Reviewerly/exHarmony huggingface dataset:
Examination of sequence of strokes by optical method

The sequence in which the writing strokes have been executed on a paper is known as sequence of strokes. Examining the sequence of strokes can help in determining the relative age of ink which is required in various cases related to document examination including examination of wills, cheques, statements, receipts, agreements etc. The current study is focused on examining the sequence of strokes executed with a ball point pen. Video Spectral Comparator 8000 (VSC8000) has been used for the current examination. The test samples and the actual questioned documents were examined under VSC8000. It was observed that depending on the pressure through which the strokes have been executed, identification of sequence of two or more intersecting writing strokes was possible.
Localized Validation Accelerates Distributed Transactions on Disaggregated Persistent Memory

Persistent memory (PM) disaggregation significantly improves the resource utilization and failure isolation to build a scalable and cost-effective remote memory pool in modern data centers. However, due to offering limited computing power and overlooking the bandwidth and persistence properties of real PMs, existing distributed transaction schemes, which are designed for legacy DRAM-based monolithic servers, fail to efficiently work on the disaggregated PM. In this article, we propose FORD, a F ast O ne-sided R DMA-based D istributed transaction system for the new disaggregated PM architecture. FORD thoroughly leverages one-sided remote direct memory access to handle transactions for bypassing the remote CPU in the PM pool. To reduce the round trips, FORD batches the read and lock operations into one request to eliminate extra locking and validations for the read-write data. To accelerate the transaction commit, FORD updates all remote replicas in a single round trip with parallel undo logging and data visibility control. Moreover, considering the limited PM bandwidth, FORD enables the backup replicas to be read to alleviate the load on the primary replicas, thus improving the throughput. To efficiently guarantee the remote data persistency in the PM pool, FORD selectively flushes data to the backup replicas to mitigate the network overheads. Nevertheless, the original FORD wastes some validation round trips if the read-only data are not modified by other transactions. Hence, we further propose a localized validation scheme to transfer the validation operations for the read-only data from remote to local as much as possible to reduce the round trips. Experimental results demonstrate that FORD significantly improves the transaction throughput by up to 3× and decreases the latency by up to 87.4% compared with state-of-the-art systems.
Visual Analytics of Co-Occurrences to Discover Subspaces in Structured Data

We present an approach that shows all relevant subspaces of categorical data condensed in a single picture. We model the categorical values of the attributes as co-occurrences with data partitions generated from structured data using pattern mining. We show that these co-occurrences are a-priori , allowing us to greatly reduce the search space, effectively generating the condensed picture where conventional approaches filter out several subspaces as these are deemed insignificant. The task of identifying interesting subspaces is common but difficult due to exponential search spaces and the curse of dimensionality. One application of such a task might be identifying a cohort of patients defined by attributes such as gender, age, and diabetes type that share a common patient history, which is modeled as event sequences. Filtering the data by these attributes is common but cumbersome and often does not allow a comparison of subspaces. We contribute a powerful multi-dimensional pattern exploration approach (MDPE-approach) agnostic to the structured data type that models multiple attributes and their characteristics as co-occurrences, allowing the user to identify and compare thousands of subspaces of interest in a single picture. In our MDPE-approach, we introduce two methods to dramatically reduce the search space, outputting only the boundaries of the search space in the form of two tables. We implement the MDPE-approach in an interactive visual interface (MDPE-vis) that provides a scalable, pixel-based visualization design allowing the identification, comparison, and sense-making of subspaces in structured data. Our case studies using a gold-standard dataset and external domain experts confirm our approach’s and implementation’s applicability. A third use case sheds light on the scalability of our approach and a user study with 15 participants underlines its usefulness and power.
Explainability in subgraphs-enhanced Graph Neural Networks

Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher than the 1-dimensional Weisfeiler-Leman isomorphism test. The new paradigm suggests using subgraphs extracted from the input graph to improve the model's expressiveness, but the additional complexity exacerbates an already challenging problem in GNNs: explaining their predictions. In this work, we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. The proposed explainer accounts for the contribution of all the different subgraphs and can produce a meaningful explanation that humans can interpret. The experiments that we performed both on real and synthetic datasets show that our framework is successful in explaining the decision process of an SGNN on graph classification tasks.
Improving the Use of Chaotic Keys in Message Cryptography

Using chaotic logistic keys in message cryptography is a popular technique; it provides a high level of message security by protecting it from being hacked. This technique becomes inefficient when the message length is big, because a chaotic key of big length requires much time to be generated. To overcome this problem this paper research will provide a novel method for short and long messages cryptography. The new method will use a complicated private key which will provide a huge key space to resist any kids of attacks. The private key will be used to divide the message into equal blocks (except the last block), and it will be also used to generate the index keys which are required to apply encryption and decryption, these keys will be generated from the obtained chaotic logistic keys. The proposed method will use a simple encryption and decryption functions, these functions will use the index keys values to distribute the source message to form the encrypted message and to retrieve the decrypted message from the encrypted one. A recommendation of message dividing into blocks will be suggested, depending on the message length: when to divide a message and how many blocks are required. The required number of message blocks will be tested for various in length messages, the throughputs will be calculated to justify the process of message dividing into blocks. The proposed method will tested using various short and long messages, the obtained results will be analyzed to prove the quality, security, sensitivity and the efficiency of the proposed method.
Cybercrime-as-a-Service &amp; AI-Enabled Threats

Some of the fast-expanding areas of concern in the realm of cybersecurity are cybercrime-as-a-service (CaaS) and AI-enabled threats. CaaS refers to the usage of online markets or platforms where people or organizations can buy or rent the equipment and services required to commit cybercrimes. Artificial intelligence-enabled threats are those that leverage machine learning and artificial intelligence to conduct cyberattacks. This article offers an overview of these two topics and suggests a way to deal with them. The suggested approach entails actions like recognizing and keeping an eye on online marketplaces and platforms that are used to buy or rent hacking tools and services, creating and implementing new security tools and technologies that can recognize and counter AI-enabled threats, regularly training staff and users in security awareness, updating and patching systems and software, and working with law enforcement to find a perpetrator.
Constructing meaningful code changes via graph transformer

Abstract The rapid development of Open‐Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long‐range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, a novel method is proposed, named Graph Transformer for learning meaningful Code Transformations (GTCT), to provide developers with preliminary and quick feedback when developers submit code changes, which can improve the quality of code changes and improve the efficiency of code review. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information of the source code limitation, the code graph embedding component captures the types and patterns of code changes by encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, the code transformation learning component uses the multi‐head attention mechanism and positional encoding mechanism to address the long‐range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code and others' taxonomy of code changes.
Master Face Attacks on Face Recognition Systems

Due to its simplicity, face authentication is currently more frequently used than authentication using a personal identification number or an unlock pattern, especially on mobile devices. This has made it a seductive target for attackers who use a demonstration assault. Traditional presentation attacks employ the victim's face or victim footage. The existence of master faces—faces that match numerous enrolled templates in face recognition systems—has been demonstrated in earlier research, and their presence increases the effectiveness of presentation attacks. In this article, we present the results of a thorough investigation of latent variable evolution (LVE), a technique frequently employed to produce master faces. To determine the characteristics of master faces, an LVE algorithm was used in a variety of settings and with many databases and/or face recognition systems.
XAI Design Goals and Evaluation Metrics for Space Exploration: A Survey of Human Spaceflight Domain Experts

The National Aeronautics and Space Administration (NASA) and its international partners are beginning a new era of human exploration by sustainably returning humans to the Moon and preparing for missions to Mars. Due to the great distances these mission will travel from Earth, reliance on Earth-based support will be diminished and greater levels of autonomy will be required. Recent advances in Artificial Intelligence (AI) such as Machine Learning (ML) and robotics are beginning to make highly autonomous systems a reality. However, these systems are highly complex and obscure to both their developers and users which inhibits trust and degrades human-autonomy teaming. Explainable AI (XAI) is a new and vibrant field exploring solutions to these issues with AI. The objective of this study is to determine user goals and metrics by which to design and evaluate XAI systems for crews and remote operators of AI systems in deep space exploration. This involved a survey-based study of scientists, engineers, and operators of spaceflight systems to determine the desired XAI outcomes, i.e. answering "what to explain?" and how to evaluate a user's level of satisfaction with an explanation. Our results will inform future XAI system development for autonomy in deep space exploration.
Automated recognition of individual performers from de-identified video sequences

Identification of individual humans from RGB image data is well-established. However, in many domains, such as in healthcare or applications involving children, ethical issues have been raised around using traditional RGB image data because individuals can be identified from these data. The widespread availability of reliable depth data, and the associated human skeleton data derived from these data, presents an opportunity to differentiate between individuals while potentially avoiding individually identifiable features. Using skeleton data only, we developed a unique 20-dimensional bone segment length feature vector for 1,761 trials (1,759,980 image frames) of data, captured from 14 participants who engaged in a one-hour group intervention playing Xbox One Kinect Bowling twice-weekly for 24 weeks. We then evaluated our novel feature using representative batch processing (k-nearest neighbour) and real-time (multi-layer perceptron) models, validated against manually-labelled ground-truth data. Our results suggest that our skeleton feature can differentiate between instances (i.e., individuals) with an accuracy over all participants of 100% for batch processing and 96.57% in real-time, and deals well with class imbalances. Our results suggest that we can reliably differentiate between individual persons using only skeleton data derived from depth image data in medical research.
Analysis of length of stay for patients admitted to Korean hospitals based on the Korean National Health Insurance Service Database

The aim of this study is not only to predict length of stay (LOS) for all patients admitted to medical institutions in South Korea, but also to unveil features that would significantly influence LOS. As our dataset, we used the 2016–2019 health checkup cohort DB data stored in the virtual server of National Health Insurance Service (NHIS) of Korea. Since this study presents the pioneer work of predicting LOS for all inpatients in South Korea to our best knowledge, we referred to extant literature completed elsewhere for feature selection with regard to LOS analysis. In this paper, we compared the prediction performance of inpatients' LOS between four machine learning models including XGBoost, Decision Tree, Random Forest, and Support Vector Machine. The results indicate that the XGBoost model shows the best performance with accuracy of 0.7472 and AUC of 0.88. Furthermore, using the XGBoost model, we discovered features that had a significant influence in predicting LOS with the use of feature importance. We found that the critical features in determining LOS were examination records which are consisted of age, the deductible insurance ratio and information with regard to one's main diagnosis and the number of sub diagnosis, the number of doctors and beds available in hospitals, cholesterol level, body mass index (BMI), and the admission month. The significance in this study is that we found the important common features that affect LOS for inpatients across medical department and we could make a generalization of our research result.
Dissertations as important scientific legacy: Preservation and access (the case study of the Library of N. D. Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences)

The authors discuss the preservation of dissertations by national scientists in the libraries of the Russian Academy of Sciences (RAS) research institutions. A share of theses defended in the Russian Empire before 1918 makes the part of the general collection. Since 1934, the scholarly manuscripts havebeen spinned off into a separate collection, since 1950, along with theses abstracts. The dissertations were transferred to the organizations where they were defended, therefore the collection comprises the works accomplished in different institutions. This could be the only location for the manuscripts of 1930–40s, as the Dissertation Collection of the Russian State Library was established after 1944. The authors argue that apart from their information value, the dissertations make the valuable source on the history of national science and society, demonstrate communications between academic organizations and continuity of scholar schools, and are of great memorial value. The mission of libraries is to preserve the scholar legacy of this country and to support accessibility of information on dissertations through e-catalogs and digitizing full texts of the Soviet-era theses.
Studying information needs of central scientific library users in a big industrial city to improve the library document resources

Scientific libraries as an essential part of science, education, sociocultural and industrial complex of a region (big academic or industrial city) are engaged in developing information and communication environment. Therefore, systematic developing of library and information space is vital. Within the framework of the project “Regional scientific libraries in the changing science and education environment” (Fundamental Studies Program of the Russian Academy of Sciences, 2022–2026), the State Public Scientific and Technical Library of the Russian Academy of Sciences Siberian Branch has been designing the model to adapt regional libraries to modern communications and new methods of scientific publications dissemination within the context of social transformations and changing paradigm of library acquisition of scientific literature and information resources, the services provided to specialists in science and education and meeting their information needs, modified user preferences, behavior and reading styles, and the concept of libraries interaction in building and using their document resources. The authors focus on the science and education space and library and information infrastructure of Novosibirsk (big science and education center) and Western Siberia (Tomsk, Kemerovo Regions and Altay Kray) and, for comparison purposes, that of Chelyabinsk, the large industrial center in the Urals. The authors characterize in brief Chelyabinsk industrial, science and academic specialization and its library system (based on the monitoring findings), as well as the primary results of the study of library users information needs (survey based); they also examine the compliance of document resources of Chelyabinsk Regional Universal Scientific Library to the user information needs to update the conceptual model of library and information services offered to students and specialists.
Axiological aspects of transforming library profession in the aspect of professional education

Technological evolution of the civilization have been increasingly define the integrated global landscape and contribute to political, economic, social and ecological changes. Innovative high-speed communication systems and wide access to information consolidate the states, economies and businesses in the more sophisticated way one can envision. This global interdependence bears the risks of increasing social and economic inequity; in particular, due to instability and social unsustainability of the weaker economies. The authors discuss the problems of the library profession digitalization affecting the transformation of library education as the condition for preserving and developing human resources in librarianship. They insist that training librarians should comply with the requirements to modern libraries. They specify the key professional competences for library specialists of a new breed, and suggest prospective vectors of training at library departments.
How are financial institutions enabling online fraud? A developmental online financial fraud policy review

Purpose The criminalization of online financial fraud is examined by analyzing the existing literature, policies and state statutes within the context of the cybercrime ecosystem. Therefore, this paper aims to investigate online fraud policies within the USA and the prevalence of such incidents to explore the effectiveness of current fraud policies. Design/methodology/approach This examination explores policies related to online fraud within the USA by defining online financial fraud incidents within the context of the cybercrime ecosystem and analyzing such incidents with routine activities theory to emphasize the current legislative inadequacies with provisional policy recommendations. Findings This research suggests online financial fraud is not unanimously conceptualized among regulating or criminal institutions. Although federal regulators have governed financial institutions, federal institutions have failed to account for the capabilities of computer-mediated and technological device use (12 USC §1829). Research limitations/implications The limited research analyzing the effectiveness of guardianship that prevents or deters internet-mitigated or dependent financial fraud crimes. Practical implications Policy recommendations include but are not limited to mandating federal and privatized financial institutions to disclose all fraudulent activity to all stakeholders (e.g. customers and local and federal criminal justice agencies). Originality/value This paper provides an innovative approach using a criminological theory and policy framework to examine the prevalence of online fraud and the regulations enacted to counteract such violations.
PyNGHam: A Python library of the NGHam protocol

The PyNGHam library is a Python implementation of the original NGHam protocol library written in C by Jon Petter Skagmo (LA3JPA) (Skagmo, 2014).The NGHam protocol is an amateur radio protocol developed to be a modern version of the AX.25 protocol, with the main improvement of introducing a forward error correction (FEC) algorithm, which considerably improves the robustness of the communication link.One of the main applications of this protocol is on small satellite projects (specifically CubeSats), supporting a reliable communication link between the ground station and the satellite.This Python implementation enables easier integration and use of this protocol in computers and embedded devices.