Reviewerly/exHarmony huggingface dataset:
Towards large-scale programmable silicon photonic chip for signal processing

Abstract Optical signal processing has been playing a crucial part as powerful engine for various information systems in the practical applications. In particular, achieving large-scale programmable chips for signal processing are highly desirable for high flexibility, low cost and powerful processing. Silicon photonics, which has been developed successfully in the past decade, provides a promising option due to its unique advantages. Here, recent progress of large-scale programmable silicon photonic chip for signal processing in microwave photonics, optical communications, optical computing, quantum photonics as well as dispersion controlling are reviewed. Particularly, we give a discussion about the realization of high-performance building-blocks, including ultra-low-loss silicon photonic waveguides, 2 × 2 Mach–Zehnder switches and microring resonator switches. The methods for configuring large-scale programmable silicon photonic chips are also discussed. The representative examples are summarized for the applications of beam steering, optical switching, optical computing, quantum photonic processing as well as optical dispersion controlling. Finally, we give an outlook for the challenges of further developing large-scale programmable silicon photonic chips.
Legal guidelines for identifying online illegal fund-raising crimes in China

Purpose This paper aims to elucidate the responsiveness of China’s judicial system in addressing the challenges of identifying online illegal fund-raising crimes that have emerged in recent years. This study systematically evaluates the efficacy and potential pitfalls of legal guidelines contained in judicial interpretations, such as holistic determination, sampling verification and presumption of the nature of funds. In addition, the research endeavors to propose pertinent recommendations for refining the existing judicial rules. Design/methodology/approach This research mainly uses a doctrinal methodology, focusing on the principal judicial interpretations formulated by the Supreme People’s Court and other central judicial entities in China. The scope encompasses the realm of online illegal fund-raising crimes as well as other cybercrimes. The analytical framework involves a comprehensive examination of these authoritative judicial documents, coupled with a theoretical and critical analysis of relevant academic materials. Findings This research underscores that while judicial interpretations serve as an effective legal strategy to confront the challenges posed by online illegal fund-raising crimes, their implementation introduces a nuanced landscape. These legal guidelines, often emanating from diverse judicial departments and tackling specific issues, carry the inherent risk of giving rise to new complexities and fostering inconsistency. Judicial authorities shall exercise prudence in both the formulation and application of these guidelines, ensuring their harmonization with existing legal norms and fundamental legal principles. Originality/value This research constitutes a critical and comprehensive examination of judicial interpretations in China pertaining to online illegal fund-raising crimes. It offers valuable insights into the country’s judicial interpretation system and its legal responses to financial crimes. The paper serves as a valuable resource for academics, law enforcement professionals, policymakers, legislators and researchers.
Towards a dynamic earthquake risk framework for Switzerland

Abstract. Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised, and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the Swiss Seismological Service (SED) using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include operational earthquake (loss) forecasting (OE(L)F), earthquake early warning (EEW), ShakeMaps, rapid impact assessment (RIA), structural health monitoring (SHM), and recovery and rebuilding efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. This paper may serve as a reference guide for other countries wishing to establish similar services for seismic risk reduction.
From Authority Work to Wikidata (Via Wikipedia): Implementing a Workflow for Original Catalogers

This is a practical reflection on how a library cataloger can learn to create Wikidata items based on Slavic language publications, and to maintain the practice as part of the workflow. We describe our experience at the University of Toronto Robarts Library, and the path to adopting a working model for regularly contributing Wikidata items based on the cataloger's original authority work for NACO.
Discrimination between right‐ and left‐handed writers based on sister lines

Forensic document examiners are often confronted with questioned documents written with ballpoint pens. Depending on the force applied (or pressure) as well as the angle between the pen and the surface, sister lines running parallel to the inked strokes can be left by the lip of the housing ball. In a real case, sister lines were observed on the left side of inked strokes of a questioned signature. To assess whether the writer of that signature was a left-handed or a right-handed writer based on this result, an experimental study was carried out. Handwritten samples and signatures from 182 right-handed and 18 left-handed writers were collected with different ballpoint pens. For every writer, the presence or absence of sister lines was recorded. Sister lines were observed in 49% of the writers of the studied population (in 50% of the right-handed and ca. 39% of the left-handed writers). Most individuals who left sister lines showed a writing angle of 50°-55°. The location of sister lines was tabulated to inform probabilities of observing sister lines at a given location, if the writer is a right-handed or left-handed writer. In the case at hand, a likelihood ratio of 48 was obtained in support of the proposition of a left-handed writer, rather than a right-handed writer. Applying Bayes' theorem, such value moves the prior probability of 15% that the writer of the questioned signature is left-handed to a posterior probability of 89%.
Building Better Theories: Prediction Intervals as a Tool for Theory Testing and Improvement

The precision with which a theory predicts behavior speaks both to the quality of that theory and to its potential utility in real-world applications. Unfortunately, predictive precision is frequently overlooked when evaluating theories in social psychology. Here we call attention to the benefits of placing more emphasis on predictive precision. We define what we mean by predictive precision, consider its role in theory evaluation from a philosophy of science perspective, and suggest a method for quantifying it: by constructing prediction intervals. A prediction interval is the range of values within which an empirical observation (e.g., a sample mean) is expected to fall some specified percentage (e.g., 95%) of the time. The size of a prediction interval reflects the degree of fluctuation in the empirical observations expected by theory, and so the precision of its prediction. Prediction intervals are useful because they simplify theory testing and provide a metric by which theory improvement—and so scientific progress—can be gauged. Prediction intervals are easily created when theories are expressed formally as agent-based models. We illustrate the process of creating and using prediction intervals in three detailed examples, each involving a different agent-based model concerned with the behavior of small interacting groups.
Relevance and creativity – a linear model

Purpose The purpose of this paper is to provide a new and useful formulation of relevance. Design/methodology/approach This paper is formulated as a conceptual argument. It makes the case for the utility of considering relevance to be function of use in creative processes. Findings There are several corollaries to formulating relevance as a function of use. These include the idea that objects by themselves cannot be relevant since use assumes interaction; the affordances of objects and how they are perceived can affect what becomes relevant but are not in themselves relevant; relevance is not an essential characteristic of objects; relevance is transient; potential relevance (what might be relevant in the future) can be distinguished from what is relevant in use and from what has been relevant in the past. Originality/value The paper shows that its new formulation of relevance brings improved conceptual and terminological clarity to the discourse about relevance in information science. It demonstrates that how relevance is articulated conceptually is important as its conceptualization can affect the ways that users are able to make use of information systems and, by extension, how information systems can facilitate or disable the co-production of creative outcomes. The paper also usefully expands investigative opportunities by suggesting relevance and creativity are interrelated.
A revisit of the normalized eight-point algorithm and a self-supervised deep solution

Abstract The normalized eight-point algorithm has been widely viewed as the cornerstone in two-view geometry computation, where the seminal Hartley’s normalization has greatly improved the performance of the direct linear transformation algorithm. A natural question is, whether there are and how to find other normalization methods that can further improve the performance for each input sample. In this paper, we provide a novel perspective and propose two contributions to this fundamental problem: (1) we revisit the normalized eight-point algorithm and make a theoretical contribution by presenting the existence of different and better normalization algorithms; (2) we introduce a deep convolutional neural network with a self-supervised learning strategy for normalization. Given eight pairs of correspondences, our network directly predicts the normalization matrices, thus learning to normalize each input sample. Our learning-based normalization module can be integrated with both traditional (e.g., RANSAC) and deep learning frameworks (affording good interpretability) with minimal effort. Extensive experiments on both synthetic and real images demonstrate the effectiveness of our proposed approach.
The Philosophy and Foundations of Vocational Technology in Education with IT Integration

The development of Information Technology (IT) has had a significant impact on vocational education in this modern era. This article aims to explore the relationship between philosophy and the foundations of vocational technology with curriculum development, teaching methods, and evaluation in the educational context. The main focus is on the importance of understanding the philosophical underpinnings of vocational technology and how philosophy can guide the implementation of information technology in vocational education. Through this analysis, it is hoped that new ways can be found to enrich students' learning experiences and prepare them for future challenges in a workforce increasingly intertwined with technology.
From Reddit to manifestos: Forensic evaluation of incel online activity

Abstract Forensic evaluators are increasingly called upon to review online collateral information, including social media posts, web forum posts, chat histories, and other sources such as manifestos. This information is especially vital when assessing members of a virtual community such as that of the involuntary celibate, or incel community. While this new wealth of information can add valuable context to the forensic assessment, it presents unique challenges for the evaluator including challenges with authenticity and interpretation. This article will present an approach to evaluations of such collateral, including a review of the relevant empirical research in this area and touch upon important areas to consider in the forensic evaluation of incel online activity.
Design of Unified Modeling Language Information System for Motorcycle Unit Selling and Buying Transactions using the Waterfall Method

This research aims to design an information system for buying and selling motorbike units using the Waterfall method. The information system for buying and selling motorbike units has an important role in supporting motorbike sales business activities, from ordering to the payment process. The design of this system was carried out by following the Waterfall method approach which consists of several stages, namely requirements analysis, system design, implementation, testing and maintenance. The needs analysis stage involves identifying business and system user needs, including required functions and system requirements. Next, system design is carried out by designing the structure, interface and transaction process flow using the notation and methods provided by the Waterfall method. In implementation, an information system for buying and selling motorbike units is developed based on the design that has been created. Then, testing is carried out to verify the success of the system in carrying out its functions and ensure conformity with the specified needs. After that, the system is ready for use and maintenance is carried out to maintain the performance and reliability of the system on an ongoing basis. By applying the Waterfall method in designing a motor unit buying and selling transaction information system, it is hoped that it can produce a system that is structured, has functions that suit business needs, and is tested overall. The Waterfall method also allows developers to have a clear understanding of each design stage before moving on to the next, thereby minimizing the risk of errors and deficiencies in the system.
Intelligent Path Planning for Cognitive Mobile Robot Based on Dhouib-Matrix-SPP Method

The Mobile Robot Path Problem looks to find the optimal shortest path from the starting point to the target point with collision-free for a mobile robot. This is a popular issue in robotics and in this paper the environment is considered as static and represented as a bidirectional grid map. Besides, the novel optimal method Dhouib-Matrix-SPP (DM-SPP) is applied to create the optimal shortest path for a mobile robot in a static environment. DM-SPP is a greedy method based on a column row navigation in the distance matrix and characterized by its rapidity to solve sparse graphs. The comparative analysis is conducted by applying DM-SPP on thirteen test cases and comparing its results to the results given by four metaheuristics the Max-Min Ant System, the Ant System with punitive measures, the A* and the Improved Hybrid A*. The outcomes acquired from different scenarios indicate that the proposed DM-SPP method can rapidly outperform the four predefined artificial intelligence methods.
The effect of employee motivation and employee engagement on job performance mediated by job satisfaction

This study aims to investigate the effect of employee motivation and employee engagement mediated by job satisfaction on job performance. This study employs explanatory research with a quantitative approach. The population in this study consisted of all employees of retail company X. The sampling technique used was saturated sampling, and the number of research samples was 150. Data was collected through surveys and documentation, and the instrument was tested using Likert scales. The data was analysed using Structural Equation Model Partial Least Squares (SEM-PLS) modelling. The results of this study indicate that employee motivation can increase job performance, while employee engagement cannot increase job performance. Furthermore, the findings revealed that employee motivation can increase job satisfaction while employee engagement cannot. Job satisfaction can increase job performance. Job satisfaction can mediate the effect of employee motivation on job performance, but it cannot mediate the effect of employee engagement on job performance. According to the findings of this study, Company X should be able to provide motivational stimulus to employees by increasing salaries and work incentives, as well as increasing training and developing employee skills, so that workers can work effectively and efficiently.
Improving the prediction of social media engagement in universities by utilizing feature selection in machine learning

This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.
FSKT‐GE: Feature maps similarity knowledge transfer for low‐resolution gaze estimation

Abstract The limited of texture details information in low‐resolution facial or eye images presents a challenge for gaze estimation. To address this, FSKT‐GE (feature maps similarity knowledge transfer for low‐resolution gaze estimation) is proposed, a gaze estimation framework consisting of both a high resolution (HR) network and low resolution (LR) network with the identical structure. Rather than mere feature imitation, this issue is addressed by assessing the cosine similarity of feature layers, emphasizing the distribution similarity between the HR and LR networks. This enables the LR network to acquire richer knowledge. This framework utilizes a combination loss function, incorporating cosine similarity measurement, soft loss based on probability distribution difference and gaze direction output, along with a hard loss from the LR network output layer. This approach on low‐resolution datasets derived from Gaze360 and RT‐Gene datasets is validated, demonstrating excellent performance in low‐resolution gaze estimation. Evaluations on low‐resolution images obtained through 2×, 4×, and 8× down‐sampling are conducted on two datasets. On the Gaze360 dataset, the lowest mean angular errors of 10.97°, 11.22°, and 13.61° were achieved, while on the RT‐Gene dataset, the lowest mean angular errors of 6.73°, 6.83°, and 7.75° were obtained.
Assessing the impact and challenges of AI-based language models on the education sector: a proposal for new assessment strategies and design

This paper examines the potential impact of AI-based language models on education, with a specific focus on the ChatGPT model developed by OpenAI. The study begins by providing an overview of the current state of AI in education, including the challenges and opportunities presented by these models. It then delves into an analysis of ChatGPT, including its capabilities, strengths and limitations. The paper also conducts a thorough analysis of potential implications and offers suggestions for assessment design to prevent students from utilising AI-based language models in educational environments. Finally, the paper concludes with a set of recommendations for assessment design, considering the specific characteristics of ChatGPT and the potential impact of AI-based language models on tourism education.
Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy

Path planning for mobile robots poses a challenging optimization problem, requiring the discovery of a near-optimal path within diverse constraints. Conventional particle swarm optimization (PSO) algorithms encounter limitations in solving constrained problems, vulnerability to local optima, and premature convergence. To address these challenges, this paper proposes a bi-population PSO algorithm with a random perturbation strategy (BPPSO), which divides particles into two subpopulations. The first subpopulation enhances global search capabilities by considering the quality of particles and the optimal solution of a randomly selected particle when updating velocities. The second subpopulation strengthens local search using a linear cognitive coefficient adjustment strategy. Moreover, a counter tracks iteration without improvement in the global best position. Upon reaching a predefined threshold, random perturbation is added to the positions of all particles in both subpopulations, increasing diversity and enhancing the ability to escape local optima. The performance of BPPSO was experimentally validated across three benchmark functions and four environment models. The results have demonstrated that the proposed BPPSO outperforms existing PSO algorithms and other established path planning algorithms in terms of path quality and running time, highlighting the feasibility of BPPSO in resolving the challenge of mobile robot path planning.
Challenges of Requirements Engineering in Agile Projects: A Systematic Review

The practice of Requirements Engineering (RE) within Agile development contexts markedly differs from that within traditional development process. As the RE discipline in Agile Software Development continues to evolve, this research highlights and assesses various challenges to refine its impact on software product development. This study describes inductive and empirical research on Agile RE challenges and practices based on an analysis of data collected in the binding academic and scientific literature. This research is also based on the guidelines of a Systematic Review of the Literature (SRL) proposed by Kitchenham. Our results indicate eight type of challenges that we grouped into four themes: suitability delimitations, on software product development projects, the formation of the development team, and the efficient participation of the client during the process. Finally, this paper contributes detailed insights into RE challenges specific to Agile project development, evaluates their resolution extent, and outlines existing research gaps in RE practices