Reviewerly/exHarmony huggingface dataset:
Blockchain in the Energy Sector for SDG Achievement

Blockchain technology finds application in multiple sectors, including renewable energy. Numerous blockchain-based applications aim to provide support in the production, management, distribution, and consumption of green energy. The benefits offered are not only technological but also social, environmental, and economic. The purpose of this study is to examine how the application of blockchain in the energy industry may affect the achievement of the Sustainable Development Goals (SDGs). This study is composed of two parts. The first part concerns the identification and analysis of the most relevant categories of blockchain applications in the energy sector and their ability to contribute to the achievement of the SDGs. A knowledge base, comprising scientific articles, gray literature, and real-world applications, has been created and analyzed. With a keyword-based approach, each application was associated with one or more SDGs. In the second part, the Sustainability Awareness Framework (SuSAF) was used to examine the findings of the first part of the study and discuss them in terms of five dimensions of sustainability. Finally, potential risks associated with the use of blockchain in the energy sector are also covered. Results reveal that tracking energy production and consumption and renewable energy communities are the applications that have the most beneficial effects, and that the benefits linked to blockchain adoption go beyond the energy sector to include the environment, the economy, industry, infrastructure, smart cities, and society.
Optimizing Incident Detection Thresholds Using the A* Algorithm: An Enhanced Approach for the California Algorithm

This paper presents an improved version of the California Algorithm (CA), focusing on threshold selection criteria. The CA is a widely recognized incidence detection algorithm used as a benchmark for comparison with newly developed incident detection algorithms. This study proposes criteria for threshold selection in CA based on the A* algorithm, which aims to find optimal thresholds using a Performance Index (PI) as a cost function. Our proposed method reduces processing time by optimizing resource utilization and establishes a standard for threshold selection in CA for comparison and evaluation purposes. Experimental results from our proposed method demonstrate its effectiveness in reducing the complexity required to determine optimal thresholds. Optimization of the CA method using the A* algorithm results in a 98.68% reduction in the number of nodes searched compared to a Complete Search Tree (CST).
An adaptive categorical effect size method based on intuitionistic meta fuzzy functions

There are several categorical effect size methods in the literature. It is not clear which method performs better for a given dataset and it is a challenging task to select the correct method for a given dataset. In this sense, to overcome the questions like "Which method should we choose?" and "Which categorical effect size method is more reliable for a given dataset?", an adaptive categorical effect size method based on intuitionistic meta fuzzy functions is introduced in the paper. Thus, the main motivation of the proposed method is to obtain more accurate outcomes by combining the results of better performing methods instead of relying on only one method. In the study, the intuitionistic fuzzy c-means clustering algorithm is adapted to meta fuzzy functions by incorporating not only membership degrees but also non-membership degrees to improve the clustering accuracy of meta fuzzy functions. Meta fuzzy functions are the linear combination of seven categorical effect size methods and the weights, which are calculated from membership grades from intuitionistic fuzzy c-means algorithm. Among the functions, the one with the lowest mean absolute percentage error is selected as the best. To evaluate the performance of the proposed method, 2 × 3, 2 × 4, and 3 × 4 contingency tables were simulated. Additionally, the performance of the proposed method is also assessed by applying it to a real-time dataset. Experimental results show that the proposed method outperforms compared to the evaluated seven categorical effect size methods in terms of mean absolute percentage error. Also, the calculated effect sizes are within the range of ±10% in terms of bias. Thus, the results verified that proposed method achieves greater reliability.
Off-Road Environment Semantic Segmentation for Autonomous Vehicles Based on Multi-Scale Feature Fusion

For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated feature fusion is used to fuse PointTensor and Cylinder for encoding and decoding. In addition, we use CARLA to build off-road environments for obtaining datasets, and employ linear interpolation to enhance the training data to solve the problem of sample imbalance. Finally, we design experiments to verify the excellent semantic segmentation ability of MAPC-Net in an off-road environment. We also demonstrate the effectiveness of the multi-layer receptive field fusion module and data augmentation.
Factors influencing the effectiveness of credit card fraud prevention in Indonesian issuing banks

The increase in online credit card transactions in the digital era has caused an increase in credit card cyber incidents. This is happening globally, including in Indonesia. Thus, it will affect a bank’s reputation as well as its financial losses. Therefore, optimal fraud risk management is needed in a banking effort to prevent credit card fraud. In response, this article intended to study credit card fraud prevention by examining the relationship between digital security required for customer data security; fraud brainstorming to identify process weaknesses; and compliance management to manage regulatory compliance. The next step was to test whether the anti-fraud specialist is competent to moderate this relationship. This study used a quantitative approach. This study included 27 Indonesian card issuers. Primary sources were used to collect data for this study. The primary data were analyzed using a structural equation model (SEM). The results of the study show that digital security, fraud brainstorming, and compliance management were positively and significantly related to the prevention of credit card fraud, at a significance level of 5%, the t-statistic has a numerical value of 6.161, 5.079, and 5.98 each. Furthermore, testing the moderating effect obtained t-statistic values of 7.330, 4.161, and 7.694. Competency results obtained with positive and significant influence moderate the relationship between these factors and credit card fraud prevention. These findings have policy implications for banking and government objectives in fighting credit card fraud through implementing prevention strategies. AcknowledgmentsThis research was conducted as part of the process of study completion at the Padjadjaran University, Bandung, Indonesia. We would like to express our sincere gratitude to the respondents who participated in this research.
Resources to develop the research publication competencies of Russian researchers

Introduction . In the last decades, due to the increased number of international research collaborations, the role of auxiliary institutions which contribute to the development of research publication competencies has increased. Competencies are required for writing, preparing, publishing, and promoting scientific articles. The formation of such competencies is carried out at different levels of higher education as part of the current Federal State Educational Standard (FSES), including prorammes of additional professional education, as well as initiatives of auxiliary university departments and non-university organisations. Aim . This article aims to analyse the resources available for the development of research publication competencies in Russia in terms of the opportunities offered by different participants of the educational process. Methodology and research methods . The study compared and analysed open access regulatory documentation, reviewed and systematised data from the websites of auxiliary university departments and non-university organisations, and used data from in-depth interviews. Results . The article describes the prerequisites for the formation of research publication competencies in higher education in Russia. An overview of effective practices to develop such competencies is given, taking into account the national context and available higher education programmes. The article provides an overview of the functionality of auxiliary university departments and non-university organisations, whose activities support publication activity. The role of academic writing teachers, consultants, translators, editors, and reviewers in the development of the competencies of the authors of scientific publications is determined. The importance of the closer interaction of all participants in the publication process is substantiated: authors, translators, and editors of texts; and teachers of scientific writing courses with editors and reviewers of scientific journals. Scientific novelty. The scientific novelty of this article refers to a detailed description of the resources for the development of research publication competencies and their compliance with the requirements of the Federal State Educational Standard, which have not been investigated in such correlation before. Practical significance . The results can be used to develop a set of measures to systematise approaches to developing research publication competencies.
Camera calibration using the dual double-contact property of circles

To mitigate the influence of the relative positional relationship between circles on camera calibration, this study explores the double contact of dual circles with dual circular points. We demonstrate that one of the generalized eigenvectors of two dual circles corresponds to the line passing through their respective centers. Moreover, we establish a methodology to identify this line from the three generalized eigenvectors and show that, regardless of the positional arrangement of the three co-planar circles, the image of the circular points can be constructed by calculating the image of this line. Consequently, the applicability of circles as calibration templates is expanded, enabling the development of a novel optimization technique for fitting circular images with enhanced calibration precision.
Still Wrestling with Monsters: A Response to Pat Norman

Abstract Žižek’s (2012) provocative revelation warns that we are ‘living in the time of monsters’, as western cultures enter ‘a new dark ages’ of permanent capitalist crisis. Žižek is referring to ideological masks that impact education and educational researchers: the greed of capitalism, twisted within its fraught political connection with democracy and citizenship, ultimately threatening the planet. If theory is provocation as Norman suggests, my response to Žižek’s provocation and Norman’s explores democracy, learning and pedagogy, and the civic purpose of education, beyond citizenship. Žižek offers philosophical insight for educational researchers to reflect upon the place of democracy, the dangers of performativity, and the purpose of education for Australian citizens. In examining democracy, citizenship, and equity, my response draws upon the philosophical and spiritual insights of Illich (1970) (Bono, Surrender: 40 songs, one story . Hutchinson Heinemann, London, 2022) and other contemporary philosophers to rethink what it means to be human. I propose that what is of utmost importance to education is receptivity and listening in the creation of new voice, new art, and new songs.
Multiauthority Ciphertext Policy-Attribute-Based Encryption (MA-CP-ABE) with Revocation and Computation Outsourcing for Resource-Constraint Devices

Fog computing accredits by utilizing the network edge while still rendering the possibility to interact with the cloud. Nevertheless, the features of fog computing are encountering several security challenges. The security of end users and/or fog servers brings a significant dilemma in implementing fog computing. The computational power of the resources constrains Internet of Things (IoT) devices in the fog-computing environment. Therefore, an attacker can easily attack. The traditional methods like attribute-based encryption (ABE) techniques are inappropriate for resource-constraint devices with protracted computing and limited computational capabilities. In this regard, we investigate a multiauthority ciphertext policy-attribute-based encryption (MA-CP-ABE) method that enables multiauthority attribute revocation and computation outsourcing. Moreover, the encryption and decryption processes of resource-constraint IoT devices are outsourced to the fog nodes. In this way, it also reduces the computational burden of the resource-constraint IoT devices. Hence, we propose MA-CP-ABE for encryption and decryption, attribute revocation and outsourcing by reducing the computational burden and securing the system. We compare the computational offloading approach with the existing techniques to prove that the proposed approach outperforms the existing approaches. The proposed method reduces the operation time for the encryption and decryption process. We outsource cryptography operations to the fog node, reducing the end user’s computational cost. Eventually, simulated outcomes are used to assess the algorithm’s computational cost.
IF-DSS: A forensic investigation framework for decentralized storage services

Decentralized storage services are growing in popularity owing to their lower costs, increased resilience, and privacy compared with traditional cloud storage services. However, these characteristics also attract malicious actors, who abuse them to create phishing URLs, distribute malware, infringe on copyrights, and conduct other crime-related activities. Investigating these services is challenging because of their censorship resistance and decentralization, which renders the existing methodologies for cloud-based storage services and peer-to-peer-based file-sharing services insufficient. To address these challenges, we introduce a novel forensic investigation framework that encompasses identifying, collecting, examining, analyzing potential evidence, and preventing the further distribution of the content. The framework works on each node, peer, gateway, and Internet area of the decentralized storage services, integrating investigation steps on both remote and local sides. The usefulness and applicability of the proposed framework were demonstrated through case studies involving phishing and large-scale file sharing using IPFS with Filecoin.
Busting up Monopoly: Methods for modern darknet marketplace forensics

Darknet marketplaces represent the most delinquent evolution step in distributing illicit goods such as drugs, steroids, firearms, warez, or leaked personal information. On the one hand, law enforcement agencies try to catch vendors, buyers, and operators of darknet marketplaces. On the other hand, the criminals mentioned above constantly stretch the limits of overlay networks, applied cryptography, and cryptocurrency pseudonymity. This paper intends to provide relevant and up-to-date (for the year 2022) information about potential ways to deal with darknet marketplaces from the perspective of investigators. The paper outlines methods (based on periodic web scraping) that may help sworn officers to gather evidence about darknet marketplace (ab)users. The potential is demonstrated in a real-life case study of the Monopoly Market. For instance, suggested approaches seem capable: monitoring the demography and activities of darknet marketplace users, estimating the number of procurements and their value, and correlating user identities with their cryptocurrency addresses. The paper also provides an applicability analysis of proposed methods on the subset of currently trending darknet marketplaces.
Forensic analysis of SQL server transaction log in unallocated area of file system

The importance of database forensics is increasing day by day as the use of databases to store sensitive corporate and personal data increases. Database forensics is a field of digital forensics that deals with database-related incidents such as data corruption, breaches, and leaks. One of the key functions of database forensics is information reconstruction, which is the tracing of actions from the time of an event to the present based on various information stored in the database. This feature allows investigators to identify unauthorized user actions and data deletion or manipulation when an incident occurs. Database log data is primarily used to reconstruct information. Database logs include transaction logs, error logs, event logs, and trace logs. Among them, we focus on the transaction log of Microsoft SQL Server (MSSQL), one of the most popular database management systems in the world. Raw-level studies have been conducted on the transaction logs of Oracle and MySQL, other databases used at the enterprise level. However, there is very little research on MSSQL transaction logs. For this reason, we analyze the internal structure of the MSSQL transaction log. Based on these finding, we present an empirical method to identify and extract transaction log records in unallocated area.
Chracer: Memory analysis of Chromium-based browsers

The web browsing activities of a user provide useful evidence for digital forensic investigations. However, existing analysis techniques that aim to analyze local artifacts (e.g., history and cache) cannot find useful data (e.g., visited URLs) if a user accesses the web using private or secret mode. Hence, string-searching and pattern-matching techniques have been proposed and used to examine user activities from a memory dump. These simple techniques are useful for identifying individual URLs visited in both normal and private modes. However, since a piece of individually detected data does not have context on how it is created, additional analysis efforts are required to properly interpret the meaning of the data. This paper proposes Chracer, a practical methodology for extracting forensically meaningful information from the virtual memory of a Chromium-based browser by systematically discovering objects of web browsing-related classes. Moreover, a proof-of-concept tool developed based on the proposed methodology demonstrates that users’ web browsing-related artifacts can be extracted effectively from the virtual memory of any Chromium-based browser, such as Google Chrome, Microsoft Edge and Brave.
Artificial intelligence clinical applications in breast diagnostic imaging

Breast cancer is the most diagnosed cancer in women worldwide, causing significant morbidity and mortality. Imaging techniques play a pivotal role in the early detection of breast cancer; digital mammography (DM) and digital breast tomosynthesis are commonly used for screening average-risk women, while magnetic resonance imaging is employed for high-risk women. Although several progresses have been made in early diagnosis, the number of breast cancer-related deaths remains high, especially among younger women and those diagnosed at advanced stages. To address this problem, new tools are needed that can enable personalized screening or new early diagnosis strategies. Artificial intelligence (AI)-base techniques can assist radiographers and radiologists in various aspects of breast cancer management, including image quality optimization, breast density evaluation, risk assessment and lesion characterization. The level of maturity of the AI technologies currently available in breast imaging is variable. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) were the first AI models introduced to aid radiologists in interpreting DM; CADe marked suspicious areas, while CADx assisted in characterizing findings. However, large-scale studies revealed limited utility and potential negative impacts on mammography interpretation. Conventional CAD systems suffered from low specificity and frequent false positives, failing to address human image perception limitations. The new generation of AI algorithms aims to overcome these limitations and assist radiologists in identifying hidden lesions. This review provides an overview of the current contributions of AI in breast cancer diagnosis, focusing on achieved results, potential objectives, and limitations in clinical practice application.
Analyzing the peeling chain patterns on the Bitcoin blockchain

Bitcoin is a widely used decentralized cryptocurrency. The proportion of Bitcoin transactions used for illegal activities is increasing. Mixing services are commonly applied to enhance anonymity and make transaction records more challenging to follow and analyze. The current research on peeling chains is generally based on heuristic algorithms to identify change addresses. However, due to the characteristics and limitations of the Bitcoin blockchain, there is no such ground truth to ensure the accuracy of each derived change address. This research analyzes the peeling chain patterns based on self-change addresses. The use of self-change addresses implies that the input address and the address used for receiving the change are controlled by the same entity. Also, each chain's transaction details and generated chain parameters are further verified for more precise results. Combining the two methods ensures the accuracy of the extracted peeling chains to some extent. And the corresponding behavior pattern of the extracted chains is studied.
Identification of data wiping tools based on deletion patterns in ReFS $Logfile

Data wiping tools permanently delete files by repeatedly overwriting data on a digital device, making file recovery impossible. Unlike the conventional deletion methods, which merely remove the file system pointer to the data, these tools are designed to entirely and irretrievably erase the data. This method can be exploited to obliterate evidence of a crime. Given the growing prevalence of such tools, a comprehensive analysis of permanent deletion behavior is essential, especially concerning the Resilient File System (ReFS). In this study, we propose a method for detecting user behavior concerning data wiping tools and algorithms in ReFS 3.7. Our approach relies on the fact that file modifications are logged in the redo record of the $Logfile, and that the opcode value of the redo record varies depending on the data wiping tool used. Since opcodes were only analyzed up to version 3.4, we analyzed the newly updated opcodes. Initially, we selected the 12 most commonly used data wiping tools for our research. In the pattern analysis phase, we applied the algorithms supported by each tool, generating a distinct deletion pattern for each one. This was accomplished by utilizing consecutive opcodes to formulate the patterns and monitor transitions in file and directory names. The patterns discerned in the $Logfile allowed us to determine which data wiping tool was deployed. The proposed methodology simplifies the identification of not only which data wiping tool has been used, but also the specific deletion behavior exhibited. We developed a tool incorporating the proposed method. Our subsequent verification confirmed the effectiveness of our methodology and tools in accurately detecting the use of comprehensive deletion tools. These findings contribute valuable insights to the acquisition of digital evidence of user deletion behavior in ReFS. Our proposed methodology will help digital forensic examiners in the detection and identification of data wiping tools' behavior.