Reviewerly/exHarmony huggingface dataset:
Engineering and Technology Education in University Studies: Driving Digital, Sustainable, and Resilient Development—A Case Study in Andalusia, Spain

An investigation was conducted to examine the impact of training in intelligent systems within engineering courses at the university level, considering the parameters and reports on business trends for 2030, as well as the Sustainable Development Goals (SDGs). The study analyzed 2309 subjects across 66 undergraduate degrees offered at nine universities in the Andalusia region, which is the most populous region in Spain. The objective of this research is to assess whether the training in intelligent systems, specifically in the fields of electrical engineering, electronics, computer science, and telecommunications, adequately prepares students in their final years of study to meet the demands of the labor and business market. Reports and trend studies have identified the urgent areas that require attention, including training in sensors, smart grids, smart cities, IoT, artificial intelligence, embedded systems, among others. The investigation provides insights into the current state of training in intelligent systems within the final years of engineering programs. These findings will inform future curriculum development and teaching guidelines, with the goal of enhancing students’ knowledge and skills in line with technological and engineering trends. The study will analyze subjects across various Spanish universities in Andalusia, considering their rankings. Based on the analysis of the results, recommendations for improvement actions and strategies to strengthen the curriculum will be proposed.
Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things

This work aims to introduce Long Short-Term Memory (LSTM) under the Internet of Things (IoT) context to enhance the accuracy and granularity of sentiment analysis in animated online education texts. It employs a multimodal data collection approach and uses IoT technology to gather multimodal textual data from students engaged in animated online education. The data includes students’ feedback texts, emotional texts, written texts, and verbal expressions during animated online education. Subsequently, a model named Information Block Bidirectional Long-Short term Memory (IB-BiLSTM) is designed and utilized to construct a sentiment classification model for animated online education texts. Experimental results demonstrate that the model achieves an accuracy of 93.92% and an F1-score of 90.34% for sentiment classification in animated online education texts and the loss function converges to around 0.14. This model effectively captures the emotional changes and evolution during students’ learning process. Thus, the proposed model holds significant potential and practical significance for enhancing animated online education’s personalization and emotional engagement. It provides valuable insights and guidance for the intelligent development of the education field.
Visual Fall Detection From Activities of Daily Living for Assistive Living

Health facilities have increased life expectancy, a key factor for the growth of the elderly population. Elderly people are at increased risk of falls, causing physical and psychological damage. Falls occur rarely compared to other activities of daily living. Due to such a class imbalance, supervised techniques are not the solution for fall detection systems. In addition, domain-level features for the fall activity are hard to generalize due to their diversity. In this work, the fall detection problem is formulated as anomaly detection in the time series where deviation from the activities of daily living is computed. On the basis of the deviation score, a fall is detected. We propose TCHA, Temporal Convolutional Hourglass Autoencoder, to learn spatial and temporal features from the videos. Hourglass units in the Temporal Convolutional Encoder help us extract multiscale features by expanding the receptive fields of neurons, reducing the information loss in deep learning methods. The proposed methodology is evaluated on the five data sets, including a compiled data set from publicly available Toyota Smarthome data set and four benchmarked datasets that include the UR-Fall dataset, IMVIA dataset, SDU dataset, and Thermal Fall dataset. Our methodology shows 4.1% superior results to existing state-of-the-art methods for unseen falls.
Using Differential Privacy to Define Personal, Anonymous, and Pseudonymous Data

Defining personal, anonymous, and pseudonymous data is a vital issue for data protection law. Current approaches adopted by legal regimes are either too absolute to be practical or too vague to be manageable. Differential privacy (DP), as a newly emergent technical tool, can help define the different categories of data by quantifiably measuring identification risks of databases. Through the selection of privacy budget in advance, data controllers can delineate the boundaries among personal, anonymous, and pseudonymous data in an auditable and reviewable way, as well as incorporate the definition into the broader practice of data risk management. This article offers concrete steps of applying this approach in practice, and argues that such an approach not only enhances certainty, consistency, and transparency, but also inspires a new model of interaction between law and technology. Recognizing that this approach is not perfect, the article then discusses some difficulties and directions for future research.
Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional Data

As one fundamental data-mining problem, ANN (approximate nearest neighbor search) is widely used in many industries including computer vision, information retrieval, and recommendation system. LSH (Local sensitive hashing) is one of the most popular hash-based approaches to solve ANN problems. However, the efficiency of operating LSH needs to be improved, as the operations of LSH often involve resource-consuming matrix operations and high-dimensional large-scale datasets. Meanwhile, for resource-constrained devices, this problem becomes more serious. One way to handle this problem is to outsource the heavy computing of high-dimensional large-scale data to cloud servers. However, when a cloud server responsible for computing tasks is untrustworthy, some security issues may arise. In this study, we proposed a cloud server-aided LSH scheme and the application model. This scheme can perform the LSH efficiently with the help of a cloud server and guarantee the privacy of the client’s information. And, in order to identify the improper behavior of the cloud server, we also provide a verification method to check the results returned from the cloud server. Meanwhile, for the implementation of this scheme on resource-constrained devices, we proposed a model for the real application of this scheme. To verify the efficiency and correctness of the proposed scheme, theoretical analysis and experiments are conducted. The results of experiments and theoretical analysis indicate that the proposed scheme is correct, verifiable, secure and efficient.
A Bi-Direction People Counting Method Based on Multiscale Clustering and Multiple Extended Target Tracking Using MIMO Radar

People counting using radar is a popular technology because of its robustness to darkness, sensitivity to human movement, noncontact wireless sensing, and no privacy issues. However, tracking and counting multiple people by radar is still a challenge. In this article, we propose a bi-direction people counting method based on multiscale clustering and multiple extended target tracking using multiple-input–multiple-output (MIMO) radar. The core idea is to correctly obtain the trajectory of each person and use its trajectory to judge the entry and exit of people. Specifically, we first perform distance, angle, and velocity estimation to obtain a large number of people target point clouds with high resolution. Second, we develop a multiscale clustering method, including microclustering, macroclustering, and precision-clustering so as to identify people target more accurately while maintaining low computational complexity. Then, we use the two-stage data association method to associate macrocluster, and then associate point clouds in macrocluster, which improves the accuracy of data association in the tracking process. Finally, we count people according to the prior edge areas and the obtained trajectories. Experimental results show that the proposed method can effectively detect and track multiple people targets close to each other, and reach the accuracy of people counting above 97%.
GROUSE: A Task and Model Agnostic Wavelet- Driven Framework for Medical Imaging

In recent years, deep learning has permeated the field of medical image analysis gaining increasing attention from clinicians. However, medical images always require specific preprocessing that often includes downscaling due to computational constraints. This may cause a crucial loss of information magnified by the fact that the region of interest is usually a tiny portion of the image. To overcome these limitations, we propose GROUSE, a novel and generalizable framework that produces salient features from medical images by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grouping</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">selecting</i> frequency sub-bands that provide approximations and fine-grained details useful for building a more complete input representation. The framework provides the most enlightening set of bands by learning their statistical dependency to avoid redundancy and by scoring their informativeness to provide meaningful data. This set of representative features can be fed as input to any neural model, replacing the conventional image input. Our method is task- and model-agnostic, thus it can be generalized to any medical image benchmark, as we extensively demonstrate with different tasks, datasets, and model domains. We show that the proposed framework enhances model performance in every test we conduct without requiring ad-hoc preprocessing or network adjustments.
Video Detection of Small Leaks in Buried Gas Pipelines

For the problem of difficult tracking of small leaks in buried gas pipelines, a video detection of small leaks in buried gas pipelines method is proposed for the detection robot inside buried gas pipelines. Firstly, collecting images and videos of leaks inside buried gas pipelines to establish a dataset. Secondly, building a video detection model for small leaks in buried gas pipelines, introduce a bidirectional feature pyramid network into YOLOv5s (You Only Look Once), and build a feature fusion network to enhance the model’s ability of fusing small leaks. Thirdly, building a small target detection layer and a small target detection head in YOLOv5s classification prediction network to enhance the model’s ability of fusing small leaks. Fourthly, the video detection model for small leaks in buried gas pipelines is trained using the dataset. Lastly, the video detection effect of small leaks of the model is verified through leak detection experiments in various situations. The experimental results show that the precision rate of this method is 94.1%, the recall is 94.8%, and the average precision is 94.5%, which has a good detection effect and strong generalization ability.
Three-Dimensional Marine Ranching Cage Inspection Path Planning Integrating the Differential Evolution and Particle Swarm Optimization Algorithms

This study addressed the autonomous planning of three-dimensional (3D) underwater inspection paths for autonomous underwater vehicles (AUVs) in marine ranching by integrating differential evolution and particle swarm optimization (PSO) algorithms. First, a modified PSO algorithm incorporating swap operators, mutation, and crossover strategies was employed to enable autonomous obstacle avoidance during the inspection of offshore net cages in fish farms situated within a 3D marine environment. This approach addresses the problem of planning full-traversal paths for multiple inspection points. Second, the performance of the proposed algorithm was assessed through comparative tests with other algorithms. The proposed algorithm demonstrated significant improvements in convergence speed, accuracy, and stability under complex scenarios involving multiple optima and intense oscillations. To validate the superiority and overall planning proficiency of the modified method, an experimental setup comprising of two distinct 3D marine cage environments with a series of checkpoints was utilized. The experimental results demonstrated the ability of the proposed algorithm to generate an optimal path while traversing all inspection points of fish farm offshore net cages. By ensuring the safety of AUVs and closely adhering to the surfaces of offshore net cages during the inspection process, the algorithm exhibits remarkable adaptability to specific application scenarios, effectively mitigating concerns related to local optima and premature convergence.
ML-Based 5G Traffic Generation for Practical Simulations Using Open Datasets

As artificial intelligence (AI) and machine learning (ML) technologies continue to advance, they are creating innovations in various industrial sectors and are increasingly influencing the development of wireless communication networks. Research advances in ML for wireless communications and networks depend essentially on the availability of datasets for testing results and attempting generalizations, but the availability of datasets obtained from real-world networks or experimental testbeds is currently very limited. To address this issue, we created a 5G traffic dataset by measuring various packet traffic and made it publicly available for anyone to use. The dataset is 328 hours long, making it difficult to handle directly and requiring preprocessing to be used for a specific purpose. To overcome these limitations, this study implements two types of traffic generators that can generate 5G traffic by improving ML models that have been verified and are widely used in academia. The traffic generator trained on the collected dataset has millions of parameters, but the total size of the model is no more than 118.12 MB, making it lightweight and easily deployable. To evaluate the difference between the generated traffic and the real traffic, the Jensen-Shannon divergence (JSD) and maximum mean discrepancy (MMD) metrics are observed, and it shows that the generated traffic is very similar to the real traffic. Our implementation and pretrained models are available on IEEE Code Ocean and GitHub.
More Arrows in the Quiver: Investigating the Use of Auxiliary Models to Localize In-View Components with Augmented Reality

The creation and management of content are among the main open issues for the spread of Augmented Reality. In Augmented Reality interfaces for procedural tasks, a key authoring strategy is chunking instructions and using optimized visual cues, i.e., tailored to the specific information to convey. Nevertheless, research works rarely present rationales behind their choice. This work aims to provide design guidelines for the localization of in-view and not occluded components, which is recurrent information in technical documentation. Previous studies revealed that the most suited visual cues to convey this information are auxiliary models, i.e., abstract shapes that highlight the space region where the component is located. Among them, 3D arrows are widely used, but they may produce ambiguity of information. Furthermore, from the literature, it is unclear how to design auxiliary model shapes and if they are affected by the component shapes. To fill this gap, we conducted two user studies. In the first study, we collected the preference of 45 users regarding the shape, color, and animation of auxiliary models for the localization of various component shapes. According to the results of this study, we defined guidelines for designing optimized auxiliary models based on the component shapes. In the second user study, we validated these guidelines by evaluating the performance (localization time and recognition accuracy) and user experience of 24 users. The results of this study allowed us to confirm that designing auxiliary models following our guidelines leads to a higher recognition accuracy and user experience than using 3D arrows.
Remote Collaborations in Metaverse under 5G Mobile Networks

For decades, people have been creating new ways to make remote communication and collaboration natural and effective. Recently, eXtended Reality (XR)-based remote collaborations enable people around the world to communicate in real-time in a mixed physical and virtual space, which breaks the limitations of geography. 5G mobile networks are considered as the enabling communication technology toward XR and metaverse, but the network demands of remote real-time collaborations in metaverse and how real-world 5G can support these applications have not been studied in depth. In this work, we introduce remote collaboration technologies and evaluate the network characteristics of remote real-time collaboration in metaverse under the 5G mobile network. Based on our evaluation results, challenges and opportunities are discussed.
Prompt Tuning in Code Intelligence: An Experimental Evaluation

Pre-trained models have been shown effective in many code intelligence tasks, such as automatic code summarization and defect prediction. These models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream tasks. However, as the inputs to pre-training and downstream tasks are in different forms, it is hard to fully explore the knowledge of pre-trained models. Besides, the performance of fine-tuning strongly relies on the amount of downstream task data, while in practice, the data scarcity scenarios are common. Recent studies in the natural language processing (NLP) field show that prompt tuning, a new paradigm for tuning, alleviates the above issues and achieves promising results in various NLP tasks. In prompt tuning, the prompts inserted during tuning provide task-specific knowledge, which is especially beneficial for tasks with relatively scarce data. In this article, we empirically evaluate the usage and effect of prompt tuning in code intelligence tasks. We conduct prompt tuning on popular pre-trained models CodeBERT and CodeT5 and experiment with four code intelligence tasks including defect prediction, code search, code summarization, and code translation. Our experimental results show that prompt tuning consistently outperforms fine-tuning in all four tasks. In addition, prompt tuning shows great potential in low-resource scenarios, e.g., improving the BLEU scores of fine-tuning by more than 26% on average for code summarization. Our results suggest that instead of fine-tuning, we could adapt prompt tuning for code intelligence tasks to achieve better performance, especially when lacking task-specific data. We also discuss the implications for adapting prompt tuning in code intelligence tasks.
Towards a Deeper Understanding of Global Covariance Pooling in Deep Learning: An Optimization Perspective

Global covariance pooling (GCP) as an effective alternative to global average pooling has shown good capacity to improve deep convolutional neural networks (CNNs) in a variety of vision tasks. Although promising performance, it is still an open problem on how GCP (especially its post-normalization) works in deep learning. In this paper, we make the effort towards understanding the effect of GCP on deep learning from an optimization perspective. Specifically, we first analyze behavior of GCP with matrix power normalization on optimization loss and gradient computation of deep architectures. Our findings show that GCP can improve Lipschitzness of optimization loss and achieve flatter local minima, while improving gradient predictiveness and functioning as a special pre-conditioner on gradients. Then, we explore the effect of post-normalization on GCP from the model optimization perspective, which encourages us to propose a simple yet effective normalization, namely DropCov. Based on above findings, we point out several merits of deep GCP that have not been recognized previously or fully explored, including faster convergence, stronger model robustness and better generalization across tasks. Extensive experimental results using both CNNs and vision transformers on diversified vision tasks provide strong support to our findings while verifying the effectiveness of our method.
<i>B<sup>2</sup>SFL:</i> A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.
Nonrigid Registration-Based Progressive Motion Compensation for Point Cloud Geometry Compression

There is a critical requirement for efficiently compressing point cloud geometries representing three-dimensional (3D) moving objects in various applications. The Moving Picture Experts Group 3D Graphics coding group (MPEG 3DG) set up an inter-exploration model for geometry-based point cloud compression (G-PCC interEM). However, the block-matching motion compensation scheme with a translational motion model has limited ability to handle dense point clouds with complex local motions. To overcome this problem, we propose a progressive non-rigid motion compensation framework for point cloud geometry compression, where the point cloud registration technique is introduced and tailored with our designed rate-distortion cost. In the coarse-grained stage, a point cloud is represented as deformable point patches, and the patch-wise non-rigid motion estimation task is formulated as a registration-based optimization problem that can be efficiently solved by the majorization-minimization method. In the fine-grained stage, we propose a block-based motion refinement to enhance the estimated motion field in the local region, followed by a multi-hypothesis motion compensation scheme enabling smooth reference reconstruction with patch-wise deformation and block-wise refined motions. Experiments demonstrate our proposed scheme outperforms several competitive platforms in terms of both coding performance and compensation quality. Compared with G-PCC interEM, our proposed framework achieves significant bitrate savings, i.e., 4.71% (32 frames) and 4.22% (200 frames), for point cloud lossless geometry compression.