing. Among the techniques available in the literature, multi-modal hashing, which can encode heterogeneous multi-modal features into compact hash codes, has received particular attention. Existing multi-modal hashing methods introduce hyperparameters to balance many regularization terms designed to make the models more robust in the hash learning process. However, it is time-consuming and labor-intensive to set them proper values. In this paper, we propose a simple, yet effective method that is inspired by the Hadamard matrix, which captures the multi-modal feature information in an adaptive manner and preserves the discriminative semantic information in the hash codes. Our framework is flexible and involves a very few hyper-parameters. Extensive experimental results show the method is effective and achieves superior performance compared to state-of-the-art algorithms.
Biometrics as a Service

In today’s world, very rapid advances in the technology used for personal life tasks have made our lives a lot easier but also riskier. Data generated, utilized, and transferred using these technologies is private, and unauthorized access to these systems can significantly damage the data owner. Traditionally many methods are used to provide security for these kinds of systems, but flaws in these methods can lead to many breaches, such as misplaced, copied, forgotten, or stolen PINs and passwords. Biometrics provides an alternative solution that can safeguard us from such problems, while identifying a person distinctively. Biometrics involves work related to the understanding and quantification of human physical and behavioral traits such as iris, facial, DNA, voice, and fingerprint recognition. The effectiveness of biometric systems is very high, as reproduction of similar traits is very difficult in practice. Hence they are being opted for in various fields such as banking, online authentication, attendance systems, and identification systems. A huge amount of data is required for all these applications, creating a need for efficient scaling and storage management. Cloud computing is an architecture that utilizes a wide network of remote servers to store data in a distributed fashion, and perform management and processing on this data over the distributed network, which can be accessed from anywhere required using the internet. Employees can use personal handheld devices to connect to the company servers securely from anywhere, increasing efficiency. What if the advantages of cloud and biometrics were to be teamed up? In Biometrics as a Service (BaaS), biometric systems and data are hosted on cloud technologies and can be accessed from the cloud whenever and wherever there is a need for biometric authentication. It saves time and leads to improved performance for the industries using it.
Subject & Course Guides: OHIO Libraries 2020 Homecoming: Mapping Memories

In this historic year, Ohio University Libraries invites students, alumni, and community members to participate in OHIO's first-ever virtual Homecoming celebration. In the tradition of Homecoming events of years past, our 2020 offerings aim to immerse par
Design and Implementation of Distributed Government Audit System Based on Multidimensional Online Analysis

With the continuous progress of the information age, e-commerce, the Internet of things and other emerging Internet areas are gradually emerging. Massive amount of structured data auditing becomes a major issue. Log files and other data can be uploaded to the cloud via the Internet to guard against potential threats. Difficulty now is how to realize the data in the field of data audit query online, interactive and impromptu. There are two main methods of data warehouse, respectively is zhang table reduction method and basic data verification method. In the age of big data, data quantity increases gradually, so that the audit speed, design of the data storage and so on will be more or less problematic. If the audit task is not completed in time, it will result in the failure to store the audit data, which will cause losses to enterprises and the government. This paper focuses on the data cube physical model and distributed technical analysis, through the establishment of a set of efficient distributed and online auditing system, so as to make the data fast and efficient auditing.
Reducing Redundant Association Rules Using Type-2 Fuzzy Logic

Data mining (DM) is an analysis extensive data in order to gain the novel and hidden information. DM is vital to research domains like statistics, artificial intelligence, machine learning, and soft computing. Association Rule Mining (ARM) in enormous databases is a fundamental topic of DM. Discovering frequent itemsets are an underlying process in ARM. Frequent itemsets are employed using statistical measures like Support (Sup) and Confidence (Conf). ARM is practiced to produce association rules (ARs) from frequent itemsets. Such rules reveal a connection between items in the actual world. Numerous algorithms have submitted to attain these rules. However, such algorithms suffer from troubles of redundancy and a sizable count of derived ARs, which renders algorithms inefficient and renders it complicated for end-users to grasp created rules. Due to these motives, this paper adopts the type-2 fuzzy association rules mining technique (T2FARM) to attain frequent items, locate all relationships among items and AR which fulfill minimum support (min sup) and minimum confidence (min conf) values in addition to prune redundant rules in. Empirical evaluations display that the proposed technique improves redundant rules pruning of DM compared to traditional fuzzy association rules (FARs).