A Comparative Analysis of Feature Selection Algorithms in Cross Domain Sentiment Classification

Abstract: Background: Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. Method: The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. Results: From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Conclusion: Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.


Knowledge Graph Embedding via Triplet Component Interactions

Abstract: Abstract In knowledge graph embedding, multidimensional representations of entities and relations are learned in vector space. Although distance-based graph embedding methods have shown promise in link prediction, they neglect context information among the triplet components, i.e., the head_entity, relation, and tail_entity, limiting their ability to describe multivariate relation patterns and mapping properties. Such context information denotes the entity structural association inside the same triplet and implies the correlation between entities that are not directly connected. In this work, we propose a novel knowledge graph embedding model that explicitly considers context information in graph e mbedding via t riplet c omponent i nteractions (TCIE). To build connections between components and incorporate contextual information, entities and relations are represented as vectors comprised of two specialized parts, enabling comprehensive interaction. By simultaneously interacting with one-hop related head and tail entities, TCIE strengthens the connections between distant entities and enables contextual information to be transmitted across the knowledge graph. Mathematical proofs and experiments are performed to analyse the modelling ability of TCIE in knowledge graph embedding. TCIE shows a strong capacity for modelling four relation patterns (i.e., symmetry, antisymmetry, inverse, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many). The experimental evaluation of ogbl-wikikg2, ogbl-biokg, FB15k, and FB15k-237 shows that TCIE achieves state-of-the-art results in link prediction.


Differential Impact of CD34+ Cell Dose for Different Age Groups in Allogeneic Hematopoietic Cell Transplantation for Acute Leukemia: A Machine Learning-Based Discovery

Abstract: Allogeneic hematopoietic cell transplantation (alloHCT) is curative for hematologic malignancies; however, it is associated with risks and complications. Identification of risk factors remain the topic of ongoing investigation. Specifically, CD34+ cell dose impact on overall survival (OS) has been investigated in various studies with contradicting conclusions. We utilized machine learning (ML) techniques to predict alloHCT outcomes based on patient and transplant-related characteristics, using single-center data to identify critical and often overlooked variable interactions. Our predictive model improves on the conventional Cox regression analysis with the ability to capture complex high-dimensional relationships. The application of SHAP (SHapley Additive exPlanations), an explainable artificial intelligence (XAI) technique, allowed us to discern new and clinically relevant feature-outcome relationships. The cohort used for developing the machine learning model covers 1153 patients across different diagnoses, transplanted between 2010 to 2019. In particular, we chose XGBoost, a tree-based ML model, that is known for its high performance with tabular data. After further investigation by SHAP, we narrowed the study cohort to 674 acute leukemia patients (AML n=549, ALL n=125) with a median age of 54 (18-76), where we identified clear interaction between CD34+ cell dose of the peripheral blood stem cell (PBSC) grafts and patient age at alloHCT for acute leukemia patients (Figure 1). Three cell dose groups (low, medium and high) were determined using maximally selected rank statistics method, separately for the younger (≤45-years-old) and older (>45-year-old) patient age groups. On univariate analysis for OS, within the younger cohort, the group with a lower CD34+ dose resulted in the highest 2-year OS: low vs. medium vs. high dose, 84.6% vs. 48.2% vs. 59.1% (overall p-value=0.034, Figure 2a). For the older cohort, the group with a low CD34+ dose resulted in the lowest 2-year OS: low vs. medium vs. high dose, 33.3% vs. 48.8% vs. 55.1% (overall p-value=0.054, Figure 2b). Multivariable analysis affirmed the interaction effect: for younger acute leukemia patients, a cell dose no higher than 4.3 × 106 CD34+/kg was associated with improved OS against the medium (hazard ratio [HR] 0.22, p=0.004) and higher (HR 0.25, p=0.009) cell dose groups. Conversely, for older acute leukemia patients, low CD34+ cell dose <3.8 × 106/kg was associated with worse OS against a high dose ³6.1 × 106/kg (HR 1.73, p= 0.009). The high dose group in older patients retained superior OS compared to low and medium dose groups combined (HR 0.73, p=0.024). Our findings suggest that CD34+ cell dose should be tailored by patient age for acute leukemia patients undergoing alloHCT, and XAI showcases excellent proficiency in revealing such interactions.


Crossing the digital frontier: are older adults ready for virtual reality workouts?

Abstract: In the dynamic web of our digital age, virtual reality (VR) is crystallizing as a promising medium for promoting physical activity in older adults, overcoming age-old barriers. This perspective article explores the intricate synergy between VR and physical activity in older adults and highlights the many challenges of aging within a virtual environment. While VR heralds the potential for multisensory interaction and cognitive enhancement, a thorough assessment of its impact is paramount. The reach of VR goes beyond immediate applications and permeates the emotional and psychological realms of the human journey. Such an exploration requires a comprehensive perspective that goes beyond a purely technical assessment. The aim of this article is not to present clear-cut solutions, but to encourage reflection on the diverse impact of VR on our aging population. We argue for a future in which technology complements, rather than eclipses, the depth of human experience.


Digital practices at school, safety and digital citizenship

Abstract: This study aims to address the massification of the internet in today's world, how much people depend on digital technologies today for almost everything in their lives, as well as the digital practices that exist today in schools, as well as security issues. online or digital and digital citizenship. Which, by the way, are topics little discussed in schools or with timid reflections still in the school environment. Because it is a bibliographical research, important contributions from authors who dominate on such subjects and address digital security issues as a whole and that the school universe is still unaware of many issues were described. Research by Cristina Paludo Santos was also described in her article Education, Digital Practices and New Network Risks regarding a project with high school students where the fragility of knowledge regarding digital security was detected, where part of it is shared in this bibliographical research. In view of the above, it is concluded in this work that digital practices exist in schools, however, the risks also and about digital security is a topic that is still little known by the school community and society in general. The risks are inevitable, but it is possible that society can be better informed about them.


Pointer Meter Reading Recognition by Joint Detection and Segmentation

Abstract: To handle the task of pointer meter reading recognition, in this paper, we propose a deep network model that can accurately detect the pointer meter dial and segment the pointer as well as the reference points from the located meter dial. Specifically, our proposed model is composed of three stages: meter dial location, reference point segmentation, and dial number reading recognition. In the first stage, we translate the task of meter dial location into a regression task, which aims to separate bounding boxes by an object detection network. This results in the accurate and fast detection of meter dials. In the second stage, the dial region image determined by the bounding box is further processed by using a deep semantic segmentation network. After that, the segmented output is used to calculate the relative position between the pointer and reference points in the third stage, which results in the final output of reading recognition. Some experiments were conducted on our collected dataset, and the experimental results show the effectiveness of our method, with a lower computational burden compared to some existing works.


An Overview of Deepfake Methods in Medical Image Processing for Health Care Applications

Abstract: AI has become a part and parcel of the medical industry. Especially machine learning-based deep learning models with their ability to handle large amount of data continuously made significant contributions to the healthcare industry. One of the most promising technological developments in the field of deep learning is “deepfake”. Deepfake is a method of synthesizing fake images, audio, or video through deep learning models. Deepfake medical image processing is used in different applications like medical image synthesizing, modality transfer, data augmentation, dataset expansion, resolution enhancement, denoising, and reconstruction of medical images. Therefore, its wide range of applications provided a new realm of opportunity for the medical professional for quick, easy, and accurate diagnosis of dis-ease. This study provides an overview of deepfake implementation in the field of medical image processing. This paper is focused on providing clarity for the research questions (RQs), What are deepfake? What are the most common methods to generate a medical deepfake? What are the major applications of deepfake in the medical field? What is the need for deepfake detection? These research questions may help the researchers and academicians to understand the domain specified.


E-Comic Based on SETS: A Digital Learning Media to Improve Student’s Character and Critical Thinking Skills

Abstract: This research aims to test the feasibility and effectiveness of electronic science comic learning media based on character education and SETS in improving students' character and critical thinking skills. This research is development research oriented towards testing product effectiveness. The data collection methods employed in this study included observation, tests, and questionnaires. The research findings indicate that, 1) the feasibility of SETS-based e-comics is measured through expert assessments of media and materials with the criteria of a very viable product; 2) The average gain test results show that SETS-based science electronic comic media is effective in improving students' character moderately by 5,04 and students' critical thinking abilities with a score of 4,03 are included in the medium category. This can be interpreted as saying that the use of SETS-based science e-comics is able to improve student behavior and influence students to be more enthusiastic and broad-minded to actively ask scientific questions.


Anti-Eavesdropping by Exploiting the Space–Time Coupling in UANs

Abstract: Due to the space–time coupling access, we find that anti-eavesdropping opportunities exist in underwater acoustic networks (UANs), where packets can be successfully received only by the intended receiver, but collide at the unintended receivers. These opportunities are highly spatially dependent, and this paper studies the case that linearly deployed sensor nodes directly report data toward a single collector. We found an eavesdropping ring centered around these linearly deployed sensor nodes, where the eavesdropper could steal all the reported data. Since the typical receiving-alignment-based scheduling MAC (TRAS-MAC) will expose the relative spatial information among the sensor nodes with the collector, the eavesdropper can locate the eavesdropping ring. Although moving the collector into the one-dimensional sensor node chain can degrade the eavesdropping ring to a point that constrains the eavesdropping risk, the collector’s location will be subsequently exposed to the eavesdropper. To efficiently protect the reported data and prevent the exposure of the collector’s location, we designed a slotted- and receiving-alignment-based scheduling MAC (SRAS-MAC). The NS-3 simulation results showed the effectiveness of the SRAS-MAC and the TRAS-MAC in protecting data from eavesdropping, which protect 90% of the data from eavesdropping in the one-eavesdropper case and up to 80% of data from eavesdropping in ten-eavesdropper cases. Moreover, unlike TRAS-MAC, which will expose the collector’s location, SRAS-MAC provides multiple positions for the collector to hide, and the eavesdropper cannot distinguish where it is.


A Novel Default Prediction Model Based on DNN and LightGBM

Abstract: Given the credit risk losses brought by Loan default to commercial banks, this paper, based on the America Express data set of the Kaggle platform, builds a hybrid model to predict customer default, to reduce credit risk. We first conduct data cleaning and feature engineering processing, dividing the data into category data and continuous data, and calculating the correlation between different features. We build DNN and lightGBM for credit default prediction. From the comprehensive results, the prediction effect of the mixed model is significantly better than that of the single prediction model. Among these models, our hybrid approach achieves the leading metric of 0.798. This metric is higher than XGBoost, LightGBM, and DNN by 0.013, 0.002, and 0.008 respectively.


Understanding and treating body image disturbances in eating disorders through body illusion interventions: a scoping review protocol

Abstract: Abstract Background We plan a scoping review aimed to synthesize what is known about the use of sensory-driven body illusion (BI) interventions for understanding and treating body image disturbance (BID) in people diagnosed with clinical eating disorders (EDs) and people with subclinical ED symptomatology. Our study will provide an outline of the current literature, identify gaps within the literature, and suggest novel directions for future research. Methods/design The scoping review process will be guided by the methodological framework of Arksey and O’Malley, subsequent recommendations by Levac et al., and the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols Extension for Scoping Reviews guidelines. The following electronic databases will be systematically searched: MEDLINE (via PubMed), Web of Science, PsycINFO, and Scopus. Furthermore, to identify additional studies, we will use a search engine such as Google Scholar, and for grey literature, we will include Proquest for Dissertations and Theses. A search strategy has been identified and agreed upon by the research team in conjunction with a research librarian. Two researchers will screen the titles and abstracts independently and then assess the full text of the selected citations for the inclusion criteria. A third reviewer will be involved in cases of disagreement. Data will be extracted, collated, and charted to summarize all the relevant methods, outcomes, and key findings in the articles. Discussion A better understanding of this topic will aid in the development and refinement of current treatments aimed at treating BID in people with EDs. Implications and recommendations for research, policy, and practice in the context of the ED community will be discussed. Systematic review registration https://osf.io/3bcm6/?view_only=83b2e8a2445d4266909992e3dfb51929


Adaptive identification of supply chain disruptions through reinforcement learning

Abstract: Proactive identification and the management of disruption risks play a crucial role in the achievement of a global supply chain’s aims. Given the velocity and volume by which such disruption events occur, it is impractical to expect supply chain managers to determine the occurrence of such events manually. Given the pressures facing global supply chains due to the COVID-19 crisis, it is important for supply chain managers to proactively identify disruption risks to their supply chains and manage them to either achieve the outcomes or develop plans by which resilience against them can be built. In this paper, we demonstrate how the integration of natural language processing and reinforcement learning, which are fundamental artificial intelligence methods, can be used to assist supply chain risk managers in the timely identification of such disruption events. We explain in detail our proposed approach, namely RL-SCRI and show its superiority over the current models in achieving its aim.


On the use of smart contracts to cope with Arrow’s Information Paradox on trade secrets exchange: method and prototype

Abstract: The state of the art contains systems for the transfer of intangible assets protected by intellectual property rights, consolidated by legal monopolies, be them attributive systems (such as patents and industrial designs) or reliant on automatic protection (such as copyright and neighboring rights). Some of these systems, or their respective methods, are described in the scientific literature. Of this set, some methods employ blockchain technology and a smaller subset also employs smart contracts technology, despite both technologies being mutually independent. Although trade secrets also constitute monopolies, computerized solutions or cryptographic environments used for other types of goods protected by other forms of intellectual property cannot be transferred to these assets, due to their sui generis nature, lacking of monopoly regardless of its non-disclosure. The need for maintenance of secrecy is due to Arrow Information Paradox, the phenomenon by which the disclosure of the secret constitutes by itself its transference. This phenomenon impairs the potential for transference of industrial secrets and prevents the adoption of the existing systems applicable to other types of intangible assets. Through this work a method for transferring industrial secrets is presented, using smart contracts embedded on a blockchain, for solving two of the three component elements of the Paradox, namely capability and reliability, and presupposes the third, relevance. The method is also validated through the development of a prototype, based on Ethereum blockchain.


Construction and Improvement of a Vocational Education and Teaching System Oriented to “Internet+”

Abstract: Given the current rapid development of informatization, people will increasingly use online teaching methods to learn for their quality improvement. Starting from the fundamental theories, such as the concept, classification, basic process, task, and method of data mining under the background of Internet Plus, this article analyzes the problems that must be considered in data mining application. Secondly, the Apriori algorithm is studied, the FP-Tree flow chart is established, and the Internet Plus vocational education teaching system model is constructed using multiple databases as data sources. Finally, the results of the teaching system are analyzed and verified, with the model analysis showing that the minimum confidence boost of the system is 0.65, and the minimum support reaches 0.03. The maintainability of the database based on the association rules Apriori algorithm is good; the data entry can be completed smoothly, and the update, deletion, and modification can also be completed smoothly.


A Comprehensive and Privacy-Aware Approach for Remote Qualified Electronic Signatures

Abstract: The current shift towards digital transactions emphasizes the need for robust Qualified Electronic Signature (QES) frameworks that safeguard integrity and privacy. Having the potential to become the leading type of adopted QES, the main challenge that Remote QESs present to end users is choosing between transmitting the entire document or only its digest to the Trust Service Provider (TSP). The first option compromises the document’s confidentiality, while the second one requires the development of signature applications compliant with advanced signature formats, a task that often needs additional time and resources. In this paper, we introduce a comprehensive strategy for remote QESs, designed for seamless integration with current client applications, while simultaneously maintaining user privacy. The main topics approached in this paper are the following: a comprehensive architecture for privacy-aware remote QES systems, relevant standards and legislation, integration scenarios for clients, and remote QES standard protocols to assure communication between client and TSP environments. Furthermore, we also explore the integration of our proposed solution with an enhanced long-term preservation service that uses Ethereum smart contracts and methodologies to implement signature applications with advanced electronic signatures via open-source libraries while ensuring document privacy. The main result of this work is a flexible on-premise module that provides the ability to sign, validate, and preserve documents, with a minimal integration effort.


Detection of arabic sign language by machine learning techniques with PCA and LDA

Abstract: People need a communication channel and a way to communicate with a person or group. Deaf people communicate with others through sign language. The remarkable and rapid development in image and video recognition systems has made researchers use this development to solve many problems, including sign language for the deaf, and reduce their suffering in communicating with ordinary people. This work aims to use and apply machine learning technology to build an automatic recognition system for Arabic Sign Language (ArSL). In this work, images of ArSL characters were recognized using four classification techniques (Naïve Bayes (NB), Decision Trees (DTs), and Adaptive Boosting), and K-Nearest Neighbor (KNN)) with the Python library and using two feature extraction algorithms (PCA & LDA). Data pre-processing steps, including grayscale conversion, Gaussian blur, histogram equalization, and resizing, are applied to enhance the data's suitability for training and testing. The work was tested with five experiments chosen with multiple ratios for training and test data. The first training is 90%, the second training is 80% of the data, the third is 75%, the fourth is 70%, and the last is 60%. The work also played a good role in interpreting ArSL, and the accuracy of the work considers the KNN algorithm more accurate in prediction.


The Role of Libraries in Creating a Safer Cyberspace: Awareness of Estonian Library Employees in Information Security

Abstract: The purpose of the study was to find out the knowledge, attitude and behavior of library employees related to information security in seven areas of the information security policy involving employees: password management, use of email, use of the Internet, use of social media, use of mobile devices, handling of information and reporting of cyber incidents. The theoretical part provides an overview of the importance of the topic in the context of libraries, the reasons why information security and cyber security must be at the same high level in libraries as, for example, in banks or healthcare institutions – we can only imagine what would happen if the digitized cultural memory of Estonia disappears or user data is leaked. The HAIS-Q (Human Aspects of Information Security Questionnaire) developed by Parsons et al. was used to collect data in order to assess information security awareness (ISA) within the organization. The method is based on three factors developed by the same authors: the Knowledge-Attitude-Behavior (KAB) model, which determines the security awareness of employees. The target group of the online questionnaire was Estonian librarians. A total of 388 librarians from public, research, school and specialist libraries responded to the survey. The mean and standard deviation of each focus area, descriptive statistics, Cronbach’s coefficient and Pearson correlation analysis were used to achieve the research objective. The results revealed that the chosen methodology is well suited for assessing the cyber literacy of not only library staff, but also the staff of all memory institutions (archives, museums) and identifying training needs.


Unveiling the correlates of university students attitude towards online education

Abstract: The education landscape in India is undergoing massive transformation due to rapid advances in Information and Communication Technologies (ICTs) and its increasing integration in education sector in India. It has transformed the way education services are planned, structured and delivered to the masses. The Corona pandemic has, per force, accelerated the use of ICTs in education sector and brought to center stage the concepts such as online education or e-learning. The present study was carried out to find out the attitude of Undergraduate students towards online education and the factors affecting it at a premier Agriculture University in North India. Using exploratory research design, the study sample comprised of 185 respondents selected using stratified random sampling method, and the data was collected using a pre-tested questionnaire which was e-mailed to the selected respondents. The study findings revealed that a majority of the respondents displayed a ‘neutral to positive’ attitude towards online education. Further, age, academic performance, family education status, Annual family income, Family size, Technology readiness, and Application skills were found to be the major factors that affected attitude towards online education. The value of Coefficient of determination, i.e. R2 (0.5729) reiterated that all the independent variables contributed 57.29% variation in the dependent variable. Hence, concerted efforts have to be made by the policy makers, administrators, and academicians concerned to make the necessary improvements in planning, implementing and strengthening online education strategies.


Sonovisual: Transforming Image Description into Dynamic Narratives

Abstract: Sonovisual is an innovative framework aimed at bridging the gap between static image descriptions and immersive, dynamic narratives. Traditional image descriptions are often limited to static textual representations, lacking the ability to fully convey the visual essence and emotional depth embedded within an image. This project introduces a novel approach that combines cutting-edge image recognition technologies with advanced natural language processing techniques, facilitating the seamless transformation of image features into engaging and vivid narratives. The core of the Sonovisual framework lies in its integration of sophisticated deep learning algorithms, enabling the extraction of intricate visual details and contextual information from images. Leveraging state-of-the-art machine learning models, the system can recognize objects, scenes, and intricate visual patterns, and subsequently generate descriptive narratives that not only encapsulate the visual content but also evoke a multisensory experience for the audience. Through the integration of adaptive storytelling techniques, Sonovisual dynamically adapts its narrative style and tone to cater to different audiences and contexts. By incorporating sentiment analysis and contextual understanding, the framework ensures that the generated narratives resonate with the emotional nuances and cultural sensitivities associated with the depicted imagery. The Sonovisual project holds promise for various applications, including enriching accessibility features for the visually impaired, enhancing educational materials with immersive visual narratives, and augmenting multimedia content with engaging storytelling elements. Additionally, its potential extends to fostering new avenues for creative expression and facilitating enhanced human-computer interaction through visually enriched storytelling experiences.


Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

Abstract: In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.


Application of the on-site P-wave earthquake early warning method based on site-specific ratios of S-waves to P-waves to the 2016 Kumamoto earthquake sequence, Japan

Abstract: Abstract The on-site P-wave earthquake early warning (EEW) based on the site-specific ratios of S-waves to P-waves has been applied to large-sized offshore earthquakes, and the efficiency of the method has been validated. However, the method requires the P-waves including earthquake ground motions radiated from a large slip area while avoiding the inclusion of S-waves. In this study, we investigated the applicability of the on-site P-wave EEW method for ground motions near an earthquake source fault region, using strong-motion data observed during the 2016 Kumamoto earthquake sequence in Japan. At first, we examined the appropriate time-window length following the arrival of the P-waves. As a result, P-waves with a time-window length of 2.56 s after the arrival at most strong-motion stations were required at least to predict appropriately S-waves for the 2016 Kumamoto earthquake sequence, including the large-sized earthquakes. On the other hand, in the case of the large-sized earthquake as the mainshock ( Mj 7.3), the method can predict within a brief time of 0.5 to 2 s in the operational use that strong ground motions exceeding a certain threshold (e.g., acceleration of 150 cm/s 2 ) will come. Moreover, we found that the method was not strongly affected by the non-linearity of soil deposits due to strong ground motions during the 2016 Kumamoto earthquake sequence. The variability of the relationship between P- and S-waves at the seismic bedrock influenced by the source and path effects is larger than the variability of the relationships between P-/S-waves at the seismic bedrock and at the ground surface by the site effects, and therefore, it hides the effect of the non-linearity of soil deposits. Graphical Abstract


Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Abstract: Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.


Management of Mathematics Learning Based on Interactive Digital Worksheets to Improve Students' Critical Thinking Ability

Abstract: The use of interactive digital technology in learning is an alternative strategy to create joyful learning activities. Therefore, interactive digital worksheet requires development by considering the critical thinking skills of elementary school students. This research determined the interactive digital media worksheet necessity based on critical thinking skills, compiling design, feasibility identification, and effectiveness test of the interactive digital media worksheet based on critical thinking skills for elementary school students. This research and development applied seven steps: (1) potential and problem identification; (2) data collection; (3) product design; (4) design validation; (5) design revision; (6) product test; and (7) product revision. The researchers collected the data with interviews, observations, questionnaires, and tests. The research results found the necessity of interactive digital student worksheets in managing mathematics learning to improve students' critical thinking skills. The interactive digital LKS design was based on critical thinking skills. The development considered the three components, such as (1) Planning; (2) implementing; and (3) evaluating. Planning consists of (1) the analysis of student characteristics; (2) the analysis of learning achievements (CP); (3) the determination of the learning objectives (TP) and the learning objective flow (ATP) and the assessment; (4) the selections of media, learning methods, and teaching materials. The implementation of the interactive digital student worksheets in learning was based on the problem-based learning model and formative and summative tests for the evaluation. The expert validation found the interactive digital worksheet was valid and reliable. The teacher responses reached the percentage of 87.5% while the student with 90%. The product test obtained an average of N-gain for the control group was 0.31 while the experimental group was 0.50, indicating an increment of students' critical thinking skills in mathematics. Thus, interactive digital worksheet implementation is useful in managing learning. The implementation was also useful for further development to manage various challenging activities and to realize meaningful learning.


Saudi EFL Students’ Perceptions Towards the Impact of YouTube on Improving Speaking Skills

Abstract: The significance of integrating technology in education, especially the widely and frequently used platform “YouTube”, is found in its ability to provide accessible and various educational resources, meeting the needs of diverse learning styles and enhancing active engagement. Since YouTube is one of the preferred applications utilized by Saudi individuals, this study investigated the impact of using YouTube on enhancing the speaking skills of EFL male and female Saudi students from different Saudi universities. This study was conducted during the first semester of the academic year 2021- 2022. A questionnaire was used as the study instrument. It was adapted from Binmahboob’s (2020) study and modified according to the study’s purposes. The questionnaire contained 17 items divided into two sections. 79 EFL students from different Saudi universities participated. Fifty-nine of them were females, while 20 were males. The findings of the study revealed that YouTube videos had a high positive perspective from the participants. In other words, it can be concluded that YouTube videos enhance the speaking skills of EFL university students. Based on these finding, some recommendations and implications were provided.


On DeFi and On-Chain CeFi: How (Not) to Regulate Decentralized Finance

Abstract: ABSTRACT This article proposes a framework to assess the factual decentralization of blockchain-based financial infrastructure, commonly referred to as ‘decentralized finance’ (DeFi). It discusses various centralization vectors along the DeFi architecture layers from a technological and legal point of view. Distinguishing between endogenous and inherited centralization, the importance of decentralization assessments for regulators is demonstrated. First, centralization vectors are a strong indication that a DeFi project may have (some) custodial properties warranting regulation. Second, centralization vectors reveal the avenues of control in projects, pointing out appropriate regulatory hooks. The article serves as an interdisciplinary introduction that analyses blockchain-based financial infrastructure and provides policymakers and regulators with a tool to identify the differences between truly independent, neutral infrastructure and fake decentralization. It concludes that the former may have very beneficial properties and can contribute towards a more open and transparent financial system, while the latter is a blockchain-based form of financial intermediation and should be regulated as such.


Regional Perspective of Using Cyber Insurance as a Tool for Protection of Agriculture 4.0

Abstract: The digitalization of the agricultural industry is manifested through the active use of innovative technologies in all its areas. Agribusiness owners have to constantly improve their security to meet new challenges. In this context, the existing cyber risks of the agrarian industry were assessed and their classification by possible consequences, such as data theft or alteration, cyber terrorism, cyber warfare, software hacking or modification, the blocking of markets and transactions on them, was proposed. Cyber insurance is an effective tool for minimizing the likelihood of cyber incidents and for comprehensive post-incident support, with the involvement of cybersecurity specialists. An algorithm for cooperation between an agricultural company and an insurance company when concluding a cyber risk insurance contract is proposed, which takes into account the needs and wishes of insurers at each stage of the interaction. To assess the need to use cyber insurance in agriculture 4.0, a methodology has been developed to evaluate the regional characteristics of cybersecurity and the digitalization of agribusiness. The results of the study show a heterogeneous need for this tool in different regions of the world.


Reservoir Computing Using Interfacial Memristors with Native SiOx Nanostructures Modified by Room-Temperature Plasma Oxidation

Abstract: We present an interface-type memristor utilizing native silicon oxide nanostructures achieved through controlled plasma oxidation engineering at room temperature. This process effectively reduces the concentration of oxygen vacancies on the surface of the native silicon oxide, facilitating oxygen-vacancy-mediated interfacial resistive switching with excellent COMS compatibility and low switching variability (1.03% for C2C and 3.96% for D2D variability). Moreover, this dynamic memristor exhibits favorable nonlinearity and short-term memory (STM) effects, which are crucial properties for the development of memristor-based reservoir computing (RC) systems. Compared with the conventional neural network, the RC system requires fewer trainable weights to achieve an accuracy of 96.7% for the Iris classification task. By the addition of virtual nodes to the reservoir with masks, the accuracy can be further increased to 100%. A simple digit recognition task is also employed, and noisy images that are not included in the original training set can be recognized correctly. These results highlight the great potential of our device for constructing reservoir computing hardware.


Privacy Protection Method for Blockchain Transactions Based on the Stealth Address and the Note Mechanism

Abstract: Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution.


Biomedical named entity recognition based on reinforcement learning label correction

Abstract: Biomedical named entity recognition is of great significance in the field of natural language processing.Deep learning approaches are mainly used at present, and the BERT-BiLSTM-CRF model is one of them. Although the BiLSTM structure in this model can capture bi-directional long-distance dependencies in sentences, there is the problem that the model performance can easily reach the bottleneck by simply adjusting the hyperparameters or changing the network structure, and the quality of its recognition results can hardly be further improved. To address the above problems, this paper adds the label correction process based on deep reinforcement learning to the BERT-BiLSTM-CRF model to form the BERT-BiLSTM-CRF-DRL model. The intelligent body Agent based on deep reinforcement learning is used as a label corrector, and the label correction threshold is set, so that the uncertainty in the annotation results of the BERT-BiLSTMCRF model is greater than the label correction threshold. The labels with uncertainty greater than this threshold in the results are processed for correction. The experimental results show that the F1 values on the datasets BC4CHEMD, BC5CR-CHEM, and NCBI-DISEASE reach 91.07%, 94.02%, and 89.56%,, which are all improved compared with the existing advanced biomedical named entity recognition models.


An Improved Fault Diagnosis Algorithm for Highly Scalable Data Center Networks

Abstract: Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.


Advancing (U-Th)/He Zircon Dating: Novel Approaches in Sample Preparation and Uncertainty Reduction

Abstract: Abstract The laser microprobe (U-Th)/He dating method is a new and efficient technique that utilizes an interoperable and integrated suite of instruments, including the excimer laser system, quadrupole helium mass spectrometer, and quadrupole inductively coupled plasma mass spectrometer. To demonstrate the applicability of this new method, we applied both the conventional and laser microprobe techniques to the Sri Lanka zircon (LGC-1). We obtained twenty-two (U-Th)/He ages on nine shards using the laser microprobe method, showing an average (U-Th)/He age of 471.1 ± 16.6 Ma (1σ). This result is generally consistent with the mean conventional age (484.1 ± 9.6 Ma) for twenty-two zircon fragments. Both are nearly equal to the age value (~476 Ma) predicted by the He diffusion model and the thermal history model of Sri Lanka highland. The variations in the laser microprobe-derived ages are most likely caused by the uncertainties in volume measurements, which is also common in other studies. We used the Mahalanobis distance technique to reduce the volume measurement bias by identifying and eliminating abnormal data.


Structural Topic Modelling-based Systematic Review for Service Robots’ Fault Diagnosis: Content, Popularity and Regional Differences

Abstract: Abstract The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots’ fault diagnosis through the application of Structural Topic Modeling (STM). A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence algorithms to address service robots’ complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of artificial intelligence, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis. Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots’ fault diagnosis.&#xD;


Towards optimal learning: Investigating the impact of different model updating strategies in federated learning

Abstract: With rising data security concerns, privacy preserving machine learning (ML) methods have become a key research topic. Federated learning (FL) is one such approach which has gained a lot of attention recently as it offers greater data security in ML tasks. Substantial research has already been done on different aggregation methods, personalized FL algorithms etc. However, insufficient work has been done to identify the effects different model update strategies (concurrent FL, incremental FL, etc.) have on federated model performance. This paper presents results of extensive FL simulations ran on multiple datasets with different conditions in order to determine the efficiency of 4 different FL model update strategies: concurrent, semi-concurrent, incremental, and cyclic-incremental. We have found that incremental updating methods offer more reliable FL models in cases where data is distributed both evenly and unevenly between edge nodes, especially when the number of data samples across all edge nodes is small.


DWS-YOLO: A Lightweight Detector for Blood Cell Detection

Abstract: Peripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.


Hardware-Aware Evolutionary Explainable Filter Pruning for Convolutional Neural Networks

Abstract: Abstract Filter pruning of convolutional neural networks (CNNs) is a common technique to effectively reduce the memory footprint, the number of arithmetic operations, and, consequently, inference time. Recent pruning approaches also consider the targeted device (i.e., graphics processing units) for CNN deployment to reduce the actual inference time. However, simple metrics, such as the $$\ell ^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> -norm, are used for deciding which filters to prune. In this work, we propose a hardware-aware technique to explore the vast multi-objective design space of possible filter pruning configurations. Our approach incorporates not only the targeted device but also techniques from explainable artificial intelligence for ranking and deciding which filters to prune. For each layer, the number of filters to be pruned is optimized with the objective of minimizing the inference time and the error rate of the CNN. Experimental results show that our approach can speed up inference time by 1.40× and 1.30× for VGG-16 on the CIFAR-10 dataset and ResNet-18 on the ILSVRC-2012 dataset, respectively, compared to the state-of-the-art ABCPruner.


Identifying galaxy cluster mergers with deep neural networks using idealized compton-y and X-ray maps

Abstract: Abstract We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning 0 ≤ z ≤ 2 from The Three Hundred project, a suite of hydrodynamic re-simulations of 324 large galaxy clusters. Mock, idealised Compton-y and X-ray maps were constructed for the sample, capturing them out to a radius of 2R200. The idealised nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase ΔM/M ≥ 0.75, and the other half serve as a control, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55 and F1 score of 0.53. Using a multichannel model relative to a single channel model, we obtain a 3% improvement in BA score, and a 6% improvement in F1 score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilise regions out to larger distances for mergers with respect to non-mergers, greater than ∼1.2R200 and ∼0.7R200 for SZ and X-ray respectively.


Fairness-Aware Graph Neural Networks: A Survey

Abstract: Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. We categorize these techniques by whether they focus on improving fairness in the pre-processing, in-processing (during training), or post-processing phases. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.


Geochemical Characteristics of Trace Elements of Hot Springs in the Xianshuihe–Xiaojiang Fault Zone

Abstract: Trace elements in hot-spring waters are indicators for tracing hydrochemical processes, such as the deep circulation of geothermal water, the degree of water–rock interaction, redox reactions, the contamination of shallow water by thermal water, etc. In this study, 74 hot springs in the Xianshuihe–Xiaojiang Fault Zone (XSHF-XJF) were chosen for an investigation of the geochemical characteristics of trace elements using classic geochemical tools and multivariate statistical analysis. The results indicated (1) the hot-spring waters were mainly derived from atmospheric precipitation as indicated by δD and δ18O values that generally increased with decreasing elevations; (2) the high concentrations of B, As, Fe, and Mn in the waters, as well as the values of the Water Quality Index (WQI), indicated that the water quality was spatially heterogeneous and the hot-spring waters are not entirely suitable for drinking; and (3) B/Cl ratios showed that hot springs have different reservoir sources in the XSHF and XJF, respectively. The leaching of surrounding rock during water circulation contributed predominantly to the trace elements of the study’s waters. Diversity of lithology was the main factor affecting their concentrations. In addition, deep circulation controlled by the fault could influence trace-element enrichment. Our results offer a guide for the exploration and use of geothermal resources.


Sign-to-Text Translation from Panamanian Sign Language to Spanish in Continuous Capture Mode with Deep Neural Networks

Abstract: Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning.


New Algorithm for Evaluation of Online Courses Based on Quality Matters Rubric Using Fuzzy Soft Expert Sets

Abstract: The field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts’ opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions.


Virtual reality natural experiences for mental health: comparing the effects between different immersion levels

Abstract: Abstract Virtual nature is an innovative approach for promoting mental health. The purpose of this study was to compare the effects on mental health outcomes between two immersion levels of virtual reality natural experiences. The study design was a cluster trial. Healthy adults were allocated to two experimental groups. Identical pre-recorded 360° videos of natural scenes and sounds were played on the two virtual reality devices, one with a higher immersive level via a head-mounted display and the other one with a lower immersive level via a smartphone. The intervention was conducted for 30 min per session, once a week for 12 weeks. Data were collected by self-reported questionnaires at the baseline and post-intervention. In total, 54 participants completed the interventions. A significantly greater effect was revealed on improving happiness, self-rated health, and physical, mental, social, and environmental quality of life, and ameliorating distress, depression, and somatization in participants who experienced the higher immersive level compared to participants who experienced the lower immersive level. Virtual reality natural experiences with high immersion are recommended to promote mental health.


A Comprehensive Exploration of Outlier Detection in Unstructured Data for Enhanced Business Intelligence Using Machine Learning

Abstract: Due to the rapid growth of online data, it is evident that social informatics faces a significant obstacle. The task of effectively utilizing this abundance of information for business intelligence purposes and extracting valuable insights from it across diverse and heterogeneous platforms presents a daunting challenge. Coordinating AI with business knowledge stands apart as an essential worry in the ongoing scene. Customarily, exceptions were many times excused as boisterous information, bringing about the deficiency of relevant data. This paper highlights the need to rethink how outliers are handled and shed light on the primary research challenges in this mining subfield. It presents a thorough scientific categorization of different Business Knowledge strategies and diagrams their ongoing application areas. Also, the paper talks about future exploration bearings and proposals to overcome any barrier concerning oddities in information examination, consequently empowering more successful business methodologies. This work plans to improve the usage of tremendous web-based information hotspots for better business insight results.


Simulation of robot arm system control using fuzzy logic

Abstract: This study explains a robot arm system control simulation using a fuzzy logic controller. The robot arm is a nonlinear system and is not easily controlled using a conventional control system since the mathematical model of the system is not easy to obtain. Considering these problems, this final project presents a simulation of a fuzzy logic controller on a robot arm system. The aim of this control is to move the robot arm at an angular speed thus the robot gets the desired torque or the right position. Determining fuzzy rules greatly influences system stability. This control network produces output in the form of torque while the robot arm system calculates the next torque from the robot. The best output results were found on the triangular curve at response 2 because of the difference in values 0.01 on the final angle obtained.


Calculation and Analysis of Key Parameters of Underwater Optical Imaging System

Abstract: When photographing objects underwater, it is important to utilize an optical window to isolate the imaging device from the water. The properties of the entire imaging system will change, and the imaging quality will decrease due to the refraction impact of the water and the window. The theoretical calculation method for air imaging is no longer relevant in this context. To analyze the unique rule, this research derives the formulas for key parameters of underwater imaging systems under paraxial circumstances. First, the optical window is modeled, then the formula for the optical window’s focal length in the underwater environment is derived, and the change rule for the focal length of various window forms underwater is condensed. For the ideal imaging system using a domed optical window, the equivalent two-optical group model of the imaging system is established, and the formula for calculating the focal length, working distance, and depth of field of the underwater imaging system is derived through paraxial ray tracing. The accuracy of the formula is verified through the comparative analysis of the formula calculation results and the Zemax modeling simulation results. It provides an important theoretical basis for the in-depth study of underwater imaging technology.


Document Storage and Verification System Using Blockchain Technology

Abstract: A banking or identity provider can set up a customer identification data verification process between reliant parties with the use of the electronic know your customer (e-KYC) system. Due to its high degree of accessibility and availability and its efficient resource usage, the majority of banks choose to implement their e-KYC system on the cloud. All of the KYC procedures used by banks rely on encryption, which is a cumbersome process that may cause consumer data to be disclosed to unaffiliated financial institutions. Blockchain technology can be used to increase the efficiency of this system because it can automate a lot of human labor and is impervious to attacks of all kinds. The distributed ledger and immutable blockchain block make the perfect addition to the KYC process. The use of smart contacts can automate the identification of fraud. Any kind of KYC can be used to store information related to KYC identity. Consequently, financial institutions can establish a shared private blockchain on their premises for the purpose of document validation. This allows the user to maintain control over their private documents while also simplifying the process for banks to obtain the records needed for compliance


Digital Renaissance in Education: Unveiling the Transformative Potential of Digitization in Educational Institutions

Abstract: Abstract This paper examines the ongoing trend of digitization within educational institutions, analyzing the potential opportunities that lie ahead. As educational landscapes evolve, embracing digitization offers institutions a pathway to unlock a myriad of prospects. This research explores the current state of digitization in educational settings, highlighting the opportunities that arise from the integration of digital technologies. The driving forces behind significant changes in higher education include escalating operating costs, the emergence of micro and nano degrees, the surge in online learning, the influx of digitally-native students, and the overarching wave of digital transformation. Students are increasingly leveraging digital technologies to showcase a diverse array of tools, integrating structured learning environments into the fabric of higher education teaching. This research provides a comprehensive overview of digital transformation in higher education, evaluating the potential of digitalization and forecasting its prospects in the near future. Key concepts such as digital transformation, virtual reality (VR) technology, artificial intelligence (AI), and Technology, Entertainment, Design (TED) courses are examined as practical and indispensable elements shaping the future of education. Through a meticulous analysis, it becomes evident that digitalization marks only the initial phase in a broader process wherein institutions harness new technologies not merely to replicate old tasks but to redefine educational paradigms. Furthermore, the paper emphasizes that digital transformation necessitates the infusion of new management practices capable of harnessing the full potential of information and communication technologies (ICT). The journey towards the digital transformation of educational institutions involves a profound shift in mindset, encompassing innovative approaches to teaching, learning, and administration. As institutions embrace this transformative journey, they position themselves to create enhanced value for students and forge dynamic collaborations with partners in the evolving landscape of higher education.


A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods

Abstract: A high-resolution catalog for the 2014 Ms 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short–long window average energy ratio algorithm (STA/LTA) and AI algorithms, is employed to retrain the CERP model. The P- and S-wave phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for the precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more detailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that (1) the seismogenic fault of the Ludian earthquake is the NW-trending Baogunao–Xiaohe Fault, (2) the Ludian aftershocks interconnected with the discontinuous NW-trending Baogunao–Xiaohe Fault, and they also intersected with the Zhaotong–Ludian Fault. (3) This suggests that the NE-trending Zhaotong–Ludian Fault may have been intersected by the NW-trending Baogunao–Xiaohe Fault, indicating that the Baogunao–Xiaohe Fault is likely a relatively young Neogene fault.


Enhanced high-spatial resolution radiographic images based on COTS CMOS image sensors applied to wood dendrochronology and densitometry

Abstract: The imaging of wood samples using X-rays for the study of dendrochronology and densitometry is predominantly conducted through analog films subsequently digitized or, in more advanced instances, with expensive flat-panel systems characterized by resolutions on the order of tenths of microns. For this reason, this work proposes as an alternative the utilization of Commercial-Off-The-Shelf (COTS) complementary metal–oxide–semiconductor (CMOS) image sensors as affordable direct detectors for the acquisition of 10.2 μm resolution radiographic images, which represents sufficient resolution to observe the small structures formed by wood cells called tracheids. The raw images are subsequently processed to mitigate the impact of non-uniformities inherent in pixel response, including the total dose effects, and artifacts present in the image generated by irregularities and defects in the materials utilized to support the wood samples. Moreover, an exhaustive study of the sensor response over different x-ray tube voltage was performed, comparing the results obtained with a simple calculation model and finding a negligible contribution of the photons above the 20 keV. The model was developed based on diverse analyses of geometry and composition of the sensor, such as Focus Ion Beam, Scanning Electron Microscopy, and Time-of-Flight Secondary Ion Mass Spectrometry, and takes into account the X-ray beam attenuations and the photon interaction with the sensor. Finally, this model enables the estimation of the transmission based on the density and thickness of the wood samples.


Analysis of strong coupling constant with machine learning and its application

Abstract: Abstract In this work, we investigate the nature of the strong coupling constant and related physics. Through the analysis of accumulated experimental data from around the world, we employ the ability of machine learning to unravel its physical laws. The result of our efforts is a formula that captures the expansive panorama of the distribution of the strong coupling constant across the entire energy range. Importantly, this newly derived expression is very similar to the formula derived from the Dyson-Schwinger equations based on the framework of Yang-Mills theory. By introducing the Euler number, $e$, into the functional formula of the strong coupling constant at high energies, we have successfully solved the puzzle of the infrared divergence, which allows for a seamless transition of the strong coupling constant from the perturbative to the non-perturbative energy regime. Moreover, the obtained ghost and gluon dressing function distribution results confirm that the obtained strong coupling constant formula can well describe the physical properties of the non-perturbed regime. In addition, we investigate the QCD strong coupling constant result of the Bjorken sum rule $\Gamma_1^{p-n}$ and the quark-quark static energy $E_0(r)$, and find that the global energy scale can effectively interpret the experimental data. The results presented in this work shed light on the puzzling properties of quantum chromodynamics and the intricate interplay of strong coupling constants at both low and high energy scales.


Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting

Abstract: The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computational training that allows providing such support is frequently hindered by various challenges related to datasets, including the scarcity of examples and imbalanced class distributions, which have detrimental effects on the production of accurate models. For a proper approach to these challenges, strategies smarter than the traditional brute force-based K-fold cross-validation or the naivety of hold-out are required, with the following main goals in mind: (1) carrying out one-shot, close-to-optimal data arrangements, accelerating conventional training optimization; and (2) aiming at maximizing the capacity of inference models to its fullest extent while relieving computational burden. To that end, in this paper, two image-based feature-aware dataset splitting approaches are proposed, hypothesizing a contribution towards attaining classification models that are closer to their full inference potential. Both rely on strategic image harvesting: while one of them hinges on weighted random selection out of a feature-based clusters set, the other involves a balanced picking process from a sorted list that stores data features’ distances to the centroid of a whole feature space. Comparative tests on datasets related to grapevine leaves phenotyping and bridge defects showcase promising results, highlighting a viable alternative to K-fold cross-validation and hold-out methods.


An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique

Abstract: The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data.


Ethical compass: The need for Corporate Digital Responsibility in the use of Artificial Intelligence in financial services

Abstract: Service research and business ethics literature intersect concerning the question of artificial intelligence (AI) service robot accountability. In financial services, there is a broad spectrum of potential ethical issues, from data usage to customer vulnerabilities. This article scrutinizes the impact of morality and where accountability resides in the use of AI service robots in financial services. To address this challenge, we discuss the role of Corporate Digital Responsibility (CDR) for firms and illustrate how to implement a conceptual framework on the ethical implications of AI service robot applications, drawing on normative ethical theory. The framework elaborates on how the locus of morality (from human to AI agency) and moral intensity combine within context-specific AI service robot applications, and how this might influence associated accountability. We provide examples of AI robots’ use for different purposes, differentiating between four 'accountability clusters': (1) professional norms, (2) business responsibility, (3) inter-institutional normativity, and (4) supra-territorial regulations cluster. We also discuss the CDR implications in different clusters. Ethical implications of using AI service robots and associated accountability challenges are relevant for a network of actors—from customers and designers to firms and the government. Implementation of the framework incorporates a range of internal and external stakeholders that firms need to consider. We also provide a CDR roadmap to incorporate a time perspective and to inform implementation efforts.


Machine Learning Training Optimization using the Barycentric Correction Procedure

Abstract: Machine learning (ML) algorithms are predictively competitive algorithms with many humanimpact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.


Application of self-supervised learning in natural language processing

Abstract: Self-supervised learning uses the label-free data learning model and has a significant impact on the NLP task. It reduces data annotation costs and improves performance. The main applications include pre-training models such as BERT and GPT, contrast learning, and pseudo-supervised and semi-supervised methods. It has been successfully applied in text classification, emotion analysis and other fields. Future research directions include mixed unsupervised learning, cross-modal learning and improving interpretability of models while focusing on ethical social issues.


Lexical Stance Markers in High-Density Passages

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Compiling Inventories of Lexical Stance Markers


Connectives: Meanings and Functions

Abstract: This chapter first considers the functional and semantic overlap between discourse connectives and discourse markers, where the latter is presented as including the former. Since the two categories share most protypical features, the fuzzy boundaries between the two categories are explained in terms of partial overlap.


Automata Theory


The Evolution of Connectives’ Meanings

Abstract: In this chapter, we first sketch a number of assumptions underlying diachronic research in order to understand how researchers sketch the emergence of discourse connectives (and discourse markers) in language. We then review the discussion about the theoretical framework underlying the diachronic evolution of discourse connectives, that is, in which conceptual terms this linguistic process is best accounted for, grammaticalization or pragmaticalization. We then turn to a general description of the evolution from clause combining strategies to coordinating and subordinating connectives. Different case studies are presented in order to illustrate typical and less typical cases of language change in the area of connectives: the semantic evolution from temporal meaning to concessive meaning of French cependant ('yet'), the peculiar semantic evolution from cause to contrast of Italian però ('but') (5.3.2), and a diachronic account of the synchronic polysemy of French alors.


Validation of Converged Teaching Materials of Artificial Intelligence and English for High School Students to Improve Machine Translation Literacy

Abstract:
Objectives This study aimed to validate AI and English Integrated Teaching Materials for High School Students to Improve Machine Translation Literacy.
 Methods To accomplish this, we organized a group of experts in the field of English education and artificial in-telligence integrated education and conducted a Delphi survey. We collected opinions on the necessity of strengthening translator utilization skills, that is, machine translation literacy, as well as the goal, structure, and content of the classes, in order to validate foundational materials for lessons that enhance the utilization skills of artificial intelligence translation systems. After revising the contents, the second Delphi survey was conducted.
 Results The validity of each sub-question was analyzed based on .49 which is the standard value for securing content validity when the number of participants in the expert group was 15. As a result of the 1st Delpi survey, it was found that it was not valid in the content structure(.33), the content of the third class(.33), and the content of the third class worksheet(.46). After revising the contents, the second Delphi survey was conducted. Statistically significant figures were secured in all items such as class necessity, design, structure, and content of each class. The validity of the artificial intelligence convergence English education contents was secured, and the Delphi survey was completed in the second round.
 Conclusions AI-English integrated education is essential to improve Machine Translation Literacy. This study is of great significance in that it has validated AI integrated education contents that can strengthen Machine Translation Literacy by understanding the principles and differences of translators, identifying the causes of trans-lation differences, and judging translation errors. However, since Machine Translation Literacy and students' English skills are inseparable concepts, continuous research will be needed on integrated education methods that can improve them together.


Designing of Goods Separation Conveyor using GM65 QR Code Scanner Sensor

Abstract: The sorting or separation process is crucial in the delivery of goods or packages, especially when viewed from the many regions in Indonesia. From the management system of separating goods which is generally still done manually, causing some goods to experience destination errors and delivery delays or commonly called Criss-Cross (CC). Delivery delays are also influenced by human error factors, so special tools are needed to separate goods based on the destination city of the goods, one of which uses a conveyor with a GM65 QR Code Scanner sensor. This sensor works based on commands from Arduino which has been programmed in such a way that it can control all inputs and outputs used in the system. In this system, the goods to be separated are made in the form of 3D mode boxes, where each box is affixed with a different QR code, namely A, B, and C. Each QR code represents the destination city of the goods to be separated, namely city A and city B, but for QR code C indicates data other than city A and city B which will be separated as foreign goods so that in this case it does not match any separation. After testing, the tool can work properly and obtain the results of separating goods in the form of a type of QR code that is read with the destination city of each item that has been separated.


Evaluasi Layanan Referensi Perpustakaan Universitas Bung Hatta

Abstract: The aim of this research is to describe the evaluation of reference services which is then seen from the CIPP Evaluation Model theory. This type of research is quantitative with descriptive methods. The variable in this research is library reference services with sub variables, namely library service evaluation using the CIPP evaluation model. The population in this study is visitors to reference services at the Bung Hatta University Library from January 2023 to November 2023 with sampling using the non-probability silling formula with an accidental sampling technique. The number of samples is 61 users. The main instrument used is a research questionnaire which has been tested for validity and reliability. Data collection was carried out through questionnaires. The data were analyzed through the stages of examination, tabulation, and drawing conclusions. The results of the research are First, in the evaluation of the context, namely the reference service function, there are 3 statements which have an average score of 3.11. This score is on a scale of 2.50-3.24 which means good. Second, in the input evaluation there are 3 sub-indicators, (1) librarian which consists of 4 statements and has an average score of 3.24. This score is on a scale of 2.50-3.24 which means good; (2) a collection consisting of 5 statements and having an average score of 3.24. This score is on a scale of 2.50-3.24 which means good; (3) a catalog consisting of 3 statements and having an average score of 3.09. This score is on a scale of 2.50-3.24 which means good. Third, in the evaluation process there are 2 sub-indicators, (1) library reference services which consist of 5 statements and have an average score of 3.25. This score is on an interval scale of 3.25-4.00 which means very good; (2) information search service which consists of 3 statements and has an average score of 3.16. This score is on a scale of 2.50-3.24 which means good. Fourth, in product evaluation there are 2 sub-indicators, (1) visitor data which consists of 3 statements and has an average score of 3.20. This score is on a scale of 2.50-3.24 which means good; (2) the level of user satisfaction which consists of 3 statements and has an average score of 3.20. This score is on a scale of 2.50-3.24 which means good.


Rethinking Smart Keyboard Layout to Aid Strong Password Creation

Abstract: In an era marked by increasing digitization and the omnipresence of smartphones, the importance of robustpassword security cannot be overstated. With the ever-growing threat of cyberattacks, there is a pressing needfor user-friendly tools that facilitate the creation of strong and unique passwords. Traditional alphanumerickeyboard layouts (physical or virtual) have remained largely unchanged for decades, relying on the sameQWERTY layout initially designed for typewriters. However, these layouts may not be optimal for generatingstrong passwords. This paper focuses on tailoring virtual keyboard layouts on smartphones specifically forstrong password creation. For this, we have performed extensive user surveys to see if the presence ofdedicated rows for digits and special characters (essential in any strong password) allows users to createstronger passwords compared to regular smartphone keyboard layout. Apart from that, we also investigatedthe optimal assignment of characters, digits, and special characters and their groupings in a single soft key.The findings from the detailed user experiment suggested optimal settings for a smartphone virtual keyboard(for Android) like- diagonal length for good typing speed (approximately between 8.38 and 9.41 cm), andkey density (0.88 to 1.21 keys/cm2) which produces the least error without sacrificing the strength ofpasswords created using those layouts. We hope the outcome of this paper will help designers to aid virtualkeyboard layouts for smartphones that can motivate and create strong passwords without sacrificing usability.


An alternative for kernel SVM when stacked with a neural network

Abstract: Many studies stack SVM and neural network by utilzing SVM as an output layer of the neural network. However, those studies use kernel before the SVM which is unnecessary. In this study, we proposed an alternative to kernel SVM and proved why kernel is unnecessary when the SVM is stacked on top of neural network. The experiments is done on Dublin City LiDAR data. In this study, we stack PointNet and SVM but instead of using kernel, we simply utilize the last hidden layer of the PointNet. As an alternative to the SVM kernel, this study performs dimension expansion by increasing the number of neurons in the last hidden layer. We proved that expanding the dimension by increasing the number of neurons in the last hidden layer can increase the F-Measure score and it performs better than RBF kernel both in term of F-Measure score and computation time.


Improving Classification Performance on Imbalanced Medical Data using Generative Adversarial Network

Abstract: In many real-world applications, the problem of data imbalance is a common challenge that significantly affects the performance of machine learning algorithms. Data imbalance means each target of classes is not balanced. This problem often appears in medical data, where the positive cases of a disease or condition are much fewer than the negative cases. In this paper, we propose to explore the oversampling-based Generative Adversarial Networks (GAN) method to improve the performance of the classification algorithm over imbalanced medical datasets. We expect that GAN will be able to learn the actual data distribution and generate synthetic samples that are similar to the original ones. We evaluate our proposed methods on several metrics: Recall, Precision, F1 score, AUC score, and FP rate. These metrics measure the ability of the classifier to correctly identify the minority class and reduce the false positives and false negatives. Our experimental results show that the application of GAN performs better than other methods in several metrics across datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.


Predicting Earthquake Magnitudes in Indonesia: Exploring the Potential of the Prophet Algorithm

Abstract: Research on earthquakes has been extensively conducted by previous studies using various methods and specific discussions. Similarly, research to predict the magnitude of earthquakes that will occur in the future has also been conducted. This study employs the Prophet algorithm to test its capability in predicting a case study's magnitude using data with numerous missing values and outliers. The study is conducted without transformation and with Box-Cox and log-transformations. Transformations are applied to handle outliers. The results indicate that across the three experiments, the difference between the predicted and actual data ranges from 0.1 to 0.5 or even more. Performance metrics reveal that the log-transform is superior to the other two experiments, with a smaller MAE of 0.27 and a MAPE of 5.96%. Nevertheless, the use of the Prophet algorithm in this case study needs further investigation with different treatments to achieve more accurate results.


A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

Abstract: Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.


Antecedents of Employee Creativity and Its Influence on Job Performance

Abstract: This study aims to determine the relationship between the antecedents of employee creativity and its effect on job performance in Police personnel in Surakarta Police Station. This research method uses quantitative methods with a total population of police personnel in Surakarta police station totaling 1083 personnel. The sampling technique of this study will refer to the criteria proposed by (Joseph F Hair et al., 2014) which are based on the Maximum Likelihood Estimation (MLE) technique. A good sample number according to MLE ranges from 100-200 samples, therefore the expected sample number is a minimum of 100 respondents and a maximum of 200 respondents (Joseph F Hair et al., 2014). Specifically for this study, the sample used was around 180-200 respondents at the Surakarta City Police Station. Data collection techniques use questionnaires that will be given randomly to the object of research. Data analysis tools using Smart-PLS applications. The results showed that Mindfulness has a significant effect on the creative process of members, Support perception of the organization has a significant effect on the meaning of work, Proactive personality has a significant effect on the meaning of work, Involvement of the creative process has a significant effect on employee creativity, Meaning of Work has a significant effect on employee creativity, Mindfulness has an indirect effect on employee creativity, in the mediation relationship First perception of support The organization has an indirect effect on employee creativity through the meaning of work, Both proactive personalities have an indirect effect on employee creativity through the meaning of work, Employee creativity has a significant effect on job performance


The Investigation of Students’ Writing Skill Improvement in GBA Implementation Using Procedure Text

Abstract: Based on the implementation of the new curriculum with GBA, this study investigates the effectiveness of GBA implementation in students'' writing skills through procedure text. This study took 41 seventh grade students of SMPIT Al Falaah Simo. This research used descriptive analysis to serve data analysis. It applied quantitative method that collects data through pre-test and post-test. Students were given a task to make procedure text about food creation of instant noodle. From the findings of the T-Test and N-Gain results, it is known that the application of GBA through Procedure text provides a significant change at the intermediate level in students'' writing skills. This means that the increase in skills has increased by about 50% of the skills in writing procedure text before the treatment. The T-Test and N-Gain results showed that the application of GBA through Procedure text provided a significant change in students'' writing skills in moderate stage. Factors such as mental unpreparedness, lack of material books, lack of time, and limited media facilities caused the increase in skills.