Reviewerly/exHarmony huggingface dataset:
Data Aegis Using Chebyshev Chaotic Map-Based Key Authentication Protocol

Networks with wireless connection between nodes are far more convenient than a wired connection and are under rapid development. One major factor that ensures a truly significant network is the security features it possesses. In traditional communication, authentication mechanisms based on ECC were used to additionally augment the trustworthiness of data security over the network. In order to enhance security, the primary focus remained on growing the key size but, this brings in the intricacy to many internal load operations that are performed in latter stages and to the workload of calculations being performed. In addition, the speed of the cryptographic processes could not be compromised. Hence, to make the process smoother and reliable, our C2MAP protocol is introduced. Particularly, Chebyshev chaotic maps are applied to relate association indulging in the mechanisms involved that are required to perform mutual authentication. The proposed protocol is based on one of the properties of the Chebyshev polynomial, i.e., the semi-group property; which differs from other traditional security strategies, by those implementing complex hash functions and corresponding cryptographic algorithms. In order to compete with the existing ECC mechanism; parameters like throughput, end-to-end delay (E2D), and packet delivery ratio are taken into account; whose corresponding calculated scrutiny would prove that the enhanced security in authentication for communication protocol would be a better choice; by visualizing them through graphical presentations.
Document Forgery Detection

Document forgery is an increasing problem for both private companies and public administrations. It can be said to represent the loss of time and resources. There are many classical solutions to these problems such as the detection of an integrated security pattern. In such cases, it is important that we resort to forensic techniques for the detection. The idea behind using these forensic techniques can also be implemented using artificial intelligence/machine learning which can be of lower cost and can provide the same or better results. The experimental result shows that multiple models have strong detection capability to detect multiple forgeries. In this paper, we have developed a different approach to detecting forgery in a document. The forgery we detect can be classified as hand-written signature forgery and copy-move forgery of any photo, text, or signature. We have developed a novel approach using capsule layers to detect a forgery in handwritten signatures. We also use ELA (Error Level Analysis) to detect any error in the compression levels of the image.
Diversification of services at the central libraries of RF constituent territories in the circumstances of service economy

The study goal is to specify the nomenclature of services types as presented at the library official websites. The authors characterize the library services as the subject of multi-aspect analysis and evaluation. They emphasize that the concept “library services” and “services by libraries” are not identical. The service economy affects diversification of services provided by the libraries. The authors complement the publications theoretical analysis with the findings of the largescale empirical study of services represented at the official websites of 77 central libraries of the RF constituent units (republican, regional territorial universal scientific libraries). The scale of diversification is demonstrated. The authors argue that the libraries have to undertake some unusual activities and to provide services foreign to their nature and social mission. The findings of the study demonstrate that services production in the libraries corresponds to 30 different types of professional activities. Many services provided by the libraries call for other than library, information, or bibliographic competences. Therefore, services quality assessment is needed; certification and licensing of the libraries providing them may be needed. The authors conclude that diversification as manifested in services inappropriate to the libraries is forced by the transfer to the market economy, development of service economy, and the focus on service-oriented government.
The Effects of Anthropomorphised Virtual Conversational Assistants on Consumer Engagement and Trust During Service Encounters

Drawing on social exchange and anthropomorphism theory, this research examines the role of virtual conversational assistants (VCA) as frontline employees. Specifically, we investigate the effects of AI-derived features, such as anthropomorphism, in building Human-Machine relationships. Drawing on a qualitative interpretivist approach, 31 semi-structured interviews were conducted with global users of Siri, Alexa and Google Assistant. Our findings suggest anthropomorphism is an important factor in understanding the development of trust within Human-Machine interactions. More specifically, the effects of a humanised voice, interactive communication capability and cognitive features evoke a sense of social presence that may positively or negatively impact user trust. We propose that the interplay between a user’s perceptions of the bright and dark sides of interacting with an AI-empowered anthropomorphised machine determines categories of trust and subsequent customer engagement behaviours with this embedded form of organisational frontline.
The library and open access resources: Threats vs opportunities

The open science initiatives, growing number of open resources (digital collections, institutional repositories, open educational resources, scientific data, etc.), changing user information needs and preferences hang like a sword of Damocles over the libraries and threaten with the absence of demand for science and education support. The purpose of the study is to identify librarians’ awareness of the world open access resources (OAR) and their role in developing library and information products and services. The author analyzes OAR structure, Russian librarians’ awareness of 50 largest OAR structured by document type (books and articles, dissertation, preprints, data sets, learning materials, patents and standards, encyclopedias and dictionaries, scientific information retrieval systems). She also emphasizes the problems of mastering global OAR by Russian academic librarians, their interest toward practical use of OAR in library and information user services. The findings of the study demonstrate the possibility for the libraries to build their value in the market of information OAR and to become essential part of the modern science information ecosystem, to assist in managing scientific content and document retrieval, and to provide subject-oriented teaching and consulting of users.
Enhancement and Optimization of Underwater Images and Videos Mapping

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel's transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G-B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.
Identifying research fronts in NLP applications in library and information science using meta-analysis approaches

Purpose The purpose of this study is to identify the research fronts by analysing highly cited core papers adjusted with the age of a paper in library and information science (LIS) where natural language processing (NLP) is being applied significantly. Design/methodology/approach By excavating international databases, 3,087 core papers that received at least 5% of the total citations have been identified. By calculating the average mean years of these core papers, and total citations received, a CPT (citation/publication/time) value was calculated in all 20 fronts to understand how a front is relatively receiving greater attention among peers within a course of time. One theme article has been finally identified from each of these 20 fronts. Findings Bidirectional encoder representations from transformers with CPT value 1.608 followed by sentiment analysis with CPT 1.292 received highest attention in NLP research. Columbia University New York, in terms of University, Journal of the American Medical Informatics Association, in terms of journals, USA followed by People Republic of China, in terms of country and Xu, H., University of Texas, in terms of author are the top in these fronts. It is identified that the NLP applications boost the performance of digital libraries and automated library systems in the digital environment. Practical implications Any research fronts that are identified in the findings of this paper may be used as a base for researchers who intended to perform extensive research on NLP. Originality/value To the best of the authors’ knowledge, the methodology adopted in this paper is the first of its kind where meta-analysis approach has been used for understanding the research fronts in sub field like NLP for a broad domain like LIS.
Charged and free services in the libraries of the constituent entities of the Russian Federation within the theory of patronized goods

The purpose of the article is to analyze the ratio of charged and free services provided by central libraries of the constituent entities of the Russian Federation (i. e. republican, territorial and regional libraries). The problem of library charged user services is examined through the optics of professional library community and the government. The authors attempt to expand theoretical foundations of library services with the new ideas of national economists focusing on the humanitarian sector and, in particular, those developing the theory of patronized goods. The theoretical analysis is complemented with the large-scale empirical study of services presented at the official websites of 77 central libraries in the RF constituent entities. The findings demonstrate that the charged services dominate while the share of free services has drastically decreased. The charged services in these libraries exceed the free ones by a factor 8.4. The authors conclude that massive and mechanistic implementation of charged services is a dangerous challenge for the libraries as it exposes them to a risk of abandoning the principle of common availability of libraries and, in long run, of users exodus.
Quantitative experimental L2 acquisition MALL studies: A critical evaluation of research quality

Abstract With more than 1,200 publications over the past two decades, experimental mobile-assisted language learning (MALL) studies targeting second/foreign language (L2) acquisition outcomes are certainly not lacking in quantity. Their research quality, on the other hand, has often been brought into question, most notably with regard to the adequacy of their assessment instruments and statistical analyses. Yet limiting the determination of research quality to the evaluation of testing procedures, and the statistical analysis of the results they produce, ignores the critical relevance of the underlying research parameters that generate the results in the first place. A comprehensive evaluation of quantitative experimental L2 acquisition MALL research quality, encompassing design as well as assessment instruments and statistical analysis, thus remains to be undertaken. The present investigation endeavors to do so based on an extensive compilation of 737 MALL studies published between 2000 and 2021. The research quality of these publications is evaluated according to four main parameters: language acquisition moderators, treatment intervention conditions, assessment instruments, and statistical analysis. These are applied according to a modified version of the Checklist for the Rigor of Education-Experiment Designs (CREED), which classifies research design quality into five levels: low, medium-low, medium, medium-high, high. With over three quarters of all studies falling within the low category, the result leaves much to be desired. Since the modified CREED algorithm developed here can equally be applied to studies from their inception, it offers a way forward to improve the research quality of future experimental MALL studies.
Pre-Training Across Different Cities for Next POI Recommendation

The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across cities to alleviate those data sparsity and imbalance problems for next POI recommendation. Recently, pre-training over a large-scale dataset has achieved great success in many relevant fields, like computer vision and natural language processing. By devising various self-supervised objectives, pre-training models can produce more robust representations for downstream tasks. However, it is not trivial to directly adopt such existing pre-training techniques for next POI recommendation, due to the lacking of common semantic objects (users or items) across different cities . Thus in this paper, we tackle such a new research problem of pre-training across different cities for next POI recommendation. Specifically, to overcome the key challenge that different cities do not share any common object, we propose a novel pre-training model named CATUS , by transferring the cat egory-level u niversal tran s ition knowledge over different cities. Firstly, we build two self-supervised objectives in CATUS : next category prediction and next POI prediction , to obtain the universal transition-knowledge across different cities and POIs. Then, we design a category-transition oriented sampler on the data level and an implicit and explicit transfer strategy on the encoder level to enhance this transfer process. At the fine-tuning stage, we propose a distance oriented sampler to better align the POI representations into the local context of each city. Extensive experiments on two large datasets consisting of four cities demonstrate the superiority of our proposed CATUS over the state-of-the-art alternatives. The code and datasets are available at https://github.com/NLPWM-WHU/CATUS.
Generalized possibilistic c-means clustering with double weighting exponents

Considering that the improved possibilistic c-means (PCM) algorithms are sensitive to noise while addressing the issue of consistency clustering in PCM, this paper proposes the generalized possibilistic c-means clustering with double weighting exponents. Firstly, double weighting exponents are introduced into the PCM algorithm, and the generalized possibilistic c-means clustering model is established. Secondly, the difference between the double weighting exponents in the generalized possibilistic clustering model is set to 1 or −1; two improved single-exponent possibilistic clustering algorithms called IPCM1 and IPCM2 are proposed, and their local convergence is strictly proven by the Zangwill theorem. Finally, the reasonable range of the weighting exponent in IPCM1 and IPCM2 algorithms is determined by mathematical optimization. Experimental results indicate that IPCM1 and IPCM2 outperform existing PCM-related algorithms; they achieve excellent clustering performance, significantly improve the robustness to noise, and decrease the sensitivity to the weighting exponent. The work of this paper has far-reaching significance for promoting the development of the possibilistic c-means clustering theory.
Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

Over the last couple of decades, the introduction and proliferation of whole-slide scanners led to increasing interest in the research of digital pathology. Although manual analysis of histopathological images is still the gold standard, the process is often tedious and time consuming. Furthermore, manual analysis also suffers from intra- and interobserver variability. Separating structures or grading morphological changes can be difficult due to architectural variability of these images. Deep learning techniques have shown great potential in histopathology image segmentation that drastically reduces the time needed for downstream tasks of analysis and providing accurate diagnosis. However, few algorithms have clinical implementations. In this paper, we propose a new deep learning model Dense Dilated Multiscale Supervised Attention-Guided (D2MSA) Network for histopathology image segmentation that makes use of deep supervision coupled with a hierarchical system of novel attention mechanisms. The proposed model surpasses state-of-the-art performance while using similar computational resources. The performance of the model has been evaluated for the tasks of gland segmentation and nuclei instance segmentation, both of which are clinically relevant tasks to assess the state and progress of malignancy. Here, we have used histopathology image datasets for three different types of cancer. We have also performed extensive ablation tests and hyperparameter tuning to ensure the validity and reproducibility of the model performance. The proposed model is available at www.github.com/shirshabose/D2MSA-Net.
VaccineChain: A checkpoint assisted scalable blockchain based secure vaccine supply chain with selective revocation

In the present era of the pandemic, vaccination is necessary to prevent severe infectious diseases, i.e., COVID-19. Specifically, vaccine safety is strongly linked to global health and security. However, the main concerns regarding vaccine record forgery and counterfeiting of vaccines are still common in the traditional vaccine supply chains. The conventional vaccine supply chains do not have proper authentication among all supply chain entities. Blockchain technology is an excellent contender to resolve the issues mentioned above. Although, blockchain based vaccine supply chains can potentially satisfy the objectives and functions of the next-generation supply chain model. However, its integration with the supply chain model is still constrained by substantial scalability and security issues. So, the current blockchain technology with traditional Proof-of-Work (PoW) consensus is incompatible with the next-generation vaccine supply chain framework. This paper introduces a model named "VaccineChain" - a novel checkpoint-assisted scalable blockchain based secure vaccine supply chain. VaccineChain guarantees the complete integrity and immutability of vaccine supply records to combat counterfeited vaccines over the supply chain. The dynamic consensus algorithm with various validating difficulty levels supports the efficient scalability of VaccineChain. Moreover, VaccineChain includes anonymous authentication among entities to provide selective revocation. This work also consists of a use case example of a secure vaccine supply chain using checkpoint assisted scalable blockchain with customized transaction generation-rule and smart contracts to demonstrate the application of VaccineChain. The comprehensive security analysis with standard theoretical proofs ensures the computational infeasibility of VaccineChain. Further, the detailed performance analysis with test simulations shows the practicability of VaccineChain.
Analysis of Digital Forensic Artifacts Data Enrichment Mechanism for Cyber Threat Intelligence

Cyber attack targeting heterogeneous devices in large-scale network environments through advanced persistent threat (APT) attacks are on the rise. Therefore, in order to improve the effectiveness of the cyber incident response system, it is necessary to apply a data enrichment mechanism for the collected digital forensic artifacts data to reinforce threat analysis and detection performance. Therefore, we designed and implemented the data enrichment mechanism for cyber threat intelligent system by analyzing the existing cyber incident response framework such as SIEM, CTI based on the aggregated digital forensic artifacts. Through this, it is expected to improve the detection performance and effectiveness when using artifact data enrichment process for analyzing cyber incidents collected from heterogeneous devices.
A Secure Framework for Vehicle Maintenance Service using Blockchain

Automotive technology is soaring and has reached an advanced phase. Despite their benefits, these advancements may expose vehicles to additional threats, particularly in terms of security and data management. This paper focuses on the application of vehicle maintenance services, specifically on maintenance history records. Currently, handling maintenance services history is a manual process which may give rise to inaccuracy and unavailability. Moreover, consumers rarely have access to these records unless they keep hardcopy-based records for the maintenance services performed on their cars. Those issues bring another concern that there are no reliable platforms or legitimate sources to retrieve the history of maintenance service records. Thus, the objectives of this paper are to identify the relevant stakeholders and determine their roles in the vehicle maintenance services industry. Hence, the list and details of stakeholder is considered and applied accordingly in the design of a secure framework that provides the necessary security goals for the maintenance services records. The proposed framework implements blockchain technology based on two use cases which are the Framework for Scheduled Maintenance Service and the Framework of Repair Maintenance Service. The use of this framework by the stakeholders in the automotive ecosystem would ensure the implementation of the required security goals in the maintenance services application. The method that is being used in this research is literature reviews which is to find out the best approach to be applied in the framework as well as listing out the stakeholder involved and their roles. Next, a secure framework has been designed to produce secure vehicle maintenance records that covers the security requirements needed. As a result, the contributions of this work are the list of stakeholders and their roles, security requirements that need to be taken into consideration and a secure framework for vehicle maintenance records involving all stakeholders.