Reviewerly/exHarmony huggingface dataset:
Students’ Perspectives of the Economic, Financial, and Psychological Effects of Online Learning and Its Gender Association: A Cross-Sectional Study in Jordanian Universities

Introduction: Jordan declared a quarantine in March 2020 and shut down all educational institutions in response to the COVID-19 outbreak.E-learning was a novel way to continue the educational process in the wake of the pandemic.The study evaluates online learning (OL), aiming to examine the economic, financial, and psychological effects of OL on students, as well as their concentration, university instructors' efficiency, and satisfaction with OL.This research also explores the gender association of OL effects in Jordanian universities.Methods: This is a cross-sectional study.A self-filled questionnaire was generated using an online form and distributed to students at public and private universities through social media.The questionnaire utilized a five-point Likert scale to assess various aspects related to OL.The main evaluation involved a Chi-square test and posthoc test to examine gender-based differences in the factors associated with OL.The reliability of the questionnaire was assessed using a Kaiser-Meyer-Olkin (KMO) value of 0.721 and a Cronbach's Alpha of 0.7.Results: A total of 720 responses were collected and analyzed.The results revealed a significant gender association with the economic/financial (P=0.001) and psychological (P=0.002)effects of OL.It was observed that 65.3% of students reported feeling depressed due to online learning.Moreover, a significant correlation was found between students' concentration and university instructors' efficiency (P=0.00).Interestingly, only 14.72% of students believed university instructors were 100% efficient in teaching theoretical and practical subjects.Additionally, 55% of the students expressed their desire for university instructors to record lectures for ease of access. Conclusion:The study concludes that OL's poor economic/financial effects disproportionately affect female students.On the other hand, male students were more likely to strongly disagree with the poor psychological effects of OL.Moreover, female students were more inclined to strongly disagree with the notion that OL has no psychological effect.Lastly, significant findings indicate that both students and university instructors share equal responsibility for the success of OL in Jordanian universities.
Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach

Objectives: The number of deaths from cardiovascular disease is projected to reach 23.3 million by 2030. As a contribution to preventing this phenomenon, this paper proposed a machine learning (ML) model to predict patients with arteriosclerotic heart disease (AHD). We also interpreted the prediction model results based on the ML approach and deployed modelagnostic ML methods to identify informative features and their interpretations.Methods: We used a hematology Electronic Health Record (EHR) with information on erythrocytes, hematocrit, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, leukocytes, thrombocytes, age, and sex. To detect and predict AHD, we explored random forest (RF), XGBoost, and AdaBoost models. We examined the prediction model results based on the confusion matrix and accuracy measures. We used the Shapley Additive exPlanations (SHAP) framework to interpret the ML model and quantify the contribution of features to predictions.Results: Our study included data from 6,837 patients, with 4,702 records from patients diagnosed with AHD and 2,135 records from patients without an AHD diagnosis. AdaBoost outperformed RF and XGBoost, achieving an accuracy of 0.78, precision of 0.82, F1-score of 0.85, and recall of 0.88. According to the SHAP summary bar plot method, hemoglobin was the most important attribute for detecting and predicting AHD patients. The SHAP local interpretability bar plot revealed that hemoglobin and mean corpuscular hemoglobin concentration had positive impacts on AHD prediction based on a single observation.Conclusions: ML models based on real clinical data can be used to predict AHD.
Image Stitching using RANSAC and Bayesian Refinement

When several pictures overlap, we can merge them into one high-resolution image which is called image stitching. To tackle this problem effectively within the images, SIFT algorithm that is robust to such noisy elements was used to identify the matching keypoints which eventually enhances the overall picture standards significantly - advancing panoramic photography as well as medical imaging alongside satellite imagery domains through improving their technical aspects vastly with potential practical use cases across various industries like healthcare, remote sensing or even general professional photography services too. Our proposed procedure shall subsequently undergo rigorous testing phases using varied datasets for efficiency analysis purposes towards validating its soundness while providing robust enough to handle all possible inputs thrown at it without faltering under any circumstances. The results of this project could have practical implications in industries such as healthcare, remote sensing, and photography.
Adversarial Data Augmentation for HMM-Based Anomaly Detection

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for generating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs). Then, we present a data augmentation and retraining technique that uses these adversarial examples to improve anomaly detection performance. Finally, we evaluate our adversarial data augmentation and retraining approach on four datasets showing that it achieves a statistically significant performance improvement and enhances the robustness to adversarial attacks. Key differences from the state-of-the-art on adversarial data augmentation are the focus on multivariate time series (as opposed to images), the context of one-class classification (in contrast to standard multi-class classification, and the use of HMMs (in contrast to neural networks).
Cryptocurrency in Practice

A cryptocurrency is a digital or virtual currency that uses cryptography for security. Cryptocurrencies are decentralized systems that allow for the creation, transfer, and verification of transactions without the need for a central authority or intermediary.
Asymmetric Measurement-Device-Independent Quantum Key Distribution through Advantage Distillation

Measurement-device-independent quantum key distribution (MDI-QKD) completely closes the security loopholes caused by the imperfection of devices at the detection terminal. Commonly, a symmetric MDI-QKD model is widely used in simulations and experiments. This scenario is far from a real quantum network, where the losses of channels connecting each user are quite different. To adapt such a feature, an asymmetric MDI-QKD model is proposed. How to improve the performance of asymmetric MDI-QKD also becomes an important research direction. In this work, an advantage distillation (AD) method is applied to further improve the performance of asymmetric MDI-QKD without changing the original system structure. Simulation results show that the AD method can improve the secret key rate and transmission distance, especially in the highly asymmetric cases. Therefore, this scheme will greatly promote the development of future MDI-QKD networks.
Folksonomia na Netflix proposta de participação dos usuários na recuperação da informação

This article aims to bring forward and propose solutions to the problem of retrieving information on the Netflix streaming platform. Therefore, it addresses briefly the origin, evolution, and diversification of streaming platforms. Special focus is given on the platform and history of Netflix, explaining how they are generally consumed by society. Comparison is made between retrievaltechnologies and information representation used in streaming platforms and folksonomy. It exemplifies situations in which these technologies do not work as expected and presents information about hashtags and their emergence. Describes how folksonomy works. It is characterized as an exploratory, descriptive, bibliographical, documentary research with a qualitativeapproach. The results show that it is possible for the user to contribute to the retrievalof information using folksonomy. In conclusion, it presents a proposal for improvements for the retrieval of information on streaming platforms with the help of folksonomy.
Multi-Modal Streaming 3D Object Detection

Modern autonomous vehicles rely heavily on mechanical LiDARs for perception. Current perception methods generally require <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> point clouds, collected sequentially as the LiDAR scans the azimuth and acquires consecutive wedge-shaped slices. The acquisition latency of a full scan ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim\!\text{100}\;\text{ms}$</tex-math></inline-formula> ) may lead to outdated perception which is detrimental to safe operation. Recent streaming perception works proposed directly processing LiDAR slices and compensating for the narrow field of view (FOV) of a slice by reusing features from preceding slices. These works, however, are all based on a single modality and require past information which may be outdated. Meanwhile, images from high-frequency cameras can support streaming models as they provide a larger FoV compared to a LiDAR slice. However, this difference in FoV complicates sensor fusion. We propose an innovative camera-LiDAR streaming 3D object detection framework that uses camera images instead of past LiDAR slices to provide an up-to-date, dense, and wide context for streaming perception. The proposed method outperforms prior streaming models and powerful full-scan baselines on the challenging NuScenes benchmark in detection accuracy and end-to-end runtime. Our method is shown to be robust to missing camera images, narrow LiDAR slices, and small camera-LiDAR miscalibration.
A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.
A FAIR catalog of ontology-driven conceptual models

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.
True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4

Large language models (LLMs) have demonstrated solid zero-shot reasoning capabilities, which is reflected in their performance on the current test tasks.This calls for a more challenging benchmark requiring highly advanced reasoning ability to be solved.In this paper, we introduce such a benchmark, consisting of 191 long-form (1200 words on average) mystery narratives constructed as detective puzzles.Puzzles are sourced from the "5 Minute Mystery" platform and include a multiple-choice question for evaluation.Only 47% of humans solve a puzzle successfully on average, while the best human solvers achieve over 80% success rate.We show that GPT-3 models barely outperform random on this benchmark (with 28% accuracy) while state-of-the-art GPT-4 solves only 38% of puzzles.This indicates that there is still a significant gap in the deep reasoning abilities of LLMs and humans and highlights the need for further research in this area.Our work introduces a challenging benchmark for future studies on reasoning in language models and contributes to a better understanding of the limits of LLMs' abilities. 1
The Impact of Knowledge Management Practices on Job Satisfaction of Moroccan Employees: Mediating Role of Organizational Commitment

Purpose: The study aims to investigate how knowledge management (KM) practices impact JS among Moroccan employees, while also exploring the mediating role of organizational commitment (OC) in this relationship. Theoretical framework: The study draws upon established theories in KM, job satisfaction (JS), and OC. It suggests that effective KM practices can enhance JS and that OC acts as a mediator in this relationship. Design/Methodology/Approach: This study employs a deductive approach and quantitative research design to explore the relationship between KM practices and JS in Moroccan SMEs. Data from 175 respondents were analyzed using linear regression with the assistance of the SPSS statistical software. Findings: Effective KM practices positively impact job satisfaction (JS) in Moroccan SMEs. Additionally, organizational commitment (OC) acts as a mediator between KM practices and JS. Implementing successful KM strategies and fostering employee commitment can enhance JS in Moroccan SMEs. Research, practical &amp; social implications: The study's implications highlight that implementing effective KM practices in Moroccan SMEs can have far-reaching benefits, including increased JS, enhanced productivity, improved talent attraction, and positive contributions to socio-economic development, emphasizing the significance of KM for organizational success and broader societal advancement. Originality/Value: The originality of this study lie in its examination of KM practices specifically within Moroccan SMEs. By focusing on this context, the study provides unique insights into the challenges and opportunities faced by these organizations, offering practical implications and recommendations that can contribute to the development of tailored KM strategies for similar SMEs in Morocco and beyond.
Gaze-based Attention Recognition for Human-Robot Collaboration

Attention (and distraction) recognition is a key factor in improving human-robot collaboration. We present an assembly scenario where a human operator and a cobot collaborate equally to piece together a gearbox. The setup provides multiple opportunities for the cobot to adapt its behavior depending on the operator’s attention, which can improve the collaboration experience and reduce psychological strain. As a first step, we recognize the areas in the workspace that the human operator is paying attention to, and consequently, detect when the operator is distracted. We propose a novel deep-learning approach to develop an attention recognition model. First, we train a convolutional neural network to estimate the gaze direction using a publicly available image dataset. Then, we use transfer learning with a small dataset to map the gaze direction onto pre-defined areas of interest. Models trained using this approach performed very well in leave-one-subject-out evaluation on the small dataset. We performed an additional validation of our models using the video snippets collected from participants working as an operator in the presented assembly scenario. Although the recall for the Distracted class was lower in this case, the models performed well in recognizing the areas the operator paid attention to. To the best of our knowledge, this is the first work that validated an attention recognition model using data from a setting that mimics industrial human-robot collaboration. Our findings highlight the need for validation of attention recognition solutions in such full-fledged, non-guided scenarios.
Detecting Personal Information in Training Corpora: an Analysis

Large language models are trained on increasing quantities of unstructured text, the largest sources of which are scraped from the Web. These Web scrapes are mainly composed of heterogeneous collections of text from multiple domains with minimal documentation. While some work has been done to identify and remove toxic, biased, or sexual language, the topic of personal information (PI) in textual data used for training Natural Language Processing (NLP) models is relatively under-explored. In this work, we draw from definitions of PI across multiple countries to define the first PI taxonomy of its kind, categorized by type and risk level. We then conduct a case study on the Colossal Clean Crawled Corpus (C4) and the Pile, to detect some of the highest-risk personal information, such as email addresses and credit card numbers, and examine the differences between automatic and regular expression-based approaches for their detection. We identify shortcomings in modern approaches for PI detection, and propose a reframing of the problem that is informed by global perspectives and the goals in personal information detection.
IDAS: Intent Discovery with Abstractive Summarization

Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents.We show that recent competitive methods in intent discovery can be outperformed by clustering utterances based on abstractive summaries, i.e., "labels", that retain the core elements while removing non-essential information.We contribute the IDAS approach, which collects a set of descriptive utterance labels by prompting a Large Language Model, starting from a well-chosen seed set of prototypical utterances, to bootstrap an In-Context Learning procedure to generate labels for non-prototypical utterances.The utterances and their resulting noisy labels are then encoded by a frozen pre-trained encoder, and subsequently clustered to recover the latent intents.For the unsupervised task (without any intent labels) IDAS outperforms the state-of-theart by up to +7.42% in standard cluster metrics for the Banking, StackOverflow, and Transport datasets.For the semi-supervised task (with labels for a subset of intents) IDAS surpasses 2 recent methods on the CLINC benchmark without even using labeled data.
Leveraging Deep Learning for IoT Transceiver Identification

With the increasing demand for Internet of Things (IoT) network applications, the lack of adequate identification and authentication has become a significant security concern. Radio frequency fingerprinting techniques, which utilize regular radio traffic as the identification source, were then proposed to provide a more secured identification approach compared to traditional security methods. Such solutions take hardware-level characteristics as device fingerprints to mitigate the risk of pre-shared key leakage and lower computational complexity. However, the existing studies suffer from problems such as location dependence. In this study, we have proposed a novel scheme for further exploiting the spectrogram and the carrier frequency offset (CFO) as identification sources. A convolutional neural network (CNN) is chosen as the classifier. The scheme addressed the location-dependence problem in the existing identification schemes. Experimental evaluations with data collected in the real world have indicated that the proposed approach can achieve 80% accuracy even if the training and testing data are collected on different days and at different locations, which is 13% higher than state-of-the-art approaches.
Remote Health Monitoring Systems for Elderly People: A Survey

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.
Deep Feature Inpainting for Unsupervised Visual Anomaly Detection

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. In this paper, we address the problem of unsupervised visual anomaly detection from the perspective of image feature restoration. We propose deep feature inpainting (DFI) where we develop a deep learning model that can automatically inpainting the normal image patterns or features from the anomalous images by eliminating the irregular structures or patterns. By comparing the anomalous features and their reconstructed counterparts, we can identify and analyze the anomalous structures in the images. Extensive experiments have been carried out to demonstrate the effectiveness of the approach.