Reviewerly/exHarmony huggingface dataset:
Personal Social Network Profile Authentication through Image Steganography

In the era of digital communication and social networking, the authenticity and integrity of personal social network profiles have become crucial for establishing trust and ensuring secure interactions. Existing methods often suffer from vulnerabilities like password theft, identity impersonation, and data breaches. To overcome these challenges, the paper introduces a new steganography method as a robust solution, leveraging the concept of hiding information within a seemingly innocent digital cover image. The proposed methodology involves imperceptible authentication and embedding profile information within a profile image or any other uploaded pictures in a profile’s timeline. This scheme is developed using a shell matrix, DNA encoding and absolute moment block truncation coding (AMBTC) compression. A shell matrix is used for concealing the private information and AMBTC compression is applied to compress large data files into smaller ones, which can speed up the network transmission of compressed code. By exploiting the redundancy in image data, the authentication data are embedded in a manner that is indistinguishable to human observers. To estimate the effectiveness of the proposed approach, wide experiments were conducted using real-world social network profiles. The results demonstrate the ability of the proposed technique to successfully embed and extract authentication data while maintaining the profile photo’s visual appearance.
Extended Object Tracking Performance Comparison for Autonomous Driving Applications

Extended object tracking is crucial for autonomous driving, as it enables vehicles to perceive and respond to their environment accurately by considering an object’s shape, size, and motion over time. Two commonly used methods for extended object tracking, Joint Probabilistic Data Association (JPDA) and Gaussian Mixture Probability Hypothesis Density (GM-PHD), were compared in autonomous vehicles using radar data. Both JPDA and GM-PHD perform well in tracking multiple extended objects, but GM-PHD demonstrates a performance advantage, especially in terms of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, which measures the accuracy of tracked object positions in comparison to their actual positions.
A Novel Information Security Framework for Securing Big Data in Healthcare Environment Using Blockchain

The Blockchain-based information security framework for health care big data environments is a framework designed for the secure storage, access, and transmission of health care data in big data environments. It combines the privacy and security advantages of encryption and decentralized networks offered by Blockchain technology with the scalability of distributed systems to provide an effective secure platform for big data applications. The framework is based on the principles of confidentiality and immutability to ensure the security and privacy of health care data. The framework is designed to support a wide range of information sources and use cases including patient records, clinical research, medical imaging, genomic data, and pharmaceutical trials. It is also designed to be compatible with existing distributed computing and data querying technologies such as Hadoop and Spark, which will help organizations to improve the accessibility of health care data. The Blockchain-based framework will also provide an audit trail, allowing hospitals and other organizations to better monitor and control access to their data. This will enable organizations to ensure compliance with HIPAA and other regulations, while providing enhanced confidentiality and privacy to users and patients.
Introducing a Chatbot to the Web Portal of a Higher Education Institution to Enhance Student Interaction

This paper introduces the implementation of a chatbot on the web portal of a higher education institution, aiming to enhance student interaction and provide seamless access to information and support services. With the increasing reliance on digital platforms for student engagement, a chatbot offers a user-friendly and efficient means of communication, catering to the diverse needs of students in a higher education setting. The chatbot developed utilizes natural language processing, machine learning, and artificial intelligence algorithms to engage in dynamic conversations with students. We use Large Language Models (LLMs), because these and vector databases are revolutionizing the way we handle and retrieve complex data structures. Their main objective is to provide instant responses, personalized guidance, and timely support for various aspects of student life within the institution, namely the following: Information Retrieval, where the chatbot acts as a virtual collaborator, offering quick and accurate responses to frequently asked questions regarding admissions, programs, course registration, financial aid, and campus facilities, reducing the need for manual information searches; Academic Support, where the chatbot assists students in academic matters, such as course selection, prerequisites, graduation requirements, and study resources. It can offer personalized recommendations based on a student’s academic profile and preferences; Campus Services, which provides information about campus services, extracurricular activities, events, and resources; and Appointment Scheduling, which facilitates appointment scheduling with academic advisors, and support staff, streamlining administrative processes and ensuring timely access to guidance and assistance. This development follows a user-centric approach, incorporating feedback from students, faculty, and administrators to ensure that the chatbot meets their specific needs and preferences. Rigorous testing and quality assurance measures are implemented to guarantee the accuracy, reliability, and security of the chatbot. In conclusion, we achieve a functional chatbot with a medium computational heaviness; in this way, it can be practical to use it in real time by the students on the institution’s web portal. The introduction of a chatbot on the web portal of a higher education institution represents a significant advancement in facilitating student interaction and support services. By providing instant and personalized responses, the chatbot streamlines communication, reduces response times, and empowers students to find information and resources efficiently. As chatbot technology continues to evolve, ongoing enhancements and refinements will ensure that it remains a valuable tool for enhancing student experiences, promoting engagement, and fostering a positive learning environment within the institution.
Exposure time and point cloud quality prediction for active 3D imaging sensors using Gaussian process regression

Abstract Setting an optimal image exposure is crucial for acquiring dense point clouds using 3D active optical sensor systems such as structured light sensors [structured light sensors (SLSs)] and active stereo sensors. One of the most common and seamless ways to optimize the image brightness of an image exposure is to adjust the camera’s exposure time. However, optimizing the image exposure alone is ineffective for acquiring surfaces of large-scale objects with a complex topology if a spatial understanding of the scene is neglected. Hence, the present paper proposes a data-driven approach using two Gaussian processes [Gaussian processes (GPs)] regression models to select a proper exposure time considering the nonlinear correlations between image exposure and the scene spatial relationships. To model these correlations, our study introduces first the generic synthesization of seven inputs and two target variables. Then, based on these inputs, two independent GPs are designed: one for predicting the measurement quality and one for estimating the exposure time. The performance and generalizability of both models are thoroughly evaluated using an SLS and an active stereo sensor. The evaluation demonstrated that the point cloud quality models adequately matched observations with an R2 exceeding 90%. Specifically, the models predicted point cloud quality with an root mean square error (RMSE) of 10%. Additionally, the assessment of the performance of the exposure time models showed a model fit with an R2 above 97%. The exposure time prediction accuracy, as evidenced by the RMSE values, was within 10% of the corresponding exposure time range for each sensor. The present research shows the potential and effectiveness to completely automate the assessment of a point cloud quality and the selection of exposure times with the help of data-driven models.
From Cloud to Crowds: Restoring Points of the Peer-to-Peer and Decentralized Internet

Focusing on intellectual property rights, file-sharing, privacy, public key encryption, free labor, and cryptocurrencies, this chapter examines the nature of the recent peer-to-peer networking to rebuild the decentralized Internet against cloud computing. Peer-to-peer networking has the potential to decentralize Internet power since it involves sharing intellectual property rather than monopolizing it, utilizing encryption technology for user privacy, and providing monetary compensation for users’ content and data production. In addition, it highlights that peer-to-peer networking spans a wide range of the ideological spectrum, from the broader dissemination of more intact libertarian market relations to the fundamental challenge to the capitalist property regime. It argues that such hybrid orientations result from the original hacker ethic oscillating between commonism and libertarianism and its persistent penetration into the various peer-to-peer networking to return the wealth and value from the cloud to the crowd.
Pengembangan Website ‘BALASAN’ Sebagai Upaya Peningkatan Minat Belajar Bahasa Isyarat Indonesia

This website development uses fun and interactive methods. This website collaborates with GERKATIN which provides Bisindo classes which discuss understanding the world of the Deaf, how to communicate with the Deaf, and learning the basics of Indonesian Sign Language to build inclusive communication relationships. In this web development, there are 3 Bisindo service options that can be chosen according to your needs. One of the services is the Bisindo class, where we can take direct (face to face) classes accompanied by experienced Deaf teachers. To be able to take this class you can choose a level, namely level 1 &amp; 2. The class schedule starts when the class quota has been met. Each class is held 10 times according to the schedule determined by GERKATIN.
Remote Sensing Image Retrieval Algorithm for Dense Data

With the rapid development of remote sensing technology, remote sensing products have found increasingly widespread applications across various fields. Nevertheless, as the volume of remote sensing image data continues to grow, traditional data retrieval techniques have encountered several challenges such as substantial query results, data overlap, and variations in data quality. Users need to manually browse and filter a large number of remote sensing datasets, which is a cumbersome and inefficient process. In order to cope with these problems of traditional remote sensing image retrieval methods, this paper proposes a remote sensing image retrieval algorithm for dense data (DD-RSIRA). The algorithm establishes evaluation metrics based on factors like imaging time, cloud coverage, and image coverage. The algorithm utilizes the global grids to create an ensemble coverage relation between images and grids. A locally optimal initial solution is obtained by a greedy algorithm, and then a local search is performed to search for the optimal solution by combining the strategies of weighted gain-loss scheme and novel mechanism. Ultimately, it achieves an optimal coverage of remote sensing images within the region of interest. In this paper, it is shown that the method obtains a smaller number of datasets with lower redundancy and higher data utilization and ensures the data quality to a certain extent in order to accurately meet the requirements of the regional coverage of remote sensing images.
Target Selection Techniques for Smartphone While Walking

To improve the performance of interaction between the user and the smartphone in walking state, target selection techniques were proposed and their performance was investigated. First, the performance of smartphone target selection in walking state was comparatively investigated to that in sitting and standing states. Then, three target selection techniques Implicit Enlargement, Attraction and Last Contact were designed for walking state and their performance was empirically evaluated together with the control technique Direct Touch. The experimental results showed that the error rate in walking state were higher than those in sitting and standing state. Implicit Enlargement showed the fastest speed and the lowest error rate. The subjects made fewer errors with Last Contact than those with Direct Touch in target selection tasks during walking. Attraction showed the worst performance. Fault-tolerant mechanisms were helpful to improve the accuracy for target selection in walking state. Last, the effectiveness and applicability of the proposed techniques were discussed.
Digital Transformations of the South African Legal Landscape

Objective : South Africa is a country with great potential for intensive development due to the active growth and adoption of digital technologies. The rapidly emerging digital landscape is transforming the legal framework, which in turn influences the digital environment. This transformative relationship determined the focus of the research, which is to identify the legal system adaptability under dynamic changes, as well as the legal landscape evolution under digitalization and technological progress. Methods : the study of the changing legal landscape required an interdisciplinary approach that combines legal analysis with ideas from sociology, economics, etc. In doing so, the formal-legal method was used to examine the key legal instruments shaping South Africa's digital environment and providing the opportunities and challenges of the interaction between digital technologies and South African law. Results : the paper provides insights into how the South African legal system is addressing digital challenges; assesses the integration of digital innovations into the legal system; highlights the transformative impact of digital technologies on traditional legal processes, including collecting evidence, dispute resolution and access to justice. Finally, it evaluates the role of digital technologies in making legal processes more efficient. Scientific novelty : the study contributes to the ongoing debate on the complex relationship between digital technologies and South African law. It shows how South African law is coping with digital complexities and substantiates new insights into the transformation of the traditional legal paradigm as a result of digitalization, as well as its implications for legal proceedings and access to justice. By delving into the adaptations, challenges and innovations arising at the intersection of law, technologies and digitalization, insights are gained into how South African law navigates the dynamic digital landscape. Practical significance : adapting the legal landscape to digitalization and technological advances is critical to ensure rapid technological progress. It also requires collaboration between government agencies, civil society, experts in law and technology. The study provides valuable recommendations and suggestions for policymakers, legal practitioners and stakeholders shaping South Africa's legal ecosystem. The author addresses the challenges of ensuring personal data privacy, enhancing electronic interactions, and countering cybercrime. The importance of introducing technological achievements while maintaining robust legal safeguards is emphasized.
Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention

The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss. However, the lack of publicly available datasets containing fraud is a major challenge. This study aims to address these issues using advanced machine learning techniques. Known for their ability to provide insight into data, decision trees are used for real-time fraud detection. In addition, deep learning techniques and artificial neural networks (ANN) are used to detect complex fraud patterns, while logistic regression is used to model the probability of fraudulent events. The accuracy of these methods, including decision trees, logistic regression, and ANN, is fully evaluated, with accuracies of 99.8%, 99.9%, and 99.94%, respectively. These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection.
A Knowledge-Integrate Cross-Domain Data Generation Method for Aspect and Opinion Co-Extraction

To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.
Assessment of Farmer Characteristics and Design Preferences on Location-based Pest and Disease Reporting Service for Digital Agriculture: A Preliminary Analysis

Abstract User interface design is critical for ensuring that agricultural technology is accessible and usable for farmers. Usability and accessibility for farmers can boost crop yields and lower losses. Therefore, it is crucial to understand the preferences and needs of farmers, particularly in terms of user interface design. This study examines the preferences of chili farmers for user interface elements in reporting chili pests and diseases and investigates how farmer characteristics (i.e. age categories and educational backgrounds) influence these preferences. Data was collected from a group of chili farmers in Batu Pahat using a survey instrument and field experimental tasks that list several user interface elements according to the tasks given in relation to disease and pest attack reporting information for chili crops. The study includes user interface-related tasks for online map services such as Google Maps and Waze, which aim to report the location of attacks. The study’s findings underscore the significance of user interface elements in shaping user experiences. Google Maps emerged as the favored navigation service, highlighting the importance of a straightforward and user-friendly interface. The study suggests that enhancing user knowledge of lesser-known functions can lead to higher satisfaction and loyalty. In agricultural applications, visual cues and intuitive design, exemplified by the “Picture Radio Button” and “Horizontal Date” elements, were well-received. Overall, this study highlights the importance of considering farmers’ demographic characteristics in designing user interfaces for agricultural technology. By doing so, agricultural technology can be made more accessible and usable for farmers, ultimately leading to more effective management of chili pests and diseases. The findings of this research will yield advantages by aiding farmers, specifically in Malaysia, in transitioning into intelligent farmers, aligning with the objectives of the Shared Prosperity Vision 2030 and the realization of smart agriculture in Malaysia. Furthermore, this endeavor is consistent with the Food and Agriculture Organization of the United Nations (FAO) initiative aimed at digitizing farmers, encompassing smallholder farmers as well.
An Efficient and Robust Method for Data Privacy and Security on a Public Cloud Using a Novel Hybrid Technique

The end user has a cost-effective and cloud-based method of storing and retrieving their personal information through remote access using some kind of network connectivity. The user may view the data at any time and from any location they want. However, the data that are stored on the cloud may not always stay in a safe state. As the data can only be accessed by the end user via the intervention of a third party, the authenticity and integrity of the data are at risk of being compromised. It is possible for many people to utilize different Web access at the same time to access and recover their information stored on the cloud. As a consequence, a user’s sensitive data are exposed, leaked, or lost in several locations. Cryptographic methods, such as the Elliptic Curve Cryptography, have been used in the development of a great deal of different algorithms and protocols, all with the intention of preserving the confidentiality and authenticity of the data (ECC). In this research, we present a safe and efficient method for exchanging data via the cloud, while simultaneously preserving both the data’s security and their integrity. The suggested system primarily operates by combining the Elliptic Curve Cryptography (ECC) technique with the Advanced Encryption Standard (AES) method to guarantee verification and maintain the solidarity of data. The findings of the experiments demonstrate that the strategy that was presented is effective and produces superior outcomes when compared to other ways that are already in use.
Integration of data acquisition system for rainfall measurement on IoT-based tipping bucket rain gauge

Abstract Rainfall is an important parameter in determining weather conditions. Rainfall data can be used for various purposes, such as for consideration in planning a civil building. If rainfall cannot be measured properly, it will result in an inaccurate design. In actual conditions, rain gauges are usually placed in locations far from power sources. However, to obtain data in digital form, a power source is required. In obtaining digital data, electronic components and monitoring systems are needed which require a power source and an internet connection to send measurement data to the website. The rainfall gauge used in this study is of the tipping bucket type. In the rain gauge used, there is a magnetic switch that functions to calculate the tip that occurs when it rains so that the rainfall value is obtained. The rainfall gauge used in this study has an accuracy of 97.85%. This tool is equipped with an IoT-based monitoring system, namely using a website. Based on the results of the tests that have been carried out, and the range of rainfall per 5 minutes that can be measured is 0 mm-74,5 mm, the range of rainfall per 1 hour that can be measured is 0 mm-894 mm, and the range of rainfall per 1 day that can be measured is 0 mm-21456 mm. In receiving data from the rainfall gauge used in this study, the Arduino Mega microcontroller and the GSM 808 module were used to send data reading the rainfall gauge to the website. On the website display, you can see the rainfall that is happening, and you can see a graph of rainfall. There is a report menu that can be used to download rainfall data per 5 minutes, per 1 hour, and per 1 day with the date selection feature of the rainfall data you want to download. Downloaded rainfall data is in the form of an Excel file.