Reviewerly/exHarmony huggingface dataset:
Quantum computing fidelity susceptibility using automatic differentiation

Automatic differentiation is an invaluable feature of machine learning and quantum machine learning software libraries. In this work it is shown how quantum automatic differentiation can be used to solve the condensed-matter problem of computing fidelity susceptibility, a quantity whose value may be indicative of a phase transition in a system. Results are presented using simulations including hardware noise for small instances of the transverse-field Ising model, and a number of optimizations that can be applied are highlighted. Error mitigation (zero-noise extrapolation) is applied within the autodifferentiation framework to a number of gradient values required for computation of fidelity susceptibility and a related quantity, the second derivative of the energy. Such computations are found to be highly sensitive to the additional statistical noise incurred by the error mitigation method
Analysis of the Questions Trend in the Transportation Technology area of the Secondary School Technology Teacher Recruitment Examination

Objectives This study attempted to understand the change and trend of content by analyzing questions in the transportation technology area in the secondary school technology teacher recruitment examination.&#x0D; Methods This study was conducted in the order of ‘literature review’, ‘data analysis criteria setting’, ‘data coding’, and ‘data analysis’. The subject of analysis is the questions on the secondary schooltechnology teacher recruitment examination from 2002 to 2022, and the evaluation area and content elements presented by the Korea Curriculum Evaluation Institute were used as the analysis criteria.&#x0D; Results First, in the secondary school technology teacher recruitment examination, the number of questions in the transportation technology area accounted for about 13.9% of the total questions and the total score was about 13.5%, and the ratio of points in the transport technology field gradually decreased as the evaluation period changed. Second, the questions in the transportation technology area differed by evaluation field, and the proportion of automobile technology field was the highest, followed by energy technology field and transportation technology basic field. Third, the questions in the transportation technology area differed by evaluation area, and among the evaluation content elements, the structure and principle of internal combustion engine cars(T321).&#x0D; Conclusions First, considering the importance of transportation technology and the balance between evaluation areas, it is necessary to newly adjust the ratio of questions in the transport technology area. Second, it is necessary to move away from automobile-centered evaluation and set questions in a wider range of transportation technology areas in line with the content system of the recent curriculum. Third, it is necessary to actively reflect the rapidly changing trend of modern transportation technology along with the revision of the curriculum in the recruitment examination.
Analysis of perception, requirement for AI education and AI teaching efficacy for secondary pre-service teacher

Objectives The purpose of this study is to suggest direction for the AI education plan at the university of education through the analysis the perception, requirement for AI education and AI teaching efficacy of secondary pre-service teachers.&#x0D; Methods The survey was conducted by sampling students from K University's College of Education in Gyeongsang nam-do by grade and major. As a research tool, development questions in the areas of AI interest, understanding, and need through review of previous studies, and artificial intelligence teaching efficacy measurement tools (AI-TEBI) were used. Finally, 198 respondents were analyzed. Chi-square test, independent t-test, and one-way ANOVA were used to analyze differences by gender, major, and grade.&#x0D; Results First, the level of understanding of AI education was low, and the level of awareness of interest and necessity was high. Second, the demand for AI utilization education was higher than understanding education, and there were differences by major and grade. Third, among the sub-elements of AI teaching efficacy, individual teaching efficacy on AI and interaction with AI were low, and expectations for AI concept recognition and AI teaching results were high.&#x0D; Conclusions The AI education experience of secondary pre-service teacher affected the positive perception of AI education. According to this result, it is necessary to operate an appropriate systematic curriculum of AI understanding and utilizing education to enhance AI teaching efficacy by major and grade. The results of this study will be used as basic data to set a plan how to conduct AI education for all majors in college of education.
Collective turnover response over time to a unit-level shock.

This work provides a theoretical explanation for the mechanisms that can drive collective turnover in response to a unit-level shock by applying event systems theory to collective turnover. Specifically, we recognize the importance of modeling a disruption phase following a shock, the social mechanisms that influence the collective turnover response, and boundary conditions on the impact of the shock on the collective turnover response. We examine collective turnover following 239 general manager departures in a large U.S. retailer from 2012 to 2014 to observe how a unit-relevant shock affects the collective turnover response across time. In doing so, we identify and explain a potential delay before the disruption phase and the cumulative abnormal voluntary turnover that occurs in the disruption phase following a unit-level shock. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Physics, motions and prediction of earthquakes from rock mechanics perspective

Earthquakes are known to be one of the natural disasters resulting in the huge losses of human lives as well as of properties as experienced in the recent great earthquakes. Ground motion characteristics, deformation and surface breaks of earthquakes depend upon the causative faults. Earthquakes are due to the temporary instability of earth’s crust resulting from the stress state changes. While the accumulation of stresses takes for very long period, which is called stick phase, the stress release occurs in a few seconds to 500-600 seconds and it is called the slip-phase. Physics, motions and prediction of earthquakes are generally considered as a topic of earth science. As Earth’s crust is made of rock mass, it is quite natural to expect to address this issue from the perspective of rock mechanics. This keynote attempts to provide such a unified treatise on the principles of rock mechanics.
Modelling the Quantification of Technical Debt

Technical Debt (TD) captures the consequences of sub-optimal decisions made during the development of a software product. If not managed properly TD can be detrimental to the product in the long-term. To manage TD it needs to be quantified (measured). Many approaches to quantify TD have been proposed, but evaluating them is difficult. We propose TDQM --- the Technical Debt Quantification Model, that captures the important concepts in TD quantification and their relationship. TDQM has been used to compare and evaluate proposed quantification approaches. TDQM will also be used to advance our knowledge about TD quantification.
Foundationally Sound Annotation Verifier via Control Flow Splitting

We propose VST-A, a foundationally sound program verifier for assertion annotated C programs. Our approach combines the benefits of interactive provers as well as the readability of annotated programs. VST-A analyzes control flow graphs and reduces the program verification problem to a set of straightline Hoare triples, which correspond to the control flow paths between assertions. Because of the rich assertion language, not all reduced proof goals can be automatically checked, but the system allows users to prove residual proof goals using the full power of the Coq proof assistant.
Innovation and Digital Competence: A Bibliometric Analysis

To integrate ICT into the classroom critically, creatively, and safely, teachers require more than operational technology proficiency. They need the development of digital competence as a dynamic element of innovation in virtual classes. From this perspective, the objective of this study was to determine the current status of documents that relate innovation and digital competence. To this end, a bibliometric analysis of 172 investigations published in Web of Science was conducted. It was found that 54% of papers were carried out during the pandemic period (2020–2021) and 74% were published in the open access modality. The journal Sustainability presents the largest body of researches. Spain is the country with the highest production and Universidad de Pablo de Olavide has the largest number of publications. In the analysis of co-authorships by country, Spain has the greatest influence. It is concluded that there is a growing accessible and collaborative research, especially between European countries, although with an increasing intervention of Latin American countries. Moreover, the existence of research recurrence on the positive effects of innovation in digital competence of teachers and students to adequately integrate ICT in higher education is recorded.
Safe Value Functions

Safety constraints and optimality are important but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to maintain formal guarantees, it is also common practice in reinforcement learning (RL) to simply modify reward functions by penalizing failures, with the penalty treated as a mere heuristic. We rigorously examine the relationship of both safety and optimality to penalties, and formalize sufficient conditions for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe value functions (SVFs):</i> value functions that are both optimal for a given task, and enforce safety constraints. We reveal this structure by examining when rewards preserve viability under optimal control, and show that there always exists a finite penalty that induces an SVF. This penalty is not unique, but upper-unbounded: larger penalties do not harm optimality. Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact. This insight suggests practical, theory-guided heuristics to design reward functions for control problems where safety is important.
An Adaptive Vector-Based Vehicles Detection for Urban Intersection Camera Sensors Under Nighttime Illumination

Vehicles detection via surveillance sensors is an intractable and essential task under nighttime urban intersection scenes. To efficiently resolve the problem that most of the current color or texture feature-based vehicles detection models easily suffer contamination from dynamic and sudden or gradual illumination variations on nighttime, an adaptive vector-based background model without complex artificial texture feature is proposed for vehicles detection in night intersection surveillance scenes. For each pixel of image, a series of filters are employed to take the values from the past at the same location and its neighborhood, and a vector is assigned to each filter to represent the neighborhood region information of the pixel. Then, the series of vectors comprising the background model and the vector of the current pixel is compared to the vectors of the background model based on the theorem of linear dependence to describe whether that pixel belongs to foreground or background. Eventually, to adapt the dynamic scenes, a random update scheme is employed to update the model. As our experimental results demonstrated in real-world nighttime urban intersection surveillance scenarios, the proposed model attains superior vehicles detection performance compared to other state-of-the-art algorithms based on extensive qualitative and quantitative evaluations.
Key performance indicators (KPIs) in Ghanaian university libraries: developing outcome-based metrics for staff performance

Purpose This study aims to audit the key performance indicators (KPIs) in Ghanaian university libraries. It aimed at finding out the KPIs used by Ghanaian university libraries and how effective they have been in correcting lapses in staff performance. Design/methodology/approach Employing a quantitative research approach, the views of 218 librarians were elicited from nine universities in Ghana. Due to the manageable size of staff population in the libraries, the census sampling technique was adopted. Findings Results indicated even though staff members were unaware of KPIs in their libraries, most of the respondents understood the concept and perceived is to be measuring the success and tracking performance in libraries. It was also revealed that most of the libraries did not have clear and measurable KPIs. Close to 80% of the libraries under study did not have clearly stated KPIs of which members of staff were aware. The study revealed that KPIs may differ from one library to the other, depending on the mission and vision statement of the library. For increased staff performance in Ghanaian university libraries the study recommends management to develop clear and measurable outcome-based metrics that will lead to high performance. This will not only address the needs of clients but also improve the libraries' efficiency in terms of information provision. Management should also have clear and measurable KPIs and make them available to library staff. Practical implications For increased staff performance in Ghanaian university libraries the study recommends management to develop clear and measurable outcome-based metrics that will lead to high performance. This will not only address the needs of clients but also improve the libraries' efficiency in terms of information provision. Management should also have clear and measurable KPIs and make them available to library staff. Originality/value The study provides information on KPIs in Ghanaian university libraries and how to effectively deal with the challenges associated with its usage.
The Self-destructive Behavioural Effects of Virtual Addiction on Cyber Crime Scene Investigation of Victimless Crimes

Virtual addiction-based activity analysis is challenging because of the vast amount of data exchange and the expansion of widespread smartphone use. The reality is that smartphones are a dynamic activity platform with multiple benefits that everybody utilizes, and people have become heavily reliant on them. A massive amount of personal data is kept on cell phones, which can be used to forecast user behaviour using log functions, and other keyword storing and mapping methodological approaches. Hackers can decode information and cause damage by examining the behaviour pattern. However, this is a third-party attack; our debate is about self-destructive conduct, such as virtual addiction, that impacts cybercrime. This study's primary goal is to examine how numerous addictions affect human cognition and cause painful personal health via virtual media-based addictive uses such as smartphones, virtual reality games, and television. This virtual consumption is strongly linked to emotional connection, human habit formation, and compulsive addictive behaviour. The study's primary insight is that virtual addiction is self- destructive, contributing to numerous victimless 118crimes. The original study premise is that when overwhelming addictive behaviours were investigated, it was found that illegal activity may result in self- destructive behaviour, such as attempted suicide, loss of money and possessions, biological and behavioural unfavourable cognition, anxiety, nervousness, and a lack of self-esteem.
Blackchain-based Identity Management Systems in Digital Forensics

The collection, storage, and analysis of big data has become an enormously crucial solution for addressing cybercrime and establishing legal cases since improvements in information advancing technologies during the last two decades. In our interconnected, digitalized society, digital identification is becoming increasingly vital. Identity management solutions are commonly used in real-world applications and are designed to make managing digital identities and activities like verification easier. There have been initiatives in recent times to offer blockchain-based access control solutions that enable individuals to share control of their own identities. A legal technique for collecting, analysing, storing, and reporting digital evidence is known as digital forensics. This type of evidence is critical to police investigations since it depicts the facts of computer hackers’ scenes and links suspected to criminal activity. As a result, it's critical to treat obtained evidence with care to make sure that it's acceptable in police investigations and court proceedings, as well as to avoid tampering or wrongdoing. Current studies have turned to blockchain technology to promote 242transparency, immutability, and auditability for managing such le gitimate evidence. In this chapter, we create a concrete blockchain-based authentication and authorization framework. The Hyperledger Composition proof of concept is also supplied, and its efficiency is reviewed. With the range of improvement for a comprehensive end-to-end program, the model has proven reasonable overhead in terms of performance and resource consumption.
Blockchain-based Privacy Preservation Technique for Digital Forensics Records

Electronic records of forensic evidence are legally admissible in courts all around the world. Electronic forensic evidence is becoming more widely shared among government agencies so that the investigators can receive a complete view of a case's history. However, maintaining an accurate evidence record and avoiding loss of vital information remains a challenge. Hence the adoption of suitable techniques in this field is a major requirement as they will make information available to authorized users promptly and securely. This research aimed to develop and evaluate a blockchain-based technique that addresses the issues of electronic evidence security, user data rights, and data integrity. In the proposed blockchain-based e-evidence record management system, the Ethereum blockchain is used to create a smart contract that contains a public key for a record saved in the decentralized InterPlanetary file system (IPFS). A proxy re-encryption theme is employed to preserve the user's privacy. In addition, we built and deployed smart contracts to manage business logic for the network's member organizations. Furthermore, this chapter proposes an access 212control policy algorithm for enhancing information accessibility between legal departments. The prototype system has been developed to implement and test the performance of the proposed technique. Police investigators can utilize the system to retrieve the case data, and validate the authorized user's consent to view it, according to the findings. The access log is stored in a visible and unassailable manner in the blockchain, which is used for audits. The proposed blockchain-based system achieves better results in terms of latency, throughput, and average storage time when compared to traditional evidence record maintenance systems that use client-server architecture.
Forensic Analysis of Online Social Network Data in Crime Scene Investigation

Online social networks (OSNs) have become a part of our everyday lives. The information created in OSNs will be examined when criminals misuse this information. Criminological evidence was assembled by analysing OSNs using online digital forensics tools. Analysing OSNs will help computerised criminology investigators forecast, identify, and prevent crimes in various fields. In mobile device forensics, forensic methods are used to recover digital evidence from mobile devices using forensically sound techniques. This chapter discusses the crimes on social networking sites, phishing, cyberbul-lying, baiting with a link, and digital evidence analysis. We discuss how the user can securely use OSNs. Moreover, we discuss how forensics departments can create evidence and submit it to the law.
Transformation of ecological-resource and ecological-geochemical functions of the lithosphere within the Stary Oskol–Gubkin mining district of the Kursk Magnetic Anomaly

The impact of mining production on the natural environment shows itself in many ways: through changes in the chemical and mechanical composition of the atmosphere, deformation of the earth&rsquo;s surface, activation of exogenous geomorphological processes, violation of hydrological regime, surface water and groundwater alteration, degradation of soil and vegetation cover, disruption of functioning (vital activity) of living organisms&mdash;from bacteria to humans. The purpose of this work was to assess the main trends of transformation of the ecological-resource and ecological-geochemical functions of the lithosphere within the Stary Oskol&ndash;Gubkin mining area of the Kursk Magnetic Anomaly. This area is a key site for such research in the central part of Russia, which is due to its natural and manmade features: the initial high quality of the natural environment, the high population density&mdash;36 people / km2, and the extremely high level of induced load as a result of the mineral mining and processing activities. The research methods included field observations, as well as soil sampling at 12 key sites and at three main rivers of the district. The ecological and geochemical studies were carried out using the quantitative atomic spectral analysis. In the process of research, the tendency of significant alkalinization of soils and surface waters was revealed to exceed background values by 30&ndash;50%. Extraction and processing of iron ore has led to a significant degradation of the ecological and resource function, which is manifested in the removal of soil cover over the areas of tens of km2. The main polluting elements are indicated, including Zn, Pb, Mo, Co, Cr, B, Cu. The trend of transformation of the macro- and mesocomponent composition of surface waters was recorded. The content of calcium, sodium, chlorides exceeds the similar indicators in other rivers by 2 to 10 times, and this excess in terms of the content of sulfates is 70 times.
Stable Control and Disturbance Rejection Strategy for Planar 2R Underactuated Robot via Intelligent Algorithm

Stable control and active disturbance rejection strategy is proposed for planar 2R underactuated robot via intelligent algorithm in this paper. At first, we build the dynamic model and describe the control characteristic. After that, we design PD controller for the active link. According to the underactuated constraint, we then optimize the parameters of controller by using the differential evolution algorithm (DEA), so that we can ensure the target states of all links could eventually be reached at the identical time. And the simulation results indicate the strategy is effective.