Reviewerly/exHarmony huggingface dataset:
Applying Eye Tracking Technique to Evaluate Poster Design: A Study on World Peace Poster

In recent years, the world has been confronted with a myriad of difficulties as a result of the pandemic and the war conflicts that have broken out between countries. In this scenario, it is important to focus on world peace because world peace might influence the growth and prosperity of the entire global community. On the other hand, eye tracking is a relatively new technique for the study of visual attention and perception in poster design. This research work examines the effectiveness of design elements in world peace poster design using an eye tracking technique used for the measurement of attention, attractiveness, and understanding of the messages conveyed using posters. We designed three novel posters on the theme of world peace (using Photoshop and Illustrator), and the eye tracking was conducted using the GazeRecorder software and a questionnaire survey with 20 participants (age ranged from 23–43 years, 47.4% male, 52.6% female) was conducted to understand the users’ reactions to the designed posters. Eye movements are used to provide a comparatively novel source of data about visual attention and evaluation of design elements. Creative poster designs that are eye-catching, have a brief message, and can be understood at a glance are more effective in the context of world peace message communication. The study highlights the use of eye tracking as an active area of research that is being applied to the study of world peace poster design. It was observed that poster 2 (P2) was more attractive as the design was significantly more original, aesthetically pleasing, and communicative to the target audience for the promotion of a world peace-related message. The study reports on people's perceptions of poster design, particularly different design elements, and their effectiveness, as well as people's attention to certain messages or information on design posters, which transmit the message and cause them to think in a certain way.
Impact of the E-brand on the Consumer's E-trust, Reliance and Resistance Towards the Chatbot

Currently, corporations are finding a lot of interest in the usage of chatbots for various tasks across a range of industries. The future of interfaces looks bright for chatbots. In order to give customers a better experience, retailers must understand how they engage with their interfaces. To formulate our research hypotheses in this chapter, we draw on two theories, namely the Stimilus-Organism-Response theory and the Social Presence theory. By concentrating on text chatbots, a new interactive technology, we have made significant contributions to the literature on interactive marketing and artificial intelligence. One of the first empirical studies on the causes and effects of consumer trust in text chatbots is the one we conducted. Just the utilitarian usage of chatbots for online customer care has been examined in prior studies. The emotional component of the human-chatbot relationship is added by our study (1). As far as we are aware, no research has yet been done to evaluate how brand experience and behaviour are affected by online brand equity. Our study adds and investigates yet another connection between brand experience and chatbot user trust (2). Our study examines the consequences of trust in the employment of chatbots as well as the attitudes of reliance and resistance (3). Our research is original because of these three contributions. By incorporating the emotional component of this contact, we hope to examine the characteristics (antecedents and repercussions) of customer trust in text chatbots in this chapter. Ultimately, the results add more valuable data for e-service providers and chatbot developers to enhance their capabilities, comprehend the consequences on user experiences, and provide a roadmap for relationship development and strategy creation.
A Secure Data Dynamics and Public Auditing Scheme for Cloud Storage

Cloud Computing is developing technology it provides very affordable data storage as well as very quick computer services. All data saved in the cloud is under the control of cloud service provider, sometimes called as cloud keeper. The validity and dependability of the data kept in the cloud storage are issues that a data owner is worried about. Any unauthorized user or person can misappropriate or manipulate data. In order to validate the privacy, dependability, and integrity of data kept in the cloud, this study proposes a safe public auditing scheme that uses TPA. This suggested auditing method combines the SHA-512 integrity check algorithm with the RSA-15360 public-key encryption algorithm. Additionally perform data dynamics.
GAN (Generative Adversarial Network)-Based Image Super-Resolution: A Technical Perspective

Generative Adversarial Network (GAN) has revolutionized the field of computer vision by enabling the generation of high-quality images with fine details. Image Super Resolution is a specific task of GAN that aims to upscale low-resolution images to high-resolution images, and this review focuses on the adversarial-learning-based methods used for this task. The review also covers the available datasets and network architectures of generative models and applications in Image Super Resolution. GAN techniques has promising results and can enhance various computer vision tasks, including medical and satellite imaging. However, GAN-based Image Super Resolution is computationally expensive and requires large amounts of data for training. Despite these challenges, this technique's potential to improve image quality and provide more detailed images is immense. Advancements in computing and data collection may help overcome these limitations and make GAN-based Image Super Resolution more accessible for researchers and practitioners in various fields.
An Efficient Saliency Detection Using Wavelet Fusion

For decades, Convolution Neural Network (CNN) is a powerful tool and is widely used because of its excellent feature extraction capability. The performance of saliency detection-based CNN has achieved great improvement since a huge amount of feature evidence can be extracted by CNN easily. The Visual quality of the saliency maps can be improved by sharpening the edges and extracting detailed information from the image. Therefore to resolve the quality issue we have proposed the fusion of wavelet transform and CNN for salient object detection. The feature fusion consists of two main module wavelet transform which use Haar-wavelet and CNN. Extensive testing reveals that the proposed module outperforms with mean absolute error 0.1624 and precision of 0.5563.
Demystifying Blockchain

People frequently claim that blockchain is a very complex technology that requires very specific skills. Indeed, blockchain developers and architects must build extensive programming knowledge; understand all protocols, networks, security, and cryptonomics; learn how to create dApps; and so on. That’s why being a blockchain developer is such a highly paid job.
A Framework for Opinion Extraction and Analysis

Opinion mining provides the knowledge of the product to the manufacturers to improve the services and products. The current opinion mining work requires sophisticated engineering of features and syntactic word embedding without considering the semantic interactions between aspect terms and opinions. It undermines the performance of most opinion mining tasks. The proposed work provides a novel framework consisting of three phases for aspect-based opinion mining. The initial phase offers FO pair extraction by means of the CoFO method from the review text corpus. The method offers the use of POS tagging for language and semantic text analysis and identifies the position of FO pairs in documents using the HITS algorithm. Further, it aligns the text using M-WAM and calculates the genuinity score to determine the final set of FO pairs. The second phase provides the extraction of aspect-related terms from respective FO pairs so as to provide aspect based summary of a product to the customer. The phase comprises of RMI–BSSL technique which performs semi-supervised learning. Finally, the third phase provides sentiment mining of each FO pair based on GWU-Clustering techniques, which use the sentiment score of all within cluster words for the final polarity calculation of the opinion word. The implemented work achieved superior performance than the other methods during the experimental analysis.
An Efficient Abstractive Summarization of Research Articles Using Pegasus Model

In the modern era, the internet has a wealth of information; it is difficult for an individual to read all the data, so the text should be condensed to speed up the procedure. Summarization is the technique of developing summaries from a large body of information, such as research papers, news stories, reports, etc. while preserving the information's original content. To rephrase and condense the original content, abstraction-based summarization employs cutting-edge Deep Learning algorithms. PEGASUS is one such deep learning algorithm that proposes a new self-supervised objective for pre-training enormous Transformer-based encoder-decoder models on massive text corpora. PEGASUS creates a single output sequence from the remaining phrases after masking the crucial sentences from an input document. Choosing significant sentences performs better than leading or randomly chosen ones in particular. The summary produced by experimental results on the Scisumm dataset outperforms the state-of-the-art in terms of both syntactic and semantic structure and has recall values of 0.9.
Segmentation of Brain Tumor Images Using Morphological Reconstruction

A common imaging method for identifying brain tumors and providing comprehensive data on tumor kind, location, size, identity, and detection is mathematical morphological reconstruction. To create a more straightforward image that is more relevant and simpler to inspect, segmentation is the act of splitting an image into several segments and explaining the separation of the questionable region from pre-processed MRI images. There are many segmentation techniques that incorporate detection tools, and the output of each technique varies. The Mathematical Morphological Reconstruction (MMR) technique will be used to examine how picture segmentation can be utilized to identify brain tumors and how valuable it may be in the medical field.
Text Detection and Recognition from the Scene Images Using RCNN and EasyOCR

Detecting text location and recognizing them from scene images remains one of the most challenging and enduring research problems in the field of computer vision. Over the past few decades, various researchers working hard to increase the accuracy of text recognition in scene images by considering several challenges. Because of this, there is a high need for commercial text recognizers for natural scenes. Conventional optical character recognition (OCR) demands clean backgrounds, crisp layouts, and neat text, which are frequently not met by natural scene images. A major challenge in scene text recognition is its orientation. There are various orientations like horizontal, vertical, diagonal and off-diagonal. Sometimes these orientations are in curved nature than straight. Another challenge is texts are embedded with un-uniform backgrounds and complicated environments. The extraction of such text is harrowing because of noisy backgrounds, diverse fonts, and text sizes. In this research, a comprehensive solution for detecting text using Faster RCNN and EasyOCR for text recognition is used to increase accuracy. The proposed algorithm is tested on benchmark datasets such as ICDAR13 and ICDAR15. F-score improves by 2.1 and 1.4% for detection on ICDAR13 and 15 datasets, respectively. Also noticed that 1.8% of improvement in recognition accuracy.
Multi-Objective Models for Sparse Optimization in Linear Support Vector Machine Classification

The design of linear Support Vector Machine (SVM) classification techniques is generally a Multi-objective Optimization Problem (MOP). These classification techniques require finding appropriate trade-offs between two objectives, such as the amount of misclassified training data (classification error) and the number of non-zero elements of the separator hyperplane. In this article, we review several linear SVM classification models in the form of multi-objective optimization. We put particular emphasis on applying sparse optimization (in terms of minimization of the number of non-zero elements of the separator hyperplane) to Feature Selection (FS) for multi-objective optimization linear SVM. Our primary purpose is to demonstrate the advantages of considering linear SVM classification techniques as MOPs. In multi-objective cases, we can obtain a set of Pareto optimal solutions instead of one optimal solution in single-objective cases. The results of these linear SVMs are reported on some classification datasets. The test problems are specifically designed to challenge the number of non-zero components of the normal vector of the separator hyperplane. We used these datasets for multi-objective and single-objective models.
Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases

Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced datasets using resampling methods and flagging a pump-and-dump from the moment of public announcement up to 60 min beforehand. We validated our proposals using data from Pumpolymp and the CryptoCurrency eXchange Trading Library to identify 351 pump signals relative to the Binance crypto exchange in 2021 and 2022. We found that the most effective approach was using the original imbalanced dataset with pump-and-dumps flagged 60 min in advance, together with a random forest model with data segmented into 30-s chunks and regressors computed with a moving window of 1 h. Our analysis revealed that a better balance between sensitivity and specificity could be achieved by simply selecting an appropriate probability threshold, such as setting the threshold close to the observed prevalence in the original dataset. Resampling methods were useful in some cases, but threshold-independent measures were not affected. Moreover, detecting pump-and-dumps in real-time involves high-dimensional data, and the use of resampling methods to build synthetic datasets can be time-consuming, making them less practical.
Research on Automatic Path Planning Method of Warehouse Inspection Robot

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Greek preschool teachers’ readiness to teach online at the onset of the COVID-19 pandemic

Abstract The COVID-19 pandemic had a significant impact around the world and has drastically changed the field of education. In addition to instructional materials, new technologies enabled access to new learning environments. However, the COVID-19 pandemic has highlighted some significant disparities between conventional information and communication technologies (ICTs) included in teacher training curricula in Greece and the new software required for remote learning and teaching. Thus, the study presented here aimed to examine the mobilisation of in-service teachers and their readiness to address teaching challenges while delivering online lessons. Online questionnaires were distributed to 110 in-service preschool teachers. The study aimed to answer four research questions about teachers’ ICT knowledge and the ways in which they used technology for work purposes, as well as the difficulties they encountered while teaching online. Evaluation of the data resulted in three main findings: (1) holding an ICT training certificate did not have a significant impact on the implementation of new technologies in distance education; (2) support from local education authorities was an important factor of the transition to online teaching; and (3) Greek teachers did not feel fully prepared to teach online.
Securing Internet of Things (IoT) Devices Through Distributed Ledger Technologies (DLTs) and World Wide Web Consortium (W3C) Standards

The tremendous growth of the Internet of Things (IoT) connected devices calls for a way for manufacturers to standardise the newly created devices for security purposes. As these devices are getting smaller and smaller so does the computational power in them. The resource constraints introduced by the devices eliminate some of the cryptographic features that can be performed by these devices. Thus, developers are looking for a secure method for automating processes and exchanging information in real-time. With that, new technologies like distributed ledger technologies (DLTs) introduce a promising solution for enabling large-scale IoT applications in a decentralized and autonomous manner. Therefore, this work aims to investigate the integration of DLTs and IoT to create a safe and secure environment for IoT devices whilst being aware of the constraints brought by these devices. In this paper, a proposed solution is presented that adopts emerging standards introduced by the World Wide Web Consortium (W3C), to ensure an internationally recognisable system and platform.
Requirements for Interoperable Blockchain Systems: A Systematic Literature Review

The lack of standards or methods guiding the development of blockchain systems has led to the existence of heterogeneous blockchains that are unable to communicate. As a result, organizations using the technology are unable to collaborate and share data with their counterparts. Extant literature discussions focus predominantly on the mechanisms used to achieve interoperability and neglect the requirements needed to achieve blockchain interoperability. This paper aims to address the blockchain interoperability challenge by exploring the fundamental requirements that blockchain systems should offer to achieve technical, semantic, legal and organizational interoperability. The study adopted the Systematic Literature Review approach and included 83 peer-reviewed articles and 5 industry reports on blockchain interoperability. The findings of the study are categorized according to the four levels of interoperability defined by the European Interoperability Framework. The findings indicate security and data confidentiality as some of the technical requirements that must be met. While semantic requirements include establishing standardized data ontologies. Legal interoperability requirements include the use of smart contracts to enforce domestic and international laws, and organizational requirements relate to establishing cooperation agreements and developing collaborative business models for data exchanges between organizations.
Design of a Tokenized Blockchain Architecture for Tracking Trade in the Global Defense Market

The trades in global defense market usually involve sensitive systems, firearms and ammunition which need special regulations and control. Controlling the flow of defense products requires a comprehensive and coordinated effort involving cooperation between manufacturers, local dealers and national law enforcement. However, ensuring transparency in the trade of goods through the defense market is difficult. Traditional approaches can be replaced with a blockchain-based ledger to solve their transparency issues. Blockchain technology and tokenization can be used for making defense sales more transparent and traceable. In this study, NFTs are utilized to build a tokenized architecture that use the Ethereum blockchain, IPFS and smart contracts to trace, perform transactions, and create digital twin certificates for the products of the defense market. Our solution offers security, high integrity, reliability, traceability, and transparency while eliminating any requirements for trusted intermediaries.
Decision-Making Based on a Conditional Fuzzy Measure

The application of a conditional logical formula of three-valued calculus in the decision-making system is considered. A conditional logical formula makes it possible to determine a conditional fuzzy measure on its basis, which is associated with the following positive aspects. First, there is no need for expert evaluation of the fuzzy measure of the truth of the conclusion for fuzzy premises, which reduces the degree of subjectivity and eliminates the need to ensure the completeness of statistical data, as well as the justification of completeness. Secondly, the proposed version of calculating conditional conclusions relatively simply allows for a multi-premise case and the ability to evaluate the importance of premises based on their priorities (in classical approaches like Mamdani, premises do not differ in their degree of importance for conclusions). Thirdly, there is no needto eva luate the degree of truth of the rules themselves for fuzzy conclusions. These advantages simplify practical use and ultimately improve the quality of decisions made, especially in the case of a large number of inputs (for exam ple, numbered in tens). An example of the practical use of the approach developed on the basis of a fuzzy conditional measure for making decisions about the correction of the learning process based on the testing results is given.
Moving Towards Blockchain-Based Methods for Revitalizing Healthcare Domain

The healthcare industry is widely regarded as one of the most critical domains in contemporary society. It plays a pivotal role in promoting the overall physical, mental, and social well-being of individuals worldwide, and efficient healthcare delivery can significantly contribute to a government’s economic development and modernization. However, ensuring the security of vast volumes of healthcare data remains a primary concern. Many traditional solutions are inadequate for addressing this particular issue. Therefore, researchers and practitioners have developed novel methods that rely on cutting-edge technology to achieve a high level of security. One such technology that has gained significant attention is blockchain, owing to its robust properties for instance decentralization, immutability, and transparency. Blockchain has demonstrated promising results in various domains, from the financial and innovative sectors to the healthcare industry. In this paper, we first outline the challenges associated with security and data management in healthcare. Subsequently, we highlight the key components and consensus mechanisms of blockchain technology. We then present an overview of blockchain applications in the healthcare field by reviewing relevant use cases, such as improving patient monitoring, controlling pharmaceutical supply chains, and ensuring the security of electronic medical records. Ultimately, we identify the constraints and challenges of existing research and provide recommendations for future works.