Reviewerly/exHarmony huggingface dataset:
Thinking Beyond the Default User: The Impact of Gender and Stereotypes on Designers’ Interpretation of Users and Their Needs

Abstract Throughout the mechanical design process, designers, the majority of whom are men, often fail to consider the needs of women, resulting in consequences ranging from inconvenience to increase risk of death or serious injury. Although these biases are well-studied in other fields of research, the mechanical design field lacks formal investigation into this phenomenon. In this study, undergraduate engineering students (n = 151) took a survey in which they read a Persona describing a student makerspace employee and a Walkthrough describing their interaction with the makerspace while completing a project. During the Walkthrough, the user encountered various obstacles, or Pain Points. Participants were asked to recall and evaluate the Pain Points that the user encountered, then evaluated their perceptions of the makerspace and user. Six different experimental conditions were used to investigate the impact of gender-stereotyped tasks and the gender of a user on designers’ interpretation of them and their needs. In addition to finding that the gender of a user impacted the way a task environment was perceived, results confirmed the presence of androcentrism, or “default man” assumptions, in the way designers view end users of unknown gender. Future work will explore methodologies of overcoming this bias so that designers are able to consider the diverse needs of a range of users.
Klasifikasi Jenis Buah Nanas Menggunakan Convolution Neural Network

Indonesia is one of the countries with gread agricultural potential. One of the products of agriculture in Indonesia is pineapple. Pineapple is a tropical plant with edible fruit and one of the maximum economically vital plants in the Bromeliaceous family. The process of selecting pineapple species is generally very dependent on human perception. The development of technology and science makes it possible to perform classification or in terms of object selection using technology based on digital image-based characteristics. Images are used as a source of information that can be used to classify objects. One of the deep learning methods used is Convolutional Neural Networks (CNN) because they have a high deep network and are widely used to image data. Deep learning in Computer Vision has good capabilities in, one of which is image classification or object classification in images, and the network in CNN has a special layer, namely the convolution layer, The image convolution process in this study uses the keras package on GoogleColab, because making a neural network model using Keras does not need to write code to express mathematical calculations individually. Testing using a sample of 120 pineapple images shows an accuracy rate of 91,66% which is considered to be able to identify 3 types of pineapple fruit.
Investigate Customer Preferences Using Online Video Reviews - Preliminary Results

Abstract The wealth of online reviews has grown exponentially, attracting the attention of many researchers who recognize their potential as a valuable source of customer feedback. Leveraging online reviews enables product designers to gain a deeper understanding of customer preferences and make informed decisions to improve product design. Traditionally, the major source of online reviews comes from e-commerce websites, such as Amazon and eBay. However, as social media platforms gain popularity, video reviews from these platforms are becoming an increasingly important source of customer feedback. Video reviews have several advantages over traditional textual reviews. They are more comprehensive, less likely to be fake, have greater coverage of the customer base, and contain more interaction in the form of comments. The paper presents a four-stage methodology to analyze video reviews from social media platforms as an alternative source of customer feedback. This involves collecting and preprocessing video reviews, extracting product features using latent Dirichlet allocation (LDA) models, analyzing sentiment using the valence aware dictionary and sentiment reasoner (VADER) package, and computing feature importance using SHAP values. To the best knowledge of the authors, very few literatures have investigated the feasibility of using video reviews as a substitute for traditional online reviews. Therefore, this paper contributes to the growing literature by demonstrating the viability of video reviews as an alternative source of online reviews to understand customer preferences and their implications for engineering design.
Counterexample Guided Neural Network Quantization Refinement

Deploying Neural networks (NNs) in low-resource domains is challenging because of their high computing, memory, and power requirements. For this reason, NNs are often quantized before deployment, but such an approach degrades their accuracy. Thus, we propose the counterexample guided neural network quantization refinement (CEG4N) framework, which combines search-based quantization and equivalence checking. The former minimizes computational requirements, while the latter guarantees that the behavior of an NN does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small NNs. Our technique successfully quantizes the networks in the chosen evaluation set, while producing models with up to 163% better accuracy than state-of-the-art techniques.
Evaluating the Vulnerability of Deep Learning-based Image Quality Assessment Methods to Adversarial Attacks

Recent studies have discovered that Deep Learning (DL) models are vulnerable to adversarial attacks in image classification tasks. While most studies have focused on DL models for image classification, only a few works have addressed this issue in the context of Image Quality Assessment (IQA). This paper investigates the robustness of different Convolutional Neural Network (CNN) models against adversarial attacks when used for an IQA task. We propose an adaptation of state-of-the-art image classification attacks in both targeted and untargeted modes for an IQA regression task. We also analyze the correlation between the perturbation’s visibility and the attack’s success. Our experimental results show that DL-based IQA methods are vulnerable to such attacks, with a significant decrease in correlation scores. Consequently, the development of countermeasures against such attacks is essential for improving the reliability and accuracy of DL-based IQA models. To support the principle of reproducible research and fair comparison, we make the codes publicly available on https://github.com/hbrachemi/IQA_AttacksSurvey.
Requirements engineering issues and challenges in Ukrainian Agile-projects

In this paper, a review of scholarly studies related to the issues of requirements engineering in Agile as well as a survey study in the Ukrainian IT community, are carried out. This paper aims to adjust the research done on this issue, to identify the current challenges of requirements engineering in Agile and their impact on the final production. Based on the results of the analysis, further research steps are planned. This paper is a preparation for the search for a technical solution to the identified issues and challenges and contributes to the theoretical knowledge of requirements engineering problems in Agile.
A novel chaotic and neighborhood search-based artificial bee colony algorithm for solving optimization problems

Abstract With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimization problems. However, the traditional ABC has some shortcomings such as under-exploitation and slow convergence, etc. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains three improved mechanisms, including Bernoulli chaotic mapping with mutual exclusion mechanism, neighborhood search mechanism with compression factor, and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and the exploration ability. To enhance the convergence efficiency and exploitation capability of the algorithm, the neighborhood search mechanism with compression factor and sustained bees are presented. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC, the results demonstrate that the proposed CNSABC has better convergence efficiency and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions.
Longitudinal and Turning Manual Operation Control for a Force Sensorless Power-Assisted Transport Cart

In this paper, we propose manual operation control for a force-sensorless power-assisted transport cart used for transportation in a factory. The magnitude and direction of the hand force applied by the operator were estimated according to the cart body sway. Based on the estimated force, the control input not only for the longitudinal but also for turning movements per the operator's intention can be obtained by using the proposed power-assist control algorithm. Our proposed method does not require a force sensor to detect the operator's hand force. Furthermore, the operator can intuitively operate the cart manually by applying hand force from any direction around the cart body. The proposed power-assist control scheme was implemented in a transport cart we developed. The experimental results obtained using the transport cart demonstrate the feasibility of the proposed manual operation control method.
From Crisis to Continuity: Exploring Students’ Perspectives on the Future of Online Learning Beyond COVID-19

As we live in the post-COVID-19 era, much research should be devoted to guiding educators and policymakers on what to retain, revise, or even eliminate from the online learning experience. This study aimed to provide a deeper understanding of the students&amp;rsquo; behavioural intention to continue using technology in the post-COVID-19 era. The study was grounded in a well-known theoretical model for assessing technology adoption, the Technology Acceptance Model (TAM), expanded by adding the following external variables: accessibility (ACC), anxiety (ANX), feedback (FB), computer playfulness (CP) and perceived enjoyment (PNJ). A total of 134 undergraduate students from both public and private universities and colleges in Oman were included in the study. Data was collected through the administration of a Likert-scale questionnaire and analysed using descriptive tests and the Smart-PLS technique. The study&amp;rsquo;s main findings revealed that ACC, ANX, CP, and PNJ had a significant impact on Perceived Ease of Use (PEOU), while no such effect was observed on Perceived Usefulness (PU). Notably, the study concludes that students exhibit a high intention to continue using technology. The study underscores the increasing familiarity of interactive technology tools among teachers and students, a trend accelerated during the pandemic. However, a recommendation is made for the development of a comprehensive framework by educational stakeholders, including policy professionals and teachers, to specify the strategic use of technology and its intended purpose.
A Visualized Bibliometric Analysis for Mapping Research Trends of Machine Learning in Academic Research

This study aims to conduct a comprehensive bibliometric analysis of machine learning research within the academic research from 2013 to 2023. The goals include comprehending how publication trends have changed over time, pinpointing important fields of interest, and clarifying how machine learning and scholarly research interact. This study's methodology combines a bibliometric framework with a qualitative descriptive approach. The data were collected from Scopus database, which consist of 4.655 documents for analysis. The analysis is executed using R studio and Biblioshiny. The result shows that the rise of machine learning in academic research between 2013 and 2023 suggests that this field of study will continues to be a trending area for academic exploration.
The Effect Of Using Semantic Mapping Strategy Toward Students Vocabulary Mastery At 8th Grade At Boarding School Jama’atul Mualimin

This research was done to find out the effect of using semantic mapping toward students vocabulary mastery. It has been done at boarding school Jama‟atul Mualimin. Most of students still have limited vocabulary. The students were difficult remember vocabulary that they have learned, they cannot use correct vocabulary in sentences and the student were not interested in the classroom. In this research, the researcher used pre- experimental research with one group pretest and post-test design. The population of this research was the 8th grade students at boarding school Jama‟atul Mualimin which consisted of one class. The sample was taken by total sampling because the school just had one class. The sample of this research was the students of 8th grade which consisted of 19 students. The result of the research indicated the score of the student‟s posttest is higher than the score of pretest. The mean score of pretest is 47,31, where the mean score of students posttest is 88,26. It means that semantic mapping gave contribution toward student‟s vocabulary mastery. In addition to test the hypothesis, the t-obtained 18,17 while t-table on = 0,05 is 1.734. It means that the t-obtained is higher than t-table. In other words, the (Ha) was accepted and Ho was rejected. Which stated there was significant effect of using semantic mapping strategy at 8th grade at boarding school Jama‟atul Mualimin. It can be concluded that, using semantic mapping strategy gave significant effect of using semantic mapping strategy toward students vocabulary mastery.
Forensic examination of security features of the <scp>CDC</scp>‐issued <scp>COVID</scp>‐19 vaccination card and suggested improvements to minimize counterfeiting

Abstract During the COVID‐19 pandemic, the United States released the CDC‐issued vaccination card to document the status of immunizations. It eventually was used as a verification system to allow patrons to safely access public venues. This is/was apparently a simple printed piece of paper with minimal security features which made it an easy target for counterfeiters with little expertise required. Those unvaccinated who wished to enter areas that required proof of the COVID‐19 vaccine were soon finding alternative illegal methods to do so which led to public health concerns. This research was divided into two parts. Part A aimed to forensically identify the security features with a video spectral comparator and a stereomicroscope. Ten “known” blank cards were collected directly from dispensaries that administered the vaccine and 68 “unknowns” from individuals who received the COVID‐19 vaccine. Part B aimed to evaluate the authentication systems employed by countries outside the United States to propose a model on how the vaccination card could be improved and what security features should be included for future instances requiring proof of immunization. The results revealed the CDC document was produced with minimal security features making the document prone to counterfeiting. Furthermore, based on the information collected from other countries' vaccination certificates, it is recommended to develop a dual authentication system, with digital and printable aspects, that contains a QR code linked to a database. Other security features may include a non‐optical brightener substrate, security fibers, a watermarked logo, offset printing, and a geographical color‐coded system.
Preface of the 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering—MaxEnt 2023

The forty-second International Conference on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (42nd MaxEnt’23) was held at the Max Planck Institute for Plasmaphysics (IPP) in Garching, Germany, from 3rd to 7th of July 2023 (https://www [...]
Visualization of Source Code and Acceptance Test Co-evolution

This research investigates how the codebase and acceptance tests have changed over time using version control. An essential requirement for this evaluation is analyzing software development and test cases used in real-world projects. The data stored in software repositories is vital for software systems. Access to prior versions of the program and the ability to differentiate between adjacent versions using acceptance tests and source code modifications can provide insight into how the software evolves. A skillful visualization method is required to achieve this level of awareness. This research presents a visualization method for tracking the concurrent evolution of source code and acceptance tests within software development projects. The case study looks at four open-source projects taken from the real world that have acceptance tests hosted on GitHub. The proposed visualization method enables the negative slope on two projects for the source code and acceptance test co-evolution to be quickly observed, and with such an observation, the project team can take necessary actions to change the course of the co-evolution.
E-MAIL SPAM DETECTION

Emails have become a ubiquitous means of personal and professional communication, often containing sensitive and confidential information. However, they are also prime targets for cybercriminals who employ techniques such as phishing to obtain private data. This paper proposes an intelligent and efficient email spam detection system that leverages data mining algorithms for classification and association. By extracting multiple features from email content, we improve classification accuracy and efficiency. The system integrates various machine learning algorithms and achieves a 30% reduction in the error rate compared to existing methods. Our approach enhances email spam detection by combining support vector machines with multi-feature extraction and classification. Key Words: Support vector machines, Email Spam, Phishing Detection, Machine Learning, Multi-Feature Extraction.
KREATIFITAS SISWA SMK NEGERI 1 AMPEK ANGKEK DALAM PEMANFAATAN LIMBAH KAYU

This research was conducted to increase student creativity in utilizing wood waste at SMK Negeri 1 Ampek Angkek, Agam Regency. The main problem in this research is that students tend to be more passive and less creative, so they are constrained in creating ideas to work on using wood waste. This study uses qualitative methods with active participation observation data collection techniques, interviews and documentation. The data analysis technique was carried out in a descriptive-interpretative manner. Improving student creativity in utilizing sawdust waste is carried out in several important stages, including; preparing materials and equipment, making designs/sketches, learning processes and being creative in making sawdust products decorative, functional and aesthetic. The results of the study show that measuring students' abilities can be seen based on their ability to design works, persistence in working on works and students' creativity in making works. Efforts made to increase student creativity are carried out by using varied learning media and displaying visual forms in the form of videos of the process of making wood products and photos of examples of wood waste products, training students' abilities in creating new ideas in choosing product forms.
A Novel Virtual Machine Placement Algorithm Using an Energy-Aware Meta-Heuristics Approach

The paramount concern associated with the Virtual Machine Placement (VMP) relates to the mapping of Virtual Machine (VMs) to the Physical Machine’s (PMs). This mapping goal is to utilize the PMs to their maximum potential. In order to attain the full proficiency from the mapping process it is required not to impede already active instances of Virtual machine. Incompetence in mapping process substantially results in resource wastage and increase in energy consumption and consequentially increasing the overall functional cost at the data canter. A meta-heuristic based algorithm for virtual machine placement is recommended to redress the aforementioned issues. A cr-Cuckoo algorithm is proposed which integrates the concept of correlation and Cuckoo Search. The work given here is contrasted with varied familiar algorithm of the domain. The acquired results exhibit a distinguished reduction in the consumption of power and count of migrations by VMs and violation in SLA.
Efektivitas Pembelajaran E-Learning Menggunakan Media Zoom Pada Materi Asam Basa

The existence of the covid-19 virus made the government issue a policy regarding learning activities from home or online learning, in order to continue in getting the educational services to improve human resources. One of applications that could be used by teachers during online learning was Zoom meeting. This research aimed at seeing the effectiveness of E-learning using Zoom Media at State Senior High School 1 Kuantan Mudik. This research used descriptive quantitative research. Questionnaire of Google form technique with survey method was used for collecting the data. The effectiveness of E-learning using zoom was seen based on 4 indicators, namely 1) learning quality, 2) learning level suitability, 3) motivating effort and, 4) timing. Indicator 1 obtained that 71% was agree that learning material was well prepared, 68% was disagree that the material was delivered well, and 56% was disagree to follow that e-learning was well conducted. Indicator 2 obtained that 82% was disagree that e-learning could replace face-to-face class, 65% was disagree that the learning objectives have been achieved, 62% was disagree that the audio and video quality of the application strongly supported e-learning. Indicator 3 obtained that 66% was disagree that the internet access was good and 71% was disagree that internet connection was not constrained during e-learning. Indicator 4 obtained that 56% was agree that the lesson was well scheduled. Overall, e-learning using zoom was considered ineffective. It could be seen from the existing problems such as learning objectives that were not achieved, the material was not delivered properly, and the internet connection was not stable.
Optimizing Malware Detection Using Back Propagation Neural Network and Hyperparameter Tuning

The escalating growth of the internet has led to an increase in cyber threats, particularly malware, posing significant risks to computer systems and networks. This research addresses the challenge of developing sophisticated malware detection systems by optimizing the Back Propagation Neural Network (BPNN) with hyperparameter tuning. The specific focus is on fine-tuning essential hyperparameters, including dropout rate, number of neurons in hidden layers, and number of hidden layers, to enhance the accuracy of malware detection. A Back Propagation Neural Network (BPNN) with dropout regularization is trained on an extensive dataset as part of the research design. Hyperparameter optimization is conducted using GridSearchCV, with experiments varying learning rates and epochs. The best configuration achieves outstanding results, with 98% accuracy, precision, recall, and F1-score. The proposed approach presents an efficient and reliable solution to bolster cybersecurity systems against malware threats.