Reviewerly/exHarmony huggingface dataset:
Secure Video Offloading in MEC-Enabled IIoT Networks: A Multicell Federated Deep Reinforcement Learning Approach

Authors:
raw_author_name: Tantan Zhao
raw_affiliation_string: Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Fan Li
raw_affiliation_string: Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China

Abstract: Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multi-cell scenarios where users are unwilling to offload data to other cells. In this paper, a secure video offloading scheme based on multi-cell federated deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a multi-cell federated DRL algorithm based on the frameworks of multi-cell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multi-cell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption and security rate.
Dynamic Spatial Focus for Efficient Compressed Video Action Recognition

Authors:
raw_author_name: Ziwei Zheng
raw_affiliation_string: School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Le Yang
raw_affiliation_string: School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Yulin Wang
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Miao Zhang
raw_affiliation_string: School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Lijun He
raw_affiliation_string: School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Gao Huang
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Fan Li
raw_affiliation_string: School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China

Abstract: Recent years have witnessed a growing interest in compressed video action recognition due to the rapid growth of online videos. It remarkably reduces the storage by replacing raw videos with sparsely sampled RGB frames and other compressed motion cues (motion vectors and residuals). However, existing compressed video action recognition methods face two main issues: First, the inefficiency caused by the usage of coarse-level information under full resolution, and second, the disturbing due to the noisy dynamics in motion vectors. To address the two issues, this paper proposes a dynamic spatial focus method for efficient compressed video action recognition (CoViFocus). Specifically, we first use a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors. Then the selected patch pair will be processed by a high-capacity two-stream deep model for the final prediction. Such a patch selection strategy crops out the irrelevant motion noise in motion vectors, as well as reduces the spatial redundancy of the inputs, leading to the high efficiency of our method in the compressed domain. Moreover, we found that the motion vectors can help our method to address the possibly happened <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static-issue</i> , which means that the focus patches get stuck at some regions related to static objects rather than target actions, which further improves our method. Extensive results on both the HMDB-51 and UCF-101 datasets demonstrate the effectiveness and efficiency of our method in compressed video action recognition tasks.
Semantic Linear Genetic Programming for Symbolic Regression

Authors:
raw_author_name: Zhixing Huang
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Yi Mei
raw_affiliation_string: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Jinghui Zhong
raw_affiliation_string: School of Computer science and Engineering, South China University of Technology, Guangzhou, China

Abstract: Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalization ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To utilize the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.
Dual-Level Knowledge Distillation via Knowledge Alignment and Correlation

Authors:
raw_author_name: Yin Yang
raw_affiliation_string: School of Computing, Clemson University, Clemson, SC, USA
raw_author_name: Hongxin Hu
raw_affiliation_string: Department of Computer Science and Engineering, University at Buffalo The State University of New York, Buffalo, NY, USA
raw_author_name: Feng Luo
raw_affiliation_string: School of Computing, Clemson University, Clemson, SC, USA

Abstract: Knowledge distillation (KD) has become a widely used technique for model compression and knowledge transfer. We find that the standard KD method performs the knowledge alignment on an individual sample indirectly via class prototypes and neglects the structural knowledge between different samples, namely, knowledge correlation. Although recent contrastive learning-based distillation methods can be decomposed into knowledge alignment and correlation, their correlation objectives undesirably push apart representations of samples from the same class, leading to inferior distillation results. To improve the distillation performance, in this work, we propose a novel knowledge correlation objective and introduce the dual-level knowledge distillation (DLKD), which explicitly combines knowledge alignment and correlation together instead of using one single contrastive objective. We show that both knowledge alignment and correlation are necessary to improve the distillation performance. In particular, knowledge correlation can serve as an effective regularization to learn generalized representations. The proposed DLKD is task-agnostic and model-agnostic, and enables effective knowledge transfer from supervised or self-supervised pretrained teachers to students. Experiments show that DLKD outperforms other state-of-the-art methods on a large number of experimental settings including: 1) pretraining strategies; 2) network architectures; 3) datasets; and 4) tasks.
IoV environment exploring coordination: A federated learning approach

Authors:
raw_author_name: Tong Ding
raw_affiliation_string: School of Software, Shandong University, Jinan, 250101, China
raw_author_name: Lei Liu
raw_affiliation_string: School of Software, Shandong University, Jinan, 250101, China
raw_author_name: Yi Zhu
raw_affiliation_string: School of Software, Shandong University, Jinan, 250101, China
raw_author_name: Lizhen Cui
raw_affiliation_string: School of Software, Shandong University, Jinan, 250101, China

Abstract: Exploring open fields with coordinated unmanned vehicles is popular in academia and industries, and one of the most impressive applicable approaches is the Internet of Vehicles (IoV). The IoV connects vehicles, road infrastructures, and communication facilities to provide sustainable solutions for task explorations. However, the coordination of acquiring information from others may risk data privacy. To this end, sharing high-quality experiences instead of raw data has become an urgent demand. This paper employs a Deep Reinforcement Learning (DRL) method to let IoV generate training data with prioritized experience and states, which can support the IoV to explore the environment more efficiently. Moreover, a Federated Learning (FL) experience sharing model is established to guarantee the vehicles' privacy. The numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental methods. The experiments also suggest that the proposed method could support agents with no explicit information to achieve the tasks.
FedGL: Federated graph learning framework with global self-supervision

Authors:
raw_author_name: Chen, Chuan
raw_author_name: Xu, Ziyue
raw_author_name: Zheng, Zibin

Abstract: Graph data are ubiquitous in the real world. Graph learning (GL) attempts to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed across different organizations, resulting in isolated data silos. To address this problem, we incorporate federated learning into GL and propose a general Federated Graph Learning framework called FedGL. FedGL is capable of obtaining a high-quality global graph model while protecting data privacy by discovering the global self-supervision information during the federated training. Specifically, we propose uploading prediction results and node embeddings to the server for discovering the global pseudo labels and global pseudo graph. These are then distributed to each client to enrich the training labels and complement the graph structure respectively, thereby improving the quality of each local model. Moreover, the global self-supervision enables information of each client to flow and be shared in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients. Finally, experimental results show that FedGL significantly outperforms baselines on four widely used graph datasets.
Anti-Drugs Chatbot: Chinese BERT-Based Cognitive Intent Analysis

Authors:
raw_author_name: Eric Hsiao-Kuang Wu
raw_affiliation_string: Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
raw_author_name: Yu-Yen Ou
raw_affiliation_string: Department of Computer Science and Information Engineering, Yuan Ze University, Taoyuan, Taiwan
raw_author_name: Chia-Ru Chung
raw_affiliation_string: Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan

Abstract: Drug abuse has always been a severe issue, but the proportion of drug abuse and addiction is rising. According to research reports, youth are motivated to access drugs mainly due to curiosity and peer influence. Additionally, youth especially lack proper knowledge and education surrounding drug abuse. Analyzing whether potential addicts intend to access drugs is helpful in preventing drug abuse and addiction. We developed an Anti-drug Chatbot for young people on a popular online social platform. We can detect potential risks, obtain warnings from the user-entered query and provide these to professional consultants for help. In this article, we present a hierarchical system with bidirectional encoder representation from transformers (BERT) to efficiently recognize and classify a user’s intent. We use the Chinese BERT-based model to utilize contextual information to perform classification and recognition. We evaluate our proposed system on our conversational dataset.
A Heterogeneous Graph Neural Network With Attribute Enhancement and Structure-Aware Attention

Authors:
raw_author_name: Guanjun Liu
raw_affiliation_string: Key Laboratory of Embedded System and Service Computing of Ministry of Education, Tongji University, Shanghai, China

Abstract: Heterogeneous information network (HIN) has been applied in a wide variety of graph analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to cast the meta-paths aside, since it solves the problem of structural information loss caused by artificially designed meta-paths. However, existing meta-path-free HGNNs fail to take into account that most node types in many HINs have no attributes, and they cannot make full use of sparse node attributes when applied to HINs with missing attributes. Furthermore, their computation of attention coefficients explores the correlations of node attributes while almost ignoring structural ones, which may limit the expression ability of the model and cause overfitting in model training. To alleviate these issues, we propose an HGNN with attribute enhancement and structure-aware attention (HGNN-AESA). First, we design an attribute enhancement module (AEM) to connect more useful attributed nodes to the target nodes. Specifically, AEM introduces a random walk with restart (RWR) strategy to obtain structural relevance scores of each node within its specific subgraph. The structural relevance scores are used to capture potentially influential attributed nodes in high-order neighborhood for each target node. Second, we propose heterogeneous structure-aware attention layers (HSALs) to learn node representations. HSALs follow a hierarchical attention framework, including node-level and type-level attention. The node-level attention aggregates feature (attribute) embeddings of same-type neighbors, and the relevant attention coefficients depend on the combination of node attributes and heterogeneous structural interventions. The type-level attention fuses all type-specific vector representations and generates the ultimate node embedding. Finally, extensive experiments on three different real-world HIN datasets demonstrate that our model outperforms state-of-the-art methods.
Circuit Implementation of Proximal Projection Neural Networks for Composite Optimization Problems

Authors:
raw_author_name: Jintao Wu
raw_affiliation_string: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
raw_author_name: Xing He
raw_affiliation_string: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
raw_author_name: Tingwen Huang
raw_affiliation_string: Department of Mathematics, Texas A&amp;M University, Doha, Qatar
raw_author_name: Junzhi Yu
raw_affiliation_string: State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, China

Abstract: Existing circuits for composite optimization problems tend to ignore the structure of nonsmooth objectives and lead to less practicability. To this end, with a proximal projection neural network (PPNN) and an inertial proximal projection neural network (IPPNN), this paper presents two novel analog circuit frameworks in presence of a nonsmooth term, where the actions of neuron are simulated via a feedback loop formed by integrator, gradient estimate, proximal operator and other basic operation models. In our circiuts, it shows that the stable output voltages are mapped to optimal solutions. Considering the nonsmooth term as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{l}_{1}}$</tex-math></inline-formula> -norm, we further design an effective analog circuit of soft thresholding operator. Theoretically, it is proved that both PPNN-based and IPPNN-based analog circuit frameworks have stable output voltages in the sense that the value of electronic components meets a certain mild proportion. Finally, two examples are simulated on Multisim 14.0 platform to validate the effectiveness and practicability of the proposed analog circuits.
Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids

Authors:
raw_author_name: Md Moniruzzaman
raw_affiliation_string: Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, ON, Canada
raw_author_name: Abdulsalam Yassine
raw_affiliation_string: Department of Software Engineering, Lakehead University, Thunder Bay, ON, Canada
raw_author_name: Rachid Benlamri
raw_affiliation_string: University of Doha for Science and Technology, Doha, Qatar

Abstract: The proliferation of electric vehicles (EVs) and the advancement of Vehicle-to-Everything (V2X) energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This paper proposes a novel Federated Reinforcement Learning (FRL) system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of Proof of State of Charge (PoSOC) as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system.
AdaDet: An Adaptive Object Detection System Based on Early-Exit Neural Networks

Authors:
raw_author_name: Le Yang
raw_affiliation_string: Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Ziwei Zheng
raw_affiliation_string: Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Jian Wang
raw_affiliation_string: Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China
raw_author_name: Shiji Song
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Gao Huang
raw_affiliation_string: Department of Automation, Tsinghua University, Beijing, China
raw_author_name: Fan Li
raw_affiliation_string: Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China

Abstract: Recent researchers have proposed adaptive inference methods with an early-exiting mechanism, which stops the inference procedure of input if the prediction is with high confidence, leading to a fine-grained resource allocation based on the complexity of inputs. However, the adaptive inference strategy can only be applied for image classification tasks, such a system for object detection is still under-investigated since it lacks an appropriate way to measure the uncertainty of detection results for multiple objects in an image. To address this issue, in this paper, we propose a novel adaptive object detection system (AdaDet) based on early-exit neural networks. By using stochastic variables to represent localization predictions, our method can measure the uncertainty of the detection result for an object, based on which we further develop an entropy-based criterion to estimate the reliability of the whole detection results for an input image. With the proposed method, samples that achieve high-confidence detection results will exit early from the detection model, leading to low computational costs for inferring these samples. While only complex images need to finish the whole computational graph to achieve better detection results during inference. We experimentally demonstrate that the proposed AdaDet enhances detection performance under anytime prediction and adaptive inference settings on the most common dataset (MS COCO) used in this field.
Collaborative Parameter Estimation of Multiple Unmanned Surface Vessels: A Robust Distributed Estimator-Based Approach

Authors:
raw_author_name: Han Shen
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Guanghui Wen
raw_affiliation_string: Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China
raw_author_name: Yuezu Lv
raw_affiliation_string: Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing, China

Abstract: In this paper, the collaborative parameter estimation of multiple unmanned surface vessels (USVs) with model structure uncertainties is studied. The considered parameter estimation problem is first converted into a distributed state and parameter joint estimation problem. Then, the robust distributed estimator is constructed to handle the inevitable model structure uncertainties, and the upper bounds of prediction and estimation error covariance matrices are derived respectively. Additionally, the upper bound of the estimation error covariance matrix is minimized by designing appropriate estimator gains. Finally, the advantages of the proposed distributed parameter estimation approach from the perspectives of information interaction and consideration of model structure uncertainties are verified via simulations and practical experiments.