Reviewerly/exHarmony huggingface dataset:
Proposal of a Modified Hash Algorithm to Increase Blockchain Security

Blockchain technology provides a novel method for storing information, performing tasks and functions, and establishing trust between participating nodes. Despite the fact that blockchain technology has received significant attention in a variety of application contexts over the last few years, the issue of privacy and security remains the primary focus of blockchain discussions. In Blockchain, the cryptographic hash function is used to secure message blocks and connect blocks in a chain. In summary, each block in the blockchain contains its own block hash as well as a hash of the block before it. This enables them to create a crypto-graphically secure linear chain of blocks. With blockchain technology, there is a new way to store information, carry out tasks and functions, and build trust between the nodes that are taking part. Hash algorithms can be used to make blockchain integration safe, and many of them can solve problems with data integrity and security that arise when using blockchain technology. But they can also have problems with time, lack of resources, and using too much memory. In this study, an algorithm to create a hash based on a chaos key using logistic maps and lightweight cryptography is proposed (hamming bird 2). Hash outputs are tested in terms of time and hardness to guess using cryptanalysis tools. The proposed algorithm has been tested using cryptanalysis site based brute force attacks. The result show that the modified hash cannot identified by site using all cryptanalysis setting that prove the strong of hash generated against attacks.
Explicit Aspect Annotation via Transfer and Active Learning

We present a semi-supervised annotation process for identifying and labelling explicit aspects of an initially unlabelled corpus. Firstly, we employ cross-domain learning to pre-annotate the initial data, deliberately excluding domain-related input features to ensure effective learning transfer. Then, we apply an active learning strategy to enhance the pre-annotation performance and enrich the learning data. We adjust the strategy to sequence labeling and address class imbalance. We evaluate this process using two unlabelled datasets in French, consisting of user opinions on beauty products and electronic devices, respectively. The results show an improved F1-score achieved by increasing and correcting 30% of the training dataset.
Brightness Conversion of Color Images on Equi-Hue Plane by Using Color Vectors

Vision is particularly important among the senses that humans perceive, and among them, the sense of color is called color vision. Since color blindness is characterized by color vision, color discrimination may be difficult and may interfere with daily life. Therefore, image color conversion is performed to facilitate color discrimination. However, since general color conversion does not preserve the hue of an image, the resulting image is unnatural for people with general color vision. In this research, brightness correction is performed on the equi-hue plane in the RGB space. Saturation is corrected according to the position of the pixel on the constant hue plane. This enables natural color conversion considering the color distance between pixels while preserving the hue of the image. We verify the effectiveness of the proposed method through various experiments.
Evaluation of Deep Learning Models for Continuous Authentication Using Behavioral Biometrics

The traditional method to authenticate users on mobile devices or applications requires usernames and passwords. This approach authenticates the user only at the entry point of an application without providing continuous authentication during the whole session. This paper explores the use of behavioral biometrics, which involves tracking the unique movements of a user while interacting with a device, for continuous authentication on a mobile banking application. As a methodology, we use binary classification and explore the performance of deep learning algorithms. A dataset is collected from 45 participants using a mobile banking application in Turkey. We train four different types of deep architectures, including Multilayer Perceptron (MLP), LSTM, bi-directional LSTM, and convolutional LSTM. The dataset includes data from both touch screens and motion sensors. The results of the experiments reveal that MLP and the convolutional-LSTM algorithms achieve the best performance on raw data from both motion sensors and touch screens. Accuracy rates are over 99.85%, and FAR, FRR, and EER are below 0.5%.
Tracking Dialog States in Goal-Oriented Dialogues using a BERT-Based Siamese Network

A dialogue state tracker is a component in a task-oriented dialogue system that monitors the current state of a conversation and gives information about its context and history to other system components. The dynamic and open-ended character of human interactions is one of the primary obstacles in dialogue state monitoring, necessitating robust and adaptable models to keep up with the quick context changes. Recently, numerous deep learning-based algorithms have been developed for this purpose. Still, these models are typically heavily-engineered and conceptually sophisticated, making them challenging to deploy, debug, and maintain in a production environment. To overcome these challenges, we offer the BERT-SIAM-DST model, a unique way to dialogue state monitoring employing a Siamese network with BERT as the base network. This model uses the robust representation capabilities of BERT and the ability of Siamese networks to record correlations between inputs to make accurate predictions regarding the current state of the discussion. In addition, the number of parameters does not increase proportionally with the size of the ontology, and the model is adaptable to alterations in the domain ontology. We test the performance of the BERT-SIAM-DST model on the standard WoZ 2.0 dataset of annotated dialogues and compare it to other approaches. Compared to numerous baseline models, the BERT-SIAM-DST model is effective at tracking the state of discussions, demonstrating the promise of BERT-based Siamese networks for this purpose.
Heuristic Optimizations of Boolean Circuits with Application in Attribute-Based Encryption

We propose a method of optimizing monotone Boolean circuits by re-writing them in a simpler, equivalent form. We use in total six heuristics: Hill Climbing, Simulated Annealing, and variations of them, which operate on the representation of the circuit as a logical formula. Our main motivation is to improve performance in Attribute-Based Encryption (ABE) schemes for Boolean circuits. Therefore, we show how our heuristics improve ABE systems for Boolean circuits. Also, we run tests to evaluate the performance of our heuristics, both as a standalone optimization for Boolean circuits and also inside ABE systems.
Assessment of benefits and disadvantages of implementing Cloud-specific solutions in Polish companies on the example of ERP systems

The rapid development of Information Technology has led to the emergence of Cloud Computing as a vital tool for businesses seeking cost-effective and efficient IT solutions. This study aims to investigate the perception, advantages, disadvantages, and future predictions related to Cloud Computing among Polish companies, particularly focusing on the implementation of Enterprise Resource Planning (ERP) systems. The research question addressed is: how is the Cloud perceived by Polish companies? A survey was conducted among Small and Medium-sized Enterprises (SMEs) utilizing Cloud-based solutions in their everyday operations. The findings indicate that the primary goals for implementing Cloud Computing services were limiting operational costs, improving work efficiency, and achieving better accessibility. Technological, organizational, economic, and social benefits were identified, with the most important being higher efficiency of IT solutions, ease of utilization of IT resources, reduction of IT-related investment risk, and the development of knowledge and stimulation of employee activity. The study also revealed potential future trends, such as IT-oriented standardization, cost reduction, and improved quality of IT technologies. However, some concerns related to security and the impact of Cloud Computing on the environment were also highlighted.
Improving TCPSO Solution Search Performance Using Gaussian Process Regression

Particle Swarm Optimization tends to converge prematurely to local solutions. To improve such a problem, TCPSO (Two-swarm Cooperative PSO) has been proposed. TCPSO employs a group of slave particles for intensive solution search and a group of master particles for global solution search. If the solution search process of TCPSO results in a small update range of solutions, the approximate shape of the function can be estimated based on known input data. This estimation can be done using Gaussian process regression to model the distribution with mean and variance, and improves the solution search process of TCPSO.
A Large and Diverse Arabic Corpus for Language Modeling

Large Language Models (LLMs) have ushered in a major paradigm shift in Natural Language Processing (NLP), where large pre-trained Language models (LMs) have become a fundamental component of most NLP tasks. These models are intelligent enough to find relevant and meaningful representations of a language without any supervision. They are used to fine-tune typical NLP tasks with substantially higher precision than conventional shallow learning techniques. However, training these models requires a massively large corpus that adequately represents a language. Due to the availability of enormous corpora, English LLMs typically perform better than their counterparts. This effort focuses on the design and development of a large Arabic corpus. The corpus comprises over 500 GB of Arabic cleaned text, intended to improve cross-domain knowledge and downstream generalization capability of LLMs. The corpus was employed in the training of a large Arabic LLM. In order to assess the efficacy of the LLM, a variety of typical NLP tasks were fine-tuned. The fine-tuned tasks exhibited a significant boost in accuracy ranging between 4.5 and 8.5%, when compared to those downstreamed from multi-lingual BERT (mBERT). To the best of our knowledge, this is currently the largest clean and diverse Arabic corpus ever assembled.
Escrow and Pairing Free CP-ABE Scheme with Forward and Backward Secrecy for Healthcare Internet of Things

Healthcare Internet of Things (HIoT) systems are a step forward in improving the efficiency and quality of patients’ vital information. The HIoT system collects, encrypts, and outsources the patients’ sensitive data over the cloud server for storage and sharing purposes. Pairing-free ciphertext-policy attribute-based encryption (PF-CPABE) is the prominent solution to provide lightweight and fine-grained access control over shared encrypted healthcare data. However, the existing PF-CPABE constructions suffer from one or more limitations, including a key escrow problem and inefficient user revocation while achieving backward and forward secrecy. In this paper, we propose an Escrow and Pairing Free CP-ABE Scheme (EPFCS) with forward and backward secrecy for HIoT. The proposed EPFCS ensures a key escrow-free HIoT system, facilitates the secure distribution of users’ secret keys without using the secure channel in PF-CPABE, and enables efficient user revocation while achieving forward and backward secrecy even in dynamic healthcare scenarios. The security analysis confirms that the proposed EPFCS ensures confidentiality, key escrow freeness, forward and backward secrecy, and resistance against key collusion attacks. Furthermore, the performance analysis demonstrates that the proposed EPFCS is more effective and efficient in aspects of communication and computation costs than the existing schemes.
BCTPV-NIZK: Publicly-Verifiable Non-interactive Zero-Knowledge Proof System from Minimal Blockchain Assumptions

Non-interactive publicly-verifiable zero-knowledge proofs (PV-NIZKs) are essential to modern cryptography. However, historically, literature has used dependencies like the need for absolute trust in a third party or the existence of a truly random oracle to construct such proofs. Recently, the focus has shifted to exploiting the decentralized trust foundation of a generic Proof-of-Stake (PoS) blockchain to build such proofs. However, such proposals make unrealistic assumptions on the blockchain itself: static adversaries, assumptions on the future behavior of the players, and the existence of first-time miners in any given sequence of n honestly mined blocks. While aiding good proofs, such assumptions undermine the practical adaption of such proof systems. This paper introduces a blockchain-based PV-NIZK (BCTPV-NIZK) system from standard blockchain assumptions. It assumes the existence of a publicly-verifiable, secret randomness generation scheme (widely studied in literature and requires no further assumptions on trust).
Unlocking the potential of ICT innovation in veterinary healthcare: The pathway to improve practices and business model

The field of animal healthcare is in a constant state of evolution, with the veterinary industry experiencing significant transformations due to the introduction of novel technologies and innovative advancements. This article aims to explore the importance of innovation and ICT in business models of veterinary practices. The article draws on existing literature on innovation and practical experiences that identifies key challenges and opportunities in animal healthcare business models. In the article the Author attempted to answer three research questions: What are the potential benefits of ICT innovation in veterinary practices in terms of efficiency, quality of care, and customer satisfaction? How can ICT innovations improve the overall business model and communication within veterinary clinics? What specific ICT tools and technologies are available and suitable for integration into veterinary practices? The aim of the study was to analyze the business model of veterinary clinics and identify tools and ICT innovations to improve the functioning of veterinary practices. For this purpose, the Author conducted relevant literature reviews, inductive and deducting reasoning, analysis with the use of business model canvas and 102 in-depth interviews (IDI). Main results from the conducted study shows, that by leveraging technology in various areas, such as value propositions, customer relationships, channels, key partners, key activities, key resources, cost structure, and revenue streams, veterinary clinics can enhance their operations and provide better services to their customers. The results of the study may be useful to other researchers as well as executives in the animal healthcare facilities.
Word Sense Disambiguation Corpus Development for Romanian Language

Research in the area of ​​the interconnection of lexical resources represents a real challenge, because it addresses the difficult problem of semantic understanding and, more precisely, the disambiguation of the meaning of the words - Word Sense Disambiguation (WSD). In the current and prospective context of the information society, the existence of the digital format of the fundamental works of a national culture is strictly necessary. It is a topical issue throughout the world of creating a representative corpus of a language accessible through the Internet, the corpus being a concrete, clear picture of the use of that language. In this study we will describe the development of a Romanian language GOLD corpus, related to the multiple meanings existing for various words. We propose a corpus annotation standard, based on three lexical resources as follows: the Thesaurus Dictionary of the Romanian Language in electronic format (eDTLR), from which we extracted a list of words with multiple meanings; from the Reference Corpus for Contemporary Romanian Language (CoRoLa) we extracted contexts in which these words were founded and from the the Romanian WordNet (RoWN) resource, we took into account the sense meaning of the word from the corpus context.
SoK: Digital Signatures and Taproot Transactions in Bitcoin

Bitcoin has emerged as one of the most disruptive innovations since the advent of the internet. Its core principle of decentralization has not only revolutionized the way transactions are made but also paved the way for development of an entirely new blockchain industry. Its security is achieved through the implementation of cryptographic constructs based on elliptic curve cryptography. In this paper, we delve into use of digital signatures in Bitcoin. We provide an overview of Elliptic Curve Digital Signature Algorithm (ECDSA) and the recently adopted Schnorr signatures. Furthermore, we discuss Taproot, a soft fork introduced in Bitcoin, which enhances Bitcoin’s versatility for complex applications. With Taproot, multiparty transactions can be designed with greater privacy for all parties involved, as well as for the underlying contracts. This paper offers a comprehensive review of both the ECDSA and Schnorr signature schemes, shedding light on their scripting capabilities within the Bitcoin ecosystem.
An approach for ontology-based research and recommendation on systems engineering projects

Recommendation systems (RS) have dramatically evolved these past few years, generating more and more accurate results and developing innovative filtering methods. A huge breakthrough was the integration of ontologies into their recommendation process. It integrated the domain's knowledge into its reasoning process, thus overcoming the limitations of the conventional RS. The main purpose of this paper is to explore the application of ontology-based RS within critical infrastructure projects implementing the systems engineering or model-based engineering approach.
Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs.

Heterogeneous graphs (HG) are an effective way of abstracting complex systems, including social, biological, and economic systems. However, modeling these graphs is challenging due to their high dimensionality, sparsity, and heterogeneity. Traditional approaches designed for homogeneous graphs struggle to handle the diverse types of entities and relationships present in HG, leading to a loss of information and potentially inaccurate embeddings. To address these challenges, we introduce a novel method, Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs, that leverages the power of triplets of (entity, relation, entity) to accurately represent the various types of relationships among nodes and links. Trans-trip effectively captures the rich semantics embedded in HGs, overcoming the challenges of global coherence and entity projection. By leveraging the flexibility and interpretability of triplets, our method can handle the multi-types nodes/links present in HG and can capture complex higher-order structures. We demonstrate the effectiveness of our proposed method on several benchmark datasets, showing that it outperforms existing embedding methods. Trans-trip provides a more accurate and interpretable representation of HG, which can be used across various fields, such as biology, social networks, and e-commerce.
Blockchain Technology Awareness Among Managers of Large Enterprise

Digital transformation presents several challenges to businesses in today's world, and one such challenge is the ability to effectively adopt innovative technologies that allow for rapid access to information while maintaining the highest standards of security. One specific IT solution that meets these requirements is blockchain technology. The objective of this article is to examine the level of awareness about blockchain technology among managers and professionals from large companies in Poland's Silesian province, as well as to observe related thematic issues in blockchain technology. The research revealed that the managers' awareness of blockchain technology was comparable across all surveyed companies, regardless of their economic sector. The only significant statistical correlations were linked to whether the managers and professionals belonged to the digital or non-digital generation.
A Survey on Security Threats and Mitigation Strategies for NoSQL Databases

With the advent of IoT devices, cloud computing, accessible mobile devices, social networking sites and other advancements in technology a huge amount of data is being generated. NoSQL databases were evolved to provide a better storage capability, scalability, improved performance for read and write operations for the enormous data generated by various systems which are continuously being read and written by large number of users. Initially it was believed to provide better security in comparison to the traditional relational database management system (RDBMS), but in due course of time NoSQL databases were also exposed to various security breaches and vulnerabilities. In this paper we studied in detail the various security vulnerabilities of MongoDB, along with the need to secure the interfaces being used to access MongoDB. We analyzed the prevention and mitigation strategies for the same. The study of this paper can be used as a best practice to secure NoSQL or MongoDB database. It suggests how to secure the queries and all the interfaces that are being used to access the database.
An Efficient Two-Party ECDSA Scheme for Cryptocurrencies

Threshold signatures have emerged as a promising solution to secure cryptocurrencies. While some signature algorithms like Schnorr, BLS, EdDSA are threshold-friendly, the structure of ECDSA makes it challenging to construct such schemes. As such the known threshold ECDSA schemes use complex zero-knowledge proofs. However, these impact their performance negatively. Further, these schemes have attempted to achieve efficiency in signature computation part while accepting complexity in the key generation. To be more specific, in the known 2-of-2 schemes the two parties need to perform key generation together to be able to run signature computation. In this work, we propose an efficient two-party ECDSA protocol that enables two parties to “aggregate” their ECDSA signature (on a single message) without participating in any kind of key generation process. Our protocol is based on additive sharing of (ECDSA) private keys and homomorphic properties of Paillier encryption. All the zero-knowledge proof we use are non-interactive. As a result, our key generation is 7x faster than state-of-the-art. In terms of overall time complexity, our scheme is comparable with state of the art 2-of-2 ECDSA scheme.
Focused ACO with node relocation procedure for solving large TSP instances

Ant Colony Optimization (ACO) is a well-known family of nature-inspired metaheuristics used to find approximate solutions for challenging optimization problems. Despite its relative simplicity and adaptability to new problem variants, the efficiency of ACO significantly deteriorates with increasing problem size, even though it often provides high-quality solutions. This paper aims to enhance the performance of ACO when solving large instances of the Traveling Salesman Problem (TSP). Specifically, we propose novel improvements to the current state-of-the-art ACO approach, Focused ACO (FACO), that include a node relocation procedure applied during the solution construction process and a greedy heuristic that preserves high-quality solutions from previous algorithm iterations. Our proposed FACO variants demonstrate a substantial performance improvement when solving TSP instances with up to 200k nodes.