Reviewerly/exHarmony huggingface dataset:
Self-Constructing Temporal Excitation Graph for Skeleton-Based Action Recognition

Graph convolutional network (GCN)-based methods have obtained remarkable performance and gained widespread attention for skeleton-based human action recognition. These methods typically apply 1-D local convolutions to model temporal correlations and simply utilize multilayer stacking to capture long-range temporal dynamics. However, the 1-D local convolution focuses on the relations between the adjacent time steps. Also, with the repeat of a lot of local convolutions, the key temporal relation with nonadjacent temporal distance may be ignored due to the information dilution. Therefore, it remains unclear how to fully explore the temporal dynamics of skeleton sequences. In this article, we propose a temporal excitation GCN (TE-GCN) to exploit a self-constructing temporal relation graph for capturing complex temporal dynamics. Specifically, the constructed temporal relation graph explicitly establishes the connections between semantically related temporal features to adaptively capture the temporal relations between the skeleton sequences. Meanwhile, to further explore the sufficient temporal dynamics concurrently, a multihead mechanism is designed to investigate multikinds of temporal relations. Extensive experiments are performed on two widely used large-scale datasets, NTU-60 RGB+D and NTU-120 RGB+D. Also, experimental results show that the proposed model obtains significant improvements by making contribution to temporal modeling for action recognition.
Hypergraph-Based Multi-robot Task and Motion Planning

In this article, we present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than the existing methods and successfully plans for problems with up to 20 objects, more than three times as many objects as comparable methods. We achieve this improvement by decomposing the planning space to consider manipulators alone, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are decomposed elements of the planning spaces and hyperarcs are transitions between elements. The existing methods use graph-based representations where vertices are full composite spaces and edges are transitions between these. Using the hypergraph reduces the representation size of the planning space for multimanipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scales exponentially in the number of robots and objects. We show that similar gains can be achieved for other multi-robot task and motion planning problems.
Privacy-Protected Person Re-Identification via Virtual Samples

Most person re-identification (re-ID) approaches are based on representation learning of pedestrian images, which assume that the person’s appearance captured by cameras in the target is fully available. However, the exposure of appearance could cause serious privacy leakages. To address this issue, we focus on a new privacy-protected person re-ID task where the person’s appearance is unavailable for training. We first overcome the dilemma of lacking real person images by utilizing the virtual pedestrian samples ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., PersonX). Then, we introduce a composition of data augmentations to simulate real conditions, where the learned model is transferred to the real target in a black way. Specifically, the background behind, surrounding illumination, pose, scale, and attributes of pedestrians from the target scene, irrelevant to privacy, are utilized to generate virtual images. With above privacy-irrelevant information, we propose a Translation-Rendering-Sampling (TRS) framework to generate images toward the distribution of the real-world dataset (including background, pose, attribute, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> ). Extensive experiments are conducted on several realistic re-ID datasets excluding the person’s appearance. The experiments show that our method outperforms the baseline significantly as well as some transfer learning methods.
Self-supervised contrastive graph representation with node and graph augmentation

Graph representation is a critical technology in the field of knowledge engineering and knowledge-based applications since most knowledge bases are represented in the graph structure. Nowadays, contrastive learning has become a prominent way for graph representation by contrasting positive-positive and positive-negative node pairs between two augmentation graphs. It has achieved new state-of-the-art in the field of self-supervised graph representation. However, existing contrastive graph representation methods mainly focus on modifying (normally removing some edges/nodes) the original graph structure to generate the augmentation graph for the contrastive. It inevitably changes the original graph structures, meaning the generated augmentation graph is no longer equivalent to the original graph. This harms the performance of the representation in many structure-sensitive graphs such as protein graphs, chemical graphs, molecular graphs, etc. Moreover, there is only one positive-positive node pair but relatively massive positive-negative node pairs in the self-supervised graph contrastive learning. This can lead to the same class, or very similar samples are considered negative samples. To this end, in this work, we propose a Virtual Masking Augmentation (VMA) to generate an augmentation graph without changing any structures from the original graph. Meanwhile, a node augmentation method is proposed to augment the positive node pairs by discovering the most similar nodes in the same graph. Then, two different augmentation graphs are generated and put into a contrastive learning model to learn the graph representation. Extensive experiments on massive datasets demonstrate that our method achieves new state-of-the-art results on self-supervised graph representation. The source code of the proposed method is available at https://github.com/DuanhaoranCC/CGRA.
Global Multi-Scale Optimization and Prediction Head Attentional Siamese Network for Aerial Tracking

Siamese-based trackers have been widely used in object tracking. However, aerial remote tracking suffers from various challenges such as scale variation, viewpoint change, background clutter and occlusion, while most existing Siamese trackers are limited to single-scale and local features, making it difficult to achieve accurate aerial tracking. We propose the global multi-scale optimization and prediction head attentional Siamese network to solve this problem and improve aerial tracking performance. Firstly, a transformer-based multi-scale and global feature encoder (TMGFE) is proposed to obtain global multi-scale optimization of features. Then, the prediction head attentional module (PHAM) is proposed to add context information to the prediction head by adaptively adjusting the spatial position and channel contribution of the response map. Benefiting from these two components, the proposed tracker solves these challenges of aerial remote sensing tracking to some extent and improves tracking performance. Additionally, we conduct ablation experiments on aerial tracking benchmarks, including UAV123, UAV20L, UAV123@10fps and DTB70, to verify the effectiveness of the proposed network. The comparisons of our tracker with several state-of-the-art (SOTA) trackers are also conducted on four benchmarks to verify its superior performance. It runs at 40.8 fps on the GPU RTX3060ti.
Validity of non-local mean filter and novel denoising method

Image denoising is an important topic in the digital image processing field. This paper theoretically studies the validity of the classical non-local mean filter (NLM) for removing Gaussian noise from a novel statistic perspective. By regarding the restored image as an estimator of the clear image from the statistical view, we gradually analyse the unbiasedness and effectiveness of the restored value obtained by the NLM filter. Then, we propose an improved NLM algorithm called the clustering-based NLM filter (CNLM) that derived from the conditions obtained through the theoretical analysis. The proposed filter attempts to restore an ideal value using the approximately constant intensities obtained by the image clustering process. Here, we adopt a mixed probability model on a prefiltered image to generate an estimator of the ideal clustered components. The experimental results show that our algorithm obtains considerable improvement in peak signal-to-noise ratio (PSNR) values and visual results when removing Gaussian noise. On the other hand, the considerable practical performance of our filter shows that our method is theoretically acceptable as it can effectively estimates ideal images.
Breast Cancer Prediction Based on Machine Learning

Breast cancer is a significant health concern, necessitating accurate prediction models for early detection and improved patient outcomes. This study presents a comparative analysis of three machine learning models, namely, Logistic Regression, Decision Tree, and Random Forest, for breast cancer prediction using the Wisconsin breast cancer diagnostic dataset. The dataset comprises features computed from fine needle aspirate images of breast masses, with 357 benign and 212 malignant cases. The research findings highlight that the Random Forest model, leveraging the top 5 predictors—"concave points_mean", "area_mean", "radius_mean", "perimeter_mean", and "concavity_mean", achieves the highest predictive accuracy of approximately 95% and a cross-validation score of approximately 93% for the test dataset. These results demonstrate the potential of machine learning approaches in breast cancer prediction, underscoring their importance in aiding early detection and diagnosis.
Harnessing the Power of ChatGPT for Mastering the Maltese Language: A Journey of Breaking Barriers and Charting New Paths

The language model, Chat Generative Pretrained Transformer (ChatGPT), which is a cutting-edge artificial intelligence (AI) technology developed by OpenAI, employs deep learning algorithms to generate human-like text. Despite its recent popularity, ChatGPT is expected to play an increasingly important role in shaping the future of technology and AI. This study used a mixed methods approach to assess the effectiveness of ChatGPT in facilitating the learning of Maltese for 41 international multilingual adult students and to explore their experiences and perceptions of using ChatGPT to learn Maltese. The participants used ChatGPT for a period of two weeks to support their Maltese learning and completed a survey to evaluate their experience and the usefulness of the tool in the Maltese learning process. An online focus group approach was employed, in which the participants discussed their use of ChatGPT for Maltese learning. According to the findings, ChatGPT had some limitations when it came to providing accurate information related to Maltese grammar and vocabulary, and sometimes produced non-existent words or incorrect answers. Additionally, the tool struggled to comprehend and respond to Maltese questions and statements. Participants also noted that ChatGPT had limitations in supporting grammar, vocabulary, creating dialogues or stories, translation, summarization, proofreading, and conversational practice, especially when compared to its effectiveness in assisting with the learning of the English language. Despite 98% of the participants indicating that ChatGPT lacked cultural context in the survey, a Palestinian participant was able to persuade others during the focus group session by demonstrating how the tool was helpful in understanding Malta's cultural context. Furthermore, ChatGPT does not provide immediate feedback to allow Maltese students to quickly correct errors and improve their language skills. The study highlights the need for improvement in the training of ChatGPT on the Maltese language, as well as collaboration with Maltese language and AI experts, to better meet the needs of Maltese students. The findings have important implications for the design and implementation of language learning programs and for the development and deployment of language learning tools for less widely spoken languages like Maltese.
The weaponization of platform governance: Mass reporting and algorithmic punishments in the creator economy

Abstract Amid wider discussions of online harassment on social media platforms, recent research has turned to the experiences of social media creators whose compulsory visibility renders them vulnerable to frequent attacks, ranging from persistent trolling to much more insidious, offline acts of violence. This study examines a contemporary form of harassment against social media creators known as “mass reporting,” or the targeted, automated flagging of creators' online content to trigger content takedowns and account bans. Mass reporting is particularly challenging for social media creators because of its weaponization of platform infrastructures for community governance, leaving creators with few avenues of support after orchestrated attacks and restricting their access to platform support. Drawing on in‐depth interviews with social media creators who have been subjected to mass reporting on their content, this study builds an understanding of the ways in which tools for platform governance, such as content reporting, can be weaponized to harass and introduce vulnerabilities for creators.
Imitation Learning through Image Augmentation Using Enhanced Swin Transformer Model in Remote Sensing

In unmanned systems, remote sensing is an approach that collects and analyzes data such as visual images, infrared thermal images, and LiDAR sensor data from a distance using a system that operates without human intervention. Recent advancements in deep learning enable the direct mapping of input images in remote sensing to desired outputs, making it possible to learn through imitation learning and for unmanned systems to learn by collecting and analyzing those images. In the case of autonomous cars, raw high-dimensional data are collected using sensors, which are mapped to the values of steering and throttle through a deep learning network to train imitation learning. Therefore, by imitation learning, the unmanned systems observe expert demonstrations and learn expert policies, even in complex environments. However, in imitation learning, collecting and analyzing a large number of images from the game environment incurs time and costs. Training with a limited dataset leads to a lack of understanding of the environment. There are some augmentation approaches that have the limitation of increasing the dataset because of considering only the locations of objects visited and estimated. Therefore, it is required to consider the diverse kinds of the location of objects not visited to solve the limitation. This paper proposes an enhanced model to augment the number of training images comprising a Preprocessor, an enhanced Swin Transformer model, and an Action model. Using the original network structure of the Swin Transformer model for image augmentation in imitation learning is challenging. Therefore, the internal structure of the Swin Transformer model is enhanced, and the Preprocessor and Action model are combined to augment training images. The proposed method was verified through an experimental process by learning from expert demonstrations and augmented images, which reduced the total loss from 1.24068 to 0.41616. Compared to expert demonstrations, the accuracy was approximately 86.4%, and the proposed method achieved 920 points and 1200 points more than the comparison model to verify generalization.
A novel model based collaborative filtering recommender system via truncated ULV decomposition

Collaborative filtering is a technique that takes into account the common characteristics of users and items in recommender systems. Matrix decompositions are one of the most used techniques in collaborative filtering based recommendation systems. Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) based approaches are widely used. Although they are quite good at dealing with the scalability problem, their complexities are high. In this study, the Truncated-ULV decomposition (T-ULVD) technique was used as an alternative technique to improve the accuracy and quality of recommendations. The proposed method has been tested with Movielens 100k, Movielens 1M, Filmtrust, and Netflix datasets, which are widely used in recommender system researches. In order to assess the performance of the proposed model, standart metrics (MAE, RMSE, precision, recall, and F1 score) were used. It is seen that while progress was achieved in all experiments with the T-ULVD compared to the NMF, very close or better results were obtained compared to the SVD. Moreover, this study may guide T-ULVD based future studies on solving the cold-start problem and reducing the sparsity in collaborative filtering based recommender systems.
A scenario framework for electricity grid using Generative Adversarial Networks

This study proposes a synthetic data framework to decrease the cost of smart metering. It offers clustering of real-time buses based on their electricity consumption levels and synthetic data generated by Generative Adversarial Networks (GANs) to provide sufficient information and data in grid modeling. GANs can be used to predict the electrical dissipation profiles of buses and their total energy consumption since they replicate the distributional features of the recorded data. This study is expected to facilitate energy distribution companies and grid operators in the progress of efficient and intellectual modeling of smart grids by stimulating the bottom-up contribution of end-users. It aims to decrease the data collection cost while allowing realistic scenarios. Furthermore, it introduces a new metric to measure the accuracy of the GANs and illustrates some empirical analysis using single consumer data.
A novel data gaps filling method for solar PV output forecasting

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. The accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.
Accelerating Scientific Applications with the Quantum Edge: A Drug Design Use Case

To address increasing demands of computational resources, scientific applications started to support different type of hardware accelerators, e.g., GPUs, TPUs, ASICs. However, due to the limitation to scalability of hardware resources posed by the Moore Law, a conspicuous amount of research is focusing in integration of Quantum Computers in the overall computing continuum. The high amount of data and the high heterogeneity of quantum architectures necessitate development and integration of additional hardware/software layers to facilitate integration of quantum hardware into the overall computing continuum. In this work, we discuss the possibility of applying edge computing to address this issue, laying the fundamentals to the concept of the Quantum Edge. Further, we present and analyse a drug design use case, identifying challenges and future research directions for Quantum Edge.
Performance Losses with Virtualization: Comparing Bare Metal to VMs and Containers

The use of virtualization technologies has become widespread with the advent of cloud computing. The purpose of this study is to quantify the performance losses caused by all kind of virtualization/containerization configurations. A benchmark suite consisting of tools that stress specific components and then four real applications commonly used in computing centers has been designed. A system to schedule the execution of these benchmarks and to collect the results has been developed. Finally, a procedure calling all the benchmark in a consistent and reproducible way either within a container or in a (virtual or not machine) has been implemented. These developments permitted then to compare bare metal with four hypervisors and two container runtimes as well as the mix of containers in the virtual machines. The results show that the performance differences vary greatly depending on the workload and the virtualization software used. When using the right virtualization software, the estimated the performance losses are around 5% for a container and 10% for a virtual machine. The combination of the two entails the addition of these losses to 15%. In the case of non-optimized software, a performance loss of up to 72% can be observed. We also observed that containers and virtual machines can over-perform bare-metal when it comes to file access. Overall we conclude that virtualization has become very mature and performance losses seems not to be a concern anymore.
EQuaTE: Efficient Quantum Train Engine for Runtime Dynamic Analysis and Visual Feedback in Autonomous Driving

This article proposes an efficient quantum train engine (EQuaTE), a novel development tool for quantum neural network (QNN) autonomous driving software, which plots gradient variances to confirm whether the QNN falls into local minima situations (called barren plateaus). Based on this runtime visualization, the stability and feasibility of QNN-based software can be tested during runtime operations of autonomous driving functionalities. This software testing of a QNN via dynamic analysis is essentially required due to undetermined probabilistic qubit states during runtime operations. Furthermore, an EQuaTE is capable of visual feedback because the barren plateaus can be identified at local autonomous driving platforms, and the corresponding information will be visualized at remotely connected cloud. Based on this visualized information at the cloud, the QNN, which is also stored at cloud, should be automatically reorganized and retrained for eliminating barren plateaus. Then, the trained parameters can be downloaded into the QNN of autonomous driving platforms.