Reviewerly/exHarmony huggingface dataset:
Guidelines for the Systematic Selection of Virtual Reality Hardware for Learning Factories

The development and operation of physical as well as virtual learning factories are complex and costly activities. For a competency-oriented development of learning factories the (virtual) factory hardware must be tailored to the curriculum and the underlying competencies. With virtual learning factories - or virtual learning scenarios in general - there are two aspects which need to be considered. Firstly, the virtual learning environment itself must be designed. This aspect contains e.g., the selection of suitable machines and production equipment. Secondly, the used virtual reality hardware must be chosen additionally for virtual learning environments. Learning factories normally undergo a continuous improvement process regarding their curriculum and offered content. Therefore, the selection of virtual reality (VR) hardware can be a challenging aspect for learning factory operators since the decision should not only be suitable for the current situation but is also a strategic decision. This publication explains and offers a guideline how learning factory operators can select suitable virtual reality hardware components in a systematic and structured way and which aspects should be considered to identify the most suitable configuration.
Cryptography—Secure communications

As we saw in the last chapter, a modern block cipher will allow two stations to communicate in private across an insecure channel. If both stations are able to agree on a secret key in advance through a secure channel, then establishing an encrypted channel can be straight forward although as we will see later, it is not without its problems. However, in many systems, the use of a “preshared key” is impractical. This means that a session key must be agreed between both stations over the insecure channel. This is a nontrivial problem that is addressed by the Transport Layer Security (TLS) protocol.
Contribution of Cyber Literature by Agus Noor to the Preparation of “Kisah - Kisah Kecil dan Ganjil Malam 1001 Pandemi”

Cyber literature is literature that is present due to the development of communication media, especially internet media. His presence became a benefit for several writers, including Agus Noor. Agus Noor developed a story idea with flash fiction or a collection of stories taken from Instagram with the use of the hashtag 1001night pandemic. The collection of stories is then combined into a whole story that is recorded. This work is closely related between the cyber world and the literary space to produce a literary work. This research aims to map the work of Agus Noor on social media in relatively deep. The approach used in this study is qualitatively exploratory. With reading without data, but focus on exploration found on Instagram accounts and books. This research will produce some narrative. First, the relationship of cyber literature with the book collection of stories by Agus Noor. Second, the usefulness of cyber literature in making story ideas and stories by Agus Noor.
The Optimal Combination between Blockchain and Sales Format in an Internet Platform-Based Closed-Loop Supply Chain

Inspired by the practice of platform retailers cooperates with blockchain technology service companies to improve the recycling rate of waste products and solve the three major pain points of recycling, this paper examines how the platform should optimize the combination of "permissioned blockchain technology (PBT) & sales format" to improve the economic, social and environmental (ESS) performance of the closed-loop supply chain (CLSC). The study first models and characterized the three major effects of PBT in the CLSC, namely trust effect, brand premium effect and recycling appeal effect. On this basis, the platform can choose to adopt a selling or agency selling format to help the upstream manufacturer sell products with or without PBT. Through analytic models, this paper investigates the impact of the implementation of PBT on product price, online service, brand goodwill, as well as demand and corporate profits. Through the comparative analysis of the platform's own profit, manufacturer' willingness to cooperate and the triple benefits of ESS in the CLSC, the optimal "PBT & sales format" combination strategy of the platform is obtained. The result indicates that when PBT effectively enhance consumer trust in the platform to recycle waste products and enhance brand favorability, the implementation of PBT has made a huge contribution to ESS. When the brand effect exceeds the reference price effect, the implementation of PBT will increase the retail price, exacerbating the double marginal effect, which is consistent with the conclusions of previous studies. When the reference price effect exceeds the brand effect, the introduction of PBT can alleviate the double marginal effect, which differs from previous studies. Another important finding is only when the commission rate is extremely low/relatively high, the manufacturer can reach a stable cooperation intention with the platform on selling/agency selling. And when the commission rate is in the medium and relatively low categories, the manufacturer's preferred sales model is different from the choice of the platform, the two can not achieve a permanent stable cooperation, and there is only a suboptimal equilibrium in the CLSC. Furthermore, combined with the platform sales cases of different categories of products, this paper derives the "PBT & sales format" combination strategy of platform enterprises for the universality of different attribute products.
Utilizing Expert Knowledge and Contextual Information for Sample-Limited Causal Graph Construction

This paper focuses on causal discovery, which aims at inferring the underlying causal relationships from observational samples. Existing methods of causal discovery rely on a large number of samples. So when the number of samples is limited, they often fail to produce correct causal graphs. To address this problem, we propose a novel framework: Firstly, given an expert-specified causal subgraph, we leverage contextual and statistical information of the variables to expand the subgraph with positive-unlabeled learning. Secondly, to ensure the faithfulness of the causal graph, with the expanded subgraph as the constraint, we resort to a structural equation model to discover the entire causal graph. Experimental results show that our method achieves significant improvement over the baselines, especially when only limited samples are given.
Requirements Engineering Education: A Systematic Literature Review

Requirements engineering is an essential activity of software development lifecycle (SDLC) and its importance has enormously grown in recent years due to exponential growth of software industry. In this context, requirements engineering education has become largely indispensable for computing graduates and trainees in software industry. Several studies have explored different dimensions of requirements engineering education and relevant pedagogical techniques over the past few years. This systematic review aims at identifying and highlighting the extant research on REE that is available and to select useful approaches that can help in expediting the growth in this field. The systematic literature review is performed to classify existing literature related to REE into four categories: method/techniques, tools, comparison studies, and frameworks used in REE. After a rigorous evaluation process, 32 primary studies were selected and classified according to these categories. Results of this SLR indicate a significant shift toward this dimension. We have highlighted several research trends and gaps that need to be addressed by the RE community.
InterCriteria Decision Making Approach for Osteoarthritis Disease Analysis

The InterCriteria decision making approach is based on two fundamental concepts: intuitionistic fuzzy sets and index matrices. This paper continues a series of research on the application of approach of InterCriteria Analysis (ICrA) on real medical data. The ICrA is applied on the results of physiotherapeutic treatment and kinesiotherapeutical program characteristics. The main goal is an improvement of general quality of patients’ life through practices of specific methodology of kinesiotherapy. The knee performs the function of a medial motor link in the kinetic chain of the lower limb. The osteoarthritis is one of the most devastating chronic conditions that affect people around the world. The paper describes achieved results from using a multicriteria decision making approach of analyzing the treatment results of patients with Osteoarthritis disease in order to improve the process of taking decisions about the treatment course.
Displacement Field Construction Based on a Discrete Model in Image Processing Problems

The problem of a displacement field calculation for an image sequence based on a discrete model is being solved. Algorithms for velocity field (displacement field) construction are in demand in various image processing tasks. These methods are used in motion detection, object movement tracking, analysis of complex images, movement correction of medical diagnostic images in nuclear medicine, radiology, etc. An optimization approach to the displacement field construction based on a discrete model is developed in the paper. The approach explores the possibility of taking into account the brightness change along the trajectories of the system. A linear model is considered. Directed optimization methods based on the analytical representation of the functional gradient are constructed to search for unknown parameters. The algorithm for displacement field construction with image partitioning into regions (neighborhoods) is proposed. This algorithm can be used to process a variety of image sequences. The results of the algorithm operation on test radionuclide images are presented.
National Identity, Multicultural Receptivity and Awareness of Human Rights Attitudes

The purpose of this study is to reveal the level of national identity, multicultural acceptability, and human rights attitudes of university students, and to analyze differences according to the demographic background. Therefore, this survey was conducted targeting 815 people in their 20s who were enrolled in a university in the metropolitan area. As a result of the study, first, it was found that university students had a higher perception of multicultural acceptance than national identity and human rights attitudes. Second, university students showed significant differences in national identity, multicultural acceptability, and human rights attitudes according to gender, grade level, foreign visit experience, and exchanges with foreigners. This study has a limitation in that it selected university students enrolled in the metropolitan area, and there is a limitation in that it was conducted only as a quantitative study. Nevertheless, it is mean that it analyzed in detail national identity, multicultural acceptability, and human rights attitudes.
Technology Management Framework for Smart University System in the Philippines

In this study, the Smart University came out of the concept of smart cities by applying the principles of smart cities to the operation of university. The Smart University is a vision where the University, as a platform, provides foundational context data to deliver the university of the future. To deal with this reality, the researcher developed a framework for transforming a traditional university into a Smart University, which is more efficient, effective, and has more involvement from both students and teachers, all working together to accomplish the shared goal of improved learning. To achieve this objective, the researchers propose to develop a Technology Management Framework that will address issues and challenges to be considered in establishing and adopting a Smart University in the Philippines. The study will also measure the level of preparedness on Smart Universities among the students, teachers, university administrators, and IT consultant and practitioners that serve in different government agencies. Finally, the paper will also address adaptability of universities and to measure the Level of Smartness of Universities in Metro Manila.
Detecting Privacy Leaks Utilizing Digital Forensics and Reverse Engineering Methodologies

Most commercial iOS forensics and data acquisition tools will effectively do the job in general, but in cases of data leaks presented, we need to look between the lines to extract all related logs and API communication that occurred inside the mobile system. Thus, in this chapter we will investigate the most recent updated tools and use more and more tools to have better evidence about our case.
Modelling and analysis of cascading failures in interoperable critical infrastructure systems

Studying the behaviour and structure of the critical infrastructure systems (CISs) and the interdependency among them is of special significance, and if it is not determined properly, in addition to failure and cascading failure, there will be a national catastrophe. One of the reasons for not using the proper technique for critical infrastructure modelling is not to specify the entire sample space of the system and express all its limitations so that in addition to displaying accurate infrastructure behaviour, cascading failures can be prevented to an acceptable extent. Therefore, there is a need for a CIS formal specification modelling to develop different instances of a CIS, and selecting the best and the failure-free instance of it. In this paper, we provide a formal declarative specification of CIS that supports detection and prevention of failure and cascading failure automatically in using Alloy. Since failures and cascading failures occur in dynamic and time-dependent environments, the proposed model considered dynamic aspects of CISs. The results show that using Alloy Analyser, all the models made and the current failures in infrastructure are determined using counter-examples and the best instance in terms of minimum and even without failure can be selected.
Overview and Analysis of the Text Mining Applications in the Construction Industry

The construction industry is experiencing a remarkable growth in data generation. The major portion of the data in the architecture, engineering and construction (AEC) domains are unstructured textual documents. Text mining (TM) has been introduced to construction industry, in order to extract underlying knowledge from unstructured data. However, there are few articles comprehensively reviewed applications of TM in the AEC domain. Thus, this study adopts a qualitative-quantitative method to conduct a state-of-the-art survey on the articles related to applications of TM in the construction industry and published between 2000 and 2021. VOSviewer software is applied to provide an overview of TM applications regarding to publication trend, active countries and regions, productive authors, and co-occurrence of keywords perspectives. Eight major application fields are identified and analyzed in depth. Five key challenges and three future directions are proposed. This review can help the research community to grasp the state-of-the-art of TM applications in the construction industry and identify the further research areas.
Short Introduction to pytest

pytest is a framework written in Python that helps in creating and automating test cases. It is easy to learn and takes a lot of the work out of writing and managing test cases. In particular, only the logic for the test cases itself needs to be implemented. Unlike some other frameworks, there is no need to learn a large number of functions to set up test assertions; one is enough.
Designing of Financial Time Series Forecasting Model Using Stochastic Algorithm Based Extreme Learning Machine

The financial forecasting research domain includes many directions, out of which forecasting of currency exchange rate attracts many researchers. The researchers have used various neural networks for the development of forecasting models due to the nonlinear and chaotic nature of exchange rate data. Different stochastic optimization algorithms have been hybridized with neural networks for further optimization of the network parameters. In this paper, Extreme Learning Machine (ELM) technique is applied to predict the exchange rate and the recently developed Sine Cosine Algorithm (SCA) is hybridized with this technique. The proposed ELM-SCA model is studied with respect to its prediction performance on a sample dataset for one-day ahead and seven-day ahead prediction. The experimental results demonstrate that the suggested ELM-SCA model produces better results than the ELM model in forecasting of SGD to INR.
Biomimicry in Microwave Photonic and Fiber Optic Sensors Embedded Soft Robotics

Biomimicry offers natural and effective solutions to solve critical challenges in wide range of emerging technologies. This paper introduces several bio-inspired dynamic microwave photonic technologies as well as biomimicry in fiber-optic sensors enhanced soft robotics.
Overview of the Predictive Data Mining Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Determining the area of a letter using Bezier curves of the third order in matrix form

Quantitative analysis of electronic texts is extremely relevant in the light of the avalanche-growing volume of information in the world. Operating with Big Data, works on text attribution, computer semantics and many other disciplines require the ability to quantify the spatial form of a text in a particular implementation. A single letter is a quantum of such calculations. It is convenient to determine the area of a letter using the properties of Bezier curves, which form all the symbols of electronic texts. In this work, we use this mathematical apparatus in matrix form to determine the area of an arbitrary symbol.
Hybrid Clustering Technique to Cluster Big Data in the Hadoop Ecosystem

Big data analytics as well as data mining play vital roles in extracting the hidden statistics. Customary advances for investigation and extraction of hidden information from data may not exert efficiently for big data because of its complex, elevated volume nature. Data clustering is a data mining technique that exacts the useful data from the data by grouping data into clusters. In big data as the data is complex and of very large volume, individual clustering techniques may not consider all the samples, which may lead to inaccurate results. To overcome this inaccuracy, the proposed method is the combination of dynamic k-means and hierarchical clustering algorithms. This proposed method can be called a hybrid method. Being a hybrid method will overcome a few drawbacks like static k value. In this chapter, the proposed method is compared with existing algorithms by using some clustering metrics.
The Legal Protection of National Cyberspace and the COVID-19 Pandemic

The resurgence of cybercrime and threats to the security of the Tunisian national cyberspace during the period of the COVID-19 pandemic represents a new era requiring new responses tailored to this environment. In addition to the protection offered by technology to this space, it is necessary to focus first on the legal protection that involves legal and institutional mechanisms relating to the definition and rules of prosecution and criminal punishments of cyber offenses. It is also a question of analyzing other legal and institutional mechanisms such as the audit of computer security or electronic certification. The examination of the legal protection of the national cyberspace allows in fact to evaluate its effectiveness in particular in this critical period of the COVID-19 pandemic in the light of the best legislative practices in the world in order to detect its insufficiencies and to see which were the answers of the Tunisian public authorities to these limits in order to guarantee a more secure national cyberspace.
Importance of videoconferencing in education and comparison of some types of applications

The given article presents information on the topic of videoconferencing and a comparison of different tools for creating effective online lessons. This study aims to determine the demand for videoconferencing and what conferencing applications could be applied in the process of education. The findings indicate that the main distinguishing feature of those applications is the content that is aimed to be shared and the number of participants. Those applications create opportunities for making the lesson more authentic. Particular attention is paid to the analysis of capacities of three applications as Microsoft Teams, Google Meets, and Zoom.
An Improved Lossless Algorithm for Text Compression

This paper provides a method of lossless, adaptive, and asymmetric text compression at bit-level and comparison among text compression techniques. To increase efficiency, it can be paired with other statistical compression techniques. This is a mathematical approach, where text data is first converted to binary and afterward, utilizing a character word length of 8-bits it additionally compresses the binary codes obtained. Our proposed algorithm, reckons an ideal character word length b holding a condition that b must be greater than 8, which raises the ratio of compression by a factor of, [b/8]. To espouse this algorithm, it is exercised as complementary with the implementation of the Huffman Algorithm to compress a source text file.
Computational Power (C)

Differential privacy implicitly assumes that the attacker has infinite computing power. This is unrealistic in practice, so it is natural to consider attackers with only polynomial computing power. This chapter of the Brief gives an overview of the corresponding notions.
Security Issues and Defenses in Virtualization

Virtualization, the process of allowing efficient utilization of physical computer hardware, is the core of many new technologies. With this comes the importance of understanding the related security aspects to avoid the compromise of underlying resources and services. In this paper, we provide an overview on the two main virtualization architectures and the different types of virtualization approaches related to those architectures. We also review the literature for virtualization security requirements and security attacks. We highlight the latest security techniques proposed in the literature. Due to the growth of cloud computing in the industry, we also discuss virtualization security in the industry. As a result, we have found that the gap between academia and industry has become very small in this field, and more importance should be given to client and service provider responsibility awareness.
Hypothesis Testing

Drawing from the properties of the normal distribution, this chapter introduces key concepts in hypothesis testing, including the Central Limit Theorem, the sampling distribution, the expected value of the mean, and the estimated standard error. These concepts are illustrated through three types of t-tests: one-sample t-test, two-samples t-tests, and dependent samples t-tests.