Reviewerly/exHarmony huggingface dataset:
Retracted: Software solution for text summarisation using machine learning based Bidirectional Encoder Representations from Transformers algorithm

Abstract Retraction: [Abdulwahid Al Abdulwahid, Software solution for text summarisation using machine learning based Bidirectional Encoder Representations from Transformers algorithm, IET Software 2023 ( https://doi.org/10.1049/sfw2.12098 )]. The above article from IET Software , published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Soft Computing Techniques to Analyze the Load Balancing in Cloud Environment

An emerging method of digital computing known as "cloud computing" has recently gained immense popularity. While there are many benefits to using the cloud over the internet, there are also serious challenges that must be addressed in order to boost the efficiency of this method. Challenges to cloud computing via the internet include load balancing, work scheduling, fault tolerance, and several security concerns. The effectiveness of the cloud may be enhanced by fixing a number of problems, one of the most pressing being load balancing. To prevent any one node from being too overburdened or underused, a system employs a technique known as load balancing. In order to increase utilization and minimize overall task execution time, load balancing algorithms are developed to distribute work fairly across available resources. In this article, a review and comparison of different load-balancing algorithms is provided. This paper provides a basis of the application of different load-balancing techniques, which utilize the approach of soft computing within the cloud computing environment.
Methodological Research on Image Registration Based on Human Brain Tissue In Vivo

As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis.
Human Activity Recognition Based on a Modified Capsule Network

Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.
Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study

Access to data is crucial for decision-making; this fact has become more evident during the pandemic. Data collected using mobile apps can positively influence diagnosis and treatment, the supply chain, and the staffing resources of health care facilities. Developers and health care professionals have worked to create apps that can track a person's COVID-19 status. For example, these apps can monitor positive COVID-19 test results and vaccination status. Regrettably, people may be concerned about sharing their data with government or private sector organizations that are developing apps. Understanding user perceptions is essential; without substantial user adoption and the use of mobile tracing apps, benefits cannot be achieved.This study aimed to assess the factors that positively and negatively affect the use of COVID-19 tracing apps by examining individuals' perceptions about sharing data on mobile apps, such as testing regularity, infection, and immunization status.The hypothesized research model was tested using a cross-sectional survey instrument. The survey contained 5 reflective constructs and 4 control variables selected after reviewing the literature and interviewing health care professionals. A digital copy of the survey was created using Qualtrics. After receiving approval, data were collected from 367 participants through Amazon Mechanical Turk (MTurk). Participants of any gender who were 18 years or older were considered for inclusion to complete the anonymized survey. We then analyzed the theoretical model using structural equation modeling.After analyzing the quality of responses, 325 participants were included. Of these 325 participants, 216 (66.5%) were male and 109 (33.5%) were female. Among the participants in the final data set, 72.6% (236/325) were employed. The results of structural equation modeling showed that perceived vulnerability (β=0.688; P<.001), self-efficacy (β=0.292; P<.001), and an individual's prior infection with COVID-19 (β=0.194; P=.002) had statistically significant positive impacts on the intention to use mobile tracing apps. Privacy concerns (β=-0.360; P<.001), risk aversion (β=-0.150; P=.09), and a family member's prior infection with COVID-19 (β=-0.139; P=.02) had statistically significant negative influences on a person's intention to use mobile tracing apps.This study illustrates that various user perceptions affect whether individuals use COVID-19 tracing apps. By working collaboratively on legislation and the messaging provided to potential users before releasing an app, developers, health care professionals, and policymakers can improve the use of tracking apps. Health care professionals need to emphasize disease vulnerability to motivate people to use mobile tracing apps, which can help reduce the spread of viruses and diseases. In addition, more work is needed at the policy-making level to protect the privacy of users, which in return can increase user engagement.
A compiler for universal photonic quantum computers

Photons are a natural resource in quantum information, and the last decade showed significant progress in high-quality single photon generation and detection. Furthermore, photonic qubits are easy to manipulate and do not require particularly strongly sealed environments, making them an appealing platform for quantum computing. With the one-way model, the vision of a universal and largescale quantum computer based on photonics becomes feasible. In one-way computing, the input state is not an initial product state <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert\boldsymbol{0}\rangle^{\otimes n}$</tex> , but a socalled cluster state. A series of measurements on the cluster state's individual qubits and their temporal order, together with a feed-forward procedure, determine the quantum circuit to be executed. We propose a pipeline to convert a QASM circuit into a graph representation named measurement-graph (m-graph), that can be directly translated to hardware instructions on an optical one-way quantum computer. In addition, we optimize the graph using ZX-Calculus before evaluating the execution on an experimental discrete variable photonic platform.
Blockchain Security and Corporate Governance

Economic globalization is a new challenge, especially for developing countries like Indonesia, because everything is interrelated and has a special impact such as the occurrence of a crisis that is evenly distributed in every country. For this reason, improving corporate governance is important. This study aims to determine the attitude of Indonesian investors in adopting Blockchain Technology as an effort to improve corporate governance in public companies in Indonesia. This study involved 170 investors and businesspeople who directly interacted with the benefits of corporate governance in a company. The data analysis of this research uses a structural equation model with SmartPLS software. The result is Perceived ease of use, Perceived usefulness, and Security have a significant influence on attitude in adopting blockchain in corporate governance, but if examined more deeply it can be seen that the coefficient and t-statistic value of security have the greatest results, so it can be said one of the important elements of blockchain adoption in the implementation of corporate governance must emphasize the high security factor, because agency theory and agency conflict which are classic problems in corporate governance are the main problems that can be solved with the presence of blockchain to reduce fraud, earnings manipulation, and earnings management.
Graph Modeling in Computer Assisted Automotive Development

We consider graph modeling for a knowledge graph for vehicle development, with a focus on crash safety. An organized schema that incorporates information from various structured and unstructured data sources is provided, which includes relevant concepts within the domain. In particular, we propose semantics for crash computer-aided engineering (CAE) data, which enables searchability, filtering, recommendation, and prediction for crash CAE data during the development process. This graph modeling considers the CAE data in the context of the R&D development process and vehicle safety. Consequently, we connect CAE data to the protocols that are used to assess vehicle safety performances. The R&D process includes CAD engineering and safety attributes, with a focus on multidisciplinary problem-solving. We describe previous efforts in graph modeling in comparison to our proposal, discuss its strengths and limitations, and identify areas for future work.
Enabling privacy-preserving multi-server collaborative search in smart healthcare

With the advancement of smart healthcare, each medical institution stores huge amounts of users' medical data in its cloud server for diagnosis and treatment. However, the traditional storage structure has obstacles to reasonable medical resources access and medical data circulation, such as data security, query privacy, and isolated data island problems. An intuitive solution is to store all the encrypted medical data in a central server. But this method absolutely depends on the central server, causing more performance disadvantages and privacy risks. Therefore, it is urgent to construct a secure and proper medical data retrieval scheme. Based on the existing data storage model, we build a multi-server search scheme to collaboratively perform diagnostic institution location, medical data search, and even cross-domain data search in this paper. The multi-server architecture solves problems of destructiveness and information over-centralization caused by the single server and enhances the reliability and practicality of the system. The utilization of hidden vector encryption, secret sharing, and secure multi-party computation realizes efficient search, identity privacy, search pattern security, and access pattern security. Security analysis demonstrates that identity privacy and query security are protected. Extensive experiments show that the scheme has better data search and data add efficiency through horizontal and vertical comparisons.
Criteria for the Dyslexic Games: A Systematic Literature Review

The advancement of technology has witnessed the increasing use of game-based intervention and it is proven beneficial for people with specific learning disorder such as Dyslexia. Initially developed for entertainment, a game is generally considered a tool with the key features of challenge, motivation, and rewards which add more fun and excitement to Dyslexia teaching and learning. Many game applications have been developed in the area; each incorporates several criteria to meet the specific needs of the dyslexics. To date, these criteria are still dispersed in numerous publications, leading to the duplication of basic games that have previously been developed by others. This study aimed to identify the dispersed criteria by adopting PRISMA approach in the systematic literature review, and analyze them to formulate a comprehensive guideline for future design and development of the dyslexic games. Five main databases were chosen which are Scopus, ACM Digital Library, EBSCO-host, Wiley and Web of Science (WOS). Out of 551 articles that have been screened, only 50 articles were eligible to be analyzed based on the relevancy to the criteria, and only 23 articles were included in the study after further screening. A total of 135 criteria were identified with some redundancies, and were further classified into four categories: device and platform; features; interface and gameplay. These criteria can be utilized by the educational game developers as reference when designing the dyslexic games. A better dyslexic-friendly games could be produced when the developers design it with their special learning needs in mind.
Character and Word Embeddings for Phishing Email Detection

Phishing attacks are among the most common malicious activities on the Internet. During a phishing attack, cybercriminals present themselves as a trusted organization or individual. Their goal is to lure people to enter their private information, such as passwords and bank card numbers, while believing that nothing malicious is happening. The attack often starts with a phishing email, which is an email that is very similar to a legitimate email, but usually contains links to malicious websites or uses some other techniques to mislead victims. To prevent phishing attacks, it is crucial to detect phishing emails and remove them from email inbox folders. In this paper, a neural network based phishing email detection model is proposed. In comparison to some earlier approaches, our model does not use manually engineered input features. It learns character and word embeddings directly from email texts, and uses them to extract local and global features using convolutional and recurrent layers, respectively. Our model is tested on the two commonly used datasets for phishing email detection, the SpamAssassin Public Corpus and Nazario Phishing Corpus, and it achieves an accuracy of 99.81 % and F_1-score of 99.74 %, which is on par or better than the current state-of-the-art approaches.
Background removal of video in realtime

Background removal for video is a computer-vision based system to remove the background from a video with ease. Creating a professional background when at home, i.e., not in a very professional environment, can be a tedious task. Not everyone has time to learn editing and the technicalities involved in having an entire setup for creating a sophisticated background and it is not practical that normal people buy green screens or blue screens just for their everyday formal meets. Our goal is to create a quick and easy solution to that by removing background in real time while also maintaining the quality of the call, having the additional benefit of adding custom backgrounds and enabling users to add effects like adjust lighting, contrast etc., we are combining all 4-5 steps in 1 single step. SelfieSegmentation module of Mediapipe helps us achieve this. The Selfie Segmentation API creates an output mask from an input image. The mask will be the same size as the input image by default. A float integer with a range of [0.0, 1.0] is assigned to each pixel of the mask. The higher the confidence that the pixel depicts a person, and vice versa, the closer the number is to 1.0.
Efficient system for CPU metric visualization

There are multiple metrics associated with the smooth and efficient working of a computer system. Some of the crucial parts are like the CPU, memory usage and GPUs. For different Operating Systems, they have their own System Software for managing and analysing their sessions. Like Task Manager in Windows, Nmon in Linux and Activity Monitor in Mac. In addition to it, there are few applications software’s which perform similar tasks with slight modifications. In this paper, a web application is proposed that will fetch these performance metrics from the user's system and display them using dynamic charts. The proposed application is a system independent tool and can be useful for all operating system. The application also can be used to determine whether or not a game is compatible with the user's system based on the system requirements.
Virtual emotion detection by sentiment analysis

As websites, social networks, blogs, and online portals proliferate on the internet, authors are producing reviews, opinions, ideas, ratings, and feedback. The emotional content of this writer may be about things like books, people, hotels, items, studies, events, and so on. These emotions have great value for businesses, for governments, and for people. The majority of the writer-generated material requires the usage of text mining algorithms and sentiment analysis, even if this information is meant to be instructive. Sentiment Analysis is a technique in Natural Language Processing (NLP) that tries to identify and extract assessments communicated within a given text. This paper intends to execute different content handling strategies in NLP and use of Valence Aware Dictionary for Sentiment Reasoning (VADER) Model that is sensitive to both polarity (positive/negative) and intensity (strength) of emotion.
Blockchain Technology

Blockchain came because of the occurrence of incredulity to single authorities by introducing the concept of network decentralization and data distribution saved in a ledger. Decentralization is used to validate discrepancies in the majority of data. The consensus mechanism collectively maintains the consistency of the ledger. A blockchain is a set of blocks containing transaction data interconnected to each other using the concept of cryptography. A mining process is an effort to add new blocks to the blockchain. The mining computer carries out the process after passing several complex mathematical problems. The fastest miner is rewarded with crypto coins. Some consensus mechanisms commonly used in blockchain are proof of work, proof of stake, practical byzantine fault tolerance, and proof of elapsed time. Blockchain network is designed and implemented in such a way that it can guarantee the security of its data, is easy to be audited, is robust to denial of service and majority attacks, and is private and confidential. The application of blockchain is not limited to finance systems; it can also be applied in health, education, supply chain, and state democracy systems.
RECENT DRONE APPLICATIONS IN MALAYSIA: AN OVERVIEW

Abstract. This study aims on an exploration of recent applications of drones in the Malaysian context. More specifically, to investigate the drone’s application status and application areas. To search the academic literature and identify the included documents, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We conducted an overview study of the total number of 54 documents (out of 202) published since 2018, which were extracted from the Scopus database. The results of the bibliometric analysis of the included documents showed that drones in Malaysia are applied in different disciplines, including agriculture, mapping, forestry, river monitoring, transportation, construction, coastal management, flood management, and other applications. Among these, agriculture and mapping are the main application areas. MDPI published the most journal articles, and IGRSM conferences published the highest number of conference papers. Furthermore, scholars from the Universiti Teknologi Malaysia have the highest contribution among others.
Higher education design: An emerging but cannot be ignored agenda

OPINION article Front. Educ., 07 February 2023Sec. Higher Education Volume 8 - 2023 | https://doi.org/10.3389/feduc.2023.1074854
Interface Design for Crowdsourcing Hierarchical Multi-Label Text Annotations

Human data labeling is an important and expensive task at the heart of supervised learning systems. Hierarchies help humans understand and organize concepts. We ask whether and how concept hierarchies can inform the design of annotation interfaces to improve labeling quality and efficiency. We study this question through annotation of vaccine misinformation, where the labeling task is difficult and highly subjective. We investigate 6 user interface designs for crowdsourcing hierarchical labels by collecting over 18,000 individual annotations. Under a fixed budget, integrating hierarchies into the design improves crowdsource workers' F1 scores. We attribute this to (1) Grouping similar concepts, improving F1 scores by +0.16 over random groupings, (2) Strong relative performance on high-difficulty examples (relative F1 score difference of +0.40), and (3) Filtering out obvious negatives, increasing precision by +0.07. Ultimately, labeling schemes integrating the hierarchy outperform those that do not — achieving mean F1 of 0.70.