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Discriminative Word Alignment Over Multiple Word Segmentations

Chinese Journal of Electronics 2025年第2期23卷 263-270页

作者： Ning Xi Xinyu Dai Shujian Huang Jiajun Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Conventional bilingual word alignment is conducted on sentence pairs with single word segmentation for languages such as Chinese, viz. Single-segmentation-based word alignment (SSWA). However, SSWA may run the risk of losing optimal word segmentation granularities or causing data sparseness in word alignment. This paper proposes Multiple-segmentation-based word alignment (MSWA). In MSWA, diverse and complementary knowledge in multiple word segmentations can be employed to lower the above risks in word alignment. Given $k$ word segmentations of a Chinese sentence, a skeleton segmentation is firstly constructed. The alignment between the skele-ton segmentation and the parallel English sentence is log-linearly modeled, where various features defined over multiple word segmentations are incorporated. The Viterbi alignment, the alignment with the highest score, is mapped back to $k$ word alignments based on $k$ segmentations respectively. Experimentally, MSWA outperformed SSWA on all $k$ segmentations in both alignment quality and translation performance.

关键词： Translation Viterbi algorithm Social sciences Education Medical services Skeleton

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Causal Label Enhancement

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 3933-3946页

作者： Liu, Xinyuan Zhu, Jihua Zheng, Qinghai Li, Zhongyu Tian, Zhiqiang Zhu, Mingchen Xi’an Jiaotong University School of Software Engineering Xi’an710049 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210008 China Fuzhou University College of Computer and Data Science Fuzhou350025 China University of California DavisCA95616 United States

Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM. © 1989-2012 IEEE.

关键词： Contrastive Learning

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Low-Carbon Geographically Distributed Cloud-Edge Task Scheduling 1

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhu, Yingjie Qi, Ji Wang, Zehao Wei, Shengjie Chen, Yan Cao, Tuo Luo, Gangyi Qian, Zhuzhong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Technology Co. Ltd. Jiangsu Suzhou China

ISBN: (数字)9789819615513

ISBN: (纸本)9789819615506

Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers are powered by renewable energy sources, due to the difficulties of using traditional power supplies or because of advancements in energy harvesting technologies. These green edge servers can cut down carbon emissions by processing tasks locally, but the inherent limitations of their computing capacity result in some tasks having to be uploaded to a data center to meet service-level agreement (SLA) requirements. To further reduce carbon emissions in cloud-edge systems, scheduling tasks to those low-carbon data centers while meeting latency constraints is highly beneficial. In this paper, we propose a low-carbon cloud-edge scheduling algorithm that utilizes Lyapunov optimization techniques and Markov approximation to address the long-term optimization problem of carbon emissions. Our algorithm guarantees provable performance, and simulation results demonstrate its effectiveness in striking a balance between carbon emissions and task latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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A fully connected polarization-entangled network via integrated spontaneous four-wave mixing engineering

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 72-79页

作者： Pingyu Zhu Yan Wang Yuxing Du Miaomiao Yu Kaikai Zhang Kun Wang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Hefei National Laboratory Hefei 230088China

Quantum communication is rapidly developing and is gradually being commercialized due to its technological *** dense communication links among multiple users in a scalable and efficient way is of great significance for realizing a large-scale quantum communication ***,we propose a novel scheme to construct a fully connected polarizationentangled network,utilizing the engineering of spontaneous four-wave mixings(SFWMs)and a path-polarization *** does not require active optical switches which limit the communication speed,or trusted nodes which lead to potential security *** required frequency channels in the network grow linearly with the number of *** experimentally demonstrate a six-user fully connected network with on-chip SFWM processes motivated by four *** user in the network receives a frequency channel,and all fifteen connections between the users are implemented *** work opens up a promising scheme to efficiently construct fully connected large-scale networks.

关键词： quantum network quantum entanglement nonlinear optics integrated optics

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Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

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Digital Communications and Networks 2024年第2期10卷 380-388页

作者： Renwan Bi Mingfeng Zhao Zuobin Ying Youliang Tian Jinbo Xiong Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Faculty of Data Science City University of Macao999078MacaoChina State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

关键词： Mobile edge crowdsensing Dynamic privacy measurement Personalized privacy threshold Privacy protection Reinforcement learning

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New Offloading Method of Computing Task Based on Gray Wolf Hunting Optimization Mechanism for the IOV

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2264-2277页

作者： Zhang, Jie Zhang, De-Gan Qiao, Meng Hong-Lin, E. Zhang, Ting Zhang, Ping University of Macau Key Laboratory of Internet of Things for Smart City Faculty of Science and Technology China Tianjin University of Technology Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology Tianjin300384 China Tianjin University of Sport School of Sports Economics and Management Tianjin301617 China

Task offloading, as an effective solution, provides low latency and sufficient computing resources for mobile users in the network. However, how to reasonably offload to reduce system overhead is a challenging issue today. This article takes user terminals, edge servers, and idle vehicles with resources as the network structure, and is inspired by the highly social nature of the gray wolf pack. It proposes a new offloading method of edge computing task based on hunting optimizing mechanism of gray wolf for the Internet of Vehicle (IOV). Firstly, an adaptive weight factor is proposed to balance the weight ratio of delay and energy consumption in the system cost under the constraints of delay and energy consumption. With delay and computing resources of vehicles and servers as constraints, a multi constraint minimization system cost problem is proposed. Secondly, the hunting process of the gray Wolf optimization algorithm is used to find the optimal solution of the unloading scheme, The Levy flight strategy was added to enhance the global search ability of the algorithm, and a dynamic weight strategy was introduced to improve the convergence performance of the algorithm. Finally, the improved gray Wolf optimization algorithm was used to solve the optimal unloading plan and minimum cost. The simulation results show that compared with traditional gray Wolf optimization algorithm offloading schemes, the proposed scheme in this paper requires lower system costs. © 2004-2012 IEEE.

关键词： Mobile edge computing

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mmMIC: Multi-modal Speech Recognition based on mmWave Radar 42

mmMIC: Multi-modal Speech Recognition based on mmWave Radar

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Fan, Long Xie, Lei Lu, Xinran Li, Yi Wang, Chuyu Lu, Sanglu Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

With the proliferation of voice assistants, microphone-based speech recognition technology usually cannot achieve good performance in the situation of multiple sound sources and ambient noises. In this paper, we propose a novel mmWave-based solution to perform speech recognition to tackle the issues of multiple sound sources and ambient noises, by precisely extracting the multi-modal features from lip motion and vocal-cords vibration from the single channel of mmWave. We propose a difference-based method for feature extraction of lip motion to suppress the dynamic interference from body motion and head motion. We propose a speech detection method based on cross-validation of lip motion and vocal-cords vibration so as to avoid wasting computing resources on nonspeaking activities. We propose a multi-modal fusion framework for speech recognition by fusing the signal features from lip motion and vocal-cords vibration with the attention mechanism. We implemented a prototype system and evaluated the performance in real test-beds. Experiment results show that the average speech recognition accuracy is 92.8% in realistic environments. © 2023 IEEE.

关键词： Speech recognition

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ASR: Efficient and Adaptive Stochastic Resonance for Weak Signal Detection 42

ASR: Efficient and Adaptive Stochastic Resonance for Weak Si...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Chen, Xingyu Liu, Jia Zhang, Xu Chen, Lijun Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Weak-signal detection underlies a variety of ubiquitous computing applications, such as wireless sensing and machinery fault diagnosis. Stochastic resonance (SR) provides a new way for weak-signal detection by boosting undetectable signals with added white noise. However, existing work has to take a long time to search optimal parameter settings for SR, which cannot fit well some time-critical applications. In this paper, we propose an adaptive SR scheme (ASR) that can amplify the original signal at a low cost in time. The basic idea is that we find that the potential parameter is a key factor that determines the performance of SR. By treating the system as a feedback loop, we can dynamically adjust the potential parameters according to the output signals and make SR happen adaptively. ASR answered two technical questions: how can we evaluate the output signal and how can we tune the potential parameters quickly towards the optimal. In ASR, we first design a spectral-analysis based solution to examine whether SR happens using continuous wavelet transform. After that, we reduce the parameter tuning problem to a constrained non-linear optimization problem and use the sequential quadratic programming to iteratively optimize the potential parameters. We implement ASR and apply it in two ubiquitous computing applications: respiration-rate detection and machinery fault diagnosis. Extensive experiments show that ASR outperforms the state-of-the-art. © 2023 IEEE.

关键词： Spectrum analysis

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DenseNet-based RFID Grouping Protocols 23

DenseNet-based RFID Grouping Protocols

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2023 ACM Turing Award Celebration Conference, CHINA 2023

作者： Wang, Tianyu Liu, Jia State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798400702334

The grouping protocol in RFID systems is to label tags according to a given partition so that tags in the identical group hold the same group ID, which makes multi-cast transmissions or aggregate queries possible and thereby improves time efficiency. Existing grouping protocols need to inform each tag individually or each group by transmitting extra filter vectors, which suffer degraded performance. In this paper, we propose a DenseNet-based grouping protocol that labels tags concurrently. The basic idea is to treat the grouping problem as a classification task. The partition information known by an RFID reader is used to train data and obtain a classifier, of which parameters can be broadcast to all tags simultaneously. Each tag can calculate its own group ID according to the parameters. Experimental results demonstrate that our grouping protocol can reduce the execution time by 53% in a real-world RFID system with about 100,000 tags, compared with the state-of-The-Art. © 2023 Owner/Author.

关键词： Efficiency

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AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization 42

AGO: Boosting Mobile AI Inference Performance by Removing Co...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xu, Zhiying Peng, Hongding Wang, Wei Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e.g., each subgraph can only contain at most one complex operator. In this paper, we propose AGO, a framework for graph optimization with arbitrary structures to boost the inference performance of deep models by removing such constraints. To create new optimization opportunities for complicated subgraphs, we propose intensive operator fusion, which effectively stitches multiple complex operators together for better performance. Further, we design a graph partitioning scheme that allows an arbitrary structure for each subgraph while guaranteeing the acyclic property among all generated subgraphs. Additionally, to enable efficient performance tuning for complicated subgraphs, we devise a divide-and-conquer tuning mechanism to orchestrate different system components. Through extensive experiments on various neural networks and mobile devices, we show that our system can improve the inference performance by up to 3.3× when compared with state-of-the-art vendor libraries and deep compilers. © 2023 IEEE.

关键词： Deep learning

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