Reviewerly/exHarmony huggingface dataset:
Enhancing Face Recognition with Deep Learning Architectures: A Comprehensive Review

The progression of information discernment via facial identification and the emergence of innovative frameworks has exhibited remarkable strides in recent years. This phenomenon has been particularly pronounced within the realm of verifying individual credentials, a practice prominently harnessed by law enforcement agencies to advance the field of forensic science. A multitude of scholarly endeavors have been dedicated to the application of deep learning techniques within machine learning models. These endeavors aim to facilitate the extraction of distinctive features and subsequent classification, thereby elevating the precision of unique individual recognition. In the context of this scholarly inquiry, the focal point resides in the exploration of deep learning methodologies tailored for the realm of facial recognition and its subsequent matching processes. This exploration centers on the augmentation of accuracy through the meticulous process of training models with expansive datasets. Within the confines of this research paper, a comprehensive survey is conducted, encompassing an array of diverse strategies utilized in facial recognition. This survey, in turn, delves into the intricacies and challenges that underlie the intricate field of facial recognition within imagery analysis.
XAI Applications in Medical Imaging: A Survey of Methods and Challenges

Medical imaging plays a pivotal role in modern healthcare, aiding in the diagnosis, monitoring, and treatment of various medical conditions. With the advent of Artificial Intelligence (AI), medical imaging has witnessed remarkable advancements, promising more accurate and efficient analysis. However, the black-box nature of many AI models used in medical imaging has raised concerns regarding their interpretability and trustworthiness. In response to these challenges, Explainable AI (XAI) has emerged as a critical field, aiming to provide transparent and interpretable solutions for medical image analysis. This survey paper comprehensively explores the methods and challenges associated with XAI applications in medical imaging. The survey begins with an introduction to the significance of XAI in medical imaging, emphasizing the need for transparent and interpretable AI solutions in healthcare. We delve into the background of medical imaging in healthcare and discuss the increasing role of AI in this domain. The paper then presents a detailed survey of various XAI techniques, ranging from interpretable machine learning models to deep learning approaches with built-in interpretability and post hoc interpretation methods. Furthermore, the survey outlines a wide range of applications where XAI is making a substantial impact, including disease diagnosis and detection, medical image segmentation, radiology reports, surgical planning, and telemedicine. Real-world case studies illustrate successful applications of XAI in medical imaging. The challenges associated with implementing XAI in medical imaging are thoroughly examined, addressing issues related to data quality, ethics, regulation, clinical integration, model robustness, and human-AI interaction. The survey concludes by discussing emerging trends and future directions in the field, highlighting the ongoing efforts to enhance XAI methods for medical imaging and the critical role XAI will play in the future of healthcare. This survey paper serves as a comprehensive resource for researchers, clinicians, and policymakers interested in the integration of Explainable AI into medical imaging, providing insights into the latest methods, successful applications, and the challenges that lie ahead.
License Agreements used in Distribution of Machine Learning Software Packages

Recently, consideration of the issues of legal regulation of artificial intelligence has become increasingly relevant. Most legal works are devoted to the study of responsibility of a legal personality that arises or does not arise from activities involving the use of artificial intelligence. However, for developers of the relevant software, the issue of intellectual property protection, namely, exclusive rights to computer programs, is more interesting. In particular, the types of licenses under which open source software products (such as MIT, GPL, BSD, Apach), their main characteristics, disadvantages and advantages are of interest. Of particular interest is the legal regulation of such license agreements in the context of cross-border turnover of software packages, as well as the existence of the open source concept. In addition, in order to dive deeper into the subject area of the study, the paper discusses basic concepts from the theory of machine learning.
Potensi Biogas Dengan Pretreatment Substrat Kulit Kopi Menggunakan Campuran Rumen Sapi

Biogas is an alternative energy source to reduce the use of fossil fuels. This research aims to explore the potential production of biogas using pretreatment of coffee husk substrate, both biologically with civet dung and chemically with NaOH solution, and compare it with the process without pretreatment. The fermentation process is carried out by adding rumen microorganisms to each digester with variations in substrate composition and fermentation time for 35 days, with sampling times at 7, 14, 21, 28, and 35 days. Biogas testing is performed by measuring the methane (CH4) content and total solids (TS) in the substrate. The results of the research show that pretreatment of coffee husk substrate has a significant impact on biogas (CH4) production. Biological pretreatment with civet dung produced the highest methane gas, reaching 42.3724% on the 28th day of fermentation with a mixture of 50% coffee husk + 25% rumen + 25% water, while the process without pretreatment produced the lowest methane gas, at 6.38433% on the 7th day of fermentation. The total solids results indicate an increase and decrease in total solids content in the reactor with various treatments. The highest total solids result was obtained with biological pretreatment at 40.15%. This research indicates that biological pretreatment of coffee husk substrate with civet dung can increase biogas production efficiency, which can be a potential solution to reduce dependence on fossil energy sources and sustainably manage coffee husk waste.Keywords: Biogas, Pretreatment, Coffee Peel, Civet Dung, Rumen Microorganisms, Production Efficiency, NaOH Solution.
Health Block: A Blockchain Based Secure Healthcare Data Storage and Retrieval System for Cloud Computing

Data in healthcare domain is highly sensitive in nature. Besides, there is need for maintaining integrity of such data. Blockchain technology has emerged to solve the problem of data integrity and non-repudiation with immutable storage in distributed repository. Thus secure data storage and retrieval in cloud environments is made possible using blockchain implementation. There are many existing healthcare systems with blockchain integration found in the literature. However, there is need for a system that supports complete set of operations that are governed by smart contracts. Another important consideration is that end users should be able to operate healthcare system without the need for knowledge of blockchain technology. Towards this end, in this paper, we proposed a Blockchain based secure healthcare data storage and retrieval system known as HealthBlock for cloud computing environments. We defined smart contract with underlying structures and functions using Solidity language for Ethereum blockchain platform. We also proposed and implemented an algorithm known as Healthcare Transactions over Blockchain (HToB). This algorithm supports secure blockchain based data storage and retrieval governed by smart contracts. Our system is evaluated using user-friendly web based client application. The experimental results showed that our system is able to ensure data integrity and non-repudiation besides reaping all benefits of blockchain technology.
Bibliometric analysis regarding the concept of blockchain accounting

The current global economic system is characterized by a high degree of globalization and significant values, a high level of security and the efficiency of the system of transmission of economic-financial information between multiple subjects involved. In this context, the question arises of the legality and morality of the operations within these transactions, because, very often, they camouflage cases of economic crime with enormous damages. With these considerations in mind, we deemed it appropriate in this research to bibliometrically analyze the concept of blockchain, which is a new secure technology. The authors carried out an analysis of the specialized literature applying the quantitative method of retrospective analysis and description of the works published with the mentioned subject. We defined two research stages: the first stage consisted of data collection using the international Web of Science (WoS) database, and the second research stage involved the processing of data exported from the WoS platform with the Full record and Cited references option through the CiteSpace program for carrying out a bibliometric analysis based on keywords.
Image emotion analysis based on semantic concepts

With the increasing number of users express their emotions via images on social media, image emotion analysis attracts much attention of researchers. For the ambiguity and subjectivity of emotion, image emotion analysis is more challenging than other computer vision tasks. Previous methods merely learn a direct mapping between image feature and emotion. However, in emotion perception theory of psychology, it is demonstrated that human beings perceive emotion in a stepwise way. Therefore, we propose a novel image emotion analysis framework that makes use of emotional concepts as middle-level feature to bridge image and emotion. Firstly, the relationship between the concept and the emotion is organized in the form of knowledge graph. The relation between the image and the emotion in the semantic embedding space is explored where the knowledge is encoded into. On the other hand, a multi-level deep metric learning method to optimize the model from both label level and instance level is proposed. Extensive experimental results on two image emotion datasets, demonstrate that the present approach performs favorably against the state-of-the-art methods on both affective image retrieval and classification tasks.
The Design and Application of a Unified Ontology for Cyber Security

Ontology enables semantic interoperability, making it highly valuable for cyber threat hunting. Community-driven frameworks like MITRE ATT &CK, D3FEND, ENGAGE, CWE and CVE have been developed to combat cyber threats. However, manually navigating these independent data sources is time-consuming and impractical in high-stakes situations. By adopting an ontology-based approach, these cybersecurity resources can be unified, enabling a holistic view of the threat landscape. Additionally, leveraging semantic query languages empowers analysts to make the most of existing data sources. This paper explores how through the application of a semantic query language (SPARQL) on a unified cybersecurity ontology, analysts can effectively exploit the information contained within these resources to strengthen their defense strategies against cyber threats.
Secured Collaboration with Ciphertext Policy Attribute Based Signcryption in a Distributed Fog Environment for Medical Data Sharing

Smart hospitals are leveraging cloud computing as a practical platform for storing and sharing medical data, enhancing medical analyses. However, entrusting sensitive medical data to third parties poses risks to patient privacy. Cloud-based data sharing also leads to many hazards, including latency challenges and bandwidth concerns when accessing data through cloud storage. To address these security flaws inherent in centralized cloud systems, this study proposes employing ciphertext policy-sensitive attribute-based signcryption with verifiable designcryption within a spatially distributed fog environment. This method signs messages using sensitive attributes and access entitlements, organized in a tree structure alongside non-sensitive attributes. The proposed approach integrates attribute-based encryption, ensuring fine-grained access control and digital signatures to guarantee data authenticity and secrecy. Unlike conventional cloud computing, the data distribution occurs in fog computing, facilitating swift data analysis. Spatial data distribution via fog nodes offers advantages such as low latency, optimized bandwidth usage, offline capabilities, enhanced privacy and security, scalability, and improved redundancy. These benefits position fog computing as a crucial element in real-time, location-based data processing for data sharing applications. The proposed method incorporates secure data authentication, access controls, and resistance against message attacks. To assess its effectiveness, a comparative analysis is conducted with existing methods, focusing primarily on computational efficiency. This scientific evaluation aims to offer insights into the advantages and limitations of the proposed approach.
Analysis of Optimal Number of Shards Using ShardEval, A Simulator for Sharded Blockchains

Blockchain-based networks have found increasing usage in various fields due to their distributed nature, immutability, public verifiability, and zero trust requirement. Monolithic blockchain systems such as Bitcoin and Ethereum are not scalable. Sharding is a promising approach among the various solutions that have been proposed in the literature to improve the scalability of such monolithic systems. However, designing, testing, and identifying critical parameters of sharding-based protocols for blockchain networks is challenging. We present ShardEval, a simulator to evaluate sharding-based protocols for blockchain networks. We have used ShardEval to identify critical parameters of a sharded blockchain network. In particular, we determine the optimal number of shards in a network with a fixed percentage of cross-shard transactions. The simulation results are validated against the rigorous theoretical analysis, confirming the correctness under reasonable assumptions. Using the insights gained from simulation and theoretical analysis, we introduce Lookup Table to improve the transaction throughput further. The theoretical framework and simulator bridge the gap between designing and testing sharding-based protocols.
Attack Graph Based Security Metrics for Dynamic Networks

Evaluating network attack graphs in today’s dynamic networks poses a challenge. Conventional metrics used for attack graph based risk assessment are inadequate due to their inability to consider temporal evolution of networks. To address this limitation, we introduce the notion of temporal attack graph, which incorporates the temporal characteristics of network configurations and vulnerabilities. It provides a notion for risk assessment by providing a more precise depiction of the network’s security state over time. In addition, we introduce two security metrics based on temporal attack graphs. By effectively capturing the temporal features of dynamic networks, these metrics enable accurate measurement of network security over time. Path-based metrics analyze whether an attacker can reach a target along a specific temporal path. These metrics help in evaluating overall robustness of the network and adopting appropriate security counter measures beforehand.
Attack on the Privacy-Preserving Carpooling Service TAROT

The widespread popularity of carpooling services has brought about several privacy concerns, including the collection and use of user location data by service providers. To address these concerns, various carpooling service schemes based on homomorphic encryption have been proposed. TAROT, proposed by Xu et al. (IEEE IOT Journal 2022), aims to be an efficient, accurate, and privacy-preserving carpooling service scheme. In this paper, we show that there is a leakage of location data for users in TAROT. Specifically, we examine a Goldwasser-Micali (homomorphic encryption scheme)-based Equality Determination Algorithm (GMEDA) used in TAROT and propose passive attacks, where honest-but-curious users collude to steal the location information of other users.
A Cycle-GAN Based Image Encoding Scheme for Privacy Enhanced Deep Neural Networks

Deep learning model training on cloud platforms typically require users to upload raw input data. However, uploading raw image data to cloud service providers raises serious privacy concerns. To address this problem, we propose a Cycle-Gan based-image transformation scheme that leverages convolutional autoencoder image encoding for domain translation. Our Cycle-GAN based image transformation scheme enhances privacy of deep neural networks while preserving model utility. In this paper, we demonstrate that our Cycle-GAN based image transformation scheme protects visual feature information of sensitive image data. We evaluate the effectiveness of our proposed method to preserve model utility using classification accuracy and robustness against reconstruction attacks using structural similarity index measure (SSIM). The classification accuracy of encoded images using our proposed method is 92.48, 91.05, 90.37 for Chest X-ray, Dermoscopy and OCT datasets, respectively. The SSIM scores for reconstruction attacks where the attacker only has access to the encoded data and corresponding labels are 0.1002, 0.0995 and 0.0329 for Chest X-ray, Dermoscopy and OCT datasets, respectively. Our results demonstrate that the Cycle GAN based encoding scheme effectively enhance privacy while preserving model utility.
PSDP: Blockchain-Based Computationally Efficient Provably Secure Data Possession

Provable Data Possession is a protocol that helps the client to verify the integrity of the data that has been stored in cloud or a remote server. It allows the client to verify that the data possessed by the server is not modified, damaged or lost i.e. the data is original in all forms without retrieving it. Probabilistic proof of possession are constructed by the model by performing random sampling to choose random blocks of data from the remote server. To verify this, client maintains a data of fixed volume. This reduces the input and output costs drastically. We have constructed an efficient and lightweight provably secure Data Possession (PSDP) using Elliptic Curve Digital Signature Algorithm (ECDSA) in the signing process in this paper.
OLAF: An Ontology Learning Applied Framework

Since the beginning of the century, research on ontology learning has gained popularity. Automatically extracting and structuring knowledge relevant to a domain of interest from unstructured textual data is a major scientific challenge. After studying the main existing methods, such as Text2Onto, we propose a new approach with a modular ontology learning framework focusing on automatically extracting knowledge from raw text sources. We consider tasks from data pre-processing to axiom extraction. Whereas previous contributions considered ontology learning systems as tools to help the domain expert craft a reusable ontology, we developed the proposed framework with full automation in mind to build a minimum viable ontology targeted at an application. Ontology Learning Applied Framework (OLAF) has been generically designed to build specific ontologies whatever the application domain, use case and text data. We implement an initial version and test the framework on an ontology-based system, a search engine for technical products.
Technology in the Classroom and Its Impact on College Graduates

The integration of technology in the classroom has become increasingly prevalent in higher education, revolutionizing the way students learn and interact with course material. This abstract provides an overview of the research conducted to investigate the impact of technology in college classrooms on the academic and professional development of graduates. The research explores the various forms of technology used in classrooms, including digital learning platforms, online resources, and interactive tools, and examines their influence on student engagement, learning outcomes, and future career success. Findings indicate that technology in the classroom has a profound impact on college graduates’ educational experiences. Students who actively engage with technology report increased motivation, enhanced critical thinking skills, and improved access to resources. However, the level of technological integration and the quality of the tools utilized play a significant role in determining outcomes. Furthermore, this research explores the relationship between technology-enhanced learning and graduates’ employability and career readiness. It examines how exposure to technology in the classroom prepares students for the demands of a rapidly evolving job market. Results suggest that graduates with a strong technological skillset and digital literacy have a competitive advantage in securing employment and adapting to the dynamic nature of modern workplaces. In conclusion, this research highlights the multifaceted impact of technology in the college classroom, shedding light on its potential benefits and challenges. It emphasizes the need for educators to employ technology strategically to maximize its positive effects on student learning and future career prospects. These findings have implications for curriculum development, instructional design, and educational policy, with the aim of preparing college graduates to thrive in a technology-driven world.
Developing Rigorous Mathematical Thinking Approach-Based Learning Materials for Sequences and Series Concept at High School Level in Tanjungpinang

Mathematics encourages logical reasoning and critical thinking. Rigorous thinking helps in analysing problems to decision-making and ensures that solutions are accurate and reliable. Prior research indicated students’ lack of those abilities when learning the concept of sequences and series. Therefore, this research aims to develop learning materials for the concept of sequences and series based on rigorous mathematical thinking (RMT) approach. The learning materials developed with the ADDIE model include mobile learning, student worksheets, and test instruments, which provide clear explanations, examples, problem-solving activities with RMT approach, and assessments enabling teachers to evaluate students’ understanding of mathematics concepts of the sequences and series. The learning materials were validated using a questionnaire by mathematics experts. The quantitative and qualitative data results show that the mobile learning, student worksheets, and test instruments are valid. Mobile learning that was developed and tested valid entails that the study can add to a new learning strategy that proposes technology and digital tools to engage students in the learning process and promote RMT.
Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation

This paper proposes an advanced expert collaborative filtering recommendation algorithm. Although ordinary expert system filtering algorithms have improved the recommendation accuracy of collaborative filtering technology to a certain extent, they have not screened the level of expertise of experts, and the credibility of experts varies. Therefore, this paper proposes an expert mining system based on signal fluctuations. The algorithm uses signal processing technology to filter the level of experts. This method introduces a kurtosis factor. Regarding the user's rating sequence as a random discrete signal, and then randomly sorting the user's ratings k times, the average kurtosis of the user is obtained. And take the kurtosis value as the credibility of expert users. Through experiments on multiple datasets including MovieLens, Jester, Booking-Crossings, and Last.fm, we have proved the advancement and reliability of our method.