Reviewerly/exHarmony huggingface dataset:
Are These Requirements Risky: A Proposal of an IoT-Based Requirements Risk Estimation Framework

Internet of Things (IoT) systems are revolutionizing traditional living to a new digital living style. In the past, a lot of investigations have been carried out to improve the technological challenges and issues of IoT and have focused on achieving the full potential of IoT. The foremost requisite for IoT software system developers seeking a competitive edge is to include project-specific features and meet customer expectations effectively and accurately. Any failures during the Requirements Engineering (RE) phase can result in direct or indirect consequences for each succeeding phase of development. The challenge is far more immense because of the lack of approaches for IoT-based RE. The objective of this paper is to propose a requirements risk management model for IoT systems. The method regarding the proposed model estimates requirements risk by considering both customers’ and developers’ perceptions. It uses multiple criteria using intuitionistic fuzzy logic and analytical technique. This will help to handle the uncertainty and vagueness of human perception, providing a well-defined two-dimensional indication of customer value and risk. The validity of the approach is tested on real project data and is supported with a user study. To the best of our understanding, literature lacks the trade-off analysis at the RE level in IoT systems and this presented work fills this prerequisite in a novel way by improving (i) requirements risk assessment for IoT systems and (ii) handling developers’ subjective judgments of multiple conflicting criteria, yielding more concrete and more observable results.
User-Defined Interaction Using Everyday Objects for Augmented Reality First Person Action Games

In this paper, we present an elicitation study to explore how people use everyday objects for augmented reality first person games. 24 participants were asked to select items from a range of everyday objects to use as controllers for three different classes of virtual object. Participants completed tasks using their selected items and rated the experience using the Augmented Reality Immersion (ARI) questionnaire. Results indicate no strong consensus linking any specific everyday object to any virtual object across our testing population. Based on these findings, we recommend developers provide the ability for users to choose the everyday objects they prefer.
ESAC Initiative

The ESAC Initiative is a community of international library practitioners who share data and best practices in order to promote the transition of scholarly publishing to open using transformative agreements. ESAC views transformative agreements as one phase of the transition to open and to a publishing landscape where scholarship will be available to all in a sustainable and equitable manner. The ESAC website shares the outputs of the community's collaborative efforts and provides information on how to participate in the community.
College Sports Multimedia Network Based on Wireless Communication and Deep Learning Network Environment

At this stage, network information technology has been fully popularized and widely used in various fields and has achieved remarkable results. In the development of physical education courses in colleges and universities, the effective use of multimedia and network-assisted teaching can greatly improve the physical quality of college students, stimulate students’ interest in physical education courses, and promote students’ all-round development. Therefore, after the teaching is over, comparative analysis is used to compare the teaching effects of the computer network educational pattern and the previous educational pattern to determine the suitability of the university sports multimedia network system. The experiment proved that the fertility and health test scores of the reference team have been partially improved (the fertility and health test scores of the reference team increased from 3.05 points to 3.87 points), and the fertility and health test scores of the experimental group students have been greatly improved (the fertility and health test scores of the experimental group increased from 3.24 points to 5.58 points), which shows that the multimedia network teaching has broken passing the constraints of times and spatial limitations. Students can learn sports knowledge even in extracurricular time. It complements the traditional teaching methods of precepts and deeds and is the development trend of modern physical education.
DIGITAL TECHNOLOGIES IN PROMOTING THE SERVICES OF COMPANIES FROM THE FIELD OF ONLINE EDUCATION: ASSESSING THE EMOTIONAL EFFECTIVENESS OF TOOLS

The article is devoted to the analysis of the emotional effectiveness of digital tools in promoting the services of companies from the field of online education. Digital technologies include many tools for promoting goods and services of companies. Each of these tools has a number of features, and in different situations different methods of promotion turn out to be effective. In order to build marketing strategies, companies need to understand the key tools of digital promotion, understand the specifics of using each of them. The study identified the key tools of digital promotion, their features, advantages and disadvantages. The market of online education in Russia has been analyzed, the target audience of educational products and companies in this area have been identified. An analysis of the results of an empirical study of users on their attitude to the tools for promoting educational products was carried out. As part of the study, a number of author's hypotheses were identified, which were tested using statistical analysis. The practical testing of hypotheses was carried out on the basis of a survey by the method of questioning respondents (Internet questionnaires) - individuals. The assessment of the attitude of citizens of the Russian Federation to the use of digital marketing tools when promoting services was studied. Respondents were asked questions with one answer, with several answers, questions based on the Likert scale. Data processing was carried out in the SPSS Statistics program. Recommendations are given to companies from the field of online education to promote their goods and services based on the studied theory and empirical research.
Linear-Time Sampling on Signed Graphs Via Gershgorin Disc Perfect Alignment

In graph signal processing (GSP), an appropriate underlying graph encodes pairwise (anti-)correlations of targeted discrete signals as edge weights. However, existing fast graph sampling schemes are designed and tested for positive graphs describing only positive correlations. In this paper, we show that for datasets with inherent strong anti-correlations, a suitable graph structure is instead a signed graph with both positive and negative edge weights, and in response, we propose a linear-time signed graph sampling method. Specifically, given an empirical covariance data matrix ${\mathbf{\bar C}}$, we first employ graphical lasso to learn a sparse inverse matrix $\mathcal{L}$, interpreted as a generalized graph Laplacian for signed graph $\mathcal{G}$. We then propose a fast signed graph sampling scheme containing three steps: i) augment $\mathcal{G}$ to a balanced graph ${\mathcal{G}_B}$, ii) align all Gershgorin disc left-ends of corresponding Laplacian ${\mathcal{L}_B}$ at smallest eigenvalue ${\lambda _{\min }}\left( {{\mathcal{L}_B}} \right)$ via similarity transform ${\mathcal{L}_p} = {\mathbf{S}}{\mathcal{L}_B}{{\mathbf{S}}^{ - 1}}$, leveraging a recent linear algebra theorem called Gershgorin disc perfect alignment (GDPA), and iii) perform sampling on ${\mathcal{L}_p}$ using a previous fast Gershgorin disc alignment sampling scheme (GDAS). Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on two political voting datasets.
Open-source platform controls qubits QICKer

QICK (Quantum Instrumentation Control Kit) unifies a quantum information processing system and its control platform, resulting in high-performance, low-cost qubit manipulation
Blind Crosstalk Correction for Color Fringe Projection Profilometry Combining the Sine and Cosine Phase-Shifting Patterns

Color crosstalk is challenging for color fringe projection profilometry, which will cause color imbalance and phase-shift deviation, thus resulting in phase error. To address this challenge, this paper presents a blind crosstalk correction method for color phase-shifting fringe projection profilometry without crosstalk calibration. Three channels of the color fringe are firstly normalized to resolve color imbalance. The phase error induced by phase-shift deviation between three channels doubles the frequency of the color fringe. Therefore, the original cosine color fringe is shifted by quarter period to generate another sine color fringe to compensate the periodic phase error. Both simulations and experiments were carried out, and their results confirmed the effectiveness and accuracy of the proposed method.
Designing with the Mind in Mind

UI design rules and guidelines are not simple recipes. Applying them effectively requires determining rule applicability and precedence and balancing trade-offs when rules compete. By understanding the underlying psychology, designers and evaluators enhance their ability to apply design rules. This two-part (140-minute) course explains that psychology. This course will be presented online only.
Analyzing the Role of User Experience for Continued Usage on the Internet of Medical Things.

Covid-19 has thrown a lot of challenges to address the gaps in our existing healthcare ecosystem, especially in the remote areas, where providing quality healthcare services is a challenge. Digital healthcare or the internet of medical things is viewed as a new age weapon to provide healthcare services to the masses at an affordable price. One such way is by connecting the end-user with physicians through mobile applications or devices, however, this demands thorough research and validation to understand the underlying needs of the users and physicians interacting in this connected eco-system In this study our focus is to identify the role of ‘ user experience for continued usage on the internet of medical things ’ or connected device eco-system. Through this study, we intend to probe and analyze the role of user interaction and user experience (UI/UX) in discovering the features and services provided through connected devices and mobile applications and their subsequent usage over a prolonged period of time. This study aims to present a theoretical framework utilizing the UTAUT-2 framework to hypnotize the identified problem statement.
Grid Search CV Implementation in Random Forest Algorithm to Improve Accuracy of Breast Cancer Data

Breast cancer is the most common cancer in women and is the second leading cause of global death. Disease diagnosis plays an important role in determining treatment strategies related to patient safety. Therefore, we need machine learning to predict disease. This paper aims to determine the best parameter values in breast cancer data using the Grid Search CV method and classify breast cancer data using the random forest algorithm. In addition, the paper aims to compare the accuracy values generated using the Grid Search CV and without the Grid Search CV. The method used to analyze breast cancer data in researchers is the Random Forest (RF) classification algorithm. In addition to using the Random Forest algorithm, this study also uses the Grid Search CV method. Grid Search CV is a method used to determine the optimal model parameters so that the classifier can predict the test data reliably. This study indicates that the highest accuracy value is obtained in the random forest algorithm using the grid search method of 0.9545. In contrast, the accuracy of the random forest algorithm without using the grid search method is 0.9480. For further research, it is suggested to develop a breast cancer dataset using the grid search cv method with other algorithms, such as Logistic Regression, Xgboost, and SVM. We can also use the same algorithm with different datasets to prove that the grid search cv method can increase accuracy.
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model

Many recent studies on large-scale language models have reported successful in-context zero-and few-shot learning ability.However, the in-depth analysis of when in-context learning occurs is still lacking.For example, it is unknown how in-context learning performance changes as the training corpus varies.Here, we investigate the effects of the source and size of the pretraining corpus on in-context learning in HyperCLOVA, a Korean-centric GPT-3 model.From our in-depth investigation, we introduce the following observations: (1) incontext learning performance heavily depends on the corpus domain source, and the size of the pretraining corpus does not necessarily determine the emergence of in-context learning, (2) in-context learning ability can emerge when a language model is trained on a combination of multiple corpora, even when each corpus does not result in in-context learning on its own, (3) pretraining with a corpus related to a downstream task does not always guarantee the competitive in-context learning performance of the downstream task, especially in the few-shot setting, and (4) the relationship between language modeling (measured in perplexity) and in-context learning does not always correlate: e.g., low perplexity does not always imply high in-context few-shot learning performance.
Towards a Systematic Threat Modeling Approach for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have attracted the research fraternity due to the prevalent use of this technology in various domains such as health, automation, and the military. The WSN consists of numerous sensor nodes deployed randomly. As these networks are deployed in a real-world scenario, security and privacy are of utmost concern. Security protects the wireless network against an adversary. Firstly design the security, identification of security threats, the security design for suggestive countermeasures is designed. Threat and attack modeling are well-known techniques for designing appropriate security solutions. Threat modeling helps in the identification of dangers that might lead to security compromise. Attack modeling includes an understanding of the threat and will help in designing the proper mitigation mechanism. This article focuses on various threat modeling approaches for identifying the threat. Finally, the article concludes with the comparative analysis of various approaches such as STRIDE, DREAD, PASTA, TRIKE.
Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

The industrial control data set has many features and large redundancy, which has a certain impact on the training speed and classification results of the neural network anomaly detection algorithm. However, features are independent of each other, and dimension reduction often increases the false positive rate and false negative rate. The feature sequencing algorithm can reduce this effect. In order to select the appropriate feature sequencing algorithm for different data sets, this paper proposes an adaptive feature sequencing method based on data set evaluation index parameters. Firstly, the evaluation index system is constructed by the basic information of the data set, the mathematical characteristics of the data set, and the association degree of the data set. Then, the selection model is obtained by the decision tree training with the data label and the evaluation index, and the suitable feature sequencing algorithm is selected. Experiments were conducted on 11 data sets, including Batadal data set, CICIDS 2017, and Mississippi data set. The sequenced data sets are classified by ResNet. The accuracy of the sequenced data sets increases by 2.568% on average in 30 generations, and the average time reduction per epoch is 24.143%. Experiments show that this method can effectively select the feature sequencing algorithm with the best comprehensive performance.
Prevention and Treatment of Sports Injuries and Rehabilitative Physical Training of Wushu Athletes

This paper is aimed at exploring the characteristics of research on prevention and treatment of sports injuries and rehabilitation physical training of Wushu athletes. It also considers the application of rehabilitation physical training in the rehabilitation of Wushu athletes. By searching literature, conducting questionnaires, and combining mathematical statistics, it studies the injury prevention and rehabilitation training of Wushu athletes. This paper chooses the level of first class and above of sports, and a total of 50 elite male and female Wushu athletes were systematically trained as subjects of study. Athletes, aged 15 to 20 years, were trained for 2 to 5 years, 35 male athletes and 15 female athletes. Different from traditional rehabilitation therapy, athletes' physical rehabilitation training is also different from traditional sports rehabilitation treatment. By evaluating the physical condition of athletes, the causes of sports injuries were analyzed, to formulate special rehabilitation training programs and carry out athletes' rehabilitation training targeted and purposeful. Record the experimental data and analyze the experimental results. The experimental results show that physical rehabilitation training can make athletes avoid the influence of unsafe factors of sports injury, improve the safety of training, and effectively prevent sports injury. The experimental results show that physical rehabilitation training combined with rehabilitation medicine has obvious advantages, which can make Wushu athletes recover quickly without sequelae.
Directions Selection for Assessing the Quality of Electronic Learning Tools for the Organization of the Educational Process of the Higher Educational Process

The article deals with issues related to the assessment of the quality of e-learning tools (ELT), its design and application in practice. The main attention is paid to the issue of assessing the availability of certain functional capabilities in the ELT, which make it possible to most effectively solve didactic tasks, as well as to motivate teachers and students. The purpose of the article is to identify the main directions for assessing the quality of electronic learning tools and to substantiate the possibility of a comprehensive assessment of the level of functionality of the ELT. Methodology. ELT as a complex information object is decomposed into simpler elements, to which various quality assessment methodologies can be applied - expert, cognitive-linguistic, methods of statistical information processing and artificial intelligence. In conclusion, it is concluded that the assessment of the quality of e-learning tools can be carried out in two ways: the assessment of content as a text in terms of the completeness of the content with the definition of a number of parameters of this assessment and the assessment of “functionality” in terms of the possibility of implementing certain functions. The totality of the selected areas, indicating the assessment tools, will determine the criteria for the quality of e-learning tools.
Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users' risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.
Modified Hill Cipher Algorithm using Myszkowski Transposition to address Known-Plaintext attack

Abstract: Hill Cipher algorithm is a Polygraphic cipher known to be vulnerable to the Known-Plaintext attack. It is a type of attack wherein the attacker has access to a pair of Plaintext and Ciphertext. Possible leaked information can be used to reveal critical data which can include the private key that is used in the encryption of the plaintext. In this study, the researchers propose an improved scheme by incorporating Myszkowski transposition in the algorithm to address the specified problem. Runtime Test and Statistical test such as the Avalanche effect were used to evaluate the performance of the algorithm. The test yielded a 25.70% faster runtime based on previous modifications and a 52.78% Avalanche Effect. Randomness test such as the Monobit test, Runs Test and Longest Run of Ones in a Block test were applied to evaluate the ciphertext produced. The criterion for the Randomness Test requires its p-value to be greater than 0.01, the evaluation showed a 0.249, 0.167 and 0.368 p-value, respectively. The proposed scheme proved that the algorithm is cryptographically secure and efficient as all tests yielded favorable results. Keywords: Cryptography, Hill Cipher, Encryption, Decryption, Key Generation
Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

Purpose The intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios. Design/methodology/approach We propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites. Findings Using the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection. Originality/value This paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.