AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

Abstract: Abstract To address the issue of tracking drift and failures in thermal infrared (TIR) tracking tasks caused by target occlusion, this study proposes an anti‐occlusion TIR target tracker named AODiMP‐TIR. This approach involves an anti‐occlusion strategy that relies on target occlusion status determination and trajectory prediction. This enables the prediction of the target's current position when it is identified as occluded, ensuring swift recapture upon reappearance. A criterion is introduced for occlusion status determination based on the classification response map of SuperDiMP. Additionally, a trajectory mapping module designed to decouple target motion from camera motion is presented, enhancing the precision of trajectory prediction. Comparative experiments with other state‐of‐the‐art trackers are conducted on the large‐scale high‐diversity thermal infrared object tracking benchmark (LSOTB‐TIR), LSOTB‐TIR100, and thermal infrared pedestrian tracking benchmark (PTB‐TIR) datasets. The results indicate that the AODiMP‐TIR performs well across all three datasets, particularly exhibiting outstanding performance in occlusion sequences. Furthermore, ablation study experiments confirm the effectiveness of the anti‐occlusion strategy, occlusion determination criterion and trajectory mapping module.


Mutual learning with reliable pseudo label for semi-supervised medical image segmentation

Abstract: Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels is crucial in this regard. Unreliable pseudo-labeling can result in the introduction of noise, leading the model to converge to suboptimal solutions. To address this issue, we propose learning from reliable pseudo-labels. In this paper, we tackle two critical questions in learning from reliable pseudo-labels: which pseudo-labels are reliable and how reliable are they? Specifically, we conduct a comparative analysis of two subnetworks to address both challenges. Initially, we compare the prediction confidence of the two subnetworks. A higher confidence score indicates a more reliable pseudo-label. Subsequently, we utilize intra-class similarity to assess the reliability of the pseudo-labels to address the second challenge. The greater the intra-class similarity of the predicted classes, the more reliable the pseudo-label. The subnetwork selectively incorporates knowledge imparted by the other subnetwork model, contingent on the reliability of the pseudo labels. By reducing the introduction of noise from unreliable pseudo-labels, we are able to improve the performance of segmentation. To demonstrate the superiority of our approach, we conducted an extensive set of experiments on three datasets: Left Atrium, Pancreas-CT and Brats-2019. The experimental results demonstrate that our approach achieves state-of-the-art performance. Code is available at: https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels.


The c-Differential-Linear Connectivity Table of Vectorial Boolean Functions

Abstract: Vectorial Boolean functions and codes are closely related and interconnected. On the one hand, various requirements of binary linear codes are needed for their theoretical interests but, more importantly, for their practical applications (such as few-weight codes or minimal codes for secret sharing, locally recoverable codes for storage, etc.). On the other hand, various criteria and tables have been introduced to analyse the security of S-boxes that are related to vectorial Boolean functions, such as the Differential Distribution Table (DDT), the Boomerang Connectivity Table (BCT), and the Differential-Linear Connectivity Table (DLCT). In previous years, two new tables have been proposed for which the literature was pretty abundant: the c-DDT to extend the DDT and the c-BCT to extend the BCT. In the same vein, we propose extended concepts to study further the security of vectorial Boolean functions, especially the c-Walsh transform, the c-autocorrelation, and the c-differential-linear uniformity and its accompanying table, the c-Differential-Linear Connectivity Table (c-DLCT). We study the properties of these novel functions at their optimal level concerning these concepts and describe the c-DLCT of the crucial inverse vectorial (Boolean) function case. Finally, we draw new ideas for future research toward linear code designs.


The Impact of Artificial Intelligence on Students in the First Three Grades in Basic Schools in the City of Amman-Jordan from The Perspective of Their Teachers

Abstract: Nowadays Artificial Intelligence (AI) affects most of our lives; it is positive in some ways, but there are limitations in others. Teachers' perceptions of AI are still poorly studied, despite ongoing debate and growing research in the field. One of its main effects is on students in the first grades; however, this study aimed to investigate the effect of AI on the first three grades of students in basic schools in the governorate of the capital Amman Jordan, from the perspective of its teachers as a case study. The study was performed using a scientific questionnaire, and 125 participants completed this survey. The findings of an analysis of teachers' beliefs regarding using AI technologies to teach the first three grades in elementary schools in Jordan's capital, Amman Governorate, are presented in detail. As a result of the significance of these axes in educational curricula and strategies, as well as related sustainability, the focus was on five axes in the questionnaire questions: the validity of information derived from artificial intelligence, supporting students' technical knowledge and conceptual knowledge, focusing on development practices for students' skills, and finally achieving educational outcomes. The findings show that in-service teachers need to be trained to use current AI-based tools more effectively. To integrate AI into regular education, teachers must participate in the process of co-designing materials while taking into account contextual circumstances and, most importantly, curricula. Teachers' input during the development can help put AI in perspective, resulting in tangible effects and significant educational advances.


Impact of Employee Engagement On Service Quality in Nigerian Public Agencies

Abstract: This study investigates the connection between employee engagement and service quality in the Asset Management Company of Nigeria (AMCON), which is a key player in Nigeria's financial sector. This study examines three key dimensions of employee engagement – absorption, vigor, and dedication – and their effects on service quality, specifically focusing on the responsiveness dimension of the SERVQUAL model. This study is important because there is a lack of representation of AMCON and similar institutions in the existing literature, particularly within the context of Nigeria's unique financial landscape. This study utilized a mixed-methods approach that included quantitative surveys and qualitative interviews with AMCON employees. The analysis employed the SERVQUAL model to evaluate service quality and multiple regression analysis to explore the influence of each engagement dimension. The findings of this study are expected to contribute to a theoretical understanding of the impact of employee engagement on service quality within the asset management sector. Additionally, this study aims to provide practical insights for AMCON, potentially improving service delivery strategies and effectiveness in fulfilling its mandate. The broader implications of this study extend to similar asset management entities in emerging economies, providing a comparative perspective, and understanding of these dynamics in diverse economic and regulatory environments.


Cloud-based Metabase GIS Data Analysis Platform Quality Management According to ISO 9126 Indicators

Abstract: Platform metabase GIS data analysis based on the cloud that has been successfully developed is an alternative solution for spatial-text data analysis. The output of this cloud-based platform not only provides accurate textual information but also precise location representation of the objects. This research examines the quality of the developed platform based on ISO 9126, which consists of six main indicators: functionality, reliability, feasibility, efficiency, maintainability, and portability. Each indicator has different sub-indicators, totaling 22 sub-indicators. The quality assessment results indicate that the platform for metabase GIS data analysis based on the cloud exhibits excellent quality, with an average test result based on the ISO 9126 indicators reaching 93%.


Deep Learning-Based Semantic Segmentation in Autonomous Driving

Abstract: Perception is a fundamental task of autonomous driving systems, which gathers all the necessary information about the surrounding environment of the moving vehicle. Then a decision-making system takes the perception data as input and provides the optimum decision given a scenario, which maximizes the safety of the passengers. In this project, we have developed variants of the U-Net model to perform semantic segmentation on urban scene images to understand the surroundings of an autonomous vehicle. The U-Net model and its variants are adopted for semantic segmentation in this project to account for the power of the U-Net in handling large and small datasets. We have also compared the best-performing variant with other commonly used semantic segmentation models. The comparative analysis was performed using three well-known models, including FCN-16, FCN-8, and SegNet. After conducting sensitivity and comparative analysis, it is concluded that the U-Net variants performed the best in terms of the Intersection over Union (IoU) evaluation metric and other quality metrics.


Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Abstract: Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques.


LatCrit

Abstract: Abstract Ever since its 1995 inception at a Puerto Rico colloquium on Critical Race Theory and Latina/o communities, Latina and Latino Critical Legal Theory (LatCrit) agendas have prioritized students and sustainability. This chapter highlights those priorities in the context of the times and ambitions that have helped to activate and shape LatCrit theory, community, and praxis as a form of organized academic activism. The authors describe LatCrit’s origins and history since 1995, its collective present, and their hopes for the future. Understanding the emergence, circumstances, and purposes of LatCrit’s development as a still thriving school of critical outsider jurisprudence and its programmatic expression of anti-subordination interventions during these past few decades brings into focus how and why LatCrit remains an “advocacy project” that might inspire other individuals or collectives, whether in academia or other parts of society, and whether focused on students or other populations. Specifically for students, we suggest a variety of ways to connect with LatCrit scholarship, events, and networks to build your own critical knowledge, professional capacity, and bottom-up advocacy projects.


A Conceptual Model of Nurse Retention Integrating Quality of Work Life and Human Resource Management: The Case of Jordan

Abstract: Worldwide, the health sector faces a major challenge in retaining its staff. In Jordan, both public and university hospitals struggle with high turnover among nursing staff, which ultimately affects their overall performance. This study aims to discuss an integrated conceptual model that is developed based on the impact of quality of nursing work life (QNWL) and human resource management (HRM) practices on nursing staff retention. The model is drawn using the socio-technical system (STS) and social exchange (SET) theories to support the significance of QNWL and nurse retention. In addition, HRM practices involving training, compensation, performance appraisal, and recruitment were used. This framework contributes to research by combining the perspectives of QNWL and HRM to understand employee retention comprehensively. The practical insights derived from this model can be used to improve nurse retention strategies in the Jordanian healthcare system and as a foundation for future empirical research.


Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

Abstract: Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.


The Problem of Classifying AI as a Modern Cultural Phenomenon

Abstract: The article examines the question of whether artificial intelligence (AI) is a phenomenon of modern culture. The criteria for the phenomenon of culture are highlighted: the criterion of value-normative certainty, the criterion of developmental impact on members of society, the criterion of compliance with the creative component of hu-man nature, the criterion of helping a person understand and express his own individuality, the criterion of pop-ularity and recognition. The compliance of AI with each of these criteria is analyzed. It is revealed that AI is in-cluded in the process of preserving and transmitting the values and norms of society, contributes to the devel-opment of society members, including creativity, as well as human expression of individuality, is very popular in modern society. Conclusion dwells upon the fact that AI corresponds to the specified criteria, so that at this stage of societal development it can be considered a cultural phenomenon.


Aligned ICTs to the CEFR in the Cuban foreign language teaching program.

Abstract: This study examines integrating information and communication technologies (ICTs) into foreign language teacher preparation aligned with the Common European Framework of Reference (CEFR) standards at a Cuban university. A mixed methods approach combined surveys, interviews, assessments and observations to evaluate effects on trainees’ communicative competence development, interpreting uneven gains across reading, writing, listening and speaking abilities. Questionnaire and assessment analysis evidenced perceived and actual progress towards CEFR alignment among fifth year versus first year students resulting from sustained ICT incorporation over their progression. However variability disadvantages persisted in some competencies. Qualitative insights emphasized technology’s enablement of contextualized practice and real-world application as instrumental for competence growth. Despite affirmative outcomes overall, optimizing consistency by motivation and engagement-focused implementations is recommended. Intensive, systematic ICT integration shows potential for helping future foreign language teachers attain key standards-based competencies. Findings inform initial teacher training advancement in Cuba and beyond.


Neuroscience as a basis for the improvement of educational ICTs

Abstract: Neuroscience, with its in-depth study of the human brain, has been identified as a vital tool for improving Information and Communication Technologies (ICT) in education. In order to provide more efficient and personalized didactic tools, the integration of neuroscientific findings into ICT design has been essential. Using qualitative methodologies involving documentary analysis, case studies, and systematic reviews, the importance of adapting ICT to the ways in which the brain naturally learns has been identified. Results indicate that adapting ICT based on neuroscientific principles leads to increased engagement, retention, and knowledge transfer among students. In addition, the use of digital tools aligned with neurodidactic strategies has proven to be more effective in creating inclusive and diversified learning environments, addressing the individual needs of each learner and promoting holistic learning.


Heavy element abundances in galactic globular clusters

Abstract: Globular clusters are considered key objects for understanding the formation and evolution of the Milky Way. In this sense, the characterisation of their chemical and orbital parameters can provide constraints on chemical evolution models of the Galaxy. We use the heavy element abundances of globular clusters to trace their overall behaviour in the Galaxy, with the aim to analyse potential relations between the hot H-burning and s-process elements. We measured the content of Cu I and s- and r-process elements (Y II, Ba II, La II, and Eu II) in a sample of 210 giant stars in 18 galactic globular clusters from high-quality UVES spectra. These clusters span a broad metallicity range and the sample is the largest that has been uniformly analysed to date, with respect to heavy elements in globular clusters. The Cu abundances did not show a considerable spread in the sample, nor any correlation with Na, indicating that the Na nucleosynthesis process does not affect the Cu abundance. Most GCs closely follow the Cu, Y, Ba, La, and Eu field stars' distribution, revealing a similar chemical evolution. The Y abundances in mid-metallicity regime GCs (-1.10 dex $<$ Fe/H $<$-1.80 dex) display a mildly significant correlation with the Na abundance, which ought to be further investigated. Finally, we do not find any significant difference between the n-capture abundances among GCs with either Galactic and extragalactic origins.


Exploring Convolutional Neural Networks for the Classification of Acute Lymphoblast Leukemia Blood Cell Images

Abstract: This paper introduces a novel approach to blood cell classification using convolutional neural networks (CNNs). Our emphasis lies in methodological insights derived from comparative analyses across various CNN architectures, implemented in Python with PyTorch such as ResNet152V2, Xception, EfficientNetB5, and EfficientNetV2M. In exploring model capabilities and limitations, we observe that the simple and shallow architectures is not sufficient to learn patterns compared to more complex networks. During research we have discovered that EfficientNetV2M demonstrate stable results with mean F 1 =0.891 score, which is higher compare to other models. Saliency maps are applied to reveal significant regions within or near cells, offering nuanced insights into morphological influences like cell shape. Extensive data augmentation allows one effectively mitigate overfitting, aligning train learning curves with validation and test splits. Our study is methodologically rich, comprising a meticulous data-specific overview, a robustness analysis through multiple model training, a systematic architecture comparison, and an in-depth examination using saliency maps.


Pressure, Opportunnity, Rationalization, dan Capability terhadap Terjadinya Fraud : Studi di Salah Satu Dinas Propinsi Sulawesi Selatan

Abstract: This study examines the influence of Pressure, Opportunity, Rationalization, and Capability on Fraud within a department in South Sulawesi Province over two months, from December to January 2022. The department's entire staff constitutes the study population, with a saturated sampling method employed to ensure a representative sample. Field research methodology was utilized, employing quantitative data collection through questionnaire surveys. Data analysis was conducted using multiple linear regression with the assistance of statistical software.The results indicate that Pressure (Opportunity), Opportunity, Rationalization, and Capability significantly impact Fraud. The results indicate that Pressure (Opportunity), Opportunity, Rationalization, and Capability significantly impact Fraud. Practical recommendations for the study's subject include; conducting training on work ethics, stress management, and understanding of fraud prevention; implementing clear and stringent anti-fraud policies; enhancing communication and transparency throughout the organization; strengthening internal supervision mechanisms and fostering a work culture that values integrity and ethics.


Sequential Polar Decoding with Cost Metric Threshold

Abstract: Polar codes have established themselves as a cornerstone in modern error correction coding due to their capacity-achieving properties and practical implementation advantages. However, decoding polar codes remains a computationally intensive task. In this paper, we introduce a novel approach to improve the decoding efficiency of polar codes by integrating the threshold-based SC-Creeper decoding algorithm, originally designed for convolutional codes. Our proposed decoder with an additional cost function seamlessly merges two established decoding paradigms, namely the stack and Fano approaches. The core idea is to leverage the strengths of both decoding techniques to strike a balance between computational efficiency and performance, with an additional method of controlling movement along a code tree. Simulations demonstrate the superiority of the proposed improved SC-Creeper decoder with tuned parameters. The improved SC-Creeper decoder achieves the performance of the CA-SCL-8 decoder in terms of high code rates and overcomes it in terms of the N=1024 code length, while simultaneously surpassing the efficiency of the traditional Fano decoding algorithm.


The Impact of HRM Practices on Employee Creativity Through the Mediating Role of Employee Engagement in the Egyptian Hotels Context

Abstract: Human Resource Management (HRM) practices play a pivotal role in organizational enhancement and association development. This study investigates the relationship between HRM practices, external factors, and their impact on employee satisfaction, performance, and creativity within Egyptian hotels. Key HRM practices considered include recruitment, selection, market-oriented training, productivity enhancement, and social security assessments.The research methodology involved a unit of analysis focusing on employees in Egyptian hotels. A convenient sampling technique was employed, with 331 respondents participating in the study. Data was collected through a questionnaire comprising 42 statements adapted from previous studies. The proposed framework positions HRM practices as independent variables, employee creativity as the dependent variable, and employee engagement as a mediator.Results indicate that recruitment, selection, performance appraisal, and career planning significantly influence employee satisfaction, while compensation, training, development, employment security, and employee involvement show partial support. Additionally, recruitment, selection, compensation, performance appraisal, and career planning significantly affect employee performance, with other factors showing partial support.Moreover, employee engagement is found to mediate the relationship between HRM practices and employee creativity. Employee satisfaction fully mediates the relationship between specific HRM practices and creativity, while employee performance partially mediates this relationship. These findings provide insights into the nuanced impact of HRM practices on employee outcomes, emphasizing the critical role of employee engagement in fostering creativity.The study concludes with practical recommendations for organizations to optimize HRM practices and enhance employee engagement for improved creativity and overall performance.


Pre-Segmented Down-Sampling Accelerates Graph Neural Network-Based 3D Object Detection in Autonomous Driving

Abstract: Graph neural networks (GNNs) have been proven to be an ideal approach to deal with irregular point clouds, but involve massive computations for searching neighboring points in the graph, which limits their application in large-scale LiDAR point cloud processing. Down-sampling is a straightforward and indispensable step in current GNN-based 3D detectors to reduce the computational burden of the model, but the commonly used down-sampling methods cannot distinguish the categories of the LiDAR points, which leads to an inability to effectively improve the computational efficiency of the GNN models without affecting their detection accuracy. In this paper, we propose (1) a LiDAR point cloud pre-segmented down-sampling (PSD) method that can selectively reduce background points while preserving the foreground object points during the process, greatly improving the computational efficiency of the model without affecting its 3D detection accuracy. (2) A lightweight GNN-based 3D detector that can extract point features and detect objects from the raw down-sampled LiDAR point cloud directly without any pre-transformation. We test the proposed model on the KITTI 3D Object Detection Benchmark, and the results demonstrate its effectiveness and efficiency for autonomous driving 3D object detection.


A Study of Online Teaching Strategies During Covid-19 of Secondary School Teachers

Abstract: During the advent of COVID-19, it became inevitable to switch from traditional face-to-face teaching to online teaching mode. Switching to the online teaching process was the only option left for uninterrupted learning. One of the ways to make the online teaching-learning experience was the ability to energize the class environment. Creating a classroom community where students can communicate after lectures and can share their ideas and thoughts. Recording of the lectures must be given to students so that they can see them later for better understanding. Effective teaching strategies implementation was utmost required to improve the online teaching and make online learning a successful experience for both teachers and the students. Descriptive survey method was used for the present study. Objectives were to study the role of teacher in online class during covid -19 pandemic, to find different types of educational strategies for making online classes, to study the role of parents and students in implementation during online classes. Sample was selected from the 7 Secondary Schools affiliated to the State Board of Gujarat in Vadodara city taking as random sampling technique which included 70 teachers. Self made questionnaire was used to achieve the objectives of the study. It has been found in present study that the maximum number of teachers were able to perform their role in helping students according to their needs, perform as a mentor, guide and friend, to inspire the students and the remaining have faced difficulty motivating their students. Able to maintain the proper interpersonal relationships, interactions were also possible between the teacher & students in online mode of teaching, but very few teachers face the difficulty in fulfilling or maintaining the interpersonal relationships. Different types of educational strategies were used in which maximum number of teachers were able to perform their interactive classroom though various activities. All the teachers were able to perform stories as strategies to clear the concept, majority of teachers are able to inspire the students and the remaining have faced difficulty with an inductive deductive approach & ICT based lesson plan in online mode of teaching. Majority of teachers were able to make good rapport with the students & Use teaching aids, various online quiz platforms in online mode of teaching, but very few teachers face the difficulty in fulfilling open forum or discussions & encourage Group feedback for engagement. The maximum number of teachers agreed that parents and students were found able to perform their roles, to perform online cultural programs, competitions & activities, to motivate online morning assemblies, dignity of online assessments, to connect in online parents and teacher meeting during online class.


Seeding Gaussian boson samplers with single photons for enhanced state generation

Abstract: Non-Gaussian quantum states are crucial to fault-tolerant quantum computation with continuous-variable systems. Usually, generation of such states involves trade-offs between success probability and quality of the resultant state. For example, injecting squeezed light into a multimode interferometer and postselecting on certain patterns of photon-number outputs in all but one mode, a fundamentally probabilistic task, can herald the creation of cat states, Gottesman-Kitaev-Preskill (GKP) states, and more. We consider the addition of a non-Gaussian resource state, particularly single photons, to this configuration and show how it improves the qualities and generation probabilities of desired states. With only two modes, adding a single-photon source improves GKP-state fidelity from 0.68 to 0.95 and adding a second then increases the success probability eightfold; for cat states with a fixed target fidelity, the probability of success can be improved by factors of up to four by adding single-photon sources. These demonstrate the usefulness of additional commonplace non-Gaussian resources for generating desirable states of light.


DEVELOPING A MEASUREMENT INSTRUMENT FOR TECHNICAL AND ANALYTICAL SKILLS IN AUDITING FOR ENHANCED FRAUD DETECTION

Abstract:
This study bridges a significant gap in forensic accounting and fraud detection by establishing a standardized measure for Technical and Analytical Skills (TAS) in external auditing. Despite the acknowledged importance of TAS in fraud detection, the absence of a universally accepted definition and measurement instrument has limited the field's advancement. This research introduces a validated TAS measurement instrument, underpinned by a novel framework that categorizes TAS into six critical dimensions: Substantive Analytical Procedures, Technical Tools and Software, Critical Thinking, Innovation and Solution Implementation, Professional Development, and Quantitative and Statistical Analysis. A structured survey among 360 auditors from international firms in Southern Africa confirmed the instrument's reliability, with Cronbach's alpha values exceeding 0.70 across all dimensions, and supported the distinctiveness of the six-factor structure through confirmatory factor analysis. The instrument's potential to enhance auditing practices and fraud detection capabilities is considerable. It offers a foundation for future research to explore its cross-cultural applicability, predictive validity, and adaptation to technological advancements. This contribution not only provides a robust tool for auditing professionals but also fosters a culture of innovation and continuous learning within the field.
 Keywords: Technical and Analytical Skills, Forensic Accounting, Fraud Detection, External Auditing, Skill Measurement, Professional Development, Confirmatory Factor Analysis, Auditing Education.


Research of Digital Characteristics of the Step Cycle in Forensic Medicine

Abstract: This article presents the results of a study devoted to the analysis of the characteristics of the human step cycle as an indicator that allows you to identify a person on video by the signs of gait. Video recordings of volunteers walking on an electric treadmill were used as the research material. A software package has been developed that allows calculating the parameters of the step cycle used for processing the material. The average values of such characteristics of the step cycle were obtained and analyzed, such as the duration of the transition period and double support as a percentage of the step cycle and seconds, the length of the step cycle in seconds, walking frequency, walking pace separately for men, women and for the entire sample as a whole. The change of the listed indicators depending on the walking speed is investigated, the change in the frequency of steps with increasing speed is analyzed. A method of identifying a person in a video based on the characteristics of a step cycle is proposed, which allows comparing the digital characteristics of steps and making a reasonable conclusion about their similarity or difference. A software package has been developed to determine the anthropometric parameters of an individual in a video based on the characteristics of the step cycle. The obtained results can be used in forensic medicine for conducting expertise on identification of a person by signs of gait.


Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

Abstract: Digital transformation and upgrading pose new requirements for talent quality. Taking human resources positions as an example, this article constructs a “3+X” talent quality evaluation index system that includes knowledge, ability, attitude, and professional skills. Based on the data characteristics of 55 employees in human resources positions, a combination weighting model, entropy method, and BP neural network model are used, and an evaluation index system and weight evaluation index system suitable for the quality characteristics of talents in China’s hydropower industry have been constructed. The research results show that employee competence is an important indicator for driving digital transformation, with knowledge and professional skills playing a relatively central role, and attitudes being less differentiated. Finally, the research significance and shortcomings of the study are summarized, and the prospects for future development are provided.


Machine Learning Driven Strategies for Safeguarding India’s Digital Environment Against Cyberstalking

Abstract: The ubiquity of the internet and mobile phones has drastically shifted the physical world into a virtual realm, encompassing individuals of all age groups, from schoolchildren to seniors. People now utilize social media and various internet-based platforms to connect and invest their time. However, within this virtual environment, the ability to conceal or impersonate one’s identity presents an opportunity for criminal activity to go unnoticed. Despite India’s progression towards becoming a digital nation, there exists a shortage of regulations and technological resources to address the challenges stemming from this digital paradigm shift. Cybercrime refers to any illicit behavior involving the use of a computer as a tool, target, or weapon. One subset of cybercrime is cyberstalking, which encompasses actions where a perpetrator employs a computer or associated technologies to induce fear in their victim. This chapter aims to delve into the definition, typology, detection, prevention, and the imperative need for cybercrime education. Its primary objective is to investigate the surge in internet stalking cases in India, exploring the definition, categories, and underlying causes of cyberstalking. Additionally, this article scrutinizes the Indian legal framework and judicial precedents related to these issues.


Multi-Interactive College English Teaching Under the Network-Based Multimedia Environment

Abstract: As a new teaching method, multi-interactive teaching has been more and more recognized by educators in this fast-growing information age due to its advantage of facilitating communication. However, more empirical studies still need to be done to investigate its effect. This study aims to investigate the impact of multi-interactive teaching method on college students’ English proficiency and their learning motivation through a quasi-experimental design. The Experimental Group (44 students) was taught using multi-interactive teaching approach while the Control Group (44 students) learned English in a conventional way over a span of 14 weeks. T-test was adopted to examine whether there was significant difference in English proficiency (listening, reading, writing, and translation) and their learning motivation between the two groups. The findings revealed that except writing, the Experimental Group obtained significantly higher scores than the Control Group. Hence, it is recommended that multi-interactive teaching be used as an effective method to promote students’ English proficiency and learning motivation in College English classes.


A Discussion of the Pedagogical Benefits of Nodal Knowledge in University Physics

Abstract: Focusing on nodal knowledge in university physics, this paper describes the definition and categorisation of nodal knowledge, revealing its characteristics and its connection to core knowledge. Through in-depth analyses, it is found that nodal knowledge is an important link in the physics subject that promotes and extends core knowledge, embodying the characteristics of fundamentality, selectivity, transition and diversity. In addition, this paper also highlights the superiority of using nodal knowledge for teaching, which not only helps students reduce the burden of memorisation, but also promotes the construction of a clear structure in the knowledge network and the understanding of the ins and outs of knowledge. Crucially, nodal knowledge makes it easier for students to understand and derive subject knowledge, thus helping them to master it more effectively.


Impact of Artificial Intelligence and Internet of Things on Performance Management: A Systematic Review

Abstract: The study aims at assessing the outcome of AI and IoT to operational efficiency in business processes for various sectors. Exploring the Synthesis of the existing literature, combined with empirical evidence, this research examines the transformation possibility of AI and IoT Technologies in the improvement of the company. The centric findings have proved that there are significant upsurges in metrics of performance such as efficiency, productivity, quality, and customer satisfaction across different sectors of industries which are like healthcare, manufacturing, and retailing. Healthcare is another example. In this sector AI and IoT combination reduced the patient queue times by 50% while in manufacturing produced more products while spending 33% less capital costs. Similar to the case of brick and mortar stores, they recorded a 25% increment in their sales through AI-aided demand forecasting and the use of IoT in the inventory management. The findings of this research therefore point the way for the tremendous role played by AI and IoT in driving operational excellence, decision making processes and innovation in performance management. The research leads to the identification of major issues and concerns such as data privacy, security and technology integration, that require additional attention. Eventually, by utilizing AI and IoT technologies, companies find new models of sustainable development and market advantages which are of great help in the context of the current business conditions.


A Systematic Performance Review of Security Methods for the Cyberworld

Abstract: Governments and organisations globally increasingly recognise the importance of cybersecurity as a critical measure against cyber threats in our highly interconnected society. Establishing robust security measures has become paramount in safeguarding sensitive information and infrastructure. Biometric systems have emerged as critical components in various sectors, including industry, civilian applications, and rhetoric, to enhance security measures. This paper provides an overview of diverse security approaches in the cyber world and examines several research works, highlighting their strengths and weaknesses in implementation. We critically analyse existing methodologies and address potential shortcomings, aiming to improve the effectiveness of security measures. Additionally, we explore emerging trends and novel research directions in the field of biometric and rhetorical security. The study delves into contemporary biometric toolkits, examining their functionalities and applications across domains. Furthermore, we discuss digital ornamental models, evaluating their efficacy in enhancing cybersecurity measures. Through comparative analysis, we identify key differences and areas for improvement in existing security frameworks. In conclusion, this paper proposes a generic computer security model tailored to address the evolving challenges of cybersecurity. We highlight potential applications of this model in society, emphasising the importance of proactive measures to mitigate cyber threats effectively. Through comprehensive analysis and innovative approaches, we aim to contribute to advancing cybersecurity practices in contemporary society.


Development of Digital Literacy Skills among Learners in Public Primary Schools in Homabay County, Kenya

Abstract: Digital literacy direct learners towards innovative, creative and transformational learning in the 21st century. It is one of the core competency in the Competency Based Curriculum. The objective of this study was to assess the availability of ICT infrastructure in public primary schools to develop learners’ digital skills. Constructivist learning theory guided the study. Concurrent embedded mixed method was used. A population of 5713 was targeted for study where a sample of 571 respondents composed of 8 head teachers, 78 teachers and 485 grade six learners were selected. Stratified random, simple random and purposive sampling procedures were used to select the study sample. Data collection instruments included interviews, questionnaires, focused group discussion and observation schedule. Descriptive statistics were used to analyse quantitative and qualitative data. Some of the key findings of the study were majority of public primary schools in Kasipul lack internet connectivity and only 32.1% have computer labs. The results further indicated that teachers have basic knowledge and skills in digital literacy. However, only 23.2% of teachers have been trained in digital literacy. The study recommends that Ministry of education in collaboration with Ministry of Information and Communication Technology enhance internet connectivity in all primary schools to support implementation of digital literacy to develop learners’ digital skills


Analysis of EFL Learners' Writing Challenges in the Digital Era

Abstract: The Writing Intensive Course emphasizes fundamental English writing elements, providing a prime chance for students to improve written communication. Educators face challenges in enhancing expressiveness, but the large millennial class size, familiar with digital technology, presents a potential advantage. Millennials, as digital natives, show enthusiasm for digital-context learning, offering an opportunity for effective instruction in writing. The primary objective of this research is to find out the problems or challenges students undergo while learning to write English in the digital era. This quantitative research examined 174 EFL students to give particular responses that work with brief reviews of their challenges in English writing. Unable to express (10.9%), improve skills (9.8%), and develop ideas (5.2%) in writing stand strong as the biggest three difficulties in English writing. The diverse challenges, collected from participant samples, reveal consistent issues for both active and nonactive students. The study emphasizes the value of digital-assisted courses, offering hope for enhanced English writing and communication skills among SL/FL learners in a more accessible and effective manner. In brief, this study highlights internal factors as the primary challenges in writing class, with students struggling with self-expression, doubts about improvement, and generating ideas. Various challenges, including optimizing roles and feedback, underscore the importance of addressing obstacles for enhanced writing proficiency in digital-assisted courses


Emotional Regulation Ability on Test Results: Factors Affecting Motivation and Learning Achievement

Abstract: The ability to regulate emotions in the acceptance of test results can impact both the increase and decrease of motivation. Unfortunately, there has not been much research explaining this phenomenon. Therefore, this study aimed to examine the influence of emotions on the acceptance of test results on students' learning motivation. This research used an ex post facto approach with a cause-and-effect correlation design. The population consisted of 300 students at SMPN 2 Kuta Selatan. Using purposive sampling, 30 students were involved as research samples. The research instrument consisted of 21 statements from the DAS questionnaire about test results, 20 statements about student motivation, and a formative test. Data were analyzed descriptively, using correlation statistics and paired sample t-tests. The research results showed that there was a significant relationship between the ability to manage depression, anxiety, and stress in the acceptance of test results. In addition, students with better emotional management skills tended to have positive motivation if their test results were disappointing. Meanwhile, students with low emotional abilities also had low motivation. For these affecting factors on students’ achievements, this study suggests a more holistic pedagogical approach and intervention as essential components in efforts to improve their learning achievement


The development of blockchain technology in manufacturing: A bibliometric analysis

Abstract: Many manufacturing organizations have adapted their governance systems, strategies, business models, and strategic processes in the "New Normal" and technologies like Blockchains have irreversibly transformed how people live and work. Despite extensive scholarly research, analysing emerging trends in Blockchain research remains critical. Unfortunately, the use of bibliometric analysis to study Blockchain has been limited, with little understanding of how its various research areas are progressing. Bibliometric analysis, a useful method for examining the development of research, has not been widely used in the context of blockchain. The progression of its numerous research areas is also yet unknown. This study seeks to fill these gaps by employing bibliometric analysis to assess scientific progress and research focus on Blockchains in manufacturing. We used Vos Viewer to conduct this bibliometric analysis to evaluate 265 papers published from 2017 to 2022 using co-authorship and co-occurrence of keywords analysis. We have identified the most productive authors, institutions, countries, and top research fields, highlighting the essential advancements in the Blockchain field. The findings demonstrate how keywords related to Blockchain have changed over time and describe the research hotspots. The originality of this research lies in its application of bibliometric analysis to examine the development and emerging trends in Blockchain research within the manufacturing domain while offering novel insights into the productivity of authors, institutions, and countries, as well as identifying research hotspots and future research directions. This study contributes to the inadequate body of literature on bibliometric analysis in Blockchain research and provides useful guidance for researchers and practitioners in the field.


Protecting ownership rights of ML models using watermarking in the light of adversarial attacks

Abstract: In this paper, we present and analyze two novel—and seemingly distant—research trends in Machine Learning: ML watermarking and adversarial patches. First, we show how ML watermarking uses specially crafted inputs to provide a proof of model ownership. Second, we demonstrate how an attacker can craft adversarial samples in order to trigger an abnormal behavior in a model and thus perform an ambiguity attack on ML watermarking. Finally, we describe three countermeasures that could be applied in order to prevent ambiguity attacks. We illustrate our works using the example of a binary classification model for welding inspection.


Application of glasses‐free augmented reality localization in precision neurosurgery and literature review

Abstract: Abstract To summarize the characteristics and therapeutic effects of glasses‐free augmented reality (AR) in neurosurgery, a case series of seven patients undergoing resection of intracranial lesions at the Neurosurgery Department of Hangzhou Xixi Hospital from November 2019 to October 2021 was collected. During the surgeries, glasses‐free AR was used for multi‐angle and multi‐modal image localization. The intraoperative positioning errors of all seven patients who underwent body surface localization with glasses‐free AR, in addition to routine surgical procedures, were less than 10 mm. These findings suggest that glasses‐free AR holds great promise for precision neurosurgery.


Pedagogical Response to the Needs of Preschool Children in a Disadvantaged Situation

Abstract: The aim of this article is to analyse the pedagogical response of Croatian ECEC teachers to the needs of preschool children in a disadvantaged situation. The quality of pedagogical practise to prevent unfavourable developmental trajectories in children in a disadvantaged situation, particularly in the Croatian system, is poorly researched. A vignette describing an imaginary child in a disadvantaged situation was used and 35 preschool teachers from 15 ECEC institutions described in writing in an online application how they would support the child in his or her development. The qualitative thematic analysis was conducted according to the five components of the strengths-based approach (relationships, empowerment, collaboration, strengths and focus on change). This approach assumes that through reciprocal relationships between children, parents and teachers, previously agreed goals are achieved and personal resources, motivation and belief in the possibility of achieving sustainable change are unleashed. The results show that ECEC teachers are predominantly oriented towards individual components of the strengths-based approach (quality of relationships, focus on children's strengths), while the implementation of the entire strengths-based approach in its entirety is lacking. It is emphasised that the strengths-based approach needs to be more strongly affirmed in Croatian ECEC system so that its principles are recognisable in the pedagogical practise of all ECEC teachers.


Abstract: International Journal of Information Technology & Decision MakingAccepted Papers No AccessBasic statistical methods in determining criteria weightsUzeyir FidanUzeyir Fidanhttps://doi.org/10.1142/S0219622024500093Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 3 August 2022 Accepted 18 February 2024 PDF download


Cryptography and DRM: A study of digital copyright protection in the gaming industry

Abstract: This paper explores the role of cryptography and encryption technology in developing digital rights management (DRM) in the gaming industry. DRM aims to protect the rights of creators by preventing unauthorized copying and distribution of digital media. Cryptography plays a crucial role in DRM, ensuring the privacy, authenticity, and integrity of data. The early stages of game DRM involved physical disc methods, such as unique disc characteristics and online activation. However, these methods faced challenges such as compatibility issues and unauthorized copying. Modern DRM technologies combine advanced encryption techniques with account-binding mechanisms, requiring users to activate and access game content on specific accounts. Cryptography plays a key role in DRM by encrypting game data into unreadable formats, ensuring that only users with valid decryption keys can access the content, and ensuring secure distribution and consumption of digital content. The paper also discusses third-party DRM technologies, such as Denuvo, which employ strong encryption and obfuscation methods. However, third-party DRM may impact performance, inconvenience users, and lead to compatibility issues. The paper emphasizes the necessity of continually refining DRM technologies to balance copyright protection and consumer rights in the evolving gaming industry.


Multilingual sentiment analysis during the pandemic using deep learning models

Abstract: High mood swings and increased mortality with the COVID-19 outbreak, mood changes are a concern. COVID-19 is extremely contagious, and by December 20, 2021, the global death toll from COVID-19 exceeded 5.35 million. Under such circumstances, people changed their original way of life and isolated themselves from home to work online. Most people in this situation expressed their emotions through social media. In order to understand the changes of people's emotions during the pandemic, this research analyzed the emotions of people's messages posted on social media in different countries through the SenWave dataset by different deep learning models. The dataset contains 6 languages (English, Arabic, Spanish, French, Italian and Chinese) with more than 70,000 tweets, in this paper, only tweets collected from Twitter are concerned, which contains 11 labels. The dataset will be preprocessed and transferred into numerical vector, then TextCNN, BERT, and BERT-TextCNN models are implemented for the above problems. BERT model reached 0.69 and 0.53 on precision and recall respectively on Twitter dataset.


Breast cancer classification based on hybrid machine learning model

Abstract: This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.


An exploration and practice of an optimized online annotation solution based on PDF slicing and SVG

Abstract: With the continuous development of network technology, online annotation has become an essential tool in fields such as education, training, and document collaboration. However, traditional annotation techniques are plagued by several limitations, including low security, non-erasable annotations after uploading, and difficulties in communication across different systems. To address these issues, this paper proposes an SVG-based online annotation technique that is built upon PDF file slicing. This approach aims to enhance the deficiencies of traditional annotation techniques and improve user interaction. The research begins by introducing the background and significance of online annotation technology, elucidating the application prospects of PDF slicing technology and SVG in annotation. The paper then provides a detailed description of the annotation algorithm and the implementation process. Additionally, it discusses the experimental setup, results, and analysis. The results indicate that the SVG-based online annotation technology can effectively enhance annotation efficiency and real-time interaction while enabling erasure of historical data and facilitating cross-platform communication. Compared to traditional annotation techniques, the SVG-based approach exhibits higher practicality and flexibility, making it suitable for various types of document annotations and collaborative scenarios.


Large language models capsule: A research analysis of In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) methods

Abstract: In the context of natural language processing (NLP), this paper addresses the growing need for efficient adaptation techniques for pre-trained language models. It begins by summarizing the current landscape of NLP, highlighting the challenges associated with fine-tuning large language models like BERT and Transformer. The paper then introduces and analyzes three categories of parameter-efficient fine-tuning (PEFT) approaches, namely, In-Context Learning (ICL)-inspired Fine-Tuning, Low-Rank Adaptation PEFTs (LoRA), and Activation-based PEFTs. Within these categories, it explores techniques such as prefix-tuning, prompt tuning, (IA)3, and LoRA, shedding light on their advantages and applications. Through a comprehensive examination, this paper concludes by emphasizing the interplay between performance, parameter efficiency, and adaptability in the context of NLP models. It also provides insights into the future prospects of these techniques in advancing the field of NLP. To summarize, this paper offers a detailed analysis of PEFT methods and their potential to democratize access to cutting-edge NLP capabilities, paving the way for more efficient model adaptation in various applications.


The metamorphosis of machine translation: The rise of neural machine translation and its challenges

Abstract: Machine translation refers to the process of using computers to translate source language into target language, which has undergone significant transformations since its inception, with the current mainstream neural machine translation achieving satisfactory translation performance. This paper overviews the three developmental stages of machine translation: rule-based machine translation, statistical machine translation, and neural machine translation, with a focus on neural machine translation. It introduces the key models that emerged in the development process of neural machine translation, namely the recurrent neural network encoder-decoder model, recurrent neural network search model, and Transformer, and compares their strengths and limitations. Other relevant technologies and models developed alongside neural machine translation are also discussed. Addressing the current challenges of neural machine translation, the paper delves into issues of overfitting, low-resource translation, structural optimization of Transformer models, and enhancement of neural machine translation interpretability. Finally, the paper explores the prospects of applying neural machine translation to multimodal translation.


Review of collaborative filtering recommendation systems

Abstract: In the era of information overload, recommender systems develop rapidly. And because the needs of information consumers are full of diversity and the information data provided by information producers is too large, to enhance the efficiency and quality of recommendations, the research community has introduced numerous approaches to optimize recommendation systems. As collaborative filtering stands as a time-tested technique in recommendation systems, This paper facilitates a swift comprehension of recent advances in collaborative filtering. It does so by examining the techniques presented across the entire collaborative filtering recommendation systems research field in recent years, especially its development in the domain of deep learning, and have a solid understanding of the field of study.


Transformers: Statistical interpretation, architectures and applications

Abstract: Transformers have been widely recognized as powerful tools to analyze multiple tasks due to its state-of art multi-head attention spaces, such as Natural Language Processing (NLP), Computer Vision (CV) and Speech Recognition (SR). Inspired by its abundant designs and strong functions on analyzing input data, we would like to start from the various architectures, further proceed to the investigation on its statistical mechanism and inference and then introduce its applications on dominant tasks. The underlying statistical mechanisms arouse our interests and intrigue us to investigate it in a higher level, and this surveys will focus on its mathematical foundations and then use the principles to try to analyze the reasons for its excellent performance on many recognition scenarios.


Soft and Hard Skills to Be Developed By a Pre-Service Teacher under Distance Learning: Case Study

Abstract: This article reflects on the development of soft and hard skills and their importance within the academic and educational course of students who study a degree in English as a foreign language under the distance modality. At the center of this study, the opinions of 46 students from various semesters underscore the fundamental role that the cultivation of these competencies plays in distance education. The qualitative approach and NVIVO software were used in a multiple case study to carry out axial coding. In this way, the information was classified into 6 categories, supported by the opinions provided by the survey participants: Personal Growth, Assertive Communication, Professional Enrichment, Digital Management, Pedagogical Confrontation, and English Proficiency. By analyzing the perspectives of these students, the research concludes that pre-service teachers have a valuable perspective on the development of competencies and recognize the integration of these competencies in their distance learning path, promoting personal and professional growth for their future roles.


Portable Referee System for Volleyball Game Based on Pressure Monitoring and Self-Powering Communication

Abstract: Sports refereeing has traditionally relied on human judgement, which can be prone to errors. However, the development of automatic referee systems has the potential to address this issue, improve training, and promote sports in the future. In this study, we present a novel and portable referee system specifically designed for volleyball games, which utilizes piezoelectric sensors to detect pressure and simultaneously harvest energy for powering the system. The system consists of two parts: a wearable glove and a device mounted on the volleyball. The glove part has five sensors fixed on the fingertips and the volleyball has eight sensors evenly distributed on its surface. The collected contact data from the sensors is wirelessly transmitted to a personal computer. Our experimental results demonstrate that the system can accurately capture a three-second contact and detect foul actions in a volleyball game. These findings highlight the potential of the system for sports and other monitoring applications.


The Impact of ChatGPT on Language Evolution: A Linguistic Analysis

Abstract: This research sought to identify changes in terms of word choice, sentence structure, and semantics introduced by ChatGPT and how the functionality of ChatGPT impacts the pragmatics of the English language: politeness, deixis, and discourse markers. This study used corpus analysis as part of the methodology involving Natural Language Processing (NLP) tools for part-of-speech tagging and sentiments, analyzing changes in wordings, syntaxes, and semantic elements generated by ChatGPT as an advanced language modal. A qualitative approach, including user surveys and comparative analysis, proved useful in clarifying the changing patterns of politeness, deixis, and discourse markers in AI-supported communication. Henceforth, ChatGPT generates neologism, unique cultural references, verbal contextual adaptations, and a wide range of technical terms. Moreover, the researchers found that user preferences for communication mediums vary, which showed a complex field of users and their expectations in the context of language generated by ChatGPT. Thus, it was observed that ChatGPT contributes significantly to developing the pragmatic aspects of language use in the modern era. Hence, the study proposes that the ChatGPT polishes the usage of politeness terms and improves its deixis along with formal discourse markers. This highlights the need for continuous improvement, especially in areas such as politeness, deixis management, and integration of discourse markers for the developers.


ANALYSIS OF NEEDS FOR DEVELOPING TEACHING MATERIALS FOR WRITING ADVERTISING TEXTS BASED ON ANDROID PLATFORM APPLICATIONS FOR CLASS VIII STUDENTS

Abstract: This research is motivated by the low ability of students to write attractive advertising texts. This is because the teaching materials available in schools are not yet able to facilitate students being able to write text advertisements well. The research carried out in this study was descriptive qualitative research, namely to describe students' needs for teaching materials that suit the students' needs


A Cognitive Semantic Analysis of Hausa Personifications

Abstract: This paper investigates the cognitive aspect of Hausa Personifications. The objective of the study is to discover the Hausa personifications found in the language expressions as a result of feature mappings from animate concepts to inanimate ones. The other objective is to come up with a cognitive semantic analysis of the Hausa personifications. The studied personifications are metaphorical and the Conceptual Metaphor Theory of Lakoff and Johnson (1980) is used as a guide frame in mapping the attributive features of the living entity to the inanimate ones. The observation technique is used in collecting the data for the study. One of the findings of the study reveals the kind of features merged between the animate and the inanimate concepts to form the Hausa personifications. Another important finding of the research indicates that the attributive features attached to animate entities are mapped upon the inanimate concepts conceptually to make the Hausa expressions personified. And finally, the study considered the analyzed Hausa personifications as similar to dead metaphors.


Blockchain Powered Medical Sector – Application, Challenges and Future Research Scope

Abstract: The recent research in the healthcare sector using computer technologies in the fourth industrial revolution helps to improve the quality of life by accessing the medical data to monitor, diagnose and treat the patient at the right time from anywhere in the world. Blockchain is one of the major recent innovations and trending research topics that plays a vital role in diverse applications like Smart cities, Healthcare industry, Smart grid, etc. Blockchain, which is fascinated with its features like secure data sharing, immutability, decentralization, and reliability in data management, has made it a prominent technology in the healthcare industry. This chapter discusses 1) The working principle of blockchain technology with its different prospectus in healthcare. 2) Advantages of blockchain technology over the Internet of Things in secured patient data management, efficient data sharing with decentralized data management accessible for authorized users using cryptography techniques. 3) Various applications of blockchain technology in healthcare, like remote patient monitoring using Internet of Things (IoT) devices for cardiac and electroencephalogram (EEG) signal monitoring to diagnose life-threatening diseases. 4) Drug traceability in the pharmaceutical drug supply chain to ensure product safety with an end-to-end tracking system and immutable transaction record. Finally, this chapter also presents the blockchain based challenges and solutions that advocate the future research scope in healthcare systems.


Consequences and Deliberations in Implementation of Blockchain and Internet of Things Integration

Abstract: Blockchain technology proposes security facilities to the Internet of Things (IoT). The things or objects used in daily life are connected to the internet to form IoT. The blockchain integral safety mechanism can deliver amenities, such as authentication, accessibility, integrity, secrecy, and authorization to the IoT applications. The uses of IoT applications are the dream turning into reality. But using this IoT still faces some challenges, mainly in the areas of security such as data consistency and reliability. The objects have interacted over the internet; they can also be supervised and controlled remotely. The use of IoT reduces time, manual work, tracking and money. With the evolution of IoT, it is essential to deliver more security for enormous amounts of data. Blockchain is a circulated network with the properties of integrity and secrecy. The blockchain maintains data security in the network of IoT. Here to discover the challenges associated with the combination of IoT and blockchain, a study is taken first, then a blockchain introduction is offered, followed by the Blockchain-based IoT requirements, demand and Quality of Service (QoS) discussed. Then, tasks faced while applying this blockchain-based IoT, such as plan, progress and deployment, are discussed. Then applications of blockchain-based IoT such as throughput, efficiency, latency, privacy, fork problem, smart contracts, legal issues, security, storage and proposed solutions are deliberated. Finally, upcoming research guidelines for the combination of IoT and blockchain are designated.


Integration of Blockchain and Internet of Things

Abstract: Customers can benefit from the Internet of Things in a number of ways, and it has the potential to transform the fundamental ways that consumers interact with technology. The pervasiveness and correspondences maintained for IoT might provide various conveniences and aids for people, but also open up many security loopholes. Blockchain, a distributed digital ledger, is finding uses in industries as diverse as finance, healthcare, utilities, agriculture, real estate, and Supplier Management. The middleman acting as guardians for specific applications in these enterprises can be removed in order to provide security and those equivalent applications can be run in a distributed way with practically no centralized power. Blockchain technology makes this feasible without sacrificing efficiency or safety, which was previously impossible. Blockchain and IoT seem to be best on their own in the respective sector in which it is applied, so businesses can try and exploit this powerful combination known as Blockchain Internet of Things (BIoT) to bring immense advancements, progressions and cutting edge innovations in the area of their interest. The term “BIoT” was created by fusing blockchain with IoT applications.


An Overview of Blockchain in the Field of Smart Healthcare System

Abstract: Rapid Blockchain is one of the most talked about technologies in the world at the moment. The origin of blockchain is a cryptocurrency called “bitcoin”. It is a secure currency that can be used as a medium of exchange worldwide. Blockchain itself is a decentralised, peer-to-peer distributed ledger capable of storing all transactions that take place on the network. This property makes blockchain useful for any type of exchange, such as data, currency and information. Blockchain protects against potential data theft or corruption in the healthcare network. It is important to maintain the integrity and validity of patient records to ensure wellness. Artificial intelligence and blockchain will provide a smart healthcare system for people around the world by extracting useful information, protecting medical data, simplifying claims processing, using patient self-generated data and systematising procedures.


Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns

Abstract: In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor-Purpura distance. We apply algebraic topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically calculating the persistence barcodes and Betti curves. By comparing the features of different types of output patterns, we uncover the complex relations between low-dimensional target signals and the underlying multidimensional spike trains.


Inclusive Description in the Glasgow School of Art Library’s Published Catalog

Abstract: This case study provides an overview of two guidance packs that the author created for the GSA Library, the first of which concerns advisory notices and the second of which concerns subject indexing and classifications. In an effort to share good praxis, this article includes real examples of item-level advisory notice text as well as a set of flowcharts that were designed to help GSA Library staff with the tasks of inclusive description.


Security Enhancement in Cloud and Edge Computing Through Blockchain Technology

Abstract: The cloud computing (CC) network is designed to tackle the security and privacy challenges of centralized cloud services by distributing computing and storage resources among networked nodes. Cloud computing, on the other hand, is restricted by the performance of linked devices, posing problems in state authorization, stats encryption, consumer privacy and more. Blockchain technology (BT) is the most popular circulated network technology right now. It is utilized in numerous fields like bitcoin, IoT, etc., to tackle the consistent issue of distributed data. The difficulties that CC networks present for security and privacy are covered in this chapter. Analysis and solutions brought to edge computing networks by BT in terms of data encryption, authentication and user privacy. In this chapter, the advantages of combining the cloud computing network with blockchain technology will be discussed. Finally, memory, workload, and latency problems for related future studies have been discussed.


Machines that work by electric current

Abstract: We are surrounded by machines that work by electric current. For example, digital devices that work by electric current like clock or transducer. In order for them to work, they require current. This paper explains a simple way to make those devices.


Randomized block quasi-Monte Carlo sampling for generalized likelihood uncertainty estimation

Abstract: Abstract Although hydrological model forecasts aid water management decisions, they normally have predictive uncertainties. Generalized likelihood uncertainty estimation (GLUE) is popular for constructing predictive uncertainty bounds (PUBs). GLUE is based on simple Monte Carlo sampling (SMCS), a technique known to be ineffective in establishing behavioural simulations. This study introduced randomized block quasi-Monte Carlo sampling (RBMC). In RBMC, each parameter's range is divided into a stipulated number of sub-blocks (Snb). Parameter's values are separately generated in each sub-block. Finally, the sub-blocks are shuffled while maintaining the sequence of generated values in each sub-block. When Snb is equal to the number of simulations, RBMC reduces to SMCS. Otherwise, each Snb leads to a separate RBMC configuration or sampling scheme. The number of RBMC-based behavioural solutions was often found to be greater than that of SMCS, in some cases, by up to 33.6%. The width of the 90% confidence interval on 95th percentile flow based on SMCS was often larger than those of RBMC, sometimes, by up to 23.4%. PUBs were found to vary in widths among RBMC configurations thereby revealing the influence of the choice of a sampling scheme. Thus, GLUE based on RBMC is recommended to take into account the said influence.


An assessment of the integration of ICTs into teaching processes by science teachers: The case of Albania

Abstract: Many studies have recently focused on the importance of the effective integration of Information and Communication Technology (ICT) tools in science education and the need for science teachers to receive adequate training and support to use them effectively. This paper aims to explore the Albanian science teachers’ perceptions and use of ICTs in teaching processes. The study provides an interpretative analysis of the opinions of science teachers teaching in the middle and high schools of several Albanian regions expressed in an online survey. Overall, the results suggest that the use of ICT by science teachers in Albania is limited by a lack of proper infrastructure, limited access to technology, and training of teachers on integrating technology. However, there are indications that the adoption of ICT in science education may increase in the future, particularly after the boost from the COVID-19 pandemic emergency.


The use of elements of neuropedagogy in the creation of virtual simulators for in-depth study of chemistry in higher education

Abstract: This study investigates the efficacy of incorporating neuropedagogical elements into virtual simulators for advanced chemistry education in universities. Utilizing a mixed-method approach, it uses questionnaires completed by 50 organic chemistry students. These questionnaires included both general and Likert scale questions, focusing on students' experiences with the PhET Interactive Simulation. The results highlight the positive impact of neuropedagogy on students' learning attitudes, cognitive abilities, and skill development, particularly in areas like critical thinking, analytical skills, and social competencies. A significant portion of students reported enhanced educational outcomes: 76% observed improved training effectiveness, 88% better understood the subject, and 96% found the virtual simulator-based learning more engaging. Furthermore, the study notes improvements in students' problem-solving abilities (68%), logical reasoning (92%), and comprehension of chemical processes (94%). These findings emphasize the value of integrating neuroscience principles in chemical education, potentially benefiting both tertiary and secondary education sectors. They indicate the necessity for educational adaptation in line with interdisciplinary research in cognitive and neurological sciences. This research not only serves as a valuable resource for chemistry teachers but also sets the stage for future empirical studies exploring neuroscience's role in teacher education. The study underscores the importance of further investigation into how teachers implement neuropedagogical techniques and the effectiveness of such applications, advocating for continuous development in educational methodologies.


Role of UPI Application Usage and Mitigation of Payment Transaction Frauds: An Empirical Study

Abstract: The objective of this investigation is to analyze the intention behind user behavior toward employing Unified Payment Interface (UPI) apps and pinpointing successful solutions for mitigating frauds linked to them. This will increase safety measures, which would lead to an escalation in usage among consumers, ultimately resulting in higher financial inclusion across India through cashless transactions. The Indian digital payment system has been transformed by these platforms, including Google Pay, PhonePe, and Paytm, which together account for over 90% of all online payments made. Users are attracted primarily due to its convenience as well as ease-of-use factors along with influence from their peers who have turned into avid users themselves. Largely, due to COVID-19 lockdown disruptions many people shifted away from traditional monetary exchanges toward Internet Banking options leading to a surge-related crime involving transactional fraudulent practices costing up to 346%. The popularity itself throughout time due again mainly because it’s easy access capabilities UPI payment app development on fraudulent activities continued. A structured questionnaire was adopted during the collection phase within Chennai City among active UPI payment apps users whose data were analyzed using multivariate statistical techniques. The result proves that predictors such as perceived security, safety, risk consistent advantageous represents key inferences, supplying impetus towards utilizing technology and providing cautionary advice where deficiencies subsist. Info dissemination, emphasizing feature/benefit analysis targeting core customer groups, delivered periodically supplemented defensive protocols, incorporating trial process adaptations can mitigate further risk associated with application utilization. Reader response, demonstrating accommodative response based on familiarity, empowering generational social structure, and enabling transformation gained momentum, demonstrates structural readjustment and solidifies the benefits borne out by increased adaption ratified effective outcomes accruing overall benefits, favourably impacting a variety of demographic profiles, and guaranteeing continued use.


STACK for Computational Science, Mathematics and Engineering e-Learners

Abstract: E-Learning platforms such as Moodle, Blackboard and Canvas have got reasonable attention in teaching and learning processes. However, when it comes to assessment and interactive learning activities they offer little service to science, engineering, and mathematics e-learners. In this work, we present the application of the System for Teaching and Assessment using Computer Algebra Kernel (STACK) as a plugin in a Learning Management System (LMS) to address the issue. Different features of STACK are demonstrated and discussed. As an LMS plugin, STACK can be used for interactive delivery of content as well as an assessment tool. Here, examples from mathematics, physical sciences and engineering are demonstrated. The use of STACK extends the applicability of LMS for a wider range of subjects to address existing inability to handle higher level mathematical and computational skills. Additionally, the use of STACK in an LMS is useful in handling tutorials for large classes especially when a blended delivery mode is preferred.


Item Enhanced Diversification in the Recommendation System using Graph Neural Network

Abstract: A recommendation system is a set of programs that utilize different methodologies for relevant item selection for the user. Graph neural networks have been extensively used in recent years to improve the quality of recommendations across all domains. A general recommendation system’s main goal is to recommend items to the user accurately, and it frequently prioritizes items that are well-liked or mainstream. If the model concentrates only on one specific item category from the users’ past preferences, then recommendations for the target user will become too obvious. To address this problem, diversity in the recommendation system is introduced. The model IG-DivRS (Item-Enhanced Graph Neural Network for a Diversified Recommendation System) is proposed. Our proposed model uses a Graph Neural Network (GNN) with the user’s interacted and non-interacted item history for diversified recommendation generation. The novelty of our proposed model is to explore the effect of non-interacted items on the target user for diversified recommendation generation. Instead of selecting random non-interacted items for the target user, we apply the DPP(Determinantal Point Process) algorithm to select the non-interacted item appropriately. The detailed experimental analysis shows that our model ID-DivRS outperforms the state-of-the-art model in accuracy and diversity.


Financial Original Voucher Classification and Verification System Based on Deep Learning

Abstract: Aiming at the problems of voucher preparation errors, numerous verification rules, lengthy audit cycle and low efficiency in the process of voucher preparation and audit of financial reimbursement in Colleges and universities, based on the implementation process of financial reimbursement process, this paper designs a framework of financial original voucher classification and verification system based on deep learning, and proposes a deep learning algorithm to identify the key contents of financial original vouchers, so as to support the voucher preparation process of financial reimbursement personnel Review and verification process of financial personnel. By testing the existing financial reimbursement process, the original voucher can be accurately classified according to the requirements of the national tax system for the classification of goods, for the identification of the type of daily reimbursement; based on the preprocessing of bills, FRCNN algorithms in deep learning can be applied to identify the key contents of bills.


Research on the Correlation Between College Students’ Financial Status and Feedback Awareness

Abstract: Student funding is an important project that ensures people’s livelihoods and warms the hearts of the people, which is related to poverty alleviation and social equity. In the continuous progress of society, China’s student aid policy is also gradually improving, and its focus has shifted from basic guarantee based aid to development based aid. But it is worth studying how college students’ feedback awareness after financial status. This article takes 51 subsidized college students from vocational colleges in Wuhan as the research sample, and uses Eviews13 software to empirically study the correlation between college students’ financial status and feedback awareness. The research conclusion shows that there is a significant correlation between financial status and feedback awareness, indicating that funded college students are grateful and know how to give back to society. Receiving drips of water when in need, and returning the kindness with a spring.


The Establishing of Practice Teaching System for Intelligent Manufacturing Engineering Major on Application-Oriented Universities

Abstract: Driven by China’s strategy of strength manufacturing country, the production mode of Chinese enterprises will change from traditional mechanical and electrical mode to digitizing, networking and intelligent production mode. Therefore, the intelligent manufacturing engineering major grows as a new major under that background. It has become the key to the construction of this major how to build a practical teaching system suitable for the intelligent manufacturing engineering major. Through cooperation with companies such as Huazhong CNC and Gaode Information, the application-oriented universities such as Wenhua college and Ezhou Vocational University have established a practical teaching system: Centered around intelligent manufacturing production lines, taking equipment of production line as the main line, focus on semi physical simulation platforms and digital twin virtual simulation platforms, taking vocational technical level certificates as the guide, taking digital design and programming as the foundation, and strengthened network data collection and communication control. Under the guidance of the practical teaching system, an intelligent manufacturing practical teaching platforms have been built. Which will be laid a solid practical teaching foundation for the construction of intelligent manufacturing engineering majors.


Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

Abstract: Environmental monitoring is increasingly reliant on image recognition through neural networks, automating the identification and classification of environmental threats. This automation optimizes resource allocation, enhances monitoring accuracy, and empowers researchers to address climate-related challenges. In response to this, we present a web application built using React.js and TensorFlow.js, which enables real-time image recognition. This application facilitates tasks ranging from climate analysis to disaster response and wildlife conservation, offering valuable insights into environmental trends. Moreover, it engages the public, promotes awareness, and contributes to conservation efforts, making it a versatile and cost-effective tool for environmental monitoring. The application leverages advancements in image recognition technology, availability of datasets, open-source frameworks, and cloud computing infrastructure. It supports a wide range of applications, promotes global collaboration, and ensures data security. While challenges exist, the potential of neural networks in environmental monitoring is promising. The web application combines the power of React.js and artificial neural networks, enhancing environmental monitoring efficacy and fostering sustainable practices on a global scale. Ongoing research and standardization efforts are essential for refining and expanding this approach.


The Preliminary Construction of an Evaluation Index System for Digital Competence of University Leaders in the Context of Digital Transformation

Abstract: Digital transformation puts forward new requirements for colleges and universities. This paper takes university leaders as an example and combines the competence model of “knowledge-ability-attitude” to construct the evaluation index system of digital competence of university leaders under the background of digital transformation. Through the expert scoring method, this paper has measured the weights of the evaluation index system with G1 method, principal component analysis and combined weighting model. The results of the study show that in the weighting system, the digital competence characteristics of leaders show a trend of “ability (0.4218) > knowledge (0.2586) > attitude (0.1965)”. Finally, based on the results of the weight calculation, the conclusions and shortcomings of this paper are summarized, and insights for future studies are presented.


Optimal Global Positioning System/European Geostationary Navigation Overlay Service Positioning Model Using Smartphone

Abstract: The potential for the use of smartphones in GNSSs (Global Navigation Satellite Systems) positioning has increased in recent years due to the emergence of the ability of Android-based devices used to process raw satellite data. This paper presents the results of a study on the use of SBAS data transmitted by the EGNOS (European Geostationary Navigation Overlay Service) system in GNSS positioning using a Xiaomi Mi8 smartphone. Raw data recorded at a fixed point were used in post-processing calculations in GPS/EGNOS positioning by determining the coordinates for every second of a session of about 5 h and comparing the results to those obtained with a Septentrio AsteRx2 GNSS receiver operating at the same time at a distance of about 3 m. The calculations were performed using the assumptions of the GNSS/SBAS positioning algorithms, which were modified with carrier-phase smoothed code observations and the content of the corrections transmitted by EGNOS.


Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion

Abstract: The algorithmic complexity vulnerability (ACV) that may lead to denial of service attacks greatly disrupts the security and availability of applications, and due to the widespread use of third-party libraries, its impact may be amplified through the software supply chain. The existing work in the field is dedicated to abstract loop or iterative patterns and fuzzing the entire application to discover algorithm complexity vulnerabilities, but they still face efficiency and effectiveness issues. Our research focuses on: (1) proposing a representation and extraction method for code features related to algorithmic complexity vulnerabilities, helping analysts quickly understand program logic; (2) providing a new ACV detecting model, focusing on the spatial complexity anomalies caused by deep recursion structures, and proposing a new filtering method; and (3) aiming at the difficulty of efficiently generating complex-data-type-related payloads using existing symbol execution techniques, a call-chain-guided payload construction method is proposed. We tested third-party components in the open-source Java Maven Repository, identified many unexposed vulnerabilities, and eight of them received Common Vulnerabilities and Exposures (CVE) identifiers, and demonstrated that our method can discover more algorithmic complexity vulnerabilities compared to existing tools with better performance.


Enhancement of Prediction Model for Students’ Performance in Intelligent Tutoring System

Abstract: Adapting Artificial Intelligence to Intelligent Tutoring System (ITS) has made teaching and learning more effective. Prediction of students’ performance has gained more interest among researchers to know whether the students master their learning before moving to another topic. For the research scope, we have analyzed numerous Bayesian Knowledge Tracing (BKT) variations in methodology and found that the most precise way to forecast students’ success is through Individualized Bayesian Knowledge Tracing (iBKT). Although iBKT makes a good prediction, iBKT does not consider other knowledge-related elements, such as learning and guess rate, and only uses students’ prior knowledge as the parameters. Due to issues concerning uncertainties in students’ interactions, this study proposes to enhance the prediction function of the iBKT using a feature relating to students’ confidence levels. Thus, this new confidence parameter is defined as P(C), assumed to improve prediction accuracy when forecasting student achievement. The prediction accuracy is tested using the attributes of the ASSISTment and Knowledge Discovery and Data Mining (KDD) datasets as input. In addition, Root Mean Square Error (RMSE) is applied to calculate the performance accuracy of iBKT and enhanced iBKT with the confidence parameter. As a result, the RMSE performance accuracy of iBKT with the confidence parameter shows a low RMSE score for both datasets. The ASSISTment dataset provides a higher prediction when applying the confidence parameter, 0.21190. Therefore, it is concluded that enhancing the confidence parameter is an effective method with accuracy improvement for predicting students’ success in ITS.


Development of Ice Jellyger Beverage Products as a Business to Train the Spirit of Entrepreneurship Among Students Through Social

Abstract: In addition to marketing products through social media and having an entrepreneurial spirit, sellers or students or entrepreneurs must choose a business that is contemporary and can compete in the social media market such as the Es Jellygar (Fresh Jelly) beverage product business. This research aims to train the spirit of entrepreneurship among students in developing and marketing Ice Jellygar beverage products. This research is an R&D research, the development model used is the 4D model (Define, Design, Develop, and Disseminate). The data analysis method used in this study was carried out through product acceptance tests on consumers. The result of this research is the development of Es Jellygar beverage products, which go through four stages of development: Define, Design, Develop, and Disseminate. The Define stage focuses on finding reference recipes, the Design Stage creates prototypes or product designs, the Develop Stage involves evaluation by experts and prototype testing, and the Disseminate Stage is about introducing products to the public through exhibitions and consumer testing. Marketing products through social media and having an entrepreneurial spirit, sellers or students or entrepreneurs must choose businesses that are contemporary and can compete in the social media market such as the Es Jellygar (Fresh Jelly) beverage product business. So it can be concluded that this business can be an opportunity for entrepreneurs or students as an easy side business, and can compete in the sales field.


STUDY OF THE INTERNET OF THINGS NETWORK CONSTRUCTION TASKS

Abstract: The emergence and development of the Internet of Things stimulates the development of telecommunications and computing technologies. The current state of this process and its prospects allow the inclusion of a large number of devices connected to communication networks. This leads to the need to select an adequate model and methodological apparatus that allows working with such a quantity. The article discusses the concept of the IoT, which determines that any devices or things can now interact with each other at any time at any point in space.