Reviewerly/exHarmony huggingface dataset:
Examination of User Emotions and Task Performance in Indoor Space Design Using Mixed-Reality

Given the COVID-19 pandemic and the resulting social distancing measures with inevitable telecommuting, capturing user emotions is essential as it affects both satisfaction and task performance. Therefore, the purpose of this study was to analyze emotions and task performance in terms of dislike and personalized decision-making in indoor spaces. To facilitate experiments with participants, a mixed-reality environment was utilized with the Pleasure, Arousal, Dominance (PAD) test and cognitive tests. The results of the experiment conducted on 30 subjects identified that aroused and discontented emotions dominated in non-preferred spaces, but pleased, important, and autonomous emotions arose in personalized spaces, as determined through sentimental analysis and statistical methods. Although negative emotions were present in the aversion space, attention and execution abilities were high compared to the personalized space, but working memory was low. By conducting stepwise regression analysis, it was found that working in a visually unfavorable space, which caused an increase in controlled or controlling emotions, improved short-term work efficiency. In addition, important emotions did not have a positive effect on any task performance. However, with pleased and contented emotions in a personalized indoor space, long-term work efficiency was increased, as explained by the Yerkes-Dodson law.
Deep Learning Side-Channel Collision Attack

With the breakthrough of Deep Neural Networks, many fields benefited from its enormously increasing performance. Although there is an increasing trend to utilize Deep Learning (DL) for Side-Channel Analysis (SCA) attacks, previous works made specific assumptions for the attack to work. Especially the concept of template attacks is widely adapted while not much attention was paid to other attack strategies. In this work, we present a new methodology, that is able to exploit side-channel collisions in a black-box setting. In particular, our attack is performed in a non-profiled setting and requires neither a hypothetical power model (or let’s say a many-to-one function) nor details about the underlying implementation. While the existing non-profiled DL attacks utilize training metrics to distinguish the correct key, our attack is more efficient by training a model that can be applied to recover multiple key portions, e.g., bytes. In order to perform our attack on raw traces instead of pre-selected samples, we further introduce a DL-based technique that can localize input-dependent leakages in masked implementations, e.g., the leakages associated to one byte of the cipher state in case of AES. We validated our approach by targeting several publicly available power consumption datasets measured from implementations protected by different masking schemes. As a concrete example, we demonstrate how to successfully recover the key bytes of the ASCAD dataset with only a single trained model in a non-profiled setting.
Oil and Vinegar: Modern Parameters and Implementations

Two multivariate digital signature schemes, Rainbow and GeMSS, made it into the third round of the NIST PQC competition. However, neither made its way to being a standard due to devastating attacks (in one case by Beullens, the other by Tao, Petzoldt, and Ding). How should multivariate cryptography recover from this blow? We propose that, rather than trying to fix Rainbow and HFEv- by introducing countermeasures, the better approach is to return to the classical Oil and Vinegar scheme. We show that, if parametrized appropriately, Oil and Vinegar still provides competitive performance compared to the new NIST standards by most measures (except for key size). At NIST security level 1, this results in either 128-byte signatures with 44 kB public keys or 96-byte signatures with 67 kB public keys. We revamp the state-of-the-art of Oil and Vinegar implementations for the Intel/AMD AVX2, the Arm Cortex-M4 microprocessor, the Xilinx Artix-7 FPGA, and the Armv8-A microarchitecture with the Neon vector instructions set.
IoT Cloud System Based Dual Axis Solar Tracker Using Arduino

Because of the urgent need for electricity and the shortage of it, humanity has searched for the most environmentally friendly and well-available types of energy. From the different types of energy that comes from the sun, wind, tides, hydro and thermal energy that emanates from the earth and is spontaneously replenished naturally, the focus has been on solar energy. Which represents the best source of energy as it is naturally available in life and does not cause any damage or pollution in particular to nature and life. Not to mention after-math effects. This project led to the development of a two-axis solar tracker with the Internet of Things (IoT) or cloud platform. The solar system improves the performance of the solar panels due to following and the sun's mobility. Four light-dependent resistors (LDRs) were used to detect sunlight and the maximum intensity of the light, two servo motors have been utilized to rotate the panels based on sunlight detection by the LDR, after that, we use the WiFi as an intermediary between the Arduino device and the cloud platform. The cloud represents the final destination for the data to be stored, manipulated and processed.
Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.
Authority without Care: Moral Values behind the Mask Mandate Response

Face masks are one of the cheapest and most effective non-pharmaceutical interventions available against airborne diseases such as COVID-19. Unfortunately, they have been met with resistance by a substantial fraction of the populace, especially in the U.S. In this study, we uncover the latent moral values that underpin the response to the mask mandate, and paint them against the country's political backdrop. We monitor the discussion about masks on Twitter, which involves almost 600k users in a time span of 7 months. By using a combination of graph mining, natural language processing, topic modeling, content analysis, and time series analysis, we characterize the responses to the mask mandate of both those in favor and against them. We base our analysis on the theoretical frameworks of Moral Foundation Theory and Hofstede's cultural dimensions. Our results show that, while the anti-mask stance is associated with a conservative political leaning, the moral values expressed by its adherents diverge from the ones typically used by conservatives. In particular, the expected emphasis on the values of authority and purity is accompanied by an atypical dearth of in-group loyalty. We find that after the mandate, both pro- and anti-mask sides decrease their emphasis on care about others, and increase their attention on authority and fairness, further politicizing the issue. In addition, the mask mandate reverses the expression of Individualism-Collectivism between the two sides, with an increase of individualism in the anti-mask narrative, and a decrease in the pro-mask one. We argue that monitoring the dynamics of moral positioning is crucial for designing effective public health campaigns that are sensitive to the underlying values of the target audience.
Machine Explanations and Human Understanding

Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human decision making. However, empirical studies have found mixed and even negative results. An open question, therefore, is under what conditions explanations can improve human understanding and in what way. To address this question, we first identify three core concepts that cover most existing quantitative measures of understanding: task decision boundary, model decision boundary, and model error. Using adapted causal diagrams, we provide a formal characterization of the relationship between these concepts and human approximations (i.e., understanding) of them. The relationship varies by the level of human intuition in different task types, such as emulation and discovery, which are often ignored when building or evaluating explanation methods. Our key result is that human intuitions are necessary for generating and evaluating machine explanations in human-AI decision making: without assumptions about human intuitions, explanations may improve human understanding of model decision boundary, but cannot improve human understanding of task decision boundary or model error. To validate our theoretical claims, we conduct human subject studies to show the importance of human intuitions. Together with our theoretical contributions, we provide a new paradigm for designing behavioral studies towards a rigorous view of the role of machine explanations across different tasks of human-AI decision making.
An interpretable decision-support model for breast cancer diagnosis using histopathology images

Microscopic examination of biopsy tissue slides is perceived as the gold-standard methodology for the confirmation of presence of cancer cells. Manual analysis of an overwhelming inflow of tissue slides is highly susceptible to misreading of tissue slides by pathologists. A computerized framework for histopathology image analysis is conceived as a diagnostic tool that greatly benefits pathologists, augmenting definitive diagnosis of cancer. Convolutional Neural Network (CNN) turned out to be the most adaptable and effective technique in the detection of abnormal pathologic histology. Despite their high sensitivity and predictive power, clinical translation is constrained by a lack of intelligible insights into the prediction. A computer-aided system that can offer a definitive diagnosis and interpretability is therefore highly desirable. Conventional visual explanatory techniques, Class Activation Mapping (CAM), combined with CNN models offers interpretable decision making. The major challenge in CAM is, it cannot be optimized to create the best visualization map. CAM also decreases the performance of the CNN models. To address this challenge, we introduce a novel interpretable decision-support model using CNN with a trainable attention mechanism using response-based feed-forward visual explanation. We introduce a variant of DarkNet19 CNN model for the classification of histopathology images. In order to achieve visual interpretation as well as boost the performance of the DarkNet19 model, an attention branch is integrated with DarkNet19 network forming Attention Branch Network (ABN). The attention branch uses a convolution layer of DarkNet19 and Global Average Pooling (GAP) to model the context of the visual features and generate a heatmap to identify the region of interest. Finally, the perception branch is constituted using a fully connected layer to classify images. We trained and validated our model using more than 7000 breast cancer biopsy slide images from an openly available dataset and achieved 98.7% accuracy in the binary classification of histopathology images. The observations substantiated the enhanced clinical interpretability of the DarkNet19 CNN model, supervened by the attention branch, besides delivering a 3%–4% performance boost of the baseline model. The cancer regions highlighted by the proposed model correlate well with the findings of an expert pathologist. The coalesced approach of unifying attention branch with the CNN model capacitates pathologists with augmented diagnostic interpretability of histological images with no detriment to state-of-art performance. The model’s proficiency in pinpointing the region of interest is an added bonus that can lead to accurate clinical translation of deep learning models that underscore clinical decision support.
A framework for pronunciation error detection and correction for non-native Arab speakers of English language

This paper examines speakers’ systematic errors while speaking English as a foreign language (EFL) among students in Arab countries with the purpose of automatically recognizing and correcting mispronunciations using speech recognition, phonological features, and machine learning. Accordingly, three main steps are implemented towards this purpose: identifying the most frequently wrongly pronounced phonemes by Arab students, analyzing the systematic errors these students make in doing so, and developing a framework that can aid the detection and correction of these pronunciation errors. The proposed automatic detection and correction framework used the collected and labeled data to construct a customized acoustic model to identify and correct incorrect phonemes. Based on the trained data, the language model is then used to recognize the words. The final step includes construction samples of both correct and incorrect pronunciation in the phonemes model and then using machine learning to identify and correct the errors. The results showed that one of the main causes of such errors was the confusion that leads to wrongly utilizing a given sound in place of another. The automatic framework identified and corrected 98.2% of the errors committed by the students using a decision tree classifier. The decision tree classifier achieved the best recognition results compared to the five classifiers used for this purpose.
BANK PROFITABILITY DETERMINANTS: EVIDENCE FROM KOSOVO AND ALBANIA

This research aims to examine the factors that impact the profitability of commercial banks in Kosovo and Albania.Profitability is crucial in the financial sector as it directly affects policymakers, regulators, and bank management.The study focuses on several determinants of bank profitability, including the number of employees, loan interest rate, non-performing loans, and total loans.The study employed quarterly secondary data spanning from 2010 to 2020, resulting in 400 observations.The analysis used multiple linear regression, influenced by the huge number of observations and the applicability of ordinary least squares (OLS) for such studies.This method allowed for a thorough examination of the interactions between the dependent and independent variables, providing a more in-depth understanding of the factors driving bank profitability.The statistical significance was determined using software such as STATA and SPSS.The dependent variables in the analysis are return on assets (ROA) and return on equity (ROE).The findings reveal that the loan interest rate, total loans, and non-performing loans significantly influence the profitability of the analyzed banks in Albania and Kosovo.Furthermore, the total number of loans and employees are statistically significant determinants of ROE.These findings provide valuable insights for bank management and policymakers in enhancing bank profitability and stability.
EFN-SMOTE: An effective oversampling technique for credit card fraud detection by utilizing noise filtering and fuzzy c-means clustering

Credit card fraud poses a significant challenge for both consumers and organizations worldwide, particularly with the increasing reliance on credit cards for financial transactions. Therefore, it is crucial to establish effective mechanisms to detect credit card fraud. However, the uneven distribution of instances between the two classes in the credit card dataset hinders traditional machine learning techniques, as they tend to prioritize the majority class, leading to inaccurate fraud pre- dictions. To address this issue, this paper focuses on the use of the Elbow Fuzzy Noise Filtering SMOTE (EFN-SMOTE) technique, an oversampling approach, to handle unbalanced data. EFN-SMOTE partitions the dataset into multiple clusters using the Elbow method, applies noise filtering to each cluster, and then employs SMOTE to synthesize new minority instances based on the nearest majority instance to each minority instance, thereby improving the model’s ability to perceive the decision boundary. EFN-SMOTE’s performance was evaluated using an Artificial Neural Network model with four hidden layers, resulting in significant improvements in classification performance, achieving an accuracy of 0.999, precision of 0.998, sensitivity of 0.999, specificity of 0.998, F-measure of 0.999, and G-Mean of 0.999.
Intercultural Communicative Competence Model Using English Literature: A Case Study at Some Islamic Universities in Indonesia

The acquisition of intercultural competence is considered paramount in enabling learners to engage comfortably with individuals from diverse linguistic and cultural backgrounds. The ability to communicate and interact appropriately with individuals from diverse cultural and linguistic backgrounds is increasingly recognized as a fundamental component of the 21st century learning skillset. To investigate the attitudes, knowledge, and skills of students in relation to intercultural activities, this study employs a literature-based instructional approach. The implementation of the Intercultural communicative competence through English Literature (ICC-EL) paradigm among 92 students enrolled in the English Education Study Program at an Islamic higher education institution is the subject of this study. The research adopted a mixed-method approach to generate both quantitative and qualitative data. Specifically, a questionnaire was administered to students upon the completion of the course to elicit quantitative data, while qualitative data was obtained through observation of the students' performance by the course lecturer. The results demonstrate that the implementation of the ICC-EL model has proven to be successful in stimulating the students' intercultural communication skills. Furthermore, the study of literary materials in the form of English novels has encouraged students to administer their awareness of intercultural communication skills. The incorporation of a variety of techniques has provided students with greater exposure to diverse languages and cultures, thereby encouraging them to manage their intercultural communication skills. The assessment of intercultural communication competency employed in the ICC-EL model is deemed appropriate and adaptable for implementation in different language teaching and evaluation contexts.
Security and Privacy Issues in Cloud Computing

&lt;p&gt;With a variety of software and hardware services available to users, cloud computing has developed in recent years as a very influential and transformational technology. It functions as a fundamental method for sharing resources via the internet, with virtualization being essential to making this sharing of cloud resources possible. The National Institute of Standards and Technology (NIST) has described cloud computing as a concept that permits easy, on-demand network access to a shared pool of reconfigurable computing resources. With little management work or communication with the cloud provider, these resources can be quickly supplied and released. A significant amount of businesses have adopted cloud computing due to the many advantages and opportunities it provides. But the quick shift to the cloud has also given rise to security worries. Cloud computing is one of the modern technology that must to be used with caution. Cloud computing does not come with strong data privacy protections. Therefore, it is very important to ensure the confidentiality of data storage in order to protect data security. The objective of this paper is to discuss about the cloud computing models, the cloud service providers, the security and privacy issues surrounding cloud computing. Furthermore, prevention strategies and best practices for security and privacy issues in cloud computing will also be discussed in this paper. &lt;/p&gt;
A Survey on Neural Topic Models: Methods, Applications, and Challenges

Abstract Topic models aim to discover latent topics and infer what topics a document contains in an unsupervised fashion. They have been prevalent for decades with wide applications like text analysis. Recently the rise of neural networks has facilitated a new research field---Neural Topic Models (NTMs). Different from conventional models, NTMs directly optimize parameters without model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a thorough survey on neural topic models carefully and widely. In detail, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and cross-lingual documents. Then we discuss developed popular applications based on NTMs. We finally highlight the challenges of NTMs to motivate future research.
Examining the Mediating Role of Job Satisfaction between Motivation, Organizational Culture, and Employee Performance in Higher Education: A Case Study in the Arab Region

This study aimed to examine the correlation among motivation, organizational culture and employee performance and the mediation role of job satisfaction in higher education.A more productive and positive work environment was created by organizations by understanding the connection among these factors.This study provided valuable insight into how to create a culture of motivation and satisfaction to improve employee performance.364 participants were selected from Yemeni and Omani universities, including academics and staff.A cross-sectional survey design was employed, with participants selected using stratified random sampling.Questionnaires were contributed online using emails and social media applications and analyzed by PLS-4.Results of Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that extrinsic motivation had a negative impact on employee performance while organizational culture had a significant positive impact in dynamic environment.Results also highlighted the positive role of job satisfaction represented by supervisoremployee engagement, incentives and promotion in enhancing performance in dynamic environment.Researchers recommended to aggressively increase job satisfaction and employee performance with extrinsic motivation in dynamic environment in Arabic region.