Reviewerly/exHarmony huggingface dataset:
Dynamic Clustering and Power Control for Two-Tier Wireless Federated Learning

Authors:
raw_author_name: Wei Guo
raw_affiliation_string: School of Science and Engineering (SSE), Future Network of Intelligence Institute (FNii), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China
raw_author_name: Chuan Huang
raw_affiliation_string: School of Science and Engineering (SSE), Future Network of Intelligence Institute (FNii), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China
raw_author_name: Xiaoqi Qin
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Lian Yang
raw_affiliation_string: Information and Communication Engineering College, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Wei Zhang
raw_affiliation_string: School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW, Australia

Abstract: Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge, where a global model is trained iteratively through the collaboration of the edge devices without sharing their data. However, due to the relatively large communication cost between the devices and parameter server (PS), direct computing based on the information from the devices may not be resource efficient. This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.
HVMM: A Holistic Virtual Machine Management Strategy for Cloud Data Centers

Authors:
raw_author_name: Piao Lv
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou, China
raw_author_name: Zhen Zhang
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou, China
raw_author_name: Yuhui Deng
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou, China
raw_author_name: Lin Cui
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou, China
raw_author_name: Longxin Lin
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou, China

Abstract: Cloud computing has emerged as an infrastructure in the era of digital economy and has been widely applied in various fields. Virtual Machine(VM) management is the key mechanism in a Cloud Data Center(CDC). A typical VM management system is responsible for VM allocation and VM reallocation, and it is usually designed to optimize specific objectives, especially for the metrics of energy consumption, resource wastage, communication cost, and Service Level Agreement Violations (SLAV). However, it is greatly challenging to optimize these metrics at the same time, and most existing VM management strategies focus on optimizing part of the above four metrics. In this paper, we propose a Holistic Virtual Machine Management (HVMM) strategy to optimize the energy consumption, resource wastage, communication cost, and SLAV simultaneously. First, we define two parameters, the Compatibility and the Performance-to-Power Ratio (PPR), for VM allocation to optimize energy consumption and resource wastage. Then, we propose a reallocation approach based on spectral clustering that can handle dynamic traffic between VMs without a priori knowledge of the traffic between VMs, and it takes a slight expense of resource wastage and energy consumption to reduce communication cost between VMs and ensures low SLAV risk. To evaluate the performance of HVMM, we compared the proposed strategy with the state-of-the-art strategies in various experiments on real-world traces. Compared with the other strategies, the resource wastage of HVMM is reduced by 62%. Simultaneously, the communication cost is reduced by about 26%, the energy consumption is reduced by about 5%, and SLAV is much lower than that of the others.
An Adaptive Post-Processing Network With the Global-Local Aggregation for Semantic Segmentation

Authors:
raw_author_name: Guilin Zhu
raw_affiliation_string: School of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Runmin Wang
raw_affiliation_string: School of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Yingying Liu
raw_affiliation_string: School of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Zhenlin Zhu
raw_affiliation_string: School of Information Science and Engineering, Hunan Normal University, Changsha, China
raw_author_name: Changxin Gao
raw_affiliation_string: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Li Liu
raw_affiliation_string: School of Systems Engineering, National University of Defense Technology, Changsha, China
raw_author_name: Nong Sang
raw_affiliation_string: School of Information Science and Engineering, Hunan Normal University, Changsha, China

Abstract: Current semantic segmentation methods mainly focus on modeling the context of the global image to obtain high-quality segmentation results. However, they ignore the role of local image patches, which contain complementary and effective context information. In this paper, we propose an adaptive post-processing network (APPNet) for semantic segmentation based on the predictions of current methods in the global image and local image patches. The key point of APPNet is the global-local aggregation module, which models the context between global predictions and local predictions to generate accurate pixel-wise representation. Furthermore, we develop an adaptive points replacement module to compensate for the lack of fine detail in global prediction and the overconfidence in local predictions. Our method can be readily integrated into existing segmentation methods (i.e., ConvNeXt, HRNet, ViT-Adapter) with little memory and without extra modification in current models. We empirically demonstrate our method brings performance improvements across diverse datasets (i.e., Cityscapes, ADE20K, PASCAL-Context, COCO-Stuff). The code and models will be publicly available at https://github.com/zhu-gl-ux/APPN.
Multi-Task Learning With Multi-Query Transformer for Dense Prediction

Authors:
raw_author_name: Xiangtai Li
raw_affiliation_string: S-Lab, Nanyang Technological University, Nanyang Ave, Singapore
raw_author_name: Haobo Yuan
raw_affiliation_string: Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
raw_author_name: Yibo Yang
raw_affiliation_string: JD Explore Academy, Beijing, China
raw_author_name: Lefei Zhang
raw_affiliation_string: Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China

Abstract: Previous multi-task dense prediction studies developed complex pipelines such as multi-modal distillations in multiple stages or searching for task relational contexts for each task. The core insight beyond these methods is to maximize the mutual effects of each task. Inspired by the recent query-based Transformers, we propose a simple pipeline named Multi-Query Transformer (MQTransformer) that is equipped with multiple queries from different tasks to facilitate the reasoning among multiple tasks and simplify the cross-task interaction pipeline. Instead of modeling the dense per-pixel context among different tasks, we seek a task-specific proxy to perform cross-task reasoning via multiple queries where each query encodes the task-related context. The MQTransformer is composed of three key components: shared encoder, cross-task query attention module and shared decoder. We first model each task with a task-relevant query. Then both the task-specific feature output by the feature extractor and the task-relevant query are fed into the shared encoder, thus encoding the task-relevant query from the task-specific feature. Secondly, we design a cross-task query attention module to reason the dependencies among multiple task-relevant queries; this enables the module to only focus on the query-level interaction. Finally, we use a shared decoder to gradually refine the image features with the reasoned query features from different tasks. Extensive experiment results on two dense prediction datasets (NYUD-v2 and PASCAL-Context) show that the proposed method is an effective approach and achieves state-of-the-art results. Code and models are available at https://github.com/yangyangxu0/MQTransformer.
Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based Approach

Authors:
raw_author_name: Dipanjyoti Paul
raw_affiliation_string: Indian Institute of Technology Patna, Patna, Bihta, India
raw_author_name: Daipayan Chakdar
raw_affiliation_string: Indian Institute of Technology Patna, Patna, Bihta, India
raw_author_name: Sriparna Saha
raw_affiliation_string: Indian Institute of Technology Patna, Patna, Bihta, India
raw_author_name: Jimson Mathew
raw_affiliation_string: Indian Institute of Technology Patna, Patna, Bihta, India

Abstract: Recent years have witnessed tremendous growth in the publication of research articles as well as in the rise of new research topics. Articles get published in a streaming manner and therefore retrieving and recommending trending topics continuously by updating the trend of topics with time will be beneficial for young researchers. The proposed topic recommendation system is a clustering-based approach that utilizes an autoencoder framework for the generation of clusters. The autoencoder framework considers articles as input in a multiview framework and produces latent data in lower-dimensional space using graph attention-based encoder and decoder networks. The latent data is then partitioned with respect to its different views and finally, a single consensus overlapping partitioning is produced by satisfying all the views. Since the publication of articles is a continuous process, to update the trending topics continuously, the clustering-based approach is kept on and applied iteratively in a sliding window manner. The generated clusters are analyzed to extract the trending topics and recommendations for future scope. An article can belong to multiple topics and the proposed method is developed by considering such criteria and therefore tested with the modified version of the multilabel scientific article data ArXiv, named CSML.ArXiv. The superiority of the proposed method can be observed from its comparisons with existing methods, and a few baseline methods with respect to cluster formation, topic extraction, and trending topic evaluation.
Efficient Pairing-Free Certificateless Signcryption Scheme for Secure Data Transmission in IoMT

Authors:
raw_author_name: Jianhong Zhang
raw_affiliation_string: College of Information Sciences and Technology, North China University of Technology, Beijing, China
raw_author_name: Chenghe Dong
raw_affiliation_string: College of Information Sciences and Technology, North China University of Technology, Beijing, China
raw_author_name: Yining Liu
raw_affiliation_string: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China

Abstract: The Internet of Medical Things (IoMT) builds a bridge between patients and doctors, facilitating patients’ being diagnosed and monitored by uploading physiological indicators without visiting the hospital. However, physiological indicators are sensitive data of patients, making it a challenge to achieve verifiability of data sources while ensuring data privacy during data transmission of IoMT. Due to its ease of deployment and the ability to provide both encryption and signature, CertificateLess SignCryption (CLSC) is suitable for designing secure data-transfer protocol in IoMT. Nevertheless, internal adversaries “malicious users" and “malicious KGC", capable of launching Type I and Type II attacks, threaten the security of present CLSC schemes, making most of them insecure. In this work, after giving an example of a recent CLCS scheme suffering Type I attack, we propose an efficient pairing-free CLCS scheme suitable for secure data transmission in IoMT based on the idea of zero-knowledge proof. It not only provides confidentiality and unforgeability of transmitted data under the Type I and Type II attacks, but also achieves lower computational and communication overhead, and public verifiability. Finally, compared with the five recent CLSC schemes, theoretical analysis and experimental testing results show that the proposed scheme outperforms the other five schemes in terms of computation and communication costs as well as security. Therefore, our scheme is better suited for constructing secure data transmission in IoMT scenarios.
Federated Domain Generalization: A Secure and Robust Framework for Intelligent Fault Diagnosis

Authors:
raw_author_name: Chao Zhao
raw_affiliation_string: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Weiming Shen
raw_affiliation_string: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

Abstract: The maturation of sensor network technologies has promoted the emergence of the Industrial Internet of Things, which has been collecting an increasing volume of monitoring data. Transforming these data into actionable intelligence for equipment fault diagnosis can reduce unscheduled downtime and performance degradation. In conventional artificial intelligence paradigms, abundant individual data distributed across clients’ devices needs to be delivered to a central storage for data analysis and knowledge extraction, which may violate data privacy requirements and neglect distribution discrepancy across different clients. To tackle the issue of privacy disclosure, an edge-cloud integrated federated learning framework is developed. Then, a two-stage training mechanism is designed to establish a domain-agnostic fault diagnosis model that can achieve satisfactory diagnostic performance on unseen target domains. Comprehensive simulated experiments on two rotating machines indicate that the proposed method possesses good generalization ability and can meet the requirement of privacy protection.
CC-FedAvg: Computationally Customized Federated Averaging

Authors:
raw_author_name: Hao Zhang
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, China
raw_author_name: Tingting Wu
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, China
raw_author_name: Siyao Cheng
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, China
raw_author_name: Jie Liu
raw_affiliation_string: Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China

Abstract: Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices. It inherently assumes a uniform capacity among participants. However, due to different conditions such as differing energy budgets or executing parallel unrelated tasks, participants have diverse computational resources in practice. Participants with insufficient computation budgets must plan for the use of restricted computational resources appropriately, otherwise they would be unable to complete the entire training procedure, resulting in model performance decline. To address this issue, we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose Computationally Customized Federated Averaging (CC-FedAvg), which allows participants to determine whether to perform traditional local training or model estimation in each round based on their current computational budgets. Both theoretical analysis and exhaustive experiments indicate that CC-FedAvg has the same convergence rate and comparable performance as FedAvg without resource constraints. Furthermore, CC-FedAvg can be viewed as a computation-efficient version of FedAvg that retains model performance while considerably lowering computation overhead.
An In-Depth Look at Forking-Based Attacks in Ethereum With PoW Consensus

Authors:
raw_author_name: Jelena Mišić
raw_affiliation_string: Department of Computer Science, Toronto Metropolitan University, Toronto, Canada
raw_author_name: Vojislav B. Mišić
raw_affiliation_string: Department of Computer Science, Toronto Metropolitan University, Toronto, Canada
raw_author_name: Xiaolin Chang
raw_affiliation_string: Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China

Abstract: In this paper, we analyze the performance of Ethereum data distribution network using a probabilistic model which allows accurate modeling of data propagation but also of forking, which happens when the blockchain maintained by the network temporarily splits into multiple versions due to a disagreement over the validity of a particular block. We also investigate the duration of inconsistent states of the ledger, which refers to the amount of time that the network remains split or partitioned. Finally, we model the block withholding attack and block slowdown attack, and analyze their impact on network performance of the network in terms of quality indicators such as block delivery time, the duration of ledger inconsistency, and forking probability. We also propose countermeasures for the block withholding attack.
Switching-Event-Based Interval Type-2 Fuzzy Control for a Class of Uncertain Nonlinear Systems

Authors:
raw_author_name: Yi Shui
raw_affiliation_string: School of Automation, Southeast University, Nanjing, China
raw_author_name: Lu Dong
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Ya Zhang
raw_affiliation_string: School of Automation, Southeast University, Nanjing, China
raw_author_name: Changyin Sun
raw_affiliation_string: School of Automation, Southeast University, Nanjing, China

Abstract: In this paper, a switching-event-based interval type-2 variable universe fuzzy tracking control strategy is proposed for a class of uncertain nonlinear systems. Remarkably, the nonlinearities and the large unknown uncertainties (including parametric and structural) can be allowed. Therefore, a novel switching-event-based mechanism (SEBM) is proposed. It not only determines when to update the control law, but also when to update the design parameters. At the same time, generous computing resources are saved. In addition, the interval type-2 fuzzy control (IT2FC) technology is introduced to resist unknown uncertainties by identifying the controlled model online. Moreover, the Lyapunov function is designed to prove that the Zeno phenomenon does not occur and the closed-loop system is asymptotically stable. Finally, two practical simulations are given to demonstrate the effectiveness of the proposed method.
Differentiable Rendering for Synthetic Aperture Radar Imagery

Authors:
raw_author_name: Jonathan I. Tamir
raw_affiliation_string: Computational Sensing and Imaging Lab, The University of Texas at Austin, USA

Abstract: There is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in optimization pipelines using first-order methods such as backpropagation. Incorporating such domain knowledge can lead to deep neural networks that are trained more robustly and with limited data, as well as the capability to solve ill-posed inverse problems. Existing efforts in differentiable rendering have focused on imagery from electro-optical sensors, particularly conventional RGB-imagery. In this work, we propose an approach for differentiable rendering of Synthetic Aperture Radar (SAR) imagery, which combines methods from 3D computer graphics with neural rendering. We demonstrate the approach on the inverse graphics problem of 3D Object Reconstruction from limited SAR imagery using high-fidelity simulated SAR data.
Federated Learning for Crowd Counting in Smart Surveillance Systems

Authors:
raw_author_name: Yiran Pang
raw_affiliation_string: Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
raw_author_name: Zhen Ni
raw_affiliation_string: Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
raw_author_name: Xiangnan Zhong
raw_affiliation_string: Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

Abstract: Crowd counting in smart surveillance systems plays a crucial role in Internet of Things (IoT) and smart cities, and can affect various aspects such as public safety, crowd management, and urban planning. Using surveillance data to centrally train a crowd counting model raises significant privacy concerns. Traditional methods try to alleviate the concern by reducing the focus on individuals, but the concern still needs to be thoroughly resolved. In this work, we develop a horizontal federated learning (HFL) framework to train the crowd counting models which can preserve privacy simultaneously. This framework enables the smart surveillance system to learn from model aggregation without accessing the private data stored on local devices. Therefore, it eliminates the need for video data transmission, reduces communication costs, and avoids raw data leakage. Due to the lack of FL crowd counting datasets, we design four non-IID (non-Independent and Identically Distributed) partitioning strategies, including feature-skew, quantity-skew, scene-skew, and time-skew, to simulate real-world FL scenarios. In addition, we present an efficient fully convolutional network (e-FCN) for each client to demonstrate the practical applicability of the proposed framework. The e-FCN adopts an encoder-decoder architecture with fewer parameters, making it communication-friendly and easier to train. This design can achieve competitive performance compared to more complex models in surveillance crowd counting in literature. Finally, we evaluate the proposed HFL framework with e-FCN under our skew strategies on multiple real-world datasets, including Crowd Surveillance, ShanghaiTech PartB, WorldExpo’10, FDST, CityUHK-X, UCSD, and MALL. Extensive experiments allow us to present our developed Federated Crowd Counting benchmark as a reference for future research and provide guidance for FL algorithm selection in smart surveillance system deployment.