Reviewerly/exHarmony huggingface dataset:
Proposed V-E-learning Model: Applying V-Model to Ensure the Quality of E-Learning System Implementation at Higher Education Institutions

Since March 2020, CORONAVIRUS (COVID-19) pandemic has affected many sectors, including education sector at the global level and at the Kingdom of Saudi Arabia. Preventative procedures have been taken, including suspending teaching and training for K-12 and higher education and moving to use distance education as an alternative to face-to-face education, with the continuation of the Corona pandemic, the Ministry of Education decided to consider the e-learning (distance education and blended education) as a strategic alternative to overcome crises threatening the educational process . With the widespread use of e-learning platforms in the Kingdom of Saudi Arabia, the interest of the quality of e-courses increased more than before. Therefore, this study proposes a model for ensuring the quality of e-learning and e-courses in higher education institutions and developing a set of evidence and suggested performance indicators that help those responsible for quality management to verify and validate the adherence to these standards. Dar Al-Uloom University was chosen as a case study of what was implemented during the Corona pandemic and the extent of the university’s commitment to this standards to ensure continuous improvement and raise the level of performance related to the e-learning system and its e-courses.
Novel use of radio frequency identification (RFID) provides a valid measure of indoor stair-based physical activity

This study sought to test the validity of radio frequency identification (RFID) technology to measure indoor stair walking. RFID and Bluetooth sensors (accelerometers with the proximity function) were placed at landings across three floors within a stairwell. Participants (n = 29) were randomized to stair walking or sedentary control and concomitantly wore RFID tags at the hip, chest and neck. An accelerometer worn at the hip captured criterion physical activity data. RFID devices detected sedentary control and stair walking participants (mean RFID detections: 15.50, 47.33 respectively). Neck lanyard RFID tags were detected most frequently. RFID correctly classified all participants in both groups. RFID-based energy expenditure estimates were strongly correlated with accelerometer estimates (r = 0.78–0.94). Percent agreement for floor detection between the Bluetooth and RFID readers (38.6%) was consistent with known limitations of Bluetooth proximity sensing. It is concluded that, during self-paced stair walking in young adults, RFID devices provide valid data on participant movement and estimated energy expenditure.
Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned

The valuable information is extracted through data mining techniques. Recently, privacy preserving data mining techniques are widely adopted for securing and protecting the information and data. These techniques convert the original dataset into protected dataset through swapping, modification, and deletion functions. This technique works in two steps. In the first step, cloud computing considers a service platform to determine the optimum horizontal partitioning in given data. In this work, K-Means++ algorithm is implemented to determine the horizontal partitioning on the cloud platform without disclosing the cluster centers information. The second steps contain data protection and recover phases. In the second step, noise is incorporated in the database to maintain the privacy and semantic of the data. Moreover, the seed function is used for protecting the original databases. The effectiveness of the proposed technique is evaluated using several benchmark medical datasets. The results are evaluated using encryption time, execution time, accuracy, and f-measure parameters.
Risk Analysis in Software Cost Estimation: A Simulation-Based Approach

Risk analysis and cost estimation are two important aspects of project planning that can either make the way or break the way to a project’s success. At the same, both these tasks are difficult and painstaking, but whether someone likes it and not, the project’s success depends heavily on them. As documented by Fredric Brooks Junior in his legendry book “The Mythical Man-Month,” planning, scheduling, and estimation have been central to software engineering since its early days in the 1970s. Present communication presents a simulation-based approach to estimate the costing schedule of a software development project. The results show that simulation is expedient as well as efficient in terms of time, effort, and cost requirement and provides pragmatic results.
Development of conceptual framework for cyber fraud investigation

The increase in the number of internet users in Indonesia as much as 175.4 million as recorded in the Datareportal.com report and 4.83 billion globally, impact the increase in the number of cyber fraud cases. Data states that 96% of fraud cases are not resolved due to fraud methods carried out online and make it difficult for legal officers to obtain evidence. Previous fraud investigation research mainly focused on fraud detection, so this research focuses on submitting a framework for investigating cyber fraud cases. The cyber fraud case requires a new framework for investigation because in this fraud case, there is digital evidence that is very prone to be damaged, lost, or modified, which makes this case unsolved. This research aims to develop a framework that is expected to help auditors to uncover cases of cyber fraud so that resolved cyber fraud cases can increase. The method used in making this framework uses Jabareen's conceptual framework development method, which consists of 6 stages, namely, Mapping the selected data source, extensive reading and categorizing of the chosen data, Identifying and naming objects, Deconstructing and categorizing the concept, Integrating concept, Synthesis, resynthesis. And make it all sense. The framework for cyber fraud investigation uses 22 digital forensic frameworks and eight frameworks for fraud audit investigations. The results of developing a framework using the Jabareen method resulted in 8 stages, integrating various concepts selected from digital forensics and fraud audits. Evaluation of framework development was carried out by giving limited questionnaires to practitioners and academics, which produced 89% for the feasibility value and needs of the framework and 67% there is no need for changes to the framework being developed.
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi-class classification problem. To build an ECNN, we propose to design a code matrix so that the minimum Hamming distance between any two rows (i.e., two codewords) and the minimum shared information distance between any two columns (i.e., two partitions of class labels) are simultaneously maximized. Maximizing row distances can increase the system fault tolerance while maximizing column distances helps increase the diversity between binary classifiers. We propose an end-to-end training method for our ECNN, which allows further improvement of the diversity between binary classifiers. The end-to-end training renders our proposed ECNN different from the traditional error-correcting output code (ECOC) based methods that train binary classifiers independently. ECNN is complementary to other existing defense approaches such as adversarial training and can be applied in conjunction with them. We empirically demonstrate that our proposed ECNN is effective against the state-of-the-art white-box and black-box attacks on several datasets while maintaining good classification accuracy on normal examples.
Coming Full Circle - From CEFR to CEFR-J and back

The CEFR-J project was launched in Japan in 2008. The CEFR-J gives a set of Can Do descriptors for 10 CEFR sub levels (Pre-A1 to B2.2) and related Reference Level Description (RLD) work, whilst including developed profiling for vocabulary, grammar, and textual features were developed. In this article, the English resources created for the CEFR-J are applied in preparing teaching resources for other major European languages as well as Asian languages. To achieve this, a series of teaching/learning resources including the CEFR-J Wordlist and Phrase List initially developed for English were translated into 27 other languages using neural machine translation. These translated word and phrase lists were then manually corrected by a team of language experts. The automatic conversion of English to other languages was evaluated against human judgments as well as frequency analysis referencing web corpora. Three types of e-learning resources were created, taking into consideration the wordlists and the phrase lists for teaching those languages to undergraduate students: (1) a flash-card app for learning vocabulary, which allows for classification by both thematic topic and CEFR level, (2) an online syntax writing tool for the study of grammar and vocabulary, and (3) an online spoken and written production corpus collection tool.
A Deep Learning approach for Animal Breed Classification - Sheep

In the field of Husbandry, its little difficult to detect the breed of animals in order to estimate the commercial value production. Usually it can be classified with the help of DNA testing but its expensive and sometime not affordable. Lameness in sheep is one of the concerns which is to be taken care. It usually happens due to inadequate of healthcare and welfare among the country serving sheep industry. This paper aims to classify the sheep according to their respective breed. Basically 4 breeds have been taken into consideration and by applying neural networks, the sheep breed has been classified. Throughout the task the accuracy of 99.97% was achieved while training the CNN model & 87.1% was achieved while testing the model. With the help of this classifier, sheep farmer can identify the breed more accurately and efficiently.
FoggySight: A Scheme for Facial Lookup Privacy

Abstract Advances in deep learning algorithms have enabled better-than-human performance on face recognition tasks. In parallel, private companies have been scraping social media and other public websites that tie photos to identities and have built up large databases of labeled face images. Searches in these databases are now being offered as a service to law enforcement and others and carry a multitude of privacy risks for social media users. In this work, we tackle the problem of providing privacy from such face recognition systems. We propose and evaluate FoggySight, a solution that applies lessons learned from the adversarial examples literature to modify facial photos in a privacy-preserving manner before they are uploaded to social media. FoggySight’s core feature is a community protection strategy where users acting as protectors of privacy for others upload decoy photos generated by adversarial machine learning algorithms. We explore different settings for this scheme and find that it does enable protection of facial privacy – including against a facial recognition service with unknown internals.
Sim2RealQA: Using Life Simulation to Solve Question Answering Real-World Events

As smart speakers continue to proliferate, question answering (QA) by smart devices is being woven into our daily lives. This study assumes question answering related to daily life events detected by context recognition systems, such as activity recognition and indoor positioning systems, e.g., answering questions like “Did my grandma eat dinner?” and “How many times did my grandpa go to the toilet?” These questions can effectively support human memory-aids, locate lost items, and monitor human activities. However, training a question-answering model requires large amounts of labeled training data (i.e., questions, answers, and the time-series of real-world event triplets) collected in a target environment. In this paper, we propose a novel simulation to real QA (Sim2RealQA) framework that completely trains a QA model with QA datasets produced in a life simulator and use it for solving real-word QA problems without answer labels. Our proposed QA model can learn a general reasoning process for QA that is independent of environments and deal with diverse types of questions specific to question answering in real-world environments, e.g., counting the number of occurrences of a real-world event and enumerating the names of those who are performing an activity together. Experiments show that using life simulations is a promising approach for solving real-world QA problems when no real-world answer labels are available.
Web-development with Laravel framework

Laravel is a useful PHP framework and we can create webapplication easily with this application.It uses the popular MVC (model-view-controller) design pattern and based the Symfony system.Laravel uses a modular package system, therefore we can expand our application with new modules.It reuses several existing components of other frameworks, which helps to create a secure operable application quickly.Some of its features and advantages: supports accessing of different databases; presents utilities which helps in web-application development; simple and fast routing engine; the web application becomes more scalable; considerable time is saved in designing.Laravel is open-source software, licensed by MIT.
Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Abstract A successful business intelligence solution can help organizations improve the quality and speed of their decision‐making processes by analyzing the consolidated information collected from their websites. Using the current Web server log standard, which indicates only the locations of served Web pages, may lead to inaccurate business analysis for data driven and frequently updated static content Web pages. A properly defined Web content usage data warehouse that captures both dynamic and static contents of web pages provides rich data source for discovering interesting business rules among users' activities. This paper demonstrates the simplicity of data extract, transform and load procedures to import raw Web content usage log to various data models for data analysis, reporting and data mining tools. In this paper, we use two data mining techniques (expected maximization and the prefixspan algorithms) for visitor grouping and path analysis to find interesting patterns in Web content usage log data, an important component of e‐commerce Web sites traffic analysis. Visitor grouping uses data clustering to group visitors/sessions with similar selected attributes' value. Path analysis mines common visiting path sequence. The ultimate goal is to enable the online merchant provide enhanced and differentiated marketing services generally to its existing and potential customers and tell what items customers are interested in instead of ambiguous URLs.
Medical image encryption scheme based on self‐verification matrix

To mitigate the shortcomings of existing medical image encryption algorithms, including a lack of anti-tampering methods and security, this report presents an anti-tampering encryption algorithm for medical images that is based on a self-verification matrix. First, chaotic coordinates generated by chaos are used to traverse all pixels in a plain image to generate a two-dimensional matrix (a self-verification matrix) with positioning information. The accurate location of illegally altered image pixels can be detected using the self-verification matrix. To improve the security of the self-authentication matrix, DNA coding is also applied to the self-authentication matrix, and the plain image is also diffused statically to destroy the pixel distribution. Next, the scrambled image and self-verification matrix are mixed and cross-scrambled. Finally, the fused image is diffused dynamically to improve the security of the encrypted image. Experimental simulation and performance analysis show that the algorithm achieves good encryption effectiveness, provides strong anti-tampering capabilities, and can accurately locate at least 4 pixels.
Hybrid Control of Digital Baker Map with Application to Pseudo-Random Number Generator

Dynamical degradation occurs when chaotic systems are implemented on digital devices, which seriously threatens the security of chaos-based cryptosystems. The existing solutions mainly focus on the compensation of dynamical properties rather than on the elimination of the inherent biases of chaotic systems. In this paper, a unidirectional hybrid control method is proposed to improve the dynamical properties and to eliminate the biases of digital chaotic maps. A continuous chaotic system is introduced to provide external feedback control of the given digital chaotic map. Three different control modes are investigated, and the influence of control parameter on the properties of the controlled system is discussed. The experimental results show that the proposed method can not only improve the dynamical degradation of the digital chaotic map but also make the controlled digital system produce outputs with desirable performances. Finally, a pseudorandom number generator (PRNG) is proposed. Statistical analysis shows that the PRNG has good randomness and almost ideal entropy values.
Empowering ISA95 compliant traditional and smart manufacturing systems with the blockchain technology

Technological advancements in the information technology domain such as cloud computing, industrial internet of things (IIoT), machine to machine (M2M) communication, artificial intelligence (AI), etc. have started to profoundly impact and challenge not only the ISA95 compliant traditional (ISA95-CTS) but also the smart manufacturing systems (SMMS). Our literature survey pinpoints that systems scalability, interoperability, information security, and data quality domains are among those where many challenges occur. Blockchain technology (BCT) is a new breed of technology characterized by decentralized verifiability, transparency, data privacy, integrity, high availability, and data protection properties. Although many researchers leveraged BCT to empower various aspects of industrial manufacturing systems, there is no study dedicated to addressing the challenges impacting the manufacturing systems compliant with the ISA95 standard. Thereby, our study aims to fill the identified research gap systematically. This paper thoroughly analyzes the challenges hampering the ISA95-CTS and SMMS and methodically addresses them with corresponding BCT capabilities. Furthermore, this paper also discusses various aspects, including the weaknesses, of BCT convergence to ISA95-CTS and SMMS.
Hopfield type of Artificial Neural Network via Election Algorithm as Heuristic Search method for Random Boolean kSatisfiability

This study proposed a hybrid computational model by incorporating Election Algorithm (EA) as a heuristics search technique in a Hopfield type of artificial neural network (HNN).The main objective is to improve the learning phase of Hopfield type artificial neural network (HNN) for optimal Random Boolean kSatisfiability representation for higher-order logic.Many researchers in the area of artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANNs) are motived by the multiple processing units that work together to learn, identify patterns and predict information which provides a powerful mechanism for optimizations/search problems and other decision-making problem.Election algorithm (EA) has been utilized due to the policy of extending the power and rule play by the political parties beyond its borders to seek endorsement from voters.This policy plays an important role in accelerating the learning process of Hopfield type of artificial neural network (HNN) for optimal random Boolean kSatisfiability representation.In this work, a different number of neurons (NN) has been manipulated invalidating the robustness and efficiency of EA in HNN for RANkSAT logical clauses.The proposed model has been compared with other existing model-based the global minima ratio, statistical error accumulations, and time complexity during the learning process.The simulated results generated have been presented in the form of graphs.Based on the result of this study, the proposed HNN-RAN-kSAT-EA agreed with the existing HNN-RANkSAT-ACO model but outperformed HNN-RANkSAT-ES in term of statistical measures used in this study.
Designing a hybrid dimension reduction for improving the performance of Amharic news document classification

The volume of Amharic digital documents has grown rapidly in recent years. As a result, automatic document categorization is highly essential. In this paper, we present a novel dimension reduction approach for improving classification accuracy by combining feature selection and feature extraction. The new dimension reduction method utilizes Information Gain (IG), Chi-square test (CHI), and Document Frequency (DF) to select important features and Principal Component Analysis (PCA) to refine the features that have been selected. We evaluate the proposed dimension reduction method with a dataset containing 9 news categories. Our experimental results verified that the proposed dimension reduction method outperforms other methods. Classification accuracy with the new dimension reduction is 92.60%, which is 13.48%, 16.51% and 10.19% higher than with IG, CHI, and DF respectively. Further work is required since classification accuracy still decreases as we reduce the feature size to save computational time.
Virtual Tourism In New Normal: Are People Going To Change Their Style Of Travel Temporarily Or Permanently?

In this Covid-19 pandemic 'new normal' era, Virtual Tourism could play a significant role in encouraging visitation and other tourism behaviors.Tourists' visit intention and VR users' continued use were determined by the hedonic motivation and technology acceptance model (TAM).However, there are limited studies on flow in virtual tourism.To address this gap, this study aims to examine the determinants affecting Visit Intention within Tourists in Indonesia.Empirical data from stay-at-home tourists during the pandemic who has been used VR application for the virtual tour were tested against the proposed research model by using structural equation modeling.The data was collected through an online survey involving 100 participants who have used and experienced virtual tourism during the pandemic.The results indicated that flow and continued use are the main factors affecting visit intention, whereas usefulness and enjoyment are the main factors affecting flow.The findings of this study provide several important implications for virtual tourism researches and practices.
Automatic image enhancement for under-exposed, over-exposed, or backlit images

Unfortunately, images acquired by users from image acquiring devices like cameras could be under-exposed, overexposed, or backlit. These under-exposed, over-exposed, or backlit images are not suitable for recognizing the information contained in the images. In this paper, we propose a new technique which corrects the brightness of already acquired images from image acquiring devices. This technique divides the image area to determine the state of brightness and operates according to the results of the state analysis without any threshold or magic value. It effectively corrects the brightness of under-exposed, overexposed, or backlit images using only image data analysis.