Reviewerly/exHarmony huggingface dataset:
Enhancing sign language recognition using CNN and SIFT: A case study on Pakistan sign language

Millions of people who have trouble hearing rely heavily on sign language as their primary means of communication. As a form of visual language, sign language is primarily used as features by the deaf and hard of hearing to communicate with one another. These features make it challenging for the average person to grasp what is being said. As a result, it becomes more difficult for deaf people to interact with the hearing community. In order to solve this communication barrier, scientists have been working on sign language recognition systems. The purpose of this research is to propose a hand gesture recognition framework for Pakistan Sign Language (PSL) by training a Convolutional Neural Network (CNN) on PSL gesture images obtained with the aid of a testbed built in the laboratory using the Kinect motion sensor for Urdu alphabets. Kinect images of hands were taken in this study under a variety of lighting conditions. A feature vector set was created from hand margin, size, shape, palm center points, and finger position using Scale Invariant Feature Transform (SIFT). Important features of PSL-based sign images were extracted using SIFT and then converted to vector points. The proposed improved CNN model was able to achieve an impressive accuracy of 98.74% on the PSL dataset, demonstrating an impressively low error rate of only 1.26%. A case study is demonstrated in the assessment of the system that has been provided. Finally, a comparison is performed between others and our work to show the efficiency of the proposed framework and legitimize the concept.
Lexicon-grammar tables standardization and implementation

&lt;span&gt;The lexicon-grammar approach is a very important linguistic approach in automatic natural language processing (NLP). It allows for the description of the lexicon of the language through readable and intuitive tables for human manual editing. However, the automatic use of the lexicon-grammar tables in the automatic NLP platforms remains difficult, given the incompatibility between the codes used to represent the properties in the lexicon-grammar tables and those used to represent the properties in the automatic NLP platforms. In this work, we present our method of standardizing the lexicon-grammar tables for the French language, since they constitute very rich lexical, syntactic, and semantic linguistic resources. First, we standardize their properties so that they can be compatible with those used in the NLP platforms. Then, to implement the standardized tables, we used a linguistic platform such as NooJ. For that, we describe the process of integrating these tables into this platform through the automatic generation of the dictionaries from these tables. Finally, to test the efficiency of the generated dictionaries, we create for some of them syntactic grammars that take into account all the grammatical, syntactic, and semantic information contained in the dictionaries.&lt;/span&gt;
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With the rapid expansion of data dimensions, subspace representation learning, a method for mapping high-dimensional data samples to their corresponding underlying low-dimensional subspaces, has become an essential process for high-dimensional data clustering. Although the existing methods have achieved reliable data representation learning and precise clustering, few of them realized that the corrupted data points in the dataset will influence the linear representation of the others. When there are multiple heavily corrupted data in a dataset, the matrix of the self-representation coefficient would be influenced by these data. Therefore, this paper proposes the segmentary group-sparsity self-representation learning (SGSSL) and segmentary group-sparsity-based spectral clustering (SGSSC) models to eliminate their influence on representation learning and clustering results. We proposed that imposing varying degrees of row sparsity and column sparsity constraints on the representation coefficient matrix can prevent corrupted data from contaminating other data during the self-representation process, thus obtaining better spectral clustering results. Extensive experiments on several real datasets demonstrate that our proposed method can perform better than several related methods in recent years.
IoT Systems for Extreme Environments

The deployment of Internet of Things (IoT) systems spans a large variety of applications, each with unique requirements. Many of these applications relate to the management of various cyber–physical systems, including road and rail traffic, electricity, water, food/goods transportation and storage, smart building management, crime/safety management, underwater systems, etc. Within this context, a growing concern is the deployment of IoT systems in extreme environments which may occur either because of the nature of the application or due to external factors. Some prominent examples of the former are 1) the IoT deployments in hazardous environments such as management of chemical or nuclear plants, management of underwater oil/gas infrastructure, mining operations, etc.; 2) IoT systems deployed specifically to manage accidents and disasters; and 3) IoT systems deployed in arctic/antarctic regions where they routinely experience extreme levels of changes in terms of temperature, wind conditions, sunlight availability, compression, etc. Such IoT systems generally are designed to specifically operate in the challenging environment they must operate in, and thus may be expected to be rather robust. However, the IoT systems designed to manage the physical infrastructures such as those in urban settings may also need to handle unprecedented and unexpected stresses due to the worldwide phenomena of aging physical infrastructure, demand that far exceeds the designed capacity, and increasingly extreme operating conditions due to climate change. This special issue covers all such scenarios and thus represents a vast and rich area for innovations.
On the Hotelling Game with Close Players

The principle of minimum differentiation stated by Hotelling in his pioneer paper was proved to be invalid 50 years later, since there is a region where the players are so close that Nash equilibrium does not exist. Some authors have contributed with amendments to the original game in the aim of ensuring the existence of Nash equilibrium independently of the location of the players. However, this work analyzes the less frequently studied case of the Hotelling game with close players under different implementations of the game, considering elastic and inelastic demand (conventional Hotelling game, the Hotelling game with reservation cost and Hotelling-Smithies game). The study is supported by means of numerical simulation of the game.
A Fact-finding Survey and Improvement Plans for Mechanical Equipment Preconstruction Verification Application

This paper presents the results of a fact-finding survey conducted among on-site mechanical equipment engineers. Additionally, it proposes improvement plans for completing the mechanical equipment preconstruction verification application. Currently, errors occur due to the unclear method of filling out the preconstruction verification application. This leads to a decrease in the quality of construction and undermines the purpose of enforcing the Machinery Equipment Act. Accordingly, this paper proposed a plan to improve the efficiency and accuracy of mechanical equipment construction. First, items for apartment housing information were added. Second, the types of registration certificates for mechanical equipment design, construction work, and supervision were specified to verify the qualifications of companies and personnel. Third, the technical level of the on-site mechanical equipment technician and the on-site mechanical equipment supervisor was required to be written so that the appropriate manpower arrangement could be confirmed. Through this, it is expected that the overall level of mechanical equipment construction can be improved by reducing errors in filling out preconstruction verification applications and increasing the transparency of construction.
Cui Bono? Software Professionals Should Always Ask “Who Benefits?”

We suggest a straightforward approach to recognizing and managing ethical challenges that arise as software is designed, developed, and deployed. The approach features two “Cui bono?” questions: “Who benefits?” and “Who is at risk?” These questions can help computing professionals deal effectively and honorably with their responsibilities.
Using Machine Learning Algorithms in Determining the Stage of Breast Cancer from Pathology Reports

Introduction: After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports.Material and Methods: A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score.Results: The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time.Conclusion: We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.
The Effect of Flashcards in Improving Vocabulary at the Eighth Grade Students of SMP Negeri 6 Palu

Students face challenges in English communication primarily due to insufficient vocabulary. Consequently, teachers should actively implement strategic measures to enhance students' mastery of vocabulary, such as incorporating activities like the use of flashcard. The objective of this study is to investigate whether the utilization of flashcard media has a positive impact on enhancing vocabulary. This research is an experimental study with a pre post test design using 2 sample groups, namely the intervention group and the control group consisting of 32 people per group. In choosing the participants, the researcher used purposive sampling. This study was conducted in SMPN 6 Palu since the participants are the students of the eighth grade. The results were analyzed using t table and t calculations to determine the effect. The result showed that the mean score of pre-test of control group is 45.13 and that the mean score post test of control group was 53, whereas the mean score of pre-test of experimental group is 45.38, and the mean score of experimental group was 81.38. t-counted (14.57) is higher that t-table (1.9988). This implies that the research hypothesis has been validated. In summary, the utilization of flashcards proves to be effective in enhancing vocabulary among eighth-grade students at SMP Negeri 6 Palu. flashcard media could be used as one of media in the classroom, because it easy and interesting in applying for the students.
Performance Analysis of Various Image Feature Extractor Filters for Pothole Anomaly Classification

Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the other hand, has an accuracy of roughly 87% when it comes to classifying potholes in low-light conditions. These findings are crucial in the selection of feature extraction filters for use by ML classifiers in the development of a robust autonomous pothole detection and classification system for improved navigation on anomalous roads and possible integration into self-driving cars.
Training iterated protocols for distillation of GHZ states with variational quantum algorithms

We present optimized distillation schemes for preparing Greenberger-Horne-Zeilinger (GHZ) states. Our approach relies on training variational quantum circuits with white noise affected GHZ states as inputs. Optimizing for a single iteration of the scheme, we find that it is possible to achieve an increased fidelity to the GHZ state, although further iterations decrease the fidelity. The same scheme, acting on coherently distorted pure-state inputs, is effective only in certain special cases. We show that radically different results can be achieved, however, when one optimizes for the output after two iterations of the protocol. In this case, the obtained schemes are more effective in distilling GHZ states from inputs affected by white noise. Moreover, they can also correct several types of coherent pure-state errors.
Exploring the Spatial Distribution and Properties of Solar Bright Points in Different Solar Areas: A Statistical Analysis of Network and Internetwork Features

Solar bright points (BPs) are small-scale magnetic features observed on the solar surface. They play a significant role in the transfer of energy and mass between the solar photosphere and the corona. This article presents a comprehensive statistical analysis of BPs in different solar areas: quiet Sun (QS), active regions (AR), and coronal holes (CH). We separate the BPs into two categories: network and internetwork. Our study aims to understand the spatial distribution and properties of BPs in these areas, which can help improve our knowledge of the Sun’s magnetic activity and energy transfer processes. Our research highlights the close relationship between the spatial distribution and characteristics of BPs and the solar magnetic field. Using machine learning, we achieved efficient and accurate BP detection, enabling analysis of a larger dataset. The prevalence of network BPs in the QS and internetwork BPs in CH suggests differences in magnetic field concentration. In AR, BPs are evenly distributed between network and internetwork areas, reflecting the complex nature of the magnetic field.
Predicting the impacts of anthropogenic drivers on management scenarios using Bayesian belief network in the Zeribar freshwater wetland, Iran

The Zeribar wetland in Iran’s western city of Marivan provides a valuable foundation for maintaining various aspects of social and cultural life, from water supply to recreational values, as well as for natural habitats. However, its different services have been put under strong anthropogenic pressure and urbanization processes. The present study is a part of ongoing efforts to improve understanding of the anthropogenic factors potential impact on management status of this vulnerable freshwater ecosystem, aiming to provide integrated management strategies using a Bayesian belief network (BBN) approach. A conceptual framework supporting the BBN was designed according to the main determining factors of the wetland’s management including “human-induced factors”, “economic factors and human capital”, “policy context” and “social and cultural issues”. The BBN model was parameterized using different data sources comprising literature review, face-to-face interviews, filed observation, model simulations, and experts’ knowledge. Stakeholders’ feedback on model accuracy and management strategies application was assessed in different workshops with representatives from locals, executives, and scientific communities. The results showed that the two subsystems of “policy context” and “economic factors and human capital” are the most important key factors influencing the target node, wetland management status. Then, fourteen alternative management scenarios (S2- S15) were generated using the improvement in prior probabilities of influencing factors, followed by evaluation and statistical comparison with the output of the current condition as the base-case scenario (S1). Under the S1 scenario, the probability of occurrence of the “Strong” state, in which the wetland is properly protected from anthropogenic disturbances, for the target node of the network was 25.8 %, while the most significant improvement in the wetland management status (66.4%) was observed after an improvement in all influencing variables was adopted (S15), suggesting a significant potential for improvement in the wetland management. According to the stakeholders’ feedback, the constructed BBN model, and the components included in the management scenarios are useful to fill a critical gap in the current management framework. The results of this work can assist decision-makers in managing the wetland more effectively by considering all socio-economic and anthropogenic dynamics.
Mastery Grading Approach in a Calculus Course

Abstract Calculus I students at our engineering school are at a wide range of preparedness levels. Many struggles with this course and the pandemic in the past two years only exacerbated the situation. In fall 2021, 33 out of 183 students (18%) in Calculus I at our engineering school received D/fail/withdraws. Students in this getaway course (first course in engineering and in college) need much more support than students in other courses. They feel anxious about their grades and lack confidence, and a sense of belonging in the classroom [1]. In cases where students were not prepared because they did not have access to academic support and educational resources before coming to college, their performance towards the beginning and throughout the semester will not be accurate and will affect their overall course grade. It is also important to remember that not all students learn the material at the same pace, and some will need a longer time to grasp the concepts. The traditional grading approach reinforces the inequities among students. Consequently, it becomes crucial to rethink how we teach these students and how we align our grading practices to their learning and well-being. To encourage students to prioritize learning over grades, to reduce their anxiety, and to improve and grow throughout the semester, we implemented the mastery grading approach in our Calculus I classes, which impacted about 90-100 first-year engineering students who take Calculus I during their first semester. There is a wide variety of practices in alternative grading systems, such as "Mastery-based Testing (MBT)" [2], "Standards-Based Grading (SBG)" [3], or "Specifications Grading (Spec)" [4]. "Mastery grading" is used as an umbrella term for these alternative grading approaches. This paper will discuss the main elements of the mastery grading design in a Calculus I class, as well as preliminary results on 1) how students' performance differs between mastery and traditionally graded sections, 2) how mastery grading affects students' stress, anxiety, mindset, and beliefs about their math ability. This study is ongoing, and data is being collected in the Fall of 2022. Students' performance will be compared using their placement test scores and their common final exam. The pre-and post-surveys will be administered to assess students' stress, anxiety, and beliefs about their math ability. The analysis will be done after the fall semester concludes.
Changing the Landscape of the Digital Workforce and DEI: A Call to Action for Engineering Education

Abstract Digital transformation has been implemented across a wide range of industries. Advanced technologies (e.g., artificial intelligence, robotics, cloud, internet of things etc.) and data are used to digitally connect organizations, automate processes, and represent each aspect of complex systems, products, and services. While digital transformation has changed the landscape of the engineering workforce, engineering programs of today remain largely based on 20th-century needs. Due to the interdisciplinary nature and interconnectedness of digital transformation, the traditionally siloed engineering disciplines require innovation to keep pace with the workforce demands. This paper explores the digital transformation workforce demands and digital competencies that are needed now and for the future of the engineering workforce. These skills extend beyond the technical, but also encompass social, emotional, and higher cognitive skills. Further, digital transformation will have tremendous impact on diverse communities - with the opportunity to either create access or create further disparity due to access to engineering education and opportunities beyond. The paper also reports on the experiences and advice of participants in the invited panel that was held at the 2023 ASEE Southeastern Regional Conference where panelists addressed the need to modernize engineering education in the era of Digital Transformation and the strategies they envision or have implemented to address opportunities and risks for diversity, equity, and inclusion.