Deteksi Perubahan Berat Badan Akseptor KB Hormonal dengan Menggunakan Model Regresi Linear Berganda

Abstract: Hormonal contraception, which uses estrogen and progesterone preparations, is a contemporary method of contraception that is popular among active birth control participants. Apart from stopping population growth, it also has negative effects on its users. The side effect of increasing body weight can occur due to the nature of the acceptor and the use of hormonal contraceptives for a long period of time. The aim of the study was to detect changes in body weight of hormonal birth control acceptors based on age, parity and length of use of contraception using a multiple linear regression model. Non-experimental quantitative analytical research method with a cross sectional design. The research was conducted at several PMBs in Palembang City in July-September 2023 with samples consisting of hormonal birth control acceptors taken using a purposive sampling technique. Data were collected using questionnaires and body weight measurements and analyzed by multiple linear regression. The results of the research show that the prediction model for weight change = 5.789 - 0.098 Age + 1.4 Parity - 0.008 Length of birth control. Weight gain will definitely affect the health of the recipient and can increase the risk of obesity if it continues. To maintain a stable weight, birth control acceptors must pay attention to their daily diet and exercise regularly. It is very important to obtain information about the effects caused by the use of hormonal birth control with a high number of parities and ages at risk.


How to implement and evaluate with scrum methodology in business subjects - an experience in an educational innovation Project

Abstract: The following paper arises from the Educational Innovation Project PIE 15-132 "Implementation and evaluation of the scrum methodology (project based learning) in the subjects of Business Creation and Management", funded by the Vice-Rectorate for Teaching and Research Staff of the University of Malaga. The idea stems from the analysis and study of the agile scrum methodology, focused on iterations and continuous reviews. The proposal consists of transferring the application of this methodology, widely used in software development environments, to subjects linked to the business environment, given the flexibility that characterises it. Its application made it possible to obtain interesting results, both related to the objectives of the subject "Business Creation", as well as others of a transversal nature, which are in great demand in today's environments.


An Assessment of Capital Structure Decisions among Listed Deposit Money Banks in Nigeria

Abstract: This study aimed to assess Capital Structure Decision among listed Deposit Money Banks (DMBs) in Nigeria by assessing the effects of total equity and debts on capital structures of DBMs in Nigeria. The study alsocorrelated asset tangibility to firm size and examined the effect on the capital structure of the Banks. The study used secondary data collected from the annual accounts and reports of the selected ten (10) banks purposively selected for twelve (12) years covering 2011- 2022. The study’s population is made up of twenty-four existing listed deposit money banks on the list on the Nigerian Exchange Group plc based on the availability of data. The study employed explanatory variables of total equity, total debt, asset tangibility, and firm size to capture financial statement components. Data was analyzed using descriptive analysis and panel regression models. Findings revealed that equity, debts, and assets tangibility have positive and significant effects on the capital structure decision of the selected banks, while the firm’ size does not. The study concluded that equity capital, debt capital, and long-term assets are relevant in capital structure decision of listed deposit money banks in Nigeria. It is recommended that management should carefully assess the appropriate mix of equity and debt that maximizes income potentials and continuity of the firm in the context of substantial tangibility of the assets.


The Impact of Financial Integration on Economic Growth in Nigeria

Abstract: The study investigated the effect financial market integration on economic growth in Nigeria over the period of thirty-two years ranging from 1990 to 2021. The study designed and specified a multiple regression model to examine the individual and joint effects of the proxies of financial market integration (trade openness, credit to private sector and government expenditure) on economic growth in Nigeria (measured in Gross Domestic Product). The model was estimated by Ordinary Least Square technique using E-views 12 statistical package. The annual time series data used were collected from Central Bank of Nigeria (CBN) statistical bulletin and analyzed with the aim of achieving the stated objectives. The study found that trade openness, credit to private sector and government expenditures have individual and joint significant effect on economic growth in Nigeria. Based on the findings, the study therefore concluded that the financial market integration plays a significant positive role in the sustenance of growth of the Nigerian economy


Green Accounting and Corporate Performance of Selected Quoted Oil & Gas Firms in Nigeria

Abstract: The objective of this study is to ascertain the relationship between green accounting and the corporate performance of quoted oil and gas firms in Nigeria. The study specifically evaluates the effect of oil spillage, gas flaring, water pollution and energy consumption disclosures on the corporate performance of quoted oil and gas firms in Nigeria. The study adopted the ex- post facto research design. The population was drawn from the ten quoted oil and gas companies listed on the Nigerian Exchange Group (NGX) as of 1st June 2022. The final sample was delimited to eight firms with annual financial information for the study period. The study relied on secondary sources of data, i.e., from annual financial statements of the oil and gas firms. The data were analysed using descriptive and inferential statistical techniques. The hypotheses were tested using the pooled OLS technique consistent with prior authors on disclosure measurements. The results showed that oil spillage disclosure has a positive non- significant effect on Tobin’s Q; there is a positive and significant effect of gas flaring disclosure on Tobin’s Q; water pollution disclosure has a negative and significant effect on Tobin’s Q; and, there is a positive non-significant effect of energy consumption disclosure on Tobin’s Q. Based on the above the study recommended that shareholders should monitor compliance of managers with the numerous laws and strategies used to reduce GHG flaring and the resulting carbon emissions in the company. Managers develop a water pollution system for managing their negative environmental impact on water. Regulators should constantly monitor firms with excessive energy consumption to pay relevant fees and fines which can encourage the move to sustainable energy consumption.


Human Resources Management Impact on College Education Quality

Abstract: The role of human resource management (HRM) in forming the educational environment and ensuring excellence at higher education institutions is great. In German universities and organizations, HRM practices are adopted to measure performance of teaching with respect to student outcomes. The research questions impact of HRM on quality education and roadblocks found while implementing for improvements. HRM’s pivotal role in raising the standards of education and its singularity as people-centeredness aspects in educational institutions when presented by a literature review. The methodology used is a descriptive qualitative study which describes the understanding by College administrators in regard to Human Resource Management and related programs. The study includes a literature review of HRM practices in higher institutions. The findings uncover difficulties in teacher recruitment and retention but highlight the role of HRM practices, including faculty development programs, mentorship initiatives, or thorough support to academic staff members when it comes to ensuring education quality. The limitations are the poor teacher training and motivations of teachers to deliver good quality education, as well as poverty statements or socioeconomic statuses on which students come from. Among the suggestions, there are filling talent gaps, increasing work process effectiveness and its smoother continuity on a long-term basis by enhancing efficiency in staff knowledge issues as well as closer cooperation between faculties and HR departments. With all this information, it is possible to conclude that HR planning for analyzing tasks, costs and workforce requirements are necessary in promoting the functions of educational institutions towards achieving academic goals.


Impact of Knowledge Application and Knowledge Conversion on Organizational Performance: An Empirical Analysis

Abstract: This work examines the effect of knowledge application and knowledge conversion on organizational performance among manufacturing firms in Ibadan Oyo State. The research design adopted is the survey research design. Stratified random sampling technique was used. The sample size of two hundred respondents was selected from six manufacturing firms in Ibadan that served as the population of this study. Of the two hundred (200) questionnaire administered to the respondents, one hundred and ninety-two (192) copies were successfully completed and retrieved for the analysis. Both descriptive and inferential statistics was used for the analysis. Results of the finding show that knowledge application has a significant positively relationship with four sub- variables of organizational performance namely, revenue (r = .492**, p = .000), financial strength (r = .538**, p = .000), public image (r = .543**, p = .000) and goodwill (r = .463**, p = .000). Knowledge conversion is also positively related to the four sub-variables of organizational performance namely, revenue (r = .442**, p = .000), financial strength (r = .537**, p = .000), public image (r = .478**, p = .000) and goodwill (r = .443**, p = .000). The study concluded that the knowledge management has a significantly effect on organizational performance. It was recommended that organizations should redefine ways to promote knowledge conversion in order to maximize human potential in the workplace. Also, organization needs to combine the technology infrastructure, processes, incentives and culture in order to motivate employees to integrate and use knowledge management, in order to keep up with ever-changing business environment, and to ensure organizational success in addressing unforeseen challenges.


A literature survey on public blockchain technology for Cryptocurrency

Abstract: Blockchain and cryptocurrencies have emerged as revolutionary technologies that have garnered substantial interest in the past few years. A blockchain functions as a distributed and transparent ledger, facilitating secure transactions without the requirement of intermediaries. Digital assets like Bitcoin and Ethereum are examples of cryptocurrencies, leveraging blockchain technology to enable secure and unrestricted transactions. These innovations have the potential to revolutionize industries such as finance, supply chain management, and healthcare. However, challenges such as regulatory concerns and scalability issues must be addressed for their widespread adoption. Overall, blockchain and cryptocurrencies offer new opportunities for innovation and financial inclusion


Elderly Fall Detection Method Using Threshold Based Method and Transfer Learning

Abstract: The lack of elderly data is one of the most challenging issues in elderly fall detection. And the simulated young data is usually used in fall detection researches. In this paper, a wearable elderly fall detection method based on threshold based method (TBM) and transfer learning is proposed to shorten the distribution difference between young data and elderly data. In the process of training, labeled young data (source domain) and unlabeled elderly data (target domain) are utilized so as to weak the adverse effects of distribution difference between young data and elderly data. Finally, the better performance with sensitivity 98.63% and specificity 98.00% is illustrated with elderly data in Sisfall dataset.


A Deep Learning Capacity Estimation Method Based on Incremental Capacity Analysis and Differential Thermal Voltammetry

Abstract: Precise estimation of Lithium-ion battery capacity is critical for the battery management system (BMS). This paper proposed an innovative method that combinates convolutional neural network and feature curves which are incremental capacity analysis (ICA) and differential thermal voltammetry (DTV). Rather than extracting feature parameters of the IC curve as done in available research, the present method uses the whole IC and DTV curves in a certain range of voltage as the input to avoid complicated annual feature extraction and correlation analysis. The result shows that the max error of capacity estimation is less than 4.46 %, the mean absolute percentage error is less than 1.29 % for each battery, and overall MAPE is below 1.19 %.


New Tuning Rules of PI+CI Controllers for First-Order Systems

Abstract: The reset control is a simple nonlinear control approach where the states of the controller are conducted to zero when a particular condition is satisfied. The PI+CI is a controller that mixes the simplicity of PI controllers with the benefits of a reset action to mitigate the fundamental limitations of linear control. However, the tuning of this kind of controller, with three parameters, two for the linear part and one for the nonlinear one, is not trivial. In this paper, simple tuning rules for PI+CI are proposed for both tracking and regulation problems, assuming first-order dynamics for the plant. The resulting control scheme, for which the reset coefficient is computed from exponential functions, is simulated and compared with an ideal PI+CI where the reset coefficient is obtained using rules available in the literature. Similar results are obtained for the tracking problem, and optimal performance based on the Integral Absolute Error (IAE) is also obtained for the regulation problem. These new rules, in contrast to those already existing in the literature, depend only on closed-loop specifications. Furthermore, the framework based on the minimization of IAE, used to obtain the proposed rules, makes it possible to consider for the first time the tracking and regulation problems simultaneously, i.e., cases where setpoint changes and disturbance arrivals can occur at the same time before reaching a new steady state. The results are validated using a set of study cases.


Calculation Model of Earthwork Volume in Open Pit Mined Area Based on Step Feature Line

Abstract: To improve efficiency and accuracy of the calculation method of earthwork volume in the open pit mined area with complex terrain, a model based on the step feature line for the open pit mined area is constructed. With the Delaunay triangulation network constructed by the tilt photogrammetry photography of UAV, the step feature lines are extracted by the algorithm of RANSAC, and a detailed calculation model of earthwork volume is constructed by encrypting triangular network with the lines. With the experiments taken, it is shown that the accuracy of the new model is obviously improved as compared with both DTM model and unconstrained Delaunay triangulation model.


Enactment of Blockchain in NFT

Abstract: Non-Fungible Tokens (NFTs) have arisen as a major term in the twenty-first century, signifying unique digital assets such as photographs, music, and movies. These tokens receive their value and legitimacy from blockchain technology, which is mostly used on the Ethereum platform. NFTs, which use smart contracts, enable the production, ownership, and trading of one-of-a-kind digital assets, revolutionizing sectors including as art, music, and gaming. While blockchain’s uses are broad, there are worries about the environmental effect of blockchain, notably Ethereum’s energy-intensive proof-of-work consensus. Despite their revolutionary potential, NFTs pose dangers connected to market instability, legal concerns, and technological weaknesses. NFT trading is enabled by a variety of third-party programmers and markets, with notable participants being OpenSea and MetaMask. Looking ahead, the future of NFTs in India is dependent on legislative changes, as the country sees an increase in interest in these digital assets among artists and producers, necessitating a balanced approach to encourage innovation while resolving possible difficulties. NFTs are a game changer in the digital economy, opening up new opportunities for producers and investors. However, environmental problems and regulatory uncertainties must be resolved in order for the NFT ecosystem to expand sustainably. Technological improvements, legislative reforms, and the continuous growth of blockchain technology will most likely influence the future of NFTs in India and throughout the world.


A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things

Abstract: The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.


A Multi-View Temporal Knowledge Graph Reasoning Framework with Interpretable Logic Rules and Feature Fusion

Abstract: A temporal knowledge graph represents temporal information between entities in a multi-relational graph. Its reasoning aims to infer and predict potential links among entities. Predicting time-aware entities is a challenging task due to significant differences in entity appearances over time, such as different timestamps and frequencies. Current embedding-based similarity-matching methods have been introduced for predicting temporal facts. However, they lack deterministic logical explainability and cannot model the dynamic evolution of entities over time. To address these challenges, we propose a novel framework for temporal knowledge graph reasoning based on multi-view feature fusion (MVFF). First, MVFF extracts logical rules and uses the Gumbel-Softmax trick to sample high-quality rules. Second, it integrates logical rules, temporal quadruples, and factual triples to capture semantic features, temporal information, and structural information to solve link prediction tasks. Through experiments on four benchmark datasets, we show that MVFF outperforms state-of-the-art methods, providing not only better performance but also interpretable results.


ISTVP: Independent single transaction verification protocol for light node using fraud proofs without collaborator

Abstract: Abstract Most blockchain users run light nodes on mobile devices. Due to limited storage and computation, light nodes cannot perform transaction validation. This shortage makes opportunities for malicious nodes to produce blocks containing invalid transactions, which results in the loss of funds for light nodes. Fraud proofs play a significant role in ensuring transaction security for light nodes. However, existing fraud proof schemes require honest collaborators and the processing of entire blocks. To address these limitations, Independent Single Transaction Verification Protocol for Light node Using Fraud Proofs without Collaborator called ISTVP is proposed that enables light nodes to independently verify transactions and generate fraud proofs without relying on collaborators or processing the entire block. To support ISTVP, SVST is introduced, an efficient block structure for single‐transaction verification. SVST not only efficiently indexes historical transaction outputs to improve verification efficiency, but also significantly reduces the storage requirements for verifying transactions to . Furthermore, the authors analyze the security of ISTVP and demonstrate that it satisfies both persistence and liveness, while maintaining the level of security of full node.


IMPACT OF WOMEN'S POLITICAL PARTICIPATION ON GOOD GOVERNANCE IN NASARAWA STATE (2012-2023)

Abstract: This study investigates the impact of womens political participation on good governance in Nasarawa State using a case study period between 2012 and 2023. The objectives of the study were; to examine the extent of womens representation in Nasarawa States political institutions, to evaluate the influence and impact of these women in policy making, budgetary allocations, and the implementation of programs that address gender-specific concerns within the state, and to analyze the socio-economic implications of womens political participation. The theoretical framework employed for this study is Kanters Glass-Ceiling Theory (1977). Citing specific literature and empirical studies on womens political participation and governance, and consulting scholarly articles, policy briefs, and reports published in reputable academic journals and accessible databases, the analysis was bolstered with evidence-based findings. Study revealed that in Nasarawa State as in other parts of Nigeria and the world, patriarchy has over the years dominated the political landscape, leading to significant challenges and barriers for women seeking political roles. However, findings suggest that womens political participation in Nasarawa State positively impacts good governance. Increased representation of women in decision-making bodies improved policy-making processes, prioritized gender-sensitive sectors, and enhanced accountability and transparency. The Study therefore advocated for increase in the number of women in political offices in Nasarawa state which could potentially contribute to better governance by reducing corrupt practices and promoting transparency in decision-making processes. KEYWORDS: Politics, Political participation, Governance, Gender Equality


Roadmap for Unconventional Computing with Nanotechnology

Abstract: Abstract In the “Beyond Moore’s Law” era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, the adoption of a wide variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber-resilience and processing prowess. The time is ripe to lay out a roadmap for unconventional computing with nanotechnologies to guide future research and this collection aims to fulfill that need. The authors provide a comprehensive roadmap for neuromorphic computing with electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets and assorted dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain inspired computing for incremental learning and solving problems in severely resource constrained environments. All of these approaches have advantages over conventional Boolean computing predicated on the von-Neumann architecture. With the computational need for artificial intelligence growing at a rate 50×faster than Moore’s law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon and this roadmap will aid in identifying future needs and challenges.


Exploration of the influencing factors of independent learning in higher vocational students based on process ability analysis

Abstract: To clarify the main factors affecting the independent learning ability of higher vocational students, corresponding data were collected via a questionnaire survey, and the data were analyzed to determine the relationships between the factors affecting the independent learning of higher vocational students and independent learning ability. A process model of the independent learning of higher vocational students is constructed, and the model is analyzed to determine the influence of different factors on independent learning to clarify the process-level relationship between them.


Integration of artificial intelligence and GPT API applied to education: challenges and potentialities of digital teaching

Abstract: Research on the integration of digital technologies in education is key to understanding and optimizing the intersection between technological advancement and pedagogical development, ensuring the training of individuals capable of navigating and contributing effectively in the society of the 21st century. In this context, this article examines the incorporation of technology in the educational environment, highlighting the vital role of digital tools in reshaping teaching practices. The study analyzes how technology, especially the application of artificial intelligences such as Chat GPT, can transform the educational process, promoting more interactive and personalized learning. The challenges of preparing educators for this new reality are discussed, while underlining the importance of training critical and adaptable students in an increasingly digitized society.


Factors Affecting Teachers’ HOTS Implementation in Chinese College EFL Classrooms

Abstract: Reviewing previous HOTS studies in China revealed that the implementation and instruction of higher order thinking skills had not been widely adopted in Chinese college EFL education. Despite the unanimous agreement on the crucial role of fostering students’ HOTS, the incorporation of HOTS is affected by many factors. With a mixed-method approach, this research aims to investigate the factors affecting Chinese college EFL teachers’ HOTS implementation from the teacher’s perspective. Data was collected from 100 Chinese college EFL teachers through quantitative questionnaires and structured interviews. The findings of this research suggest that implementing HOTS in the Chinese college EFL classroom is challenged by various factors, particularly those related to teacher, student, institutional, and environmental factors. The results also provide empirical evidence of the insufficient knowledge and proficiency in implementing HOTS among the surveyed teachers during their language teaching process. This implies the need to establish a supportive HOTS-promoting environment involving teachers, students, and administrators in successfully implementing HOTS instructions in Chinese college EFL classrooms.


Fusion of digital technologies and active methodologies in contemporary education

Abstract: This study addressed the fusion of digital technologies and active methodologies in contemporary education, aiming to understand its impact on the teaching and learning process. The research problem focused on the need to investigate how the combination of these elements can influence current education. The overall objective was to analyze the benefits, challenges, and integration strategies of these approaches, as well as their impact on teaching and learning. The methodology involved a comprehensive literature review, data collection, and analysis of research and case studies. The results indicated that the integration of digital technologies and active methodologies promotes more engaged, personalized, and collaborative learning. However, challenges such as the need for teacher training and overcoming technological barriers were identified. Legislation and educational policies play a importante role in promoting these approaches. The final considerations highlight the ongoing importance of the fusion of digital technologies and active methodologies in contemporary education and the need to overcome the identified challenges.


Secondary education and technological innovation: educational tools for modern learning

Abstract: This study addressed the impact of technological innovation in high school and the implementation of modern educational tools. The central problem investigated was the effective integration of these technologies into the educational environment and their effect on student performance and engagement. The overall objective was to analyze the impact of these innovations, identify the most effective tools, and explore inclusive and equitable implementation practices. The methodology adopted was a literature review, allowing for a comprehensive analysis of the topic. The results indicated that despite challenges related to equity of access and resistance to change, educational technologies offer significant opportunities to revitalize teaching and learning. The analysis highlighted the need for a holistic approach in adopting technologies, emphasizing the transformation of pedagogical practices and educator training. The final considerations emphasized the potential of technological innovations to enrich the educational experience, provided they are accompanied by appropriate pedagogical strategies and equitable accessibility.


Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Abstract: Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.


Empirical analysis of optimized portfolio allocation based onMarkowitz and index models

Abstract: There is a need to achieve a balance between asset returns and risks. This has remained a central focus of financial market research. It has served as a crucial reference for investment decision-making. There has been a weakness as this financial investment theory relies on qualitative analysis. This method has lacked robust quantitative methods. The resurgence and expansion of Western economies have led to a flourishing financial investment activities. This has prompted the emergence of the Modern Portfolio Theory (MPT). The theory was pioneered by Harry Markowitz in 1952. MPT has rapidly evolved over the period. It has attracted numerous scholars and yielded substantial research outcomes (Markowitz, 1991).


A case study on digital training: benefits and unexplored opportunities

Abstract: Abstract In an era characterized by the increasing complexity of products and the rapid turnover of the workforce across different companies, there is a growing need to invest significantly in quick and efficient training methods. Concurrently, the advancement of digitalization has rendered certain training practices anchored to paper-based materials obsolete. Numerous companies are directing their investments toward digital training, yet the optimal format to exploit the full advantages of digitalization remains unclear. This study undertakes a comparison of four distinct digital versions of the same training process with the aim of comprehending the tangible benefits. The findings indicate that to fully capitalize on the advantages of digital technology, a complete rethinking of training practices is necessary.


The attribute classification model in intelligent question answering system based on domain knowledge graph

Abstract: The knowledge graph-based question answering system aims to find the connection between the internal knowledge behind the problem and the known knowledge base triplet. This paper proposes a framework for knowledge graph question answering (ZDNN-KGQA), which divides intelligent question answering based on knowledge graphs into four steps: named entity recognition, entity disambiguation, attribute classification, and answer selection. In the named entity recognition step, this paper proposes a BERT-BiLSTM-CRF model that combines an attention mechanism to identify entities in question sentences. In the entity disambiguation step, this paper utilizes the BERT-Softmax model to extract the accurate entities from the candidate entity set. In the attribute classification step, it is converted into a problem of text similarity. This paper proposes the use of BERT-Softmax and Word2vec models to extract attributes intext problems, effectively improving entity recognition performance and question semantic parsing performance. The average F1 value of this system on the test dataset provided by the NLPCC-ICCPOL 2016 KBQA task is 0.8974, which is close to the optimal performance level.


Pengembangan Sistem Informasi Manajemen Kepegawaian pada Yayasan Pondok Pesantren Nurul Huda Sukaraja

Abstract: Along with advances in information technology, the Nurul Huda Islamic Boarding School Foundation still relies on managing personnel data manually without adopting a personnel management information system. This research aims to design a personnel information system that includes employee data management, including rank, leave applications, attendance, further studies, and monitoring of employee performance. In this research, the methodology used is Extreme, which is used to model process flow. In contrast, Data Flow Diagrams (DFD) are used to model data flow in a system, helping to clearly describe processes, data, and interactions between system components. The system development method used is extreme programming. Then, the system will be tested using the Black Box Testing method. The results show that the system is 100% valid and 100% compatible.


Decoding 2.3 Million ECGs: Interpretable Deep Learning for Advancing Cardiovascular Diagnosis and Mortality Risk Stratification

Abstract: Abstract Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.


On logistic regression versus support vectors machine using vaccination dataset

Abstract: The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.


AUGMENTED REALITY ADVERTISEMENTS WITHIN THE REALM OF PHYGITAL MARKETING: CASE STUDIES OF ADVERTISING EXAMPLES

Abstract: Technology has evolved to be a concept of constant development and innovation, significantly impacting both individual and societal life. It has become a fundamental building block that influences personal inclinations, choices, and decisions, and is already present in many phases of human life. Individuals react to technological advancements they encounter in their daily lives, as they are not just consumers in the current era, but also producers. The responses to technological advancements extend to influencing purchasing activities and shopping behaviors of individuals. This study has been developed to analyze the role and usage of Augmented Reality technology, one of these technological advancements, in the transforming field of marketing and specifically in advertising. The evolution of the marketing concept has brought about new approaches, as is the case in almost every developing field. Among these new approaches, the influence of digitalization is acknowledged. Within these definitions, the concept of phygital marketing is included. The phenomenon of transferring real-life actions to a virtual (digital) environment has given way to the transfer of virtual (digital) environments into the physical, real-life aspect. The term "phygital", a combination of physical and digital, can be summarized as the reflection of digital phenomena, especially in the physical dimension, into everyday life. One of the application areas where the impact of phygital concept in marketing is evident is the use of Augmented Reality technology in advertisements, which defines the scope of this study


Autonomous collision avoidance method based on improved artificial potential field in the traffic separation scheme waters

Abstract: In order to realize the autonomous navigation of ships in the complex waters under the traffic separation system (TSS), taking the Chengshanjiao TSS waters as an example, an improved autonomous collision avoidance algorithm is proposed based on the artificial potential field (APF) method. By establishing five kinds of potential fields and their force functions, including the gravitational function of the goal point, the repulsion function of the target ships (TS), the gravitational function of the centerline of the traffic lane, the velocity function of the ship, and the boundary repulsion function of the navigation lane, finally the resultant force of them is calculated to guide the navigation direction of the ship, during the navigation process in this TSS water. Multiple groups of comparative experiments demonstrated that under the improved algorithm, the ship could avoid collisions with TS in four typical scenarios, according to the requirements of the 1972 International Regulations for Preventing Collisions at Sea (COLREGS) and good seamanship practice, simultaneously the ship could proceed in the navigable lane and keep safe distance with the boundaries and separation zones. The method proposed in this paper is expected to provide basic theoretical basis and technical support for the safe navigation of autonomous ships in TSS waters.


A study on the correlation between the English score of collegeentrance Examination and the score of the first CET-4

Abstract: English is the most widely used language in the world. Nowadays, the world is showing a trend of globalization. As a bridge connecting us with the world, the importance of English is self-evident. The study of high school is to let us master the ability to use English communication and to strengthen our ability to use English further when we enter the university. The college entrance examination is a standard reference examination for high school students in China after completing the basic learning stage. Its purpose is to comprehensively check whether the students who have completed the high school courses have met the requirements stipulated in the syllabus. After years of practice, it has become one of the most authoritative examinations in China. [1] In recent years, many colleges and universities have paid great attention to the CET-4 test, and most have linked the CET-4 test with a bachelor’s degree. Students cannot get the CET-4 certificate in universities, and the schools will not award bachelor’s degrees to the students. China’s College entrance examination and CET-4, as two of the most objective level examinations in China, have strong reliability and validity in the field of English learning and have a certain continuity over the years, which can objectively and honestly reflect the English learning level of students from high school to college. [2] The English score of the college entrance examination can also reflect the English level of first-year college students when they first enter school. While CET-4 represents a student’s English level at the university level.


Scalable measurement error mitigation via iterative bayesian unfolding

Abstract: Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead.


Magic behaviors in non-Markovian environment

Abstract: Magic as an essential resource in fault-tolerant quantum computation may shed insight into quantum information processing of open quantum system. In this work, we study the dynamics of magic for atom system immersed in non-Markovian reservoir. It is found that magic decay process can be delayed and adjusted by the strength of classical driving field and non-Markovian memory effect. In addition, it is shown that the magic state is less susceptible to decay the process of system-environment coupling than stabilizer state, and for two atoms coupling to two independent reservoirs, magic can be generated. The results may be beneficial for theory and experiment of quantum computation and information in open quantum system.


A Proposal of Naturalistic Software Development Method

Abstract: Naturalistic programming purports to include natural language elements in programming languages to increase software expressiveness. Even though natural language is inherently ambiguous, it is richer and thus more expressive than any artificial language. Currently, the Naturalistic Programming Paradigm (NPP) is supported by its conceptual model and three general-purpose naturalistic programming languages that can generate executable binary code. Nevertheless, to date, no research efforts have been concentrated on applying the NPP within a software development process. To address this gap, in this article, we propose a naturalistic software development method to test the advantages of the NPP. The method focuses on the analysis and design stages of the software development process and seeks to contribute to closing the gap between the problem and the solution domains. We also present an example of an implementation using Cal-4700, a naturalistic programming language, showing the differences in expressiveness of programming with a traditional programming language, like Python.&nbsp;


Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

Abstract: Due to the recent developments in communications technology, cognitive computations have been used in smart healthcare techniques that can combine massive medical data, Artificial intelligence, federated learning, Bio-inspired computation, and the Internet of Medical Things. It has helped in knowledge sharing and scaling ability between patients, doctors, and clinics for effective treatment of patients. Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results. Since the subject’s speech can be remotely recorded and submitted for further examination, it offers a quick, economical, dependable, and non-invasive prospective alternative detection approach. However, the two main requirements of this are higher accuracy and lower computational complexity and, in many cases, these two requirements do not correlate with each other. This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy. A Cascaded Perceptual functional link artificial neural network (PFLANN) is used to capture the non-linearity in the data for better classification performance with low computational complexity. The proposed model is being tested for multiple respiratory diseases and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.


Optimasi Centroid Awal Algoritma K-Medoids Menggunakan Particle Swarm Optimization Untuk Segmentasi Customer

Abstract: Customer segmentation is an important strategy in a company, it affects good customer relationships which will result in increased profits. Grouping customers in data mining can use several algorithms, but K-Medoids is the right choice because it can reduce noise and outlier sensitivity. However, the selection of cluster centers is still random and has an effect on the results of clustering, so it is necessary to improve the k-medoids algorithm so that the resulting cluster value can be optimal. Particle Swarm Optimization is an optimization algorithm that is often used and has been proven to improve the results of a clustering. In this case, optimization using Particle Swarm Optimization (PSO) in the selection of the initial cluster center needs to be applied to the k-medoids algorithm so that the results of the cluster can be optimal. The results of the study showed the Davies-Bouldin Index (DBI) value for K-Medoids K 2 = 0.379, K 3 = 0.283, and K 4 = 0.593, while the DBI value PSO + K-Medoids K 2 = 0.088, K 3 = 0.226, and K4 = 0.363. The DBI value shows that PSO optimization on K-Medoids to determine the initial centroid is proven to improve the results of clustering than standard K-Medoids.


Software security: Threats, solutions and challenges

Abstract: Software security is of great concern as computers have entered almost all walks of life and people at large have become dependent on technology for not only entertainment and communication but for performing tasks involving money and a lot of stake. Software security not only involves securing the software but also user data and communication media. This paper states the several types of security threats that exist since the time networking has evolved, namely, malware, Trojans, viruses, denial of service attacks, and many more. This paper reviews several measures to address these threats. It includes logging, anti-malware, network security methods, and encryption methods. It has been identified that a lot of work has been done to deal with security threats, and it is not only limited to the protection of software but also extends to the protection of data and networks. The existing methods make extensive use of artificial intelligence, and it is identified that there is a need to develop a model that is able to identify known as well as unknown threats. There is a huge scope for research in this area.


An Analytical Inquiry into the Impact of International Trade on Poverty Reduction in Nigeria

Abstract: This work is propelled by the alarming rate at which poverty has continued to increase, thereby aiding socio-economic challenges in Nigeria. The work through the application of the ARDL and ECM framework of data analysis on secondary data sourced from the Central Bank of Nigeria, calibrated the impact of international trade on poverty reduction in Nigeria from 1981-2021. The result of the study has proven that the relationship between the nation's non-oil export and poverty is negative. However, evidence from the study more so alludes that the country's export of crude oil overtime has not had any meaningful effect of reducing poverty in Nigeria, rather the rate of poverty has continued to rise with the increase in oil exports. Empirical evidence from the study further revealed that Nigeria's imports within the scope of the work has had a significant negative relationship with poverty, which negates the theoretical condition of their expected relationship. Hence, to overturn this positive trend between oil exports and poverty rate, the Nigerian government needs to train and equip the unskilled poor Nigerian with requisite skills and knowledge that would make them gainfully employed in the oil industry, more so the country is admonished to restrict its imports to just commodities that cannot be produced locally.


Inclusive Virtual Reality Learning: Review and 'Best-Fit' Framework for Universal Learning

Abstract: The rise of Virtual Reality (VR) in educational contexts has highlighted the need to design Virtual Reality Learning Applications (VRLAs) that prioritize inclusivity, accommodating a spectrum of learner needs. Despite the surging interest, there is a noticeable gap in research that delves into the specifics of creating VRLAs that are rooted in inclusive educational theory. This research sought to extract insights and recommendations for the development of VRLAs tailored for diverse student populations. The intention was to scrutinize research focused on the inclusive design elements of VRLAs, leading to the establishment of preliminary Inclusion Guidelines for VR Learning (IGVRL). Adopting the "best-fit" framework synthesis technique, the research anchored its findings in the Universal Design for Learning (UDL) framework. UDL was developed to mold learning experiences to meet the requirements of heterogenous learners. Using UDL as a coding framework, a comprehensive literature review was undertaken, adhering to the SPIDER search strategy. The review of literature revealed distinct design recommendations that facilitate inclusive learning within VRLAs. Information was systematically categorized based on UDL's nine classifications and subsequently distilled into the preliminary IGVRL. It's pertinent to note that these guidelines, while offering a foundational perspective, necessitate further in-depth evaluations for validation. The analytical process brought to the fore several themes that UDL did not adequately encompass, such as the nuances of embodied learning, the focus on VR contents and their immersive properties, and the pivotal role of collaboration and cooperation in VRLAs. These insights underscore the further need for research in these areas. Although some facets of VR accessibility were discussed, a deeper exploration into this domain was identified as crucial, reiterating the importance of accessibility in underpinning inclusive education. The research underscores the potential of VRLAs in promoting inclusivity within educational settings and introduces the preliminary IGVRL for VRLA design, specifically targeting K-12 contexts. This paper emphasizes the continuum of research required to refine and validate these guidelines, ensuring their applicability and efficacy in diverse educational scenarios.


An Improved Variational-Based Model for Denoising and Segmentation of Vector-Valued Images

Abstract: Preserving important features such as edges is one of the main concerns in models for denoising and segmenting vector-valued (colour) images. The Rudin-Osher-Fatemi (ROF) model is a well-known variational-based image denoising model that is capable of reducing image noise while preserving image edges. However, the ROF model is not formulated for denoising colour images and is less effective in preserving corners and weak edges. On the other hand, a variational-based selective segmentation model for colour images called the selective distance segmentation (DSS2) model has recently been proposed, which can effectively partition or extract a specific object in an image. However, the DSS2 model has problems in segmenting colour images with noise, which may result in poor segmentation. Therefore, in this research, we first modify the ROF model to denoise vector-valued images by including the edge detector and extending the formulation into a vector-valued framework. Second, we reformulate the DSS2 model by incorporating the modified ROF model as a new fitting term in the DSS2 model. Peak signal-to-noise ratio (PSNR) is used to measure the image quality, while Jaccard and Dice similarity index are used to evaluate the segmentation quality. The comparison between our proposed model and existing model shows that our model is more effective as indicated by higher PSNR, Jaccard and Dice similarity index values.


Evaluating the Impact of Financial Stability and Monetary Stability on Economic Growth: Evidence from an Emerging Economy

Abstract: Financial stability and monetary stability both make an essential contribution to sustainable industrial growth and well-established industrial sectors contribute to sustainable economic development. The fundamental objective of this study is to create an FSI index to quantify financial strain in Pakistan. While utilizing monthly data starting from 2004-11 to 2017-7 the Financial Stress Index has four indicators which include TED spread, yield spread, stock price volatility, and exchange rate volatility through principal component analysis (PCA). An ARDL co-integration model is used to investigate the long-term and short-term relationships between time series data. The primary takeaways from this research are that financial stability and industrial production are positively correlated, while inflation and industrial production are negatively correlated. This study fills a knowledge gap by analyzing the impact of financial and monetary stability on economic growth in Pakistan using the Financial Stress Index. Lastly, this study sheds light on the financial and monetary concerns of Pakistan.


Simulating quantum backﬂow on a quantum computer

Abstract: Abstract Quantum backﬂow is a counterintuitive eﬀect in which the probability density of a free particle moves in the direction opposite to the particle’s momentum. If the particle is electrically charged, then the eﬀect can be viewed as the contrast between the direction of electric current and that of the momentum. To date, there has been no direct experimental observation of quantum backﬂow. However, the eﬀect has been simulated numerically (using classical computers) and optically (using classical light). In this study, we present the ﬁrst simulation of quantum backﬂow using a real quantum computer.&#xD;


Assessing EFL Listening and Speaking Skills During Remote Teaching

Abstract: The purpose of the present study was to analyse how English as a Foreign Language (EFL) listening and speaking skills were assessed during remote teaching as a result of the COVID-19 pandemic. The participants were 174 senior high school students in public and private institutions, 102 EFL teachers, 32 high school authorities, and 80 students’ parents. The methodology comprised a quantitative approach in which students’, teachers’, and authorities’ surveys were analysed. The instruments employed consisted of a five-point Likert scale that included strongly agree, agree, neutral, disagree, and strongly disagree regarding items related to assessment. The findings suggest that for assessing listening and speaking skills the teachers in the research mostly used online technological tools such as quizzes, chatrooms, and blogs. Furthermore, the assessment instruments included oral presentations, questioning, as well as listening and speaking tests. Finally, formative and summative assessments were mainly employed to evaluate students’ listening and speaking skills during the pandemic.


FACIAL EXPRESSION EMOTION RECOGNITION USING LOCAL BINARY PATTERNS HISTOGRAMS

Abstract: Local Binary Patterns (LBP) histograms are used as texture descriptors by the proposed facial expression emotion detection system to identify various face expressions in photos. Localized facial patterns are represented by histograms formed by the extraction of LBP features based on pixel intensity comparisons after pre-processing operations such as picture normalization and scaling. Using a labeled emotion dataset, these histograms are used as feature vectors to train a machine learning model. The model's performance is evaluated using evaluation measures, such as accuracy, precision, recall, and F1 score. The system, which was developed with OpenCV and scikit-learn, shows a reliable and useful method for recognizing facial expressions that can be used in practical settings. Key Words: Facial Recognition, Deep Learning, Image Recognition


Untangling the Processes of Bitcoin: An Organizational Learning Perspective

Abstract: Bitcoin is a complex phenomenon, whether in terms of the macro factors affecting its price or its role in the global energy infrastructure. However, extant literature pays too little attention to exploring the internal mechanisms of the protocol to be able to link them to how they affect the visible characteristics of Bitcoin. This paper uses secondary data from highly reputable Bitcoin-focused sources to systematically map the processes that enable Bitcoin to function as a peer-to-peer cash system. Novelty is achieved by applying the established and versatile “4I” organisational learning framework to provide a new lens through which to understand how the processes within Bitcoin enable and facilitate different types of changes to the protocol. Further insights are provided to organisational learning from Bitcoin, in relation to managing mission-critical changes to organisational systems. In addition, it presents an option for dealing with irreconcilable internal differences to “hard-fork” part of the organisation. While the scope of this paper is limited to secondary data, opportunities for further research, including primary data collection, are outlined to explore how Bitcoin knowledge disseminates within communities or companies.


Aksesibilitas Pemustaka Penyandang Disabilitas Di Dinas Kearsipan Dan Perpustakaan Provinsi Sumatera Barat Berdasarkan Standar IFLA Checklist

Abstract: This research aims to analyze the accessibility of library and archives services for people with disabilities in the West Sumatra Provincial Archives and Library Service, the accessibility of users with disabilities in the West Sumatra Province Archives and Library Service based on IFLA Checklist standards, and the obstacles faced in fulfilling accessibility for people with disabilities at the Provincial Archives and Library Service West Sumatra. This research uses qualitative research with descriptive methods. Data collection techniques use observation, interviews and documentation studies. The data validation technique uses triangulation, namely triangulation of data sources. The data validation technique uses the Miles and Hubermen model which consists of data reduction, data presentation, drawing conclusions and verification. The results of this research are as follows. Firstly, accessibility at the West Sumatra Provincial Archives and Library Service has not yet been fully realized because there are still many shortcomings found in the implementation of accessibility services for people with disabilities, such as the ease of library users in meeting their needs has not been fully realized and the only library material collections available are books with braille printing. Second, accessibility at the West Sumatra Archives and Library Service based on the IFLA Checklist Standard reached 40.54%. Thus, it can be said that accessibility for users with disabilities in the Archives and Library Service is still not in accordance with the IFLA Checklist standards. The research results from the IFLA Checklist standard amounted to 111 items which were 45 items which were appropriate while which were 66 items which were not appropriate. Third, the obstacles faced by the West Sumatra Province Archives and Library Service in fulfilling accessibility for disabled users from various aspects such as the lack of available funding, lack of technical knowledge of implementation, and limited human resources who are competent in providing services for users with special needs or disabilities.


The Linked Factor of Customer Satisfaction and Loyalty in F&B Industry: A Study of Operational Performance Factor

Abstract: The focus of this research is to investigate how operational performance affects customer satisfaction and loyalty in the food and beverage industry. Starbucks has been selected as this research object. Conducting a survey and referring to other studies of journals and articles to support the assisting data are the research methods employed. According to the research results, 71 % of respondents are pleased with current Starbucks services and products. The studies also revealed that the majority of customers had heard of Starbucks on social media. The study’s findings reveal that operation success is significantly linked to customer satisfaction. Customer satisfaction has been described as a significant mediator between service quality and customer loyalty. Having a clear understanding of the hypothesized relationship in the study encourages Starbucks to figure out the best action to gain or increase customer loyalty by providing good service quality to build a loyal customer base and customer satisfaction.


Bitcoin on Economic Growth (Case of 8 Asia Countries)

Abstract: In recent years, the price of Bitcoin has risen sharply on balance, despite some fluctuations. The latest price surge of the largest cryptocurrency prompted talk about Bitcoin's future. This research was conducted to confirm the impact of cryptocurrency which is Bitcoin on economic growth in Asian countries. Many studies have talked about cryptocurrencies on the technological side, but there are still few references to research that analyzes cryptocurrencies with economic growth. Using panel data regression, this study correlates cryptocurrencies, in this case, Bitcoin, as a type of cryptocurrency that is best known with GDP as a variable that represents economic growth. This study also has 4 other variables as control variables which will ensure the results are not biased. Namely the Internet (I), Capital (K), Labor (L), and Technology (T), as in Solow's growth theory which reviews growth through its supply side, the function of production inputs. This study took samples from 8 Asian countries with the most Bitcoin transactions (Japan, China, India, Singapore, Indonesia, Malaysia, Thailand, and Hongkong) from 2014 to 2020.


Analysis of Web Vulnerability Using Open-Source Scanners on Different Types of Small Entrepreneur Web Applications in Malaysia

Abstract: Most of Malaysia’s small entrepreneurs have switched to online platforms as an alternative to their physical businesses. Social media sites such as TikTok, Instagram, Twitter, and Facebook provide free advertising tools and convenient access to a broader global target. However, security issues on these websites remain questionable as users are exposed to web attacks due to the vulnerabilities on the websites. Considering the cost and lack of awareness of the importance of cybersecurity, some organizations find it not profitable to invest in securing their websites. Therefore, this paper aims to test Malaysian small entrepreneurs’ web applications using open-source scanners and analyse the results of web vulnerabilities detected. To do so, two types of open-source scanners, OWASP ZAP and IRONWASP, were installed to scan five websites found through advertisements on social media sites. The web vulnerability identification was based on the top 5 OWASP web vulnerability reports, and the results showed that five types of web vulnerabilities were detected. The analysis of the results showed that the top 5 web vulnerabilities in Malaysian small entrepreneurs’ websites are the ‘Missing Session Timeout’ vulnerability with 81.84 percent, the ‘Sensitive Information Passed as Clear Text in GET URL’ vulnerability with 14.74 percent, and the ‘Session ID Cookies not Marked Secure’ vulnerability with 2.47 percent. This paper provides security analysis on Small Medium Enterprise (SME) websites for future enhancement and consideration during development and implementation to avoid possible attacks. Therefore, developers are advised to handle these vulnerabilities by carefully managing the session timeout, and users are recommended to log out from the websites immediately after they are done.


Modern Implementation of the Natural-Philosophical Concept of Cognition on the Basis of the Empirical-Metaphysical General Systems Theory

Abstract: The article examines the relationship between philosophy and science, as well as the possible role of general systems theory in the formation of the field of consolidated philosophical and scientific knowledge. It is argued that the existing arguments concerning the incompatibility of philosophy with the criteria of scientificity only point to the existence of differences between philosophical knowledge and knowledge within the framework of private sciences. At the same time, philosophy is the methodological and paradigmatic basis of all sciences. It is suggested that it is expedient to return to the natural-philosophical concept of cognition, according to which the core of science is philosophy, and private sciences act as tools of cognition of separate subject areas. The methodological basis for the realization of the natural-philosophical concept today can be the general theory of systems, supplemented by the ontological component. In the article the idea of a common field of knowledge formed by philosophy and all private sciences is put forward. This field of cognition is formed by areas of three types: metaphysical knowledge, private sciences, and areas of generalized or universal knowledge, the cogni-tion of which can be carried out only by means of the general theory of systems. An extended version of the general theory of systems is proposed – the empirical-metaphysical general theory of systems, which, in the author’s opinion, possesses the necessary capabilities for the realization of philosophical cognition. The pro-posed general theory of systems includes an epistemological component (metaphysics of material being) as well as an ontological component. А cognition of complex non-deterministic objects within the framework of empirical-metaphysical general theory of systems is realized by a wide set of tools: collections of patterns and primitives of systems, secondary laws and properties of objects, evolutionary way of cognition and laws of evo-lutionary growth of complexity of systems, as well as methodology of formation of reliable knowledge.


Stress Detection and Mitigation through Discovery of Optimal Lecture Delivery Method for STEM-Enrolled University Students

Abstract: The high availability of technology-related jobs is currently not fulfilled by enough supply of technology graduates, specifically the ones coming from science, technology, engineering, and mathematics (STEM) courses. This is related to the low enrolment rate of STEM-based courses at Universities. The notion that STEM subjects are intense and difficult to pass could be the reason of such phenomena. This research experimented different lecture delivery methods comprising of different seating arrangements, inclusion or exclusion of break, group size, and room size to investigate the generated levels of stress among STEM students, so that the method that would induce minimum stress could be discovered. The analysis of the generated heart rates as stress indication concludes that the level of difficulty of STEM subject’s topic is the one that most affected students’ level of stress, while inclusion of break during class is imperious to destress students. Class arrangement with far seating was discovered to be potentially more stressful for certain students indicated by the high level of resting heart rate in one of the experiments. Importantly, it was also found that regardless of lecture delivery method, STEM subjects in general remained challenging that was suggested by the consistently high recorded heart rates, which was also a sign of active learning engagement - besides implying the level of stress. Finally, gender-wise, there is no visible data pattern that could hint that certain gender is more vulnerable to stress when handling STEM subjects.


Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification

Abstract: The classification of human face shapes, a pivotal aspect of one&rsquo;s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation for accurate feature extraction. Our methodology utilizes a publicly available dataset of female celebrities, comprising five face shape classes: heart, oblong, oval, round, and square. By augmenting this dataset during training, we magnify its diversity, enabling better generalization and enhancing the model&rsquo;s robustness. With the EfficientNetV2S neural network, we employ transfer learning, leveraging pretrained weights to optimize accuracy, training speed, and parameter size. The result is a highly efficient and effective model, which outperforms state-of-the-art approaches on the same dataset, boasting an outstanding overall accuracy of 96.32%. Our findings demonstrate the efficiency of our approach, proving its potential in the field of face shape classification. The success of our methodology holds promise for various applications, offering valuable insights into beauty analysis, cosmetic recommendations, and personalized healthcare.


The Mediating Effect of School Adjustment on the Relationship between a Creative School Environment and Children's Happiness

Abstract: This study examined the mediating effect of children's school adjustment on the relationship between a creative school environment and children’s happiness. For this purpose, an analysis was performed of data from the 12th Panel Study on Korean Children conducted in 2019. The participants were 1,393 fifth-grade elementary school students. Data were analyzed using descriptive statistics, one-way ANOVA, Pearson correlation analysis, confirmatory factor analysis, and structural equation modeling using SPSS 25.0 and AMOS 23.0 programs. The principal results were as follows. Firstly, there were significant positive correlations among creative school environment, children's school adjustment, and happiness. Secondly, the creative school environment significantly affected children's school adjustment and happiness. Thirdly, children's school adjustment had a positive effect on their happiness. Fourthly, the creative school environment had an indirect effect on children's happiness through school adjustment. The results thus demonstrate that school is a social environment that strongly influences children's happiness. Therefore, to improve children's school happiness, it is essential to consider the relationship between a creative school environment and school adjustment.


Extrinsic Calibration for Large FOV Based on Inverse Depth Parameterized Bundle Adjustment

Abstract: Abstract To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for 3D-DIC in large field of view. The method combines image feature algorithms with DIC techniques to extract matching point pairs from the left and right images of binocular cameras. These matching point pairs are then homogenized within the field of view. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large field of view applications, the calibration control points' spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient calibration control points, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add calibration control points within the field of view. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method's reliability and accuracy in large field of view 3D measurement are verified through experiments.&#xD;


Gibbs sampling the posterior of neural networks

Abstract: Abstract In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an efficient Gibbs sampler. For small models, the Gibbs sampler attains similar performances as the state-of-the-art Monte Carlo Markov chain methods, such as the Hamiltonian Monte Carlo or the Metropolis adjusted Langevin algorithm, both on real and synthetic data. By framing our analysis in the teacher-student setting, we introduce a thermalization criterion that allows us to detect when an algorithm, when run on data with synthetic labels, fails to sample from the posterior. The criterion is based on the fact that in the teacher-student setting we can initialize an algorithm directly at equilibrium.


Software Managers' Guide to Operational Excellence

Abstract: The secret to being a great engineering leader? Setting up the right checks and balances.


Automated and interactive selective collection system in the educational context of the early years of elementary education

Abstract: Striving for a more sustainable future, selective waste collection is essential to minimize environmental impacts and promote societal awareness. This project aims to raise awareness among children in the early years of elementary school. It teaches them interactively about the importance of selective waste collection through the reinforcement and practice of sustainable actions in a playful manner. The automated trash bin, constructed using electronic components, is controlled through the Arduino platform with the assistance of sensors and actuators. Thus, when a child approaches the automated trash bin, a sound is emitted to indicate the type of material the bin receives. This solution aims to teach non-literate children how to perform selective waste collection in an interactive and playful manner, instructing them on the separation and disposal of waste using the trash bin. The final prototype underwent preliminary evaluation by 64 individuals through an online questionnaire comprising nine closed-ended questions. The results were positive regarding the effectiveness of this tool's proposal.


Participatory Action Research in the Development of Higher Education with Digital Media

Abstract: The digitalisation of higher education has versatile potential for students and especially for pre-service teachers. Unfortunately, this potential is insufficiently used in higher education institutions. This article displays a project focusing on developing university teacher education with digital media. For this purpose, the applied model of Participatory Action Research for Higher Education will be illustrated, reflected, and discussed to share the findings and experiences of the process and facilitate future development projects. Furthermore, conditions for success, challenges, and recommendations based on this project will be provided.


Reviewing Maple Viewing (Momijigari, 1899)

Abstract: Abstract Maple Viewing (1899) is considered the earliest surviving Japanese film. Photographed by the cinematographer Shibata Tsunekichi, the film records Kabuki actors Ichikawa Danjurō IX and Onoe Kikugorō V performing in a play of the same name. There is no doubt that Maple Viewing is an early “surviving” work. Yet can we call Maple Viewing “Japanese” simply because it was photographed in Japan by a Japanese cinematographer? If we imagine that it was produced for non-Japanese viewers in the context of an Orientalist imagination, what would its classification as “Japanese” amount to? Moreover, we can also question its status as a “film” in turn-of-the-century discourse. What was Maple Viewing understood to be when it was first exhibited? Focusing on the work of Shibata, this chapter examines early Japanese filmmaking in terms of the influence of Japanese art and culture on European fine art, as well as Japanese reactions to “Japonisme.”


Harnessing AI and Gut Microbiome Research for Precision Health

Abstract: The gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.


AI-Powered Energy Management System for Industry

Abstract: The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world’s energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward—transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability


The Role of Explainable AI in Building Trust and Confidence in Automated Business Processe

Abstract: Computer-based intelligence (AI) has shown significant potential in various applications, but it requires a clear understanding of the local area. This issue falls within the Reasonable simulated intelligence (XAI) field, which is crucial for the practical application of AI models. This article discusses the current work in XAI, defining explainability in machine learning and proposing a new definition. It also discusses ongoing commitments related to AI models, including Profound Learning strategies. The article suggests Dependable Computerized reasoning, a method for large-scale AI execution in real-world contexts with reliability, model logic, and responsibility at its core. The goal is to provide a comprehensive taxonomy for newcomers and professionals to embrace AI's benefits.


Video Action Recognition Using Depth Motion Maps

Abstract: This thesis presents an algorithm for modifying Depth Motion Map (DMM) pyramid analysis based action recognition. It extends the work presented by Chengwu Liang to better handle incoming data. Morphological image transforms are used to remove incoming noise created by the inaccuracies in a Microsoft Kinect when the subject is stationary. This avoids the problem of motion being registered in frames where there is no motion. It also uses a non-linear cost metric on the motion energy to ensure some degrees of shift invariance in the Z axis, which is towards or away from the camera. In addition, advanced information fusion methods are adopted to improve feature representation, leading to better performance. Experiments are conducted on video data containing a variable number of stationary frames at the start and end of an action. The experiments are conducted with many different classifiers. The results show that a performance benefit is realized due to the use of these enhancement methods.


Image Enhancement for the Improved Extraction of Local Image Features Using Image Offsets

Abstract: In this thesis, an algorithm for improved feature extraction is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Comparative keypoint detection testing on the enhanced images is conducted to test if more keypoints can be extracted using the method. Keypoint strength experiments are also conducted as well as quality assessment experiments to test the validity of the proposed algorithm as an image enhancement method. Image matching experiments though SIFT and SURF are also conducted. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam, Heinly, and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or similar matching accuracy compared with related enhancement algorithms using SIFT and SURF.


Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Abstract: Recommendation systems are widely used in various areas to personalize recommendations to users. However, they are vulnerable to shilling attacks in which malicious users try to promote their products or diminish their competitors. Therefore, detecting shilling attacks can significantly improve the quality of recommender systems. With the increasing complexity of attacks and changes in attackers' behaviour, more advanced approaches are required to find the hidden patterns in data. This thesis proposes a CNN-based hybrid model and architecture that integrates self-learning and flexible aspects of CNN with other supervised learning methods to enhance shillings attacks detection on collaborative filtering recommendation systems. We also introduce a new metric, the F-compatible score, to measure the compatibility ratio of merging the CNN with any other classifier in a given aggregate. This measure helps in monitoring the enhancement level of the detection results of shilling attacks. Two different approaches are proposed in this thesis, user-based, and item-based. The experimental analysis used three benchmark datasets: the Movie-Lens 100K, Netflix, and Movie-Lens 1M. A comprehensive comparative analysis is also completed to assess the performance of both individual-based and hybrid-based detection models. Experimental results show that the proposed hybrid models performed well on most attack profiles and reached an F1-score of up to 99%. We concluded that the superiority of hybrid models over individual models depends on the sparsity level of data and the divergence of results.


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.


PENERAPAN MODEL PEMBELAJARAN DISCOVERY LEARNING BERBASIS POWERPOINT PADA MATA PELAJARAN EKONOMI UNTUK MENINGKATKAN MINAT BELAJAR SISWA DI SMP NEGERI 3 SATU ATAP ARAMO

Abstract: This research aims to investigate the influence of implementing the Discovery Learning model based on PowerPoint on students' interest in learning economics, specifically on economic activities such as production, distribution, and consumption, at SMP Negeri 3 Satu Atap Aramo. This study employs the Classroom Action Research (CAR) approach with a qualitative method aimed at exploring the learning process in addressing specific issues. The research was conducted at SMP Negeri 3 Satu Atap Aramo with a population of seventh-grade students, totaling 20 students. The results of the research indicate that out of the total sampled students, 25% of them obtained scores ranging from 50 to 56, another 25% of students scored between 66 and 75, and the remaining 50% of students scored between 76 and 98. This research aims to provide a deeper understanding of the effectiveness of specific teaching methods on students' learning interests in the context of economics.


Scattering amplitude from quantum computing with reduction formula

Abstract: Utilizing the Lehmann-Symanzik-Zimmermann reduction formula, we present a new general framework for computing scattering amplitudes in quantum field theory with quantum computers in a fully nonperturbative way. In this framework, one only has to construct one-particle states of zero momentum, and no wave packets of incoming particles are needed. The framework is able to incorporate scatterings of bound states, and is ideal for scatterings involving a small number of particles. We expect this framework to have particular advantages when applied to exclusive hadron scatterings. As a proof of concept, by simulations on classical hardware, we demonstrate that in the one-flavor Gross-Neveu model, the fermion propagator, the connected fermion four-point function, and the propagator of a fermion-antifermion bound state obtained from our proposed quantum algorithm have the desired pole structure crucial to the implementation of the Lehmann-Symanzik-Zimmermann reduction formula.


Online Teaching Effect Evaluation and Analysis Using Combined Weighting Technique

Abstract: An evaluation index system of online teaching satisfaction was established based on the data of a questionnaire survey. In addition, an evaluation index system of an online teaching effect with four first-level indexes and 14 second-level indexes was constructed from the aspects of pre-class preparation, video production, classroom teaching, and after-class guidance. Then, a combination weighting–Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model was established using the analytic hierarchy process (AHP), Criteria Importance Through Inter-criteria Correlation method, and TOPSIS model. The objective is to evaluate the online teaching effect of 10 engineering technology universities in Henan Province. Results show that the online teaching effect of these universities is generally satisfactory. In addition, video production and classroom teaching are important factors affecting online teaching, which, however, is slightly influenced by pre-class preparation. On this basis, this study proposes to enhance online assessment, improve students’ attention in class, strengthen teacher–student communication, actively perfect teaching resources, and create a good learning environment.


Enhanced Privacy for Health Records Using Blockchain Technologies

Abstract: This project deals with the security of digital medical history by implementing advanced measures such as encryption using blockchain methods, access control. Thanks to the integration of encryption, blockchain, dynamic access control and security even after decryption, a robust privacy framework is built to meet evolving cyber threats and strengthen security and availability of health data. This conventional method of storage deprives doctors and researchers of capitalizing on modern technologies and healthcare engines to perform clinical tests, improve patient care, and contribute to future clinical research [1]. By empowering patients using blockchain technology, this project is transforming healthcare data management to improve patient care and promote the development of a reliable healthcare environment, which means remarkable progress towards improving Privacy for Health Records Using Blockchain Technologies. Key Words: Cloud Storage Security, Blockchain Technology, Health Records, Privacy and Security, Blockchain Algorithms, Keccak, Keras, Tesseract


Perceived Effects of Language and Culture Suppression in the Catalan Region of Spain

Abstract: The Catalan language has had a complex history. The suppression of the Catalan language had a profound impact on the Catalan culture and identity. This study addresses the interaction between language and culture according to students in a Catalan university. The purpose was to evaluate their insights and report the perceived connection between language and culture. An experimental study was conducted to investigate the interrelationship between language and culture and whether it is perceived that language loss affects cultural loss, and both are intertwined. 67 undergraduate university students between the ages of 18 and 22 years participated in this study. They responded to a questionnaire that included general questions on history, language, and culture. The results suggest that the participants feel there is an interdependent connection between language loss and culture loss. This research puts into focus people’s identification with their own language, culture, traditions, and history, which reinforces the difficulty of separating one’s cultural and linguistic history.


Cyber Security: Study on Attack, Threat, Vulnerability

Abstract: Abstract: The main aim of this research is to examine the various aspects of cyber infrastructure, encompassing both hardware and software systems, networks, enterprise networks, intranets, and the occurrence of cyber intrusions. The paper intends to elucidate the significance of network intrusions and cyber-theft, along with delving into the factors contributing to the proliferation of cybercrime. Furthermore, it provides a comprehensive definition of cyber security and its role in addressing network intrusion and cyber theft, while also exploring the drivers behind the surge in cybercrime and their repercussions. The authors conclude by suggesting preventive measures and potential solutions to mitigate the risks associated with cyber attacks. They emphasize that while technological advancements can aid in minimizing the impact of cyber attacks, human behavior and psychological tendencies remain significant vulnerabilities. Despite this, the authors express optimism in reducing cyber attacks through investment in organizational education campaigns that address psychological susceptibilities.


Exploring the Correlation between Students' Attitudes towards AI and Their Learning Outcomes

Abstract: This research study aims to investigate the intricate connection between students' attitudes toward Artificial Intelligence (AI) and the correlates of their learning outcomes. With the growing integration of AI in various educational settings, understanding how students perceive AI and how these perceptions correlate with their academic performance is of vital importance. This research employs a descriptive and correlational approach, combining surveys and learning outcomes data analysis to delve into the changing aspects of this relationship. By exploring students' attitudes toward AI, their acceptance levels, and their learning outcomes, this study seeks to provide insights that can inform educators, policymakers, and AI developers on optimizing AI's role in education for enhanced student success. Also, the findings indicate a significant relationship between students' attitudes toward AI and their learning outcomes, emphasizing the importance of considering these attitudes in understanding and predicting educational outcomes. One of the key recommendations is that institutions should consider integrating more comprehensive AI education into their curriculum to bridge the gap in actively seeking out information about AI. This proactive approach can contribute to a more informed and adaptive student body, ultimately fostering a positive and constructive environment for AI integration in education.