Reviewerly/exHarmony huggingface dataset:
Academician B. V. Voitsekhovsky’s Book Collection

The study of personal libraries (book collections) is timely due to the implementation of the state policy to expose library collections and study regional book (documentary) heritage. One can note an increasing interest of book historians, specialists in the field of library and book study activities in private book collections of famous people, outstanding cultural and scientific figures, as well as personal libraries of scientists. A holistic look at the book collections of scientists through the prism of the scientist’s book environment complements and enriches the idea of the researcher’s creative laboratory, and provides additional material on the history of science and book culture. The scientist’s personal library acquires a memorial status and contributes to preservation of his memory. The article is dedicated to the memory of Bogdan Vyacheslavovich Voitsekhovsky, a famous scientist, who made a great contribution into the national science and economy, the main years of whose life were associated with the Siberian Branch (SB) of the USSR Academy of Sciences – SB RAS and Novosibirsk Academgorodok. The purpose of this article is to characterize the personal library of Academician B. V. Voitsekhovsky stored in the Central Scientific Library of UrB RAS. The work reveals the composition of the library, characterizes the owner’s records and inscripts; determines the status of the researcher’s book collection as a personal (memorial) library, reflecting the main directions of the scholar’s scientific and inventive activities. This will permit to add to the data on the history of Russian science, with the aim of popularization of historical and cultural heritage.
Wikipedia in the System of National Scientific Sovereignty: Literature Review

The object of the article is to review scientific articles, to evaluate Wikipedia in publications of native library science periodicals. Wikipedia is considered from the point of view of scientific activity management. The table is presented, which shows relative pros and cons of this electronic encyclopedia. Such disadvantages of this electronic resource as incomplete reflection of scientific subject areas, lack of professional categorization of electronic resources, influence from foreign copyright are noted, further direction of Wikipedia research in the system of national scientific sovereignty is determined as a subject-by-article analysis of the articles of this encyclopedia within the framework of certain scientific areas and, especially, in the field of library science and librarianship.
Popularization of Books from the Scientific and Technical Library Collections: Holding Intellectual Games and Book Illustrative Exhibitions

The Central Scientific and Technical Library for Construction and Architecture (CSTL C&amp;A) in Moscow is the leading library of the Russian Federation in these areas. The library stock contains more than 1 million items of specialized literature, including foreign books and periodicals, and its readers are scholars and researchers from construction and architectural organizations, professors from the higher education institutions, as well as students of specialized colleges and institutes. Starting from 2022, the library has been actively engaged in cultural and educational activities, introducing new formats of events and developing interaction with audiences of different age groups, attracting more and more students from specialized colleges and universities. The purpose of the article is to present the experience of CSTL C&amp;A in popularizing books from its collections by means of holding intellectual games and book illustrative exhibitions. The prerequisites for the introduction of a new format of educational activities into the cultural activities of the institution are considered, the course of the game is described and nuances of its preparation are analyzed. The e xperience presented by the author at the XXVII Annual Conference of the Russian Library Association “Library – the cultural code of Russia” can be useful for libraries with collections of limited usage, since it provides visitors with the opportunity to familiarize themselves with literature during the event.
Analysis of Directional Projection Characteristics Based on Convolution Neural Network Algorithm and Application of Cultural Relics Authenticity Identification

In recent years, with the rapid development of computer technology, intelligent algorithms have been widely used in all walks of life. More and more data processing methods have been applied in ancient ceramic identification. This paper introduces the identification algorithm of archival cultural relics based on a cascaded deep convolution neural network, the smooth identification algorithm of cultural relics image, the shape identification based on geometric constants, the theoretical basis of cultural relics identification, cultural relics are physical relics combined with human values in a certain sense. We should not only respect the value and significance of cultural relics themselves but also transmit the ideological connotation and spiritual significance given by predecessors to the present through protection and restoration.
User centric AI-based similar defect recommendation system

Software defect is unwanted but unavoidable side product of software development process. Managing the defect from origin to fix is long cycle and consume lot of effort. Resolving the defect is one of crucial task in the software defect lifecycle. There are many defects which have been fixed in past can give hint for resolving the new defect. However, it is very tedious task to keep track of all the defects in system and get a list of similar or technically duplicate defect. In this paper, we have explained our novel work of retrieving lexically and technically similar defect based on user’s focus. We have leveraged technique of Natural language processing (NLP) to analyze the historical defects in the system and build a model to retrieve similar defects. We have taken user-centric approach to develop the software. This work also opens various possibilities for further research and apply AI on software defects to improve the process of software development overall.
Federated Learning For IoT Devices in Smart Cities: A Particle Swarm Optimation-Based Approach

Smart cities witnessed significant advancements in integrating Internet of Things (IoT) wheareas the IoT-generated big data in smart cities raised concerns about privacy and security. This research paper addresses privacy and security concerns in utilizing IoT-generated big data in smart cities. It proposes a novel approach that combines federated learning (FL) with the particle swarm optimization (PSO) algorithm to enhance prediction accuracy while ensuring data privacy. The FL approach distributes the learning process across IoT devices preserving data privacy and leveraging collective intelligence. Extensive evaluation using privacy and security metrics tailored for smart cities which demonstrates the effectiveness of the proposed approach. It achieves high classification accuracy, maintains data privacy and mitigates privacy and security risks. This research contributes to knowledge on privacy and security in big data analysis by providing insights for smart cities and IoT-generated data. The findings have important implications for responsible development and use of IoT-generated data in smart cities.
Mobile Device Forensics, challenges and Blockchain-based Solution

Digital forensics is crucial in investigating cybercrimes, focusing on collecting, analyzing, and preserving digital evidence. With technology deeply integrated into our lives, digital forensics plays a vital role in uncovering evidence related to criminal activities, cybersecurity incidents, and fraud. However, mobile devices pose unique challenges due to their diverse nature and complex data storage structures. In this context, blockchain technology emerges as a promising solution. Blockchain, a decentralized and immutable digital ledger, provides a secure framework for storing and verifying digital transactions. Its features, such as tamper resistance and cryptographic security, align well with the needs of mobile device forensics. By leveraging Blockchain, digital evidence's integrity and authenticity can be enhanced by securely recording the chain of custody. Additionally, Blockchain addresses data privacy and security concerns by offering secure data storage and access controls. Furthermore, Blockchain enables secure collaboration among investigators through its distributed nature, allowing for shared and auditable repositories of forensic data. This paper explores the intersection of mobile device forensics and blockchain technology, highlighting the challenges faced and the potential benefits of utilizing Blockchain. It proposes a blockchain-based mobile cloud forensic framework to reliably record forensic findings and resolve issues in mobile cloud forensics. Integrating blockchain technology in mobile device forensics offers improved trustworthiness and reliability of digital evidence, overcomes privacy and security concerns, and enables efficient collaboration among forensic teams. This research aims to inspire further advancements in mobile device forensics, enhancing the capabilities of digital investigators and contributing to more effective cybercrime investigations. Finally, we proposed a blockchain-based mobile cloud forensic framework to reliably record the forensic findings and resolve the issues with mobile cloud forensics.
Movie character re-identification by agglomerative clustering of deep features

In this paper, we present a method for agglomerative clustering of characters in a video. Given a video edited with humans, we seek to identify each person with the character they represent. The proposed method is based on agglomerative clustering of deep face features, using first neighbour relations. First, the heads and faces of each person are detected and tracked in each shot of the video. Then, we create a feature vector of a tracked person in a shot. Finally, we compare the feature vectors and we use first neighbour relations to group them into distinct characters. The main contribution of this work is a person re-identification framework based on an agglomerative clustering method, and applied to edited videos with large scene variations.
Education Abstract: Optical Computing for AI Acceleration

Emerging AI applications such as large language models, vision transformers, graph convolutional networks, and other deep neural networks require massive computational resources. Contemporary electronic platforms are struggling to keep up with the demands of these emerging AI applications. In this paper, we discuss how optical computing can enable light-speed acceleration of AI workloads, and how cross-layer design can overcome the many challenges with it.
Education Abstract: Thermal Challenges and Mitigation in 3D DRAM

Novel memory technologies such as 3D-stacked DRAMs [2], offering substantial memory bandwidth, have been commercialised in an attempt to break the memory wall. High Bandwidth Memory (HBM) [2], a modern 3D DRAM standard providing bandwidth as high as 1 TBps, is used in modern GPUs and AI processors running memory-intensive workloads. The vertical integration leads to higher memory density, better performance due to short interconnect lengths, reduced power consumption, and smaller form factor. Figure 1 shows the architecture of 3D DRAM (HBM2E memory), consisting of two stacks (Stack 0 and Stack 1) with each stack comprising four DRAM dies stacked vertically. The DRAM dies are physically connected with fast through-silicon via (TSV) interconnect and micro-bumps. Each die supports two physical channels and each channel consists of several banks that are organised into rows and columns, similar to a conventional DRAM.
Work-in-Progress: NAPMAE: Generalized Data-Efficient Neural Architecture Predictor with Masked Autoencoder

Predictor-based Neural Architecture Search (NAS) offers a promising solution for enhancing the efficiency of traditional NAS methods. However, it is non-trivial to train the predictor with limited architecture evaluations for efficient NAS. In this paper, we propose a self-supervised transformer-based model that leverages unlabeled data to learn meaningful representations of neural architectures. After pre-training with a masked autoencoder, the predictor can adapt to various performance metrics estimation with limited supervised data. Experimental results demonstrate that our predictor requires less labeled data and achieves superior performance compared to existing predictors.
Design and implementation process of a pico-hydro power generation system for teaching and training

In Mexico the development of remote laboratories is incipient, in 2020 the National Council of Humanities, Sciences, and Technologies (CONAHCYT) created a network of virtual laboratories with nine of its public research centers in order to create a virtual space that enables the development of experimental and research activities in a distance modality. However, access to virtual laboratories is limited only to its members, and the platforms are still under development. With this motivation, this article presents a multi-institutional project for the design, development, and implementation of a pico-hydraulic system for small-scale power generation for teaching and training purposes with the aim of responding to the current need for distance or virtual teaching of practical knowledge due to the coronavirus disease (COVID‑19) caused by the SARS-CoV-2 virus. For the development of the system, technological innovation tools (QFD, TRIZ) were used, with which the design requirements were obtained considering the users (students and teachers) and the renewable energy-related competence of the curricula. Finally, the satisfaction of the users (students and teachers) with the use of the designed system shows the advantage of including it as part of the practical activities of the subjects to improve the development of power generation, transformation, and transmission system projects.
Work-in-Progress: Unishyper, A Reliable Rust-based Unikernel for Embedded Scenarios

Unishyper is a reliable Rust-based unikernel with good performance for embedded scenarios. It relies on Rust Features to enable customization at fine granularity. To achieve high reliability, Unishyper makes full use of the Rust language features to reduce memory safety bugs, ensure safe resource management, and achieve fault handling and recovery. Finally, Unishyper achieves high performance through safe multi-threading model as well as the single-privilege-level and single-address-space design.
Digital technologies for conducting dictations in Ukrainian

At present, modern information technologies are developing very rapidly. A lot of different software tools are being created to improve and simplify people's lives. This is especially true in the context of distance learning. In the context of online learning, the traditional form of conducting dictations needs to be transformed. Therefore, it is possible to propose automation of the process of conducting and checking dictations using software. The paper analyzes scientific research and publications of the current state of language synthesis technologies and text similarity testing. With the help of C# and the language synthesis libraries of Microsoft, Google, Amazon, software has been developed that allows the user to listen and type text, and then automatically check it with the initial sample. The conducted testing for different types of errors showed the possibility and expediency of development the system for conducting and checking dictations. Follow-up activities will focus on improving the effectiveness of the program. The use of such programs will help improve the organization of conducting and checking dictations during distance learning and self-training of students.
Genomic Data Analysis with Variant of Secure Multi-Party Computation Technique

The increasing availability of genomic data for research purposes necessitates innovative approaches to ensure privacy while facilitating collaborative analysis. This study explores the integration of a variant of Secure Multi-Party Computation (SMPC) techniques into genomic data analysis. The conventional challenges of sharing sensitive genetic information among multiple entities, such as research institutions or healthcare providers, are addressed by leveraging advanced cryptographic protocols. The research focuses on the development and implementation of a secure framework for collaborative genomic data analysis using an adapted SMPC variant. This variant is designed to efficiently handle the complexities of genetic data while ensuring robust privacy preservation. By encrypting individual genomic inputs and enabling computations without revealing the raw data, the proposed SMPC variant facilitates joint analyses, contributing to advancements in personalized medicine, disease research, and genetic epidemiology. The variants of SMPC, namely oblivious transfer protocol, is used, this allows the receiver to obtain one out of several pieces of information forwarded by the sender without revealing which one they obtained. It can be integrated into SMPC protocols for enhancing the privacy with less effort and cost. The proposed mechanism involves the validation of the SMPC variant through simulations using real-world genomic datasets and assessing its performance in terms of computational efficiency and privacy preservation. Results from experiments demonstrate the feasibility and effectiveness of the proposed technique in enabling secure multi-party genomic data analysis. This research contributes to the evolving landscape of privacy-preserving techniques in genomics, offering a promising avenue for collaborative research without compromising the confidentiality of sensitive genetic information.
Anti Forensik Voice Note Menggunakan Whatsapp Mod

Currently there is a social media application that provides additional features in the form of voice changers by utilizing the voice note feature of Whatsapp Mod which can blur the identity of the voice owner. In several articles, Whatsapp Mod claims that this application is an anti-banned application, so it is feared that Whatsapp Mod is an application that uses anti-forensic techniques in it. This study aims to prove whether voice modifiers using voice notes on Whatsapp Mod can be a form of anti-forensic activity which can later make it difficult for investigators to conduct investigations. This will be proven by the use of a special method in handling audio forensics in which there are four stages, namely Acquisition, Enhancement, Decoding, and Voice Recognition. Audio forensics analysis that was carried out between voice recordings modified using Whatsapp Mod voice notes and original voice recordings showed results that were not identical. This proves that the voice modifier using the Whatsapp Mod voice note is considered an effective anti-forensic tool.
Web-Based Information Systems for Political Parties and Community Organizations Using Extreme-Programming Methods

Badan Kesatuan Bangsa dan Politik is expected to be able to carry out its tasks and functions as a promoter of government affairs in the area of national unity and politics. However, there is still a frequent process of verification of registration files of association organizations and political parties, which is interrupted so that the registration process becomes obstructed. There are no available data on the existence and management of both social organizations and political parties. Delivery of accountability reports for political party funds is always late so the financial administration process is hampered, causing low budget absorption. Delivery of data desks of elections of heads of district or general elections is still manual, so late incoming reports cause information to the leadership to be unupdated. The research is aimed at building a web-based information system that provides registration, verification and filing features for political parties and association organizations. The method used is Extreme Programming with phases of planning, design, encoding, and testing. The results of this study resulted in a reliable and easy-to-use information system and showed 100% accuracy of the black box test results.
Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore)

This research aims to analyze reviews in understanding the opinions and emotions expressed by users regarding the Google Translate application on the Google Play Store using sentiment analysis. By using the Support Vector Machine (SVM) method in sentiment analysis of the Google Translate application, to get a better understanding of how users respond to the application. This can help developers improve the application experience, responding better to user needs and preferences. This user review analysis uses the SVM method. The measuring tool in this research uses, firstly, the Indonesian Lexicon as a tool to obtain positive and negative results, secondly, term frequency–inverse document frequency (tf-idf) as a support for the results of the evaluation. Google Translate app has a dataset of 1000 user reviews collected from Google play store. The results of analysis using Support Vector Machine produced 95% accuracy, with "no" as the result of the most positive and negative reviews out of 1580 reviews.
Attacks on Rollups

A rollup is a network, implemented via smart contracts on a blockchain, that aims to scale that slow but general purpose blockchain. The rollup executes transactions and posts the resulting state root, along with the transaction data, to a blockchain they are built on. As a result, the state root of the rollup network is always recorded on the underlying blockchain. The underlying blockchain is used to derive the state of the rollup itself, meaning that the rollup state cannot be changed arbitrarily or would be easily detected (subject to how its state is updated and recorded on the underlying blockchain). In turn, the rollup inherits some security from its underlying blockchain --- but the rollup network itself is not immune to direct attacks. Some attacks are like other network-level attacks (e.g., denial-of-service attacks) while others are a result of the rollup's connection to its underlying blockchain (e.g., re-organization attacks). In this work, we collect a list of known attacks on rollups and illustrate their impact.
Can Smart Contracts Become Smart?

Despite the growth in the number of decentralized applications (DApps) supported by the Ethereum blockchain, we can observe the narrow scope of these DApps, concentrated within the fintech and games areas. A cause for the lack of range of DApps lies in the fees for transactions sent to backing smart contracts. While consistent steps have been made to overcome cost efficiency problems, introducing rollups as a secondary layer solution, intertwined accessibility and security drawbacks still persist. Measures addressing some of these issues like account abstraction were independently proposed. These solutions bring changes in transaction handling that often exceed the scope of smart contracts, where the core of DApp logic resides. Integrating such measures often requires the use of new frameworks and understanding the changes in the transaction flow, which can prove challenging to a DApp developer. A question is whether the current landscape of solutions proposed for increasing usability is capable of producing a consistent impact on DApp scope trends. In this position paper we try to answer this, raising also the matter of impact on DApp engineering.