Improving the risk management process in quality management systems of higher education

Abstract: The purpose of this paper is to improve the risk management process in the quality management system of higher education, taking into account the hazardous factors that increase the probability of occurrence and severity of consequences of undesirable events, as well as favorable factors that reduce the probability of occurrence and severity of consequences of hazardous events. The basis of risk management in the quality management systems of higher education institutions is the "Bowtie" method, which involves six main steps of identifying inconsistency, determining the impact of hazardous and favorable factors according to the impact group, ranking hazardous and favorable factors, calculating risk, substantiating precautionary measures and checking calculations. To rank hazardous and favorable factors, the authors used the "Decision Making Trial and Evaluation" method (hereinafter-DE-MATEL), which is based on paired comparison and decision-making tools based on graph theory. An improved process is proposed for risk assessment, which differs from the known ones by the presence of the identification of the cause-and-effect relationship "hazard (inconsistency)-hazardous event-consequences", identification of hazardous and favorable factors of the internal and external environment that affect the probability and/or the degree of severity of a hazardous event-the appearance of an inconsistency, which is carried out after the inconsistency has been determined; determination of causal hazardous and favorable factors by an acceptable method. Registers of inconsistencies (hazards), hazardous and favorable factors have been developed and proposed based on the requirements for accreditation of educational programs and the international standard ISO 9001:2015, which will allow, based on a risk-oriented approach, to provide a basis for setting the goals of a higher education institution under martial law in order to guarantee effective implementation of the mission and strategy. They are proposed for decision-making in the quality management systems of educational organizations on the substantiation of precautionary or corrective measures based on ranking the risks from identified inconsistencies, which are determined taking into account the impact of the entire set of identified significant hazardous and favorable factors. The value of this paper is to improve the quality risk management process in educational organizations, taking into account the impact of hazardous and favorable factors, and to develop an appropriate step-by-step algorithm of actions and a risk assessment form.


Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study

Abstract: Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening.Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts.This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening.The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts.


Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

Abstract: A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.


Towards large-scale programmable silicon photonic chip for signal processing

Abstract: 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

Abstract: 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.


Quantum Neural Network model for Token allocation for Course Bidding

Abstract: Quantum computer has shown the advantage over the classical computer to solve some problems using the laws of quantum mechanics. With a combination of knowledge of machine learning and quantum computing, Quantum neural networks adapted the concept from classical neural networks and apply parameterized quantum gates as neural network weights. In this paper, we present an application of quantum neural networks with real-world data to predict token price used in a course bidding system. The experiments were carried out on the Qiskit quantum simulator. The result shows that quantum neural networks can achieve a good prediction result compared to the classical neural network. The best model configuration has the lowest RMSE 6.38%. This approach opens an opportunity to explore the benefit of quantum machine learning in many research fields in the future.


Towards a dynamic earthquake risk framework for Switzerland

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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: 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

Abstract: 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: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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: 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

Abstract: 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

Abstract: 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

Abstract: 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


Wavelet-based information theory in quantitative assessment of AFM images’ quality

Abstract: The quantitative assessment of the image quality produced by atomic force microscopy (AFM) is an ongoing and challenging task. In our study, we demonstrate Shannon's application of information theory for measuring image quality. Specifically, we propose quantifying the loss of image information due to the various distortion processes by exploring the relationship between image information based on the information channel capacity (ICC), spectral image representation, and visual quality. Since the ideal image is unavailable, the power and noise spectrum, the critical input information for the image quality evaluation, must be robustly estimated in the proposed method. The classical, most popular Welch method for spectral estimation uses an average of several windowed periodograms and can produce biased spectrum estimates. Therefore, in our work, we discuss an alternative technique based on the wavelet transform that can be applied to solve this challenging problem, specifically in the case of noisy, uncertain AFM measurements. Finally, we validate the performance of the enhanced ICC-wavelet-based algorithm with noisy measurement AFM data.


WaterPairs: a paired dataset for underwater image enhancement and underwater object detection

Abstract: Abstract Due to its importance in marine engineering and aquatic robotics, underwater image enhancement works as a preprocessing step to improve the performance of high-level vision tasks such as underwater object detection and recognition. Although several studies exhibit that underwater image enhancement algorithms can boost the detection accuracy of detectors, no work has focused on studying the relationship between these two tasks. This is mainly because current underwater datasets lack either bounding box annotations or high-quality reference images, based on which detection accuracy or image quality assessment metrics are calculated. To examine how underwater image enhancement methods affect underwater object detection tasks, we provide a large-scale underwater object detection dataset with both bounding box annotations and high-quality reference images, namely, the WaterPairs dataset. The WaterPairs dataset offers a platform for researchers to comprehensively study the influence of underwater image enhancement algorithms on underwater object detection tasks. We will release our dataset at https://github.com/IanDragon/WaterPairs once this paper is accepted.


Multimodal abstractive summarization using bidirectional encoder representations from transformer with attention mechanism

Abstract: In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism. The purpose of the video summarization is to increase the speed of searching for a large collection of videos so that the users can quickly decide whether the video is relevant or not by reading the summary. Initially, the data is obtained from the publicly available How2 dataset and is encoded using the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder and the Long Short Term Memory (LSTM) encoder. The textual data which is embedded in the embedding layer is encoded using a bidirectional GRU encoder and the features with audio and video data are encoded with LSTM encoder. After this, BERT based attention mechanism is used to combine the modalities and finally, the BI-GRU based decoder is used for summarizing the multimodalities. The results obtained through the experiments that show the proposed MAS-BERT has achieved a better result of 60.2 for Rouge-1 whereas, the existing Decoder-only Multimodal Transformer (D-MmT) and the Factorized Multimodal Transformer based Decoder Only Language model (FLORAL) has achieved 49.58 and 56.89 respectively. Our work facilitates users by providing better contextual information and user experience and would help video-sharing platforms for customer retention by allowing users to search for relevant videos by looking at its summary.


Redox conditions of Datangpo-type manganese ores constrained by statistical analysis of pyrite framboids and iron isotopes

Abstract: Datangpo-type manganese ores which originated in the Cryogenian Datangpo Formation, are overlaid and underlaid by Nantuo diamicite and Tiesi'ao silistones, respectively. A thorough understanding of the manganese mineralization process is currently limited by the continued uncertainty regarding the metallogenic redox condition of the Mn-bearing rock series. This study employed statistical analysis of pyrite framboid sizes and iron isotope (δ56Fe) to determine the redox conditions and constrain the mineralization process of Datangpo-type manganese ores in South China, considering the Gaolou in Chongqing, Yanglizhang in Guizhou, and Minle and Zhenxing in Hunan as examples. The results showed that Tiesi'ao siltstones mainly developed relatively larger diameter (7.29–7.68 μm) framboidal pyrites, whereas mudstones in the Datangpo Formation developed relatively smaller diameter (2.63–5.56 μm) framboidal pyrites. Furthermore, manganese ores produce non-framboidal pyrites. In three profiles, framboidal pyrite concentrations were found to be negatively correlated with Mn contents. These characteristics suggested that siltstones, mudstones, and manganese ores were deposited in oxic-anoxic, anoxic-euxinic, and oxic-anoxic conditions, respectively. The δ56Fe of whole rocks (δ56FeWR) exhibited a range from −0.73 ‰ to +0.48 ‰ (average of −0.32 ‰), whereas pyrite (δ56FePy) varied from +0.03 ‰ to +0.83 ‰ (average of +0.36 ‰). Furthermore, δ56FePy in manganese ore (average = +0.27 ‰) were lighter than those in mudstone (average = +0.49 ‰). These results of this study demonstrated that oxidation deposition played a role in pyrite formation, with manganese ores exhibiting more oxic conditions compared to mudstones. The findings of this study suggested that idiomorphic pyrite formation was similar to that of manganese ore, in which Mn precipitated in the form of manganese (hydro)oxides under oxic seawater, and then converted into rhodochrosite under anoxic sulfidic diagenetic conditions, with the involvement of microbes. This research has the potential to enhance comprehension regarding redox conditions and the regulation of sedimentary manganese ores via redox transformation.


Blockchain in asset management: An extensive review of opportunities and challenges

Abstract: Blockchain technology has emerged as a transformative force within the financial sector, particularly in asset management. This paper provides a comprehensive review of the opportunities and challenges associated with integrating blockchain in asset management practices. The opportunities presented by blockchain in asset management are multifaceted. The decentralized and immutable nature of blockchain enables increased transparency and security in the tracking and transfer of assets. Smart contracts, a key feature of blockchain, automate and streamline various processes, reducing operational costs and minimizing the risk of errors. This technology also facilitates real-time settlement and enhances liquidity, contributing to increased efficiency in asset management workflows. However, along with these opportunities come substantial challenges. Regulatory uncertainties, interoperability issues, and the need for industry-wide standards are significant hurdles that the asset management sector must overcome to fully harness the potential of blockchain. Security concerns related to the custody and protection of digital assets, as well as the scalability of blockchain networks, also demand careful consideration. Moreover, the integration of blockchain in asset management necessitates a paradigm shift in organizational structures and industry practices. Stakeholders must adapt to new ways of managing and transferring assets, embracing the cultural and technological changes inherent in blockchain adoption. Education and collaboration among industry participants are crucial for navigating the complexities of this transformative technology. In conclusion, this extensive review highlights the immense potential of blockchain in revolutionizing asset management while shedding light on the formidable challenges that need to be addressed for successful implementation. As the industry continues to explore and adopt blockchain solutions, collaboration and proactive measures will be essential in maximizing the benefits and mitigating the risks associated with this groundbreaking technology.


A survey on multi-agent reinforcement learning and its application

Abstract: Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper presents a comprehensive survey of MARL and its applications. We trace the historical evolution of MARL, highlight its progress, and discuss related survey works. Then, we review the existing works addressing inherent challenges and those focusing on diverse applications. Some representative stochastic games, MARL means, spatial forms of MARL, and task classification are revisited. We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications. We also address critical operational aspects, such as hyperparameter tuning and computational complexity, which are pivotal in practical implementations of MARL. Afterward, we make a thorough overview of the applications of MARL to intelligent machines and devices, chemical engineering, biotechnology, healthcare, and societal issues, which highlights the extensive potential and relevance of MARL within both current and future technological contexts. Our survey also encompasses a detailed examination of benchmark environments used in MARL research, which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios. In the end, we give our prospect for MARL and discuss their related techniques and potential future applications.


The Effect of Storytelling Learning Using Role Playing Model Assisted by Audiovisual Media on Students Written and Storytelling Skills of Students Grade V Elementary School in Pringsurat Sub-District Temanggung District

Abstract: The Role Playing learning approach aims to make learning more meaningful for students. The Role Playing learning approach, combined with multimedia resources, stands out as a chosen and viable strategy for overcoming emerging challenges. In this study, experimental research with a quantitative approach was applied to assess the impact of using storytelling learning models paired with various media on storytelling skills and writing stories in a fifth-grade elementary school class. Based on the assessment process that has been carried out, the results in this study are an assessment of typical differences in comprehension tests between experimental and control groups supporting the acceptance of H1 versus H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. Therefore, this study ensures that the Role Playing learning model has a significant influence on students' storytelling skills. Similarly, the evaluation of typical differences in comprehension tests between the experimental group and the control group also favored the acceptance of H1 over H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. In summary, role playing has a significant influence on students' storytelling skills. Assessment of typical differences in comprehension tests between the experimental group and the control group led to acceptance of H1 versus H0. With a bidirectional significance probability value of 0.005, H1 is accepted, and H0 is rejected because 0.000 is less than 0.05. In conclusion, Role Playing significantly affects students' ability to tell stories and write stories. Keywords: role playing, storytelling skills, story writing skills, audio-visual media.


Experimental Integration of Quantum Key Distribution and Post‐Quantum Cryptography in a Hybrid Quantum‐Safe Cryptosystem

Abstract: Abstract Quantum key distribution (QKD) and post‐quantum cryptography (PQC) are the two counter measures against cryptographic attacks via quantum computing. While QKD offers information theoretic security but limited authentication scalability, PQC facilitates scalable authentication in high density networks but is not information theoretic secure. Therefore, an ideal quantum‐safe framework should efficiently leverage the complementarity of both techniques. However, despite growing efforts in integrating both, current realizations have focused on channel authentication, and a complete cryptosystem addressing both hybrid authentication and hybrid key exchange is yet to be demonstrated. Here, an authenticated hybrid key exchange protocol is introduced that incorporates PQC and QKD in a modular and information‐theoretic secure architecture. The quantum‐safe protocol is inherently resilient to catastrophic cryptographic failures and provides both forward and post‐compromise security. As proof‐of‐concept implementation, the cryptosystem on a QKD hardware prototype is integrated, with the QKD processing, PQC key exchange and secret state masking via physical unclonable functions (PUFs) all running on a single field programmable gate array (FPGA). This work paves the way for the deployment of versatile and modular quantum‐safe networks that exploit the complementarity of PQC and QKD.


Multiple Dimensions of Machismo: Linguistic Considerations for Latino Sexual Minority Men in the United States

Abstract: The literature differentiates between two domains of machismo: traditional machismo and caballerismo. Research has largely focused on measuring machismo among English speakers. We evaluated whether Estrada’s (2011) 2-factor model of machismo was invariant across language (English vs. a direct Spanish translation). A series of multigroup confirmatory factor analyses were conducted between respondents who completed the survey in English ( n = 428) and Spanish ( n = 102). Analyses suggested the hypothesized 2-factor model did not fit across language groups. While the traditional 2-factor structure emerged in the English language data, exploratory factor analysis indicated a 3-factor structure of machismo among Spanish-speaking respondents. One of the new factors (inherent machismo), among Spanish-speaking respondents, was associated with internalized heterosexism, suggesting that the new factor structure may capture the belief that masculine men are superior. These findings suggest there is a need for the development of culturally appropriate Spanish language assessment.


KolamNetV2: efficient attention-based deep learning network for tamil heritage art-kolam classification

Abstract: Abstract In India, kolam, commonly referred to as rangoli, is a traditional style of art. It involves using rice flour, chalk, or coloured powders to create elaborate patterns and motifs on the ground. Kolam is a common daily ritual in many regions of India, especially in South India, where it is seen as a significant cultural tradition and a means to greet visitors. Unfortunately, as a result of people’s hectic lives nowadays, the habit of drawing kolam on a regular basis is dwindling. The art of making kolam patterns is in danger of disappearing as so many individuals no longer have the time or space to do it on a regular basis. Therefore, it is imperative that ancient art be conserved and digitally documented in order to enlighten our next generation about kolam and its classifications. Deep learning has become a powerful technique because of its ability to learn from raw image data without the aid of a feature engineering process. In this article, we attempted to understand the types of Kolam images using the proposed deep architecture called KolamNetV2. KolamNetV2 comprises EfficientNet and attention layers, ensuring high accuracy with minimal training data and parameters. We evaluated KolamNetV2 to reveal its ability to learn the various types in our challenging Kolam dataset. The experimental findings show that the proposed network achieves fine enhancement in performance metrics viz, precision-0.7954, recall-0.7846, F1score-0.7854 and accuracy-81%. We compared our results with state-of-the-art deep learning methodologies, proving the astounding capability. Graphical Abstract


Information Tools for Organizing Distance Education Within the Framework of Digitalization of the Educational Process

Abstract: The article is devoted to the study of information means of an educational institution for the organization of distance education within the framework of digitalization of the educational process, which sets out the principles and means of effective functioning of distance education. Specialized automated information systems developed with the active use of computer and telecommunications tools make it possible to globally increase the efficiency of distance learning. The purpose of this work is to draw conclusions based on the results of a study of information means for organizing distance education using automated information systems. The results of the study allow us to conclude that the effective functioning of the mechanism of continuous education using information systems contributes to the implementation of educational activities of students, ensuring an improvement in the quality of professional competencies applicable in the professional practice of a future specialist. The results of the study allow us to conclude that the effective functioning of the mechanism of continuous education using information systems contributes to the implementation of educational activities of students, ensuring an improvement in the quality of professional competencies applicable in the professional practice of a future specialist.


Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation

Abstract: This paper addresses few-shot semantic segmentation and proposes a novel transductive end-to-end method that overcomes three key problems affecting performance. First, we present a novel ensemble of visual features learned from pretrained classification and semantic segmentation networks with the same architecture. Our approach leverages the varying discriminative power of these networks, resulting in rich and diverse visual features that are more informative than a pretrained classification backbone that is not optimized for dense pixel-wise classification tasks used in most state-of-the-art methods. Secondly, the pretrained semantic segmentation network serves as a base class extractor, which effectively mitigates false positives that occur during inference time and are caused by base objects other than the object of interest. Thirdly, a two-step segmentation approach using transductive meta-learning is presented to address the episodes with poor similarity between the support and query images. The proposed transductive meta-learning method addresses the prediction by first learning the relationship between labeled and unlabeled data points with matching support foreground to query features (intra-class similarity) and then applying this knowledge to predict on the unlabeled query image (intra-object similarity), which simultaneously learns propagation and false positive suppression. To evaluate our method, we performed experiments on benchmark datasets, and the results demonstrate significant improvement with minimal trainable parameters of 2.98M. Specifically, using Resnet-101, we achieve state-of-the-art performance for both 1-shot and 5-shot Pascal-[Formula: see text], as well as for 1-shot and 5-shot COCO-[Formula: see text].


Parallel GEMM-based convolution for deep learning on multicore RISC-V processors

Abstract: Abstract We address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication ( gemm ) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm .


Gender-transformative action, social norms and economic empowerment

Abstract: Feminist advocacy for ‘gender transformative’ approaches to development, public policy and humanitarian action that account for social norms has surged in recent years. This article intervenes in the debate around norms and implications for transformative approaches. We draw on a unique set of quantitative, global ‘gender data’ collected in 2020 and 2021 and examine how social norms inform women’s experiences of economic empowerment, as well as how these relationships map onto the current debates around interventions to address social norms and the form these interventions ought to take. Our data show that social norms matter for access to and control over resources; in addition, they illustrate that an individual belief in gender equality is fairly common around the world but that such individual beliefs frequently do not coincide with what people think their neighbours believe. These findings suggest a need for consideration of factors beyond individual attitudes towards and beliefs in gender-transformative interventions for women’s economic empowerment.


@llegra: a chatbot for Vallader

Abstract: Abstract Extinct and endangered languages have been preserved primarily through audio conservation and the collection and digitization of scripts and have been promoted through targeted language acquisition efforts. Another possibility would be to build conversational agents like chatbots or voice assistants that can master these languages. This would provide an artificial, active conversational partner which has knowledge of the vocabulary and grammar and allows one to learn with it in a different way. The chatbot, @llegra, with which one can communicate in the Rhaeto-Romanic idiom Vallader was developed in 2023 based on GPT-4. It can process and output text and has voice output. It was additionally equipped with a manually created knowledge base. After laying the conceptual groundwork, this paper presents the preparation and implementation of the project. In addition, it summarizes the tests that native speakers conducted with the chatbot. A critical discussion elaborates advantages and disadvantages. @llegra could be a new tool for teaching and learning Vallader in a memorable and entertaining way through dialog. It not only masters the idiom, but also has extensive knowledge about the Lower Engadine, that is, the area where Vallader is spoken. In conclusion, it is argued that conversational agents are an innovative approach to promoting and preserving languages.


APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

Abstract: Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants.


Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification

Abstract: In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware executable adversarial examples of PE, we conduct two white-box attacks, Jacobian-based Saliency Map Attack (JSMA) and Carlini and Wagner attack (C&W). An adversarial payload was injected into the DOS header, and a section was added to the file to preserve the PE functionality. The attacks successfully evaded the CNN model with a 91% evasion rate, whereas the RNN model evaded attacks at an 84.6% rate. Two defense mechanisms based on distillation and training techniques are examined in this study for overcoming adversarial example challenges. Distillation and training against JSMA resulted in the highest reductions in the evasion rates of 48.1% and 41.49%, respectively. Distillation and training against C&W resulted in the highest decrease in evasion rates, at 48.1% and 49.9%, respectively.


Agent-Guided Non-Local Network for Underwater Image Enhancement and Super-Resolution Using Multi-Color Space

Abstract: The absorption and scattering of light in water usually result in the degradation of underwater image quality, such as color distortion and low contrast. Additionally, the performance of acquisition devices may limit the spatial resolution of underwater images, resulting in the loss of image details. Efficient modeling of long-range dependency is essential for understanding the global structure and local context of underwater images to enhance and restore details, which is a challenging task. In this paper, we propose an agent-guided non-local attention network using a multi-color space for underwater image enhancement and super-resolution. Specifically, local features with different receptive fields are first extracted simultaneously in the RGB, Lab, and HSI color spaces of underwater images. Then, the designed agent-guided non-local attention module with high expressiveness and lower computational complexity is utilized to model long-range dependency. Subsequently, the results from the multi-color space are adaptively fused with learned weights, and finally, the reconstruction block composed of deconvolution and the designed non-local attention module is used to output enhanced and super-resolution images. Experiments on multiple datasets demonstrated that our method significantly improves the visual perception of degraded underwater images and efficiently reconstructs missing details, and objective evaluations confirmed the superiority of our method over other state-of-the-art methods.


Pure Decoherence of the Jaynes–Cummings Model: Initial Entanglement with the Environment, Spin Oscillations and Detection of Non-Orthogonal States

Abstract: A model based on pure decoherence for the Jaynes–Cummings spin–boson system, coupled through its integral of motion to an infinite bosonic bath, is proposed and examined. The properties of the spin oscillation process suggest an initial entanglement between the environment and the spin–boson degrees of freedom. The study demonstrates that the potential applicability of the Jaynes–Cummings model in detecting non-orthogonal bosonic states is preserved in the presence of pure decoherence.


SEGCN: a subgraph encoding based graph convolutional network model for social bot detection

Abstract: Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler-Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.


AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Abstract: The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.


Efficient astrodynamics-informed kinodynamic motion planning for relative spacecraft motion

Abstract: In this paper, we present a computationally efficient sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state while avoiding collisions with obstacles and minimizing the total fuel required for the transfer. In our previous work, we presented an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that can regularly find fuel-efficient and collision-free transfer solutions for relative spacecraft motion – without requiring an initial guess of the solution. In this work, we improve the computation efficiency of the AIKMP algorithm. We demonstrate that by using a linearized Lambert solution (LLS) instead of a full Lambert solution as the steering law for the developed motion planning algorithm, along with an additional tree-pruning module, the computation efficiency of the AIKMP algorithm can be improved by ≈96%. The new pruning module added to the AIKMP algorithm also significantly reduces the storage requirement by the algorithm. Using different example relative transfer scenarios comprising two spacecraft and multiple obstacles/keep-out zones, we demonstrate the performance of the proposed efficient kinodynamic orbital motion planner and show that the algorithm efficiently computes near-optimal, fuel-efficient, and collision-free trajectories for spacecraft relative transfer problems.


Analysis of Criminal Landscape by Utilizing Statistical Analysis and Deep Learning Techniques

Abstract: This research intends to provide law enforcement organizations with a deep learning model that uses trends in past crimes to forecast future crimes. The study will enable them to dispatch security patrols to the most susceptible regions and take preventive actions. The experimental study demonstrates that the LSTM-based deep learning method beats the conventional ARIMA model. Multiple parameter tuning techniques were examined to create an optimized model, such as different LSTM layers, epochs, and batch sizes. The developed model has a training accuracy of around 90%, while on the test data, the minimum and highest accuracy levels were around 75%.


A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT

Abstract: Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified.


The impact of intelligent personal assistants on Mandarin second language learners: interaction process, acquisition of listening and speaking ability

Abstract: Intelligent personal assistants (IPAs) are becoming increasingly interactive, convenient and intelligent, and their potential for improving second language skills has been proven by many studies. However, these studies have mainly concentrated on secondary and higher education groups, and little is known about the use of IPAs by second language learners in a sensitive period. Moreover, few studies have analyzed learners’ discourse and behavior. In this study, an intelligent personal assistant called MSLIPA is introduced, and 44 Mandarin second language learners in the second grade of elementary school are recruited and randomly assigned into an experimental group (communicated with MSLIPA) and a control group (communicated with their peers). Both groups completed five Mandarin exchange classes. The researchers explored the impact of IPA on second language learners from two aspects: the interaction process and the acquisition of listening and speaking ability. The results show that learners engage in more rounds of dialog and use more interaction strategies when interacting with IPAs than with their peers; through the LDA analysis of the text data, it is found that the communication topics between the learners and the IPA are rich; conversations with IPAs can significantly improve learners’ listening and speaking ability, but there is no significant difference in the improvement brought about by conversations with peers. These findings indicate that the IPA has potential in Mandarin second language learning in the sensitive period, but longer-term research on technology implementation and strategy application is needed.


Demonstration of Autonomous Driving Control for a Retrofitted Wheel Loader

Abstract: Most research on automating gravel pile transport using wheel loaders has been performed primarily through simulations. Thus, studies should evaluate the usefulness of automatic gravel pile transportation by demonstrating it with an actual wheel loader. This study demonstrates automatic driving control using a retrofitted 3-ton wheel loader for gravel pile transportation. The driving model of a retrofitted wheel loader, in which multiple control systems are interlocked, is considered a simple control model with one input and one output for the pedal and vehicle velocity as well as for the steering wheel and steering angular velocity. In this study, we propose a simple and practical method for constructing a driving model via simple response analysis using an actual machine by constructing a feedforward control model based on control input/output using step responses. In this study, feedforward control was applied to the translation of the vehicle, which has a large dead time. By generating the path following the target point from the vehicle predicted position after the dead time from the driving model, the appropriate control input value calculation considering the dead time was performed. By applying the proposed method to a retrofitted wheel loader in a real environment and evaluating the control performance through control experiments, the effectiveness of the proposed method in practice was demonstrated.


An old approach to a novel problem: effect of combined balance therapy on virtual reality induced motion sickness: a randomized, placebo controlled, double-blinded study

Abstract: The objective of this study was to investigate the impact of a rehabilitation program aimed at addressing vestibular and proprioceptive deficits, which are believed to underlie the pathophysiology of motion sickness.A total of 121 medical students with motion sickness participated in this study and were randomly divided into intervention (n = 60) and placebo control (n = 61) groups. The intervention group underwent combined balance, proprioception, and vestibular training three times a week for 4 weeks, while the control group received placebo training. The study assessed various measurements, including the Virtual reality sickness questionnaire (VRSQ), tolerance duration, enjoyment level measured by VAS, stability levels using Biodex, and balance with the Flamingo balance test (FBT). All measurements were conducted both at baseline and 4 weeks later.There was no significant difference in pre-test scores between the intervention and control groups, suggesting a similar baseline in both groups (p > 0.05). The results showed a significant improvement in VRSQ, tolerance duration, VAS, Biodex, and FBT scores in the intervention group (p < 0.05). While, the control group showed a significant increase only in VAS scores after 4 weeks of training (p < 0.05). A statistically significant improvement was found between the groups for VRSQ (p < 0.001), tolerance duration (p < 0.001), VAS (p < 0.001), Biodex (p = 0.015), and FBT scores (p < 0.05), in favor of the intervention group.A combined balance training program for motion sickness proves to be effective in reducing motion sickness symptoms, enhancing user enjoyment, and extending the usage duration of virtual reality devices while improving balance and stability. In contrast, placebo training did not alter motion sickness levels. These findings offer valuable insights for expanding the usage of virtual reality, making it accessible to a broader population.


Double-Constraint Fuzzy Clustering Algorithm

Abstract: Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.


Entrepreneurial Orientation and Innovation Performance of Saudi Firms: A Sequential Mediation Model of Absorptive Capacity and Innovation Capability

Abstract: This study examines the relationship between entrepreneurial orientation (EO) and innovation performance (IP) using a sequential mediation model of absorptive capacity (AC) and innovation capability (ICAP). We conduct a literature review, develop and test hypotheses using structural equation modelling (SEM) and data collected from 225 Saudi small and medium enterprises (SMEs) operating in several sectors in Saudi Arabia. The findings reveal statistically significant relationships between the EO dimensions (proactive innovativeness and risk-taking) and AC and ICAP. However, no direct relationship is found between the EO dimensions and IP. Therefore, AC mediates the relationship between the EO dimensions and ICAP. In addition, ICAP mediates the relationship between the EO dimensions and AC to IP, while AC does not mediate the relationship between the EO dimensions and IP. To the best of our knowledge, limited research has simultaneously explored the impact of EO, AC, ICAP, and IP in the Saudi context. Our study adds to the existing body of knowledge in the fields of entrepreneurship, innovation, and strategic management by providing empirical evidence specific to the Saudi context. It also displays the significance of managers turning strategic EO dimensions into innovation outcomes through AC and ICAP as a contribution to managerial practices.


Anomaly Detection Methods in Autonomous Robotic Missions

Abstract: Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.


Facilitating and automating usability testing of educational technologies

Abstract: Abstract Usability evaluation is a key element to ensure a positive user experience with any software and it is especially important in educational software tools where there are many different actors involved (lecturers, students, administrators, etc.). However, evaluating usability is not an easy task for nonexpert evaluators. To facilitate this evaluation task, this article presents a Methodology for Usability Testing (MUT) and a system (CALMUT) that assists nonexpert evaluators in the application of the methodology by automatizing the calculations and facilitating their interpretation. This can be very useful for learning and instructional designers but also to people involved in the decision of introducing or not a new educational software. To develop the proposal, a literature review of different usability metrics, methods, and systems was carried out first, followed by a selection and adaptation for novice usability evaluators. This article also presents a case study where lecturers tested the usability of an educational software following the proposal and shows that using MUT and CALMUT helps people without previous experience detect the main usability problems of educational systems before deciding whether to use them or not.


A Light Multi-View Stereo Method with Patch-Uncertainty Awareness

Abstract: Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image's feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.


Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

Abstract: Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors' preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.


Implementation of JPEG XS entropy encoding and decoding on FPGA

Abstract: Abstract JPEG XS is the latest international standard for shallow compression fields launched by the International Organization for Standardization (ISO). The coding standard was officially released in 2019. The JPEG XS standard can be encoded and decoded on different devices, but there is no research on the implementation of JPEG XS entropy codec on FPGAs. This paper briefly introduces JPEG XS encoding, proposes a modular design scheme of encoder and decoder on FPGA for the entropy encoding and decoding part, and parallelizes the algorithm in JPEG XS coding standard according to the characteristics of FPGA parallelization processing, mainly including low-latency optimization design, storage space optimization design. The optimized scheme in this paper scheme enables encoding speeds of up to 4 coefficients/clock and decoding speeds of up to 2 coefficients/clock, with a 75% reduction in encoding and decoding time. The maximum clock frequency of the entropy encoder is about 222.6 MHz, and the maximum clock frequency of the entropy decoder is about 127 MHz. The design and implementation of the FPGA-based JPEG XS entropy encoding and decoding algorithm is of great significance and provides ideas for the subsequent implementation and optimization of the entire JPEG XS standard on FPGAs. This work is the first in the world to propose the design and implementation of an algorithm that can implement the JPEG XS entropy encoding and decoding process on FPGA. It creates the possibility for the effective application of JPEG XS standard in more media.


Global Digital Compact: A Mechanism for the Governance of Online Discriminatory and Misleading Content Generation

Abstract: With the continuous development of artificial intelligence (AI), algorithmic discrimination and discriminatory and misleading content (DMC) generated by AI have given rise to many negative effects in cyberspace, such as racial and gender discrimination, misinformation, and so on. The growing concern in society over AI governance urgently necessitates the establishment of an effective mechanism to supervise and govern AI-generated DMC. In this article, the discriminatory and misleading contents of AIGC (Artificial Intelligence Generated Content) were extracted according to Text Classification Model and then classified by Naive Bayesian algorithm. The results showed that under the Global Digital Compact (GDC), countries differed in their degrees of discrimination related to race, gender, religion, and age. The racial discrimination accounted for the highest proportion in the United States, with a score of 0.15; that in Britain and France took up a share of 0.06 and 0.07, respectively; and merely 0.03 in Germany. Discriminatory content of racial discrimination (M1) and gender discrimination (M2) in science and technology industry was relatively low, accounting for 0.05 and 0.08, respectively. Analyzing data within the Global Digital Compact (GDC) illuminates the disparities and trends in DMC generation across various countries, cities, industries, and individual users. This analysis provides valuable references for subsequent research and problem-solving initiatives under the compact. Furthermore, GDC plays a pivotal role in addressing issues related to AI-generated DMC, contributing significantly to the creation of a secure, reliable, and equitable cyberspace.


Investigating the Effectiveness of Augmented Reality Technology in Science Education in Terms of Environmental Literacy, Self-Regulation, and Motivation to Learn Science

Abstract: Augmented Reality (AR) is a technology that provides users with an engaging learning experience by placing interactive virtual 3D models in natural environments. It is known that the use of AR technology in science education has many educational benefits. This study investigated the impact of AR technology on the development of environmental literacy, self-regulation skills, and motivation for science learning, which are increasingly important skills. Acquiring these high-level skills is essential for supporting students’ development in preventing environmental problems, addressing the motivation decrease seen primarily due to distance learning, and acquiring the self-regulation skills that have gained importance due to distance learning. The study was conducted with a quasi-experimental design on fifth-grade students, with one experimental (34) and one control (36) group. The findings were analyzed through MANOVA and content analysis. The results showed that AR technology supports environmental sensitivity, positive behavior, and the development of environmental knowledge and skills. In addition, AR technology has significant contributions in terms of self-regulation. Finally, it is recommended to use AR technology in science education to develop these variables and positively affect participants’ motivation for science learning.


Feeling safe together vs. longing for touch. Affective, multisensorial contact in inclusive intra-active virtual/physical dance during the COVID-19 pandemic

Abstract: This article explores how feelings of safety were experienced through inclusive virtual/physical dance in relation to experiences of touch during the COVID-19 pandemic in Finland. The following are the measures I took to achieve the aim of this study. First, I introduce the context through previous studies and an example from the ethnographic material I gained from the inclusive X-Dance festival organized in June 2021 in Finland. Second, I explore how inclusive virtual/physical dance might help us experience feelings of safety through multisensorial experiences of touch related to communality when responding to wellbeing challenges caused by isolation. Using Laura U. Mark's theory about haptic looking and Karen Barad's term intra-action, I discuss different possibilities to approach touch as a feeling sense, as affective multisensorial contact, and through relations between different materialities. Third, I contrast these ideas with sensations of longing for the physical touch that virtual/physical dancing evokes. These experiences of longing invite me to reflect on the ambivalence about feelings of safety related to intentions to restore experiences of touch and communality through digitality. I introduce two examples from my interviews with dancers during the pandemic to discuss these ambivalences. I reflect on these interviews through Magdalena Górska's theory about corpo-materiality and corpo-affectivity. I contribute to discussions on feelings of safety by showing that multi-sensorial experiences and anti-normative understandings of body and touch enabled by the non-verbal language of dance may help us to contribute toward more inclusiveness in society, allowing us to generate holistic experiences of safety, which is another critical aim for post-pandemic times.


Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Abstract: Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mi>o</mml:mi><mml:mi>U</mml:mi></mml:mrow></mml:math> loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.


A closed-form analytical variational approach to thermally induced thin-film delamination

Abstract: Thin polymeric films applied on stiff substrates are prone to delamination upon thermal loading. Different thermal expansion coefficients and different elastic properties can cause localized stress concentrations in the interface between applied layer and substrate. The present work proposes a variational approach to compute residual stresses and strains within the film. Using finite element calculations, it is shown that the presented closed-form analytical solution provides accurate interfacial stresses. Employing a critical distances failure criterion, these stresses are used to predict thermally induced crack nucleation. The proposed model is validated using experimental data of thermally loaded epoxy–glass specimens. It is in good agreement with the test data and provides predictions for thin-film delamination.


Smart hotels but not necessarily smart decisions: the smartness paradox

Abstract: Purpose This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service. Design/methodology/approach This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments. Findings Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance. Research limitations/implications This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination. Originality/value The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.


Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model-Based Augmentation

Abstract: Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to a degraded online reinforcement learning performance. In this paper, we propose a model-based data augmentation strategy to maximize the benefits of offline reinforcement learning pre-training and reduce the scale of data needed to be effective. Our approach leverages a world model of the environment trained on the offline dataset to augment states during offline pre-training. We evaluate our approach on a variety of MuJoCo robotic tasks, and our results show that it can jumpstart online fine-tuning and substantially reduce—in some cases by an order of magnitude—the required number of environment interactions.


An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

Abstract: Abstract The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.


A real-time wearable AR system for egocentric vision on the edge

Abstract: Abstract Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR system, analysing its specifications, overall architecture and core algorithmic components. DARLENE comprises AR glasses and a wearable computing node responsible for several time-critical computation tasks. These include computer vision modules developed for the real-time analysis of dynamic scenes supporting functionalities for instance segmentation, tracking and pose estimation. To meet real-time requirements in limited resources, concrete algorithmic adaptations and design choices are introduced. The proposed system further supports real-time video streaming and interconnection with external IoT nodes. To improve user experience, a novel approach is proposed for the adaptive rendering of AR content by considering the user’s stress level, the context of use and the environmental conditions for adjusting the level of presented information towards enhancing their situational awareness. Through extensive experiments, we evaluate the performance of individual components and end-to-end pipelines. As the proposed system targets time-critical security applications where it can be used to enhance police officers’ situational awareness, further experimental results involving end users are reported with respect to overall user experience, workload and evaluation of situational awareness.


Confidence partitioning sampling filtering

Abstract: Abstract The confidence partitioning sampling filter (CPSF) method proposed in this paper is a novel approach for solving the generic nonlinear filtering problem. First, the confidence probability space (CPS) is defined, which restricts the state transition in a bounded and closed state space in the recursive Bayesian filtering. In the posterior CPS, the weighted grid samples, represented the posterior PDF, are obtained by using the partitioning sampling technique (PST). Each weighted grid sample is treated as an impulse function. The approximate expression of the posterior PDF, as key for the PST implementation, is obtained by using the properties of the impulse function in the integral operation. By executing the selection of the CPS and the PST step repeatedly, the CPSF framework is formed to achieve the approximation of the recursive Bayesian filtering. Second, the difficulty of the CPSF framework implementation lies in obtaining the real posterior CPS. Therefore, the space intersection (SI) method is suggested to obtain the approximate posterior CPS. On this basis, the SI_CPSF algorithm, as an executable algorithm, is formed to solve the generic nonlinear filtering problem. Third, the approximate error between the CPSF framework and the recursive Bayesian filter is analyzed theoretically. The consistency of the CPSF framework to the recursive Bayesian filter is proved. Finally, the performances of the SI_CPSF algorithm, including robustness, accuracy and efficiency, are evaluated using four representative simulation experiments. The simulation results showed that SI_CSPF requires far less samples than particle filter (PF) under the same accuracy. Its computation is on average one order of magnitude less than that of the PF. The robustness of the proposed algorithm is also evaluated in the simulations.


Program Behavior Dynamic Trust Measurement and Evaluation Based on Data Analysis

Abstract: Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security protection methods for industrial control terminals have coarse detection granularity, and are unable to effectively detect and prevent attacks, lacking real-time responsiveness to attack events. Therefore, this paper proposes a real-time dynamic credibility evaluation mechanism based on program behavior, which integrates the matching and symmetry ideas of credibility evaluation. By conducting a real-time dynamic credibility evaluation of function call sequences and system call sequences during program execution, the credibility of industrial control terminal application program behavior can be judged. To solve the problem that the system calls generated during program execution are unstable and difficult to measure, this paper proposes a partition-based dynamic credibility evaluation method, dividing program behavior during runtime into function call behavior and system call behavior within function intervals. For function call behavior, a sliding window-based function call sequence benchmark library construction method is proposed, which matches and evaluates real-time measurement results based on the benchmark library, thereby achieving symmetry between the benchmark library and the measured data. For system call behavior, a maximum entropy system call model is constructed, which is used to evaluate the credibility of system call sequences. Experiment results demonstrate that our method performs better in both detection success rate and detection speed compared to the existing methods.