Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption

Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server. Although FL preserves local data privacy to some extent, it has been shown that information about clients’ data can still be inferred from model updates. In recent years, various privacy-preserving schemes have been developed to address this privacy leakage. However, they often provide privacy at the expense of model performance or system efficiency, and balancing these tradeoffs is a crucial challenge when implementing FL schemes. In this manuscript, we propose a Privacy-Preserving Federated Learning (PPFL) framework built on the synergy of matrix encryption and system immersion tools from control theory. The idea is to immerse the learning algorithm — a Stochastic Gradient Decent (SGD) — into a higher-dimensional system (the so-called target system) and design the dynamics of the target system so that: trajectories of the original SGD are immersed/embedded in its trajectories; and it learns on encrypted data (here we use random matrix encryption). Matrix encryption is reformulated at the server as a random change of coordinates that maps original parameters to a higher-dimensional parameter space and enforces that the target SGD converges to an encrypted version of the original SGD optimal solution. The server decrypts the aggregated model using the left inverse of the immersion map. We show that our algorithm provides the same level of accuracy and convergence rate as the standard FL with a negligible computation cost while revealing no information about the clients’ data.
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning

We propose Deterministic Sequencing of Exploration and Exploitation (DSEE) algorithm with interleaving exploration and exploitation epochs for model-based RL problems that aim to simultaneously learn the system model, i.e., a Markov decision process (MDP), and the associated optimal policy. During exploration, DSEE explores the environment and updates the estimates for expected reward and transition probabilities. During exploitation, the latest estimates of the expected reward and transition probabilities are used to obtain a robust policy with high probability. We design the lengths of the exploration and exploitation epochs such that the cumulative regret grows as a sub-linear function of time.
Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous underwater robot swarms

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.
Autonomous navigation of interconnected tethered drones in a partially known environment with obstacles

Systems of tethered multicopters are multi-copter drones connected one to the other by an electric tether providing energy and communication. They are capturing the interest of researchers and industry due to their versatility and prolonged flight time. The tether enables continuous power supply from ground and fast, reliable, all-to-all communication. Nevertheless, it couples the vehicles, introducing range limitations and challenging control and navigation problems, in particular in presence of obstacles that are not fully known a-priori. To solve these challenges, a new approach based on a combination of off-line and real-time optimization is proposed. An off-line mission planning method is used to find optimal configurations for these peculiar systems in the nominal environment, guaranteeing safety with regards to the presence of the tether and obstacles. Furthermore, an on-line, real-time, reactive tracking algorithm based on Model Predictive Control is presented to bring the system to the aforementioned optimal configuration, coping with the presence of unexpected static obstacles. Such procedure also ensures collision avoidance, both for the vehicles and for the tether connecting them. The mentioned contributions are validated through simulation with realistic models of the drones and tethers.
XAIoT - The Future of Wearable Internet of Things

The addition of Machine Learning and other Artificial Intelligent (AI) algorithms has expanded the capabilities of the Internet of Things (IoT) framework. However, the Artificial Internet of Things (AIoT), has also brought forward many issues with the combination, mainly lack of explanations and transparency. This lack of explanations of what AI models are doing is problematic in many fields, especially in the medical field. Explainable Artificial Intelligence (XAI) allows users to be given more in depth knowledge with the background processes of the model prediction. Enabling XAI into the IoT framework would develop a system that would not only capture Big Data more effectively, but also allow the system to be more transparent and explainable, causing wider adoption of the framework. This review focuses on current work done in AIoT and how it can be vastly improved with XAIoT. We propose an Explainable Artificial Intelligent Internet of Things (XAIoT) framework for monitoring physiological health using a smart watch.
A Digital Twin Enabled Internet of Living Things (IoLT) Framework for Soil Carbon Management

Due to the lack of cost-effective methods for soil carbon content accounting, soil carbon emissions cannot be managed properly for a long time. The traditional methods are labor intensive which usually need tedious preprocessing and expensive analyzers to quantify the total soil carbon content. In this paper, we propose that a Digital Twin with AIoLT framework could perfectly solve the challenge mentioned above. By using an AIoLT-enabled proximity radar sensor to measure soil carbon content in real-time, our proposed soil carbon management digital twin (SCMDT) has the ability to deal with these soil carbon data to be “smart” big data, which are from missionaware sampling strategies. We also provide an evaluation metric for our proposed SCMDT in order to quantify its performance.
End-to-End Mobile Robot Navigation using a Residual Deep Reinforcement Learning in Dynamic Human Environments

Safe navigation through human crowds is key to enabling practical mobility ubiquitously. The Deep Reinforcement Learning (DRL) and the End-to-End (E2E) approaches to goal-oriented robot navigation have the potential to render policies able to tackle localization, path planning, obstacle avoidance, and adaptation to change in unison. In this paper, we report an architecture based on convolutional units and residual blocks being able to enhance adaptability to unseen and dynamic human environments. In particular, our scheme outperformed the state-of-the-art baselines SOADRL and NAVREP by about 13% and 18% on average success rate, respectively, throughout 27 unseen and dynamic navigation instances. Furthermore, our approach avoids the explicit encoding of positions and trajectories of moving humans compared to the standard models. Our results show the potential to render adaptive and generalizable policies for unknown and dynamic human environments.
Reducing Microservices Interference and Deployment Time in Resource-Constrained Cloud Systems

In resource-constrained cloud systems, e.g., at the network edge or in private clouds, it is essential to deploy microservices (MSs) efficiently. Unlike most of the existing approaches, we tackle this issue by accounting for two important facts: (i) the interference that arises when MSs compete for the same resources and degrades their performance, and (ii) the MSs’ deployment time. In particular, we first present some experiments highlighting the impact of interference on the throughput of MSs co-located in the same server, as well as the benefits of MSs’ parallel deployment. Then, we formulate an optimization problem that minimizes the number of used servers while meeting the MSs’ performance requirements. In light of the problem complexity, we design a low-complexity heuristic, called iPlace, that clusters together MSs competing for resources as diverse as possible and, hence, interfering as little as possible. Importantly, clustering MSs also allows us to exploit the benefit of parallel deployment, which greatly reduces the deployment time as compared to the sequential approach applied in prior art and by default in state-of-the-art orchestrators. Our numerical results show that iPlace closely matches the optimum and uses 21-92% fewer servers compared to alternative schemes while proving to be highly scalable. Further, by deploying MSs in parallel using Kubernetes, iPlace reduces the deployment time by 69% compared to state-of-the-art solutions.
Multimodal Fusion Framework Based on Statistical Attention and Contrastive Attention for Sign Language Recognition

Sign language recognition (SLR) enables hearing-impaired people to better communicate with able-bodied individuals. The diversity of multiple modalities can be utilized to improve SLR. However, existing multimodal fusion methods do not take into account multimodal interrelationships in-depth. This paper proposes SeeSign: a multimodal fusion framework based on statistical attention and contrastive attention for SLR. The designed two attention mechanisms are used to investigate intra-modal and inter-modal correlations of surface Electromyography (sEMG) and inertial measurement unit (IMU) signals, and fuse the two modalities. Statistical attention uses the Laplace operator and lower quantile to select and enhance active features within each modal feature clip. Contrastive attention calculates the information gain of active features in a couple of enhanced feature clips located at the same position in two modalities. The enhanced feature clips are then fused in their positions based on the gain. The fused multimodal features are fed into a Transformer-based network with connectionist temporal classification and cross-entropy losses for SLR. The experimental results show that SeeSign has accuracy of 93.17% for isolated words, and word error rates of 18.34% and 22.08% on one-handed and two-handed sign language datasets, respectively. Moreover, it outperforms state-of-the-art methods in terms of accuracy and robustness.
A New Chaos-Based PRNG Hardware Architecture Using the HUB Fixed-Point Format

Chaotic systems have been applied in many applications involving instrumentation and measurements, such as in sensors and control systems, due to their pseudorandom proprieties. However, reproducing chaos in digital systems is challenging because of the dynamical degradation of chaotic digital systems and, consequently, their intrinsic periodic orbits. Many techniques have been used to guarantee a sufficiently large period, making them suitable for such applications. Nevertheless, few articles pay attention to the effects of using different numerical representations on chaos degradation and, at the same time, keeping key design parameters, such as few logical resources and power consumption. Thus, this article aims to provide a new hardware architecture for a chaos-based pseudorandom number generator (PRNG) using the Half-Unit-Biased (HUB) format for fixed-point numbers by bi-coupling the tent map in conjunction with the Bernoulli map, causing a significant impact on its logical resources and performance. Results show that the HUB format is more effective than the standard fixed-point numerical representation and that the proposed approach is chaotic, with pseudorandomness.
RETRACTED: Theory and method for evaluating the importance of college course teaching for future education: From virtual reality to metaverse

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219329.
Proposed spatio‐temporal features for human activity classification using ensemble classification model

Summary Classifying human actions from still images or video sequences is a demanding task owing to issues, like lighting, backdrop clutter, variations in scale, partial occlusion, viewpoint, and appearance. A lot of appliances, together with video systems, human–computer interfaces, and surveillance necessitate a compound action recognition system. Here, the proposed system develops a novel scheme for HAR. Initially, filtering as well as background subtraction is done during preprocessing. Then, the features including local binary pattern (LBP), bag of the virtual word (BOW), and the proposed local spatio‐temporal features are extracted. Then, in the recognition phase, an ensemble classification model is introduced that includes Recurrent Neural networks (RNN 1 and RNN 2) and Multi‐Layer Perceptron (MLP 1 and MLP 2). The features are classified using RNN 1 and RNN 2, and the outputs from RNN 1 and RNN 2 are further classified using MLP 1 and MLP 2, respectively. Finally, the outputs attained from MLP 1 and MLP 2 are averaged and the final classified output is obtained. At last, the superiority of the developed approach is proved on varied measures.
An Image-Encipherment Algorithm Using a Combination of a One-Dimensional Chaotic Map and a Three-Dimensional Piecewise Chaotic Map

One-dimensional and three-dimensional piecewise chaotic maps are used to propose an image-encipher technique in this article. First, the logistic map is used to construct the pseudo-random sequence. After that, this sequence is used to scramble the plain image. Next, the three-dimensional piecewise chaotic map has produced a mask of the chaotic sequence. After doing some preprocessing steps on the mask, a bit-wise XOR operation with the mask is applied to the shuffled image. The suggested algorithm is used to encipher and decipher a different range of images. To check the algorithm security and efficiency, the algorithm performance was calculated using multiple statistical tests and compared to several recent algorithms. Furthermore, numerical simulations and experimental data are also used to validate the proposed algorithm’s resistance to various attacks.
Dark web <i>jihad</i>: exploring the militant Islamist information ecosystem on The Onion Router

This paper maps communication channels exploited by the Salafi-jihadist violent extremist organisations (VEOs) and their followers between March 2020 and June 2022 on The Onion Router (TOR). It argues that the true scale of digital jihadist presence on TOR has remained insignificant for years. Militant Islamists have mostly used .onion domains as backup propaganda dissemination channels, which enable content takedown policies introduced by countering violent extremism stakeholders to be circumvented. Aside from propaganda distribution, TOR attracts Salafi-jihadist VEOs and their followers for other reasons, as it facilitates anonymous communication, crowdfunding, sharing of terrorist manuals or the organisation of terrorist attacks.
Stiffening Cello Bridges with Design

In instruments of the violin family, the bridge is the part in charge of transferring the vibrational energy of the strings into the body and therefore contributes greatly to the sound of the instrument. The bridge needs to be light enough to efficiently transmit the strings’ movement yet rigid enough to support the static load of the strings. Historically, there have been several attempts at solving this problem with different designs, arriving in the early 1800s at the two current models: the French and the Belgian. Recently, in Cremona, Italy, the Amorim family of luthiers has developed a new cello bridge design. Inspired by their work, we study the influence of the shape of the legs of the cello bridge on its static and vibrational behavior through parametric modeling and simulations using the Finite Element Method. In particular, we perform displacement and modal analysis for different boundary conditions, providing in addition a detailed description of the mode shapes. We also compute and compare Frequency Response Functions for the different geometries. Our results show that shape can indeed be used to control the vibrational and static responses of the cello and consequently tune its sound.
Modified Kleene Star Algorithm Using Max-Plus Algebra and Its Application in the Railroad Scheduling Graphical User Interface

In max-plus algebra, some algorithms for determining the eigenvector of irreducible matrices are the power algorithm and the Kleene star algorithm. In this research, a modified Kleene star algorithm will be discussed to compensate for the disadvantages of the Kleene star algorithm. The Kleene star algorithm’s time complexity is O(n(n!)), and the new Kleene star algorithm’s time complexity is O(n4), while the power algorithm’s time complexity cannot be calculated. This research also applies max-plus algebra in a railroad network scheduling problem, constructing a graphical user interface to perform schedule calculations quickly and easily.
An accurate and adaptable deep learning-based solution to floating litter cleaning up and its effectiveness on environmental recovery

Ending water pollution is urgent for environmental recovery globally. Floating litter cleaning up solution, as an essential strategy for reducing pollution, is formulated in a few studies. In autonomous waste collection systems like unmanned surface vehicles (USV), floating litter cleaning up is very challenging on natural water surfaces due to the complex environmental factors seriously degrading litter detection accuracy, lowering efficiency, or even causing the failure of collection. The impact of the cleaning-up solution on environmental recovery has yet to be discussed. To fill knowledge gaps, we propose an accurate and adaptable deep learning solution based on Faster RCNN to address this challenge through the first-time implementation of the attention mechanism at the C3 stage of ResNet50 to effectively extract the useful feature information and compress the negative influence of the complex environmental factors. We here first discuss the effectiveness of our cleaning-up solution in water pollution-reducing and waste recycling for resource-saving, to the best of our knowledge. The extensive experimental results on our self-built dataset show that our solution is superior to the state of the arts in the accuracy and adaptability of floating litter detection under the different complex scenes. Our solution improves the effectiveness of floating waste collection a lot. It significantly contributes to pollution mitigation, ecosystem, resource-saving, and human health. It is suggested that stakeholders should pay more attention to improving cleaning-up solutions. This work strives to shed light on it based on deep learning for environmental recovery and sustainability.
High-NA polarization-independent isotropic spatial differential metasurface

Metasurface-based optical computing has been a topic of interest for many researchers, aimed at implementing high-speed and efficient image processing in small equipment. However, the existing designs cannot work well in the aspects of spatial resolution, polarization and azimuth angle simultaneously. Here, we present a 2D differential metasurface with a honeycomb photonic structure as the basic unit, which not only satisfies isotropy and polarization independence, but also has a NA as high as 0.44. Through numerical verification, the proposed metasurface can produce the result of second-order differential operation of Gaussian functions in 1D space, and can be used to implement edge detection of objects in an image as well. Our results provide new opportunities for optical analog computing and computer vision applications.
Multi-Agent Path Finding on Strongly Connected Digraphs

On an assigned graph, the problem of Multi-Agent Pathfinding (MAPF) consists in finding paths for multiple agents, avoiding collisions. Finding the minimum-length solution is known to be NP-hard, and computation times grows exponentially with the number of agents. However, in industrial applications, it is important to find feasible, suboptimal solutions, in a time that grows polynomially with the number of agents. Such algorithms exist for undirected and biconnected directed graphs. Our main contribution is to generalize these algorithms to the more general case of strongly connected directed graphs. In particular, given a MAPF problem with at least two holes, we present an algorithm that checks the problem feasibility in linear time with respect to the number of nodes, and provides a feasible solution in polynomial time.
Finite-Time Privacy-Preserving Quantized Average Consensus with Transmission Stopping

Due to their flexibility, battery powered or energy-harvesting wireless networks are deployed in diverse applications. Securing data transmissions between wireless devices is of critical importance in order to avoid privacy-sensitive user data leakage. In this paper, we focus on the scenario where some nodes are curious (but not malicious) and try to identify the initial states of one (or more) other nodes, while some other nodes aim to preserve the privacy of their initial states from these curious nodes. We present a privacy-preserving finite transmission event-triggered quantized average consensus algorithm. Its operation is suitable for battery-powered or energy-harvesting wireless network since it guarantees (i) efficient (quantized) communication, and (ii) transmission ceasing (which allows preservation of available energy). Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy. We conclude with a comparison of our algorithm against other algorithms in the existing literature.