Reviewerly/exHarmony huggingface dataset:
STUDENTS’ ADAPTABILITY IN ONLINE LEARNING BASED ON MALAYSIAN STUDENTS’ PERCEPTION

The education field has undergone a new norm due to the pandemic of COVID-19 and it can be seen that new techniques and improvements can be applied among the educators and students. In a Malaysian context, it is revealed that a majority of universities have enhanced the quality of teaching via online platforms such as Cisco Webex, Google Meet, Zoom and many more that provide the medium for instructional delivery. However, it is also imperative to view the situation from the students’ perspectives in order to identify the issues that have occurred throughout the online learning process. The current study is conducted to identify the level of adaptability among students towards online learning and it consists of several components such as physical, spiritual, and emotional aspects. The respondents were selected through purposive sampling from selected local universities in Malaysia where about 316 students participated. This study was done quantitatively, and the results were analysed using Smart PLS 3.2.8. The findings showed that there were high values of Cronbach alpha and composite reliability which indicates that students agreed with the items in all the constructs in terms of physical (α=0.879, CR=0.906), spiritual (α =0.863, CR=0.901), emotional (α=0.953, CR=0.961) and adaptability (α=0.913, CR=0.931). The validity (AVE) of all the constructs were also in the acceptable range. It reveals that the physical, spiritual, and emotional components are imperative in order to ensure students could adapt with the new norm of online learning during the pandemic of COVID-19. Other than that, it shows that a balance well-being could hinder problems related to tough learning situation especially in online learning. Keywords: Online learning; Malaysian student, Well-being, Pandemic, Adaptability
Behavior Recognition Algorithm Using Unsupervised Learning for Home Elderly

The behavior recognition is an important issue in homecare and has received lots of attention. This paper proposed a behavior recognition algorithm (BRA) which applies unsupervised learning on sensor data collected for the elderly living along. The proposed BRA consists of three phases, including Regular Action Discovery, Behavior Discovery and Behavior Matching. Plenty of experiments show that the proposed BRA exhibits good performances in terms of the recall and precision.
Enhanced Hybrid Touch Screen Display for Industrial Applications

A Hybrid model with resistive touch and laser grid system is the solution proposed to rectify the problems faced by the existing industrial touch screen displays. Placing laser grid above the touch screen has improved the touch recognition and also reduces the possibility of erroneous inputs. Lasers and phototransistors are placed above the bezel of the display to form the laser grid. Laser grid forms a 2-dimensional co-ordinate system for navigation of input location on the display. In order to avoid unnecessary inputs caused by non-human entities like dust, flies, etc., inputs should be taken by both laser grid and resistive touch panel simultaneously. Touch input is taken into account only when the co-ordinates from both resistive touch screen and laser grid are the same. If not, co-ordinates from laser grid alone are taken to calibrate the resistive touch screen. If the calibration is not successful, then the touch functionality is fully taken over by laser grid system.
Development of Optimal Transmission Rate of the Kinematic Chain by using Genetic Algorithms Coded in Mathcad

Part of machine element, require time to design, to calculate and to produce it. Production of helical gears passes through these processes. In this article is considered that production of these gears was made by milling machine. The goal is to minimize the time that needed to calculate the kinematic chain that is used to create the helical profile of gear. Kinematic chain contains the gears train with given set gears of the milling machine that mounted between driven shaft and raw part. Gear train usually is with two or three steps. An optimization algorithm will be used to find the correct tooth numbers of the gears train that gives the required transmission rate with acceptable accuracy. Genetic algorithm (GA) method has been used to find the optimal solution where the numbers of the teeth were variables and the transmission rate was the objective function.
The Effects of Technology on Legal Practice

It is difficult to come up with any events after World War II that have led our entire global society to recognise that before The Times They Are A‐Changin’1 as clearly as today’s global crisis has done. The COVID-19 pandemic and its aftermath have revealed that almost everything we once considered stable and sustainable is actually built on quite shaky ground. But the crisis has also brought out the best in our coexistence, seeing that societies in many countries have shown that they are capable of finding creative solutions to overcome the current challenges. Digital technologies have played a crucial role in the world’s response to the COVID-19 crisis. Just think of modern methods of telecommunication such as video conferencing, which have made an immense contribution to maintaining the economy and work processes, or the various corona tracking apps, which try to help stopping the spread of the virus. It can be assumed that the harmful consequences of the pandemic would have grown even greater if those digital solutions had not been available. Just as almost every area of life is affected by the pandemic, so are the law itself and legal practice.
Convolutional Neural Network for Offline Signature Verification via Multiple Classifiers

The signature process is one of the most significant processes used by organizations to preserve the security of information and protect it from unwanted penetration or access. As organizations and individuals move into the digital environment, there is an essential need for a computerized system able to distinguish between genuine and forged signatures in order to protect people's authorization and decide what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.
The road ahead for augmented reality

A heads-up look at augmented reality head-up displays.
Assessment of the possibilities of writing skills development in English as a foreign language through the application of emoji as conceptual elements

This paper evaluates the application possibilities of the emoji available in the WhatsApp chat app to the development of writing skills in English as a Foreign Language among University students. The quasi-experimental design of the research was developed with a sample of students (N=22) that made up the census population of a subject pertaining the specialization in Foreign Language/CLIL of the Degree in Primary Education at the University of Cadiz, Spain. The explanatory nature of the study favoured a mixed approach, incorporating quantitative and qualitative types of data, collected from three research tools: an initial questionnaire on student preferences related to emoji choice and use; an academically directed activity involving the creation of a narrative text from a series of closed topics which necessarily incorporated emoji; and a final interview to ascertain students' opinions about the possible application of emoji in the foreign language classroom. The results show potential benefits in the use of emoji as representatives of the conceptual field for the development of writing skills in a foreign language. The study concludes with a series of recommendations based on the theoretical principles and the results with the aim of implementing the use of emoji in the processes of language teaching-learning.
Mobile phones: An indispensable tool for migrants

“Sometimes I have to choose between food and internet connectivity, to keep in touch with my family back home. When I need money, I make a call to them via WhatsApp, and they send it very quickly.” This is how Mamadou, a 22-year-old from Niger, sums up the key role that smartphones now play in the lives of migrants. The fact that refugees can spend up to a third of their budget on internet access – according to the Rabat office of the UNHCR, the United Nations Refugee Agency – is proof of how important these portable devices are.
A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification

To overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC&#x2019;s discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively.
Emotion Detection using Social Media Data

Abstract: Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models. Keywords: Hashtags, Sentiment Analysis, Facial Recognition, Emotions.
Convergence of Blockchain and Artificial Intelligence in IoT for the Smart City

By exploiting developments in new technology, the smart city is considered an intelligent civilization in the digital age. The increasing use of blockchain technology, in particular, has ushered in a new smart city ecosystem based on digital technology. From risk management and financial services to cryptocurrencies, and from the Internet of Things (IoT) to public and social services, a wide range of blockchain applications offer answers to issues. Furthermore, the combination of blockchain and Artificial Intelligence (AI) technology is transforming the smart into sustainable ecosystems. This chapter discusses various essential criteria for the confluence of blockchain and AI technology, which will aid in the formation of a sustainable and eco-friendly smart society. We go over blockchain security enhancements and summarize the major features that can be used to build various blockchain-AI-based intelligent transportation systems. We discuss various essential criteria for the confluence of blockchain and AI technology, which will aid in the formation of a sustainable and eco-friendly smart society.
EM Corpus: a comparable corpus for a less-resourced language pair Manipuri-English

In this paper, we introduce a sentence-level comparable text corpus crawled and created for the less-resourced language pair, Manipuri (mni) and English (eng).Our monolingual corpora comprise 1.88 million Manipuri sentences and 1.45 million English sentences, and our parallel corpus comprises 124,975 Manipuri-English sentence pairs.These data were crawled and collected over a year from August 2020 to March 2021 from a local newspaper website called 'The Sangai Express.'The resources reported in this paper are made available to help the low-resourced languages community for MT/NLP tasks 1 .
Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.
Adoption and Use of Web Technologies by Librarians in Open Distance e-Learning at the University in South Africa

Web technologies are now adopted by libraries worldwide to facilitate information services. The objective of this study was to examine how subject librarians’ use web technologies to support students in an online, distance and elearning university in South Africa. Data was collected from 68 of the 135 participants who were administered with an online questionnaire, and analysed to generate information about web technology adopter categories and examine the types of the web technologies used by the librarians. Technical support and network issues top the challenges the librarians encountered. The librarians recognise the pertinence of web technologies in their services but their performance is hampered by technology, administrative and environmental factors. There is need to practically examine the meaning and nature of library technical support in the library, and extent of performance of existing library technical support services based on the opinion and experiences of UNISA librarians.
Accusative case assignment to the verbal internal argument in the Albanian language and the corresponding case assigning model

The following paper aims at shedding some light on the Albanian language case system with a special focus on the assignment of the accusative case. As a member of the vast Indo-European family Albanian language is characterized by an inflected case system and as so a free word order. Traditionally, we are taught and we still teach to the coming generations that accusative case is assigned mostly by the verb to that sentence noun phrase syntactically representing the direct object and semantically introducing the Theme or the Patient. Moreover in Albanian accusative is also assigned by another morphological category bearing the distinctive features [+noun;+verb], namely the proposition. Furthermore, as a researcher in the field of generative syntax, I have a stake in analyzing certain exceptional cases of accusative case assignment to the subject NP of the Albanian subjunctive clause. In conclusion, I was tempted to adopt Chomsky’s reconciling proposal in accusative case assignment under the specifier-head structural and schematic relation.
AI and Blockchain Monitoring Autonomous Vehicles Management Project

The autonomous vehicle management system is the subject of this chapter. This entails operating a vehicle or system in a distributed environment. Artificial intelligence and message exchanges (blockchain) are both present in this scenario. Simultaneously, we must prevent catastrophic collapse when it comes to autonomous vehicle management.
Statistical Survey of Students’ Performance: Online Education to COVID-19 in Bangladesh

Pandemic situation stops natural movement in the globe but still not curb the spread of education.Formal education could be transformed into online education in smart version of teaching learning system especially for university students in Bangladesh.Such in shutdown and worse pandemic tenure schooling are brought into students' residence in the dynamic form just like students globalization.How the university students are performing with online learning and technology is being adopted in classroom have outlined in our study.To evaluate new achievement in online education: online survey is designed to collect feedback from Bangladesh and Nepal undergraduate university students in Bangladesh at COVID-19 lockdown period.Survey data is compiled using google forms part of google drive apps and students corresponding email accounts, both student and teacher performance was recorded in BdREN vSession.Responses are discussed demographically in result discussion section.This article indicates offline classroom is not only an option for teaching learning system, technology based education is entering as advanced and alternative way however its' in worst pandemic or challenging situation, and then characterize future education image for students.
Small Prime Divisors Attack and Countermeasure against the RSA-OTP Algorithm

One-time password algorithms are widely used in digital services to improve security. However, many such solutions use a constant secret key to encrypt (process) one-time plaintexts. A paradigm shift from constant to one-time keys could introduce tangible benefits to the application security field. This paper analyzes a one-time password concept for the Rivest–Shamir–Adleman algorithm, in which each key element is hidden, and the value of the modulus is changed after each encryption attempt. The difference between successive moduli is exchanged between communication sides via an unsecure channel. Analysis shows that such an approach is not secure. Moreover, determining the one-time password element (Rivest–Shamir–Adleman modulus) can be straightforward. A countermeasure for the analyzed algorithm is proposed.
Using Quaternary stratigraphy to help locate a hazardous waste treatment site

The Ontario Waste Management Corporation (OWMC) is an Ontario government Crown corporation with the mandate to establish a province-wide long-term program to treat and dispose of hazardous and liquid industrial waste. One of the major tasks evolving from the OWMC’s mandate is the establishment of a treatment and disposal facility. All publicly owned waste management facilities in Ontario are regulated under the Environmental Assessment Act. The inventory stage is intended to establish the nature of the problem and the framework within which the facilities are to be sited. The amount, type and sources of waste are considered and the available transportation, administrative and planning functions must be established. The environmental inventory establishes the physical, biological and social setting of the study area. The evaluation process identifies the various options for dealing with the problem. Options are then assessed on the basis of environmental, social, economic, technical and planning impact.
Slovene-Medium and Bilingual Schools in Italy: Towards a New Paradigm?

Over the past years, Slovene-medium kindergartens and primary schools in the region Friuli Venezia Giulia (FVG) have been trying to curb the trend of deteriorating linguistic skills among pupils by improving teaching methods, adjusting teaching contents, and developing teaching skills.The result of such efforts is not promising and the linguistic skills in the Slovene language continue to deteriorate from one generation of minority youth to the next.The paper presents the results of a recent preliminary study on the challenges faced by the aforementioned educational institutions, based on in-depth interviews and focus groups with teachers.The study seems to suggest that the adopted approach, which focuses primarily on 'linguistic' or 'didactic' issues, might be far too constrained to bring about the desired results, and a wider, contextual approach should be adopted to improve linguistic skills.
Teaching Programming Experiences in Times of COVID-19 in the State of Zacatecas

The COVID-19 pandemic has caused the suspension of face-to-face classes; therefore, greater importance has been given to virtual education. For this reason, it is important to review the new situations in the programming teaching process. This paper aims to interpret the experiences lived by teachers in in the subjects of Fundamentals of Programming and Data Structure in the implementation of virtual education in times of COVID-19 in the Technological Institutes Decentralized from the State of Zacatecas, for this reason, the quantitative, non-experimental research was used. The instrument used was a digital survey addressed to teachers from the Technological Institutes. According to the results obtained, the main problems during virtual education were the lack of connection by geographical location and the lack of adaptation to virtual education. Teaching virtual programming was a good pedagogical experience, the teachers expressed that they gave personalized attention in small groups; however, they mentioned that it was difficult to measure the student&#x2019;s progress, and the subject contents were not seen with the required degree of complexity. For teaching programming, teachers used platforms such as Moodle, Classroom and Microsoft Teams, virtual classrooms such as Meet and Zoom and digital resources such as videos, presentations and practical manuals. The most important challenges are to have the infrastructure required for the implementation of virtual classes, and the responsibility, commitment and initiative by teachers and students.