Reviewerly/exHarmony huggingface dataset:
Virtual Laboratory Learning Experience in Engineering: An Extended Technology Acceptance Model (TAM)

This research presents an enhanced version of the Technology Acceptance Model (TAM), which integrates usability and learning objectives to evaluate the effect of adopting virtual laboratories on student performance. Data from 97 participants, gathered via a survey, was used to examine the correlation between perceived usefulness (PU), perceived ease of use (PEOU), and intention to use (IU) concerning their experiences and outcomes with a simulation-based virtual laboratory. A custom questionnaire was designed to assess TAM components, namely PU, PEOU, and IU, specifically in the context of virtual laboratory learning environments. Findings indicate that the integration of specific laboratory learning objectives fosters the creation of a more credible and authentic simulation tool. This, in turn, optimizes the learning process and enhances students' learning outcomes. The refined TAM model provides valuable insights into the determinants of user acceptance of virtual laboratories and offers practical guidance for educators and instructional designers to construct more effective and efficient virtual laboratory environments in educational contexts.
Generating User Interface by Composing Models

Rapid development of technology allows the organizations to work on the interactive environments in any work and to co-operate on it to achieve its their goal. Therefore, we tried to solve the problem of designing proper, strong, and adequate UIs, satisfying the user and meeting his requirements. In this work, we propose to generate the user interface by creating three different types of models. These models are Dialogue model, Presentation model and Application model.
Using ECLAT Algorithm for Privacy Preserving on Publishing Data

many organizations such as online organizations, hospitals, and universities use massive amounts of customer information and use Database systems to enter their data. Many services and benefits are provided by these organizations to the public. The organizations want to publish the data and information for the purpose of research and analysis to obtain many benefits and advantages to improve their work. On the other hand, the data include individuals' sensitive and private information, therefore, organizations are concerned about the security as well privacy; in fact privacy is an important issue when organizations publish their data. This paper suggests a technique to preserve the privacy of the information. The proposed technique uses the ECLAT algorithm which is efficiently mined using the vertical data format. In addition to the ECLAT algorithm, the proposed technique uses some extra steps to process and manage data and to be prepared and secure for publishing.
Optimization and Influence of Tilt Angle on PV Module Performance: Experimental Study

The aim of this experimental study is to optimize the tilt angle of solar photovoltaic (PV) panels installed in Al-Bayda city using various methods, which are generally classified into three categories; one variable method such as the latitude angle method and the declination angle method. The second category includes the clearness index method and the Kern and Harris method, which are based on NASA database specifically the solar radiation on a horizontal surface. Finally, the Liu and Jordan method and the Riendle method, which are based on isotropic and anisotropic geographical factor of tilted surface radiation. The experimental work was carried out at the laboratory of the Faculty of Engineering at Omar Al Mukhtar University, during the period July to November 2021. The tilt angle was varied from 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> to 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> . In the methods applied, the variances were limited to at least 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> for each month. It was found that the ideal tilt is 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> in the summer and 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> in the winter while the optimized annual slope is 30 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> . However, one of the contributions of this study is boosting the power output to more than 6% when varying the tilt angle monthly.
Phishing Website Detection Using a Hybrid Approach Based on Support Vector Machine and Ant Colony Optimization

The Internet has become the most useable tool for people worldwide to communicate and transact business deals, transform money and many other daily activities that require the exchange of sensitive information. As a result, it has become a major platform for cybercrime. In addition, phishing is one of the most common threats faced by internet users. A great number of legitimate and phishing websites come across to the life every day. Therefore, effective methods must be found to deal with the detection and prevention of different types of phishing. Machine learning (ML) techniques have become very popular in phishing website detection by predicting whether a webpage is phishing or legitimate based on datasets obtained from previously extracted features from many other web pages. Thus, this study aims to introduce a phishing website detection approach that utilizes Support Vector Machine (SVM) combined with the Ant Colony Optimization (ACO) algorithm. In addition, the Deep Belief Network (DBN) is also considered to select the best features from the extracted features. The collected dataset contains 12,000 samples. The phishing Uniform Resource Locators (URLs) were retrieved from the PhishTank website, whereas the legitimate URLs were collected from the open datasets of the University of New Brunswick. The performance of the proposed approach was evaluated using accuracy, precision, recall and F1-score metrics. The proposed approach achieved an accuracy of 97.54% surpassing the traditional SVM model by a percentage of 9.58%, and achieved better results on all metrics used.
A Simulation-Based Analysis Study of Different Data Centers’ Networks Employing MPLS technique as a Fault Tolerance Mechanism

This paper presents a simulation analysis study for different data center networks employing MPLS technique as a fault tolerance mechanism to ensure availability via a backup paths. However, to illustrate the efficiency of using MPLS, each data center (namely: Fat Tree, Diamond, DCell, c-Through) is built with two scenarios networks one with MPLS and another without MPLS, so 8 scenarios are evaluated. In each scenario, the servers are used to run the applications (FTP, HTTP, VoIP and database, hence, VoIP, HTTP, FTP, and database applications are used to evaluate the performance of different data center networks.
Can Large Language Models Better Predict Software Vulnerability?

The analysis of software for software vulnerability has matured over the years. In the beginning, linear approaches were tried to isolate known vulnerabilities via after-the-fact static analysis. As neural nets and machine learning became useful, more dynamic approaches were applied, It will be interesting to see what the new wave of Generative Artificial intelligence can contribute to the realm of assessing software vulnerabilities.
Measuring the Benefits of CI/CD Practices for Database Application Development

Modern software development practices automate software integration and reduce repetitive software engineering work. Automation reduces the time it takes from defining software requirements to deploying the software in production. However, when it comes to database applications, the database integration and deployment are often executed manually, making it costly and error-prone. To mitigate this, we extended current software development methodologies by designing a CI/CD pipeline that takes into consideration the database setting. We report on two industrial case studies in which we implemented a newly designed pipeline and we measure the benefits of integration and deployment automation in database development projects. From a quantitative perspective, we found that introducing CI/CD pipelines reduces failed deployments, improves stability and increases the number of executed deployments. From a qualitative perspective, we interviewed the developers before and after the implementation of the CI/CD pipeline and the results show the CI/CD pipeline brings clear benefits to the development team (i.e., reduced cognitive load). This finding puts current database release practices driven by business expectations such as fixed release windows in question.
Automatically Generating Dockerfiles via Deep Learning: Challenges and Promises

Containerization allows developers to define the execution environment in which their software needs to be installed. Docker is the leading platform in this field, and developers that use it are required to write a Dockerfile for their software. Writing Dockerfiles is far from trivial, especially when the system has unusual requirements for its execution environment. Despite several tools exist to support developers in writing Dockerfiles, none of them is able to generate entire Dockerfiles from scratch given a high-level specification of the requirements of the execution environment. In this paper, we present a study in which we aim at understanding to what extent Deep Learning (DL), which has been proven successful for other coding tasks, can be used for this specific coding task. We preliminarily defined a structured natural language specification for Dockerfile requirements and a methodology that we use to automatically infer the requirements from the largest dataset of Dockerfiles currently available. We used the obtained dataset, with 670,982 instances, to train and test a Text-to-Text Transfer Transformer (T5) model, following the current state-of-the-art procedure for coding tasks, to automatically generate Dockerfiles from the structured specifications. The results of our evaluation show that T5 performs similarly to the more trivial IR-based baselines we considered. We also report the open challenges associated with the application of deep learning in the context of Dockerfile generation.
Adding Generic Role- and Process-based Behaviors to Smart Contracts using Dynamic Condition Response Graphs

Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed independently and cannot share a joint access control policy. We propose to use Dynamic Condition Response Graphs for role-based and declarative access control for smart contracts. We show that they allow to capture and visualize a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports straight-forward declaration of access control rights, improved code auditing, programming error reduction and improves users’ understanding of smart contracts.
Video Prediction using Recurrent Neural Network

With the advent of recent technologies, Deep Learning, a subset of machine learning, has gained popularity in solving problems in a variety of domains. One vast field of application for Deep Learning approaches is in the generation of video frames. While Video Interpolation has come a long way, Deep Learning based Video Prediction remains a prominent area of research. We implement a recurrent neural network for the task of video prediction, and then analyze the performance of the model for several different generation ratios and examine the impact of variations in the videos on the model’s ability to stick close to the ground truth. The model is tested on raw videos from Youtube of the ‘Comedian’ category. The quality of the model is evaluated using quantitative and qualitative metrics. The potential use case can be in video streaming to reduce the transmission bandwidth and to generate frames that are not present in the video.
An Experience Report on Assessing Software Engineer’s Outputs in Practice

The success of a software organization relies heavily on the quality of its products and services, which in turn are influenced by the knowledge, capability, and experience of the software engineers involved in development processes. It is popular to apply quantitative assessments of software engineers for quality assurance. However, the extent to which it benefits software organizations and how it can be effectively implemented in industrial settings remains unclear. One global Information and Communications Technology (ICT) enterprise has implemented a quantitative assessment practice of software engineer’s outputs to improve its engineering capability and product and service quality. To investigate the benefits and experiences of adopting this practice in industrial settings, we conducted an empirical study using a mixed-method approach (i.e., archive analysis, interviews, and surveys). The results indicate that this practice can benefit the ICT enterprise in terms of standardizing development processes, optimizing team structures, and offering suggestions for training and management, etc. Meanwhile, this paper reports on the best practices to tackle the challenges during the adoption of the practice in the ICT enterprise, e.g., customization for teams and synergy of quantitative and qualitative assessment. In addition, we discuss the implications and recommendations of institutionalizing quantitative engineer assessment in software organizations. For organizations intending to improve software quality from the human aspect, this study provides empirical references on how to implement quantitative engineer assessment meanwhile mitigate potential risks.
Characterizing The Impact of Culture on Agile Methods: The MoCA Model

Agile methods are well-known approaches in software development and used in various settings, which may vary wrt. organizational size, culture, or industrial sector. One important facet for the successful use of agile methods is the strong focus on social aspects. We know, that cultural values influence the behaviour of humans. Thus, an in-depth understanding of the influence of cultural aspects on agile methods is necessary to be able to adapt agile methods to various cultural contexts. In this paper we focus on an enabler to this problem. We want to better understand the influence of cultural factors on agile practices. The core contribution of this paper is MoCA: A model describing the impact of cultural values on agile elements.
Hybrid Model for Email Spam Prediction Using Random Forest for Feature Extraction

With the advancement in world wide web, the way to communicate among individuals, via internet, is changed and thus, various platforms become popular such as email. Numerous organizations and people make the deployment of email as major sources of communication. This platform is extensively utilized in spite of alternative means, such as electronic messages, and social networks. However, this technology is more prone to malicious activities. The malicious users target this free mail structure and send a huge number of useless messages, for attaining revenues, or stealing personal data or IDs, to harm its users. Thus, there is necessity to discover the methods for detecting the email spam. The spam is detected in email in different phases in which the data is pre-processed, features are extracted, and the mails are classified. This work introduced a new model to predict the email spam. This approach implements the random forest in order to extract the features. Eventually, the spam is predicted using logistic regression model. The proposed model is implemented in python using anaconda.
Tech-It-Easy: An Application for Physically Impaired People Using Deep Learning

Physical limitations are and always will be a barrier to daily progress. Technology advancements are assisting everyone in leading simpler lives. Using the same technologies, we can provide a significant and beneficial answer to the issues associated with physical impairment. This essay discusses the use of technology to improve daily life for those who have physical or sensory disabilities. Not just for those who have hearing loss, but also as a tool for those who have speech disability, sign language is a vital means of communication. People without disabilities have a hard time understanding sign language, and specialists are frequently the only ones who can. Hence, a tool for sign language interpretation becomes necessary. Although Braille is a reading and writing system used by people who are blind. Braille is less popular among persons who are visually impaired, as it is time-consuming to manually translate every text into braille. Our study examines the issues raised by these two deficits and looks for technical remedies. Text to audio conversion is a piece of technology that can revolutionize the way visually impaired individuals communicate currently. It is simple and has been done effectively for the past ten years to convert written text to audio. In addition to sign language interpreters, a relatively new concept for assisting the education of the blind is to translate speech into sign language. The technologies stated above are anticipated to significantly improve the daily lives of people with physical disabilities, and this project can be further customized to match any suitable smart object.
Breast Cancer Prediction Using Different Machine Learning Algorithms: A Comparative Study

According to the World Health organization’s (WHO) 2020 report, 2.3 million new cases of breast cancer were recorded, and 685,000 women died due to breast cancer. To treat breast cancer early, a lot of research has been proposed using different types of techniques in the past few years. In recent years, machine learning algorithms (MLAs) have become popular for detection due to their improved accuracy and performance. This paper used 13 supervised machine learning (SML) techniques, namely: Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Adaptive Boosting (AB), Categorical Boosting (CB), Light Gradient Boosting Machine (LGBM), Multi-Layer Perceptron (MLP), and Extra trees (ET) to predict the outcomes of the Wisconsin Breast Cancer Original (WBCO) dataset from the UCI repository. When all thirteen algorithms were evaluated and compared, MLP outperforms them all with the highest accuracy (98.76%). This accuracy value is 0.56% greater than the recently reported accuracy value of 98.2% for the MLP classifier for the same dataset.
Using GUI Test Videos to Obtain Stakeholders’ Feedback

In software projects, stakeholders can give valuable feedback on software demonstrations. Demonstrating software early and responding to feedback is crucial in agile development. However, it is difficult for stakeholders who are not on-site customers but end users, marketing people, or designers, etc. to give feedback in an agile development environment. Successful Graphical User Interface (GUI) tests, which show the working GUI with expected software behaviors, can be documented and then demonstrated for feedback. In our new concept, GUI tests are recorded, extended, and demonstrated as videos. A GUI test is divided into several GUI unit tests, which are specified in Gherkin, a semi-structured natural language. For each GUI unit test, a video is generated during test execution. Test steps specified in Gherkin are traced and highlighted in the video. Stakeholders review these generated videos and provide feedback, e.g., on misunderstandings of requirements or on inconsistencies. To evaluate the impact of videos in identifying inconsistencies, we asked 22 participants to identify inconsistencies between (1) given requirements in regular sentences and (2) demonstrated behaviors from videos with Gherkin specifications or from Gherkin specifications alone. Our results show that participants tend to identify more inconsistencies from demonstrated behaviors which are not in accordance with given requirements. They tend to recognize inconsistencies more easily through videos than through Gherkin specifications alone. We conclude that GUI test videos can help stakeholders give feedback more effectively. By obtaining early feedback, inconsistencies can be resolved, thus contributing to higher stakeholder satisfaction.
Social Engineering Defender (SE.Def): Human Emotion Factor Based Classification and Defense against Social Engineering Attacks

One of the weakest links in any security system is neither the devices used nor the programs running on them; but the human beings using these devices. Most cyberattacks are initiated by human error. Hackers always use the most accessible and effective social engineering techniques to attack. Simply put, it is the art of manipulating people into sharing sensitive and confidential information. This research proposes a framework with four modules, namely, a source analyzer, a content classifier and analyzer, a link analyzer, and a risk reporting module, as a social engineering defender system for categorizing the risks before the email reaches the inbox of the user. Before it reaches the end user’s inbox, the system blocks the emails the social engineering defender has marked as “very high risk”.
An Analysis of Situational Intelligence for First Responders in Military

Situational awareness is the sense and knowledge of one’s immediate surroundings. In safety-critical sectors, maintaining situational awareness is essential for performance and error prevention. Situational awareness (SAW) is crucial for the success of activities in many different domains, such as surveillance, humanitarian aid, and search and rescue efforts. SAW is however susceptible to enemy attacks. By giving users enhanced coverage, it increases survivability and mission capability. Recently, Smart gadgets used data to address crisis scenarios and provide real-time tracking to protect law enforcement personnel out in the field. Despite these developments, it might be challenging for first responders to get a precise feel of their surroundings due to an abundance of field data. Security teams need to be able to quickly transform this data into actionable intelligence using a few instruments at their disposal, including body cameras, fingerprint scanners, and facial recognition software. Officers can cut through the noise to acquire actual real-time situational awareness by integrating heterogeneous information into a cohesive platform. Therefore, the proposed work examines potential mitigation measures while considering hostile threats and assaults against SAW systems. Additionally, information and alarms can be instantly sent between operators and field officers using vital interface features. The optimization of the AutoML system is proposed for fusion of sensor data. AutoML classification with Bayesian and ASHA (Asynchronous successive halving algorithm) is used for situational forecasting and decision-making awareness, IoT is used to monitor data gathered from Kaggle and sensor readings, while thingspeak cloud is used to monitor sensor output.