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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Xie, Heng Cui, Jizhou Cao, Yuhang Chen, Junjie Tao, Jianhua Fan, Cunhang Liu, Xuefei Wen, Zhengqi Lu, Heng Yang, Yuguang Lv, Zhao Li, Yongwei Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China School of Information Science and Technology ShanghaiTech University Shanghai China Ximalaya Inc. Shanghai China College of Computer and Information Engineering Tianjin Normal University Tianjin China Department of Automation Tsinghua University Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400702709

Sentiment analysis plays a crucial role in interpreting human interactions across different modalities. This paper addresses the Cross-Cultural Multilingual Humour Detection Sub-Challenge in the MuSe 2023 Multi-modal Sentiment Analysis Challenge, which aims to identify humor instances in cross-cultural situations using multimodal data. In this paper, we explore the unique window length processing of audio data to more accurately and fully capture humor information. Furthermore, we employ a data augmentation technique for text to bridge the gap between the train set and the test set. This augmentation involves converting German and English samples in the database to each other, as well as translating both languages into Chinese. These techniques aim to enhance the model's generalization across different languages and alleviate the scarcity of humor samples. For multimodal fusion, we use the late fusion technique applied to different Gated Recurrent Unit (GRU) models with modality-specific weights obtained through gradient descent. Experimental results demonstrate the effectiveness of our approach, achieving an Area Under the Curve (AUC) score of 0.872 on the MuSe-Humor test set. This performance ranked second place in the sub-challenge. © 2023 ACM.

关键词： Sentiment analysis

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Ultra Low Power High Speed DFT Implementation For ASIC SoC 9

Ultra Low Power High Speed DFT Implementation For ASIC SoC

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rao, K. Madhava Sampath Kumar, Pattem Reddy, T. Vasudeva Nilima, D. Saikumar, K. Khlaif, Abbas Mahdi B V Raju Institute of Technology Dept of Ece Medak dt Telangana Narsapur502313 India Malla Reddy College of Engineering Department of Ece Maisammaguda Secunderabad Dhulapally500100 India Mlr Institute of Technology Department of Computer Science And Information Technology Telangana Hyderabad India Malla Reddy University School of Engineering Computer Science Dulapally Maisammaguda Telangana Hyderabad500100 India Al-Ayen University Scientific Research Center Thi-Qar64001 Iraq

ISBN: (纸本)9798350394474

An ultra-low power DFT architecture suitable for MIMO-OFDM applications & it was introduced in this study. Several radix such as 8/16/32 dependent optimization methods are performed to create variable lengths of 4k/8k points, a necessity in today's 4G and 5G communication technologies, which are plagued by a number of power and performance concerns with DFT processors. These technologies are widely used but have a higher energy footprint than others. Consequently, this work uses a novel approach called "SBOX"and the concept of parallelism to boost performance and reduce power consumption, all of which are essential for an advanced DFT architecture. Parallelism and the proposed SBOX model allow for fewer multiplications and faster processing times. Cadence-allegro-17.2 is used for the design and verification of the full implementation;this software is also used for simulation, synthesis, layout, and power analysis. The area required for 8-point s-box DFT is 105.15 um2, it requires 7mW of power, and it can achieve a throughput of 59.33GBPS. In addition, the throughput of 57.33GBPS may be achieved by 16-point s-box DFT with a footprint of 123.35um2, using 9mW of power. Also, the throughput of 32-point s-box DFT is 56.45GBPS on a chip that uses 10.24 mW and occupies 132.35um2. With MIMO in 5G technology, this layout becomes more practical for usage in telecommunications. The performance is enhanced over existing architectures thanks to the optimized radix 8 s-box DFT. © 2024 IEEE.

关键词： MIMO systems

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise Knowledge graphs Predictive models Fingerprint recognition Transformers

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

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Intelligent Automation & Soft Computing 2023年第11期38卷 169-183页

作者： Laith Abualigah Serdar Ekinci Davut Izci Raed Abu Zitar Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Computer Engineering Batman UniversityBatman72100Turkey MEU Research Unit Middle East UniversityAmmanJordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and *** study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter *** approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and *** study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and *** highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous *** study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe *** research provides a promising avenue for further research and development in this area.

关键词： Cruise control system FOPID controller artificial hummingbird algorithm elite opposition-based learning

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TagVibra: push the limit of RFID-based vibration sensing 9

TagVibra: push the limit of RFID-based vibration sensing

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Li, Jian Li, Ping Zhu, Biaokai Dong, Chong Gong, Chao Lv, Zhao Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Anhui University of Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China Shanxi Police College Taiyuan030401 China

ISBN: (纸本)9798350331240

RFID-based mechanical vibration detection is considered a promising method for many Internet of Things (IoT) applications. However, existing methods are affected by ambient interference and leakage from the reader signature, which makes it difficult to obtain high-precision detection results. To overcome this limitation, this paper introduces TagVibra, a high-precision non-contact vibration sensing system based on RFID technology. By leveraging the nonlinear effect inside the tag and the modulation mechanism of RFID, TagVibra can achieve sub-millimeter level vibration detection accuracy. We implement TagVibra on our Universal Software Radio Peripheral (USRP) platform and evaluate its performance via experiments. The experimental results show that TagVibra can detect vibration with amplitude as low as 0.3mm, and the average estimation error below 0.7Hz when the vibration frequency is 100Hz. © 2023 IEEE.

关键词： Frequency estimation

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Switching between topological edge states using phase-change materials

Switching between topological edge states using phase-change...

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2023 Active Photonic Platforms, APP 2023

作者： Veronis, Georgios Huang, Yin Shen, Yuecheng School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Hunan Changsha410012 China State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510275 China

ISBN: (纸本)9781510665088

We introduce non-Hermitian plasmonic waveguide-cavity structures based on the Aubry-Andre-Harper model to realize switching between right and left topological edge states using the phase-change material germanium-antimony-tellurium (GST). The structure unit cells consist of a metal-dielectric-metal (MDM) waveguide side-coupled to MDM stub resonators with modulated distances between adjacent stubs. In such structures the modulated distances introduce an effective gauge magnetic field. We show that switching between the crystalline and amorphous phases of GST leads to a shift of the dispersion relation of the optimized structure so that a right topological edge state for the crystalline phase, and a left topological edge state for the amorphous phase occur at the same frequency. Thus, we realize switching between right and left topological edge states at that frequency by switching between the crystalline and amorphous phases of GST. Our results could be potentially important for developing compact reconfigurable topological photonic devices. © 2023 SPIE.

关键词： Gages

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IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks

arXiv

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arXiv 2024年

作者： Lin, Junchao Ling, Zenan Feng, Zhanbo Xu, Jingwen Liao, Minxuan Zhou, Feng Hou, Tianqi Liao, Zhenyu Qiu, Robert C. School of EIC Huazhong University of Science and Technology China Department of Computer Science and Engineering Shanghai Jiao Tong University China Center for Applied Statistics School of Statistics Renmin University of China China Lagrange Mathematics and Computation Research Center of Huawei China School of Science Wuhan University of Technology China

Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range dependencies (LRD) in underlying graphs while effectively mitigating the over-smoothing problem. However, IGNNs rely on computationally expensive fixed-point iterations, which lead to significant speed and scalability limitations, hindering their application to large-scale graphs. To achieve fast fixed-point solving for IGNNs, we propose a novel graph neural solver, IGNN-Solver, which leverages the generalized Anderson Acceleration method, parameterized by a tiny GNN, and learns iterative updates as a graph-dependent temporal process. To improve effectiveness on large-scale graph tasks, we further integrate sparsification and storage compression methods, specifically tailored for the IGNN-Solver, into its design. Extensive experiments demonstrate that the IGNN-Solver significantly accelerates inference on both small- and large-scale tasks, achieving a 1.5× to 8× speedup without sacrificing accuracy. This advantage becomes more pronounced as the graph scale grows, facilitating its large-scale deployment in real-world applications. The code to reproduce our results is available at https://***/landrarwolf/IGNN-Solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Formal Methodology for Verifying Side-Channel Vulnerabilities in Cache Architectures 1

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23rd International Conference on Formal engineering Methods, ICFEM 2022

作者： Jiang, Ke Zhang, Tianwei Sanán, David Zhao, Yongwang Liu, Yang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Information and Communication Technologies Singapore Institute of Technology Singapore Singapore ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9783031172441

ISBN: (纸本)9783031172434

Security-aware CPU caches have been designed to mitigate side-channel attacks and prevent information leakage. How to validate the effectiveness of these designs remains an unsolved problem. Prior works assess the security of architectures empirically without a formal guarantee, making the evaluation results less convincing. In this paper, we propose a comprehensive methodology based on formal methods for security verification of cache architectures. Specifically, we design an entropy-based noninterference reasoning framework with two unwinding conditions to assess the information leakage of the cache designs. The reasoning framework quantifies the dependency relationships by the mutual information between the distributions of input and output of side channels. Given a cache design, we formalize its behavior specification along with the cache layouts into an abstract state machine, to instantiate the parameterized reasoning framework that discloses any potential vulnerabilities. We use our methodology to assess eight state-of-the-art cache architectures to demonstrate reliability as well as flexibility. © 2022, Springer Nature Switzerland AG.

关键词： Side channel attack

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LET-Net: locally enhanced transformer network for medical image segmentation

LET-Net: locally enhanced transformer network for medical im...

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作者： Ta, Na Chen, Haipeng Liu, Xianzhu Jin, Nuo College of Computer Science and Technology Jilin University Changchun130012 China College of Computer Hulunbuir University Hulunbuir021008 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China Southampton Business School University of Southampton SouthamptonSO17 1BJ United Kingdom

Medical image segmentation has attracted increasing attention due to its practical clinical requirements. However, the prevalence of small targets still poses great challenges for accurate segmentation. In this paper, we propose a novel locally enhanced transformer network (LET-Net) that combines the strengths of transformer and convolution to address this issue. LET-Net utilizes a pyramid vision transformer as its encoder and is further equipped with two novel modules to learn more powerful feature representation. Specifically, we design a feature-aligned local enhancement module, which encourages discriminative local feature learning on the condition of adjacent-level feature alignment. Moreover, to effectively recover high-resolution spatial information, we apply a newly designed progressive local-induced decoder. This decoder contains three cascaded local reconstruction and refinement modules that dynamically guide the upsampling of high-level features by their adaptive reconstruction kernels and further enhance feature representation through a split-attention mechanism. Additionally, to address the severe pixel imbalance for small targets, we design a mutual information loss that maximizes task-relevant information while eliminating task-irrelevant noises. Experimental results demonstrate that our LET-Net provides more effective support for small target segmentation and achieves state-of-the-art performance in polyp and breast lesion segmentation tasks. © 2023, The Author(s).

关键词： Image enhancement

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