Reviewerly/exHarmony huggingface dataset:
The All-Russian Library Congress 2022 as a Factor of Professional Interaction and Development

The All-Russian Library Congress — the 26th Annual Conference of the Russian Library Association (RLA) — took place on May 16—20, in Nizhny Novgorod, the Library Capital of Russia 2022. The Congress was organized at the support of the Ministry of Culture of the Russian Federation and the Government of the Nizhny Novgorod Region. Its topic is “Libraries and cultural diversity”. The event was held in face-to-face and remote formats. In total, more than 1300 experts took part in the Congress (about 900 in person, 400 remotely) from 67 regions of Russia and 12 foreign countries. More than 200 thousand viewings were collected by open broadcasts of two plenary sessions in VKontakte. The professional program of the Congress consisted of two plenary sessions, 56 breakout sessions (518 reports), special events and master classes. The work was carried out at 12 sites. Traditional training schools of acquisitions librarian, library blogger, cataloguer and restorer took place. Special event was dedicated to the 70th anniversary of the journal “Bibliotekovedenie” (Library Science). The 22nd Exhibition of Publishing Products, New Information Technologies, Goods and Services traditionally accompanied the Congress. The participants of the Congress discussed the first results of implementation of the “Strategy for the Development of Librarianship in the Russian Federation for the period up to 2030”, which determined the work of libraries. The RLA actively contributes to the preparation of the Professional Standard “Specialist in Library and Information Activities”, which is currently being approved by the Ministry of Labour and Social Protection of the Russian Federation. The event focused on current problems of work in libraries, including their digital transformation; educational programs within the framework of the Federal Project “Creative People”; the creation of model municipal libraries of new generation, their further development, etc. The Congress made decisions on the development of methodical recommendations for online training of librarians, methodological materials on library statistics; on the publication of collection of recommendations on conservation, restoration, storage of large-format documents of various types; on the preparation of application for the interdepartmental project on the development of National model of related library data based on the IFLA LRM conceptual model, etc. The Congress announced the results of the RLA elections for 2022—2025. At the end of the event, Murmansk was announced the Library Capital of Russia 2023.
Secure enrollment token delivery for Zero Trust networks using blockchain

Zero Trust Networking (ZTN) is a security model where no entity in a network infrastructure is trusted. The first bastion of security for achieving ZTN is to have strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ the use of JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A JWT can be intercepted by a third party and the information of the payload can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT, and the blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides the enrollment functionality, while our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.
Base Systems for Docker Containers - Security Analysis

Docker based containerization is currently one of the most popular methods of delivering and creating of a software. It allow multiple teams to standarize their work, but also to reduce disadvantages of virtual machines that can impact performance and usability. This work concerns security of base systems, focusing on distroless. Base container images are one of the critical parts of a cloud environment. The results of analysis presented here allow for independent and objective comparison of advantages and disadvantages of various containers' base systems which are widely used in orchestration platforms such as Kubernetes and OpenShift.
Vectorizing Images of Any Size

We propose a novel algorithm for converting quantized raster color images to resolution-independent scalable vector graphics (SVG). Starting from the discontinuity set of the input image, the algorithm connects the pieces of curves separating two constant regions to reconstruct the apparent contours of objects and interpret T-junctions and saddle points. This structure is depixelized by curve affine shortening, which requires maintaining the topology of the discontinuity set during filtering. The resulting Hierarchical Curve-based Vectorization (HCV) algorithm compares favorably to several state-of-art vectorization algorithms and software for color-quantized photos and pixel art <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Improving Biomedical ReQA With Consistent NLI-Transfer and Post-Whitening

Retrieval Question Answering (ReQA) is an essential mechanism of information sharing which aims to find the answer to a posed question from large-scale candidates. Currently, the most efficient solution is Dual-Encoder which has shown great potential in the general domain, while it still lacks research on biomedical ReQA. Obtaining a robust Dual-Encoder from biomedical datasets is challenging, as scarce annotated data are not enough to sufficiently train the model which results in over-fitting problems. In this work, we first build ReQA BioASQ datasets for retrieving answers to biomedical questions, which can facilitate the corresponding research. On that basis, we propose a framework to solve the over-fitting issue for robust biomedical answer retrieval. Under the proposed framework, we first pre-train Dual-Encoder on natural language inference (NLI) task before the training on biomedical ReQA, where we appropriately change the pre-training objective of NLI to improve the consistency between NLI and biomedical ReQA, which significantly improve the transferability. Moreover, to eliminate the feature redundancies of Dual-Encoder, consistent post-whitening is proposed to conduct decorrelation on the training and trained sentence embeddings. With extensive experiments, the proposed framework achieves promising results and exhibits significant improvement compared with various competitive methods.
ORCID coverage in research institutions—Readiness for partially automated research reporting

Reporting and presentation of research activities and outcome for research institutions in official, normative standards are more and more important and are the basis to comply with reporting duties. Institutional Current Research Information Systems (CRIS) serve as important databases or data sources for external and internal reporting, which should ideally be connected with interfaces to the operational systems for automated loading routines to extract relevant research information. This investigation evaluates whether (semi-) automated reporting using open, public research information collected via persistent identifiers (PIDs) for organizations (ROR), persons (ORCID), and research outputs (DOI) can reduce effort of reporting. For this purpose, internally maintained lists of persons to whom an ORCID record could be assigned (internal ORCID person lists) of two different German research institutions—Osnabrück University (UOS) and the non-university research institution TIB—Leibniz Information Center for Science and Technology Hannover—are used to investigate ORCID coverage in external open data sources like FREYA PID Graph (developed by DataCite), OpenAlex and ORCID itself. Additionally, for UOS a detailed analysis of discipline specific ORCID coverage is conducted. Substantial differences can be found for ORCID coverage between both institutions and for each institution regarding the various external data sources. A more detailed analysis of ORCID distribution by discipline for UOS reveals disparities by research area—internally and in external data sources. Recommendations for future actions can be derived from our results: Although the current level of coverage of researcher IDs which could automatically be mapped is still not sufficient to use persistent identifier-based extraction for standard (automated) reporting, it can already be a valuable input for institutional CRIS.
Semantic relatedness in DBpedia: A comparative and experimental assessment

The evaluation of semantic relatedness between web resources is still an open challenge. This paper focuses on knowledge-based methods that provide an alternative to corpus-based approaches and generally rely on the availability of knowledge graphs. In particular, we selected 10 methods from the existing literature, organized by adjacent resources, triple patterns, and triple weights-based methods. They were implemented and evaluated using DBpedia as a reference RDF knowledge graph. Since DBpedia is under constant development, the experimental results of these proposals are not comparable in the literature. For this reason, in this work, these methods were experimented with the same DBpedia release and 14 well-known golden datasets at once. Based on this experimentation, the weighting of RDF triples combined with the evaluation of all the directed paths linking the compared resources is the best strategy for computing semantic relatedness in DBpedia.
SPOT: Secure and Privacy-Preserving PrOximiTy Protocol for e-Healthcare Systems

This paper introduces SPOT, a Secure and Privacy-preserving prOximity based protocol for e-healthcare systems. It relies on a distributed proxy-based approach to preserve users’ privacy and a semi-trusted computing server to ensure data consistency and integrity. The proposed protocol ensures a balance between security, privacy and scalability. As far as we know, in terms of security, SPOT is the first one to prevent malicious users from colluding and generating false positives. In terms of privacy, SPOT supports both anonymity of users being in proximity of infected people and unlinkability of contact information issued by the same user. A concrete construction based on structure-preserving signatures and NIWI proofs is proposed and a detailed security and privacy analysis proves that SPOT is secure under standard assumptions. In terms of scalability, SPOT’s procedures and algorithms are implemented to show its efficiency and practical usability with acceptable computation and communication overhead.
Towards practical Quantum Credit Risk Analysis

Abstract In recent years a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced [1]. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio’s asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model’s inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.
Optimizing Deep Transformers for Chinese-Thai Low-Resource Translation

In this paper, we study the use of deep Transformer translation model for the CCMT 2022 Chinese $$\leftrightarrow $$ Thai low-resource machine translation task. We first explore the experiment settings (including the number of BPE merge operations, dropout probability, embedding size, etc.) for the low-resource scenario with the 6-layer Transformer. Considering that increasing the number of layers also increases the regularization on new model parameters (dropout modules are also introduced when using more layers), we adopt the highest performance setting but increase the depth of the Transformer to 24 layers to obtain improved translation quality. Our work obtains the SOTA performance in the Chinese-to-Thai translation in the constrained evaluation.
Engineering Education Roadmap of the Future Trend of Basic Metaverse based on VR with cooperation between the government and the private sector

This article presents an educational roadmap of future trends of Metaverse in VR-based by collaborating between the School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL) with iMAKE company to make a part-time learning plan. The objectives were to measure the achievement and evaluate satisfaction with the development of part-time learning skills in technology on the topic “Basic Metaverse based on VR”. The sample group was students in a double-degree bachelor's degree program (Dual Degree) between the School of Engineering and the Faculty of Science, KMITL: Bachelor of Engineering (IoT System and Information Engineering) and Bachelor of Science (Industrial Physics) for 16 students by selecting a specific sample group, the engineering education program has a systematic process. The results showed that the achievement of part-time learning skills development in technology on the topic “Basic Metaverse based on VR” higher than the set criteria 74 %, the overall satisfaction is at a very good level, the mean satisfaction was 4.636 and the sample standard deviation was 0.39.
Chocolate-chip cookie-like pumice from the 2021 Fukutoku-Oka-no-Ba eruption: views from SNS-related geology

Pumice ejected from the Fukutoku-Oka-no-Ba (FOB) eruption of August 2021 arrived at the Nansei Islands after 2-months drifting, and subsequently arrived at Kanto area to the east and Taiwan, Philippines, and Thailand to the west. Deposition of huge amount of the drift pumice, most of which had a chocolate-chip cookie resemblance in appearance, is a drastic event and thus attracted SNS account holders, so that the arrival timeline can be traced via their daily posts. Gathering such huge amount of information from the internet can be a powerful tool to investigate and predict a geologic phenomenon that affects wide area in a short time scale. We introduce the drift pumice from FOB with respect to their deposition style, petrographic characteristics, and comparison with the previous drift pumice phenomena observed in Japan. Their impact to the Japanese society was well reflected to the buzz on Twitter. Finally, we discuss how the scientist should behave on the internet to provide correct information in an effective way.
CADIA: A Success Story in Breast Cancer Diagnosis with Digital Pathology and AI Image Analysis

The rise of Digital Pathology during the past few years is leading to the digitisation of the pathology field; the widespread use of Whole Slide Images (WSI) and the digitisation of the diagnostic process have allowed the introduction of AI-based methods to aid some parts of the process. In this framework, the CADIA project was raised in response to the Galician healthcare system digitisation needs. CADIA aims to develop an AI-based medical image analysis solution for the diagnosis of several pathologies and its demonstration on breast cancer diagnosis from WSIs. In this paper, we describe the development of CADIA, from the capture of requirements to the deployment and integration of the solution into the healthcare system infrastructure. We describe the opportunities, challenges and lessons learned during the project development.
Blockchain Architectures, the Potential of Web3 for Decentralized Participatory Architecture - Collaborative objects on the Blockchain

This paper explores the potential of blockchain technology and the Web3 for a decentralized participatory architecture. In this context, the polyvalent capacity of a block in a blockchain is at the center of this investigation. Blockchain innovations in cryptography and efficient block validation and creation systems have led to autonomous blocks that act as decentralized, transparent, and secure Web3 assets. Following our previous research on collaborative objects that enable real-time participatory design activities, a case study project H=N BLOCK+A is developed that implements blockchain principles at both the conceptual and infrastructural levels. At the conceptual level, architectural blocks are speculated and applied as autonomous and decentralized Web3 assets, i.e., a decentralized kit of parts/blocks/NFTs/applications that can form a crazy patchwork of heterogeneous compatible blocks. At the infrastructural level, an existing sustainable blockchain is facilitated to embed a decentralized design methodology that enables real-time participatory co-creation of a collective architectural form.
Improved Scheme of Practical Byzantine Fault Tolerance Algorithm based on Voting Mechanism

With the continuous development of cryptocurrencies, blockchain technology has received extensive attention, and the performance of blockchain systems is constrained by consensus algorithms. In view of the problems of high communication complexity and complex consensus process in PBFT algorithm, this paper An improvement scheme of the PBFT algorithm based on the voting mechanism is proposed. Before running the consensus protocol, the nodes in the network vote to generate a set of consensus nodes, and the remaining nodes are used as accounting nodes. At the same time, the consensus process of the traditional PBFT consensus algorithm is optimized. In the improvement scheme, the master node sends a Commit message to the consensus node and the accounting node to enter the Commit state. When the master node down, the consensus node with the highest number of votes is selected as the new master node, which reduces the probability of malicious nodes becoming master nodes and improves the system stability. The experimental results show that the improved scheme proposed in this paper reduces the communication overhead in the consensus process to a certain extent, also improves the consensus delay and throughput.
Implementation of SM4 block cipher on CUDA GPU and its analysis

SM4 is a symmetric key algorithm developed by the China National Cryptographic Authority. In this paper, the parallel implementation of SM4 block cipher commonly used in China was performed on GPU. The SM4 block cipher has an implementation that uses an 8-bit Sbox table and an implementation that uses a 32-bit T-table. Measuring the performance of each of the two table implementations, the T-table implementation performed approximately 0.75× worse than the Sbox table implementation. Additionally, Implemented SM4 to use shared memory for better performance. The result is a performance improvement of approximately 1.06× ~ 1.19× when using shared memory in the Sbox table implementation.
Security with JWT

Security is one of the most important aspects of application development. There are so many things that can go wrong so fast with security. A rapidly evolving domain, there are constantly new threats, exploits, and breaches happening at all times. From the largest public-facing applications to the smallest, every application is at risk of security breaches.
P-PseudoLabel: Enhanced Pseudo-Labeling Framework With Network Pruning in Semi-Supervised Learning

Semi-supervised learning (SSL) methods for classification tasks exhibit a significant performance gain because they combine regularization and pseudo-labeling methods. General pseudo-labeling methods only depend on the model’s prediction when assigning pseudo-labels, but this approach often leads to the generation of incorrect pseudo-labels, due to the network being biased toward easy classes or to the presence of confusing samples in the training set, which further decreases model performance. To address this issue, we propose a novel pseudo-labeling framework that dramatically reduces the ambiguity of pseudo-labels for confusing samples in SSL. We operate our method, called Pruning for Pseudo-Label (P-PseudoLabel), using the Easy-to-Forget (ETF) Sample Finder, which compares the outputs of the model and the pruned model to identify confusing samples. Next, we perform negative learning using the confusing samples to decrease the risk of providing incorrect information and to improve performance. Our method achieves better performance than those of recent state-of-the-art SSL methods on the CIFAR-10, CIFAR-100, and Mini-ImageNet datasets, and is on par with the state-of-the-art methods on SVHN and STL-10.
Rethinking Video Rain Streak Removal: A New Synthesis Model and a Deraining Network with Video Rain Prior

Existing video synthetic models and deraining methods are mostly built on a simplified video rain model assuming that rain streak layers of different video frames are uncorrelated, thereby producing degraded performance on real-world rainy videos. To address this problem, we devise a new video rain sy nthesis model with the concept of rain streak motions to enforce a consistency of rain layers between video frames, thereby generating more realistic rainy video data for network training, and then develop a recurrent disentangled deraining network (RDD-Net) based on our video rain model for boosting video deraining. More specifically, taking adjacent frames of a key frame as the input, our RDD-Net recurrently aggregates each adjacent frame and the key frame by a fusion module, and then devise a disentangle model to decouple the fused features by predicting not only a clean background layer and a rain layer, but also a rain streak motion layer. After that, we develop three attentive recovery modules to combine the decoupled features from different adjacent frames for predicting the final derained result of the key frame. Experiments on three widely-used benchmark datasets and a collected dataset, as well as real-world rainy videos show that our RDD-Net quantitatively and qualitatively outperforms state-of-the-art deraining methods. Our code, our dataset, and our results on four datasets are released at https://github.com/wangshauitj/RDD-Net .