Reviewerly/exHarmony huggingface dataset:
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels

Authors:
raw_author_name: Vasileios Tsouvalas
raw_affiliation_string: Eindhoven University of Technology, Eindhoven, The Netherlands
raw_author_name: Aaqib Saeed
raw_affiliation_string: Eindhoven University of Technology, Eindhoven, The Netherlands
raw_author_name: Tanir Ozcelebi
raw_affiliation_string: Eindhoven University of Technology, Eindhoven, The Netherlands
raw_author_name: Nirvana Meratnia
raw_affiliation_string: Eindhoven University of Technology, Eindhoven, The Netherlands

Abstract: Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices, in reality, label noise can naturally occur in FL and is closely related to clients’ characteristics. Due to scarcity of available data and significant label noise variations among clients in FL, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, whereas prior FL studies rely on excessive on-device computational schemes or additional clean data available on the server. We propose FedLN , a framework to deal with label noise across different FL training stages, namely FL initialization, on-device model training, and server model aggregation, able to accommodate the diverse computational capabilities of devices in an FL system. Specifically, FedLN computes per-client noise level estimation in a single federated round and improves the models’ performance by either correcting or mitigating the effect of noisy samples. Our evaluation on various publicly available vision and audio datasets demonstrates a 22% improvement on average compared to other existing methods for a label noise level of 60%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 4.8% increase on average in models’ recognition performance, highlighting that FedLN can be useful for improving FL services provided to everyday users.
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

Authors:
raw_author_name: Zhichao Han
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Liuao Pei
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Chengyang Li
raw_affiliation_string: Huzhou Institute, Zhejiang University, Huzhou, China
raw_author_name: Changjia Ma
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Chao Xu
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Shaojie Shen
raw_affiliation_string: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
raw_author_name: Fei Gao
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

Abstract: As a fundamental component of autonomous driving systems, motion planning has garnered significant attention from both academia and industry. This paper focuses on efficient and spatial-temporal optimal trajectory optimization in unstructured environments using compact convex approximations of vehicle shapes. Conventional approaches typically model the task as an optimal control problem by discretizing the motion process in state configuration space. However, this often results in a tradeoff between optimality and efficiency since generating high-quality motion trajectories often requires high-precision discretization of the dynamic process, which imposes a substantial computational burden. To address this issue, we leverage the differential flatness property of car-like robots to simplify the trajectory representation and analytically formulate the spatial-temporal joint optimization problem with flat outputs in a compact manner, while ensuring the feasibility of nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a collision-free driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.
Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning

Authors:
raw_author_name: Jiahui He
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
raw_author_name: Zhijun Cheng
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
raw_author_name: Bo Guo
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Abstract: Anomaly detection based on telemetry data is a major issue in satellite health monitoring, given that it can identify unusual or unexpected events to avoid serious accidents and ensure the safety and reliability of operations. This study proposes a jointly optimal one-class support vector machine with dictionary learning (JODL-OC) framework for multivariate anomaly detection in telemetry. Dictionary learning (DL) extracts the correlation and local dynamics of time series into a sparse matrix, and the one-class support vector machine (OCSVM) detects anomalies in the transformed sparse space. The optimisation objective of DL is modified by introducing a novel regularisation term called soft-boundary OCSVM, so that the JODL-OC seamlessly integrates DL, sparse representation, and classifier training into a unified model to jointly learn the features and decision boundary. Furthermore, we propose an iterative approach to solve the optimisation function and obtain the optimal JODL-OC framework for anomaly detection. A case analysis based on satellite antenna telemetry data reveals that the proposed method improves the detection precision, F1-score, FPR and FAR compared to other existing anomaly detection methods, particularly to illustrate the superior performance in correlation anomalies.
A deep model towards accurate boundary location and strong generalization for medical image segmentation

Authors:
raw_author_name: Bing Wang
raw_affiliation_string: College of Mathematics and Information Science, Hebei University, Baoding, 071000, Hebei, China
raw_author_name: Peipei Geng
raw_affiliation_string: College of Mathematics and Information Science, Hebei University, Baoding, 071000, Hebei, China
raw_author_name: Ying Yang
raw_affiliation_string: Hebei University Affiliated Hospital, Baoding, 071000, Hebei, China
raw_author_name: Xuedong Tian
raw_affiliation_string: College of Cyber Security and Computer, Hebei University, Baoding, 071000, Hebei, China
raw_author_name: Guochun Zhang
raw_affiliation_string: College of Mathematics and Information Science, Hebei University, Baoding, 071000, Hebei, China

Abstract: Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis and monitoring. However, due to the complexity of medical images and the limitations of image acquisition, most of the current segmentation models still face the challenges of imprecise boundary positioning and poor generalization ability, which greatly affect their application in clinical practices. To address these issues, we propose a new deep learning-based medical image semantic segmentation network (BLG-Net). To improve the accuracy of object boundary location, a Local–global module (Logo) is proposed. It uses the combination of a self-attention mechanism and a cross-attention mechanism to model intra-sample and inter-sample long-range dependencies from local and global perspectives, respectively, to help recover the spatial structures. To further enhance the generalization ability of the BLG-Net, we develop a generalization enhancement module (GEM), which can adaptively adjust the classifier weights of the network according to the features of the test images extracted during the test process to alleviate the distribution shift issues. Besides, we design a multi-granularity loss (MGL) function, which can guide the network to learn the object region and its boundary details at the pixel, patch, and map levels, respectively. Extensive experiments on five medical image segmentation tasks demonstrate the improvements of our method. We further verify the model’s generalization ability to unseen data on two other popular datasets with different modalities.
A new approach of contrast enhancement for Medical Images based on entropy curve

Authors:
raw_author_name: Bhupendra Gupta
raw_affiliation_string: PDPM Indian Institute of Information Technology Design and Manufacturing, Dumna Airport Road, Jabalpur, 482005, India
raw_author_name: Subir Singh Lamba
raw_affiliation_string: PDPM Indian Institute of Information Technology Design and Manufacturing, Dumna Airport Road, Jabalpur, 482005, India

Abstract: X-ray, MRI, and CT scan images etc, are widely used for the detection of various prevalent diseases. The efficient identification of the disease depends very much on the quality of the image. Sometimes due to environmental conditions such as insufficient and nonuniform illumination, shacking and movement of objects or the imaging device, and some other fault in the device, the resultant image may have low contrast and the presence of noise or artifacts. In such situation, a contrast enhancement algorithm that can efficiently improve the contrast without increasing noise and artifacts can play an important role. In this article, we propose a simple yet efficient method for these types of images by using the image’s entropy curve and homomorphic filtering. It enhances the contrast of medical images without increasing the content of noise and artifacts. Also, the performance of the proposed method is evaluated by a complete qualitative and quantitative analysis.
Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation

Authors:
raw_author_name: Ping Li
raw_affiliation_string: Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
raw_author_name: Yu Zhang
raw_affiliation_string: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
raw_author_name: Li Yuan
raw_affiliation_string: School of Electrical and Computer Engineering, Peking University, Beijing, China
raw_author_name: Huaxin Xiao
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha, China
raw_author_name: Binbin Lin
raw_affiliation_string: School of Software Technology, Zhejiang University, Hangzhou, China
raw_author_name: Xianghua Xu
raw_affiliation_string: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

Abstract: Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial–temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high efficiency.
NeatIBP 1.0, a package generating small-size integration-by-parts relations for Feynman integrals

Authors:
raw_author_name: Yang Zhang
raw_affiliation_string: Interdisciplinary Center for Theoretical Study, University of Science and Technology of China, Hefei, Anhui 230026, China; Max-Planck-Institut für Physik, Werner-Heisenberg-Institut, D-80805, München, Germany; Peng Huanwu Center for Fundamental Theory, Hefei, Anhui 230026, China

Abstract: In this work, we present the package NeatIBP, which automatically generates small-size integration-by-parts (IBP) identities for Feynman integrals. Based on the syzygy and module intersection techniques, the generated IBP identities' propagator degree is controlled and thus the size of the system of IBP identities is shorter than that generated by the standard Laporta algorithm. This package is powered by the computer algebra systems Mathematica and Singular, and the library SpaSM. It is parallelized on the level of Feynman integral sectors. The generated small-size IBP identities can subsequently be used for either finite field reduction or analytic reduction. We demonstrate the capabilities of this package on several multi-loop IBP examples. Program Title: NeatIBP CPC Library link to program files: https://doi.org/10.17632/ms85fpfm7b.1 Developer's repository link: https://github.com/yzhphy/NeatIBP Licensing provisions: GPLv3 Programming language: Mathematica, Singular, C Nature of problem: The difficulty of the IBP reduction of Feynman integrals stems from the large size of the IBP system. Solution method: We apply the module intersection method to generate IBP relations by algebraic geometry methods, and compare the smaller IBP system we generate with that from Laporta's algorithm. NeatIBP generates the module intersection in Singular [1] and selects relevant and independent IBP relations via an algorithm relying on the library SpaSM [2]. The workflow is parallelized by a task manager written in Mathematica [3]. W. Decker, G.-M. Greuel, G. Pfister, and H. Schönemann, Singular 4-3-1 – A computer algebra system for polynomial computations, http://www.singular.uni-kl.de, 2022. The SpaSM group, SpaSM: a Sparse direct Solver Modulo p, http://github.com/cbouilla/spasm, 2017. Wolfram Research, Mathematica 13.2, https://www.wolfram.com/mathematica/.
Light-field 3D image parallel encryption based on the state transition diagram of Maximum Length Cellular Automata

Authors:
raw_author_name: Ying Cui
raw_affiliation_string: Southwest China Institute of Electronic Technology, Chengdu, 610036, China
raw_author_name: Junfeng Guo
raw_affiliation_string: School of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China
raw_author_name: Qiming Wu
raw_affiliation_string: School of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China
raw_author_name: Xiang Dai
raw_affiliation_string: Southwest China Institute of Electronic Technology, Chengdu, 610036, China
raw_author_name: Chunbao Li
raw_affiliation_string: Southwest China Institute of Electronic Technology, Chengdu, 610036, China
raw_author_name: Xiaowei Li
raw_affiliation_string: School of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China
raw_author_name: Shouxin Liu
raw_affiliation_string: School of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China

Abstract: In our work, we proposed a parallel encryption scheme based on the state transition diagram of Maximum Length Cellular Automata (MLCA) to protect 3D images. Compared with the previous random mode decomposition (RMD) encryption scheme and DNA sequences and chaotic systems scheme, our scheme effectively reduces the time consumption, especially for the RME scheme, by up to 5 times. Meanwhile, leveraging MLCA's abundant rules, high parallelism, and straightforward hardware implementation, our encryption scheme exhibits flexible key selection, parallel processing capabilities, and easy hardware implementation. The results of key space analysis, differential attack analysis, and statistical analysis verify the high-security level achieved by our proposed parallel encryption scheme.
A dynamic state sharding blockchain architecture for scalable and secure crowdsourcing systems

Authors:
raw_author_name: Zihang Zhen
raw_affiliation_string: College of Computer and Cyber Security, Fujian Normal University, Engineering Research Center of Cyber Security and Education Informatization, Fujian Province University, Fuzhou, Fujian, 350117, China
raw_author_name: Xiaoding Wang
raw_affiliation_string: College of Computer and Cyber Security, Fujian Normal University, Engineering Research Center of Cyber Security and Education Informatization, Fujian Province University, Fuzhou, Fujian, 350117, China
raw_author_name: Hui Lin
raw_affiliation_string: College of Computer and Cyber Security, Fujian Normal University, Engineering Research Center of Cyber Security and Education Informatization, Fujian Province University, Fuzhou, Fujian, 350117, China
raw_author_name: Sahil Garg
raw_affiliation_string: Electrical Engineering Department, École de Technologie Supérieure, Montreal, Canada
raw_author_name: Prabhat Kumar
raw_affiliation_string: Department of Software Engineering, LUT School of Engineering Science, LUT University, 53850 Lappeenranta, Finland
raw_author_name: M. Shamim Hossain
raw_affiliation_string: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia

Abstract: Currently, the crowdsourcing system has serious problems such as single point of failure of the server, leakage of user privacy, unfair arbitration, etc. By storing the interactions between workers, requesters, and crowdsourcing platforms in the form of transactions on the blockchain, these problems can be effectively addressed. However, the improvement in total computing power on the blockchain is difficult to provide positive feedback to the efficiency of transaction confirmation, thereby limiting the performance of crowdsourcing systems. On the other hand, the increasing amount of data in blockchain further increases the difficulty of nodes participating in consensus, affecting the security of crowdsourcing systems. To address the above problems, in this paper we design a blockchain architecture based on dynamic state sharding, called DSSBD. Firstly, we solve the problems caused by cross sharding transactions and reconfiguration in blockchain state sharding through graph segmentation and relay transactions. Then, we model the optimal block generation problem as a Markov decision process. By utilizing deep reinforcement learning, we can dynamically adjust the number of shards, block spacing, and block size. This approach helps improve both the throughput of the blockchain and the proportion of non-malicious nodes. Security analysis has proven that the proposed DSSBD can effectively resist attacks such as transaction atomic attacks, double spending attacks, sybil attacks, replay attacks, etc. The experimental results show that the crowdsourcing system with the proposed DSSBD has better performance in throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, etc., while ensuring security.
APGVAE: Adaptive disentangled representation learning with the graph-based structure information

Authors:
raw_author_name: Qiao Ke
raw_affiliation_string: School of Mathematics and Statistics, Northwestern Polytechnical University, China
raw_author_name: Xinhui Jing
raw_affiliation_string: School of Software, Northwestern Polytechnical University, China
raw_author_name: Marcin Woźniak
raw_affiliation_string: Faculty of Applied Mathematics, Silesian University of Technology, Poland
raw_author_name: Shuang Xu
raw_affiliation_string: School of Mathematics and Statistics, Northwestern Polytechnical University, China
raw_author_name: Yunji Liang
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, China
raw_author_name: Jiangbin Zheng
raw_affiliation_string: School of Software, Northwestern Polytechnical University, China

Abstract: Neural networks are used to learn task-oriented high-level representations in an end-to-end manner by building a multi-layer neural network. Generation models have developed rapidly with the emergence of deep neural networks. But it still has problems with the insufficient authenticity of generated images, the deficiency of diversity, consistency, and unexplainability in the generation process. Disentangled representation is an effective method to learn a high-level feature representation and realize the interpretability of deep neural networks. We propose a general disentangled representation learning network with variational autoencoder network as the basic framework for the image generation process. The graph-based structure of the priors is embedded in the last module of the deep encoder network to build the feature spaces by the class, task-oriented, and task-unrelated information respectively. Meanwhile the priors should be adaptively modified with the task relevance of a generated image. And the semi-supervised learning is further involved in the disentangled representation network framework to reduce the requirements of label and extend the majority of feature space under the task-unrelated feature assumption. Experimental results show that the proposed method is efficient for various types of images and has a good potential for further research and development.