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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Xiaotao Li, Jiang Su, Xiangdong Zhang, Fujun College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Sentence ordering is the task of rearranging a set of unordered sentences into a coherent and logically consistent sequence. Recent work has primarily used pre-trained language models, achieving significant success in the task. However, existing sentence ordering corpora are predominantly in English, and comprehensive benchmark datasets for non-English languages are unavailable. Meanwhile, current datasets often insert specific markers into paragraphs, inadvertently making the logical sequence between sentences more apparent and reducing the models' ability to handle genuinely unordered sentences in real applications. To address these limitations, we develop C3LRSO, a high-quality Chinese sentence ordering dataset that overcomes the aforementioned shortcomings by providing genuinely unordered sentences without artificial segmentation cues. Furthermore, given the outstanding performance of large language models on NLP tasks, we evaluate these models on our dataset for this task. Additionally, we propose a simple yet effective parameter-free approach that outperforms existing methods on this task. Experiments demonstrate the challenging nature of the dataset and the strong performance of our proposed method. These findings highlight the potential for further research in sentence ordering and the development of more robust language models. Our dataset is freely available at https://***/JasonGuo1/C3LRSO. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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intelligent Logistics Service Quality Assurance Mechanism Based on Federated Collaborative Cache in 5G+ Edge Computing Environment 16th

Intelligent Logistics Service Quality Assurance Mechanism Ba...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Liu, Yiwen Fu, Jinrong Zhao, Zikai Yang, Yahui Peng, Ling Qu, Taiguo Feng, Tao School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China School of Foreign Languages Huaihua University Huaihua418000 China

ISBN: (纸本)9783031580529

Aiming at the problem of quality assurance of intelligent logistics service in 5G+ edge computing environment, this paper proposes a mechanism based on federated cooperative cache, which aims to utilize the computing and storage resources of edge nodes to realize rapid processing and sharing of logistics data and improve the efficiency and reliability of logistics services. This paper first analyzes the characteristics and challenges of intelligent logistics services under 5G+ edge computing environment, and then introduces the concept and principle of federated cooperative cache, as well as its application scenarios and advantages in intelligent logistics services. Then, this paper designs an intelligent logistics service quality assurance mechanism based on federated cooperative cache, including five modules such as data partitioning, data transmission, data fusion, data access and data update, and gives the corresponding algorithms and processes. Finally, this paper verifies the effectiveness and performance of the proposed mechanism through simulation experiments. Compared with the traditional centralized cache and distributed cache, the proposed mechanism can reduce the data transmission delay, improve the data hit rate and data consistency, so as to ensure the quality of intelligent logistics services. In the future, the federated collaborative cache mechanism can be further optimized to consider the needs of multiple scenarios. And explore the application potential of other areas to drive the continuous development and innovation of intelligent logistics services. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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Training-free diffusion model adaptation for variable-sized text-to-image synthesis 23

Training-free diffusion model adaptation for variable-sized ...

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Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Zhiyu Jin Xuli Shen Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University and UniDT Technology

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.

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Easy Scaling: The Most Critical Consideration for Choosing Analytical Database Management Systems in the Cloud Era 15

Easy Scaling: The Most Critical Consideration for Choosing A...

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15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Liu, Jie Du, Genyuan Computer Science Division Western Oregon University MonmouthOR United States School of Information Engineering Xuchang University Henan Xuchang China Henan International Joint Laboratory of Polarization Sensing and Intelligent Signal Processing Henan Xuchang China

ISBN: (纸本)9789897586712

Analytical database management systems offer significant advantages for organizations practicing data-driven decision-making. ADBMSs rely on massively parallel processing for performance improvement, increased availability and other computation related resources, and improved scalability and stability. In this position paper, we argue that (1) Gustafson-Barsis' Law aligns well with use cases suitable for Cloud-based ADBMS, still, neither Amdahl's law nor Gustafson's law is sufficient in guiding us on answer the question "how many processors should we use to gain better performance economically", and (2) ADBMS's capability of utilizing parallel processing does not translate directly into easy scaling, specially scaling horizontally by adding more instances or nodes to distribute the workload at will, so when costs are somewhat controllable, allowing easy scaling should be by far the most critical consideration for choosing an ADBMS. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Database systems

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ALEDAR: An Attentions-based Encoder-Decoder and Autoregressive model for workload Forecasting of Cloud Data Center 25

ALEDAR: An Attentions-based Encoder-Decoder and Autoregressi...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Sun, Wei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing China

ISBN: (纸本)9781665405270

Effective workload forecasting can provide a reference for resource scheduling in cloud data centers. Compared with the normal single data center, the multi-data center has a more complicated architecture design and provides more diverse computing services. The traditional load forecasting models need too much manual intervention to set up the parameters, meanwhile, the emerging neural network methods are not sensitive to the prediction scale and cannot capture the long-term associations between input features. To address these challenges, we propose a hybrid model named Attentions-based LSTM Encoder-Decoder network and Autoregressive model (ALEDAR), which combines neural network and statistical learning methods, to analyze the linear and nonlinear characteristics of the load sequence over time in a multi-cloud data center environment. ALEDAR uses a dual attention-based Encoder-Decoder architecture to extract the relationships between historical workload data, the design can also avoid the deterioration of prediction effect caused by long-range data scale, the output layer is composed of a three-layer perceptron. Moreover, ALEDAR employs an autoregressive module to capture the linear trend of the load sequence and eliminate scale insensitivity of input and output data. The experimental results show that our approach is adaptive and can improve the performance of host workload prediction in both single cloud data center and multi-cloud data center environments. We evaluate the performance of the proposed algorithm on real-world data sets and observe consistent improvement of 9.7% - 34.2% over the state-of-the-art baselines. © 2022 IEEE.

关键词： Forecasting

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Lightweight 6-bit S-boxes with DPA Resistance

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IEEE Transactions on Network Science and Engineering 2025年

作者： Yan, Liuyan Li, Lang Song, Qingling Hengyang Normal University College of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Lightweight S-boxes have a significant impact on cryptographic algorithms for resource-constrained devices in the Internet of Things (IoT). Previous research on such S-oxes has focused on low area and good cryptographic properties, which neglects the resistance to side-channel analysis, especially differential power analysis (DPA). The reVisited transparency order (VTO) is one of the best indicators for evaluating S-boxes against DPA attacks so far. Therefore, this paper presents a scheme for designing 6-bit S-boxes. The designed S-boxes are suitable for lightweight block ciphers and have a certain ability to resist DPA attacks. Specifically, we first identify 23 3-bit S-boxes with the optimal cryptographic properties and the smallest hardware area through systematic screening. Then, the paper investigates the constructions of lightweight 6-bit S-boxes based on traditional algorithm structures. Finally, a novel structure named F-LM-F is proposed for designing lightweight 6-bit S-boxes by combining Feistel structure and Lai-Massey structure. It has been proven through comparison that the S-boxes under F-LM-F structure achieve fewer fixed points and lower VTO than the 6-bit S-box of BipBip cipher, with a 17.61% reduction in hardware area and a 13.58% decrease in CPU cycles © 2013 IEEE.

关键词： Side channel attack

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A practical adversarial attack on Graph Neural Networks by attacking single node structure 24

A practical adversarial attack on Graph Neural Networks by a...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Chen, Yang Ye, Zhonglin Zhao, Haixing Meng, Lei Wang, Zhaoyang Yang, Yanlin School of Computer Science Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai810008 China

ISBN: (纸本)9798350319934

In recent years, Graph Neural Networks (GNNs) have performed significantly in node classification tasks. Re-search on GNNs adversarial attacks has recently attracted attention. Many works have demonstrated that single node attacks can fool GNNs in extreme scenarios. Existing single node attacks can be divided into two main types: 1) attacking the node features;2) injecting a single fake node. However, they have several major drawbacks: 1) they set the attack node features, which makes them not efficient;2) they require higher privileges and don't have strict budget constraints, which makes them less concealable and easier to notice. In this paper, we propose an attack with lower privileges and strict budget constraints -Single Node Structure Attack (SNSA). Specifically, SNSA is set more efficient structure attacks and does not require higher privileges to change links. Compared to other attack models, the attack space of SNSA is small and not easily detected by GNNs. Furthermore, we study the influence of the attack node selection on SNSA and observe that it is optimal to use a gradient to select an attack node. Experiments on three common datasets demonstrate the effectiveness of our model. © 2022 IEEE.

关键词： Graph neural networks

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Solar evaporator coupled with strong/weak convection and its cascade utilization

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Science Bulletin 2025年第8期70卷 1264-1274页

作者： Zhengtong Li Jia-Han Zhang Jing Zhang Peng Yan Tao Yang Chao Yang Peng Lin Taotao Meng Meng An Yingtang Zhou Xingtao Xu The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentState Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjing 210098China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal ProcessingSchool of Electronic Information EngineeringInner Mongolia UniversityHohhot 010021China Ningbo Key Laboratory of Green Petrochemical Carbon Emission Reduction Technology and Equipment Zhejiang Institute of Tianjin UniversityNingbo 315201China Department of Mechanical Engineering The University of TokyoTokyo113-8656Japan Marine Science and Technology College Zhejiang Ocean UniversityZhoushan 316022China College of Material Science and Engineering Nanjing Forestry UniversityNanjing 210037China

Modulating the macro/nanoarchitecture of evaporators to effectively harness diverse renewable energy sources is of paramount importance for optimizing the performance of solar-driven interfacial *** by the geometric structure of a windmill,we designed an innovative solar evaporator that expertly harnesses both strong and weak *** the purification of heavy metal wastewater,the maximum evaporation rate can reach 4.95 kg m^(−2)h^(−1)under one sun irradiation by introducing an ultralow wind flow(0.1 m s^(−1)),yielding an evaporation rate that is twice that of traditional ***,the gradual deposition of inorganic salt sediments on the evaporator surface is clearly *** address this issue,we present several innovative proof-of-concept cascade treatments that significantly extend the evaporator’s operational *** innovative design and exceptional performance of this solar evaporator open avenues for advancements in sustainable water treatment,energy generation,and environmental remediation.

关键词： Interfacial evaporation Petiole-based solar evaporators Secondary flow Cascade utilization Wastewater treatment

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Robustness study of non-uniform scale-free hyper-network structure

Robustness study of non-uniform scale-free hyper-network str...

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2022 International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2022

作者： Zhou, Bin Ma, Xiujuan Ma, Fuxiang School of Computer Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

The relationship between the internal structure of the hyper-edge and robustness of the hypernetwork has not yet been investigated. Aiming at this problem, this paper proposes a hypernetwork capacity-load model with non-uniform load distribution. And obtained the robustness of the non-uniform scale-free hyper-network under different internal structures of the hyperedge. The simulation reveals that the robustness of the non-uniform scale-free hyper-network is closely related to the internal structure of the hyper-edge. The non-uniform scale-free hypernetwork is most robust when the nodes inside the hyper-edges are fully connected. The results show that the internal structure of hyper-edge has a large impact on the overall robustness of the non-uniform scale-free hyper-network. © 2022 Copyright for this paper by its authors.

关键词： Artificial intelligence

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HPPNET: MODELING THE HARMONIC STRUCTURE AND PITCH INVARIANCE IN PIANO TRANSCRIPTION 23

HPPNET: MODELING THE HARMONIC STRUCTURE AND PITCH INVARIANCE...

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23rd International Society for Music Information Retrieval Conference, ISMIR 2022

作者： Wei, Weixing Li, Peilin Yu, Yi Li, Wei School of Computer Science and Technology Fudan University China Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9781732729926

While neural network models are making significant progress in piano transcription, they are becoming more resource-consuming due to requiring larger model size and more computing power. In this paper, we attempt to apply more prior about piano to reduce model size and improve the transcription performance. The sound of a piano note contains various overtones, and the pitch of a key does not change over time. To make full use of such latent information, we propose HPPNet that using the Harmonic Dilated Convolution to capture the harmonic structures and the Frequency Grouped Recurrent Neural Network to model the pitch-invariance over time. Experimental results on the MAESTRO dataset show that our piano transcription system achieves state-of-the-art performance both in frame and note scores (frame F1 93.15%, note F1 97.18%). Moreover, the model size is much smaller than the previous state-of-the-art deep learning models. © Weixing Wei, Peilin Li, Yi Yu, and Wei Li.

关键词： Harmonic analysis

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