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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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Fairness Auditing, Explanation and Debiasing in Linguistic Data and Language Models 1

Fairness Auditing, Explanation and Debiasing in Linguistic D...

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Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Manerba, Marta Marchiori Computer Science Department University of Pisa Italy KDD Laboratory ISTI National Research Council Pisa Italy

This research proposal is framed in the interdisciplinary exploration of the socio-cultural implications that AI exerts on individual and groups. The focus concerns contexts where models can amplify discriminations through algorithmic biases, e.g., in recommendation and ranking systems or abusive language detection classifiers, and the debiasing of their automated decisions to become beneficial and just for everyone. To address these issues, the main objective of the proposed research project is to develop a framework to perform fairness auditing and debiasing of both classifiers and datasets, starting with, but not limited to, abusive language detection, thus broadening the approach toward other NLP tasks. Ultimately, by questioning the effectiveness of adjusting and debiasing existing resources, the project aims at developing truly inclusive, fair, and explainable models by design. © 2023 CEUR-WS. All rights reserved.

关键词： Natural language processing systems

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Self-Training with Label-Feature-Consistency for Domain Adaptation 28th

Self-Training with Label-Feature-Consistency for Domain Ad...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xin, Yi Luo, Siqi Jin, Pengsheng Du, Yuntao Wang, Chongjun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031306778

Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to address the domain shift. Recently, self-training has been used in UDA, which exploits pseudo-labels for unlabeled target domains. However, the pseudo-labels can be unreliable due to distribution shifts between domains, severely impairing the model performance. To address this problem, we propose a novel self-training framework-Self-Training with Label-Feature-Consistency (ST-LFC), which selects reliable target pseudo-labels via label-level and feature-level voting consistency principle. The former means target pseudo-labels generated by a source-trained classifier and the latter means the nearest source-class to the target in feature space. In addition, ST-LFC reduces the negative effects of unreliable predictions through entropy minimization. Empirical results indicate that ST-LFC significantly improves over the state-of-the-arts on a variety of benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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Quantum Machine Learning and Recent Advancements

Quantum Machine Learning and Recent Advancements

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Manjunath, T.D. Bhowmik, Biswajit Brics Laboratory National Institute of Technology Karnataka Surathkal Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350322309

Quantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning enhances the performance in multiple ways. This paper studies different aspects of quantum machine learning. It introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area. We look at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc. Finally, we present the literature survey to encourage researchers and academicians. © 2023 IEEE.

关键词： Support vector machines

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Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective Optimization 23

Effects of Including Optimal Solutions into Initial Populati...

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2023 Genetic and Evolutionary Computation Conference, GECCO 2023

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798400701191

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms. © 2023 ACM.

关键词： Evolutionary algorithms

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A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms 12

A Benchmark and Scoring Algorithm for Enriching Arabic Synon...

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12th Global Wordnet Conference, GWC 2023

作者： Ghanem, Sana Jarrar, Mustafa Jarrar, Radi Bounhas, Ibrahim Department of Computer Science Birzeit University Palestine Palestine LISI Laboratory of Computer Science for Industrial System INSAT Carthage University Tunisia JARIR Joint group for Artificial Reasoning and Information Retrieval Tunisia

ISBN: (纸本)9781713890881

This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value. Given a mono/multilingual synset and a threshold (a fuzzy value [0 − 1]), our goal is to extract new synonyms above this threshold from existing lexicons. We present twofold contributions: an algorithm and a benchmark dataset. The dataset consists of 3K candidate synonyms for 500 synsets. Each candidate synonym is annotated with a fuzzy value by four linguists. The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm. Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate. Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE). The dataset and a demo page are publicly available at https://***/synonyms. © 2023 12th Global Wordnet Conference, GWC 2023. All rights reserved.

关键词： Ontology

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Comparative Study between Global and Local Explainable Models 14

Comparative Study between Global and Local Explainable Model...

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14th International Conference on Intelligent Systems: Theories and Applications, SITA 2023

作者： Dafali, Sidi Mohamed Kissi, Mohamed El Beggar, Omar University Hassan Ii Casablanca Laboratory Lim Department of Computer Science Faculty of Sciences and Techniques Mohammedia Morocco

ISBN: (纸本)9798350308211

Despite opaque machine learning models outperform transparent models, users are still unable to comprehend and trust their ***, Explainable Artificial Intelligence (XAI) becomes a prominent research area. The main objective of this discipline is providing new techniques, tools and models that explain how opaque machine learning models operate to give predictions or simply offer some information about system *** this article, we present an experimental evaluation of explainable models used in XAI, namely: LIME, SHAP, ANCHOR and EBM. Those models are applied on the results of two opaque machine learning models: Random Forest and XGBoost. Our experimental evaluation covers various aspects for comparison including:•Examining the explainability coverage of methods.•Comparing explanations provided by each method. © 2023 IEEE.

关键词： Lime

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Impact of Stress During COVID-19 Pandemic 9

Impact of Stress During COVID-19 Pandemic

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Angalakuditi, Hindu Bhowmik, Biswajit National Institute of Technology Karnataka BRICS Laboratory Department of Computer Science and Engineering Surathkal Mangalore575025 India

ISBN: (纸本)9798350397376

The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.

关键词： COVID-19

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Design and Implementation of a Vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach 40

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Xu, Yin Zhang, Xichong Xiao, Mingjun Wu, Jie Liu, An Zhang, Sheng School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Temple University Department of Computer and Information Sciences Philadelphia United States School of Computer Science and Technology Soochow University Suzhou China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350317152

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines. © 2024 IEEE.

关键词： Game theory

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