Reviewerly/exHarmony huggingface dataset:
Emotion Analysis With COVID-19 Based On On-line Text

With the expansion of the social network service (SNS) market, a large amount of data is accumulated and analyzed, so people's emotions can be confirmed in people's thoughts and opinions. The coronavirus infection-19 (COVID-19), which has recently had a great impact on the world, has been so influential that it has changed people's daily lives. In the beginning, it gave people great fear and shock, but as time went on, people got used to it. Now that we have reached the era of ‘End Corona’, we can feel that people’s feelings about COVID-19 are changing. This study identifies emotions by using the NRC Emotional Dictionary with SNS data related to COVID-19, examines changes in people's emotions about COVID-19 over time, and then investigates how emotions change using LDA and language network. The analysis results indicate that the most felt emotions are 'fear' and 'trust', and the main cause of the change in emotion is 'vaccine'. In addition, in the early stage of vaccine development, ‘fear’ is low due to trust in the vaccine, but ‘fear’ is felt again due to the side effects of the vaccine, and it is confirmed that there is a difference in feelings about COVID-19 between the early stage of 2020 and the late stage of 2022.
Resampling Technique for Imbalanced Class Handling on Educational Dataset

Educational data mining is an emerging field in data mining. The need for accurate in identifying student accomplishment on a course or maybe an upcoming course can help the institution to build technology-aided education better. Educational data mining becoming a more important field to be studied because of its potential to produce a knowledge base model to help even the teacher or lecturer. Like another classification task, educational data mining has a common and frequently discovered problem. The problem that occurred in educational data mining specifically and classification tasks generally is an imbalanced class problem. An imbalanced class is a condition where the distribution of each class is not in the same proportion. In this research, it is found that the class distribution is severely imbalanced and it is a multiclass dataset that consists of more than two class labels. According to the problem stated beforehand, this paper will focus on the imbalanced class handling and classification with several methods on both of it such as Linear Regression, Random Forest and Stacking for classification and SMOTE, ADASYN, and SMOTE-ENN for the resampling algorithm. The methods are being evaluated using a 10-fold cross-validation and an 80-20 splitting ratio. The result shows that the best performance coming from the Stacking classification on ADASYN resampled dataset evaluated using an 80-20 splitting ratio with a 0.97 F1 score. The result of this study also shows that the resampling technique improves classification performance. Even though the no-resampling classification result produced a decent result too, it can be caused by several things such as the general pattern of the data for each class is already been good from the start. Thus, there is no real drawbacks if the original data is processed.
An Analysis of the Deliberation and Task Performance of an Active Logic Based Agent (Student Abstract)

Active logic is a time-situated reasoner that can track the history of inferences, detect contradictions, and make parallel inferences in time. In this paper, we explore the behavior of an active-logic based agent on different sets of action selection axioms for a time-constrained target search task. We compare the performance of a baseline set of axioms that does not avoid redundant actions with five other axiom sets that avoid repeated actions but vary in their knowledge content. The results of these experiments show the importance of balancing boldness and caution for target search.
Concerns with using Python in Machine Learning Flight Critical Applications

Python is a programming language that is proving irresistible to the Machine Learning development community. The characteristics that make it irresistible may not be well suited for flight critical applications, e.g., Python is an interpreted programming language reliant upon a virtual machine to execute bytecode. Programming languages used in flight critical applications have higher assurance expectations than non-flight critical applications. For aviation applications, Python may be appropriate for the development of Machine Learning models, but Python does not appear to be appropriate for the implementation of those Machine Learning models for deployment on flight critical applications. This paper explores concerns for using Python in those applications and offers potential courses of action to alleviate these concerns: (1) certify/qualify/mature Python, (2) transition from development in Python to implementation in a certified/qualified/maturity programming language, or (3) development and implementation in an environment with certification/qualification/maturity pedigree.
EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems

We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems. Our EasyRec framework is superior in the following aspects:first, EasyRec adopts a modular and pluggable design pattern to reduce the efforts to build custom models; second, EasyRec implements hyper-parameter optimization and feature selection algorithms to improve model performance automatically; third, EasyRec applies online learning to adapt to the ever-changing data distribution. The code is released: https://github.com/alibaba/EasyRec.
WAT: Improve the Worst-Class Robustness in Adversarial Training

Deep Neural Networks (DNN) have been shown to be vulnerable to adversarial examples. Adversarial training (AT) is a popular and effective strategy to defend against adversarial attacks. Recent works have shown that a robust model well-trained by AT exhibits a remarkable robustness disparity among classes, and propose various methods to obtain consistent robust accuracy across classes. Unfortunately, these methods sacrifice a good deal of the average robust accuracy. Accordingly, this paper proposes a novel framework of worst-class adversarial training and leverages no-regret dynamics to solve this problem. Our goal is to obtain a classifier with great performance on worst-class and sacrifice just a little average robust accuracy at the same time. We then rigorously analyze the theoretical properties of our proposed algorithm, and the generalization error bound in terms of the worst-class robust risk. Furthermore, we propose a measurement to evaluate the proposed method in terms of both the average and worst-class accuracies. Experiments on various datasets and networks show that our proposed method outperforms the state-of-the-art approaches.
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility

Contrastive self-supervised learning (CSL) has managed to match or surpass the performance of supervised learning in image and video classification. However, it is still largely unknown if the nature of the representations induced by the two learning paradigms is similar. We investigate this under the lens of adversarial robustness. Our analysis of the problem reveals that CSL has intrinsically higher sensitivity to perturbations over supervised learning. We identify the uniform distribution of data representation over a unit hypersphere in the CSL representation space as the key contributor to this phenomenon. We establish that this is a result of the presence of false negative pairs in the training process, which increases model sensitivity to input perturbations. Our finding is supported by extensive experiments for image and video classification using adversarial perturbations and other input corruptions. We devise a strategy to detect and remove false negative pairs that is simple, yet effective in improving model robustness with CSL training. We close up to 68% of the robustness gap between CSL and its supervised counterpart. Finally, we contribute to adversarial learning by incorporating our method in CSL. We demonstrate an average gain of about 5% over two different state-of-the-art methods in this domain.
SPECIAL COURSE "PROFESSIONALLY ORIENTED MEDIA EDUCATION IN THE DISCIPLINE "ENVIRONMENTAL SAFETY" FOR FUTURE EMPLOYEES OF ENGINEERING SPECIALITIES

The modern educational process in the field of applied ecology is largely based on the use of the internet.Applied ecology, in turn, includes technoecology: environmental aspects of economic sectors, use of natural resources, nature (environmental) protection, environmental economics and environmental management, environmental audit, environmental marketing, environmental law, environmental standardisation, environmental management and environmental protection, environmental monitoring and environmental forecasting, radiation and space ecology.Media literacy has become a key feature of a specialist's professionalism.Its acquisition encourages the development of media education technologies.Contemporary researchers and practitioners have examined various aspects of using the media and their products in the educational process.Universities are offering special courses in media education, which indicates both extensive and intensive development of media didactics in higher education, which is enriched with the latest technologies, terms, concepts and at the same time penetrates the newest areas of knowledge.Our study proposes a programme of a new special course "professionally oriented media education in the discipline "environmental safety" for future engineering professionals and provides examples of the application of media education methods and tools in teaching the discipline "environmental safety in the oil and gas industry".
Denoising Noisy Neural Networks: A Bayesian Approach With Compensation

Deep neural networks (DNNs) with noisy weights, which we refer to as noisy neural networks (NoisyNNs), arise from the training and inference of DNNs in the presence of noise. NoisyNNs emerge in many new applications, including the wireless transmission of DNNs, the efficient deployment or storage of DNNs in analog devices, and the truncation or quantization of DNN weights. This paper studies a fundamental problem of NoisyNNs: how to reconstruct the DNN weights from their noisy manifestations. While prior works relied exclusively on the maximum likelihood (ML) estimation, this paper puts forth a denoising approach to reconstruct DNNs with the aim of maximizing the inference accuracy of the reconstructed models. The superiority of our denoiser is rigorously proven in two small-scale problems, wherein we consider a quadratic neural network function and a shallow feedforward neural network, respectively. When applied to advanced learning tasks with modern DNN architectures, our denoiser exhibits significantly better performance than the ML estimator. Consider the average test accuracy of the denoised DNN model versus the weight variance to noise power ratio (WNR) performance. When denoising a noisy ResNet34 model arising from noisy inference, our denoiser outperforms ML estimation by up to 4.1 dB to achieve a test accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60\%$</tex-math></inline-formula> . When denoising a noisy ResNet18 model arising from noisy training, our denoiser outperforms ML estimation by 13.4 dB and 8.3 dB to achieve test accuracies of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$60\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$80\%$</tex-math></inline-formula> , respectively.
C.S. Technopoly: A Megagame for Teaching and Learning Cybersecurity

In this paper we present our ongoing research where we are attempting to integrate sustainable development issues into a megagame designed to teach cybersecurity. There are several serious games that have been developed to teach and inform individuals about sustainability issues but none that deal specifically with both cybersecurity and sustainability issues. A Megagame is a multiplayer game with between 30-40 players who play in teams of 3-5 players that take on specific roles in dealing with complex problems that cover subject matters ranging from science fiction and heroic fantasy to political, economic, historical, and even cyber conflicts. We have built and tested a megagame entitled CS -Technopoly using the socio-technical framework of sustainability proposed by Geels and integrated it further with the Security by Consensus Model proposed by Kowalski. The intended learning objectives of the game, such as teaching adversarial and sustainable systems thinking by exposing the students to cyber threat intelligence reports and cyber security investments decision making, were tested by performing semi-structured interviews of a stratified sample of the participants. Preliminary results from 11 interviews from the two first trials of CS Technopoly indicate that the participating security experts found that C.S. Technopoly would be a useful tool for team building and improving collaboration between security departments and upper strategic management
Dynamic stability of VR headsets: the effect of contact area on displacement in user motion

Since the metaverse was introduced, virtual reality (VR) head-mounted displays (HMDs) are increasingly being used for VR gaming, sports and 3D films. Additionally, the Pancake optical module has made HMDs smaller and lighter, which greatly increased its comfortability. However, during real use, there will inevitably be a lot of user head mouvements, such as shaking, lowering and raising, etc., which will cause certain displacement of the HMDs due to its inertia. Then the relative displacement of HMDs may affect the pressure and pressure-bearing area of the head, which leads to user discomfort.Currently, the research on the comfort of HMDs primarily focuses on the static scenarios without mouvement. This paper investigated the relative displacement and user comfort of the HMDs in motion. The main purpose of this study is to determine the relationship between HMDs' displacement and the contact area in order to provide design guidances and insights for HMDs, particularly the binding area. This paper also proposed an effective experimental method for measuring the relative displacement of HMDs, which can be used to assess the dynamic stability.A simple VR environment was bulit to guide the participant’s head movements by moving circular lightened dots, for simulating the head mouvements in daily life. At the same time, a wearable ruler sticker was designed to measure the displacement of the HMDs relative to the head.The experimenter took measurements of participants’ heads and assisted them to get familiar with the HMDs, and then pasted ruler stickers on the participants’ faces. The participants put on the HMD and adjust it to a comfortable position. Then they completed the head movement under the guidance of the VR environment and repeated it for 5 times. The same procedure was repeated after changing the contact area of the binding with 40mm*40mm foam cotton pieces. The HMD’s position was recorded before and after the mouvement.The results shows that, the HMDs exhibit a significant displacement relative to the head, after performing typical head motions, which causes some slight discomfort. Meanwhile, after doing a data correlation with SPSS, it can be found that the contact area of the binding is one of the main reasons for HMDs’ displacement. And the displacement can be greatly reduced and the pressure comfort rarely changes when the contact area of the bindings is increased, which has a positive impact on the overall comfort evaluation. Besides, we believe that in addition to the contact area, the mass distribution, pressure, and materials also have an effect on the relative displacement of HMDs, which might be the focus of future research. In future, new prototypes will be developed to increase the accuracy of environmental variables control and the evaluation of different factors.
Exploring Writing Anxiety of the ESL University Students in Malaysia

In this study, engineering students at UiTM had their levels, types, and causes of writing anxiety evaluated. The participants were 172 diploma engineering students. The Second Language Writing Anxiety Inventory (SLWAI) and Causes and Strategies to Alleviate Writing Anxiety (CSWAS), which were developed by the researcher based on classroom observations and previous research on this issue, revealed a high degree of writing anxiety, with somatic anxiety being its main type, as reflected in a racing heart, dry mouth, sweating, butterfly in the stomach, twitching, and yawning as its main sources. It was concluded that fear of the feedback from teachers, lack of confidence, insufficient subject understanding, weak writing skills, and pressure with time-constraint are responsible for this deficit. It was suggested that the educational system in Malaysia should adopt a less critical and intimidating tone, concentrating on the writing requirements and expectations of ESL students rather than setting lofty objectives and applying pressure for flawless work. This study provides some recommendations for those working in the sector that will help improve the writing abilities of ESL students in Malaysia.
A Study on Advantages and Disadvantages of Digital Exhibitions in Exhibition Spaces and Their Future Orientations in the Post-Pandemic Era

The rapidly changing digital technology and the pandemic have drawn people's attention to the directions and methods of exhibition space management. The purpose of this study was to investigate the current status of exhibition spaces at home and abroad, to study the trends of digital technology and virtual exhibitions, and the technologies, limitations, advantages, and disadvantages of offline and online exhibitions, and to collect examples of the application of various digital technologies and virtual exhibition techniques. For this purpose, a survey was conducted with chi-square tests.&#x0D; The results showed strong expectations and interest in virtual exhibition spaces using digital technologies. They also showed that advances in science and technology are narrowing the gap between the real and virtual worlds, expanding the scope of exhibition content, and freeing it from temporal and spatial limitations. However, on the other hand, as offline exhibitions that favor empathy and emotional sympathy through a sense of the presence of exhibits are important, in addition to the advancement of digital technology, the exhibition producers and social engagement factors in exhibition planning should be reflected together.
Sistem Informasi Monitoring Barang Produksi Menggunakan Metode FIFO

Gold or aurum (Au) is a precious metal, because it is stable, does not change substances, does not oxidize&#x0D; in normal air, has stable properties, and is a pure element. In the current era, there are many companies&#x0D; in the jewelry sector that produce various processed products from gold, such as bracelets, rings,&#x0D; necklaces, etc. Given the importance of data collection for companies, it is necessary to monitor and&#x0D; collect data on production goods. In the face of developments in the modern era today requires a system&#x0D; to improve the quality of goods management. No PT. HARTADINATA ABADI TBK (HRTA) is engaged in&#x0D; manufacturing gold jewelry and jewelry. The company has a product marketing distribution network with&#x0D; wholesalers, retailers and own stores. Those who have data collection problems are still using the manual&#x0D; system. The author makes a web-based goods monitoring information system using qualitative descriptive&#x0D; analysis research methods. This type of research is generally used in social phenomenology. One of these&#x0D; social studies is related to guidance and counseling research. The system is made using the HTML, CSS,&#x0D; PHP, javascript and database programming languages using MYSQL, with the Object Oriented Software&#x0D; Engineering (OOSE) development method emphasizing use cases, so this system can be used to support&#x0D; the performance of the head of the polishing division and the polishing admin of the division.&#x0D;
UCoL: Unsupervised Learning of Discriminative Facial Representations via Uncertainty-Aware Contrast

This paper presents Uncertainty-aware Contrastive Learning (UCoL): a fully unsupervised framework for discriminative facial representation learning. Our UCoL is built upon a momentum contrastive network, referred to as Dual-path Momentum Network. Specifically, two flows of pairwise contrastive training are conducted simultaneously: one is formed with intra-instance self augmentation, and the other is to identify positive pairs collected by online pairwise prediction. We introduce a novel uncertainty-aware consistency K-nearest neighbors algorithm to generate predicted positive pairs, which enables efficient discriminative learning from large-scale open-world unlabeled data. Experiments show that UCoL significantly improves the baselines of unsupervised models and performs on par with the semi-supervised and supervised face representation learning methods.
Study on Factors Affecting Employee Turnover in Banking and Automotive Sector of Pakistan

This research is based on a study to investigate the impact of various factors affecting employee turnover in Pakistan's banking and automotive sector. A comprehensive literature review has been performed based on various hypotheses developed. Further, a survey strategy has been adopted to collect quantitative data. To test the hypothesis number of statistical methods have been implemented using SmartPLS. Both measurement and structural models have been developed for detailed analysis of variables affecting employee turnover. Results obtained from various analyses have also been discussed. Hence, this study examines the effect of employee engagement, job satisfaction, job insecurity, and work overload mediated through job stress and performance management on the intentions of an employee switching their job role. This study will not only assist in improving the effectiveness of retention strategies but also give updates about future retention strategies by analyzing the data collected and its results. The discovery of this study would encourage the uplifting of retention practices in other sectors as well.
Deep Manifold Attack on Point Clouds via Parameter Plane Stretching

Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.