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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

引用

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

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Placement of Charging Sites for Off-Service Battery Electric Bus in Transit Networks 8

Placement of Charging Sites for Off-Service Battery Electric...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Sobhani, Ehsan Yassine, Abdulsalam Ameli, Amir Riahinia, Shahin Department of Computer Science Lakehead University Thunder BayON Canada Department of Software Engineering Lakehead University Thunder BayON Canada Department of Electrical and Computer Engineering Lakehead University Thunder BayON Canada

ISBN: (纸本)9798350382150

Battery electric buses (BEBs) have received significant recognition as an environmentally conscious and sustainable means of transportation. Placement of charging stations to ensuring efficient and consistent charging of BEBs is a significant challenge in an electrified public transit network. This paper introduces the Constrained Greedy Clustering (CGC) algorithm, a data-driven approach for hierarchically determining optimal charging placement locations. The algorithm aims to minimize energy consumption and optimize charging load distribution in transit networks using clustering techniques. This improves the placement of charging sites for off-service BEBs in transit networks, aiming to minimize energy consumption and balance the charging sites’ load. ©2024 IEEE.

关键词： Energy efficiency

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Fairness with adaptive weight in network attack detection 29

Fairness with adaptive weight in network attack detection

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wen, Xuezhi Wang, Nan Sun, Yuanlin Xu, Fan Zhang, Dalin Zhao, Xibin Beijing Jiaotong University School of Software Engineering Beijing China University of Science and Technology of China School of Computer Science and Technology Anhui China Tsinghua University School of Software Engineering Beijing China

ISBN: (纸本)9798350330717

Network attacks aim to exploit vulnerabilities inherent in network protocols, which is widely used in many real-world applications. In the process of network anomaly detection, most methods train the model by minimizing the average empirical risk of all samples. However, due to the uneven distribution of samples from different protocols, detection models tend to be biased against minority protocols groups. To address this issue, we propose an adaptive weight assignment method for network attack detection, which emphasizes more on error-prone samples in prediction and enhances adequate representation of minority groups for fairness. We conduct experiments on two widely used datasets, KDD and NSLKDD. According to the results, our method achieves better performance than state-of-the-art methods for classification and regression tasks, and is robust to label noise in the test dataset. © 2023 IEEE.

关键词： Adaptive weight Attack detection Fairness

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Real-Time Road Signs Detection and Recognition for Enhanced Road Safety 15

Real-Time Road Signs Detection and Recognition for Enhanced ...

引用

15th International Conference on Innovations in Information Technology, IIT 2023

作者： Teklesenbet, Hermon B. Demoz, Nahom H. Jabiro, Igor H. Tesfay, Yonatan R. Badidi, Elarbi UAE University CIT Department of Computer Science and Software Engineering Al-Ain United Arab Emirates

ISBN: (纸本)9798350382396

Road sign detection and recognition play a critical role in improving driver safety and awareness in the modern traffic era. This paper describes the development and evaluation of a Road Sign Detection and Recognition System (RSDRS). Our system leverages computer vision techniques and mobile application technology to provide drivers with real-Time visual and auditory feedback based on detected traffic signs. The implementation of RSDRS involves two basic phases: developing a robust recognition model and creating an Android application. The model, trained with the German Traffic Sign Recognition Benchmark (GTSRB) dataset and the YOLOv5s object detection algorithm, serves as the core component for accurate traffic sign recognition. The Android application captures live video frames, integrates seamlessly with the recognition model, and provides intuitive feedback to the driver. The performance evaluation reveals the exceptional capabilities of our system, with an average accuracy of 99.3% and a fast response time of 0.73 milliseconds. Metrics such as recall, precision, and F1 score highlight the model's ability to maintain accuracy while minimizing false positives. Real-world applicability is paramount, and our system excels in a variety of environments and lighting conditions, as evidenced by rigorous testing. © 2023 IEEE.

关键词： computer vision

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DrDiff: Drug Response Prediction Through Controllable Diffusion-GE and Graph Attention Network 27

DrDiff: Drug Response Prediction Through Controllable Diffus...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Choi, Seungyeon Seo, Samgmin Choi, Jonghwan Park, Chihyun Park, Sanghyun Department of Computer Science Yonsei University Seoul Korea Republic of Division of Software Hallym University Chuncheon Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9781643685489

The accurate prediction of drug responses based on the genomic profile of a patient is essential to progress in the field of precision *** advent of various deep-learning algorithms based on publicly available large-scale omics datasets is the driving force behind research in this *** characteristics of biological datasets, characterized by high dimensions and low sample sizes, pose challenges of overfitting and limited generalization in prediction ***, constructing prediction models using biological data such as gene expression is further complicated by the need to account for the complex relationships among genes, which exacerbates the aforementioned *** address these challenges, we propose a drug response prediction framework (DrDiff) that integrates a denoising diffusion probabilistic model (DDPM) based data augmentation module with a graph attention network based drug response prediction *** proposed model showed a 10% higher AUC than the state-of-the-art models for drug response prediction for the six drugs considered in the study, suggesting the superior generalization performance of DrDiff over other baseline ***, we demonstrated the feasibility of generative models, which form one of the modules of the proposed framework, in overcoming the fundamental limitations of omics *** experiments bear out the feasibility of generative models, which form one of the modules of the proposed framework, in augmenting gene expression data. © 2024 The Authors.

关键词： Prediction models

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Review of Power Grid Optimization Scheduling Methods Based On Deep Reinforcement Learning 7

Review of Power Grid Optimization Scheduling Methods Based O...

引用

7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Zherui, Yang Zhen, Zhang Fangzhou, Li Nan, Dai Yiying, Yan Ke, Wang Dayu College Hohai University China College of Energy and Electrical Engineering Hohai University China College of Computer Science and Software Engineering Hohai University China

ISBN: (纸本)9798350359558

In recent years, with the introduction of energy conservation and emission reduction targets, the introduction of renewable energy has become an important initiative. However, due to the many factors that affect the new energy units, the traditional scheduling method becomes inappropriate. If a more intelligent scheduling method is not developed, the lack of flexible energy scheduling capacity will further lead to problems such as insufficient wind power consumption level, inefficient power grid decision-making, high operating costs, and unstable energy supply. In order to solve the above problems and improve the environmental protection, efficiency, economy and safety of the power grid, this paper summarizes the application of artificial intelligence in the field of power grid scheduling, analyzes SAC, A3C and TD3 algorithms in detail, analyzes their mechanism through the algorithm structure, and summarizes the advantages and disadvantages of each algorithm and its application scenarios. In addition, this paper also analyzes the application of artificial intelligence in unit combination, economic scheduling optimization and optimal power flow optimization. Different from other review papers, this paper deeply analyzes several problems that may arise in the application of the algorithm, and specifically analyzes the future optimization direction and corresponding optimization methods, so as to improve the level of experience combination, fault handling, comprehensive consideration, interpretability and other aspects of the future agent. © 2024 IEEE.

关键词： Reinforcement learning

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Impacts of Earthing Variations on Overvoltage in Meshed Multi-terminal DC Grid 50

Impacts of Earthing Variations on Overvoltage in Meshed Mult...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bader, Amir Ar-Rahman Ukil, Abhisek Dept. of Engineering Science The University of Auckland Auckland New Zealand Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

This paper investigates the impact of different earthing methods on overvoltage within modular multilevel Voltage Source Converter (MMC VSC) based Direct Current (DC) transmission and distribution grids, particularly in meshed multi-terminal (MTDC) configurations. A bipolar MTDC grid is modelled and simulated in PSCAD/EMTDC to investigate the influence of earthing on midpoint overvoltages. The transient response of the grid under DC fault scenarios is analysed through various case-studies, including solid earthing placement, variations in earthing impedance, and the application of surge arresters. The findings show the critical role of earthing in mitigating overvoltage, with low-resistive earthing options proving to be the most effective trade-off. © 2024 IEEE.

关键词： bipolar dedicated metallic return earthing meshed MTDC overvoltage

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NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework 24

NNVISR: Bring Neural Network Video Interpolation and Super R...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tong, Yuan Hu, Mengshun Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://***/tongyuantongyu/vs-NNVISR. © 2024 ACM.

关键词： Open systems

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Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection 29

Few-shot Message-Enhanced Contrastive Learning for Graph Ano...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Fan Wang, Nan Wen, Xuezhi Gao, Meiqi Guo, Chaoqun Zhao, Xibin University of Science and Technology of China School of Computer Science and Technology Anhui China Beijing Jiaotong University School of Software Engineering Beijing China Tsinghua University School of Software Engineering Beijing China

ISBN: (纸本)9798350330717

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies. © 2023 IEEE.

关键词： Deep-GNN few-shot graph anomaly detection

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Beyond Data Collection: Safeguarding User Privacy in Social Robotics 25

Beyond Data Collection: Safeguarding User Privacy in Social ...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Dorafshanian, Mahboobeh Aitsam, Muhammad Mejri, Mohamed Di Nuovo, Alessandro Laval University Department Of Computer Science & Software Engineering QC Canada Sheffield Hallam University Department Of Computing Sheffield United Kingdom

ISBN: (纸本)9798350340266

In an era marked by the advanced capabilities of social robots in personal and public spaces, the issue of pervasive data collection by these entities becomes increasingly pertinent. Social robots, deployed by government entities, hospitals, and corporations, are at the forefront of gathering sensitive personal data, necessitating careful consideration of privacy concerns. The vast amounts of data collected by these robots, while beneficial for decision-making and fostering research, also pose significant privacy risks. In particular, the challenge intensifies when robots collect and potentially share data that includes sensitive personal information. This paper presents a user-friendly Differential Privacy (DP) library that addresses this challenge. The library incorporates a risk threshold and evaluates the potential impact of data disclosure to accurately quantify privacy levels. Designed for nontechnical users, it enables the secure release of statistical data without the risk of privacy breaches. With privacy breaches and re-identification becoming increasingly common, this library offers a robust solution for safeguarding individuals' privacy while facilitating the sharing of valuable insights. © 2024 IEEE.

关键词： Sensitive data

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