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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Xiang Qin, Zhijin Tao, Xiaoming Lu, Jianhua Letaief, Khaled B. Tsinghua University Department of Electronic Engineering Beijing China Beijing China State Key Laboratory of Space Network and Communications Beijing China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350351255

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity. © 2024 IEEE.

关键词： Image communication systems

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PRIVACY-PRESERVING ONLINE SHARING CHARGING PILE SCHEME WITH DIFFERENT NEEDS MATCHING

arXiv

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arXiv 2023年

作者： Huang, Zhiyu School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

With the development of electric vehicles, more and more electric vehicles have difficulties in parking and charging. One of the reasons is that the number of charging piles is difficult to support the energy supply of electric vehicles, and a large number of private charging piles have a long idle time, so the energy supply problem of electric vehicles can be solved by sharing charging piles. The shared charging pile scheme uses Paillier encryption scheme and improved scheme to effectively protect user data. The scheme has homomorphism of addition and subtraction, and can process information without decryption. However, considering that different users have different needs, the matching is carried out after calculating the needs put forward by users. This scheme can effectively protect users' privacy and provide matching mechanisms with different requirements, so that users can better match the appropriate charging piles. The final result shows that its efficiency is better than the original Paillier scheme, and it can also meet the security requirements. © 2023, CC BY.

关键词： Piles

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Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle

Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Pa...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Youtian Lin Zuozhuo Dai Siyu Zhu Yao Yao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China Alibaba Group Fudan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce Gaussian-Flow, a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and rendering speeds, our approach harnesses recent advancements in point-based 3D Gaussian Splatting (3DGS). Specifically, a novel Dual-Domain Deformation Model (DDDM) is proposed to explicitly model attribute deformations of each Gaussian point, where the time-dependent residual of each attribute is captured by a polynomial fitting in the time domain, and a Fourier series fitting in the frequency domain. The proposed DDDM is capable of modeling complex scene deformations across long video footage, eliminating the need for training separate 3DGS for each frame or introducing an additional implicit neural field to model 3D dynamics. Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction. Our proposed approach showcases a substantial efficiency improvement, achieving a 5 x faster training speed compared to the per-frame 3DGS modeling. In addition, quantitative results demonstrate that the proposed Gaussian-Flow significantly outperforms previous leading methods in novel view rendering quality. Project page: https://***/projects/Gaussian-Flow.

关键词： Training Deformable models Solid modeling Three-dimensional displays Deformation Frequency-domain analysis Fitting

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Evaluating Learning-to-Rank Models for Prioritizing Code Review Requests using Process Simulation

Evaluating Learning-to-Rank Models for Prioritizing Code Rev...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Lanxin Yang Bohan Liu Junyu Jia Junming Xue Jinwei Xu Alberto Bacchelli He Zhang State Key Laboratory of Novel Software Technology Software Institute Nanjing University China Department of Informatics University of Zurich Switzerland

In large-scale, active software projects, one of the main challenges with code review is prioritizing the many Code Review Requests (CRRs) these projects receive. Prior studies have developed many Learning-to-Rank (LtR) models in support of prioritizing CRRs and adopted rich evaluation metrics to compare their performances. However, the evaluation was performed before observing the complex interactions between CRRs and reviewers, activities and activities in real-world code reviews. Such a pre-review evaluation provides few indications about how effective LtR models contribute to code reviews. This study aims to perform a post-review evaluation on LtR models for prioritizing CRRs. To establish the evaluation environment, we employ Discrete-Event Simulation (DES) paradigm-based software Process Simulation Modeling (SPSM) to simulate real-world code review processes, together with three customized evaluation metrics. We develop seven LtR models and use the historical review orders of CRRs as baselines for evaluation. The results indicate that employing LtR can effectively help to accelerate the completion of reviewing CRRs and the delivery of qualified code changes. Among the seven LtR models, LambdaMART and AdaRank are particularly beneficial for accelerating completion and delivery, respectively. This study empirically demonstrates the effectiveness of using DES-based SPSM for simulating code review processes, the benefits of using LtR for prioritizing CRRs, and the specific advantages of several LtR models. This study provides new ideas for software organizations that seek to evaluate LtR models and other artificial intelligence-powered software ***&materials: https://***/s/a033e99cd2a61e64c8bc.

关键词： Measurement Analytical models Codes Organizations software

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using 1 times1 convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance;ii) Compared to building multi-view representations directly from scratch, extracting them automatically using 1 times1 convolutions is more economical, and may be more effective since 1 times1 convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability. © 2022 IEEE.

关键词： Convolution

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ding, Yi Zhao, Lu Yuan, Liming Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability. © 2022 IEEE.

关键词： Aggregates

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Achieving Distributed and Privacy-Preserving Cross-Chain Transactions in Account-Model Blockchain Systems

Achieving Distributed and Privacy-Preserving Cross-Chain Tra...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Zhang, Chuan Wang, Weijie Zhang, Weiting Nie, Jiangtian Liang, Jinwen Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China Beijing Institute of Technology School of Computer Science & Technology Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Nanyang Technological University School of Computer Science and Engineering Singapore Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350333336

Cross-Chain is an emerging technology to enable transaction circulation across different blockchain systems, breaking down transaction isolation among decentralized ecosystems. Existing cross-chain solutions are often constructed based on centralized trusted third parties, which introduce additional threats to users' identities due to potentially compromised risks on the centralized third parties. To exclude the single-point compromised risk in centralized systems, this paper focuses on a fully decentralized scenario and develops a distributed cross-chain transaction scheme, which enables cross-chain transaction exchanges while ensuring the unlinkability between payees and payers. Specifically, we first propose a cross-chain cryptocurrency exchange scheme by leveraging Ethereum's smart contracts, which achieves transaction exchanges in decentralized settings. Then, we leverage zero-knowledge succinct non-interactive argument of knowledge (i.e., zk-SNARKs) to explore a privacy-preserving method for cross-chain transactions in account-model blockchain systems, such that the unlinkability between payees and payers and the privacy of cross-chain participants can be guaranteed. Security analysis and experimental evaluations demonstrate that the proposed scheme achieves the security goal at acceptable costs. © 2023 IEEE.

关键词： Smart contract

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zsLLMCode: An Effective Approach for Code Embedding via LLM with Zero-Shot Learning

arXiv

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arXiv 2024年

作者： Xian, Zixiang Huang, Rubing Cui, Chenhui Fang, Chunrong Chen, Zhenyu School of Computer Science and Engineering Macau University of Science and Technology Macau Taipa999078 China Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute Guangdong Zhuhai519099 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

The advent of large language models (LLMs) has greatly advanced artificial intelligence (AI) in software engineering (SE), with code embeddings playing a critical role in tasks like code-clone detection and code clustering. However, existing methods for code embedding, including those based on LLMs, often depend on costly supervised training or fine-tuning for domain adaptation. This paper proposes a novel zero-shot approach, zsLLMCode, to generate code embeddings by using LLMs and sentence embedding models. This approach attempts to eliminate the need for task-specific training or fine-tuning, and to effectively address the issue of erroneous information commonly found in LLM-generated outputs. We conducted a series of experiments to evaluate the performance of the proposed approach by considering various LLMs and embedding models. The results have demonstrated the effectiveness and superiority of our method zsLLMCode over state-of-the-art unsupervised approaches such as SourcererCC, Code2vec, InferCode, and TransformCode. Our findings highlight the potential of zsLLMCode to advance the field of SE by providing robust and efficient solutions for code embedding tasks. © 2024, CC BY.

关键词： Zero-shot learning

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From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation

arXiv

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arXiv 2024年

作者： Zhao, Haofei Liu, Yilun Tao, Shimin Meng, Weibin Chen, Yimeng Geng, Xiang Su, Chang Zhang, Min Yang, Hao School of Computer Science and Engineering Northeastern University Shenyang China Huawei China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evolution, QE has yielded a wealth of results. This article provides a comprehensive overview of QE datasets, annotation methods, shared tasks, methodologies, challenges, and future research directions. It begins with an introduction to the background and significance of QE, followed by an explanation of the concepts and evaluation metrics for word-level QE, sentence-level QE, document-level QE, and explainable QE. The paper categorizes the methods developed throughout the history of QE into those based on handcrafted features, deep learning, and Large Language Models (LLMs), with a further division of deep learning-based methods into classic deep learning and those incorporating pre-trained language models (LMs). Additionally, the article details the advantages and limitations of each method and offers a straightforward comparison of different approaches. Finally, the paper discusses the current challenges in QE research and provides an outlook on future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine translation

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Time series classification based on Unet with 2D transformation

Time series classification based on Unet with 2D transformat...

引用

International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Mingsen Du Yunxia Wang Yanxuan Wei Xiaona Wu Cun Ji School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method.

关键词： Accuracy Time series analysis Transforms Feature extraction Propagation losses Data models Data mining Convolutional neural networks Proposals Internet of Things

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