Reviewerly/exHarmony huggingface dataset:
Revolutionizing engineering education: Creating a web-based teaching platform for immersive learning experiences

Innovative pedagogy is more oriented towards the use of new technology in future education. Virtual and augmented realities, as well as the metaverse, are today the catalysts for the development of future teaching tools. They allow students to discover, via their smartphones or headsets, destinations around the world while remaining in their classrooms or even from their homes. These technologies are also used to put into practice, especially for the engineering sciences, lessons, and practical work by creating immersive experiences for users. This will significantly change the role of the teacher and the student in digital spaces. The realization of VR or AR learning platforms requires multidisciplinary skills ranging from 3D modeling to web and mobile platforms. In this paper, we present a method for creating a teaching platform for engineering sciences based on WebXR. We have created in our virtual environment a lesson about the Arduino board to train STEM students.
Conflict evidence fusion method based on improved conflict coefficient and belief entropy

Research on conflict evidence fusion is an important topic of evidence theory. When fusing conflicting evidence, Dempster-Shafer evidence theory sometimes produces counter-intuitive results. Thus, this work proposes a conflict evidence fusion method based on improved conflict coefficient and belief entropy. Firstly, the proposed method uses an improved conflict coefficient to measure the degree of conflict, and the conflict matrix is constructed to get the support degree of evidence. Secondly, in order to measure the uncertainty of evidence, an improved belief entropy is proposed, and the information volume of evidence is obtained by the improve entropy. Next, connecting with the support degree and information volume, We get the weight coefficient, and use it to modify the evidence. Finally, using the combination rule of Dempster for fusion. Simulation experiments have demonstrated the effectiveness and superiority of the proposed method in this paper.
Enhancing temple surveillance through human activity recognition: A novel dataset and YOLOv4-ConvLSTM approach

Automated identification of human activities remains a complex endeavor, particularly in unique settings like temple environments. This study focuses on employing machine learning and deep learning techniques to analyze human activities for intelligent temple surveillance. However, due to the scarcity of standardized datasets tailored for temple surveillance, there is a need for specialized data. In response, this research introduces a pioneering dataset featuring Eight distinct classes of human activities, predominantly centered on hand gestures and body postures. To identify the most effective solution for Human Activity Recognition (HAR), a comprehensive ablation study is conducted, involving a variety of conventional machine learning and deep learning models. By integrating YOLOv4’s robust object detection capabilities with ConvLSTM’s ability to model both spatial and temporal dependencies in spatio-temporal data, the approach becomes capable of recognizing and understanding human activities in sequences of images or video frames. Notably, the proposed YOLOv4-ConvLSTM approach emerges as the optimal choice, showcasing a remarkable accuracy of 93.68%. This outcome underscores the suitability of the outlined methodology for diverse HAR applications in temple environments.
Energy-Efficient Communications for Improving Timely Progress of Intermittent-Powered BLE Devices

Battery-less devices offer potential solutions for maintaining sustainable Internet of Things (IoT) networks. However, limited energy harvesting capacity can lead to power failures, limiting the system’s quality of service (QoS) . To improve timely task progress, we present ETIME, a scheduling framework that enables energy-efficient communication for intermittent-powered IoT devices. To maximize energy efficiency while meeting the timely requirements of intermittent systems, we first model the relationship between insufficient harvesting energy and task behavior time. We then propose a method for predicting response times for battery-less devices. Considering both delays from multiple task interference and insufficient system energy, we introduce a dynamic wake-up strategy to improve timely task progress. Additionally, to minimize power consumption from connection components, we propose a dynamic connection interval adjustment to provide energy-efficient communication. The proposed algorithms are implemented in a lightweight operating system on real devices. Experimental results show that our approach can significantly improve progress for timely applications while maintaining task progress.
A Classical Architecture for Digital Quantum Computers

Scaling bottlenecks the making of digital quantum computers, posing challenges from both the quantum and the classical components. We present a classical architecture to cope with a comprehensive list of the latter challenges all at once , and implement it fully in an end-to-end system by integrating a multi-core RISC-V CPU with our in-house control electronics. Our architecture enables scalable, high-precision control of large quantum processors and accommodates evolving requirements of quantum hardware. A central feature is a microarchitecture executing quantum operations in parallel on arbitrary predefined qubit groups. Another key feature is a reconfigurable quantum instruction set that supports easy qubit re-grouping and instructions extensions. As a demonstration, we implement the widely-studied surface code quantum computing workflow, which is instructive for being demanding on both the controllers and the integrated classical computation. Our design, for the first time, reduces instruction issuing and transmission costs to constants, which do not scale with the number of qubits, without adding any overheads in decoding or dispatching. Our system uses a dedicated general-purpose CPU for both qubit control and classical computation, including syndrome decoding. Implementing recent theoretical proposals as decoding firmware that parallelizes general inner decoders, we can achieve unprecedented decoding capabilities of up to distances 47 and 67 with the currently available systems-on-chips for physical error rate p = 0.001 and p = 0.0001, respectively, all in just 1 μs.
SQMetrics: An Educational Software Quality Assessment Tool for Java

Over the years, various software quality measurement models have been proposed and used in academia and the software industry to assess the quality of produced code and to obtain guidelines for its improvement. In this article, we describe the design and functionality of SQMetrics, a tool for calculating object-oriented quality metrics for projects written in Java. SQMetrics provides the convenience of measuring small code, mainly covering academic or research needs. In this context, the application can be used by students of software engineering courses to make measurements and comparisons in their projects and gradually increase their quality by improving the calculated metrics. Teachers, on the other hand, can use SQMetrics to evaluate students’ Java projects and grade them in proportion to their quality. The contribution of the proposed tool is three-fold, as it has been: (a) tested for its completeness and functionality by comparing it with widely known similar tools, (b) evaluated for its usability and value as a learning aid by students, and (c) statistically tested for its value as a teachers’ aid assisting in the evaluation of student projects. Our findings verify SQMetrics’ effectiveness in helping software engineering students learn critical concepts and improve the quality of their code, as well as in helping teachers assess the quality of students’ Java projects and make more informed grading decisions.
Z-number based fuzzy neural network for system identification

In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) based on the integration of Z-valued fuzzy logic and neural networks is proposed. Z-valued fuzzy rule base is presented and its inference process is described using interpolative approximate reasoning. Accordingly, the structure of the Z-FNN is proposed using a distance measure and interpolative approximate reasoning scheme. Based on presented architecture the learning algorithm of Z-FNN is designed. The updating of the unknown parameters of the network is carried out using Genetic Algorithms (GA). The proposed Z-FNN system is utilized for dynamic plant identification. The effectiveness of Z-FNN has been tested by comparing its performance with the performances of other fuzzy systems available in the literature. The proposed approach has been proven to be a suitable alternative for the identification of nonlinear systems characterized by uncertain and imprecise information.
Elementary probabilistic operations: a framework for probabilistic reasoning

The framework of elementary probabilistic operations (EPO) explains the structure of elementary probabilistic reasoning tasks as well as people’s performance on these tasks. The framework comprises three components: (a) Three types of probabilities: joint, marginal, and conditional probabilities; (b) three elementary probabilistic operations: combination, marginalization, and conditioning, and (c) quantitative inference schemas implementing the EPO. The formal part of the EPO framework is a computational level theory that provides a problem space representation and a classification of elementary probabilistic problems based on computational requirements for solving a problem. According to the EPO framework, current methods for improving probabilistic reasoning are of two kinds: First, reduction of Bayesian problems to a type of probabilistic problems requiring less conceptual and procedural competencies. Second, enhancing people’s utilization competence by fostering the application of quantitative inference schemas. The approach suggests new applications, including the teaching of probabilistic reasoning, using analogical problem solving in probabilistic reasoning, and new methods for analyzing errors in probabilistic problem solving.
Privacy-Enhanced Federated Learning for Non-IID Data

Federated learning (FL) allows the collaborative training of a collective model by a vast number of decentralized clients while ensuring that these clients’ data remain private and are not shared. In practical situations, the training data utilized in FL often exhibit non-IID characteristics, hence diminishing the efficacy of FL. Our study presents a novel privacy-preserving FL algorithm, HW-DPFL, which leverages data label distribution similarity as a basis for its design. Our proposed approach achieves this objective without incurring any additional overhead communication. In this study, we provide evidence to support the assertion that our approach improves the privacy guarantee and convergence of FL both theoretically and empirically.
A Comparative Performance Evaluation of Algorithms for the Analysis and Recognition of Emotional Content

Sentiment Analysis is highly valuable in Natural Language Processing (NLP) across domains, processing and evaluating sentiment in text for emotional understanding. This technology has diverse applications, including social media monitoring, brand management, market research, and customer feedback analysis. Sentiment Analysis identifies positive, negative, or neutral sentiments, providing insights into decision-making, customer experiences, and business strategies. With advanced machine learning models like Transformers, Sentiment Analysis achieves remarkable progress in sentiment classification. These models capture nuances, context, and variations for more accurate results. In the digital age, Sentiment Analysis is indispensable for businesses, organizations, and researchers, offering deep insights into opinions, sentiments, and trends. It impacts customer service, reputation management, brand perception, market research, and social impact analysis. In the following experimental research, we will examine the Zero-Shot technique on pre-trained Transformers and observe that, depending on the Model we use, we can achieve up to 83% in terms of the model’s ability to distinguish between classes in this Sentiment Analysis problem.
Resource saving via ensemble techniques for quantum neural networks

Abstract Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conducted experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices.
An Obstacle Avoidance Path Planning and Evaluation Method for Intelligent Vehicles Based on the B-Spline Algorithm

To meet the real-time path planning requirements of intelligent vehicles in dynamic traffic scenarios, a path planning and evaluation method is proposed in this paper. Firstly, based on the B-spline algorithm and four-stage lane-changing theory, an obstacle avoidance path planning algorithm framework is constructed. Then, to obtain the optimal real-time path, a comprehensive real-time path evaluation mechanism that includes path safety, smoothness, and comfort is established. Finally, to verify the proposed approach, co-simulation and real vehicle testing are conducted. In the dynamic obstacle avoidance scenario simulation, the lateral acceleration, yaw angle, yaw rate, and roll angle fluctuation ranges of the ego-vehicle are ±2.39 m/s2, ±13.31°, ±13.26°/s, and ±0.938°, respectively. The results show that the proposed algorithm can generate real-time, available obstacle avoidance paths. And the proposed evaluation mechanism can find the optimal path for the current scenario.
Neural Network-Based Human Motion Predictor and Smoother

Abstract Though continuous advances in the field of human pose estimation, it remains a challenge to retrieve high-quality recordings from real-life human motion using commodity hardware. Therefore, this work focuses on predicting and improving estimates for human motion with the aim of achieving production quality for skinned mesh animations by off-the-shelf webcams. We take advantage of recent findings in the field by employing a recurrent neural network architecture to (1) predict and (2) denoise human motion, with the intention of bridging the gap between cheap recording methods and high-quality recording. First, we propose an LSTM to predict short-term human motion, which achieves competitive results to state-of-the-art methods. Then, we adapt this model architecture and train it to clean up noisy human motion from two 3D low-quality input sources, and hence mimic a real-world scenario of recording human motion which yields noisy estimates. Experiments on simulated data show that the model is capable of significantly reducing noise, and it opens the way for future work to test the model on annotated data.
Dataset Condensation via Expert Subspace Projection

The rapid growth in dataset sizes in modern deep learning has significantly increased data storage costs. Furthermore, the training and time costs for deep neural networks are generally proportional to the dataset size. Therefore, reducing the dataset size while maintaining model performance is an urgent research problem that needs to be addressed. Dataset condensation is a technique that aims to distill the original dataset into a much smaller synthetic dataset while maintaining downstream training performance on any agnostic neural network. Previous work has demonstrated that matching the training trajectory between the synthetic dataset and the original dataset is more effective than matching the instantaneous gradient, as it incorporates long-range information. Despite the effectiveness of trajectory matching, it suffers from complex gradient unrolling across iterations, which leads to significant memory and computation overhead. To address this issue, this paper proposes a novel approach called Expert Subspace Projection (ESP), which leverages long-range information while avoiding gradient unrolling. Instead of strictly enforcing the synthetic dataset's training trajectory to mimic that of the real dataset, ESP only constrains it to lie within the subspace spanned by the training trajectory of the real dataset. The memory-saving advantage offered by our method facilitates unbiased training on the complete set of synthetic images and seamless integration with other dataset condensation techniques. Through extensive experiments, we have demonstrated the effectiveness of our approach. Our method outperforms the trajectory matching method on CIFAR10 by 16.7% in the setting of 1 Image/Class, surpassing the previous state-of-the-art method by 3.2%.
Display-Semantic Transformer for Scene Text Recognition

Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some noisy (distorted and blurry, etc.) images. In order to address the aforementioned issues, this study builds upon the most recent findings of the Vision Transformer, and our approach (called Display-Semantic Transformer, or DST for short) constructs a masked language model and a semantic visual interaction module. The model can mine deep semantic information from images to assist scene text recognition and improve the robustness of the model. The semantic visual interaction module can better realize the interaction between semantic information and visual features. In this way, the visual features can be enhanced by the semantic information so that the model can achieve a better recognition effect. The experimental results show that our model improves the average recognition accuracy on six benchmark test sets by nearly 2% compared to the baseline. Our model retains the benefits of having a small number of parameters and allows for fast inference speed. Additionally, it attains a more optimal balance between accuracy and speed.
Double Transfer Learning to Detect Lithium-Ion Batteries on X-Ray Images

With the soaring popularity of electronic gadgets, Lithium-Ion Batteries (LIB) have witnessed a remarkable surge. The inspiration behind this study arises from the urgent need to automate the identification of batteries in diverse contexts, such as electronic waste recycling facilities or security screening at airports. Ultimately, it strives to minimize health hazards associated with battery recycling by enabling more accurate sorting with minimal human involvement. In this paper, we applied double transfer learning to eight cutting-edge object detectors, unlocking the potential of X-Ray images in recognizing and categorizing electronic mobile devices (EMD) along with their embedded Lithium-Ion batteries (LIB).
Scalable Convolutional Neural Networks for Decoding of Terminated Convolutional Codes

We present a convolutional neural network (CNN) for the decoding of a terminated convolutional code (CC). For this use cases, an unlimited amount of labeled training data can be generated. However, the number of code words, i.e., pattern, to be learned by the CNN increases exponentially with the dimension of the code. Therefore, scalability of the neural network is of critical importance. The CNN is trained with a CC with small code word length, i.e., a limited number of code words. Therefore the training complexity is feasable. As the CNN is properly matched to dimension and structural properties of the CC, it can actually learn the structure of the CC. This allows to upscale the CNN decoder to match to CCs with larger code dimension. A retraining of weights is not required. The upscaled CNN successfully decodes code words it has never seen during the training process (generalization).
Shot Boundary Detection with Augmented Annotations

In recent years, deep learning approaches have been considered to provide state-of-the-art results in shot boundary detection. These approaches revolve around the need for large annotated datasets. The quality of the annotations is crucial to the robustness of the algorithm. Having graphical tools to verify the correct annotation of the original datasets, as well as the correct generation of synthetic datasets is a must. In this paper we propose a framework that allow the visual inspection of the datasets, incorporating the option of editing the annotations manually, as well as annotations from other algorithms, generating a set of augmented annotations. In addition, we benchmark the performance of TransNet in three scenarios, 1) using the datasets with their original annotations, 2) using automatically generated annotations, and 3) using the combination of the previous annotations, as augmented annotations. We conclude that the usage of augmented annotations significantly improves the network results.
Video Scene Segmentation Based on Triplet Loss Ranking

Scene segmentation is the task of segmenting the video in groups of frames with a high degree of semantic similarity. In this paper, we contribute to the task of video scene segmentation with the creation of a novel dataset for temporal scene segmentation. In addition, we propose the combination of two deep models to classify whether two video frames belong to the same or a different scene. The first model consists of a triplet network that is composed of 3 instances of the same 2D convolutional network. These instances correspond to a multi-scale net that performs frame embedding efficiently based on their similarity. We feed this network with an efficient triplet sampling algorithm. The second model is responsible for classifying whether these embeddings correspond to frames from different scenes by fine-tuning a siamese network.