Reviewerly/exHarmony huggingface dataset:
Research on College Students’ Course Selection Recommendation Model Based on Big Data and Cloud Computing

Abstract When students choose courses online, they need to compare and analyze the difficulty of courses, professional relevance and interest, so as to make choices. Therefore, the decision-making process of course selection is the decisive link of online course selection. In the educational administration management system, the course selection system directly faces students and serves them. According to the past management experience, this system involves many links, including majors, teachers, courses, credits, teaching places, students and so on. By means of modern information technology, it is an important measure in the reform of higher education and teaching to integrate the course selection under the credit system with the information network, and to develop an online course selection system for students that adapts to the credit system management mode, which is also the key to ensure the smooth implementation of the credit system teaching reform. In order to improve the optimal allocation performance of students’ course selection and improve the utilization efficiency of course resources, a design method of students’ optimal course selection scheme model based on big data analysis and cloud computing technology is proposed.
“Your Data Can Go to Anyone”

AbstractThe ubiquity of digital devices and mass user platforms has led to exponential growth in personal data generation. While personal data can be usefully employed to personalize and customize digital experiences and interactions, it can also be used to track and profile individuals for a range of purposes. In this chapter, we report on a two-year research project that worked with over 200 primary school students (aged 8–12 years) in Australia and Uruguay with the goal of developing critical data literacies. The project coupled code-based digital methods and participatory design and involved working with teachers and students to develop an innovative pedagogical approach to this complex social problem. Our findings provide valuable insights into children’s understandings of the technical, social, and cultural issues that shape the development of critical data literacies. While some issues are unique to each cultural context, others are common across Australia and Uruguay. These include: insights into the materiality of personal data and how to work with these new “texts” in educational settings, and the increasing need to bring students, teachers and families together as key agents to help reshape the data literacy field. The chapter concludes by considering how educators and researchers might re-imagine the dominant forms of literacy education to include a context-relevant critical data literacy praxis.
Building Precise Local Submarine Earthquake Catalogs via a Deep-Learning-Empowered Workflow and its Application to the Challenger Deep

Submarine active faults and earthquakes, which contain crucial information to seafloor tectonics and submarine geohazards, can be effectively characterized by precise submarine earthquake catalogs. However, the precise and rapid building of submarine earthquake catalogs is challenging due to the following facts: (i) intense noise in ocean seismic data; (ii) the sparse seismic network; (iii) the lack of historical near-field observations. In this paper, we built a deep-learning-based automatic workflow named ESPRH for automatically building submarine earthquake catalogs from continuous seismograms. The ESPRH workflow integrates Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking, PickNet for phase refinement, REAL for earthquake association and rough location, and HypoInverse, HypoDD for precise earthquake relocation. We apply ESPRH to the continuous data recorded by an array of 12 broadband Ocean Bottom Seismographs (OBS) near the Challenger Deep at the southern-most Mariana subduction zone from Dec. 2016 to Jun. 2017. In this study, we acquire a high-resolution local earthquakes catalog that provides new insights into the geometry of shallow fault zones. We report the active submarine faults by seismicity in Challenger Deep which is the deepest place on Earth. These faults are a significant reference for submarine geological hazards and evidence for serpentinization. Hence, the ESPRH is qualified to construct comprehensive local submarine earthquake catalogs automatically, rapidly, and precisely from raw OBS seismic data.
Decentralized Authorization in Web Services Using Public Blockchain

Web services often determine whether to provide access on their resources to a service requesting entity based on the latter’s credentials, which may not always be available with a single authority. More commonly, there is a need for getting them verified from multiple external sources in a decentralized manner. This kind of architecture is also more robust against security and privacy attacks as compared to a centralized system. However, it is imperative that authorization by the independent sources be done in a transparent and verifiable manner. In this paper, we propose a method for decentralized authorization using the Ethereum blockchain. We consider the underlying authorization model to be Attribute-based Access Control (ABAC) and hence, the credentials to be verified are the attributes of the users making access requests to the web service. In ABAC, a user is granted or denied access to an object based on her attributes as well as those of the requested object using a set of rules (called the ABAC policy). We use a public blockchain, namely Ethereum, for transparent authorization of attributes by multiple sources to allow the web service to take an access decision. It ensures that the authorization data is immutable and helps in building trust between the users, web service providers and attribute certifying authorities. We have made a prototype implementation of our proposed architecture on the Rinkeby Ethereum test network. Extensive experiments show its scalability in realistic scenarios.
On Deep Learning Classification of Digitally Modulated Signals Using Raw I/Q Data

The paper considers the problem of deep-learning-based classification of digitally modulated signals using I/Q data and studies the generalization ability of a trained neural network (NN) to correctly classify digitally modulated signals it has been trained to recognize when the training and testing datasets are distinct. Specifically, we consider both a residual network (RN) and a convolutional neural network (CNN) and use them in conjunction with two different datasets that contain similar classes of digitally modulated signals but that have been generated independently using different means, with one dataset used for training and the other one for testing.
THEORETICAL FUNDAMENTALS OF INNOVATION OF HIGHER EDUCATION IN UKRAINE

Innovative education is the development and expansion of quality education, a vital way to develop the creativity, abilities and talents of high quality students.This should be a key point in the organization of higher education in Ukraine.The introduction of innovative technologies allows to conduct the educational process and develop the system of higher education at a qualitatively new level.Thus, innovative activity in the educational process is aimed at transforming existing forms and methods of teaching and education, creating new goals and means of its implementation, so it is one of the types of productive, creative activities of people.Definitions of the general context of the study of vocational training are organized into the following categories of concepts: «innovative development», «innovation», «innovation process», «innovation», «innovation», «innovation», «intellectualization», «innovation», «innovations», «innovation process».Professional training of future specialists in free economic education should be perceived as a process of forming the readiness of future specialists for professional activity and is a prerequisite for achieving their high competitiveness in the labor market, should cover not only traditional areas but also new substructures, including innovation.Scientists see the essence of the application of innovations in the implementation of new ideas, changes brought to the stage of practical use, and consider them as activities that stimulate the progressive development of the information component through semantic, structural and organizational renewal and technological modernization of all processes.Today the educational space of Ukraine is actively filled with pedagogical innovations.As a result, the higher education system faces the challenge of transitioning to active learning using innovative tools or technologies.The new paradigm of education development necessitates appropriate changes in the professional training of future specialists in higher education.
Subjective Quality Assessment of User-Generated Content Gaming Videos

Benefited from the rapid development of the digital game industry, the growing popularity of online user-generated content (UGC) videos for games has accelerated the development of perceptual video quality assessment (VQA) models specifically for gaming videos. As a novel UGC type, gaming videos are recorded by gamers and uploaded to major streaming media platforms such as YouTube and Twitch, and have been extremely popular among the audience. However, there is little work on VQA research related to gaming videos and understanding their characteristics. In order to promote the development of the gaming VQA model, we created a new UGC gaming video VQA resource, named LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, composed of 600 authentic UGC gaming videos and 18,600 subjective quality ratings collected from an online subjective study. We also compared and analyzed several state-of-the-art (SOTA) VQA models on the new database. To support work in this field, the new database will be publicly available through the link: https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html.
ANDROID-BASED FINANCIAL STATEMENT PRESENTATION MODEL

&lt;p&gt;This study aimed to help improve school financial management by diverting the recording of financial data manually and presenting it manually through an android-based financial report presentation model to Muhammadiyah Vocational Schools in Medan City. The android-based financial report presentation model is carried out using descriptive qualitative methods based on the level of effectiveness and efficiency of using tools in the process of analyzing school financial data and supporting government efforts to reduce community activities outside the home, including education workers in order to reduce the spread of the COVID-19 virus as well as in efforts to reduce the spread of the COVID-19 virus in improving the quality of education through the use and use of technology. This research was carried out in several stages, starting from the stage of determining the problem, finding supporting data, and reaching the stage of data analysis; a model was obtained that was able to improve time efficiency in the presentation of financial statements when this model was applied to Muhammadiyah Vocational Schools in particular and other schools in Indonesia. Medan city and other regions, compared to the manual recording and presentation of financial statements.&lt;/p&gt;
Enabling Multi-Material 3D Printing for Designing and Rapid Prototyping of Deformable and Interactive Wearables

Deformable surfaces with interactive capabilities provide opportunities for new mobile interfaces such as wearables. Yet current fabrication and prototyping techniques for deformable surfaces, that are both flexible and stretchable, are still limited by complex structural design and mechanical surface rigidity. We propose a simplified rapid fabrication technique that utilizes multi-material 3D printing for developing customizable and stretchable surfaces for mobile wearables with interactive capabilities embedded during the 3D printing process. Our prototype, FlexiWear, is a dynamic surface with embedded electronic components that can adapt to mobile body shape/movement and applied to contexts such as healthcare and sports wearables. We describe our design and fabrication approach using a commercial desktop 3D printer, the interaction techniques supported, and possible application scenarios for wearables and deformable mobile interfaces. Our approach aims to support rapid development and exploration of deformable surfaces that can adapt to body shape/movement.
A Quantitative Study of Risk Scores and the Effectiveness of AI-Based Cybersecurity Awareness Training Programs

Cybersecurity awareness training plays a dynamic role for organizations in certifying resources' accessibility. This paper determines the correlation between an employee's risk score and the effectiveness of AI-based security awareness training that deals with cyber threats. The research uses the Unified Theory of Acceptance and Use of Technology to update prior research, revealing that at-risk employees' behavior and information security awareness training implementation make up successful interventions. However, those studies did not discuss AI training, and so this research fills that literature gap. This study used a quantitative research design. The researcher analyzed survey responses using Pearson's Correlation and an independent t-test to determine statistically significant relationships and differences between employees' risk scores and an AI-based security awareness training programs' effectiveness. The calculations came from a sample of 200 participants from two different organizations. The Pearson product correlation of employee's risk scores and the effectiveness of the security awareness training program was statistically significant. The researcher also conducted an independent-samples t-test to compare the employees' risk scores by gender. There were no significant differences in scores. Male was higher than female ones. The mean difference was minimal. The findings herein help interpret the role of information security awareness training in the workplace, promoting behavioral changes that would impede data violations by including the users' vulnerability and the severity of intimidation, and the response to a threat in prognosticating behavior intentions.
VETA: Visual eye-tracking analytics for the exploration of gaze patterns and behaviours

Eye tracking is growing in popularity for multiple application areas, yet analysing and exploring the large volume of complex data remains difficult for most users. We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner. The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration. The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly. The system was motivated by the need to analyse eye-tracking data collected from an 'in the wild' study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains. Results suggest that, thanks to state-of-the-art visualisation techniques and by providing context with videos, our system could enable an improved analysis of eye-tracking data through interactive exploration, facilitating comparison between different participants or conditions, thus enhancing the presentation of complex data analysis to non-experts. This research paper provides four contributions: (1) analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data; (2) a highly dynamic system to visually explore and present complex eye-tracking data; (3) insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community.
Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model

This study examines factors influencing patients' adoption of the IoT for e-Health Management System (e-HMS). A conceptual framework is built by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model to achieve the research objectives. The constructs viz. performance expectations, perceived healthcare threat, facilitation, and trust were employed to analyze the behaviour intention of healthcare IoT devices in the medical context. A questionnaire was prepared and circulated to 210 IoT healthcare users. Further, a synergy of Smart-PLS and ANN was used to determine factors influencing adopting the IoT for e-HMS. The study yields a novel insight that would render vital benefits to users and service providers. The outcomes of this research can assist IoT inventors, medicinal specialists, and vendors to enhance the optimization of e-HMS. The results make some new reporting facts to improve IoT 4.0 practices in the e-healthcare system and structural model assessment.
Paternalistic Leadership and Job Embeddedness With Relation to Innovative Work Behaviors and Employee Job Performance: The Moderating Effect of Environmental Dynamism

The current study strives to examine the determinants of employee innovative work behavior and job performance. Therefore, an integrated research model is developed with the help of paternalistic leadership style and job embeddedness theory to investigate employee behavior toward innovative work behavior. The research model is extended with the moderating effect of environmental dynamism between the relationship of innovative work behavior and employee job performance. Data were collected from 411 employees working in small medium enterprises. For inferential analysis, the structural equation modeling (SEM) technique is used. Results of the structural equation modeling revealed that altogether paternalistic leadership style and factors underpinning job embeddedness theory have explained 52.1% of the variance in employee innovate work behavior. The findings of this research suggest that managers and policy makers should focus on benevolent leadership, moral leadership, and on-the-job embeddedness to boost employee job performance and innovative work behavior.
Quantum channel marginal problem

Given a set of local dynamics, are they compatible with a global dynamics? We systematically formulate these questions as quantum channel marginal problems. These problems are strongly connected to the generalization of the no-signaling conditions to quantized inputs and outputs and can be understood as a general toolkit to study notions of quantum incompatibility. In fact, they include as special cases channel broadcasting, channel extendibility, measurement compatibility, and state marginal problems. After defining the notion of compatibility between global and local dynamics, we provide a solution to the channel marginal problem that takes the form of a semidefinite program. Using this formulation, we construct channel incompatibility witnesses, discuss their operational interpretation in terms of an advantage for a state-discrimination task, prove a gap between classical and quantum dynamical marginal problems, and show that the latter is irreducible to state marginal problems.
Analysis of the Reliability and Validity of the Self-Determination Questionnaire Using Rasch Model

Self-determination can affect students to have a positive way of thinking and acting, also to make realistic choices so they can make a decision responsibly.This study aimed to develop a questionnaire to measure student self-determination and validate it.This study was conducted in 2019, involved 406 university students as participants consisting of 78 males and 328 females, aged 20-24 years in Universitas Pendidikan Indonesia.This study utilized the quantitative approach as its method and there are 18 questionnaires with a 7-point Likert scale as an answer choice.The data analysis used the Rasch model, "i.e." Winsteps software.Study results show that the Cronbach Alpha value, which measures the interaction between the person and items, is 0.96, which falls into the excellent category of performance.In addition, the Person Reliability of 0.94 demonstrates that the respondents have consistently correctly answered questions, even in the outstanding category.The items in the self-determination questionnaire have high reliability so that they can be used to measure the self-determination of university students.
Continuous-variable quantum sensing of a dissipative reservoir

We propose a continuous-variable quantum sensing scheme, in which a harmonic oscillator is employed as the probe to estimate the parameters in the spectral density of a quantum reservoir, within a non-Markovian dynamical framework. It is revealed that the sensing sensitivity can be effectively boosted by (i) optimizing the weight of the momentum-position-type coupling in the whole probe-reservoir interaction Hamiltonian, (ii) the initial quantum squeezing resource provided by the probe, (iii) the noncanonical equilibration induced by the non-Markovian effect, and (iv) applying an external driving field. Our results may have some potential applications in understanding and controlling the decoherence of dissipative continuous-variable systems.
Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings. However, the extent to which this extrapolation relies on robust reasoning is unclear. In this paper, we investigate how well these models reason with terms that are less frequent in the pretraining data. In particular, we examine the correlations between the model performance on test instances and the frequency of terms from those instances in the pretraining data. We measure the strength of this correlation for a number of GPT-based language models (pretrained on the Pile dataset) on various numerical deduction tasks (e.g., arithmetic and unit conversion). Our results consistently demonstrate that models are more accurate on instances whose terms are more prevalent, in some cases above $70\%$ (absolute) more accurate on the top 10\% frequent terms in comparison to the bottom 10\%. Overall, although LMs exhibit strong performance at few-shot numerical reasoning tasks, our results raise the question of how much models actually generalize beyond pretraining data, and we encourage researchers to take the pretraining data into account when interpreting evaluation results.
Migrating CUDA to oneAPI: A Smith-Waterman Case Study

In order to tackle the programming challenges related to heterogeneous computing, Intel recently introduced oneAPI, which is a new programming environment that allows code developed in the Data Parallel C++ (DPC++) language to be run on different devices such as CPUs, GPUs, and FPGAs, among others. To handle CUDA-based legacy codes, oneAPI provides a compatibility tool (dpct) that facilitates the migration to DPC++. In view of the large amount of existing CUDA-based software in the bioinformatics context, this paper presents our experiences porting SW#db, a well-known sequence alignment tool, to DPC++ using dpct. From the experimental work, it was possible to prove the usefulness of dpct for SW#db code migration and the cross-vendor GPU, cross-architecture portability of the migrated DPC++ code. In addition, the performance results showed that the migrated DPC++ code reports similar efficiency rates to its CUDA-native counterpart, or even better in some tests (by approximately 5%).