Reviewerly/exHarmony huggingface dataset:
Assessing EFL Listening and Speaking Skills During Remote Teaching

Authors:
raw_author_name: Carmen Benitez-Correa
raw_author_name: Cesar Ochoa-Cueva

Abstract: The purpose of the present study was to analyse how English as a Foreign Language (EFL) listening and speaking skills were assessed during remote teaching as a result of the COVID-19 pandemic. The participants were 174 senior high school students in public and private institutions, 102 EFL teachers, 32 high school authorities, and 80 students’ parents. The methodology comprised a quantitative approach in which students’, teachers’, and authorities’ surveys were analysed. The instruments employed consisted of a five-point Likert scale that included strongly agree, agree, neutral, disagree, and strongly disagree regarding items related to assessment. The findings suggest that for assessing listening and speaking skills the teachers in the research mostly used online technological tools such as quizzes, chatrooms, and blogs. Furthermore, the assessment instruments included oral presentations, questioning, as well as listening and speaking tests. Finally, formative and summative assessments were mainly employed to evaluate students’ listening and speaking skills during the pandemic.
FACIAL EXPRESSION EMOTION RECOGNITION USING LOCAL BINARY PATTERNS HISTOGRAMS

Authors:
raw_author_name: Mr.B Rajesh

Abstract: Local Binary Patterns (LBP) histograms are used as texture descriptors by the proposed facial expression emotion detection system to identify various face expressions in photos. Localized facial patterns are represented by histograms formed by the extraction of LBP features based on pixel intensity comparisons after pre-processing operations such as picture normalization and scaling. Using a labeled emotion dataset, these histograms are used as feature vectors to train a machine learning model. The model's performance is evaluated using evaluation measures, such as accuracy, precision, recall, and F1 score. The system, which was developed with OpenCV and scikit-learn, shows a reliable and useful method for recognizing facial expressions that can be used in practical settings. Key Words: Facial Recognition, Deep Learning, Image Recognition
Aksesibilitas Pemustaka Penyandang Disabilitas Di Dinas Kearsipan Dan Perpustakaan Provinsi Sumatera Barat Berdasarkan Standar IFLA Checklist

Authors:
raw_author_name: None Nurizzati Nurizzati

Abstract: This research aims to analyze the accessibility of library and archives services for people with disabilities in the West Sumatra Provincial Archives and Library Service, the accessibility of users with disabilities in the West Sumatra Province Archives and Library Service based on IFLA Checklist standards, and the obstacles faced in fulfilling accessibility for people with disabilities at the Provincial Archives and Library Service West Sumatra. This research uses qualitative research with descriptive methods. Data collection techniques use observation, interviews and documentation studies. The data validation technique uses triangulation, namely triangulation of data sources. The data validation technique uses the Miles and Hubermen model which consists of data reduction, data presentation, drawing conclusions and verification. The results of this research are as follows. Firstly, accessibility at the West Sumatra Provincial Archives and Library Service has not yet been fully realized because there are still many shortcomings found in the implementation of accessibility services for people with disabilities, such as the ease of library users in meeting their needs has not been fully realized and the only library material collections available are books with braille printing. Second, accessibility at the West Sumatra Archives and Library Service based on the IFLA Checklist Standard reached 40.54%. Thus, it can be said that accessibility for users with disabilities in the Archives and Library Service is still not in accordance with the IFLA Checklist standards. The research results from the IFLA Checklist standard amounted to 111 items which were 45 items which were appropriate while which were 66 items which were not appropriate. Third, the obstacles faced by the West Sumatra Province Archives and Library Service in fulfilling accessibility for disabled users from various aspects such as the lack of available funding, lack of technical knowledge of implementation, and limited human resources who are competent in providing services for users with special needs or disabilities.
The Linked Factor of Customer Satisfaction and Loyalty in F&amp;B Industry: A Study of Operational Performance Factor

Authors:
raw_author_name: Yuxin Chen
raw_author_name: Yifei Chen

Abstract: The focus of this research is to investigate how operational performance affects customer satisfaction and loyalty in the food and beverage industry. Starbucks has been selected as this research object. Conducting a survey and referring to other studies of journals and articles to support the assisting data are the research methods employed. According to the research results, 71 % of respondents are pleased with current Starbucks services and products. The studies also revealed that the majority of customers had heard of Starbucks on social media. The study’s findings reveal that operation success is significantly linked to customer satisfaction. Customer satisfaction has been described as a significant mediator between service quality and customer loyalty. Having a clear understanding of the hypothesized relationship in the study encourages Starbucks to figure out the best action to gain or increase customer loyalty by providing good service quality to build a loyal customer base and customer satisfaction.
Analysis of Web Vulnerability Using Open-Source Scanners on Different Types of Small Entrepreneur Web Applications in Malaysia

Authors:
raw_author_name: None Alya Geogiana Buja

Abstract: Most of Malaysia’s small entrepreneurs have switched to online platforms as an alternative to their physical businesses. Social media sites such as TikTok, Instagram, Twitter, and Facebook provide free advertising tools and convenient access to a broader global target. However, security issues on these websites remain questionable as users are exposed to web attacks due to the vulnerabilities on the websites. Considering the cost and lack of awareness of the importance of cybersecurity, some organizations find it not profitable to invest in securing their websites. Therefore, this paper aims to test Malaysian small entrepreneurs’ web applications using open-source scanners and analyse the results of web vulnerabilities detected. To do so, two types of open-source scanners, OWASP ZAP and IRONWASP, were installed to scan five websites found through advertisements on social media sites. The web vulnerability identification was based on the top 5 OWASP web vulnerability reports, and the results showed that five types of web vulnerabilities were detected. The analysis of the results showed that the top 5 web vulnerabilities in Malaysian small entrepreneurs’ websites are the ‘Missing Session Timeout’ vulnerability with 81.84 percent, the ‘Sensitive Information Passed as Clear Text in GET URL’ vulnerability with 14.74 percent, and the ‘Session ID Cookies not Marked Secure’ vulnerability with 2.47 percent. This paper provides security analysis on Small Medium Enterprise (SME) websites for future enhancement and consideration during development and implementation to avoid possible attacks. Therefore, developers are advised to handle these vulnerabilities by carefully managing the session timeout, and users are recommended to log out from the websites immediately after they are done.
Modern Implementation of the Natural-Philosophical Concept of Cognition on the Basis of the Empirical-Metaphysical General Systems Theory

Authors:
raw_author_name: Andrey A. Gribkov

Abstract: The article examines the relationship between philosophy and science, as well as the possible role of general systems theory in the formation of the field of consolidated philosophical and scientific knowledge. It is argued that the existing arguments concerning the incompatibility of philosophy with the criteria of scientificity only point to the existence of differences between philosophical knowledge and knowledge within the framework of private sciences. At the same time, philosophy is the methodological and paradigmatic basis of all sciences. It is suggested that it is expedient to return to the natural-philosophical concept of cognition, according to which the core of science is philosophy, and private sciences act as tools of cognition of separate subject areas. The methodological basis for the realization of the natural-philosophical concept today can be the general theory of systems, supplemented by the ontological component. In the article the idea of a common field of knowledge formed by philosophy and all private sciences is put forward. This field of cognition is formed by areas of three types: metaphysical knowledge, private sciences, and areas of generalized or universal knowledge, the cogni-tion of which can be carried out only by means of the general theory of systems. An extended version of the general theory of systems is proposed – the empirical-metaphysical general theory of systems, which, in the author’s opinion, possesses the necessary capabilities for the realization of philosophical cognition. The pro-posed general theory of systems includes an epistemological component (metaphysics of material being) as well as an ontological component. А cognition of complex non-deterministic objects within the framework of empirical-metaphysical general theory of systems is realized by a wide set of tools: collections of patterns and primitives of systems, secondary laws and properties of objects, evolutionary way of cognition and laws of evo-lutionary growth of complexity of systems, as well as methodology of formation of reliable knowledge.
Stress Detection and Mitigation through Discovery of Optimal Lecture Delivery Method for STEM-Enrolled University Students

Authors:
raw_author_name: None Okta Nurika
raw_author_name: None Muhamad Hariz Bin Muhamad Adnan

Abstract: The high availability of technology-related jobs is currently not fulfilled by enough supply of technology graduates, specifically the ones coming from science, technology, engineering, and mathematics (STEM) courses. This is related to the low enrolment rate of STEM-based courses at Universities. The notion that STEM subjects are intense and difficult to pass could be the reason of such phenomena. This research experimented different lecture delivery methods comprising of different seating arrangements, inclusion or exclusion of break, group size, and room size to investigate the generated levels of stress among STEM students, so that the method that would induce minimum stress could be discovered. The analysis of the generated heart rates as stress indication concludes that the level of difficulty of STEM subject’s topic is the one that most affected students’ level of stress, while inclusion of break during class is imperious to destress students. Class arrangement with far seating was discovered to be potentially more stressful for certain students indicated by the high level of resting heart rate in one of the experiments. Importantly, it was also found that regardless of lecture delivery method, STEM subjects in general remained challenging that was suggested by the consistently high recorded heart rates, which was also a sign of active learning engagement - besides implying the level of stress. Finally, gender-wise, there is no visible data pattern that could hint that certain gender is more vulnerable to stress when handling STEM subjects.
Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification

Authors:
raw_author_name: Petra Grd
raw_author_name: Igor Tomičić
raw_author_name: Ena Barčić

Abstract: The classification of human face shapes, a pivotal aspect of one&amp;rsquo;s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation for accurate feature extraction. Our methodology utilizes a publicly available dataset of female celebrities, comprising five face shape classes: heart, oblong, oval, round, and square. By augmenting this dataset during training, we magnify its diversity, enabling better generalization and enhancing the model&amp;rsquo;s robustness. With the EfficientNetV2S neural network, we employ transfer learning, leveraging pretrained weights to optimize accuracy, training speed, and parameter size. The result is a highly efficient and effective model, which outperforms state-of-the-art approaches on the same dataset, boasting an outstanding overall accuracy of 96.32%. Our findings demonstrate the efficiency of our approach, proving its potential in the field of face shape classification. The success of our methodology holds promise for various applications, offering valuable insights into beauty analysis, cosmetic recommendations, and personalized healthcare.
Extrinsic Calibration for Large FOV Based on Inverse Depth Parameterized Bundle Adjustment

Authors:
raw_author_name: Shuiqiang Zhang
raw_author_name: Haiyang Gong
raw_author_name: Zikang Xu
raw_author_name: Zhilong Su
raw_author_name: Dongsheng Zhang

Abstract: Abstract To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for 3D-DIC in large field of view. The method combines image feature algorithms with DIC techniques to extract matching point pairs from the left and right images of binocular cameras. These matching point pairs are then homogenized within the field of view. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large field of view applications, the calibration control points' spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient calibration control points, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add calibration control points within the field of view. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method's reliability and accuracy in large field of view 3D measurement are verified through experiments.&amp;#xD;
Gibbs sampling the posterior of neural networks

Authors:
raw_author_name: Giovanni Piccioli
raw_author_name: Lenka Zdeborova

Abstract: Abstract In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an efficient Gibbs sampler. For small models, the Gibbs sampler attains similar performances as the state-of-the-art Monte Carlo Markov chain methods, such as the Hamiltonian Monte Carlo or the Metropolis adjusted Langevin algorithm, both on real and synthetic data. By framing our analysis in the teacher-student setting, we introduce a thermalization criterion that allows us to detect when an algorithm, when run on data with synthetic labels, fails to sample from the posterior. The criterion is based on the fact that in the teacher-student setting we can initialize an algorithm directly at equilibrium.
Software Managers' Guide to Operational Excellence

Authors:
raw_author_name: Kate Matsudaira
raw_affiliation_string: SoFi in Issaquah, WA, USA

Abstract: The secret to being a great engineering leader? Setting up the right checks and balances.
Participatory Action Research in the Development of Higher Education with Digital Media

Authors:
raw_author_name: Julian Küsel
raw_affiliation_string: Ludwigsburg University of Education
raw_author_name: Silvija Markic
raw_affiliation_string: Ludwig-Maximilians-Universität München

Abstract: The digitalisation of higher education has versatile potential for students and especially for pre-service teachers. Unfortunately, this potential is insufficiently used in higher education institutions. This article displays a project focusing on developing university teacher education with digital media. For this purpose, the applied model of Participatory Action Research for Higher Education will be illustrated, reflected, and discussed to share the findings and experiences of the process and facilitate future development projects. Furthermore, conditions for success, challenges, and recommendations based on this project will be provided.
Harnessing AI and Gut Microbiome Research for Precision Health

Authors:
raw_author_name: Ritcha Saxena
raw_affiliation_string: Department of Biomedical Sciences, University of Minnesota School of Medicine, Duluth, Minnesota, USA.
raw_author_name: Vikas Sharma
raw_affiliation_string: Department of Computer Sciences, Delhi Technological University, New Delhi, India

Abstract: The gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.
AI-Powered Energy Management System for Industry

Authors:
raw_author_name: None Prathmesh Gaikwad
raw_affiliation_string: Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India
raw_author_name: None Kapil N. Vhatkar
raw_affiliation_string: Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India

Abstract: The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world’s energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward—transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability
Video Action Recognition Using Depth Motion Maps

Authors:
raw_author_name: Ryan Tan

Abstract: &lt;p&gt;This thesis presents an algorithm for modifying Depth Motion Map (DMM) pyramid analysis based action recognition. It extends the work presented by Chengwu Liang to better handle incoming data. Morphological image transforms are used to remove incoming noise created by the inaccuracies in a Microsoft Kinect when the subject is stationary. This avoids the problem of motion being registered in frames where there is no motion. It also uses a non-linear cost metric on the motion energy to ensure some degrees of shift invariance in the Z axis, which is towards or away from the camera. In addition, advanced information fusion methods are adopted to improve feature representation, leading to better performance. Experiments are conducted on video data containing a variable number of stationary frames at the start and end of an action. The experiments are conducted with many different classifiers. The results show that a performance benefit is realized due to the use of these enhancement methods.&lt;/p&gt;
Image Enhancement for the Improved Extraction of Local Image Features Using Image Offsets

Authors:
raw_author_name: Jonathan Psaila

Abstract: &lt;p&gt;In this thesis, an algorithm for improved feature extraction is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Comparative keypoint detection testing on the enhanced images is conducted to test if more keypoints can be extracted using the method. Keypoint strength experiments are also conducted as well as quality assessment experiments to test the validity of the proposed algorithm as an image enhancement method. Image matching experiments though SIFT and SURF are also conducted. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam, Heinly, and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or similar matching accuracy compared with related enhancement algorithms using SIFT and SURF.&lt;/p&gt;