Reviewerly/exHarmony huggingface dataset:
Communication-Efficient Federated Multi-Task Learning with Sparse Sharing

Federated multi-task learning (FMTL) is a promising technology to deal with the severe data heterogeneity issue in federated learning (FL), where each client learns individual models locally and the server extracts similar model parameters from the tasks to keep personalization for models of clients. Hence, it is essential to precisely extract the model parameters shared among tasks. On the other aspect, the limitation of communication resources would also restrict the model transmission, and thus influence the FMTL performance. To address the above issues, we propose a novel FMTL with Sparse Sharing (FedSS) mechanism that allows clients to share model parameters dynamically according to diversified model structures under limited communication resources. Particularly, we present an adaptive quantization approach for task relevance, which serves as a metric to evaluate the extent of model sharing across tasks. The objective function is formulated to minimize the model transmission latency while ensure the FMTL learning performance via a joint bandwidth allocation and client selection strategy. Closed-form expressions for the optimal client selection and bandwidth allocation are derived based on a alternating direction method of multipliers (ADMM) algorithm. Numerical results show that the proposed FedSS outperforms the benchmarks, and achieves efficient communication performance.
Digital Signature Technology of Mobile Phone Verification Code based on Biometrics

As the most common way of mobile phone identity authentication, verification code has many security risks. In order to enhance its security, this paper proposes a technology which uses biometrics to sign the verification code digitally. Specifically, the user’s biometric value is taken as the private key, by which the verification code from the remote server is signed digitally. Then, the verification code signed is returned to the remote server. In addition, this technology involves biometric extraction, hash function, elliptic curve signature algorithm, and identity based cryptography. Biometrics are unique and irreproducible, and do not require network transmission and key management. Signing the verification code makes it authentic, complete, and undeniable. Analysis results are presented to demonstrate our technology can improve the security and practicality of the verification code.
Alternate Learning based Sparse Semantic Communications for Visual Transmission

Semantic communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data. In this paper, in order to tackle the non-differentiability of channels, we propose an alternate learning based SemCom system for visual transmission, named Spars-eSBC. Specially, SparseSBC leverages two separate Deep Neural Network (DNN)-based models at the transmitter and receiver, respectively, and learns the encoding and decoding in an alternate manner, rather than the joint optimization in existing literature, so as to solving the non-differentiability in the channel. In particular, a "self-critic" training scheme is leveraged for stable training. Moreover, the DNN-based transmitter generates a sparse set of bits in deduced "semantic bases", by further incorporating a binary quantization module on the basis of minimal detrimental effect to the semantic accuracy. Extensive simulation results validate that SparseSBC shows efficient and effective transmission performance under various channel conditions, and outperforms typical SemCom solutions.
TTA-GEP: Terrain Traversability Analysis with Geometry and Environmental Perception for the Path Planning of Planetary Rovers

Terrain traversability analysis (TTA), the key to the navigation of planetary rovers, is significant to the safety of the rover. Therefore, owing to its complexity, the Martian terrain is worth analysing comprehensively based on the terrain variability and hazard level. In this work, we propose a novel method for terrain traversability analysis for the path planning of planetary rovers by integrating Martian terrain geometry features with terrain semantic information, which includes geometry and environmental perception (GEP). Specifically, we deploy semantic segmentation to classify common terrain types, such as rocks, bedrocks, and sand, obtaining semantic information as one part of terrain traversability analysis at the same time. Simultaneously, the point cloud is generated by using binocular images from the planetary rover navigation camera (Navcam) to construct a 2.5D elevation map of the environment to analyse the geometric characteristics of the terrain. Besides, we implement path planning based on the results of TTA-GEP. Overall, our proposed method improves the performance of the terrain traversability analysis and reduces the risk of planetary rovers while detecting in an unstructured environment.
Cloud VR Video Streaming Processing Algorithm Based on Edge Cloud Collaboration

This paper concerns a Virtual Reality(VR) video stream processing based on edge-cloud collaboration. Since VR video streaming demands high resolution and frame rates, current VR terminals often struggle to achieve optimal decoding speeds. This paper proposes a video stream processing algorithm that leverages edge cloud collaboration, combining the strong computing power and large delay of cloud servers with the weak computing power and fast response of edge servers. According to the current communication situation and the computing power distribution situation of the server, the video decoding task is split and allocated to the cloud server and the edge server respectively. The algorithm utilizes the different features of both servers to minimize latency. In the process of task splitting, it is necessary to allocate computing resources to different tasks of edge servers with different computing power. In this paper, the problem of computing resource allocation is modeled as a constrained problem and transformed into an unconstrained problem, which is then optimized by applying the Sequential Least SQuares Programming optimization algorithm(SLSQP). The simulation results demonstrate that the proposed algorithm outperforms existing methods in reducing video transmission time.
Frequency Hopping Signal Detection in Low Signal-to-Noise Ratio Regimes

The detection of unauthorized frequency hopping (FH) signals has several applications in securing the radio frequency spectrum and achieving spectrum awareness in both tactical and cyber-physical systems. However, the blind detection of adversary FH signals is a challenging task, particularly in low signal-to-noise ratio (SNR) regimes, due to the adoption of dynamic hopping patterns. In this study, we propose a cyclo-stationary signal features-based blind FH signal detection scheme to address this challenge. Our proposed scheme consists of two steps: (i) feature extraction, where cyclic features are extracted from the spectral correlation function of the signals, and (ii) feature classification, where the extracted features are associated with ON/OFF detection states using a trained support vector machine (SVM) classifier. We leverage both binary and one-class SVM classifiers to enable adversary FH signal detection with and without pre-existing signal labels. Extensive simulations are conducted to verify the efficacy of the proposed FH signal detection scheme in low SNR regimes. Simulation results also provide insights into the interplay of various system parameters, such as the numbers of cyclic features and emission bandwidth, on the detection performance of the proposed SVM classifiers.
AI-assisted Action in Edge Computing System: A Joint Latency and Accuracy Oriented Approach

Human pose estimation is a crucial problem in computer vision, and it has numerous applications in diverse fields such as virtual reality, surveillance, human-computer interaction, and action assistance. With the advent of edge computing, it is a promising paradigm to perform real-time artificial intelligence (AI)-assisted action based on pose estimation at the edge. However, task scheduling optimization for human pose estimation in edge computing is a challenging problem, due to the limited computing resources. In this paper, we propose a novel framework for task scheduling optimization in human pose estimation at the edge. Our framework takes computing resources scheduling and task scheduling decision into account, with the objective of maximizing the quality of service (QoS) of the system. We use multiple depth cameras at different locations to build three-dimensional (3D) poses to maintain the accuracy of estimation and to assist in guiding action. We evaluate our proposed framework on a real-world dataset. The results demonstrate its effectiveness in improving system delay and estimation accuracy in comparison with benchmark methods. We also verify the sensitivity of our proposed framework, which can provide insights into optimal parameter settings for different scenarios.
TDoA and Monostatic Radar Data Fusion for Single Object Localization and Tracking

In this paper, we present the fusion of passive Time-Difference-of-Arrival (TDoA) and active monostatic radar measurements in the context of single object localization and tracking. We assume a target equipped with a wireless transmitter so that the passive-TDoA and active-radar sensors can simultaneously detect it. For the localization problem, we base our approach on the Constrained-Weighted-Least-Squares (CWLS) method, which has better accuracy than the Least-Squares (LS) and Weighted-LS approaches. Nevertheless, CWLS suffers from estimation bias when the measurement noise is large. We show that it is possible to reduce such a bias by using both TDoA and monostatic radar measurements. For the tracking problem, we base our approach on Bayesian filtering. Therefore, we detail the TDoA, radar and combined measurement models required for the filtering process regardless of the Bayesian Filter implementation. We show that the combined measurements result in enhanced tracking capabilities, e.g., improved reliability and larger tracking area compared to the radar-only case. In addition, we present other possible combinations of radar and TDoA measurements that one could encounter in practical scenarios. Finally, we present experimental results to validate and support our work.
Opportunistic Transmission of Distributed Learning Models in Mobile UAVs

In this paper, we propose an opportunistic scheme for the transmission of model updates from Federated Learning (FL) clients to the server, where clients are wireless mobile users. This proposal aims to opportunistically take advantage of the proximity of users to the base station or the general condition of the wireless transmission channel, rather than traditional synchronous transmission. In this scheme, during the training, intermediate model parameters are uploaded to the server, opportunistically and based on the wireless channel condition. Then, the proactively-transmitted model updates are used for the global aggregation if the final local model updates are delayed. We apply this novel model transmission scheme to one of our previous work, which is a hybrid split and federated learning (HSFL) framework for UAVs. Simulation results confirm the superiority of using proactive transmission over the conventional asynchronous aggregation scheme for the staled model by obtaining higher accuracy and more stable training performance. Test accuracy increases by up to 13.47% with just one round of extra transmission.
Fairness Oriented Spectrum Auction for Blockchain-assisted Dynamic Spectrum Sharing

Leveraging the unique characteristics of blockchain, secure and efficient dynamic spectrum sharing (DSS) can be achieved, which has been regarded as a promising solution to meet the spectrum requirement in future wireless communication systems. However, proper incentive mechanism with guaranteed fairness is essential for blockchain-enabled DSS. In this paper, we investigate fairness-oriented spectrum auction, where multiple access points can share resources on the blockchain platform with smart contract. Specifically, we propose a fairness factor to adjust users’ satisfaction considering both the historical spectrum allocation results and current spectrum auction results. Then, an improved virtual auction mechanism is proposed to balance the long-term satisfaction of participants. Simulation results show that the multi-round fairness-based auction algorithm (FBAA) can enhance the fairness of spectrum allocation and increase the number of radio users served.
A Practical Fast Model Inference System Over Tiny Wireless Device

The utilization of machine learning models has become prevalent in various wireless devices to deduce the network status from various tracing data, e.g., link capacity, channel fading, etc. To cope with the increasing complexity of the network environment, deep neural models are leveraged to mine the high-dimensional network tracing data for a variety of intelligent applications. However, due to the limited resource that allocated to the network stack process, it is infeasible to train deep neural models due to the constrained computing power and absence of large-scale labeled data. Besides, the network device can barely support quick inference of large model, thus cannot support promptly response to the network conditions. In this paper, we propose a practical fast model inference system that can run high accuracy model over tiny wireless devices that are constrained in both memory and CPU power. Specifically, we design a knowledge-distillation based training method for a light-weight model that deployed at device side that can migrate the knowledge from a well-trained deep model. It is shown that our system can support fast model inference over tiny devices, which can greatly improve the network throughput in a multi-user access system by inferring the transmission collision from channel error, and thus can improve the accuracy of the link adaptation. We have conducted practical experiments to verify our system and discuss the possible extensions.
Optimal Honeypot Allocation using Core Attack Graph in Cyber Deception Games

Honeypots appear today as a defense strategy to trap intelligent cyber attackers who can detect traditional security measures. The scalability of existing algorithms for solving some classes of game theory is very limited due to large-scale networks. This paper opens the door to a new approach to allocate honeypots in the network, to increase attackers’ costs, during the lateral movement of the APT attack. We use the core attack graph that can show the main routes an attacker can take toward the goal. This allows the defender to use a limited number of honeypots focusing its efforts only on critical nodes over the main attacker routes. The effectiveness and scalability of the proposed approach are evaluated over different network topologies, a varying number of honeypots, network size, and density. Numerical results show that the defender reward over the core attack graph is quite similar to that obtained on the original attack graph while significantly reducing the defender’s actions and computation time.
Long-term Incentive Mechanism for Federated Learning: A Dynamic Repeated Game Approach

Federated learning (FL) is capable of using the local data sets from large-scale nodes for distributed model training. In FL tasks, training and updating are usually repeated ping-pong processes, in that the model training process between devices (workers) and task publisher (TP) needs to be repeated for multiple rounds towards the global model convergence. However, a worker is typically selfish to save its local resource, and even with an incentive mechanism in place at the beginning of model training, a worker may not be honest to participate in all training rounds, leading to poor performance in global model convergence. To enable long-term cooperation in FL, however, has rarely been considered in the existing literature which motivates our work. In this paper, the multi-round FL is modeled as a dynamic repeated game. To exploit the long-term cooperation gain, a general trigger strategy is deployed as the punishment for free-riding and the Nash equilibrium (NE) of the repeated game is derived. Based on the game theoretic analysis, we develop a NE-driven incentive mechanism to guide the TP selects the most effective wages to motivate workers towards long-term cooperation and avoid midway free-riding. Simulation results show the effectiveness of our proposal.
Probabilistic Client Sampling and Power Allocation for Wireless Federated Learning

Despite the many known benefits of Federated Learning (FL), in the wireless environment, its performance is significantly impacted by the statistical and system heterogeneities among the local data sets and local clients. Therefore, judicious sampling of clients and resource allocation among them are of vital importance in FL. In this work, we consider the online joint optimization of probabilistic client sampling and power allocation to improve the training performance of wireless FL. Our optimization is based on a new convergence bound for non-convex loss functions under probabilistic client sampling, which considers the different data ratios and gradient norms among clients. We propose a new algorithm based on the Lyapunov optimization framework, termed PCSPA, that accounts for how the statistical and system heterogeneities affect both the convergence rate and training time of FL, as well as the long-term power constraints and the expected number of sampled clients. Experiments on image classification with wireless FL show that the proposed algorithm can substantially outperform conventional separate optimization strategies and a state-of-the-art joint optimization method.
Invited Paper: Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning

In this paper, we investigate the operation of an aerial manipulator system, namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. More specifically, we develop a motion planning model based on Time To Collision (TTC), which enables a quadrotor UAV to navigate around obstacles while ensuring the manipulator’s reachability. Additionally, we utilize a model-based Q-learning model to independently track and control the desired trajectory of the manipulator’s end-effector, given an arbitrary baseline trajectory for the UAV platform. Such a combination enables a variety of actuation tasks such as high-altitude welding, structural monitoring and repair, battery replacement, gutter cleaning, sky scrapper cleaning, and power line maintenance in hard-to-reach and risky environments while retaining compatibility with flight control firmware. Our RL-based control mechanism results in a robust control strategy that can handle uncertainties in the motion of the UAV, offering promising performance. Specifically, our method achieves 92% accuracy in terms of average displacement error (i.e. the mean distance between the target and obtained trajectory points) using Q-learning with 15,000 episodes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Comparing Avatar and Face-to-Face Collaboration in VR Education: Concept and Preliminary Insights

Virtual education is gaining prominence, providing opportunities for dynamic interactive content, such as Digital Twins, and novel collaboration modalities, including options for remote classrooms. In this work, we present a concept that leverages Digital Twins for interactive group work in engineering education through the use of Virtual Reality technology. We propose an experimental investigation to compare diverse collaboration alternatives facilitated by Virtual Reality, specifically face-to-face and avatar-based interaction. Preliminary findings are discussed, along with their implications for the design and implementation of future experiments in this emerging field.
What's the Deal with CG? The Usage of Mixed Reality Applications as a Means to Facilitate Meaningful Experiences in Teaching and Learning Computer Graphics

Due to the thematic diversity, as well as the spatial awareness required to learn and master computer graphics content, teaching it can be challenging. To alleviate these problems, a large body of past research has explored experiential learning approaches and implemented them as interactive 3D desktop applications or mobile augmented reality (AR) apps. As promising as these approaches are, their success is often hampered by an installation required to use the needed software. In this article, we offer a possible solution through the usage of a WebXR browser interface mixed reality computer graphics program (MRCG). Furthermore, we offer a teaching framework that takes up theoretical principles taught in classroom teaching as interactive, platform-independent AR representations and transforms them into concrete educational experiences in the sense of David A. Kolb's experiential learning cycle. Following the established pedagogical concept, a comprehensive teaching framework is presented consisting of an experiential WebApp, theoretical topics computer graphics within the computer graphics domain and supplementary assessment elements. Besides creating the new educational framework, the emphasis is on its practical implementation, as well as requirements for application design and curricular transformation. The key results include a critical assessment of difficulties and possibilities associated with applying the given experience-based teaching strategy from a technical and educational standpoint.
Online Profiling and Adaptation of Quality Sensitivity for Internet Video

A key to video streaming systems is knowing how sensitive quality of experience (QoE) is to quality metrics (e.g., buffering ratio and average bitrate). In the conventional wisdom, such quality sensitivity should be profiled by offline user studies because QoE is equally sensitive to quality metrics everywhere for an entire genre of videos. However, recent studies show that quality sensitivity varies substantially both across videos and within a video, giving rise to a new potential for improving QoE and serving more users without using more bandwidth. Unfortunately, offline profiling cannot capture the variability of quality sensitivity within a new video (e.g., a new TV show episode or live sports event), if users join to watch it within a short time window.
Efficient Hardware Implementation of Elliptic-Curve Diffie–Hellman Ephemeral on Curve25519

Hardware architecture optimized for implementing the elliptic-curve Diffie–Hellman ephemeral (ECDHE) on 256-bit Montgomery elliptic curves presents unique challenges, particularly for resource-constrained IoT and mobile devices. This work aims to provide an efficient hardware implementation of ECDHE on Curve25519, including a dedicated finite state machine (FSM) designed to handle point multiplication and ECDHE operations, utilizing constant-time algorithms and a unified memory block for resource management. Additionally, we introduce an optimized modular computation unit that covers modular addition, subtraction, multiplication, and inversion. Our proposed hardware architecture enhances the efficiency of ECDHE operations while maintaining low resource utilization, considerably reduced latency, and low power consumption. Synthesized on the Xilinx Artix-7 platform, our design boasts 64,000 Slices and a clock speed of 102 MHz, and it computes an ECDHE scalar multiplication operation in 1.1 ms, consuming 117 mW. The proposed hardware design can be applied to various platforms, including mobile devices and IoT systems.