Reviewerly/exHarmony huggingface dataset:
A Joint Siamese Attention-Aware Network for Vehicle Object Tracking in Satellite Videos

Remote sensing object tracking is a novel and challenging problem due to the negative effects of weak features and background noise. In this paper, from the perspective of attention-focus deep learning, we propose a Joint Siamese Attention-Aware Network (JSANet) for efficient remote sensing tracking which contains both self-attention and cross-attention modules. First, the self-attention modules we propose emphasize the interdependent channel-wise coefficient via channel attention and conduct corresponding space transformation of spatial domain information with spatial attention. Second, the cross-attention is designed to aggregate rich contextual interdependencies between the siamese branches via channel attention and excavate association produces reliable correspondence with spatial attention. In addition, a composite feature combine strategy is designed to fuse multiple attention features. Experimental results on the Jilin-1 satellite video datasets demonstrate that the proposed JSANet achieves state-of-the-art performance in terms of precision and success rate, demonstrate the effectiveness of the proposed methods.
Deep Reinforcement Learning-Based Self-Scheduling Strategy for a CAES-PV System Using Accurate Sky Images-Based Forecasting

Compressed air energy storage (CAES) is a scalable and clean energy storage technology with great potential in renewables accommodation. From the point of view of the facility owner participating in the energy market, the profit of a CAES-PV system’s coordinated operation is still at a notable risk. This paper addresses this problem by using a novel model-free deep reinforcement learning (DRL) method to optimize the CAES energy arbitrage in the presence of a sky images-based short-term solar irradiance forecasting model. To overcome the risk associated with the highly intermittent solar power productions, and thus efficient participation in an electricity market, a hybrid forecasting model based on 2-D convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) units is developed to capture high levels of abstractions in solar irradiance data, especially during cloudy days. Moreover, the thermodynamic characteristics of the CAES facility are considered to achieve more realistic real-time scheduling results. The comparative results based on a realistic-based case study demonstrate the effectiveness and applicability of the proposed framework compared to the state-of-the-art methods in the recent literature.
An Empirical Study of Reward Explanations With Human-Robot Interaction Applications

Explainable AI techniques that describe agent reward functions can enhance human-robot collaboration in a variety of settings. However, in order to effectively explain reward information to humans, it is important to understand the efficacy of different types of explanation techniques in scenarios of varying complexity. In this letter, we compare the performance of a broad range of explanation techniques in scenarios of differing reward function complexity through a set of human-subject experiments. To perform this analysis, we first introduce a categorization of reward explanation information types and then apply a suite of assessments to measure human reward understanding. Our findings indicate that increased reward complexity (in number of features) corresponded to higher workload and decreased reward understanding, while providing direct reward information was an effective approach across reward complexities. We also observed that providing full or near full reward information was associated with increased workload and that providing abstractions of the reward was more effective at supporting reward understanding than other approaches (besides direct information) and was associated with decreased workload and improved subjective assessment in high complexity settings.
Weakly Supervised Video Anomaly Detection via Transformer-Enabled Temporal Relation Learning

Weakly supervised video anomaly detection is a challenging problem due to the lack of refined frame-level labels in training videos. Most prior works typically address it with the multiple instance learning paradigm, which divides a video into multiple snippets and trains a snippet classifier to distinguish anomalies from normal snippets via video-level classification loss. However, these solutions are limited in the insufficient representations. In this paper, we propose a novel weakly supervised temporal relation learning framework for anomaly detection, which efficiently explores the temporal relation between snippets and enhances the discriminative powers of features using only video-level labelled videos. To this end, we design a transformer-enabled feature encoder to convert the input task-agnostic features into discriminative task-specific features by mining the semantic similarity and position relation between snippets. As a result, our model can make a more accurate anomaly detection for current video snippet based on the learned discriminative features. Experimental results indicate that the proposed method is superior to existing state-of-the-art approaches, which demonstrates the effectiveness of our model.
A Framework for Analyzing Crime Dataset in R Using Unsupervised Optimized K-means Clustering Technique

At present, the criminals are becoming more and more sophisticated in committing any sort of crime. Nowadays, the intelligence, law enforcement agencies, and the police department are facing issues in analyzing large volumes of data and classifying the crimes separately. Analysis of crime is very important, so that we can identify patterns or trends in the crimes committed. For this reason, we can use a data mining unsupervised technique known as K-means clustering. Data mining is the process of extracting unknown knowledge from a small or huge dataset, data warehouse, or repositories. Clustering is a process in which the data items are grouped based on a specific attribute. K-means clustering is done based on the means of the data items. In this paper, one can understand about K-means clustering, the procedure, and implementation of the clustering method. This system can be used for analyzing the crimes, understanding the trends and the most crime-prone places.
From Simulation to Reality: A Learning Framework for Fish-Like Robots to Perform Control Tasks

The fish-like robot is one of the typical underwater robots, which has the advantage of high maneuverability with low noise due to its bioinspired structure and biomimetic locomotion. However, it is challenging to efficiently design motion controllers for such robots to achieve satisfactory performance on specific control tasks in the real underwater environment, since the complex fluid-structure interaction exists during their swimming and exact dynamic models are absent. In this article, we propose a learning framework, incorporating a simulation system and a training methodology, to autonomously and fast train in simulation to create control policies that are capable of directly applying to a type of physical fish-like robots to perform motion control tasks. First, we construct a simulation system combining a data-driven environment and a computational fluid dynamics (CFD)-based environment, thus well balancing the simulation accuracy and the calculation speed. Second, we design a training methodology to train deep reinforcement learning (DRL)-based policies for the robot in our constructed simulation system to perform a specific control task. Then, we use two typical motion control tasks to verify our proposed framework. One is the path-following control task, which is a one-objective problem with dense rewards, while the other is the pose control task which is a two-objective problem with sparse rewards. For each task, the DRL-based control policy trained by our learning framework is directly deployed on the physical fish-like robot to perform the task in the real world. Experimental results show that the policies trained in simulation still work well in the real world, and perform even better in terms of control accuracy and stability compared with the traditional control methods, thus demonstrating the effectiveness of our learning framework.
Enabling Trust and Privacy-Preserving e-KYC System Using Blockchain

The electronic know your customer (e-KYC) is a system for the banking or identity provider to establish a customer identity data verification process between relying parties.Due to the efficient resource consumption and the high degree of accessibility and availability of cloud computing, most banks implement their e-KYC system on the cloud.Essentially, the security and privacy of e-KYC related documents stored in the cloud becomes the crucial issue.Existing e-KYC platforms generally rely on strong authentication and apply traditional encryption to support their security and privacy requirement.In this model, the KYC system owner encrypts the file with their host's key and uploads it to the cloud.This method induces encryption dependency and communication and key management overheads.In this paper, we introduce a novel blockchain-based e-KYC scheme called e-KYC TrustBlock based on the public key encryption method binding with the client consent enforcement to deliver trust, security and privacy compliance.In addition, we introduce attribute-based encryption to enable the privacy preserving and fine-grained access of sensitive transactions stored in the blockchain.Finally, we conduct experiments to show that our system is efficient and scalable in practice.
MsanlfNet: Semantic Segmentation Network With Multiscale Attention and Nonlocal Filters for High-Resolution Remote Sensing Images

With the development of deep learning, remote sensing image semantic segmentation has produced significant advances. The majority of existing methods use fully convolutional network (FCN) that lacks fine-grained multi-scale representation and fails to extract global context information. Thus, we improve FCN by adding two modules&#x2014;multi-scale attention (MSA) and non-local filter (NLF). The MSA module enhances the network&#x2019;s fine-grained multi-scale representation capability and allows modeling the inter-dependencies of feature maps among different channels. The NLF module can capture global context information by sequential using fast Fourier transform, parameter learnable filters and inverse fast Fourier transform. By using MSA module for encoder and NLF module for decoder in the FCN framework, MsanlfNet can obtain both fine-grained multi-scale spatial feature and global context information, thus achieving a balance between performance and computational effort. Experimental results on the remote sensing semantic segmentation public data sets demonstrate that our method can achieve better performance. The code is available at https://github.com/xyuanLin/MsanlfNet.
Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

The goal of this paper is to develop a continuous optimization-based refinement of the reference trajectory to ‘push it out’ of the obstacle-occupied space in the global phase for Multi-rotor Aerial Vehicles in unknown environments. Our proposed approach comprises two planners: a global planner and a local planner. The global planner refines the initial reference trajectory when the trajectory goes either through an obstacle or near an obstacle and lets the local planner calculate a near-optimal control policy. The global planner comprises two convex programming approaches: the first one helps to refine the reference trajectory, and the second one helps to recover the reference trajectory if the first approach fails to refine. The global planner mainly focuses on real-time performance and obstacles avoidance, whereas the proposed formulation of the constrained nonlinear model predictive control-based local planner ensures safety, dynamic feasibility, and the reference trajectory tracking accuracy for low-speed maneuvers, provided that local and global planners have mean computation times 0.06 s (15 Hz) and 0.05 s (20 Hz), respectively, on an NVIDIA Jetson Xavier NX computer. The results of our experiment confirmed that, in cluttered environments, the proposed approach outperformed three other approaches: sampling-based pathfinding followed by trajectory generation, a local planner, and graph-based pathfinding followed by trajectory generation.
A Comparative Analysis of GAN-Based Methods for SAR-to-Optical Image Translation

Unlike optical sensors, Synthetic Aperture Radar (SAR) sensors acquire images of the Earth&#x2019;s surface with all-weather and all-time capabilities, which is vital in a situation such as a disaster assessment. However, SAR sensors do not offer as rich visual information as optical sensors. SAR-to-Optical image-to-image translation generates optical images from SAR images to benefit from what both imaging modalities have to offer. It also enables multi-sensor image analysis of the same scene for applications such as heterogeneous change detection. Various architectures of Generative Adversarial Networks (GANs) have achieved remarkable image-to-image translation results in different domains. Still, their performances in SAR-to-Optical image translation have not been analyzed in the remote sensing domain. This paper compares and analyses the state-of-the-art GAN-based translation methods with open-source implementations for SAR-to-Optical image translation. The results show that GAN-based SAR-to-Optical image translation methods achieve satisfactory results. However, their performances depend on the structural complexity of the observed scene and the spatial resolution of the data. We also introduce a new dataset with a higher resolution than the existing SAR-to-Optical image datasets and release implementations of GAN-based methods considered in this paper to support the reproducible research in remote sensing.
Efficient Watermarking Method Based on Maximum Entropy Blocks Selection in Frequency Domain for Color Images

False-positive problem (FPP) is a one of the challenging tasks for the researchers. It authenticates the wrong owner to access the multimedia content. To overcome, the FPP problem, this paper introduces an efficient watermarking method based on the selection of highest entropy blocks. In this method, cover and watermark images are initially shuffled through Arnold transform. Then, the encrypted images are further processed by a 2-level discrete wavelet transform followed by singular value decomposition. The proposed method has been evaluated with geometrical, filtering, noise, and contrast adjustment attacks on the standard image datasets against five recently developed watermarking methods. The simulation results reveal that the proposed method outperforms the existing methods.
A Novel Cable-Driven Parallel Robot With Movable Anchor Points Capable for Obstacle Environments

Cable-driven parallel robots (CDPRs) have the characteristics of large workspace and fast response speed, which attracted lots of interests in the past decade. Most of the CDPRs use flexible cables to manipulate the moving platform while the length of cables is changed by the winches fixed on the base. Consequently, interferences among the driving cables, the moving platform, and the environment become inevitable problems, which significantly limit the effective workspace of CDPRs and its performance in complex multiobstacle environment. This article proposes a novel CDPR with movable anchor points (M-CDPR). The movable anchor point of each driving cable can move along the track on the fixed frames. Besides, we establish the kinematics and dynamics of the M-CDPR, and propose the collision detection method and the obstacle avoidance algorithm. Simulation analyzes indicate that the wrench-feasible workspace of the M-CDPR increases 456.06% compared with traditional CDPRs in the same dimension. Moreover, the M-CDPR can realize obstacle avoidance without changing the desired trajectory under different conditions when there exist multiple static obstacles, or both moving and static obstacles. In addition, a prototype is fabricated, consisting of eight driving cables and eight movable anchor points. Experimental results show that the proposed M-CDPR performed well in an environment containing a number of static and/or moving obstacles without any interference.
BabyPose: Real-Time Decoding of Baby’s Non-Verbal Communication Using 2D Video-Based Pose Estimation

People use various communication methods to interact with each other, such as speech and body movements. However, communication methods in babies differ according to their developmental periods. Babies may not be able to express themselves verbally, but they do have their own communication methods. Naturally, it becomes important for parents to understand these signs/poses. Human pose estimation is extensively employed in different applications such as video surveillance, sports analysis and medical support/aid. The goal of this research is to assist new parents by addressing pose-based real-time body movements of babies (such as arching back, head banging, kicking legs, rubbing eyes, stretching, sucking fingers) and making sense of their activities. This is the first study to estimate poses on babies. In this paper, a baby pose dataset is created from 156 video clips through online video sharing platforms. Key-points are obtained from three different pose estimators - OpenPose, AlphaPose, and KAPAO. Different LSTM models are used to recognize the babies’ activities and different performance metrics are used to compare these models. The best model has 99% accuracy and 0.0712 loss ratio. Also, babies are tracked in real-time via DeepSORT algorithm. Experimental results show that the proposed system is very promising and filling a gap in making sense of baby poses and monitoring them.
Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions

The federated learning framework is designed for massively distributed training of deep learning models among thousands of participants without compromising the privacy of their training datasets. The training dataset across participants usually has heterogeneous data distributions. Besides, the central server aggregates the updates provided by different parties, but has no visibility into how such updates are created. The inherent characteristics of federated learning may incur a severe security concern. The malicious participants can upload poisoned updates to introduce backdoored functionality into the global model, in which the backdoored global model will misclassify all the malicious images (i.e., attached with the backdoor trigger) into a false label but will behave normally in the absence of the backdoor trigger. In this work, we present a comprehensive review of the state-of-the-art backdoor attacks and defenses in federated learning. We classify the existing backdoor attacks into two categories: data poisoning attacks and model poisoning attacks, and divide the defenses into anomaly updates detection, robust federated training, and backdoored model restoration. We give a detailed comparison of both attacks and defenses through experiments. Lastly, we pinpoint a variety of potential future directions of both backdoor attacks and defenses in the framework of federated learning.
Object Tracking in Satellite Videos: A Spatial-Temporal Regularized Correlation Filter Tracking Method With Interacting Multiple Model

Target occlusion is common in satellite videos, which makes object tracking difficult because most state-of-the-art trackers are not robust to occlusion, particularly complete occlusion. In this letter, we propose a novel correlation filter algorithm with an interacting multiple model (IMM) for object tracking in satellite videos that combines the strength of the correlation filter and the IMM. When the target is occluded, we utilize the IMM to predict target position. Therefore, the proposed tracker is robust to occlusion. The experimental results demonstrate that our tracker performs favorably when the target is occluded and achieves excellent performance compared with state-of-the-art methods.
Generative AI and Intellectual Property Rights

Since the inception of AI, researchers have tried to generate novel worksWork in media ranging from musicWorksMusic through text to images. The quality of worksWork produced by generativeAIGenerative AI-systems is starting to reach levels that make them usable in contexts where until now human creations are employed. In addition, new contexts are emerging in which humans unskilled in a creative domain can generate worksWork by cooperating with generative AI-toolsAITools. GenerativeAIGenerative AI could lead to an abundance of individually customized content, where worksWork are generated for a particular user in a specific situation and presented once, perhaps never to be repeated again. These developments challenge core concepts of Intellectual Property Rights: "authorship"Authorship obviously, but also "work"Work. Although the content produced by generative systems is new, these systems are often trained on a corpus of (parts of) existing worksWork produced by humans. Hence, practices of (un)authorised imitation need to be considered. In this chapter we want to study these questions, which are emerging in all creative domains, with generativeAIGenerative AI for musicWorksMusic as the central example.
Accelerating Decentralized Federated Learning in Heterogeneous Edge Computing

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively train AI models without exposing local data. In order to avoid the possible bottleneck of the parameter server (PS) architecture, we concentrate on the decentralized federated learning (DFL), which adopts peer-to-peer (P2P) communication without maintaining a global model. However, due to the intrinsic features of EC, e.g., resource limitation and heterogeneity, network dynamics and non-IID data, DFL with a fixed P2P topology and/or an identical model compression ratio for all workers results in a slow convergence rate. In this paper, we propose an efficient algorithm (termed CoCo) to accelerate DFL by integrating optimization of topology Construction and model Compression. Concretely, we adaptively construct P2P topology and determine specific compression ratios for each worker to conquer the system dynamics and heterogeneity under bandwidth constraints. To reflect how the non-IID data influence the consistency of local models in DFL, we introduce the consensus distance, i.e., the discrepancy between local models, as the quantitative metric to guide the fine-grained operations of the joint optimization. Extensive simulation results show that CoCo achieves 10&#x00D7; speedup, and reduces the communication cost by about 50% on average, compared with the existing DFL baselines.
A Trust and Reputation System for IoT Service Interactions

The rise of the Internet has enabled new types of relationships and online domains where trust must be computed rather than earned. However, classic trust and reputation techniques designed to support computational trust among Internet entities do not meet the scalability requirements and the resource constraints typical of Internet of Things (IoT) devices. This paper provides a general framework for managing computational trust where Blockchain technology and reputation systems are used in conjunction. Our framework relies on a reward-penalty scheme to offer a customizable, secure, and scalable trust architecture for IoT devices and the services they access. We implemented and tested a smart contract-based proof-of-concept, carrying out an analysis of its cost and security to verify the practical viability of our solution.