Deep-learning-based head pose estimation from a single RGB image and its application to medical CROM measurement

Abstract: Abstract For human beings, neck movement will be degraded due to aging, trauma, musculoskeletal disorders, or degenerative diseases. Cervical range of motion (CROM) measurement is one of the popular quantitative neck examinations. Despite radiography is considered as the gold standard, it suffers from invasiveness, radiation exposure, and expensiveness. Recently, vision-based methods have been applied for CROM measurement but achieve large errors and require depth camera. On the other hand, deep neural networks provide good performances on head pose estimation (HPE) from a single image, thus promising for medical CROM measurement. We propose to use CNN networks to extract pyramidal or multi-level image features, which are passed to cross-level attention modules for feature fusion and then to a modified ASPP module and a multi-bin classification/regression module for spatial-channel attention and Euler angle conversion/prediction, respectively. The proposed technique was evaluated on public datasets, such as 300W_LP, AFLW2000, and BIWI, to verify its superior performances (with mean MAE = 3.50°, 3.40°, and 2.31° for different experimental protocols) than state-of-the-art methods. Our pre-trained model was also evaluated with our own collected dataset from hospital for CROM measurement. It also achieved the lowest MAE of 4.58° among other methods and conformed with a medical standard of 5 degrees except the pitch angle (which has a MAE of 5.70°, larger than the standard and the yaw (MAE = 3.60°) and roll angles (MAE = 4.44°)). In general, HPE technique is feasible for CROM measurement and shows its advantages of speed, non-invasiveness, free of anatomical landmark and low cost of operation.


An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction

Abstract: Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.


Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets

Abstract: Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.


Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems

Abstract: In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process.


Game Analysis of Dual-channel Supply Chain Based on Different Rights Structure

Abstract: Based on members' different bargaining power in a dual channels system, this paper discusses the impact ofthree games on channel pricing, demand and members' profits by building Manufacturer Stackelberg, Retailer Stackelberg and Vertical Nash game model.The study finds that:(1) When the coefficient of cross-price elasticity of demand is 0, the effects of the three game strategies on the competitive equilibrium solution of the two-channel supply chain are undifferentiated. (2) When the coefficient of cross-price elasticity of demand is not 0, under the Stackelberg game dominated by the retailer (manufacturer), the traditional channel (direct channel) has the highest pricing, and the demand of the traditional channel (direct channel) is the smallest, while the demand of the traditional channel (demand of the direct channel) is the largest in the Stackelberg game dominated by the manufacturer (retailer); both manufacturers and retailers are willing to choose to give up their power and do the Stackelberg game. Both manufacturers and retailers are willing to give up power in favor of being a follower of the Stackelberg game, and the Nash game is always a strict downside for the game participants. Finally, the validity of the conclusions is further tested by case studies.


Virtual Reality Technology Transitions and Reshape the Aesthetic Education Paradigm

Abstract: With the rapid development of technology, virtual reality technology has gradually penetrated into the field of education. The traditional aesthetic education model is limited by the lack of time and space and interaction. The virtual reality technology is based on its immersive and interactive characteristics. Provides new possibilities. Elapse the aesthetic education from three aspects: immersive experience, interactive teaching and time and space reshaping. Through the comparative analysis of the case, the actual application and innovation teaching model of virtual reality technology in aesthetic education is discussed, and the virtual reality technology and aesthetic education are better integrated to provide more effective and innovative ways to cultivate students' aesthetic ability.


Factors Influencing Investment Decisions among College Students: A Comparative Analysis Between India & Nigeria

Abstract: Investment is done for the purpose to gain profitable returns. Investing in a well-diversified portfolio offers a multitude of benefits, including capital appreciation to grow wealth over time. As a student, managing finance can be challenging, but exploring opportunities for passive income can make a significant difference in financial stability and independence. This study endeavors to comparatively analyze the factors influencing investment decisions among college students in India and Nigeria. Demographics play a pivotal role in shaping investment choices, and understanding the differences in the attitude of these countries is vital for effective financial education initiatives. The study reveals that there is a positive relationship between different factors that students consider when making these decisions. Factors such as financial security, return on investment (ROI), and risk minimization are found to have a significant impact on their choices.


Web-Based Car Rental Information System At Cv. Mandarental Cirebon

Abstract: The increasingly rapid development of information technology and science currently demands the existence of information systems that can support existing science and technology—currently CV. Mandarental Cirebon is growing and wants to increase car bookings for all renters in this city. The rental system used is web-based, but the features on the website still need to be completed to make it easy for customers to rent a car at CV. Problems that occur include inaccurate data, slow data processing, and waste of time. So, the information system within the company is very important to help and facilitate the business process to improve service to consumers. With this information system, it is hoped that it will provide more accurate information so that problems that occur in optimizing service to customers can be resolved at CV. Mandarental Cirebon. The methodology used in building this website includes several systematic steps. Data collection is carried out by observing to collect information, images, and other materials needed for website design.


Integrate between information systems engineering and software engineering theories for successful quality engineering measurement of software: Valid instrument pre-results

Abstract: An extensive number of instruments and systems assessment tools are weak and not good enough in the appraisal of systems’ quality engineer success measurement. Thus, the comprehension of systems’ success is very serious. One of the purposes of this research topic is to develop a successful, novel, validated instrument for measuring system quality success based on the integration between theories of information systems (Seddon and DeLone & McLean) and software engineering theory (ISO 25010). To ensure the quality of the instrument before use, eight academic experts have validated. The reason for expanding the number of experts to eight is to accurately build and evaluate the instrument because this instrument is the first one erring. After expert validation done successfully, researchers started the process of instrument pre-test. Pre-test verification and validation results done by test the instrument of 74 users. The results of the statistical testing were perfect. The Composite reliability proposed value is 0.7, The average variance extracted value is 0.5. Cronbach’s alpha is higher than 0.7. The value of Spearman’s reliable rhea is >0.6. Results approved that this instrument is strong and perfect to be used as a valid tool for system success measurement.


The relationship between perceived learning, academic performance and academic engagement in virtual education for university students

Abstract: This study aimed to determine whether the three dimensions of academic engagement (cognitive, emotional and behavioral) were positively associated with perceived learning and academic performance. The participants were 301 university students from Lima. Structural equation models were used to test the proposed theoretical relationship between the variables. The results indicated that the model showed satisfactory fit indices (CFI = 0.956, TLI = 0.949, RMSEA = 0.043, SRMR = 0.062). Perceived learning was found to be predicted by cognitive engagement (β = 0.447, p < 0.01) and emotional engagement (β = 0.230, p < 0.05). However, there was no statistically significant relationship between behavioral engagement and perceived learning (β = 0.035, p = 0.840). On the other hand, academic performance was predicted by behavioral engagement (β = 0.393, p < 0.05) but not by cognitive (β = -0.164, p = 0.301) or emotional (β = 0.001, p = 0.991) engagement. The study highlights the importance of fostering academic engagement in university students to enhance both their academic performance and perceived learning.


Pemanfaatan Media Audio Visual pada Kegiatan Bercerita dalam Meningkatkan Perkembangan Bahasa Anak di TK Khalifah 23 Palembang

Abstract: Language is an important component in early childhood development. Thus, a stimulus is needed to increase this development with various activities, namely storytelling activities using audio-visual media. This research aims to be one way to improve children's abilities in language development through storytelling activities through audio-visual media. This research will be carried out in 2 cycles, using classroom action research methods, improvement plans, objects, and reflection. The place where this research was carried out was TK Khalifah 23 Palembang. Based on the results of research activities that have been carried out, the results obtained are cycle I children's language abilities from a total of 15 children, 8 children (53%) have developed very well, 4 children (27%) have developed as expected. 3 children (20%) have started to develop. In the improvement cycle II, the results obtained were that 13 children (87%) had received very good grades, 2 children (13%) had obtained good grades and were developing according to expectations. So we can conclude that storytelling activities using audio-visual media carried out at TK Khalifah 23 Palembang have been able to improve children's language skills. What can be seen from each cycle is that there is a very significant increase.


Music Education Teaching Quality Evaluation System Based on Convolutional Neural Network

Abstract: Music education, as a quality training education, plays an important role in promoting the all-round development of students. Music evaluation is often highly subjective. However, the objective quantification can be achieved to a certain extent through the construction of music education and teaching quality evaluation system. In this study, CNN algorithm is applied to the evaluation model of music education teaching. Compared with the traditional manual feature extraction method, CNN algorithm can automatically capture the target depth features. In the experiment, the effectiveness of convolutional neural network is evaluated by means of model comparison and analysis. Taking GTZAN database as the experimental training sample, 2000 Mel spectra, 1500 training sets and 500 test sets were obtained, and the relevant parameters were initialised. The research results show that the dense network model has the highest accuracy rate in the training set in the identification and analysis of the relevant features of Mel spectrum. The model’s value is about 0.917, which is 0.024, 0.041, 0.086 and 0.098 higher than Res Net model, Perception Net model, VGG Net model and Alex Net model, respectively. Meanwhile, its loss curve effect, [Formula: see text]-value and AUC value reached the optimal level in all models, with the highest [Formula: see text]-value and AUC value reaching 0.926 and 0.922, respectively. In addition, in the test set, the average accuracy of this model is also better than other models. The comprehensive analysis shows that the model performs well in the evaluation system, which plays an important role in the evaluation of music education and teaching quality.


Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform

Abstract: The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing - a technique divergent from conventional computing methods - to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models traditionally challenged by the intricacy of computing their gradient. Our proposed methodology, termed “quantum mode training”, blends standard gradient updates with an off-gradient direction derived from RBM ground state samples. This approach significantly improves the training efficacy of RBMs, outperforming traditional gradient methods in terms of speed, stability, and minimized converged relative entropy (KL divergence). This study not only highlights the capabilities of the Dynex platform in progressing unsupervised learning techniques but also contributes substantially to the broader comprehension and utilization of neuromorphic computing in complex computational tasks.


Design and Development of e-MARZ: A Practical Guide to Implementing Cost-effective VR Experiences for Music Education

Abstract: The study introduces e-MARZ, a Virtual Reality (VR) software designed to tackle challenges faced by music educators in enhancing music appreciation lessons within the Malaysian lower secondary school music curriculum. The research aims to improve student engagement, provide immersive and interactive learning experiences, and develop a user-friendly VR application. A video production process was employed to create 360-degree music performance videos, which were integrated into the e-MARZ software via interactive modules. This article mainly discusses the development and evaluation aspects, utilising the modified ADDIE Instructional model, m-ADDIE, during the development phase. The e-MARZ prototype underwent evaluation using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ) for quantitative data collection and analysis to assess usability, with a sample of five music education experts and 82 secondary school music students. Limitations of the study include the use of 360 media of live performances tailored for secondary school students' music appreciation and employing 3DOF (degrees of freedom) instead of the more advanced 6DOF, potentially impacting the generalisability of findings and the scope of achievable VR experiences. Despite these limitations, the high usability scores obtained during the evaluation, as measured by SUS and PSSUQ, indicate the software's effectiveness in addressing its intended challenges. The e-MARZ software offers an immersive, user-friendly VR experience that is cost-effective and compatible with various devices. This research provides insights into producing cost-effective, user-friendly VR applications using 360 media, promoting accessible VR application development in education. As a result, the study transforms students' engagement with music appreciation, fostering a more immersive, interactive, and enjoyable learning experience.


Exploring the Potential of Wireless Integrated Configuration Technology as a Learning Tool

Abstract: The objective of this study was to determine the feasibility of utilising wireless integrated configuration technology among students enrolled in networking courses. The proposed method entails using Raspberry Pi as the key hardware component to facilitate wireless configuration, hence facilitating seamless connectivity and communication across wireless devices. The networking solution facilitates the transport of data, exchange of resources, and cooperation in a wireless manner, eliminating the need for physical cables or wires. The research methodology for this study entails descriptive analysis in the form of a survey, which will have an impact on the quantitative findings. The results of the needs study found that over 90% of students agreed that the use of wireless configuration devices would improve understanding and interest them in doing network configuration. Based on the findings from interview sessions with experts, it has been demonstrated that the utilisation of wireless integrated configuration technology possesses the capacity to engender an enjoyable learning experience for students. This technology enables students to engage in configuration tasks wirelessly, utilising numerous devices concurrently. Additionally, experts believe that students can learn how to configure it technically as well as theoretically. The results of this study were successful in determining the level of potential for the development of wireless devices and their usage in networking courses.


Assessing The General Sentiment of Malaysian Manufacturers in Adopting Blockchain Technology to Improve Supply Chain Processes

Abstract: The popularity of blockchain technology has seen a dramatic increase throughout the year of 2021 and 2022, with numerous use cases in various global industries. This includes the use of cryptocurrencies, non-fungible tokens, decentralized autonomous organizations, and others. In Malaysia, there is also an apparent increase in general awareness towards the technology, with Bank Negara Malaysia and Securities Commission actively participating in the Committee for Blockchain and Distributed Ledger Technology. This study specifically examined at the Malaysian manufacturing industry, attempting to assess their general sentiments towards blockchain technology and how it could help to improve various supply chain processes. The quantitative study received 114 valid responses from management level employees with Malaysian manufacturers, enabling the researchers to assess the task technology fit, perceived ease of use (PEOU), perceived usefulness (PUSE), and technology readiness index (TRI). The analysis indicated that there is indeed a high task technology fit in the Malaysian manufacturing sector. In addition, this industry showed a positive sentiment towards blockchain technology, with high scores in PEOU, PUSE, and TRI. The high TRI numbers also had a positive significant relationship on the decision to adopt this technology within the manufacturing companies to improve various supply chain processes.


Deep Learning Approach for Classification of Tweets in Detecting Cyber Truculent

Abstract: Identification and Classification of truculent tweets is a serious problem. Studies have shown how truculent tweets on Twitter have caused anxiety and in extreme cases death of victims. Various researches have applied Machine Learning approaches while others Deep Learning in classifying toxic sentences. Impressive results have been obtained using Deep Learning approaches, the focus of the research is to develop an extended model for deep learning classification using cyber tweets. The Model developed in this research work used labelled dataset (twitter_parsed_dataset.csv), Maximum Entropy was used to extract keywords and entities. One-Dimensional Convolutional Neural Network (1d-CNN) was used to detect truculent in tweets. The experimental result from the developed model consider four preprocess datasets for classification, the Unigram, Bigram, Trigram and N-gram. The result that was obtained for Accuracy 96.1%, Precision 93.6%, Recall 73.7% and F1-Score 83.8% for different test during classification. The result obtained from the developed model for accuracy, 96.1% was compared with related work Banerjee’s accuracy of 93.97%. The developed model can be used for auto detection of truculent messages in cyberspace.


A Novel Approach to Text Summarization Using Machine Learning

Abstract: Text summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive and abstractive summarising techniques are the two primary categories of text summary methods. The paper also presents several algorithms that have been proposed for text summarization, including TextRank, Seq2Seq, and BART. TextRank is a simple and fast algorithm that works well for short documents, Seq2Seq is a deep learning-based approach that generates high-quality summaries, and BART is a transformer-based algorithm that provides the best results on benchmark datasets. The obtained ROUGE Score after passing TextRank, BART, and Seq2SEq algorithm significant also.


Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation

Abstract: The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.


Pareidolia of Meanings of Digital Axiology

Abstract: Virtuality with its inherent heterogeneity, diffuseness and fragmentation unfolds before us the space of new challenges of ethical and axiological nature. The topology of the digital is the topology of the eternally ongoing and unfinished, a “non-space” that cannot be subsumed under any particular framework by setting boundaries. The unboundedness of virtual space requires a revision of the essence and form of the classical axiological system. Thus, the article attempts to define the modality of values of modern digital culture, to describe the transformation of values from a hierarchical structure through rhizomatic to the modern, most chaotic and atomic state. Conclusion dwells upon the fact that digital axiology is not the transition of the habitual system of values into a digital format, it is the opening of a new field/dimension of values and meanings that cannot be stated, concretized, but can be represented by the metaphor of a pareidolic illusion.


Recognition of Handwritten Mathematical Expressions Using Systems of Convolutional Neural Networks

Abstract: Accurate recognition of handwritten mathematical expressions has proven difficult due to their two-dimensional structure. Various machine-learning techniques have previously been employed to transcribe handwritten math, including approaches based on convolutional neural networks (CNNs) and larger encoder/decoder-based models. In this work, we explore a CNN-based method for transcribing handwritten math expressions into the typesetting language known as LaTeX. This approach utilizes machine learning not only for classifying individual characters but also for extracting individual characters from handwritten inputs and determining what forms of two-dimensionality exist within the expression. This approach achieves significant reliability when recognizing common mathematical expressions.


An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels

Abstract: Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.


Object Detection for Enhanced Visuals Using Deep Learning

Abstract: Object detection serves as a foundational aspect in computer vision, finding utility across a spectrum of applications spanning autonomous driving to security surveillance. The primary objective of this endeavor was to engineer a resilient and effective object detection framework leveraging deep learning methodologies, subsequently assessing its efficacy on real-world datasets. The project leveraged contemporary neural network architectures alongside modern tools for data preprocessing and evaluation. Keywords: Single shot Detector (SSD), Pre-Processing, Deep Learning, Image Processing, Classification.


Bridge Construction Risk Assessment Based on Variable Weight Theory and Cloud Model

Abstract: In order to effectively prevent the occurrence of risky accidents during bridge construction, this study proposes a bridge construction risk assessment method based on variable weight theory and the cloud model theory. Firstly, the fishbone diagram was used to identify risk factors in constructing a bridge construction risk index system. Secondly, according to the cloud model theory, the comment cloud model of each risk index was established by using the forward cloud generator. Finally, the risk factor weights were quantified according to the intuitionistic fuzzy analytic hierarchy process (IFAHP). Combined with the variable weight theory, a zoning variable weight function was constructed and the weights were reallocated. Through the mutual aggregation of the comment cloud model and weights, the risk level of construction bridges was obtained. The method takes full account of the fuzziness and randomness existing in the evaluation process, optimizes the distribution of weight values of indicators, and uses Delphi iteration to effectively eliminate the subjective defects of individuals. A construction bridge in Changchun was used as an example for risk assessment, and the advance of the method was well verified. The results demonstrate that the method is highly feasible and effective after accuracy verification and sensitivity analysis.


A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

Abstract: The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.


Metaheuristic Optimization Algorithms: an overview

Abstract: Metaheuristic optimization algorithms are known for their versatility and adaptability, making them effective tools for solving a wide range of complex optimization problems. They don't rely on specific problem types, gradients, and can explore globally while handling multi-objective optimization. They strike a balance between exploration and exploitation, contributing to advancements in optimization. However, it's important to note their limitations, including the lack of a guaranteed global optimum, varying convergence rates, and their somewhat opaque functioning. In contrast, metaphor-based optimization algorithms, while intuitively appealing, have faced controversy due to potential oversimplification and unrealistic expectations. Despite these considerations, metaheuristic algorithms continue to be widely used for tackling complex problems. This research paper aims to explore the fundamental components and concepts that underlie optimization algorithms, focusing on the use of search references and the delicate balance between exploration and exploitation. Visual representations of the search behavior of selected metaheuristic algorithms will also be provided.


Prescribed intelligent elliptical pursuing by UAVs: A reinforcement learning policy

Abstract: This paper proposes a prescribed intelligent elliptical pursuing algorithm driven by deep reinforcement learning (DRL) for Unmanned Aerial Vehicles (UAVs), aiming to achieve an optimal performance without violating appointed-time constraints. Specifically, an unknown system dynamics estimator (USDE) with merely one filtering coefficient is employed to counteract uncertainties effectively. Subsequently, an elliptical enclosing controller is devised for UAVs to achieve asymptotic convergence towards the target ellipse orbit. Particularly, a proximal policy optimization (PPO)-based optimal learning term is contrived to achieve control optimality related to consumption and efficiency, where a refined reward function is established to characterize the appointed-time constraints. The salient merit is that an efficient learning paradigm is formulated to fulfill a near optimal appointed-time enclosing free from prescribed performance control. Finally, a plenty of simulations substantiate the feasibility of developed algorithm.


A Study on the Safety Accident Prevention System in conjunction with the Internet of Things

Abstract: Recently, in an Internet of Things environment, devices (objects) with built-in sensors or communication functions are connected to the Internet by utilizing a physical network that allows objects equipped with IoT sensors and communication chips to automatically exchange real-time data without human intervention. It can collect surrounding information, exchange this information with other devices, and even make appropriate decisions. It can be said to be a huge state system of wired and wireless networking objects based on attached sensors and chips. Here, we provide a practical and systematic approach to overcome the limitations of preventing safety accidents such as safety education, safety equipment, and on-site safety inspections by securing a strong safety net against repeated occurrences of habitual electric shock accidents and to overcome the continuous occurrence of accidents despite multifaceted efforts to achieve zero accidents. Through transformation, the need to develop a system that combines accident prevention and IoT technology is emerging. With the implementation of the Severe Accident Punishment Act, key requirements such as setting safety and health goals and management policies, a dedicated safety and health organization, risk assessment, budget, safety and health management manager, deployment of professional personnel, listening to workers


Resource Hyperlinks

Abstract: Abstract In this chapter, links to resources in quality improvement are provided from leading journals and websites. Included are the BMJ QI series, the AURUM Institute, journals on learning health systems, and NHS England, NHS Scotland, and Ireland Health Service Executive (HSE) resources. Resources on key focus areas are provided, including driver diagrams, measurement tools, microsystems, systems convening, and person-centred care. Leads to quality improvement organizations and quality improvement videos have been listed.


Extend the Ant Colony Optimization Algorithm for virtualization Technologies to improve the Resources utilization in On-Premises Datacenters

Abstract: Over the past few decades, there has been an increasing demand for computational power, which has fueled the growth of on-premises data centers. In recent years, virtualization techniques have been introduced to enhance data center resource utilization. These techniques consolidate multiple workloads onto fewer servers, reducing the need for physical devices to support an organization's IT infrastructure. Virtualization technologies have increased IT agility by allowing for quicker deployment of virtual machines (VMs), which in turn facilitates faster application and service rollouts, improves disaster recovery capabilities, and reduces carbon emissions, leading to significant cost savings for organizations. In this paper, we enhance the Ant Colony Optimization Algorithm (ACO) by applying it to virtualization. We simulate the ACO for virtual machine resource management. Our evaluation results demonstrate that the proposed algorithm can further improve resource utilization and reduce carbon emissions. Keywords: Virtualization, Datacenter, Resource utilization, Ant Colony Optimization.


Anti-deepfake rules will be contested despite harms

Abstract: Headline INT: Anti-deepfake rules are contested despite harms


Analysis of Patient Health Using Arduino and Monitoring System

Abstract: The proposed project is a health monitoring system using Arduino that measures key health parameters such as heart rate, blood oxygen level, and body temperature. The system consists of sensors such as DHT11 for temperature, MAX30100 for heart rate and blood oxygen level, and an Arduino Nano for processing the data. The measured results are displayed on an LCD screen and a buzzer sounds when the results are ready. The system has been designed with simplicity and ease-of-use in mind, making it accessible for personal use. While the system has limitations such as sensor accuracy and lack of IoT functionality, it still has potential for improving individual health monitoring and wellness. In the future, potential areas for development include the inclusion of more sensors, IoT functionality, and machine learning algorithms for personalized insights and recommendations. Overall, the proposed health monitoring system is a promising step towards empowering individuals to take a more active role in monitoring their health and wellness.


Water Meter Reading Application System Development using Image Processing A Case Study from Sindangsari Village Water Services

Abstract: PAMDES is a drinking water company managed by local villagers. The water meter data are read and recorded manually without any technology, which is ineffective and inefficient. Digital image processing can be implemented to read and record the water meter data automatically. When implemented in the water meter, it can help PAMDES officers to read the data without the internet, without changing the conventional water meter device, and the water meter data can be read and recorded effectively and efficiently. This research used the agile method, one of the methods used in the Software Development Life Cycle (SDLC). The method is done repetitively within a short period of time. The output of this research is an application with a digital image processing model that can read water meter data up to 82% in normal conditions and still can be improved. This research aims to make the water meter data reading and recording more effective and efficient and to contribute to the transformation of Sindangsari village into a digitalized village.


Lack of CBN reform may fuel more corruption in Nigeria

Abstract: Significance Cardoso has promised to prosecute any entities involved in making fraudulent claims. The Deloitte audit is one of three concurrent investigations into the bank’s management under the leadership of former Governor Godwin Emefiele (2014-23). Impacts Backdoor fuel subsidies may increase fiscal pressures on the government. There may be little appetite for prosecuting most of the officials indicted by the reports, with Emefiele serving as a scapegoat. When the CBN’s monetary policy committee meets for the first time since July 2023, they are likely to increase rates.


Sustainable Development for Smart Healthcare using Privacy-preserving Blockchain-based FL Framework

Abstract: Artificial Intelligence (AI) methods need to learn from an adequately large dataset to achieve clinical-grade accuracy and validation, which is vital in the healthcare field. However, sensitive medical data is usually fragmented, and not shared due to security and patient privacy policies. In this context, our work aims at classifying abdominal and chest radiographs by applying Federated Learning (FL) without exchanging patient data. FL framework has been implemented on distributed data across multiple clients. In the framework, a multilayer perceptron is used as a deep learning model for the classification task. FL is a novel approach in which machine learning models are built with the collaboration of multiple clients controlled by a central server or service provider. FL model ensures data privacy and security by retaining the training data decentralized. FL model provides security and privacy for patients by training individual models in distributed clients and sharing merely the model weights.


The Impact of the Dynamicity and Non-dynamicity of Assessment on EFL Learners' Productive Skills: Attitude in Focus

Abstract: The possible effects of dynamic evaluation (DE) and non-dynamic (non-DE) evaluation on the productive skills of Saudi EFL students were examined in this study. This study also looked at how Saudi EFL students felt about utilizing DE in their writing and speaking sessions. To achieve these objectives, sixty-four Saudi intermediate EFL students were split into two groups and selected using the convenience sample approach. Then, a pre-test was given to both groups for two skills: speaking and writing. After that, one group was taught speaking and writing using dynamic evaluation, while the other group was taught using NDE. Following eighteen training sessions, the groups were given posttests in speaking and writing, and the dynamic evaluation group was also given a perception questionnaire. The speaking and writing posttests for the two groups showed a substantial difference that favored the experimental group. The speaking and writing posttests demonstrated that the DE group fared better than the non-DE group. The results also pointed out that the DE group members had favorable opinions of the evaluation process. It was concluded that one of the best ways to help EFL students advance in their English language learning is to use DE in the classroom. Teachers and course designers may be convinced to incorporate dynamic evaluation into their lesson plans and courses by the consequences of this research.


Big Data Analysis of TikTok in the Education of Traditional Chinese Opera

Abstract: Opera is the essence of traditional art accumulated in Chinese history and civilization. In China, the inheritance of opera culture has been developed as an educational cause. However, in the era of the Internet, the once glorious and popular opera is also facing an existential crisis of losing a large number of audiences. How to reconstruct the communication ecology of opera with the help of new media has become an urgent problem for opera and media circles. In this paper, through big data of Douyin (Tiktok), to analyze the education of traditional Chinese opera, the ultimate hope as an innovative communication form combining traditional opera and mobile new media, short video of opera can expand the acceptance and influence of opera and promote the popularization and development of opera.


Methodological Aspects of Teachers’ Work in the Electronic Information Educational Environment

Abstract: Currently, it is becoming more and more relevant not just the use of information technologies in the educational process, but the conduct of the educational process in a specially designed electronic information educational environment. As practice shows, not all teachers use such a practice for conducting the educational process. Many teachers still prefer to teach classes according to the traditional classroom system. And there are good reasons for this. This article discusses the method of activating the interest and motivation of teachers for the use of electronic information educational environment in their professional activities. The method is to organize and conducting a refresher course “The work of a teacher in the electronic information environment of an educational organization of higher education”. The article provides a brief review of the structure and content of this course, an analysis of the effectiveness of the course based on a survey of students who have completed training. According to the results of the study, measures aimed at activating the interest of teachers in using electronic information educational environment are proposed. The study was conducted at Tver State Technical University. The practical significance of the research is to gain experience in the development and implementation of training courses on the use of electronic information educational environment in the educational process. The authors determined the optimal structure of the course, the content and conducted a statistical assessment of its effectiveness. The effectiveness of the course was determined based on the calculation of the proportion of listeners who, after completing the course, practice remote technologies in their teaching activities, in particular, work in an electronic information educational environment. The scientific novelty is the application of the author’s method for developing the course and its application as a special tool for activating the demand for distance learning technologies.


Optimizing neural network training with Genetic Algorithms

Abstract: In modern society, computer plays an important role among all human beings. Through the increasing development of technology, some problems happened gradually. In order to solve and regenerate the country, individuals should test their strengths. This paper discusses how to use genetic algorithms to optimize neural network training. As an important tool of machine learning, neural networks have made remarkable achievements in dealing with complex tasks. However, the training process of neural networks involves a lot of hyperparameter adjustment and weight optimization, which often requires a lot of time and computing resources. In order to improve the efficiency and performance of neural network training, humans should introduce genetic algorithms as an optimization method. Experiments are conducted on several common datasets to compare the performance of neural network training with Genetic Algorithm optimization against the traditional method. The results indicate that using Genetic Algorithms significantly improves the convergence speed and performance of neural networks while reducing the time and effort spent on hyperparameter tuning. Neural networks optimized using the Genetic Algorithm outperform their counterparts trained under the same time frame.


Deciphering differential exhumation in the Gangdese orogen in southern Tibet using exposed porphyry alteration systems and geomorphic analysis

Abstract: Topography and exhumation vary strongly in the Tibetan Plateau, reflecting different geodynamics and surface processes. The Gangdese orogen in southern Tibet is characterized by west-to-east variations in topography and occurrences of the Miocene porphyry copper deposits. We integrated remote sensing based hydrothermal alteration mapping and geomorphological analysis to detect patterns of differential exhumation since the Miocene. High abundances of propylitic and potassic alteration of the eastern Miocene granitoids are identified, indicating &gt;2 km of exhumation since the Miocene. The western Gangdese has intensive phyllic and argillic alteration representing ∼1 km of exhumation. Geomorphic analyses also show E-W changes that most likely resulted from differential rock uplift histories. Channel steepness index values indicate the eastern Gangdese has channel gradients between 211 m1.02 and 457 m1.02, compared to values between 30 m1.02 and 160 m1.02 in the west. Linear inversion of tributaries of the Yarlung river shows a pulse of rock uplift between 25 Ma and 7 Ma with a maximum rate of 110 m/ m.y. in the east. In contrast, the western Gangdese exhibits slower and persistent rock uplift rates between 20 m/m.y. and 40 m/m.y. since 20 Ma. The integrated results record differential exhumation and rock uplift since the Miocene. We invoke the geometric variations of the subducted Indian lithosphere to interpret the E-W differential exhumation since the Miocene. Steeper subduction of the Indian lithosphere beneath the eastern Gangdese induced mantle upwelling, triggering rapid rock uplift and deep exhumation. Conversely, the shallower subduction of the Indian slab in the west generated slow and prolonged rock uplift with less exhumation. Our study refines the exhumation process of the Gangdese since the Miocene.


An Elixir for Blockchain Scalability with Channel based Clustered Sharding

Abstract: Blockchain refers to distributed ledger technology which stores records without the help of a central authority. Born with bitcoin, this brainstorming technology finds its applications in healthcare, land registry, education, pharmaceutical industry, digital records, manufacturing companies and so on. The properties of blockchain such as immutability, distributed nature, tamper-resistant made it a disruptive technology in many applications. The highlighting feature of this pioneering technology is the distributed storage of ledger on all the nodes of the network. This helps to achieve decentralization without the trust for third party. The transactions are proposed, executed, validated and are then added as blocks to the blockchain. The problems with all the blockchain framework is scalability with respect to storage space and throughput. Scalability is the most significant factor to be considered in this big data era. This article proposes a solution called Channel Based Clustered Sharding (CBCS) approach for Hyperledger fabric blockchain framework. In this work, a lookup table is maintained which helps in forwarding the transactions to the clustered shards for validation. The CBCS approach helps in parallel transaction processing which in turn improves scalability and throughput of the system. The performance of the proposed work is measured with the help of Hyperledger caliper, a benchmarking tool for the performance analysis of Hyperledger fabric. The results show that the performance of the proposed system is increased from 3000 tps to 30,000 tps.


Extracting Explicit and Implicit Aspects Using Deep Learning

Abstract: The proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people’s attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis. Doi: 10.28991/ESJ-2024-08-01-05 Full Text: PDF


Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.


Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems

Abstract: In view of the global crisis that has increased the use of online learning, it is imperative to comprehend the factors that affect users' perceptions and behaviors when utilizing e-learning systems. In order to examine the impact of quality factors on user satisfaction and continuance intention using e-learning systems, this study integrates the Information Systems Success Model (ISSM) with the Technology Acceptance Model (TAM). The aim of this research is to shed light on the relationships between the e-learning systems' quality, perceived usefulness, perceived ease of use, user satisfaction, and intention to continue using them. This research employed partial least squares structural equation modeling (PLS-SEM) to assess the research model. The analysis was grounded in survey data collected from a randomly selected sample of 372 students at Arab Open University in Saudi Arabia. The study's results confirm that information quality for platforms and courses positively influences perceived usefulness, system quality, and perceived ease of use. Additionally, perceived usefulness and ease of use are significantly linked to user satisfaction, supporting the notion that enhancing information quality contributes to higher user satisfaction and encourages continued engagement. The developers of e-learning systems and educational institutions may use these findings to enhance the design, content, and usability of their platforms.


CHSH Bell Tests For Optical Hybrid Entanglement

Abstract: Abstract Optical hybrid entanglement can be created between two qubits, one encoded in a single photon and another one in coherent states with opposite phases. It opens the path to a variety of quantum technologies, such as heterogeneous quantum networks, merging continuous- and discrete-variable encoding, and enabling the transport and interconversion of information. However, reliable characterization of the non-local nature of this quantum state is limited so far to full quantum state tomography. Here, we perform a thorough study of Clauser-Horne-Shimony-Holt (CHSH) Bell inequality tests, enabling practical verification of quantum nonlocality for optical hybrid entanglement. We show that a practical violation of this inequality is possible with simple photon number on/off measurements if detection efficiencies stay above 82%. Another approach, based on photon-number parity measurements, requires 94% efficiency but works well in the limit of higher photon populations. Both tests use no postselection of the measurement outcomes and they are free of the fair-sampling hypothesis. Our proposal paves the way to performing loophole-free tests using feasible experimental tasks such as coherent state interference and photon counting.


Abstract: Economic Management and Big Data Application, pp. 69-82 (2024) No AccessStudy on Market Effect of Agricultural Product Incentive Policy for Quality and SafetyXiaohua Fan, Rui Li, Jialin Luo, Kun Wei, and Xinyue ZhangXiaohua FanLaw School, Central South University, Changsha, 410000, Chinawww.csu.edu.cn, Rui LiSchool of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, Chinawww.uestc.edu.cn, Jialin LuoBusiness and Tourism School, Sichuan Agricultural University, Chengdu, 611800, Chinawww.sicau.edu.cn, Kun WeiBusiness and Tourism School, Sichuan Agricultural University, Chengdu, 611800, Chinawww.sicau.edu.cn, and Xinyue ZhangBusiness and Tourism School, Sichuan Agricultural University, Chengdu, 611800, Chinawww.sicau.edu.cnhttps://doi.org/10.1142/9789811270277_0007Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: The agricultural product incentive policy for quality and safety aims to promote the overall continuous improvement of the agricultural products market’s quality and safety level. But its market effect in the case of information asymmetry determines its actual role. Based on the lemon market theory, this study designed a two-stage single-group post-test experiment. The first stage simulated the consumer’s agricultural product selection behavior in the case of information asymmetry, and the second stage verified the market effect of the agricultural product incentive policy for quality and safety. The results show that under the condition of information asymmetry, the agricultural product incentive policy for quality and safety is indeed conducive to improving the consumers’ perceived quality. Only when the improved perceived quality exceeds the overall average quality of the market, are consumers willing to pay and buy. This study verifies the market effect of agricultural product incentive policy for quality and safety, and reveals its boundary conditions when it works, which has great guiding significance for practice. Keywords: Agricultural productsIncentive policyMarket effectExperimental research FiguresReferencesRelatedDetails Recommended Economic Management and Big Data ApplicationMetrics History KeywordsAgricultural productsIncentive policyMarket effectExperimental researchPDF download


Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness

Abstract: In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.


Head Movement Invariant Eye Tracking System

Abstract: The human visual framework, consisting of the eyes, optic nerve, and brain, gives us the sense of sight to capture and interact with our surroundings. The eye, which provides stimulus to generate sight, is a complex organ that collects light and effectively converts it to an electro-chemical impulse inside neurons.Eye trackers are an important input modality for human-computer interfaces with vision-based applications. Eye-tracking systems have been proven to be valuable assistive devices for people with motor disabilities. Despite significant improvements in eye-tracking technology over time, current eye trackers in the market are complex and require intricate calibration procedures. Hence, there is still room for further research to develop an eye-tracking system with simpler calibration procedures.This paper presents the development of a head movement invariant real-time eye-tracking system with simpler calibration procedures that can operate non-intrusively. The complexity of setting up the system is also significantly reduced. Experiments conducted with a sample set of users showed an eye gaze accuracy improvement of over 11% compared to the existing setups commonly available in the market.


Teacher’s Agency as an Indicator of Leadership Potential Development

Abstract: Nonlinearity, multi-contextuality, new normality, constant variability of the modern world are reflected in politics, economics, education and other spheres of human life. Professional and personal self-determination and orientation of a person is difficult in a situation of diversity of various social environments and spaces, functional and semantic uncertainty of educational contexts. In this regard, the importance in the field of education is acquired by the teacher’s transprofessionalism, his ability to continuously develop, independently build interaction with the multifunctional environment, showing his agency as one of the indicators of leadership potential development. The purpose of the study is to operationalize the content of the concept of the teacher’s agency and its manifestation as an indicator of the development of leadership potential, taking into account the ongoing changes in the modern world. Methodology. The understanding of the essence of the terms “agency” and “leadership potential” of the teacher was carried out using the method of semantic analysis of their interpretations available in domestic and foreign sources. The use of activity-based and acmeological approaches is due to the need to determine the key mechanism for implementing the teacher’s agency in the comparison of significant categories (agency, leadership and management). General scientific research methods were used: theoretical analysis and synthesis, systematization and generalization of scientific literature on the research topic. Results. Connectivism (social polyvalence) is proposed as the main mechanism for the manifestation of the teacher’s agency as a teacher’s ability to find and maintain conditions, forms, ways and means of communication with representatives of different professions, social groups and statuses, beliefs, ages for organizing joint actions, events, productive activities. This made it possible to establish the key role of agency in the development of a teacher’s leadership potential – the creation of an atmosphere of cooperation within and outside the educational organization. Conclusion. The conclusion is made that the teacher, as a carrier and subject of public relations, needs to develop his agency and leadership potential, respectively, for orientation and adaptation in a changing world in the interests of sustainable development of regions and the country as a whole.


Research on Strategies for Improving the Quality of English Blended Teaching in Vocational Colleges through Network Informatization Resources

Abstract:
The blended teaching mode of &ldquo;online+offline&rdquo; has gradually become a widely used teaching mode due to its flexible teaching form and rich and vivid course resources. In the blended teaching mode, this article mainly focuses on how to improve the teaching quality of vocational English courses. Firstly, an analysis was conducted on the learning situation of English courses among vocational college students, including their source structure, learning characteristics, and current development status of English courses. In order to facilitate a structured analysis of students&rsquo; learning ability in English courses, a learning ability analysis model for vocational college students was constructed, and a survey questionnaire was designed to analyze their learning behavior and data. Finally, through data analysis, it was found how to use network information resources to improve the quality of English blended learning courses.

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End-to-end Visual Grounding Based on Query Text Guidance and Multi-stage Reasoning

Abstract:
Visual grounding locates target objects or areas in the image based on natural language expression. Most current methods extract visual features and text embeddings independently, and then carry out complex fusion reasoning to locate target objects mentioned in the query text. However, such independently extracted visual features often contain many features that are irrelevant to the query text or misleading, thus affecting the subsequent multimodal fusion module, and deteriorating target localization. This study introduces a combined network model based on the transformer architecture, which realizes more accurate visual grounding by using query text to guide visual feature generation and multi-stage fusion reasoning. Specifically, the visual feature generation module reduces the interferences of irrelevant features and generates visual features related to query text through the guidance of query text features. The multi-stage fused reasoning module uses the relevant visual features obtained by the visual feature generation module and the query text embeddings for multi-stage interactive reasoning, further infers the correlation between the target image and the query text, so as to achieve the accurate localization of the object described by the query text. The effectiveness of the proposed model is experimentally verified on five public datasets and the model outperforms state-of-the-art methods. It achieves an improvement of 1.04%, 2.23%, 1.00% and +2.51% over the previous state-of-the-art methods in terms of the top-1 accuracy on TestA and TestB of the RefCOCO and RefCOCO+ datasets, respectively.

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How to Build a Blogging Website

Abstract: Abstract: This paper explores the variegated domain of blogging, with focusing on the platform OmniSpectra. Here, I present an in-depth explanation of website’s UI/UX design, usage, architecture, and backend mechanisms. The study explores the unique characteristics of OmniSpectra that distinguishes it from other popular blogging websites, highlighting its inventive method in nurturing inclusive and compelling virtual communities of both content consumers and content creators. The paper will show in-depth workings of different techniques such as blog recommendation, data collection and curation that are used in the website to provide a benchmark user experience. The paper investigates the website’s role in shaping individual’s narrative and perception based to users’ interactions to different blogs, this has been achieved by getting a constant sentiment analysis feedback from users as well as the content created by the creators using AI models. All in all, after reading this paper, the reader will get a high-level idea of how to create a blogging website and add to it different modules according to their specific requirements. All this is explained by taking the example of OmniSpectra


Prioritized Text Detergent: Comparing Two Judgment Scales of Analytic Hierarchy Process on Prioritizing Pre-Processing Techniques on Social Media Sentiment Analysis

Abstract: Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Making (MCDM) methods, the Analytic Hierarchy Process (AHP) is preferred for handling complicated decision-making challenges using several criteria. The Consistency Ratio (CR) scores were used to examine pair-wise comparisons to evaluate the AHP. This study used two judgment scales to get the most consistent judgment. Firstly, the Saaty judgment scale (SS), then the Generalized Balanced Scale (GBS). It investigated whether two different AHP judgment scales would affect decision-making. The main criteria for prioritizing pre-processing techniques in sentiment analysis are Punctuation, Spelling, Number, and Context. These four criteria also contain sub-criteria. GBS pair-wise comparisons are closer to the CR value than SS, reducing the alternatives’ weight ratios. This paper explains how AHP aids logical decision-making. Prioritizing pre-processing techniques with AHP can be a paradigm for other sentiment analysis stages. In short, this paper adds another contribution to the Big Data Analytics domain.


Natural Language Processing For Requirement Elicitation In University Using Kmeans And Meanshift Algorithm

Abstract: Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the needs of stakeholders and the goals of the organization. The research objectives in this research to the process collected and integrating data from multiple sources and ensuring interoperability. Conclusion in this research is determining is the clustering algorithm help the collection data and elicitation process has a somewhat greater impact on the ratings provided by professionals for pairs that belong to the same cluster. However, the influence of POS tagging on the ratings given by professionals is relatively consistent for pairs within the same cluster and pairs in different clusters.


Model optimization method based on the MOON algorithm

Abstract: Federated Learning is a revolutionary approach to machine learning. Its purpose is to enable multiple participants to collaboratively train machine learning models without the need to share local data. The main objective is to address issues related to data privacy and security. In traditional machine learning, data is typically centralized and stored in a single location or on cloud servers for training. However, this centralized training approach carries risks of potential data leakage, especially concerning sensitive and critical information. Industries such as healthcare and finance, which involve sensitive data, place a premium on safeguarding data privacy. Furthermore, in cases where data cannot be easily transferred or is subject to privacy regulations, centralized methods may face limitations. Federated Learning revolutionizes the conventional approach by introducing a decentralized model training process. It maintains data decentralization while achieving collaborative model optimization, greatly enhancing data privacy and security. The MOON algorithm, an integral part of federated learning, contributes to its novelty. As a significant component, the MOON algorithm facilitates new possibilities for federated learning. In this article, the research will elaborate on the MOON algorithm within the context of federated learning. And this article will delve into its description and optimization, elucidating how it enhances federated learning.


The Role of Artificial Intelligence Applications in Enhancing the Quality of Online Higher Education

Abstract: The aim of this research is to assess and quantify the impacts of leveraging artificial intelligence (AI) applications on enhancing the quality benchmarks in online higher education programs in the kingdom of Saudi Arabia. Specifically, the study will survey a sample of 200 instructors and students involved in the online education process to evaluate their perceptions of how AI-enabled functionalities influence key quality indicators. This research follows a quantitative methodology to meet the core research objectives of assessing the impacts of AI adoption on online education quality. The target population encompasses all students and instructors engaged in remote higher education programs across universities in Saudi Arabia. Based on sample size power calculations, a sample of 200 participants will be recruited, including both undergraduate and graduate-level students enrolled in fully online degree courses as well as instructors directly responsible for teaching remote classes. The findings of the research revealed that a Pearson Correlation of 0.784 and a p-value of 0.039 (less than significance level of alpha=.05) suggests a significant positive association between AI Applications and the Quality of Online Higher Education provided by teachers. And a Pearson Correlation of 0.861 and a p-value of 0.022 (less than significance level of alpha=.05) suggests a significant positive association between AI Applications and the Quality of Online Higher Education for students.


Analysis of Book Preferences Among Visitors in Library System

Abstract: Library is a place that contains various resources and materials. Many invaluable knowledge can be found in the library. By analysing the library’s data, it is possible to obtain information that can further improve its services. This research aims to extract information from Multimedia University (MMU) library and present insightful visualization of the information to enhance the library administration. At present, the library does not have information on the book preferences of the library users. The book preferences statistics can be relatively helpful as the library will know what books can be imported in the future. By doing so, more people will visit the library and they will have more related books to use as reference or to read. In addition, there are no existing dashboards to display information on all borrowers, no visitor. In the absence of this, this research adopts the data science methodology to determine the book preferences of library users by using machine learning techniques such as clustering and classification. Lastly, a dashboard will be developed to display all the findings which includes statistics on the visitors and book preferences.


Design of a Sign Language-to-Natural Language Translator Using Artificial Intelligence

Abstract: This paper describes the results obtained from the design and validation of translation gloves for Colombian sign language (LSC) to natural language. The MPU6050 sensors capture finger movements, and the TCA9548a card enables data multiplexing. Additionally, an Arduino Uno board preprocesses the data, and the Raspberry Pi interprets it using central tendency statistics, principal component analysis (PCA), and a neural network structure for pattern recognition. Finally, the sign is reproduced in audio format. The methodology developed below focuses on translating specific preselected words, achieving an average classification accuracy of 88.97%.


Random Forest based estimate to assess the damages of future earthquakes: preliminary results

Abstract: In this paper we present a case study where the Random Forest (RF) Classifier, has been used to estimate the damage to buildings caused by a (possible) future earthquake, starting from the data of past earthquakes. This prelaminar work is based on the Shakedado dataset, which contains information on buildings and ground shaking parameters for the six major earthquakes that occurred in Italy between 1981 and 2012. We perform the following two conceptual experiments • E1: Assume that the sequence that hit Emilia has just ended and the data relating to the other major earthquakes happened in the past (L’Aquila, Pollino, and Irpinia) are available, then calculate the level of damage for each building in the Emila dataset. • E2: Assume that the sequence that hit Pollino has just ended and the data relating to the other major earthquakes happened in the past (L’Aquila, Emilia) are available, then calculate the level of damage for each building in the Pollino dataset. Both training and test datasets contain only masonry buildings located within 10 km of the main shock of each sequence. The results show the RF algorithm’s ability to discriminate between buildings with light/no damage from those with medium/severe damage, with a good accuracy, especially for E1.


A Bilingual Basque–Spanish Dataset of Parliamentary Sessions for the Development and Evaluation of Speech Technology

Abstract: The development of speech technology requires large amounts of data to estimate the underlying models. Even when relying on large multilingual pre-trained models, some amount of task-specific data on the target language is needed to fine-tune those models and obtain competitive performance. In this paper, we present a bilingual Basque–Spanish dataset extracted from parliamentary sessions. The dataset is designed to develop and evaluate automatic speech recognition (ASR) systems but can be easily repurposed for other speech-processing tasks (such as speaker or language recognition). The paper first compares the two target languages, emphasizing their similarities at the acoustic-phonetic level, which sets the basis for sharing data and compensating for the relatively small amount of spoken resources available for Basque. Then, Basque Parliament plenary sessions are characterized in terms of organization, topics, speaker turns and the use of the two languages. The paper continues with the description of the data collection procedure (involving both speech and text), the audio formats and conversions along with the creation and postprocessing of text transcriptions and session minutes. Then, it describes the semi-supervised iterative procedure used to cut, rank and select the training segments and the manual supervision process employed to produce the test set. Finally, ASR experiments are presented using state-of-the-art technology to validate the dataset and to set a reference for future works. The datasets, along with models and recipes to reproduce the experiments reported in the paper, are released through Hugging Face.


Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN

Abstract: In the domain of e-commerce, customer reviews wield significant influence over business strategies. Despite the existence of various recommendation methodologies like collaborative filtering and deep learning, they often encounter difficulties in accurately analyzing sentiment and semantics within customer feedback. Addressing these challenges head-on, this paper introduces BERTFusionDNN, a novel framework merging BERT for extracting textual features and a Deep Neural Network for integrating numerical features. We assess the efficacy of our approach using a Women Clothing E-Commerce dataset, benchmarking it against established techniques. Our method adeptly extracts valuable insights from customer reviews, fortifying e-commerce recommendation systems by surmounting barriers associated with deciphering both textual nuances and numerical intricacies. Through this endeavor, we pave the way for more robust and effective strategies in leveraging customer feedback to optimize e-commerce experiences and drive business success.


S.L.X. : Student’s online exchange

Abstract: Students online exchange (SLX) is a pioneering initiative designed to meet the unique needs of students by fostering a culture of mutual assistance and resource sharing. Addressing financial constraints and daily challenges, SLX provides a specialized platform where students can seamlessly exchange resources with the spirit of helping one another. Unlike traditional sharing models, SLX emphasizes the transformative power of mutual assistance over charity, promoting a community-driven approach. This synopsis offers insights into SLX's distinctive features, objectives, and societal impact, bridging existing gaps in the literature and presenting a novel resource-sharing paradigm. Through SLX, students collaboratively share academic materials and daily necessities, establishing a supportive environment for their educational journey. SLX endeavors’ to reshape the community narrative, making helping an integral part of student life. Key Word: Students online exchange (SLX), Mutual assistance, Resource sharing, Financial-constraints, Community-driven approach, Novel resource-sharing paradigm, Academic materials, Transformative power, Community narrative.


Roles of international agricultural organizations in economy revamp: The Nigerian experience

Abstract: The enduring problems of hunger and poverty in Nigeria are mostly the result of the agriculture sector's failure to have the desired positive impact on the populace. Hence, the study reviewed the roles of international agricultural organizations in economy revamp: the Nigerian experience. In retrospect, a few governments did make an effort to make a statement about agriculture through the creation of farm settlement programs and state-owned agricultural development organizations. However, welfare concerns more than ever served as justification for these activities rather than absolute economic need. The intervention of International Agricultural Organization (IAO) through various types of international agricultural programmes became significant by concentrating on sustainable agricultural practices, technology transfer, capacity building, and policy advocacy, modernizing agriculture, infrastructure development, policy advocacy and reforms, global agricultural trade , market access and trade, climate resilience, research and development, international research and collaboration, food security and agricultural development. To mention but a few, World Bank, International Fund for Agricultural Development and United State Agencies for International Development have contributed to agriculture revamp in the Nigerian economy. Challenges Affecting the Nigerian Agricultural Economy in International Business include: Infrastructure Deficiency, Limited Access to Finance, Inadequate Research and Development, Policy Instability and Inconsistency. It was presented in the study that by providing solutions to these challenges will contribute to a way forward to revamp agriculture in the Nigerian economy, making it more robust, sustainable, and inclusive.


Unsupervised anomaly detection of polluted telemetry data based on a reconstruction model with adaptive weighting

Abstract: Anomaly detection of telemetry data is a promising way for ensuring the reliability of spacecraft and success of space mission. This study proposes a reconstruction model with adaptive weighting for unsupervised anomaly detection of polluted telemetry data. The reconstruction model integrates the advantages of typical reconstruction models and can generate high-quality results. An adaptive weighting module is designed to against the data pollution issue by assigning different weights to training data samples. Experiments are conducted on two public telemetry datasets, and the results verify the effectiveness of our method.


Efficient high-resolution human pose estimation with integrated attention mechanism

Abstract: Deep learning has brought about a revolutionary transformation in the field of human pose estimation, offering significant advantages. Traditional approaches often seek performance improvement by expanding and deepening networks, resulting in increased parameters and complexity. In response to this challenge, we introduce DNNet, a novel framework rooted in HRNet. Its unique basic blocks incorporate feature mapping, channel weighting, and the fusion of different receptive fields. In comparison to HRNet, DNNet stands out with fewer parameters, lower computational complexity, and more resilient lightweight features. By leveraging features at various resolutions, DNNet's performance is further enhanced. Extensive validation on both the COCO dataset and a dataset focused on dangerous driving behavior reveals that DNNet's accuracy is comparable to that of HRNet. Notably, DNNet achieves a remarkable 69% reduction in parameters and a 59% decrease in computational complexity under similar accuracy conditions. In practical real-world applications, when applied to a dataset addressing dangerous behavior, DNNet outperforms both ShuffleNet and MobileNet in accuracy, highlighting its adaptability and efficacy in diverse scenarios. DNNet's unique features position it as a promising solution in the field of human pose estimation, providing a balanced trade-off between accuracy, efficiency, practical applicability, and the organic fusion of different receptive fields.


Research on the Integrated Solution of Physical Education Based on Smart Campus

Abstract: In order to improve the physical education experience for students, this study investigates the idea of an integrated solution based on a smart campus for physical education. Within the physical education curriculum, the study focuses on the integration of smart fitness monitoring and wearables, interactive fitness equipment and gamification, mobile applications and customized workout plans, smart facilities, indoor navigation, and data-driven curriculum enhancements. The data gathered from a study involving two groups of students, an experimental group with access to smart campus solutions and a control group without such access, was analyzed using a well-designed framework. The data study looks at how the smart campus solutions affect the students' academic performance, fitness levels, motivation, and adherence to exercise regimens. The analyzed findings point to a number of advantages of using smart campus technologies into physical education. Compared to the control group, students in the experimental group showed higher levels of academic performance, increased motivation, improved exercise adherence, and fitness. Students who had access to smart campus solutions reported much better experiences with physical education overall. The results imply that smart campus technologies have the potential to produce a physical education learning environment that is more interesting, focused on the needs of the students, and effective. Smart campus solutions help optimize curriculum design, boost motivation, and enhance academic performance by utilizing data-driven insights and personalized learning experiences.


The Impact of a Mobile App on Student-Alumni Communication and Engagement: A Case Study

Abstract: Abstract: The purpose of this study was to examine the effects of a mobile application that connects college students and alumni on their communication, networking, and career development. The app was developed by a team of students as a novel idea to foster strong alumni relations and provide mentorship opportunities for current students. The study used a mixed-methods approach, combining quantitative data from surveys and qualitative data from interviews. The surveys were conducted before and after the app was launched, and the interviews were conducted after the app was used for three months. The results showed that the app was beneficial to both students and alumni, as it increased their frequency and quality of communication, enhanced their sense of belonging and community, and improved their skills and experiences. The paper discusses the implications, limitations, and recommendations of the study for future research and practice.


Trusted Data Acquisition in Microelectronics Manufacturing as a Basis for ML-Optimized Processing

Abstract: In the digitization of manufacturing processes, one of the major goals is the connection of production facilities and the use of data to digitize business processes. In order to optimize manufacturing processes and maximize the quality of the resulting product, further process information and data directly from the work piece and from the manufacturing environment are required to achieve a holistic system view in addition to the selected data that the manufacturing systems already provide. Within the SiEvEI 4.0 project, a research consortium from industry and academia works on process digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for trustworthy distributed manufacturing and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The basic concepts of this approach have been presented at IMAPS 2021. As an update, this paper reports on the actual experimental evaluation of these concepts in two different assembly lines, including data acquisition, data handling and AI processing with the goal to optimize processing targeting higher production yield and product quality. Specific for this work is the focus on high mix/low volume SMD assembly using fully automated equipment and Solder Ball Application. As a result, the paper presents the experimental validation of the manufacturing process digitization and the use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.


Autonomous Driving System Driven by Artificial Intelligence Perception Fusion

Abstract: Perception, as the information input module of the automatic driving system, determines the lower limit of the entire automatic driving system. Both autonomous driving perception and robot perception are constantly approaching the real physical world through digital methods, and this real physical world is only limited to the scope of human perception, such as lane lines, traffic lights, driving obstacles, and so on. The main premise of this process is that humans already know the categories or properties of the physical world, and only allow machines and systems to replicate human responses. Whether it is a pure visual route or a multi-source fusion route, the essence is the difference between the perceptual system schemes, one focusing on the vertical and the other on the horizontal. The pure vision solution represented by Tesla or the multi-source sensor fusion file represented by Waymo. In fact, the perception module of the automatic driving system usually has multiple sensors to achieve information redundancy and information complementarity through multiple dimensions, but there is the possibility of information conflict between different sensors. This paper aims at the advantages of perception-driven artificial intelligence to achieve breakthroughs in autonomous driving innovation, and analyzes how perception fusion drive is applied to the practical application of autonomous driving, so as to analyze the future development prospects of artificial intelligence.


Anomaly Detection in Heterogeneous Data Using Graph Convolutional Networks

Abstract: Closed-circuit television (CCTV) surveillance systems are ubiquitous tools for ensuring public and private safety. However, manual monitoring of surveillance footage is arduous and time-consuming, often resulting in delayed identification and response to potential threats. In this paper, we present a novel real-time threat detection system tailored for CCTV surveillance, employing cutting-edge deep learning methodologies, particularly graph convolutional networks (GCNs), to detect and classify high-movement levels within video frames. By treating videos as segments and delineating between anomalous (potentially threatening) and normal (safe) segments, our system facilitates continuous real-time monitoring of surveillance footage, enabling the timely identification of potential threats such as abuse, burglaries, explosions, shootings, fighting, shoplifting, road accidents, arson, robbery, stealing, assault, and vandalism. To assess the efficacy of our system, we conducted extensive experiments on a large-scale dataset comprising CCTV footage, yielding promising results. Additionally, we introduce a novel, comprehensive dataset comprising 128 hours of real-world surveillance videos, encompassing various anomalies and normal activities. This dataset serves dual purposes: general anomaly detection, and the recognition of specific anomalous activities. Our experimental findings demonstrate the superior performance of our method in anomaly detection compared to state-of-the-art approaches. Furthermore, we provide an analysis of several recent deep learning baselines for anomalous activity recognition, revealing the challenging nature of our dataset and the potential it holds for future research endeavors. Through the integration of advanced deep learning techniques and comprehensive dataset creation, our proposed system stands to significantly enhance the efficiency and effectiveness of CCTV surveillance, facilitating faster response times and bolstering security for individuals in diverse environments.