Reviewerly/exHarmony huggingface dataset:
Analyses and Comparisons of UAV Path Planning Algorithms in Three-Dimensional City Environment

Path planning for unmanned aerial vehicle (UAV) is a crucial problem especially in complicated three-dimensional (3D) city environments. Until recently, several algorithms have been proposed to realize the UAV operations in 3D city environment, but the existing algorithms only focus on the ideal conditions, including known obstacles and deterministic UAV parameters. However, the complicated city environment leads to a lot of randomness. In this way the evaluations of different path planning algorithms in a city environment become indispensable for the UAV operations. In this paper three classic UAV path planning algorithms are selected to make the detailed analyses and comparisons, namely A* algorithm, random-rapidly tree algorithm (RRT), ant colony algorithm (ACO). Three scenarios are designed and applied to test the mentioned algorithms above, considering different sizes of city operation scenarios, different altitudes between starting point and destination point, and different densities of obstacles in the flying environment. The simulation results show that A* algorithm works well in all three scenarios. Similarly, ACO is especially suitable for large scale scenes with a great amount of height differences between starting and destination points. To some extent RRT is the worst of the three in the designed scenarios because of the characteristics of random walking when locating the optimum solutions.
HARMONY: Heterogeneity-Aware Hierarchical Management for Federated Learning System

Federated learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. However, despite its emerging applications in many areas, real-world deployment of on-device FL is challenging due to wildly diverse training capability and data distribution across heterogeneous edge devices, which highly impact both model performance and training efficiency. This paper proposes Harmony, a high-performance FL framework with heterogeneity-aware hierarchical management of training devices and training data. Unlike previous work that mainly focuses on heterogeneity in either training capability or data distribution, Harmony adopts a hierarchical structure to jointly handle both heterogeneities in a unified manner. Specifically, the two core components of Harmony are a global coordinator hosted by the central server and a local coordinator deployed on each participating device. Without accessing the raw data, the global coordinator first selects the participants, and then further reorganizes their training samples based on the accurate estimation of the runtime training capability and data distribution of each device. The local coordinator keeps monitoring the local training status and conducts efficient training with guidance from the global coordinator. We conduct extensive experiments to evaluate Harmony using both hardware and simulation testbeds on representative datasets. The experimental results show that Harmony improves the accuracy performance by 1.67% - 27.62%. In addition, Harmony effectively accelerates the training process up to $3.29\times$ and $1.84\times$ on average, and saves energy up to 88.41% and 28.04% on average.
Spiking Neural Networks for Gesture Recognition Using Time Domain Radar Data

Gesture recognition using luminance invariant radar sensors is vital due to its extensive use in human-machine interfaces. However, the necessity for computationally expensive radar data pre-processing steps represented by fast Fourier transforms to get range and Doppler features are regarded as a contemporary concern. In this work, we present a solution for gesture recognition that relies on time-domain radar data applied to an event-driven, sparse, and end-to-end trained spiking neural network architecture. Using the proposed solution, it is possible to discriminate between 10 different gestures in a gesture dataset recorded using a 60 GHz frequency-modulated continuous-wave radar sensor, with a mean test accuracy of 93.1%.
An Analysis of Crypto Gas Wars in Ethereum

Several years after NFTs first appeared, people began to use NFT to prove ownership of assets in diverse domains. Then the values of NFTs rapidly increased and accordingly the interests in NFTs have grown. Because Ethereum has the most active transactions and has a lot of users, many projects use Ethereum to deploy their NFT smart contracts. However, Ethereum has a chronic disadvantage of low scalability. During the NFT drop period for famous and popular NFTs, users are crowding the event, resulting in a large number of transactions to get NFTs. The low scalability leads to a fierce competition called the Crypto Gas War. In this work, we choose three famous NFT drop events to collect on-chain data of Ethereum over the drop period in order to analyze the impact of the Crypto Gas War on the Ethereum network in detail. In addition, we analyze the collected data to uncover critical and hidden problems, and present insights to solve them.
Crowded event management in smart cities using a digital twin approach

One challenge of any smart city is the management of crowded events (concerts, protests, marathons, etc.). For civil servants in charge of management, in-advance attendance prevision, real-time situational awareness and its evolution forecasting are crucial to resource assignment. These massive events put under stress public resources, organization and safety of smart cities. In this paper, we describe an ongoing effort to model urban layout, sensors deployed, and citizen information (from social networks and smartphone application) to deal with these situations. We use the concept of a digital twin applied to a city by modelling different flows of information which are integrated with a 3D virtual representation with forecasting possibilities. The main contribution of this paper is the architecture proposed and GUI using the augmented virtuality concept. The main purpose of our proposal is to facilitate the knowledge of the situation and the management of this type of event.
Analysis And Improvement of AES Key Expansion Algorithm

Based on the analysis of the Advanced Encryption Standard (AES) algorithm and the existing improved algorithms, the AES key extension algorithm is improved and optimized to address the shortcomings of the existing traditional algorithm and the improved algorithm, i.e., the strong correlation between the round keys of the AES key extension algorithm brings a layer of security risks to the AES algorithm as a whole. The three improvement ideas of irreversible improvement strategy, introduction of random number strategy and word shift strategy and the combination of the three ideas are proposed. The three improvement ideas are designed from three perspectives of reducing the correlation between round keys, reducing the correlation between round keys and optimizing the running time of the algorithm, and the corresponding three algorithm models are designed and the three improvement algorithms are simulated for several times. By analyzing the security and efficiency of the algorithm theoretical and experimental results, it is shown that the improved algorithm can ensure the efficient operation of the algorithm on the premise of ensuring the security of the key expansion algorithm and improve the overall anti-attack performance of the AES algorithm to a certain extent.
CNN-based Euler’s Elastica Inpainting with Deep Energy and Deep Image Prior

Euler’s elastica constitute an appealing variational image inpainting model. It minimises an energy that involves the total variation as well as the level line curvature. These components are transparent and make it attractive for shape completion tasks. However, its gradient flow is a singular, anisotropic, and nonlinear PDE of fourth order, which is numerically challenging: It is difficult to find efficient algorithms that offer sharp edges and good rotation invariance. As a remedy, we design the first neural algorithm that simulates inpainting with Euler’s Elastica. We use the deep energy concept which employs the variational energy as neural network loss. Furthermore, we pair it with a deep image prior where the network architecture itself acts as a prior. This yields better inpaintings by steering the optimisation trajectory closer to the desired solution. Our results are qualitatively on par with state-of-the-art algorithms on elastica-based shape completion. They combine good rotation invariance with sharp edges. Moreover, we benefit from the high efficiency and effortless parallelisation within a neural framework. Our neural elastica approach only requires 3 × 3 central difference stencils. It is thus much simpler than other well-performing algorithms for elastica inpainting. Last but not least, it is unsupervised as it requires no ground truth training data.
Privacy Protected Preservation of Electric Vehicles’ Data in Cloud Computing Using Secure Data Access Control

Cloud computing provides a ubiquitous data storage and access mechanism for organizations, industries, and smart grids to facilitate their operations. However, the concern in cloud storage systems is to secure data access control toward authentication for sensitive data, such as the electric vehicles (EVs) requesting information for attending a charging service. Consequently, denying an authentic user’s request will result in delaying the requested service, thereby leading to service inefficiency. The role-based access control (RBAC) plays a crucial role in securing and authenticating such time-sensitive data. The design mechanism of roles is based on skills, authority, and responsibilities for organizations. For EVs, the roles are based on the type of membership, such as permanent, occasional, or one-time. In this paper, we propose a new RBAC access control and privacy-preserving information access method toward the coarse-grained measure control and privacy protection in the cloud storage system for EVs. The data can be encrypted and decrypted based on the types of users who possess appropriate access permission toward authorized and unauthorized users according to their roles specified by role-based access control policies. The proposed approach has been simulated with various role-based scenarios, and the efficiency was evaluated against state-of-the-art role-based access-control techniques.
One-Cycle Pruning: Pruning Convnets With Tight Training Budget

Introducing sparsity in a convnet has been an efficient way to reduce its complexity while keeping its performance almost intact. Most of the time, sparsity is introduced using a three-stage pipeline: 1) training the model to convergence, 2) pruning the model, 3) fine-tuning the pruned model to recover performance. The last two steps are often performed iteratively, leading to reasonable results but also to a time-consuming process. In our work, we propose to remove the first step of the pipeline and to combine the two others in a single training-pruning cycle, allowing the model to jointly learn the optimal weights while being pruned. We do this by introducing a novel pruning schedule, named One-Cycle Pruning (OCP), which starts pruning from the beginning of the training, and until its very end. Experiments conducted on a variety of combinations between architectures (VGG-16, ResNet-18), datasets (CIFAR-10, CIFAR-100, Caltech-101), and sparsity values (80%, 90%, 95%) show that not only OCP consistently outperforms common pruning schedules such as One-Shot, Iterative and Automated Gradual Pruning, but also that it drastically reduces the required training budget. More-over, experiments following the Lottery Ticket Hypothesis show that OCP allows to find higher quality and more stable pruned networks.
An opinion mining methodology to analyse games for health

Despite the positive impact of games for health on players’ health, users tend to stop playing them after a short period of time, leading benefits to fade. It is therefore important to understand how to sustain interest and, in this way, preserve the health benefits of games for health. This could be achieved by continuously reviewing user feedback after product launch and using this information to inform (re)design and better address user needs. With the growth of social media, user opinions became widely available in public forums. This abundance of information affords us the possibility of, through the application of natural language processing and sentiment analysis techniques, tapping into user opinions and automatically analysing and extracting knowledge from them. This paper introduces a methodology that analyses user comments posted on YouTube about the Just Dance game, to automatically extract information about Usability, User Experience (UX), and Perceived Health Impacts related to Quality of Life (H-QoL). In doing so, the methodology uses a pre-established vocabulary, based on the English lexicon and its semantic relations, to annotate the presence of 38 concepts (five of Usability, 18 of UX, and 15 of H-QoL) and to analyse sentiment. The results of the information extraction and processing are displayed on a dashboard that allows for the exploration and browsing of the results, which can be useful to better understand the opinions and impacts perceived by users and to inform the (re)design of games for health. The methodology proposed builds upon over 500,000 user comments collected from over 32,000 videos.
Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments

A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times. MRL involves learning reliable and robust representations of information from heterogeneous sources and fusing them. However, in practice, the data acquired from different sources are typically noisy. In some extreme cases, a noise of large magnitude can completely alter the semantics of the data leading to inconsistencies in the parallel multimodal data. In this paper, we propose a novel method for multimodal representation learning in a noisy environment via the generalized product of experts technique. In the proposed method, we train a separate network for each modality to assess the credibility of information coming from that modality, and subsequently, the contribution from each modality is dynamically varied while estimating the joint distribution. We evaluate our method on two challenging benchmarks from two diverse domains: multimodal 3D hand-pose estimation and multimodal surgical video segmentation. We attain state-of-the-art performance on both benchmarks. Our extensive quantitative and qualitative evaluations show the advantages of our method compared to previous approaches.
Head Gestures Based Movement Control of Electric Wheelchair for People with Tetraplegia

People who have suffered a spinal cord injury (SCI) may experience temporary or permanent loss of motor function, sensory function, and autonomic function. Tetraplegia patients are only able to move their upper body parts, such as the head, neck, and shoulder. They require wheelchair assistance to move around for their daily activities. The existing electric wheelchairs, on the other hand, rely on the users' upper arm for control which makes it difficult for the tetraplegia patients to control it. To address this issue, this project developed a control system in which control can be performed by head gesture. A gyro accelerometer is used to detect the user's head gesture. A microcontroller connected to the sensor will read the data and translate it into instructions to control the movement of the electric wheelchair based on the pre-defined head motion patterns. To obtain an average test result of the system's functionality, the system was tested on a healthy adult subject. The average maneuvering error of the trial run using electric wheelchair model on the smooth surface was 3.18cm and an average 5.2cm on the rough tar road surface. Thus, the developed control system can be assumed to be effective in detecting head gesture and that it accurately maneuvers the electric wheelchair according to the head gesture pattern.
Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm

Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot.
Evolutionary Multitask Optimization: Fundamental research questions, practices, and directions for the future

Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years.It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds.However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date.This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved.Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: (i) the plausibility and practical applicability of this paradigm; (ii) the novelty of some proposed multitasking methods; and (iii) the methodologies used for evaluating newly proposed multitasking algorithms.As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track.Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.
Quasi-Equilibrium Feature Pyramid Network for Salient Object Detection

Modern saliency detection models are based on the encoder-decoder framework and they use different strategies to fuse the multi-level features between the encoder and decoder to boost representation power. Motivated by recent work in implicit modelling, we propose to introduce an implicit function to simulate the equilibrium state of the feature pyramid at infinite depths. We question the existence of the ideal equilibrium and thus propose a quasi-equilibrium model by taking the first-order derivative into the black-box root solver using Taylor expansion. It models more realistic convergence states and significantly improves the network performance. We also propose a differentiable edge extractor that directly extracts edges from the saliency masks. By optimizing the extracted edges, the generated saliency masks are naturally optimized on contour constraints and the non-deterministic predictions are removed. We evaluate the proposed methodology on five public datasets and extensive experiments show that our method achieves new state-of-the-art performances on six metrics across datasets.
Retracted: Aspect‐based sentiment analysis employing linguistics content over social media for Web of Things

Abstract The above article from IET Communications, published online on 12 November 2022 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
End-to-End Network for Pedestrian Detection, Tracking and Re-Identification in Real-Time Surveillance System

Surveillance video has been widely used in business, security, search, and other fields. Identifying and locating specific pedestrians in surveillance video has an important application value in criminal investigation, search and rescue, etc. However, the requirements for real-time capturing and accuracy are high for these applications. It is essential to build a complete and smooth system to combine pedestrian detection, tracking and re-identification to achieve the goal of maximizing efficiency by balancing real-time capture and accuracy. This paper combined the detector and Re-ID models into a single end-to-end network by introducing a new track branch to YOLOv5 architecture for tracking. For pedestrian detection, we employed the weighted bi-directional feature pyramid network (BiFPN) to enhance the network structure based on the YOLOv5-Lite, which is able to further improve the ability of feature extraction. For tracking, based on Deepsort, this paper enhanced the tracker, which uses the Noise Scale Adaptive (NSA) Kalman filter to track, and adds adaptive noise to strengthen the anti-interference of the tracking model. In addition, the matching strategy is further updated. For pedestrian re-identification, the network structure of Fastreid was modified, which can increase the feature extraction speed of the improved algorithm by leaps and bounds. Using the proposed unified network, the parameters of the entire model can be trained in an end-to-end method with the multi-loss function, which has been demonstrated to be quite valuable in some other recent works. Experimental results demonstrate that pedestrians detection can obtain a 97% mean Average Precision (mAP) and that it can track the pedestrians well with a 98.3% MOTA and a 99.8% MOTP on the MOT16 dataset; furthermore, high pedestrian re-identification performance can be achieved on the VERI-Wild dataset with a 77.3% mAP. The overall framework proposed in this paper has remarkable performance in terms of the precise localization and real-time detection of specific pedestrians across time, regions, and cameras.