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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Zhaobo Gan, Rui Yuan, Pingpeng Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9782493814104

Speech recognition is becoming prevalent in daily life. However, due to the similar semantic context of the entities and the overlap of Chinese pronunciation, the pronoun homophone, especially "他/她/它 (he/she/it)", (their pronunciation is "Tā") is usually recognized incorrectly. It poses a challenge to automatically correct them during the post-processing of Chinese speech recognition. In this paper, we propose three models to address the common confusion issues in this domain, tailored to various application scenarios. We implement the language model, the LSTM model with semantic features, and the rule-based assisted Ngram model, enabling our models to adapt to a wide range of requirements, from high-precision to low-resource offline devices. The extensive experiments show that our models achieve the highest recognition rate for "Tā" correction with improvements from 70% in the popular voice input methods up to 90%. Further ablation analysis underscores the effectiveness of our models in enhancing recognition accuracy. Therefore, our models improve the overall experience of Chinese speech recognition of "Tā" and reduce the burden of manual transcription corrections. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Speech recognition

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intelligent Assistant for Multivariant Analysis 26

Intelligent Assistant for Multivariant Analysis

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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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Beamforming made Malicious: Manipulating Wi-Fi Traffic via Beamforming Feedback Forgery 24

Beamforming made Malicious: Manipulating Wi-Fi Traffic via B...

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30th International Conference on Mobile computing and Networking, ACM MobiCom 2024

作者： Xu, Mingming He, Yinghui Li, Xin Hu, Jingzhi Chen, Zhe Xiao, Fu Luo, Jun School of Computer Science Nanjing University of Posts and Telecommunications China Singapore Singapore Intelligent Networking and Computing Research Center and School of Computer Science Fudan University China

ISBN: (纸本)9798400704895

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text beamforming feedback information (BFI): though similar risks have been exposed using emulation platforms (e.g., USRP), they have never proven realistic till this day. In this paper, we propose BeamCraft, the first attack to manipulate traffic in commodity Wi-Fi systems;it differs significantly from existing attacks either staying only on emulation platforms with limited real-world applicability or jamming communications by brute force. The core idea of BeamCraft involves corrupting beamforming decisions by injecting crafted BFIs that feed an access point (AP) with erroneous information on channel states. To mount a covert yet purposeful attack, we develop i) a joint location and transmit power selection strategy to evade detection by victims and ii) a novel BFI forgery method to effectively manipulate AP's beamforming decisions. We implement BeamCraft using commodity Wi-Fi devices and perform extensive evaluations with it;the results reveal that BeamCraft effectively manipulates Wi-Fi traffic while maintaining a low exposure rate. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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LBA-EC: Load Balancing Algorithm Based on Weighted Bipartite Graph for Edge computing

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Chinese Journal of Electronics 2023年第2期32卷 313-324页

作者： SHAO Sisi LIU Shangdong LI Kui YOU Shuai QIU Huajie YAO Xiaoliang JI Yimu School of Computer Science Nanjing University of Posts and Telecommunications Institue of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center

Compared with cloud computing environment, edge computing has many choices of service providers due to different deployment environments. The flexibility of edge computing makes the environment more complex. The current edge computing architecture has the problems of scattered computing resources and limited resources of single computing node. When the edge node carries too many task requests, the makespan of the task will be delayed. We propose a load balancing algorithm based on weighted bipartite graph for edge computing(LBA-EC), which makes full use of network edge resources, reduces user delay, and improves user service experience. The algorithm is divided into two phases for task scheduling. In the first phase, the tasks are matched to different edge servers. In the second phase, the tasks are optimally allocated to different containers in the edge server to execute according to the two indicators of energy consumption and completion time. The simulations and experimental results show that our algorithm can effectively map all tasks to available resources with a shorter completion time.

关键词： Energy consumption Processor scheduling Load management Scheduling Bipartite graph Delays Servers

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ECLAIR: A Centralized Al-Powered Recommendations System in a Multi-Node EXAIT System 31

ECLAIR: A Centralized Al-Powered Recommendations System in a...

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31st International Conference on Computers in Education, ICCE 2023

作者： Wijerathne, Isanka Flanagan, Brendan Dai, Yiling Ogata, Hiroaki Academic Center for Computing Media Studies Kyoto University Japan Center for Innovative Research and Education in Data Science Kyoto University Japan

ISBN: (纸本)9786269689019

Educational recommender systems are increasingly becoming a core feature of modern educational systems. Often the recommender component of a system is tightly integrated, or might be remotely located without accessing data from other local systems This paper proposes a framework called ECLAIR in which local educational systems can work and share data with a global recommender system that spans multiple educational institutions. In particular an Al-driven recommendation system is intricately integrated within a multi-node learning management system. Situated at the intersection of large-scale data analysis and personalized education, ECLAIR efficiently processes heterogeneous data from diverse LMS, while ensuring data security and privacy. The proposed ECLAIR's architecture, data pipeline, and processing mechanisms are explored in detail, focusing on its seamless integration with the existing infrastructure. By leveraging MongoDB change streams and relational databases, ECLAIR guarantees real-time data synchronization, secure storage, and efficient processing. Its unique Ingestor tool transforms selected xAPI data points into a relational table format, bolstering system functionality. The successful launch of ECLAIR serves as a testament to the potential of Al in enhancing personalized learning experiences, improving data security, and bolstering system efficiency. Nevertheless the paper emphasizes the need for ongoing research, specifically concerning privacy-preserving mechanisms and efficient management of data heterogeneity. It demonstrates ECLAIRs pivotal role in the rapidly evolving landscape of eLearning and its potential for future advancements, scalability, and adaptability, setting a new precedent for Al-powered eLearning recommendation systems. © 2023 Asia-Pacific Society for Computers in Education.

关键词： Recommender systems

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KunquDB: An Attempt for Speaker Verification in the Chinese Opera Scenario 27th

KunquDB: An Attempt for Speaker Verification in the Chine...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhou, Huali Lin, Yuke Liu, Dong Li, Ming School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China

ISBN: (纸本)9783031783463

This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations. We introduce KunquDB, https://***/KunquDB, a relatively large-scale, well-annotated audio-visual dataset comprising 339 speakers and 128 h of content. Originating from the Kunqu Opera Art Canon (Kunqu yishu dadian), KunquDB is meticulously structured by dialogue lines, providing explicit annotations including character names, speaker names, gender information, vocal manner classifications, and accompanied by preliminary text transcriptions. KunquDB provides a versatile foundation for role-centric acoustic studies and advancements in speech-related research, including Automatic Speaker Verification (ASV). Beyond enriching opera research, this dataset bridges the gap between artistic expression and technological innovation. Pioneering the exploration of ASV in Chinese opera, we construct four test trials considering two distinct vocal manners in opera voices: stage speech (ST) and singing (S). Implementing domain adaptation methods effectively mitigates domain mismatches induced by these vocal manner variations while there is still room for further improvement as a benchmark. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Speech recognition

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TIGA: Towards Efficient Near data Processing in SmartNICs-based Disaggregated Memory Systems 24

TIGA: Towards Efficient Near Data Processing in SmartNICs-ba...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Duan, Zhuohui Yu, Zelin Liu, Haikun Liao, Xiaofei Jin, Hai Zheng, Shijie Wu, Sihan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400706011

Memory disaggregation, facilitated by Smart Network Interface Cards (SmartNICs), has emerged as a cost-effective approach for sharing memory resources in data centers. However, current SoC-based SmartNICs face several challenges for supporting near-data processing (NDP) in disaggregated memory (DM) systems effectively, such as inefficient resource allocation for SmartNICs employed in NDP, and the lack of collaboration between SmartNICs on data nodes and CPUs on compute nodes. To address these issues, we propose TIGA, an efficient NDP framework for SmartNICs-based disaggregated memory systems. We propose an adaptive resource allocator to fully utilize the SoC cores among NDP engines automatically, and a SmartNIC-CPU cooperative computing mechanism to schedule NDP tasks among CPUs and SmartNICs. We prototype TIGA with FPGAs and evaluate it with several typical workloads. Experimental results show that TIGA significantly improves the efficiency of NDP tasks in DM systems compared with state-of-the-art SmartNIC-based co-processing schemes. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Storage allocation (computer)

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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

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International Journal of Hybrid intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning 7

Tensor Graph Convolutional Network for Dynamic Graph Represe...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application For Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

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Federated Neuro-Symbolic Learning 41

Federated Neuro-Symbolic Learning

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41st International Conference on Machine Learning, ICML 2024

作者： Xing, Pengwei Lu, Songtao Yu, Han College of Computing and Data Science Nanyang Technological University Singapore IBM Thomas J. Watson Research Center Yorktown Heights United States

Neuro-symbolic learning (NSL) models complex symbolic rule patterns into latent variable distributions by neural networks, which reduces rule search space and generates unseen rules to improve downstream task performance. Centralized NSL learning involves directly acquiring data from downstream tasks, which is not feasible for federated learning (FL). To address this limitation, we shift the focus from such a one-to-one interactive neuro-symbolic paradigm to one-to-many Federated Neuro-Symbolic Learning framework (FedNSL) with latent variables as the FL communication medium. Built on the basis of our novel reformulation of the NSL theory, FedNSL is capable of identifying and addressing rule distribution heterogeneity through a simple and effective Kullback-Leibler (KL) divergence constraint on rule distribution applicable under the FL setting. It further theoretically adjusts variational expectation maximization (V-EM) to reduce the rule search space across domains. This is the first incorporation of distribution-coupled bilevel optimization into FL. Extensive experiments based on both synthetic and real-world data demonstrate significant advantages of FedNSL compared to five state-of-the-art methods. It outperforms the best baseline by 17% and 29% in terms of unbalanced average training accuracy and unseen average testing accuracy, respectively. Copyright 2024 by the author(s)

关键词： Federated learning

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