Reviewerly/exHarmony huggingface dataset:
"Suç Gelirlerinin Aklanması ve Terörizmin Finansmanının Önlenmesi Amacıyla Avrupa Parlamentosu ve Konseyi’nin Fonların ve Kripto Varlık Transferleriyle İlgili 2023/1113 Sayılı Tüzüğüne (Regülasyon) Genel Bir Bakış"

Advancing technology has lead the way for major changes in payment systems. Payments for purchases of goods and services, can be made with crypto-assets, besides cash, electronic money or virtual assets. Crypto-assets are preferred by individuals due to lack of intermediary institution such as a bank, high transaction speed, low costs, global and encrypted. Crypto-assets encrypted through blockchain technology or other methods provide a significant level of anonymity in point of transfers. The feature of anonymity, offers individuals a noteworthy opportunity in terms of money laundering and financing of terrorism. In order to prevent these circumstances, “Regulation 2023/1113 of The European Parliament And Of The Council on Information Accompanying Transfers of Funds and Certain Crypto-Assets” has been enacted. In this study, the provisions of the Regulation regarding obligations of payment service and crypto asset service providers that enable the transfer of funds and crypto assets, definitions related to crypto assets, suspicious transaction reporting and the nature of the sanctions to be applied are examined.
Investigating the Potential of Enterprise Service Bus as a Fundamental Facilitator for Future Information Technology Infrastructure

The Enterprise Service Bus (ESB) is a new integration infrastructure, and this article comprehensively explains the technology. It discusses the history of EAI and the technologies that form the backbone of an ESB—namely, Message-Oriented Middleware (MOM) and Service-Oriented Architecture (SOA). It details an ESB's basics and then presents Mule, an open-source ESB solution. Finally, an example app is provided to show the various benefits that an ESB can provide. Users can apply this framework with the help of ESB solutions, although these products have varying approaches and features. Integrating disparate applications by linking them over a communication bus is central to the ESB design; this bus ensures that each application can talk to it separately without needing to be aware of the presence of the others. Instead of the fragile and difficult-to-manage point-to-point integration, this decoupling of systems allows for more effective communication and eliminates the need for dependent systems
Using ensemble models to detect deepfake images of human faces

Deepfakes, synthetic media generated through advanced artificial intelligence techniques, are a rising threat to content authenticity. This paper explores deepfake detection methods to discern manipulated media from genuine content. We evaluate two convolutional neural network (CNN) architectures - EfficientNetB4 and EfficientNetB4 with attention mechanisms - on the Forensics++ and DeepFake Detection Challenge datasets. Our key contributions are: 1) Demonstrating that integrating attention enhances model performance, with EfficientNetB4 attention achieving superior accuracy over baseline EfficientNetB4 in both intra-dataset and cross-dataset scenarios; 2) Elucidating attention’s efficacy in improving deepfake detection by concentrating on manipulated regions. Our experiments highlight attention’s potential in advancing state-of-the-art deepfake detection. As deepfakes grow increasingly realistic, robust techniques like attention become imperative for multimedia forensics. This paper provides valuable insights toward developing adaptable deepfake detection systems to preserve content integrity.
Modeling of a directed acyclic graph for causal inference

The paper proposes an algorithm for causal inference based on a set of input data with compliance with the proposed restrictions. This article is presents assumptions about the data set that affect the choice and accuracy of the causal method. The algorithm for constructing the structure of a directed acyclic graph is given. The adequacy of the algorithm was checked on a test mathematical model, which allowed the analysis to be carried out without a randomized experiment. The proposed algorithm allows extrapolation to reveal a causal model with specified assumptions and the possibility of stricter restrictions on the input data set.
Design of Flood Barrier with Developed IoT-Based Flood Detection and Monitoring Systems

Due to Indonesia's tropical climate, it experiences frequent heavy rainfall, which is the primary cause of flooding. Despite various solutions implemented, the issue of flooding persists. Consumers demand an effective emergency flood protection solution to prevent water from entering homes at any time. This product is a flood barrier specifically designed for installation at the front door of houses. The development of this flood barrier product aims to address user needs and ensure flood resistance. To achieve this, the Kano Method and Quality Function Deployment (QFD) are utilized in the product design process, aligning it with customer desires. The Finite Element Analysis (FEA) is employed in testing to assess the flood barrier's ability to contain water effectively. Moreover, the data representing the user requirements is collected from residents in the Baleendah area. Those residents houses have been affected by flooding. This area faces significant economic losses due to flooding, estimated at IDR 5,490,292,000,000. Bandung Regency is projected to have the highest frequency of flooding incidents in West Java in 2022, contributing to the broader issue of flooding in the province and country. The developed flood barrier product has successfully met most customer needs, leading to customer satisfaction. The product offers a reasonable price, customer customization options, and user-friendly features. The integration of Internet of Things (IoT) capabilities has been well received by users. The flood barrier product also demonstrates its efficacy in retaining floodwater through its low equivalent elastic strain, equivalent stress, and deformation values against hydrostatic pressure. These attributes enable the product to withstand floodwaters and prevent their entry into households.
Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.
A Hybrid Stacked CNN Model with Weighted Average Ensembling for Effective Face Recognition

The discipline of computer vision has given a lot of attention to facial recognition. Automated face recognition is extensively employed in various practical scenarios, including systems for streamlining immigration checkpoints, intelligent monitoring of visual data, and authentication of personal identity. Depending on the situation, it may be divided into the two separate duties of facial verification and face identification. This study proposed a hybrid stacked CNN model for face recognition system. The models have mastered the art of making their own inferences. The models are then combined to predict a class value using a cutting-edge method called weighted average ensembling. A more accurate estimate should be produced by the new assembly process. Pre-trained CNN models are used in our proposed method: AlexNet, Resnet50V2, and VGG-19, Yolov5, VGG-16 and ResNet50. When applied to Tufts dataset images, our suggested model successfully achieved 98.05% accuracy. We have also used the Discrete Wavelet Transform (DWT) method for denoising, SegNet for Image segmentation for better performance of the model proposed.
IoT Safeguarding in Saudi Tourism Sector: Crafting a Preliminary Security Model for Enhancing Cyber Resilience

Incorporating the Internet of Things (IoT) has transformed technological landscapes, facilitating seamless communication across diverse devices and systems. However, this increased connectivity exposes critical sectors, including government and tourism, to elevated cybersecurity risks. There is a lack of knowledge regarding how organizations within the Saudi tourism sector address the cybersecurity risks associated with IoT systems. While much of the existing IoT literature concentrates on adopting IoT systems, a better understanding can be attained by proposing a preliminary research model encompassing the most significant factors influencing IoT security and related cybersecurity attacks. Despite limited empirical research on IoT security adoption in the Saudi tourism sector, this study seeks to address this gap. Motivated by this concern, this research investigates IoT security among Saudi organizations in the government tourism sector by developing a research model. inspired by the Technology Acceptance Model (TAM) literature. The model incorporates a total of eight factors (privacy, confidentiality, data integrity, access control, availability, trust, IoT standards and policies, and IoT Awareness) and seven cybersecurity attacks (denial of service (DoS &amp; DDoS), replay attack, eavesdropping attack, man-in-the-middle (MiTM) attack, spoofing attack, Sybil attack, and physical attack) identified from various literature sources. The proposed research model is a valuable tool for understanding IoT security in Saudi tourism, offering guidelines for organizations considering introducing IoT security measures. These guidelines highlight specific factors that tourism organizations should consider, enhancing the likelihood of successful IoT security adoption in the tourism context. Additionally, this study encourages IoT researchers to replicate the research in another industry sector within Saudi Arabia or other countries, particularly within the Arabian Gulf region.
Investigating the effect of distance on the implementation of RCNN automatic detection technique to the human body

The identification of humans constitutes a crucial component of monitoring systems, given the significance of the timely detection of individuals. Despite advancements in people detection systems, detecting humans at long distances remains challenging. In this study, we employed the Region-based Convolutional Neural Network (RCNN) approach to training a system on images captured at varying distances between the camera and individuals. The results demonstrate promising outcomes, with the system achieving a maximum detection recall of 1 for identifying people at distances of up to 40 meters and maximum precision of 1 for identifying people at distances of up to 50 meters.
The LOST: An Attempt to Combine Film and Game

The combination of computer graph model and game modeling has become a research hotspot. The paper focuses on developing a linear role-playing game (RPG) called "LOST" using Maya and Unity software. The study aims to detail the development process and provide a reference for future game development using cinematography techniques. The paper begins with an introduction to the background and purpose of the study. This is followed by an analysis of the design and implementation of the game's main features, such as plot, gameplay, models, and computer graphics (CG). The authors emphasize the importance of carefully crafting and polishing the game's main quests to ensure a complete and enjoyable gameplay experience for the player. They also discuss the trade-off between performance and quality in the design of models and the impact of pre-made animations on the game’s visual quality and storage space. Finally, the authors evaluate the strengths and weaknesses of the game and suggest areas for improvement, such as adding more content and variety to the combat system. Overall, the paper provides a comprehensive overview of the development of LOST" and serves as a valuable reference for future game development projects.
WORDLE Game Prediction Based on BP Neural Network and Other Models

The purpose of this paper is to study the factors that affect the difficulty level of words in word games and the related content of their reported results. This paper provides a detailed analysis of the factors that influence the difficulty level of a word and the variability of reported results in word games. The research highlights the efficacy of using a BP neural network for predicting the distribution of reported results, and the findings have important implications for both game developers and players. After dividing the difficulty of words in word games, the model in this article has achieved an accuracy of over 80% in predicting their difficulty. By providing insight into the factors that influence the difficulty level of a word and the variability of reported results, the paper can help game developers design more engaging and challenging word games, while also assisting players in improving their word game performance.
Melo Harmony: Exploring Emotion in Crafting AI-Generated Music with Generative Adversarial Network Powered Harmony

This research paper delves into the convergence of artificial intelligence (AI) and music composition by examining the integration of emotion in crafting AI-generated music through Generative Adversarial Network (GAN)-powered harmony. The primary goal is to demonstrate that AI-generated music can effectively evoke and communicate emotions, enhancing its artistic and expressive potential. This paper presents a comprehensive framework for harmonization using GANs, infusing emotional awareness into the generated compositions, and outlines future directions for practical application and validation.
Educational Status of a Popular Korean Traditional Music and Development Plan Using Interviews with Middle and High School Music Teachers

In this study, creative Korean traditional music, which is taught and learned educationally without clear definitions such as new folk songs, new Korean traditional music, creative Korean music, Korean traditional music, Korean traditional music, fusion Korean music, crossover, and world music presented in various textbooks, is called popular Korean traditional music. Understanding popular Korean traditional music will allow teachers and students to experience more diverse forms of Korean traditional music and will give them an easier access to traditional Korean music, which can only be felt difficult. To this end, it was possible to investigate and analyze popular Korean traditional music contained in textbooks and grasp the educational status of popular Korean traditional music to see what musical elements are emphasized educationally. Moreover, 38 incumbent teachers working as middle and high school music teachers across the country presented educational implications and development plans through actual educational cases, awareness, teaching, and interviews of learning about Korean traditional music. Through the above research, we will find educational significance for popular Korean traditional music, which is unclearly educated, and provide students with an opportunity to understand the current creative traditional music and improve their artistic sense expressed in traditional music.
Peningkatan Literasi Digital untuk Menangkal Hoaks pada Siswa SMP

Students are vulnerable group to being provoked by hoax news, this is because their thinking and emotional management skills are still immature. Hoax reporting has an impact on prejudice, hostility between groups and problems managing emotions due to accessing hate news. The aim of this activity is to increase digital literacy among students and provide journalistic training so that students are able to use social media healthily and avoid hoax news. The implementation was carried out at the private school SMP Teachers also admit that students often don't focus in class because they use their cellphones to check social media or play games. The activities provided were conducting digital literacy education and journalism training for 23 junior high school students. Measurements were carried out by assessing the results of the Pre-Test and posttest regarding digital literacy material, assignment of news writing tasks and participant satisfaction. The results show that there is a very significant increase in students' digital literacy knowledge. The level of student satisfaction with activities is in the good category. Suggestions for development for further activities include monitoring digital literacy regarding the use of social media.
Reducing Features Within an Extensive Network of Medical Subject Headings Metadata to Enhance Deep Predictions

Feature reduction in a large amount of Medical Subject Headings (MeSH) metadata for deep prediction involves selecting a subset of relevant features to improve the efficiency and effectiveness of deep learning models. MeSH is a controlled vocabulary thesaurus used for indexing articles in the life sciences and biomedical fields. We analyze a disease-symptom network exploiting MeSH metadata and configure a deep model for disease prediction based on symptoms. Dimension reduction techniques have yielded positive results in optimizing a large amount of Medical Subject Headings (MeSH) metadata for deep prediction with good accuracy. Therefore, our result highlights that decrease in the severity or degree of symptoms associated with a disease correlates with an improvement in the accuracy of disease prediction. This finding may have important implications for disease prediction models in healthcare: interpretation of results, clinical significance, practical implications.
Data Sharing based on Facial Recognition Clusters

The evolution of computer vision technologies has led to the emergence of novel applications across various sectors, with face detection and recognition systems taking center stage. In this research paper, we present a comprehensive examination and implementation of a face detection project that harnesses the cutting-edge face recognition model. Our primary aim is to create a reliable and effective system that can be seamlessly integrated into functions allowing users to input their image to capture their facial features, subsequently retrieving all images linked to their identity from a database. Our strategy capitalizes on the dlib library and its face recognition model, which com- bines advanced deep learning methods with traditional computer vision techniques to attain highly accurate face detection and recognition. The essential elements of our system encompass face detection, face recognition, and image retrieval. Initially, we employ the face recognition model to detect and pinpoint faces within the captured image. Following that, we employ facial landmarks and feature embeddings to recognize and match the detected face with entries in a database. Finally, we retrieve and present all images connected to the recognized individual. To validate the effectiveness of our system, we conducted extensive experiments on a diverse dataset that encompasses various lighting conditions, poses, and facial expressions. Our findings demonstrate exceptional accuracy and efficiency in both face detection and recognition, rendering our approach suitable for real-world applications. We envision a broad spectrum of potential applications for our system, including access control, event management, and personal media organization.
Graphical Authentication System Using Image Panels

The use of alphanumeric usernames and passwords is the most widely used technique for authentication. It is found that this technique has serious drawbacks. For instance, users frequently select passwords that are simple to remember. On the other hand, it may be challenging to recollect a complicated password. The creation of an OTP is another option, but it can take some time and comes with a risk (losing it in the allotted time). These existing methodologies have some disadvantages. A graphical authentication is the best to remember and hard to guess. So, a graphical authentication technique is proposed to address the problems of low security, shoulder surfing, dictionary and brute force attacks. In this methodology, the user must register by providing the required information and by selecting a panel from 3-5 images. This methodology is tested using entropy and proved that this approach is efficient than the existing methods..
Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art

Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos.