Reviewerly/exHarmony huggingface dataset:
Image Inpainting Using Lightweight Transformer Neural Network Based on Channel Attention

Image inpainting is an important image processing application in repairing images with damaged or undesirable contents. While traditional approaches based on pixel or patch matching lack the ability to generate novel contents for the missing regions in an image, generative neural network has shown its potential in making up meaningful contents for the masked areas. On the other hand, to bridge the gaps between visible and missing regions, both traditional and deep-learning based approaches utilize self-similarity property of an image. In generative learning, this self-similarity property is explored using attention mechanisms. Since structural information may have multiple meanings depending on neighborhood context but a single attention mechanism can only provide one interpretation, applying transformer architecture with multiple attentions in image inpainting should further improve the final results by giving multiple interpretations to self-similarity information. The limitation of applying transformer is that complexity of an attention mechanism is proportional to the square of the number of pixels in an image. This complexity could grow exponentially and become infeasible for capacity-limited hardware if image resolution is high. In this paper, we propose to replace spatial-attention based transformer with one based on channel attention. Thereby, the complexity is reduced to the square of the number of channels which does not depend on spatial resolution and can be easily controlled as a network parameter. To remedy the loss of spatial information because of channel attention, a spatial gating mechanism is added after the channel-attention transformer. After this new lightweight transformer architecture generates the coarse result, an attention-based refinement network is used to generate the final result. Experimental results show that the new transformer architecture can produce inpainting results comparable to state-of-the-art approaches with far less network parameters.
Blockchain Technology Accelerating Industry 4.0

Competitive industrial environments impose significant requirements on data sharing as well as the accountability and verifiability of related processes. Here, blockchain technology emerges as a possible driver that satisfies demands even in settings with mutually distrustful stakeholders. We identify significant benefits achieved by blockchain technology for Industry 4.0 but also point out challenges and corresponding design options when applying blockchain technology in the industrial domain. Furthermore, we survey diverse industrial sectors to shed light on the current intersection between blockchain technology and industry, which provides the foundation for ongoing as well as upcoming research. As industrial blockchain applications are still in their infancy, we expect that new designs and concepts will develop gradually, creating both supporting tools and groundbreaking innovations.
Blockchain for Data Sharing

The success of data-driven applications motivates organizations to make business decisions using the insights provided by the data. Such a data-driven application model is widely employed in the Internet of Things, Supply Chain Monitoring, and Smart Cities. The application developers and data consumers expect high-quality data from reliable data sources. This chapter reviews the importance and benefits of data in numerous application domains. Besides, multiple data sharing modalities and the importance of trust, security, and privacy are discussed. Multiple blockchain-based data sharing solutions are also discussed to illustrate how blockchain technology increases trust and reliability guarantees for the relevant stakeholders. Lastly, the chapter concludes with open challenges in developing and adopting data-driven applications.
Blockchain Technology for E-Governance Applications

Blockchain technology or distributed ledger technology since its inception as a cryptocurrency platform has captured the imagination of computer scientists, policy makers, and governments alike. The continuous evolution of blockchain technology from generation 1.0 to 3.0 has brought forth multiple powerful platforms for creating trust among mistrusting entities, the ability to automate transactions through smart contracts, and multiple customized platforms for specific applications by Maesa and Mori (J Parallel Distrib Comput 138:99–114, 2020). This evolution has attracted the attention of policy makers in various countries—they noticed the opportunity to build trust between citizens and governments through the mediation of this technology (International Journal of Recent Technology and Engineering (IJRTE) 8(4):5795–5802, 2019). E-Governance solutions with blockchain integrated in the backend or as mediation platforms have received traction as evidenced by multiple pilots by various governments worldwide. The National Blockchain Project at the Indian Institute of Technology Kanpur has been funded by the Government of India to develop technology and solutions for various E-Governance applications. In this chapter, we discuss the National Blockchain projects and various solutions being worked out by the project team.
Low-Cost Mobile-Based Augmented Reality Viewer: Bridging the Gap Between Affordable AR Glasses and Smartphones

This research project aims to enhance the accessibility of Augmented Reality (AR) technology by developing a low-cost mobile-based AR viewer. The study delves into the hardware and software setup, conducting a comprehensive feature analysis of both the mobile-based AR viewer and traditional AR glasses. Emphasizing the accessibility advantages inherent to the mobile-based AR viewer while candidly acknowledging its limitations, the research also explores the broader implications for AR technology adoption and outlines strategies for future improvements in this evolving field.
Data storage operations protection in mobile cloud computing

Cloud computing is revolutionizing Customer Internet computing and the IT industry by integrating with mobile environments through remote access technologies. This growth involves using smartphones and sensors as data collection nodes interacting with the cloud. However, widespread adoption faces a hurdle in data security concerns, as users are hesitant about entrusting sensitive data to public clouds operated by external providers. To tackle barriers in cloud computing, we urgently require innovative and secure management architectures. This paper introduces a comprehensive framework addressing user concerns, focusing on secure data sharing in the cloud. The framework covers aspects like transport, aggregation, usage, and destruction of sensitive data, emphasizing the semi-trusted nature of the cloud to boost user confidence and address security concerns. One key aspect of the proposed framework is the integration of the Kerberos protocol over the network. This protocol plays a pivotal role in establishing secure and authenticated communication channels, ensuring the confidentiality and integrity of the data being transferred within the cloud infrastructure. Additionally, the paper introduces a user process protection method based on a virtual machine monitor. This method contributes to enhancing overall system security by providing a layer of isolation and protection for user processes operating in the cloud environment. The combined integration of the Kerberos protocol and the virtual machine monitor-based user process protection method forms a cohesive approach towards realizing robust system functionalities. By offering a secure foundation for data management in the cloud, this framework addresses the current limitations and instills a sense of trust among users, fostering a more widespread and confident adoption of cloud computing techniques.
Wearable eye trackers

Abstract Sight interpreting/translation (SiT) refers to the mode of communication in which source language information is received via reading and target language output is produced either in oral form or sign language. The cognitive aspect of SiT has been examined empirically with participant-, product-, and process-oriented methods, as well as multi-method approaches. Previous SiT research has benefitted from eye tracking technology due to the dominance of visual input during SiT. However, previous studies have used screen-based eye trackers, where stimuli are confined to the screen, and the potential impact of elements beyond the screen cannot be captured, leading to an oversight of the interactional aspect of SiT. Thus, we suggest wearable eye trackers in future SiT studies to thoroughly explore the underlying cognitive mechanisms of this modality. Moreover, we argue that future SiT studies can benefit from triangulating eye tracking data with subjective data, such as interviews, to better understand SiT.
Cognition and behaviour of reception in museum spaces

Abstract In recent years, there has been a growing interest in translation studies within museum settings, enabling museums to enhance visitor engagement and create a more meaningful visiting experience. Within cognitive translation studies, the use of eye tracking technology has emerged as a widely adopted method for investigating translators’ allocation of visual attention and cognitive resources. However, the application of eye tracking in museum translation studies has received limited attention. This study provides a critical review of the utilisation of eye tracking to examine visitors’ perception and reception in museum settings from both behavioural and cognitive perspectives. The article begins by selecting and categorising the reviewed papers. It then describes the methods employed for collecting and analysing eye movement data, followed by summaries and critical comments on the findings from the existing literature. This article further evaluates the reliability, validity, and practicality of existing eye tracking research in museum translation, and finally proposes the potential directions for future cognitive translation studies in museums.
Web Technology Grounded Effects of Task Scheduling in Distributed and Cloud Systems

Abstract One definition of the word “distributed system” describes it as “a set of entities that collaborate in order to find a solution to a problem that cannot be solved by a single entity using their own resources.” This description of a distributed system is an example of a distributed system. As the number of algorithms that are mathematically complicated continues to increase, distributed computing systems have emerged as a direct result of this trend. The optimization of a distributed computing system has been accomplished via the development of methods for the distribution of work and the scheduling of jobs. Because of this, the system has been able to be used in a more efficient manner. Task scheduling refers to the process of selecting the order in which actions are carried out in response to a given set of circumstances. On the other hand, task allocation is the process of allocating tasks to the processors in a system that are the most fit for taking on those tasks. This procedure determines which processors are assigned the jobs. Within the context of distributed systems, the objective of this article is to provide a detailed review of the several approaches to task scheduling that have been used by researchers.
RTVD-Net:An real-time violence detection method based on pre-training of human skeleton images

Video violence detection has received increasing attention in recent years. Identifying violent behavior can effectively combat criminal behavior and prevent public safety in monitoring places. In this paper, we propose a real-time violence detection network, called RTVD-Net. Firstly, we introduce the lightweight pose estimator YOLO-Pose into violence detection tasks and proposes a pre-training method based on YOLO-Pose to extract human skeleton features. Secondly, The 2DCNN model(ACTION-Net) was used to classify violent behavior in videos. We propose a Weight selection module that takes the parameters generated from bone data as a weight coefficient, and a Keyframe Weight Assignment that connects bone information with RGB features to generate the final detection results. The method proposed in this article was comprehensively experimented and ablated on the Hockey Fights dataset and RWF2000 dataset. The experimental results showed that the accuracy of RTVD-Net was superior to the most advanced methods before, and the speed could reach 33 fps, enabling real-time detection of violent behavior. In addition, RTVD-Net can also perform well in violent behavior in multi person scenarios.
Offline Signature Verification Using a 2D Attention Encoder-Decoder Network

Verifying an individual's Chinese handwritten signature is a vital biometric technology that is widely used in banking, finance, and legal business. Forged signatures for the purpose of deception endanger these industries' interests. As a result, this paper proposes a network based on 2D Attention to verify the authenticity of the signature. In this paper, we have developed a Chinese handwritten signature dataset (CNSig) and proposed an offline signature verification network (att-OfSVNet) based on 2D Attention. The att-OfSVNet model includes two weight-sharing Encoders and Decoders. The two weight-sharing Encoders receive the inverted genuine and the inverted test signature image, and the Decoder reduces the dimension and concatenates the two extracted feature images. We use 2D Attention to fuse the features extracted by the Encoder and Decoder, which minimizes the information loss in the convolutional layers during the extraction process and enhances the effect of feature extraction by the Encoder. The experimental results show that our att-OfSVNet achieves satisfactory performance on other handwritten signature datasets in three different languages: CEDAR, BHSig-B, and BHSig-H, and it also demonstrates good generalization ability in cross-lingual tests.
Impact of Training Data Quality on Deep Speckle Noise Reduction in Ultrasound Images

Speckle noise reduction is an essential step in ultrasound image analysis. One of the challenges in speckle noise reduction is removing noise without significantly losing image detail. Various studies have been conducted using image processing and deep learning approaches. This research offers a simple framework using a deep learning method, which shows that the quality of the images used in the training process influences the performance of the denoising results using the trained network. The training data in the form of ultrasound images in this study was processed separately using various speckle noise reduction methods. We also compare with one of the pre-trained networks, namely denoising convolutional neural networks (DnCNNs). The research shows that denoising results using training data processed using the hybrid speckle noise method provide high image edge preservation performance. The tests in MATLAB reveal a significant reduction in the speckle noise of the ultrasound image, with a peak signal-to-noise ratio of 20.68 dB, a mean structural similarity index measure (MSSIM) of 0.83, and Pratt's Figure Of Merit metric indicating an edge preservation index value reaching 91.76%. Reducing speckle noise using this approach takes less time, ensuring well-maintained edge information and clear visibility of image details, making it applicable for ultrasound diagnosis.
METHODS FOR FORMING ORGANIZATIONAL AND MANAGEMENT COMPETENCES OF MASTER’S STUDENTS IN SOCIAL-CULTURAL ACTIVITIES

The purpose of the scientific article is to study various ways of forming the organizational and managerial competencies of master’s students in socio-cultural activities, to study the scientific and theoretical views of pedagogue-scientists, as well as to make suggestions. These methods provide a holistic approach to the formation of skills and knowledge necessary for the successful management of socio-cultural activities. Theoretical, comparative, classification and descriptive methods were used to cover the topic of the scientific article.
INNOVATIVE COMPETENCY AS A CORE COMPETENCE OF FOREIGN LANGUAGE TEACHERS

The paper aims to analyse and review the existing previous research on the topic innovative competency of foreign language teachers, and uses narrative method as a research method. Based on the findings, the author defined the term innovative competency, and classified its components, levels, conditions analysing existing research papers.
The Security of Delegated Proof-of-Stake Wallet and Stake Pools

The proof-of-stake paradigm is used to implement a robust ledger, i.e., immutable and decentralized collection of records via the blockchain data structure, achieving similar security properties of the proof-of-work paradigm without the drawback of centralization risks and energy high costs and, at the same time, allowing a richer and more complex system, where spending transactions are not the only transactions. For example, it allows the delegation, or staking, transaction, where the users delegate the rights over their stake. This crucially important action allows the creation of stake pools, i.e., nodes that accumulate stake. In this chapter, we review the basic security properties in this blockchain-based consensus protocol with a short review of the proof-of-work design, comparison, and known attacks to proof-of-stake-based protocol. Finally, we focus on the security of delegated proof of stake and its wallet, the main contents of this chapter. We introduce main requirements for a wallet, address, and account framework and its security in the presence of stake pools and newly identified attacks named malleability attacks.
Illicit Blockchain Content: Its Different Shapes, Consequences, and Remedies

Augmenting public blockchains with arbitrary, nonfinancial content fuels novel applications that facilitate the interactions between mutually distrusting parties. However, new risks emerge at the same time when illegal content is added. This chapter thus provides a holistic overview of the risks of content insertion as well as proposed countermeasures. We first establish a simple framework for how content is added to the blockchain and subsequently distributed across the blockchain's underlying peer-to-peer network. We then discuss technical as well as legal implications of this form of content distribution and give a systematic overview of basic methods and high-level services for inserting arbitrary blockchain content. Afterward, we assess to which extent these methods and services have been used in the past on the blockchains of Bitcoin Core, Bitcoin Cash, and Bitcoin SV, respectively. Based on this assessment of the current state of (unwanted) blockchain content, we discuss (a) countermeasures to mitigate its insertion, (b) how pruning blockchains relates to this issue, and (c) how strategically weakening the otherwise desired immutability of a blockchain allows for redacting objectionable content. We conclude this chapter by identifying future research directions in the domain of blockchain content insertion.
Bitcoin Blockchain System: An Overview of Security and Privacy Aspects

Apart from creating a billion-dollar worth of cryptocurrency ecosystem, Bitcoin revolutionized the whole domain of cryptocurrencies, and it largely influenced many other application areas (e.g., healthcare, supply-chain management, real estate) with its underlying technologies such as blockchain, consensus algorithms, and decentralized data management. Due to the reasons mentioned above, Bitcoin has attracted massive attention from the research community. Since its launch in 2009, the Bitcoin market capital has grown significantly, and it is now worth approximately 887 billion dollars. This huge and rapid growth is one of the key motivations for malicious entities to identify and exploit weaknesses to disrupt its normal functionality for financial reasons, and the same is also a reason for the researchers to discover and provide patches or solutions for the identified security- and privacy-related vulnerabilities in the system. Most of these security and privacy threats are on the critical underlying technologies of Bitcoin; thus, they are also valid for other Bitcoin-like cryptocurrencies. This chapter starts with an overview of the Bitcoin system by explaining the working methodology and interactions between its various building blocks. In the process, we conclude Bitcoin's fundamental features and underlying structures and provide insights at the core of the Bitcoin protocol and its networking infrastructure. We also discuss the significant security and privacy threats to the Bitcoin system and the countermeasures proposed in the state of the art.
Supporting Secure Trusted Manufacturing via Blockchain

Conventional manufacturing systems are shifting toward smart manufacturing where a wide range of devices are connected to the Internet, thanks to advances in Internet of Things (IoT) technology. The high connectivity of such devices introduces security risks because malicious nodes may attempt to compromise or tamper with data generated by the devices. This in turn highlights the security and data trust challenges in smart manufacturing. In this chapter, we discuss blockchain as a solution to address the outlined challenges. We study the state-of-the-art solutions proposed in the literature to enhance security and establish trust in manufacturing. Next, we introduce a blockchain-based framework to secure smart manufacturing by analyzing the data logs produced by the devices. Ultimately, we envision a blockchain-based framework to enforce trustworthiness in Digital Twins (DTs) that mirror every facet of the industrial processes to help achieve predictive maintenance.
Blockchain-Based Distributed and Secure Digital Forensic Investigation Systems

Lawful evidence management by law enforcement agencies during the Digital Forensics (DF) investigation is of supreme importance since it convicts suspects of crimes. Therefore, a secure and efficient evidence management system should have certain features such as tamper-resistant, traceability, auditability, privacy preservation, and fine-grained access control. Unfortunately, the state-of-the-art DF is facing new challenges due to the recent technological advancements in various areas, such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), communication technologies, and cloud computing, which are heavily being used in our daily lives. These technologies are also the primary sources for evidence extraction in most crimes. Hence, forensic experts need novel tools and methodologies to keep pace with these new technologies. The inherent properties of blockchain, such as transparency, immutability, secure anonymity, and auditability, make it a suitable solution to address DF's new challenges. To this end, we provide a compact survey on state-of-the-art blockchain-based DF investigation techniques along with their advantages and disadvantages. We will discuss all critical issues and challenges involved in forensic investigations and evidence management systems, focusing on security and privacy challenges. Moreover, blockchain-based solutions that target specific service areas such as IoT and cloud computing forensics will be discussed in detail due to their usage in many application domains. Finally, we will present the challenges that existing blockchain-based forensics solutions face, along with possible ways of addressing them.