Reviewerly/exHarmony huggingface dataset:
An Overview of Deepfake Methods in Medical Image Processing for Health Care Applications

Authors:
raw_author_name: D Jude Hemanth
raw_affiliation_string: Department of ECE, Karunya Institute of technology and science, Coimbatore, India

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

Authors:
raw_author_name: Ani Widyawati
raw_affiliation_string: Ph.D. candidate, Yogyakarta State University, Indonesia
raw_author_name: Suyanta .
raw_affiliation_string: Prof, Yogyakarta State University, Indonesia
raw_author_name: Heru Kuswanto
raw_affiliation_string: Prof, Yogyakarta State University, Indonesia
raw_author_name: A.K. Prodjosantoso
raw_affiliation_string: Prof, Universitas Negeri Yogyakarta, Indonesia

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

Authors:
raw_author_name: Yan Wang
raw_affiliation_string: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
raw_author_name: Fei Ji
raw_affiliation_string: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
raw_author_name: Quansheng Guan
raw_affiliation_string: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
raw_author_name: Hao Zhao
raw_affiliation_string: School of Computer Science and Information Engineering, Guangzhou Maritime University, Guangzhou 510725, China
raw_author_name: Kexing Yao
raw_affiliation_string: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
raw_author_name: Weiqi Chen
raw_affiliation_string: School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou 510521, China

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

Authors:
raw_author_name: Tianxiao Xu
raw_affiliation_string: Pace University, New York, United States
raw_author_name: Yeming Cai
raw_affiliation_string: Wuhan University, Wuhan, China

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

Authors:
raw_author_name: Ana Tajadura-Jiménez
raw_affiliation_string: UCL Interaction Centre, University College London, London, WC1E 6BT, UK
raw_author_name: Milagrosa Sánchez-Martín
raw_affiliation_string: Department of Psychology, Universidad Loyola Andalucía, Avda. de Las Universidades S/N, 41704, Dos Hermanas, Seville, Spain

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

Authors:
raw_author_name: Hamed Aboutorab
raw_affiliation_string: School of Business, University of New South Wales, Canberra, ACT, Australia
raw_author_name: Omar K. Hussain
raw_affiliation_string: School of Business, University of New South Wales, Canberra, ACT, Australia
raw_author_name: Morteza Saberi
raw_affiliation_string: School of Computer Science, University of Technology Sydney, NSW, Australia
raw_author_name: Farookh Khadeer Hussain
raw_affiliation_string: School of Computer Science, University of Technology Sydney, NSW, Australia
raw_author_name: Daniel Prior
raw_affiliation_string: School of Business, University of New South Wales, Canberra, ACT, Australia

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

Authors:
raw_author_name: Yan Zhang
raw_affiliation_string: Anyang Normal University, China

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

Authors:
raw_author_name: Iulian Aciobăniței
raw_affiliation_string: Faculty of Information Systems and Cyber Security, Military Technical Academy “Ferdinand I”, 050141 Bucharest, Romania
raw_author_name: Ștefan-Ciprian Arseni
raw_affiliation_string: Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
raw_author_name: Mihai Togan
raw_affiliation_string: Faculty of Information Systems and Cyber Security, Military Technical Academy “Ferdinand I”, 050141 Bucharest, Romania; Research and Innovation Department, CertSIGN S.A., 050881 Bucharest, Romania

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

Authors:
raw_author_name: Atheer Sabri
raw_affiliation_string: Communications Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
raw_author_name: Alaa Ali
raw_affiliation_string: Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

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

Authors:
raw_author_name: Kate-Riin Kont
raw_affiliation_string: Internal Security Institute, Estonian Academy of Security Sciences, Tallinn, Estonia

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

Authors:
raw_author_name: Fang Han
raw_author_name: Yingjie Liu
raw_author_name: Wen Peng

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

Authors:
raw_author_name: Seiji Tsuno
raw_affiliation_string: Railway Technical Research Institute, 2-8-38 Hikari-Cho, Kokubunji, Tokyo, 185-5840, Japan
raw_author_name: Masahiro Korenaga
raw_affiliation_string: Railway Technical Research Institute, 2-8-38 Hikari-Cho, Kokubunji, Tokyo, 185-5840, Japan
raw_author_name: Hiroe Miyake
raw_affiliation_string: University of Tokyo, 1-1-1 Yayoi, Bunkyo-Ku, Tokyo, 113-0032, 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

Authors:
raw_author_name: Esraa Q. Naamha
raw_affiliation_string: Department of Computer Science, University of Technology-Iraq, Baghdad, Iraq
raw_author_name: Matheel E. Abdulmunim
raw_affiliation_string: Department of Computer Science, University of Technology-Iraq, Baghdad, Iraq

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.