Reviewerly/exHarmony huggingface dataset:
LogRF: An Approach to Human Pose Estimation Using Skeleton Landmarks for Physiotherapy Fitness Exercise Correction

Human pose and gesture estimation are crucial in correcting physiotherapy fitness exercises. In recent years, advancements in computer vision and machine learning approaches have led to the development of sophisticated pose estimation models that accurately track and analyze human movements in real time. This technology enables physiotherapists and fitness trainers to gain valuable insights into their client’s exercise forms and techniques, facilitating more effective exercise corrections and personalized training regimens. This research aims to propose an efficient artificial intelligence method for human pose estimation during physiotherapy fitness exercises. We utilized a multi-class exercise dataset based on human skeleton movement points to conduct our experimental research. The dataset comprises 133 features derived from human skeleton movements during various exercises, resulting in high feature dimensionality that affects the performance of human pose estimation with machine learning and deep learning methods. We have introduced a novel Logistic regression Recursive Feature elimination (LogRF) method for feature selection. Extensive experiments demonstrate that using the top twenty selected features, the random forest method outperformed state-of-the-art studies with a high-performance score of 0.998. The performance of each applied method is validated through a k-fold approach and further enhanced using hyperparameter tuning. Our proposed study assists specialists in identifying and addressing potential biomechanical issues, improper postures, and incorrect movement patterns, which are essential for injury prevention and optimizing exercise outcomes. Furthermore, this study enhances the capabilities of remote monitoring and guidance capabilities, allowing physiotherapists to support their patient’s progress with prescribed exercises continually.
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set. FedCBMIR distributes an unsupervised FE to collaborative centers for training without sharing the data set, resulting in shorter training times and higher performance. FedCBMIR was evaluated by mimicking two experiments, including two clients with two different breast cancer data sets, namely BreaKHis and Camelyon17 (CAM17), and four clients with the BreaKHis data set at four different magnifications. FedCBMIR increases the F1 score (F1S) of each client from 96% to 98.1% in CAM17 and from 95% to 98.4% in BreaKHis, with 11.44 fewer hours in training time. FedCBMIR provides 98%, 96%, 94%, and 97% F1S in the BreaKHis experiment with a generalized model and accomplishes this in 25.53 fewer hours of training.
Enhanced fractional prediction scheme for effective matrix factorization in chaotic feedback recommender systems

The biggest wish of the e-commerce industry is to forecast and estimate the taste and repugnance of users by utilizing user ratings for various products. Chaotic users' feedback drives the e-commerce industry to develop efficient, robust, and smart recommendation systems with the capability to deal with the inconsistencies in users' rating patterns and provide extremely related, appropriate, and timely recommendations. A few fractional order-based solutions for an effective matrix factorization procedure are suggested to boost recommender systems' performance regarding recommendations speed and precision. We further investigate a newly proposed enhanced fractional stochastic gradient descent (EFSGD) technique for accelerating recommendations speed through efficient matrix factorization. The Faa di Bruno fractional derivative used in EFSGD exploits the users' historical feedback for providing accurate predictions. As a result of the growing computational complexity of recommendation models, it will a challenge for EFSGD to extract group of prominent features and ignore the cluster of redundant users' and items' latent attributes for increasing the recommendation speed and reducing the computational complexity respectively. Therefore, (a) to resolve chaotic users' ratings patterns problem and (b) reduce the complexity by identifying and selecting groups of highly correlated latent features, an innovative elastic net regularized enhanced fractional adaptive model is developed for efficient matrix factorization. The proposed model for recommender systems outperforms baseline and state-of-the-art in terms of convergence speed, computational cost, and prediction accuracy. The substantial performance of the suggested strategy is verified through various latent factors, ratings prediction-based valuation measures, learning-rates, and fractional order values. Whereas the authenticity is verified through Movie-Lens and FilmTrust datasets.
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection

We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 18,945 vulnerable functions spanning 150 CWEs and 330,492 non-vulnerable functions extracted from 7,514 commits. Our dataset covers 295 more projects than all previous datasets combined.
Efficient Memory Management for Large Language Model Serving with PagedAttention

High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2--4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM's source code is publicly available at https://github.com/vllm-project/vllm.
Beyond borders: exploring the impact of augmented reality on intercultural competence and L2 learning motivation in EFL learners

This mixed-methods study investigates the impact of augmented reality (AR) on the development of intercultural competence and L2 (second language) learning motivation among Chinese English as a Foreign Language (EFL) learners. The research comprised forty-eight intermediate-level learners who were randomly assigned to either an experimental group, receiving AR-based language instruction, or a control group, receiving traditional instruction.Both groups underwent pre- and post-tests to assess their intercultural competence and L2 learning motivation. The experimental group engaged with an AR application, which exposed learners to a variety of cultural scenarios, customs, and norms.The results indicate that the experimental group, exposed to AR-based instruction, demonstrated significantly higher levels of intercultural competence and L2 learning motivation in comparison to the control group.Qualitative data analysis further elucidated that AR-based instruction enhanced learners' engagement, motivation, and deepened their cultural understanding. This study highlights the potential of augmented reality as a powerful tool for fostering the development of intercultural competence and L2 learning motivation within the EFL context, suggesting promising opportunities for innovative pedagogical approaches in language education.
Context-Aware Auto-Encoded Graph Neural Model for Dynamic Question Generation using NLP

Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware auto-encoded graph neural model. Our approach involves constructing a graph representation of the input text, where each node in the graph corresponds to a word or phrase in the text, and the edges represent the relationships between them. We then use an auto-encoder model to learn a compressed representation of the graph that captures the most important information in the input text. Finally, we use the compressed graph representation to generate questions by dynamically selecting nodes and edges based on their relevance to the context of the input text. We evaluate our approach on four benchmark datasets (SQuAD, Natural Questions, TriviaQA, and QuAC) and demonstrate that it outperforms existing state-of-the-art methods for dynamic question generation. In the experimentation, to evaluate the result four performance metrics are used i.e. BLEU, ROUGE, F1-Score, and Accuracy. The result of the proposed approach yields an accuracy of 92% on the SQuAD dataset, 89% with QuAC, and 84% with TriviaQA. while on the natural questions dataset, the model gives 79% accuracy. Our results suggest that the use of graph neural networks and auto-encoder models can significantly improve the accuracy and effectiveness of question generation in NLP. Further research in this area can lead to even more sophisticated models that can generate questions that are even more contextually relevant and natural-sounding.
Augmented reality aided object mapping for worker assistance/training in an industrial assembly context: Exploration of affordance with existing guidance techniques

In recent years, the affordances of immersive technologies in the industrial context have enabled the onsite worker to have increased potential towards handling the complex procedural and cognitive tasks involved in the strategy of manufacturing an appropriate product. This article describes the potential of Augmented Reality (AR) in terms of upgraded workplace understanding, training, and assisting semi-skilled/new workers in an industrial production environment. The different modes of instruction visualization techniques are investigated for the execution of assembly tasks and explored their impact in accordance to the efficiency of task execution, completion time, error rate perception, cognitive load, and independence. Progress outcomes of 24 participants are explored for real-time execution of a Reverse osmosis (RO) booster pump assembly in terms of different modes of visualization, such as the conventional document method, virtual /desktop mode, and object mapping-based AR. Additionally, the user studies on different visualization devices involving hand-held visualization devices (HHVD) and Head-mounted visualization devices (HMVD) are investigated and validated in terms of adaptability, acceptability, and usability. Furthermore, the effectiveness of the AR-aided object mapping technology versus other methods is explored through the feedback received from both experts and academia participants, which increases the insight of the technology and provides an intelligent mechanism for semi-skilled/new workers to handle complex or challenging operations in the production environment. The studies indicate that object mapping-based AR visualization facilitates increased assembly task efficiency and the minimum number of errors compared to the other guidance modes. Notably, the HMVD affords an increased realism experience, minimized errors, and less completion time compared to the HHVD.
Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse

Metaverse seamlessly blends the physical world and virtual spaces through ubiquitous communication and computing equipment and infrastructure. In intelligent transportation systems, the vehicular Metaverse can provide a fully immersive and hyperreal travel experience (e.g., via augmented reality head-up displays, AR-HUDs) to drivers and passengers in autonomous vehicles (AVs) through roadside units (RSUs). However, providing real-time and immersive services requires effective physical-virtual synchronization between AVs and virtual simulators. This paper proposes a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In physical-to-virtual synchronization, digital twin (DT) tasks generated by AVs are offloaded for execution in RSUs with future route generation. In virtual-to-physical synchronization, virtual simulators customize diverse and personalized AR content via generative AI models based on user preferences. Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and virtual simulators for RSUs to provide real-time and effective services. Finally, property analysis and experimental results demonstrate that the proposed mechanism is strategy-proof and adverse-selection free while increasing social surplus.
Exploiting one-dimensional exponential Chebyshev chaotic map and matching embedding for visually meaningful image encryption

As a result of their prominent initial value sensitivity, pseudo-randomness, and unpredictability nature, chaotic maps are widely used in multimedia security. However, the majority of chaotic maps now in use have issues like narrow parameter scope, poor traversability, and insufficient chaotic performance. To address these issues, a one-dimensional exponential Chebyshev (1-DEC) chaotic map is constructed in this paper which performs satisfactorily. Through the use of a bifurcation diagram, Lyapunov exponents, sample entropy, and permutation entropy, the chaotic dynamics of the 1-DEC are thoroughly examined. Subsequently, a visually meaningful image encryption (VMIE) scheme combined with matching embedding based on 1-DEC was presented. Specifically, a cyclic shift confusion method across rows and columns (CSCARC) is proposed to eliminate the correlation of adjacent pixels. Then, an adaptive embedding method based on image energy and dynamic matching (AEIEDM) is proposed to fuse the privacy information into carrier images. In addition, secret image share and authentication information is introduced in this scheme, which improves disaster-tolerance performance and implements the distributed management and storage of data. Subscribers have the capabilities of authentication, management, and data integrity checking. Ultimately, the simulation results and analysis demonstrate that the proposed scheme possesses satisfactory performance in terms of security and robustness, and the visual quality of the steganographic images was above 50.9 dB.
Originality and the future of copyright in an age of generative AI

This paper takes the occasion of French DJ David Guetta's use of generative AI tools to create lyrics and a voice in the style of Eminem, which he then used in one of his concerts, as the basis for an exploration of the shifting meaning of creativity and originality in the age of generative AI. Our main contention is that the Guetta form of creativity with generative AI tools differs in certain important respects from what has come before. The paper describes an iterative, dynamic process of conception, prompting, generation, refining, and deployment to characterise creativity in this context. Nevertheless, we contend that copyright – specifically the concept of originality as articulated in US federal law – is a sufficiently durable legal mechanism that can manage these new cultural forms, and that the two basic requirements of modern copyright law (a tangible medium of expression and a modest degree of creativity) remain relevant in identifying the scope of legal protection. The paper argues that the David Guetta story reveals something more general about creativity in a digital age, namely that while hybrid-networked (i.e., human – corporate – machine) creators have always created hybrid-networked cultural forms (i.e., creations that blend human and technology-constituted elements), such hybridity becomes increasingly visible and complex in the context of a new world of generative AI. At the very least, earlier – and influential – models of creativity as human-driven involving creation ex nihilo become harder to sustain in a new age of generative AI. But this does not mean copyright or notions of originality are redundant or that copyright law cannot accommodate Guetta and other cases. Such an account seems important as it challenges the hegemonic and reductive view that AI “generates” artistic works autonomously and avoids reducing the copyright issues raised by such creative works to the related but distinct question of whether learning models rely on copyrighted data. As such, copyright law should remain an important mechanism to facilitate genuine creators who are using AI systems in innovative and unique ways to push the boundaries of their creativity.
Blockchain-enabled smart agriculture: Enhancing data-driven decision making and ensuring food security

Agriculture plays a vital role in global food security, but conventional practices have led to environmental degradation and health risks due to excessive use of agricultural nutrients and chemicals. In this era of increased consumer awareness and preference for organic food, the challenge lies in making informed choices amidst various crop production options. To address this, the present research introduces a groundbreaking study that utilizes blockchain technology to record and trace information on crop production from seeds to end-user consumption, enabling easy consumer choices. This study introduces a model that integrates Internet of Things (IoT) sensors to monitor the impact of selected chemicals and nutrients used in agriculture to enhance production yields. To ensure sustainability, the study employs the low-energy adaptive clustering hierarchy (LEACH) protocol, which optimizes energy consumption and network stability, thereby avoiding disruptions. Results indicate that different fertilizers affect soil physical properties such as temperature and moisture. These parameters are monitored using the Agriculture Vercel App, equipped with temperature, moisture, and light sensors in agricultural fields. The app provided threshold readings via alert notifications, revealing significant fluctuations in temperature (12%–43%) and moisture (83%–41%) concentrations upon the application of specific fertilizers like "Agricultural lime" and "Calcium Ammonium Nitrate." Our IoT-based model is linked with private blockchain technology to prioritize consumer choice and facilitate transparency. This integration enhances food security while mitigating environmental degradation risks. By providing a comprehensive record of crop production practices, our model empowers consumers to make informed decisions aligned with their health and environmental concerns. This research demonstrates the potential of combining IoT and blockchain technologies to revolutionize agriculture, ensure sustainable practices, and promote food security. The findings offer significant implications for policymakers, farmers, and consumers, fostering a more sustainable and health-conscious approach to agriculture worldwide.
A multi-strategy particle swarm algorithm with exponential noise and fitness-distance balance method for low-altitude penetration in secure space

UAV technology is at the forefront of current research and plays an important role in agriculture, public safety and the military. Low altitude trajectory is a technology for UAVs to fly safely in situations full of various threats, so the safety and stability of the flight path is an important challenge. Current swarm intelligence (SI) algorithms are widely used in path planning problems due to their high robustness and global search capability, but most of them do not take into account the universality of such problems and flight control in safe space, so it is particularly important to improve the ability of SI to plan paths in multiple environments. To better improve the competitiveness of SI in such problems, this paper proposes a multi-strategy particle swarm algorithm based on exponential noise to solve low altitude penetration in secure space, and the algorithm is abbreviated as MEPSO. A selective opposition (SO) strategy is introduced in the traditional particle swarm algorithm to dynamically update the position of individuals and improve the flexibility of the algorithm; then uses exponential noise and fitness-distance balance (FDB) to expand the search space and prevent the algorithm from falling into a local optimum, further enhance the optimization capability of the algorithm. Comparing MEPSO with 4 basic algorithms and 7 variants of other algorithms, the results show that the minimum and mean costs of MEPSO in simple environments are 72.5850 and 72.7472, respectively. It also shows that the minimum and mean cost in complex environments are 73.2984 and 73.3312, respectively. MEPSO has better global optimization capability than other algorithms. The safety and stability of the paths in both environments are high, which verifies that the path planning capability of MEPSO is reasonable and effective in the low altitude penetration in secure space.
GCL-YOLO: A GhostConv-Based Lightweight YOLO Network for UAV Small Object Detection

Precise object detection for unmanned aerial vehicle (UAV) images is a prerequisite for many UAV image applications. Compared with natural scene images, UAV images often have many small objects with few image pixels. These small objects are often obscured, densely distributed, or in complex scenes, which causes great interference to object detection. Aiming to solve this problem, a GhostConv-based lightweight YOLO network (GCL-YOLO) is proposed. In the proposed network, a GhostConv-based backbone network with a few parameters was firstly built. Then, a new prediction head for UAV small objects was designed, and the original prediction head for large natural scene objects was removed. Finally, the focal-efficient intersection over union (Focal-EIOU) loss was used as the localization loss. The experimental results of the VisDrone-DET2021 dataset and the UAVDT dataset showed that, compared with the YOLOv5-S network, the mean average precision at IOU = 0.5 achieved by the proposed GCL-YOLO-S network was improved by 6.9% and 1.8%, respectively, while the parameter amount and the calculation amount were reduced by 76.7% and 32.3%, respectively. Compared with some excellent lightweight networks, the proposed network achieved the highest and second-highest detection accuracy on the two datasets with the smallest parameter amount and a medium calculation amount, respectively.