Reviewerly/exHarmony huggingface dataset:
Evaluating the Usability of Website Interfaces in Fiji

User interfaces are vital in disseminating information and providing services through websites. A web interface must be user-friendly, easy to use and satisfy users' needs. A website must possess usability features to attract more customers and produce the maximum desired outcome with minimal effort. Fiji has witnessed intensification in websites and web interfaces over recent years. However, there is a lack of study on assessing the effectiveness of these websites. To fulfil this shortcoming, this study aims to evaluate the usability of website interfaces in Fiji.
A Decentralized Employee Performance Appraisal Framework for Recruitment, Performance Prediction and Ranking using Permissioned Blockchain and Machine Learning

Recruitment is a crucial task for Human Resource Management (HRM) and therefore determines the selection of skilled employees - who play key roles in a company's success. Employees perform well only when their skill set matches their job requirements. However, the current recruitment system fails to provide a single solution that verifies employee records and predicts employee-job compatibility. This paper proposes a recruitment system using blockchain, a machine learning model, and a multi-criteria decision analysis method. Here, blockchain technology is used to hold employee records in an encrypted manner, including their performance appraisals, in a decentralized system that allows data verification by supported organisations. QR code is used to verify encrypted employee records which are then decrypted within the system and used to predict their performance for the hiring company, using a classification model. Here, after comparing the performance of different classification models, the best-performing model, SVM Classification, has been used. Finally, the system ranks eligible candidates, based on employee records including their predicted performance appraisal rating, using a TOPSIS algorithm. The SVM classification model and TOPSIS algorithm are deployed in a Streamlit application, which predicts employee performance appraisal ratings and ranks candidates by generating scores using TOPSIS. The scores are then sorted from highest to lowest, therefore ranking candidates from most suitable to least suitable for a job role.
Enhancing Cluster Quality of Numerical Datasets with Domain Ontology

Ontology-based clustering has gained attention in recent years due to the potential benefits of ontology. Current ontology-based clustering approaches have mainly been applied to reduce the dimensionality of attributes in text document clustering. Reduction in dimensionality of attributes using ontology helps to produce high quality clusters for a dataset. However, ontology-based approaches in clustering numerical datasets have not been gained enough attention. Moreover, some literature mentions that ontology-based clustering can produce either high quality or low-quality clusters from a dataset. Therefore, in this paper we present a clustering approach that is based on domain ontology to reduce the dimensionality of attributes in a numerical dataset using domain ontology and to produce high quality clusters. For every dataset, we produce three datasets using domain ontology. We then cluster these datasets using a genetic algorithm-based clustering technique called GenClust++. The clusters of each dataset are evaluated in terms of Sum of Squared-Error (SSE). We use six numerical datasets to evaluate the performance of our ontology-based approach. The experimental results of our approach indicate that cluster quality gradually improves from lower to the higher levels of a domain ontology.
A Comparative Analysis of Swarm Intelligent Algorithms

A Black box program is one where the user cannot see the inner workings or one which has no side effects and the function of which need not be examined. Nature-inspired computing (NIC) or bio-inspired computing focuses on designing and developing computer algorithms and models using principles adopted from biology. Swarm intelligent algorithms (SIA) are bio-inspired algorithms developed based on the interactions between living organisms such as flocks of birds, ants, and fish. Swarm intelligence (SI) is one of the computational intelligence techniques used to solve complex problems. It is a reliable technique that has been used for treating several optimization problems. The two Swarm intelligent algorithms used for this research are particle swarm optimization (PSO) and Artificial Bee Colony (ABC) algorithm. The corresponding modified guided algorithms PSO-Guided and ABC-Guided are proposed. The proposed guided approach utilizes the current global best solution to guide the algorithm out of the local minima. The study finds that the guided methods outperform the standard algorithms in finding the best solution.
Analysis of Differences in Motivation Focusing on Differences in National Character

This study analyses differences in motivation with a focus on differences in the national character. The turnover rate of foreign workers in the Japanese IT industry is increasing. One possible reason for the increase in turnover is the failure of Japanese companies to set goals and provide treatment that matches the values of foreign workers. Therefore, this study analyses differences in motivation with a focus on national character differences. Interviews will be conducted with Japanese and Vietnamese people, and the results of the interviews will be analysed to estimate differences in motivation.
Airline Twitter Sentiment Classification using Deep Learning Fusion

Since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. Blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. Businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LSTM architecture for sentiment analysis. For comparison of the proposed system, LSTM alone also tested for similar dataset. LSTM was given an accuracy of 91% and the proposed ensemble framework with LSTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques.
Applicable Cyber Security Recommendations to Prevent Cyber Attacks in Universities

when it comes to IT, cyber security is of the utmost importance. Information security has evolved into a major challenge in the modern world. The term “cyber-crimes” immediately brings to mind the worrisome growth of a potential security risk in the cyber realm. Many organizations across the world, including governments, corporations, and educational institutions, are actively working to tackle cybercrime. Despite these measures, many continue to worry about their personal data being compromised in cyberspace. This article will discuss why it's so important for universities to train their students in cybersecurity, what challenges they face, and what solutions may be implemented to keep student data safe.
Cardiovascular Disease Detection Based on Interpretable and Explainable AI

This paper presents details of studies conducted to investigate interpretable and explainable machine learning and AI models for cardiovascular disease detection based on the publicly available Cleveland dataset. The study involves evaluating the interpretability and explainability capabilities of tradition shallow machine learning models and their potential for implementation under low resource settings, with limited training data available for model building, as compared to high performing deep learning models, requiring massive training datasets.
Blockchain-based Reviewer Selection

Universities require their students to undertake projects as partial fulfilment of the students' degree. These projects typically go through an independent external review process so that academic standards are upheld. Finding appropriate external reviewers with the domain knowledge is a time-consuming process. In this paper, we propose a system that helps advisors find examiners for their students' projects. The system uses machine learning techniques to identify the subject areas of the students' projects from the text of students' dissertations. It then matches scholars who have research interests in the identified subject areas. The system is implemented using a blockchain and aims to provide a transparent environment that allows scholars to freely participate in the system. In this project, we collected a set of papers from several top journals and conferences. Using this dataset, we have evaluated the performance of several classification algorithms, e.g., multinomial Naive Bayes, K-Means Clustering with multinomial logistic regression and Labeled-LDA. The experimental study shows that the weighted multinomial Naive Bayes model performs best among the three models in terms of accuracy and computational time.
Improving Mergesort Performance by Optimizing Branch Prediction

By detecting arrays with a low degree of presort-edness, an adaptive mergesort algorithm can switch to a branch free implementation in order to reduce the runtime by up to 30% compared to traditional implementations and almost 50% compared to the mergesort based Timsort, while retaining the great performance of branch based variants on partially presorted arrays. By using a fast method of detecting the degree of presortedness, no prior knowledge about the array composition is required for the algorithm.
Design and Fabrication of Quad Bike For Physically Challenged Person

A Quad-chair pro - sports wheelchair, is an attempt to make a wheelchair powered electrically for the physically challenged people corresponding to almost 6% of the world population, estimating approximately 200 million, facing challenges to drive speedy vehicles in roads which are worst affected or with uneven surfaces. Thereby this project makes it convenient for them to travel faster, save time and reduce their dependency on other individuals in boarding public transports or travelling form one place to another. It is designed with the intentions of providing high speed, high safety, reliability and eco-friendly measures. It can be partially or fully automated depending on an individual's degree of impairment and needs.
Applicability Analysis of Blockchain Technology

Blockchain technology has been generating great interest by a variety of industry sectors. Realizing the strength of blockchain technology beyond its successful application in the cryptocurrency arena, researchers have been evaluating and using blockchain for applications such as supply chain management, the energy industry, health sectors, and much more. Some of the features include smart contracts, decentralization, consensus, immutable distributed ledgers, cryptographic hashing, and digital signatures. There are multiple types of blockchain and different consensus models. However, it is often a significant undertaking to determine if an application requires a blockchain. What kind of blockchain and consensus model is best suited for a given scenario? This paper addresses this challenge by evaluating different blockchain solutions along with the consensus models to determine the applicability for different use cases. We propose a guideline and applicability analysis framework (AAF) to determine whether an application needs a blockchain solution or not. The AAF is divided into 6 domains, 11 subdomains, and 45 controls. It is designed to ingest detailed user requirements to perform a weighted evaluation that is built on mathematical constructs to determine the scenario in which a blockchain-based solution is appropriate. Moreover, this article also includes an example of evaluating applicability through AAF with the help of a use-case scenario.
Development of Interactive ASL (American Sign Language) Learning App

The ASL (American Sign Language) is one of the most used sign languages today due to the increased population of people with hearing loss and speech impediment. The interactive and fun learning platform for these individuals are less which causes communication gap between impaired and unimpaired people, trouble availing services and difficult learning sign language for abled individuals. In this paper, the basics of ASL, alphabets are animated according to the sign language using Blender software to model a 3D character and using Unity software, developed an interactive basic ASL learning app. It has features like AR (Augmented Reality) and audio for all signs which can be heard with respect to the letter typed by the user. The AR is integrated in Unity software with the Vuforia plug-in. The app was developed successfully with instant response by the 3D character model with letter input and for AR, detection of target image was found to be within seconds.
Predicting Habitual Activities for Reducing Perceived Latency

Human activities are usually habitual, following some patterns. The patterns make these activities predictable. A number of our activities these days are online, and these activities require us to access resources from online sources, incurring data transfer latencies. Being able to predict the patterns in online activities allows automatic prefetching of these resources so that the perceived latency of accesses can be vastly reduced. While there is a lot of prior research to predict short-term activities, research relating to predicting long-term habitual activities is sparse. This paper attempts to address the general area of how long-term habitual activities can be predicted. It proposes a model using long short-term memory (LSTM) capable of learning long-term habitual activities and predicting future activities based on this model. Our experimental evaluation shows that the prediction accuracy is promising.
Web-based Signing of English Text

Deaf people use sign language to communicate. There is a misconception that deaf people can read the text written in their surrounding spoken language easily. In fact, sign languages have linguistic structures quite different from their surrounding spoken languages. Therefore, a surrounding spoken language is foreign to most deaf people. Hence, the best way to help deaf people understand written text is to convert the text to signs in sign language. This paper presents a system that translates English text to American Sign Language (ASL) gloss and uses an avatar to show the signs. The system has a database that stores the sign pose data for various descriptions in sign gloss. First, the system uses a transformer-based model to translate English to ASL. Then, it looks up the sign pose data database to find the sign pose data for the descriptions in the sign gloss. The sign pose data is used to generate the animations of the signs by an avatar. The system provides a web-based interface, making it widely accessible across multiple platforms. Experiments show that the translation from text to sign language gloss achieves a good accuracy.
A Novel Security Vulnerability Detection Mechanism Using Information Flow Tracking on a Complex SOC

Non-Coherent access (NCA) is the mechanism used to exercise Non-Coherent (NC) transactions. These NC transactions commonly read or configure various System-on-Chip (SOC) registers. In SOC, NCA control mechanisms are implemented at various levels, defining restrictions on accessing sensitive resources from NC transactions generated by agents like Central processing unit (CPU), peripheral, co-processors, and so on. With increasing SOC complexity, the number of agents increases, and their communication path via interconnected Network-on-Chip (NOC) busses and routers NOC, referred to as Non-Coherent transaction path (NCP), gets more complex, resulting in multiple NCPs corresponding to the NCA of a single register from various agents. As a result, any NCP access violation across multiple N CPs can expose critical register data to unauthorized agents, allowing attackers to modify or access critical data via any malicious SW, as recently demonstrated by SW-Mounted physical Side-Channel attacks. Henceforth, in a complex SOC, validating multiple N CPs access control security vulnerabilities for numerous SOC registers is crucial. Therefore, in this paper we propose a novel approach to exploit all registers NCA security vulnerabilities in complex SOC. For the first time, the Information flow tracking (IFT) approach is utilized to define the allowable NCPs that can transfer data to the given register while respecting the SOC Access control architecture. This technique is used to threat model all SOC registers NCA. Where, we model the detected threats to mimic real-world attack scenarios by generating test scenarios and unified coverage, which is compatible with validation platforms like presilicon simulation, emulation, and post-silicon environment. The proposed methodology outperforms the current RAL-based access control validation method, which is limited to only a Simulation environment.
Analysis of Trending NFTs using Time-Series Techniques

The financial ecology has changed thanks to blockchain technology. The first known instance of blockchain may be found in a 2008 whitepaper written by a person using the alias Satoshi Nakamoto. Non-Fungible Tokens, or NFTs, are a blockchain product that has attracted a lot of public interest. A NFT is a digital property that is implemented via blockchain. It is used to prove ownership and authenticity as it cannot be duplicated, replaced, or divided. An NFT's ownership is documented in the blockchain and is transferrable by the owner, enabling the sale and trading of NFTs. Through this paper we try to establish a relationship between NFT value and various factors such as social media, OpenSea data and so on. We aim to achieve this by using Support Vector Machine, Recurrent Neural Networks, Regression for accurate results. Thus, predicting the value of the NFT precisely.
Estimating creativity drawing features from hand drawing logs

Students should engage in creative works in schools. However, there is no sensor to measure on the spot whether they engage in creative works. This work assumes engagement in creative works shows up in their writing features with pens. It uses a tablet and an electronic pen to acquire the writing features. It applies Random forests to detect the writing features. The results have showed that the features appear in writing pressure, velocity, and pen tilt angle.
Improved Yolov5S normative identification algorithm of power place operators

In the process of electric power construction, there are a variety of non-standard or abnormal behaviors, such as not wearing a safety helmet, smoking, talking on the phone, falling, etc., which should be discovered in time to avoid safety accidents. Aiming at the existence of false detection and missing detection in the recognition of small targets such as smoking and telephone, an improved yolov5 method is proposed. Add Coordinate Attention mechanism in Neck of yolov5 to enhance the ability of the model to accurately locate the target. Accordingly, a small object detection layer is added to prevent the loss of shallow semantic information to some extent. Aiming at the problem of insufficient feature fusion, the original FPN+PANet was replaced by weighted bidirectional feature pyramid BiFPN to enhance the expression ability of multi-scale features. SIoU border loss function is used to improve the model recognition accuracy. The experimental results show that, compared with the yolov5s model, the improved yolov5s model has 3.7%, 2.0% and 1.9% improvement in recall rate, mean mean of average accuracy (mAP) and accuracy, respectively, among which the improvement of small targets such as smoking and phone call is the most significant.
A Method of Passive Localization Based on Unscented Kalman Filter

This paper considers the target localization problem for cruise missile. Missile position, missile attitude and frame angles are used to calculate the target position. A passive localization algorithm based on unscented Kalman filter by observing the same target point multiple times is provided. We also provide a state initialization method to make the algorithm more suitable to engineering. The performance is demonstrated with simulated data in a six degree of freedom model.
Multimodal Fusion Authentication Method for Handheld Operation and Maintenance Terminal Considering Multi-factors

Under the background of mass access of handheld operation and maintenance terminals in smart grid, hardware resources are limited, password resources are insufficient, and security cannot meet the requirements of sensitive information protection. To solve this problem, this paper proposes a multi-mode fusion identity authentication method and system for handheld operation and maintenance terminals in distribution network that takes into account multiple factors. It can integrate multiple factors to participate in authentication. On the premise of ensuring the identity security of the operation and maintenance terminals accessing the cloud master station, it extracts various physical and behavioral characteristics of the handheld operation and maintenance terminals to form an identity authentication fusion model with multi-mode characteristics, Continuously carry out implicit identity authentication for handheld operation and maintenance terminals, and enhance the stability of authentication, anti-forgery and anti-attack performance on the premise of improving the authentication accuracy.
Power Terminal’s Identity Authentication Method Based on Unclonable Fingerprint for Electric Vehicles to Smart Grid

EV (Electrical Vehicle) is one of the critical parts of the future smart grid ecology. Through the bidirectional flow of energy between EVs and the grid, V2G (Vehicle-to-grid) guarantees the resilience of the smart grid while reducing greenhouse gas emissions. However, V2G relies on vulnerable vehicle-to-vehicle communications that are not under well-established physical/information protection measures, making them vulnerable to attacks that threaten users’ private information and the security of the power grid. To defend against the aforementioned attacks, we propose a lightweight V2G identity authentication method based on the unclonable fingerprint of EVs, which ensures user confidentiality and physical security. According to the simulation performance analysis, the proposed scheme is better than the mainstream V2G identity authentication scheme.
“You can’t improve until you measure”: A Need Finding Study on Repurposed Clinical Indicators for Professional Learning

Hospitals generate large amounts of administrative data from healthcare delivery.Much of this data is for summative quality and safety (Q&S) reporting rather than formative professional learning.Little is known about what clinicians want to learn about their practice from this data and how they want to see that information.To address this, we designed a need finding study, run at a large metropolitan hospital.This paper describes the study design, based on semi-structured interviews which used four static visualisations prototypes.Our participants, nine specialists, valued the repurposing of clinical indicators (CIs) for professional learning.They particularly valued peer comparison and trends for benchmarking and highlighted the need for risk-adjusted results that are carefully selected to ensure fair and meaningful comparisons.Our contributions are: (1) insights and needs from participant free comments, (2) comments around our four visualisation prototypes, and (3) design recommendations when presenting CIs for professional learning.