Reviewerly/exHarmony huggingface dataset:
Domains of case changing and case maintaining movements

With recent developments in Case Theory, movements in which a DP acquires a different case to the one it would have received had it not moved have been accepted as a possibility. In this paper we examine a number of such movements from a variety of languages to attempt to characterise and understand them more fully. Based in Dependent Case Theory, our analysis claims that case change does not really happen, but case assignment is allowed to be delayed under certain circumstances creating the illusion of one case over-writing another. In explicating these circumstances, we are not only able to provide a better understanding of when ‘case change’ can and can’t happen, but also develop the theory in ways which address certain conceptual problems that it faces.
What Drives Generation Z to Behave Security Compliant? An Extended Analysis Using the Theory of Planned Behaviour

Cyber security remains a relevant topic for organisations. While companies invest in expensive security tools security awareness training often is neglected, even though human error still accounts for a large part of cyber incidents (Gartner, 2022). At the same time there is currently an important generational shift, as Generation Z (Gen Z) is starting to enter the workforce and is said to be soon overtaking millennials. However, Gen Z is said to experience a lot more security related issues compared to older generations. This paper aims to understand and analyse security behaviour for Gen Z by using the theory of planned behaviour to understand what influences the security behaviour of this generation. The theory of planned behaviour has been often utilised in previous research to understand how security behaviour is formed and how it can be influenced, however, it has not yet been researched in the context of Gen Z. The results conclude that Gen Z has a rather indifferent attitude towards security and generally values convenience much more than security. This can be led back to the lack of security awareness education as opposed to older generations that often experienced a training in the work environment. Gen Z is highly influenced by their environment; therefore, a company should strive to create a security culture. Lastly, Gen Z experiences a low perceived behavioural control, as they feel a lack of control regarding to the internet and lack education how to behave more secure. This gap could be closed by providing a suitable security awareness training.
Gender Recognition of Bangla Names Using Deep Learning Approaches

The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person’s name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows.
Lackluster Adoption of Cryptocurrencies as a Consumer Payment Method in the United States—Hypothesis: Is This Independent Technology in Need of a Brand, and What Kind?

Cryptocurrencies were supposed to replace traditional payment methods when they were invented over 13 years ago, but adoption by the general consumer is still lacking, at least in the United States. Instead, crypto is often used as a speculative investment, by illicit actors, or for use cases unrelated to everyday purchases. A literature review on general adoption barriers and interviews with experts has only unearthed factors like usability, performance, and political drivers, among other barriers. Brand as an adoption barrier is mostly missing from literature, at least for cryptocurrencies. This led to the formation of a hypothesis related to crypto’s lack of adoption as a payment method. A framework is being designed based on the technology adoption model to find out if “brand” has an impact on cryptocurrency adoption, which was paradoxically designed to be brandless and not needing any institutional trust. The intent is to focus on what “Bitcoin 2.0” might look like, and to also delve further and gauge perceptions about various types of brands getting involved in the next generation of cryptocurrencies, including traditional banks, governments, technology companies, and also some of the decentralized and hybrid consortia currently vying to get consumers to use stablecoins, nation-issued cryptocurrencies, and other forms of digital instruments. While other studies had focused on trust, early adopter usability, or performance of blockchain networks, this work intends to focus on the general consumer’s perceptions about digital money, and the types of brands and evolution of this instrument liable to increase uptake.
360-degree panoramic video generation using virtual and actual multiple cameras

This paper proposes a technique for 360-degree panoramic video generating with multiple actual and virtual cameras in real-time. Our method acquires multiple images consisting of real images from physical cameras and virtual images from virtual cameras by GPU-based texture and encoding. Then, our method generates 360-degree panoramic images by ORB feature detection and matching to virtual and real images, automatic homography estimation based on the random absent consensus algorithm, multi-band blending and produces high-resolution video stream by Nvidia encoder based Hardware accelerated video encoding. From experimental results, we verified that our method takes at least 45fps at 2K resolution and 33fps at 4K resolution when transmitting the head mounted display stereo, enabling real-time 360-degree panoramic video generation as well as transmission.
Area Day-ahead Photovoltaic Power Prediction by Just-In-Time Modeling with Meso-scale Ensemble Prediction System

Photovoltaics (PV) output prediction, which is indispensable for power system operation, can affects demand and supply adjustment adversely when large prediction error occurs. Thus, the reduction of large error as well as average error is required in PV power prediction. In 2019, the operation of the Meso-scale Ensemble Prediction System (MEPS) of numerical weather prediction started from the Japan Meteorological Agency, and the amount of forecasting information would be potentially useful for the improvement of PV power prediction. However, very few studies on inputting multiple meteorological elements of the MEPS have been reported. In this paper, we newly develop the prediction model for an area day-ahead PV power output composed of Just-In-Time Modeling (JIT Modeling) with multiple elements of the MEPS. The developed method achieves precise forecasts with low computational load by both selecting meteorological elements valid for improving prediction accuracy and adequately devising the structure of JIT Modeling. Some numerical examples demonstrating the effectiveness of the developed method are also presented. In particular, the proposed method reduces large error significantly.
Pixel-Changes-Based RGB Channels for a Novel Underwater Image Dehazing Technique

The field of underwater imaging is plagued by the problem of haze, which is an obscuring effect caused by water molecules and suspended particles absorbing and scattering light rays before the rays reach the camera. This proposed technique exploits individual RGB color channels and analyzes small pixel deltas in order to dehaze underwater images and increase perceived visual accuracy. A GARCH model is employed to estimate transmission maps because it can accurately estimate human visual perception by exploiting the volatility of pixel changes within color channels that result from changes in pixels between enhanced image features. Background light is estimated using differential light emissions in different color wavelengths. The proposed system is evaluated based on entropy, UIQM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">norm</inf> , and UCIQE, then compared with existing state-of-the-art methods. The results demonstrate that this technique is superior in both visual analysis and performance evaluation metrics. Future research should examine the effect of GARCH alone on the algorithm and also explore the use of other networks such as Hopfield neural networks to alternatively implement the algorithm.
Evaluation of Students’ Learning Through Reflection on Doing Based on Sentiment Analysis

Abstract In this paper, we address the following question: How can instructors leverage assessment instruments used to process information gleaned from design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve the course in the future? A take-away is unstructured text written by students in AME4163: Principles of engineering design to record what they understand by reflecting on authentic and immersive experiences that occur throughout the semester. The immersive experiences include lectures, assignments, reviews, building, testing, and post-analysis for the design of an electro-mechanical system to address a given customer need. In the context of a take-away, a student then writes a learning statement that is a structured sentence written as a triple, i.e., (Experience—Learning—Value). Between 2019 and 2021, we collected about 10,000 take-aways and learning statements from almost 400 students. In this paper, we address the question from the perspective of students’ feelings and use dictionary-based sentiment analysis to evaluate students’ subjective feelings toward what they are learning. Through quantitative results, we get an overview of students’ sentiments, analyze the underlying reasons, and provide evidence-based guidance to instructors on how to improve the delivery of the course in the future. Our focus in this paper is on explaining the method using data from AME4163, which is general and can be extended to other courses.
Differential Preserving in XGBoost Model for Encrypted Traffic Classification

The classification of encrypted traffic is becoming ever more relevant in the field of network security management and cybersecurity. Most users are currently using encrypted traffic, which can easily lead to privacy threats, and attackers can identify user behavior through the information obtained. VPN encrypted tunnel is the most popular encrypted tunnel method at present. This paper proposes to use the XGBoost model to classify VPNs and Non-VPNs, normalizing the features extracted from encrypted traffic. Experiments are performed on the public dataset ISCX VPN-nonVPN, and the results show that the XGBoost model has an accuracy of 92.4%. To illustrate the advantages of this model, it is compared with the other 5 classification algorithms. At the same time, this paper applies differential privacy technology to the classification model of encrypted traffic and reduces privacy threats by obfuscating data features.
Robust TPMB Filtering Using Sensors With Limited Sensing Range Under Nonuniform Clutter Background

This study proposes a novel solution using sensors with limited sensing range to estimate the random appearance or disappearance of multi-object trajectories with unknown object detection profiles under nonuniform clutter background. Specifically, assuming that no objects occur within the sensors’ observable area, the unknown clutter intensity involving the average false alarms per scan and nonuniform clutter density is first estimated by adopting the joint combination of maximum likelihood (ML) and model-based clustering methods. Then, the unknown object detection profile can be calculated by marginalizing the fluctuating signal-to-noise ratio (SNR) in a random manner. Further, the TPMB filter is utilized to estimate trajectories from the first principle, after which the estimated parameters of clutter intensity and the detection probability are fed to the TPMB filtering, thereby improving the performance on completeness and robustness of estimation trajectories. Simulation and experimental results demonstrated that our proposed solution exhibits excellent tracking accuracy compared with state-of-the-art solutions involving robust MS generalized LMB (R-MS-GLMB) and dynamic parameter GLMB (DP-GLMB) filters.
Intelligent Adjustment of Temperature Control Parameters Based on Deep Reinforcement Learning for Stretch Blow Molding Machine

Stretch blow molding is the main technology used in the production of PET bottles. The stretch blow molding machine is usually composed of preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Among them, the temperature control of the heating system is one of the key factors affecting the quality of PET bottle, especially in the face of the environmental temperature changes greatly between morning and evening in some seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process is highly dependent on experience of the personnel. It has become a production challenge for the bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent adjustment technique of temperature control parameters. The proposed method can improve the problems such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. The experimental results show that the proposed method can adjust the temperature control parameters automatically in the heating process to eliminate the effect of the environmental temperature change and to control the surface temperature of the preforms stably within ±3 °C of the target temperature (the requirement is within±5 °C).
Real-time Arabic Sign Language Recognition using CNN and OpenCV

Sign language is the way of interacting for hearing impaired people. Each Sign language has multiple dialects. The world suffers from a lack of interaction between impaired and hearing individuals. This work proposes an application that recognizes Arabic Sign Language (ArSL) using Convolutional Neural Network (CNN) and OpenCV. The CNN model is trained using ArSL2018 with over 5000 images with 32 categories. Next, the OpenCV is used to capture a real-time frame. After that, the CNN model and OpenCV method are combined to recognize 32 letters. The model shows a weighted-average F-measure of 95%. Similarly, the macro-average F-measure was 95%. Finally, the application is run and the prediction of the letters in real-time is successful.
Template matching via bipartite graph and graph attention mechanism

Abstract Most existing template matching algorithms are global matching between the template target and the search region, which makes the matching process retain a lot of unfavourable background information and ignore the structure and local information of the template target. To address this problem, a template matching algorithm based on bipartite graph and graph attention mechanism is proposed in this paper. The algorithm models the similarity matching problem between template features and search region features as a complete bipartite graph, realises local‐to‐local information transfer between the two, and uses the graph attention mechanism to apply weights between local information to obtain a learnable embedding network module. In addition, in terms of feature representation, a multi‐level feature fusion module based on CNN is introduced, which improves the representation of a target by fusing features with different representational meanings of the target. Experimental results on several typical datasets show that the proposed algorithm achieves leading performance in terms of accuracy and efficiency compared to the two state‐of‐the‐art CNN‐based template matching algorithms, Deep‐DIM and QATM.
How Personality Traits Impacts on Cyber Security Behaviors of SMEs Employees

Human behavior is seen as the primary risk within small enterprises. Attackers aim to achieve their destructive goals by exploiting end-users flaws, and they prefer to attack people rather than systems. As a result, human vulnerabilities endanger electronic systems, data confidentiality, and reliability. Because of their recognized susceptibility to cyber security, cyber-attackers are increasingly targeting small and medium-sized enterprises (SMEs) rather than large organizations. Previous studies have shown that employees are the weakest point in cyber security. For successful cybersecurity practices in companies, security risks and employees' decision-making processes should be considered more attentively. Personality is one of the most important psychological characteristics influencing human conduct. Personality traits influence people's decision-making processes and boost the performance of employee behaviors. The research discusses psychological aspects, end-point cyber-attacks, personality traits, high and low scores of employees' behaviors, and risk-taking behavior; this study further provides implications for SME employees to enhance security behaviors based on personality traits experiences. This research helps SMEs in better understanding the causes behind employees' cyber security behavior.
ESSA Scheduling Algorithm for Optimizing Budget-Constrained Workflows

Workflows are a systematic approach for defining various scientific applications of distributed systems. They break down complicated, data-intensive processes into minor activities that can be executed serially or in parallel according to the type of application. Cloud systems need to allocate resources and schedule workflows efficiently. Despite many studies on job scheduling and resource provisioning, an efficient solution isn't found. Therefore, techniques are required to enhance resource utilization for optimal cloud computing platforms. Hence, user and provider quality of service (QoS) goals, like shortening workflows and ensuring budget limits with low energy utilization, must be considered. Enhanced Salp Swarm Optimization (ESSA) is designed to optimize makespan and QoS metrics in cloud systems. A Virtual Machine (VM's) compute capacity is related to Central Processing Unit (CPU) and memory. Size and memory demand is considered for tasks in the workflow, and task execution time is evaluated using both CPU and memory. The collated experimental outcomes convey that the newly presented technique boosts the workflows' energy utilization (up to 89%) and pushes the normalized makespan results to 3.2ms.
A Smart Contract-Based Access Control Framework For Smart Healthcare Systems

Abstract Security faces huge challenges in Internet of Things (IoT) environments. In particular, conventional access control standards and models tend to be less tailored for IoT due to the constrained nature of smart objects. Usually, a powerful third party is used to handle the access control logic. However, this third party is lacking in transparency and could harm user privacy. Therefore, providing a distributed access control solution, while considering transparency and privacy-preserving awareness in IoT smart systems, is of paramount importance. The described issue can be addressed using the emergent Blockchain technology that provides a promising choice to build a new generation of decentralized and transparent access control solutions. This paper proposes a smart contract-based access control framework for IoT smart healthcare systems, which is based on smart contracts to provide a distributed and trustworthy access control, combined with the GTRBAC model to express fine-grained access control policies while considering temporal authorization constraints. To prove the feasibility and validity of the proposed framework, this paper also provides a detailed technical description and an initial implementation and execution. An experimental evaluation shows that security properties’ analyses on smart contracts achieved the best possible evaluation with no vulnerabilities found, and the cost of access control operations increases linearly as the number of policy constraints increases. Besides, a comparative analysis reveals that the proposed approach can achieve good results with low gas costs and latency.
Language choice in official information materials on COVID-19 in the Philippines: a language justice perspective

ABSTRACTABSTRACTEvery individual’s language rights are just as important and, often, even equate to healthcare rights during the COVID-19 pandemic. During this crisis, essential and lifesaving information and updates about local and international efforts to respond to and mitigate the impacts of the pandemic have to be made available using language(s) accessible and understandable to as many groups of people as possible. Using the lens of language justice, this paper analysed 238 official - information materials on COVID-19 from social media pages of the Department of Health (DOH) regional offices in hopes of unpacking the injustice that those who speak minor languages in multilingual Philippines experience. The hegemonic presence of major languages in the materials was observed as a majority of the information materials were published in Tagalog and English, whereas the Mother Tongues were unequally represented. The researchers argue that more Filipinos are at risk of contracting the virus, as the languages in which lifesaving information are presented are limited and unequal. Autonomy and decentralisation of local government offices and representation of speakers of the minority languages in the production and publication of materials will be an inclusive, sustainable, and participatory community approach to COVID-19 as a language rights emergency.KEYWORDS: language justicelanguage rightslanguage choiceCOVID-19language injusticePhilippine languages Disclosure statementNo potential conflict of interest was reported by the author(s).Notes to contributorsEdward Jay M. Quinto is Associate Professor at the School of Social Sciences and Education, Mapúa University, Manila, Philippines. He obtained his Ph.D. in Applied Linguistics from De La Salle University in 2018.Angela Cailou Gando, Anicia Mae Nantin, and Maxine Jecri S. Novilla are student-researchers from the Young Innovators Research Center, Mapua University, Manila, Philippines. They were Humanities and Social Sciences strand students who graduated from the Senior High School Department of the same university in 2021.
Questions for teaching phrase building with automatic feedback

<h2>Abstract</h2> One of the important skills when languages is writing correct words (tokens) in the correct order. It requires training until mastery, but teachers' time is limited so they often cannot provide feedback on enough exercise for all their students. Automatic feedback can alleviate this problem, but its quality and efficiency require thorough study. We developed a question that tokenizes students' answers, determines mistakes in token placement using the Longest Common Subsystem algorithm, and generates automatic feedback. It also catches typos. The question was used to study the efficiency of automatic feedback during teaching English as a foreign language and programming.