Reviewerly/exHarmony huggingface dataset:
The Effectiveness of E-Learning Service Quality in Influencing E-Learning Student Satisfaction and Loyalty at Telkom University

Blended learning method through the LMS system at Telkom University is relatively new. This makes a big change in the learning process, especially during the Covid pandemic which requires online learning. Online learning or e-learning raises pros and cons at Telkom University. This study uses a quantitative method and causality with SEM PLS as the analysis technique. Sampling techniques used are purposive sampling with a sample of 334 active Telkom University students spread across all majors starting from the Class of 2015 - 2020. The results highlight how the quality of e-learning services impacts the quality of the e-learning system, the quality of teaching and e-learning materials, the quality of staff and e-learning support staff, and more. The satisfaction of e-learning users at Telkom University is significantly impacted by the quality of e-learning services by 85.9%. In Telkom University students, e-learning user happiness has a strong impact on e-learning user loyalty by 80%. The use of the e-learning system determines the satisfaction and loyalty of Telkom University students because the form of teaching is carried out through LMS so that students experience easy access to materials and learn independently.
Broccoli leaf diseases classification using support vector machine with particle swarm optimization based on feature selection

Broccoli is a plant that has many benefits. The flower parts of broccoli contain protein, calcium, vitamin A, vitamin C, and many more. However, in its cultivation, broccoli plants have obstacles such as the presence of pests and diseases that can affect production of broccoli. To avoid this, the authors build a model to identify diseases in broccoli through leaf images with a size of 128x128 pixels. The model is constructed to classify healthy leaves, and disease leaves using the image processing method that uses machine learning stages. There are several stages, including K-Means segmentation, colour feature extraction, and classification using SVM (Support Vector Machine) with RBF kernel and PSO (Particle Swarm Optimization) for reduce dimensionality data. The model that has been built compares the SVM model and the SVM-PSO model. It produces good accuracy in the training of 97.63% and testing accuracy of 94.48% for SVM-PSO and 85.82% for training, and 86.25% for testing in the SVM model. Therefore, this proposed model can produce good results in categorizing healthy and diseased leaves in broccoli.
Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention

In the fields of medical care and research as well as hospital management, time series are an important part of the overall data basis. To ensure high quality standards and enable suitable decisions, tools for precise and generic imputations and forecasts that integrate the temporal dynamics are of great importance. Since forecasting and imputation tasks involve an inherent uncertainty, the focus of our work lay on a probabilistic multivariate generative approach that samples infillings or forecasts from an analysable distribution rather than producing deterministic results. For this task, we developed a system based on generative adversarial networks that consist of recurrent encoders and decoders with attention mechanisms and can learn the distribution of intervals from multivariate time series conditioned on the periods before and, if available, periods after the values that are to be predicted. For training, validation and testing, a data set of jointly measured blood pressure series (ABP) and electrocardiograms (ECG) (length: 1,250=ˆ10s) was generated. For the imputation tasks, one interval of fixed length was masked randomly and independently in both channels of every sample. For the forecasting task, all masks were positioned at the end. The models were trained on around 65,000 bivariate samples and tested against 14,000 series of different persons. For the evaluation, 50 samples were produced for every masked interval to estimate the range of the generated infillings or forecasts. The element-wise arithmetic average of these samples served as an estimator for the mean of the learned conditional distribution. The approach showed better results than a state-of-the-art probabilistic multivariate forecasting mechanism based on Gaussian copula transformation and recurrent neural networks. On the imputation task, the proposed method reached a mean squared error (MSE) of 0.057 on the ECG channel and an MSE of 28.30 on the ABP channel, while the baseline approach reached MSEs of 0.095 (ECG) and 229.1 (ABP). Moreover, on the forecasting task, the presented system achieved MSEs of 0.069 (ECG) and 33.73 (ABP), outperforming the recurrent copula approach, which reached MSEs of 0.082 (ECG) and 196.53 (ABP). The presented generative probabilistic system for the imputation and forecasting of (medical) time series features the flexibility to handle masks of different sizes and positions, the ability to quantify uncertainty due to its probabilistic predictions, and an adjustable trade-off between the goals of minimising errors in individual predictions and minimising the distance between the learned and the real conditional distribution of the infillings or forecasts.
Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis

Histopathology is the most accurate way to diagnose cancer and identify prognostic and therapeutic targets. The likelihood of survival is significantly increased by early cancer detection. With deep networks' enormous success, significant attempts have been made to analyze cancer disorders, particularly colon and lung cancers. In order to do this, this paper examines how well deep networks can diagnose various cancers using histopathology image processing. This work intends to increase the performance of deep learning architecture in processing histopathology images by constructing a novel fine-tuning deep network for colon and lung cancers. Such adjustments are performed using regularization, batch normalization, and hyperparameters optimization. The suggested fine-tuned model was evaluated using the LC2500 dataset. Our proposed model's average precision, recall, F1-score, specificity, and accuracy were 99.84%, 99.85%, 99.84%, 99.96%, and 99.94%, respectively. The experimental findings reveal that the suggested fine-tuned learning model based on the pre-trained ResNet101 network achieves higher results against recent state-of-the-art approaches and other current powerful CNN models.
Are teachers ready to immerse? Acceptance of mobile immersive virtual reality in secondary education teachers

The increasing mainstream adoption of immersive virtual reality (iVR) in education has triggered research about key variables explaining acceptance of iVR by teachers. In this study we adopted the UTAUT2 acceptance model as a theoretical framework enriched with the variable personal innovativeness. 379 Flemish secondary education teachers watched a video about iVR learning experiences, after which an online survey concerning their perceptions was administered. General linear modeling was performed to test the hypotheses. Results indicate performance expectancy, social influence, facilitating conditions, hedonic motivation and personal innovativeness to be significantly associated with behavioural intention to use. No moderating effect of age, gender or experience was observed. The results account for 54% of the variance in behavioural intention to use. The findings help to understand which factors are key in the acceptance of mobile iVR by secondary education teachers and might help defining successful iVR implementation strategies.
Open-set face identification with automatic detection of out-of-distribution images

One of main issues in face identification is the lack of training data of specific type (bad quality image, varying scale or illumination, children/old people faces, etc.). As a result, the recogni-tion accuracy may be low for input images which are not similar to the majority of images in the dataset used to train the feature extractor. In this paper, we propose that this issue is dealt with by the automatic detection of such out-of-distribution data based on the addition of a preliminary stage of their automatic rejection using a special convolutional network trained using a set of rare data collected using various transformations. To increase the computational efficiency, the decision about the presence of a rare image is made on the basis of the same face descriptor that is used in the classifier. Experimental research confirmed the accuracy improvement of the proposed approach for several datasets of faces and modern neural network descriptors.
Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks

Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on belief attribution in Theory-of-Mind tasks. These tasks have shown both successes and failures. We consider in particular a recent purported success case, and show that small variations that maintain the principles of ToM turn the results on their head. We argue that in general, the zero-hypothesis for model evaluation in intuitive psychology should be skeptical, and that outlying failure cases should outweigh average success rates. We also consider what possible future successes on Theory-of-Mind tasks by more powerful LLMs would mean for ToM tasks with people.
Global Counterfactual Explainer for Graph Neural Networks

Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCFExplainer, a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary. Extensive experiments on real graph datasets show that the global explanation from GCFExplainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers.
WSODet: A Weakly Supervised Oriented Detector for Aerial Object Detection

In contrast to natural objects, aerial targets are usually non-axis aligned with arbitrary orientations. However, mainstream weakly supervised object detection (WSOD) methods can only predict horizontal bounding boxes (HBBs) from existing proposals generated by offline algorithms. To predict oriented bounding boxes (OBBs) for aerial targets while testing images end-to-end without proposals, WSODet is designed leveraging on layerwise relevance propagation (LRP) and point set representation (RepPoints). To be specific, based on the mainstream WSOD framework, LRP on multiple instance learning branch (MIL-LRP) is conducted to decrease the uncertainty and ambiguity of feature map. Then, a pseudo oriented label generation algorithm is designed to obtain OBB pseudolabels, which serve as supervision to train an oriented RepPoint Net under the guidance of improved oriented loss function (IOLF). During the test, input images are sent to oriented RepPoint branch (ORB) to obtain OBB predictions without proposals. Extensive experiments on the detection in optical remote sensing images (DIOR), Northwestern Polytechnical University (NWPU) VHR-10.v2, and HRSC2016 datasets demonstrate the effectiveness of our method to predict precise oriented aerial objects, achieving 22.2%, 46.5%, and 43.3% mAP, respectively. Moreover, training jointly with ORB boosts the results of the original WSOD framework compared with the existing WSOD methods even if there is no specific design for the original structure.
Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.
What North American Archaeology Needs to Take Advantage of the Digital Data Revolution

ABSTRACT Today, there is a growing movement to use accumulated archaeological information to contribute to discussions of general issues facing human societies, including our own. In this regard, the archaeological record is most unique and helpful when viewed at broad comparative scales. Most relevant data for these sorts of analyses are collected through the cultural resource management (CRM) process. Still, by and large, interpretation remains limited to individual projects, and data integration across projects is nearly nonexistent. What would it take for CRM to achieve real data integration? In this article, we discuss these issues and suggest one potential solution. The most pressing need we identify is for data products that integrate the primary data emanating from CRM at broad spatial and temporal scales, which are suitable for research by archaeologists and other social scientists. We argue that the time is right for the discipline to invest in organizations that produce such products.
Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.
A Systematic Review on Food Recommender Systems for Diabetic Patients

Recommender systems are currently a relevant tool for facilitating access for online users, to information items in search spaces overloaded with possible options. With this goal in mind, they have been used in diverse domains such as e-commerce, e-learning, e-tourism, e-health, etc. Specifically, in the case of the e-health scenario, the computer science community has been focused on building recommender systems tools for supporting personalized nutrition by delivering user-tailored foods and menu recommendations, incorporating the health-aware dimension to a larger or lesser extent. However, it has been also identified the lack of a comprehensive analysis of the recent advances specifically focused on food recommendations for the domain of diabetic patients. This topic is particularly relevant, considering that in 2021 it was estimated that 537 million adults were living with diabetes, being unhealthy diets a major risk factor that leads to such an issue. This paper is centered on presenting a survey of food recommender systems for diabetic patients, supported by the PRISMA 2020 framework, and focused on characterizing the strengths and weaknesses of the research developed in this direction. The paper also introduces future directions that can be followed in the next future, for guaranteeing progress in this necessary research area.
Light-driven spin chemistry for quantum information science

Molecular systems can host multiple electron spin qubits that have promising properties for quantum applications.
Reverse Supply Chain and Pharmaceutical Waste Collection Management Utilizing Location-Routing Model

Nowadays, the increased amount and variety of waste, and the health risks caused by them are considered among the substantial issues of human societies today. Pharmaceutical wastes are among the most dangerous polluting wastes of the environment in the world. They include expired products and unused medicines. This study aims to investigate pharmaceutical waste collection management utilizing the location-routing model in a reverse supply chain. To do so, pharmaceutical waste from pharmacies and hospitals is collected and then transfer to collection centers, where waste classification and future decisions are planned. This process is done in two stages, the first includes pharmacies and hospitals, and collection centers, and the second stage includes collection centers, disposal centers, and airports. In the first stage, pharmaceutical waste is collected, and in the second level, this waste is either safely disposed of or recycled, or medicines that can be reused are sent to third-world countries. Finally, by creating an optimization model, the transportation costs, the construction cost of collection centers, disposal costs, and the cost of producing carbon dioxide will be minimized. Reducing carbon dioxide production helps make this chain greener.
Real-Time Decoding for Fault-Tolerant Quantum Computing: Progress, Challenges and Outlook

Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be mitigated by developing higher-quality qubits. Quantum error correction (QEC) will thus be necessary to ensure fault tolerance. QEC protects the logical information by cyclically measuring syndrome information about the errors. An essential part of QEC is the decoder, which uses the syndrome to compute the likely effect of the errors on the logical degrees of freedom and provide a tentative correction. The decoder must be accurate, fast enough to keep pace with the QEC cycle (e.g., on a microsecond timescale for superconducting qubits) and with hard real-time system integration to support logical operations. As such, real-time decoding is essential to realize fault-tolerant quantum computing and to achieve quantum advantage. In this work, we highlight some of the key challenges facing the implementation of real-time decoders while providing a succinct summary of the progress to-date. Furthermore, we lay out our perspective for the future development and provide a possible roadmap for the field of real-time decoding in the next few years. As the quantum hardware is anticipated to scale up, this perspective article will provide a guidance for researchers, focusing on the most pressing issues in real-time decoding and facilitating the development of solutions across quantum and computer science.
Vacuum-enhanced charging of a quantum battery

Quantum batteries are quantum systems that store energy which can then be used for quantum tasks. One relevant question about such systems concerns the differences and eventual advantages over their classical counterparts, whether in the efficiency of the energy transference, input power, total stored energy, or other relevant physical quantities. Here, we show how a purely quantum effect related to the vacuum of the electromagnetic field can enhance the charging of a quantum battery. In particular, we demonstrate how an anti-Jaynes-Cummings interaction derived from an off-resonant Raman configuration can be used to increase the stored energy of an effective two-level atom when compared to its classically driven counterpart, eventually achieving full charging of the battery with zero entropic cost.