Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semant...Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.展开更多
Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networ...Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.展开更多
Binary Code Similarity Detection(BCSD)is vital for vulnerability discovery,malware detection,and software security,especially when source code is unavailable.Yet,it faces challenges from semantic loss,recompilation va...Binary Code Similarity Detection(BCSD)is vital for vulnerability discovery,malware detection,and software security,especially when source code is unavailable.Yet,it faces challenges from semantic loss,recompilation variations,and obfuscation.Recent advances in artificial intelligence—particularly natural language processing(NLP),graph representation learning(GRL),and large language models(LLMs)—have markedly improved accuracy,enabling better recognition of code variants and deeper semantic understanding.This paper presents a comprehensive review of 82 studies published between 1975 and 2025,systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence(AI)techniques.Particular emphasis is placed on the role of LLMs,which have recently emerged as transformative tools in advancing semantic representation and enhancing detection performance.The review is organized around five central research questions:(1)the chronological development and milestones of BCSD;(2)the construction of AI-driven technical roadmaps that chart methodological transitions;(3)the design and implementation of general analytical workflows for binary code analysis;(4)the applicability,strengths,and limitations of LLMs in capturing semantic and structural features of binary code;and(5)the persistent challenges and promising directions for future investigation.By synthesizing insights across these dimensions,the study demonstrates how LLMs reshape the landscape of binary code analysis,offering unprecedented opportunities to improve accuracy,scalability,and adaptability in real-world scenarios.This review not only bridges a critical gap in the existing literature but also provides a forward-looking perspective,serving as a valuable reference for researchers and practitioners aiming to advance AI-powered BCSD methodologies and applications.展开更多
Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these appro...Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message:"fixing 'null' pointer exception"). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.展开更多
基金supported by the National Natural Science Foundation of China (Nos. NSFC 61925105, 62322109, 62171257 and U22B2001)the Xplorer Prize in Information and Electronics technologiesthe Tsinghua University (Department of Electronic Engineering)-Nantong Research Institute for Advanced Communication Technologies Joint Research Center for Space, Air, Ground and Sea Cooperative Communication Network Technology
文摘Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.
基金supported by the Natural Science Foundation of China under Grants 61971084,62025105,62001073,62272075the National Natural Science Foundation of Chongqing under Grants cstc2021ycjh-bgzxm0039,cstc2021jcyj-msxmX0031+1 种基金the Science and Technology Research Program for Chongqing Municipal Education Commission KJZD-M202200601the Support Program for Overseas Students to Return to China for Entrepreneurship and Innovation under Grants cx2021003,cx2021053.
文摘Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.
文摘Binary Code Similarity Detection(BCSD)is vital for vulnerability discovery,malware detection,and software security,especially when source code is unavailable.Yet,it faces challenges from semantic loss,recompilation variations,and obfuscation.Recent advances in artificial intelligence—particularly natural language processing(NLP),graph representation learning(GRL),and large language models(LLMs)—have markedly improved accuracy,enabling better recognition of code variants and deeper semantic understanding.This paper presents a comprehensive review of 82 studies published between 1975 and 2025,systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence(AI)techniques.Particular emphasis is placed on the role of LLMs,which have recently emerged as transformative tools in advancing semantic representation and enhancing detection performance.The review is organized around five central research questions:(1)the chronological development and milestones of BCSD;(2)the construction of AI-driven technical roadmaps that chart methodological transitions;(3)the design and implementation of general analytical workflows for binary code analysis;(4)the applicability,strengths,and limitations of LLMs in capturing semantic and structural features of binary code;and(5)the persistent challenges and promising directions for future investigation.By synthesizing insights across these dimensions,the study demonstrates how LLMs reshape the landscape of binary code analysis,offering unprecedented opportunities to improve accuracy,scalability,and adaptability in real-world scenarios.This review not only bridges a critical gap in the existing literature but also provides a forward-looking perspective,serving as a valuable reference for researchers and practitioners aiming to advance AI-powered BCSD methodologies and applications.
基金This work was(partially)supported by the Key-Area Research and Development Program of Guangdong Province of China under Grant No.2020B010164002the National Natural Science Foundation of China under Grant Nos.61902441,61722214 and 61976061+2 种基金the China Postdoctoral Science Foundation under Grant No.2018M640855the Fundamental Research Funds for the Central Universities of China under Grant Nos.20wkpy06 and 20lgpy129the Opening Project of Guangdong Key Laboratory of Big Data Analysis and Processing under Grant No.202003.
文摘Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message:"fixing 'null' pointer exception"). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.
