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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis 3

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Roshan Ahmed, N. Shridevi, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Center for Advanced Data Science Chennai India

ISBN: (纸本)9798350372120

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis. © 2024 IEEE.

关键词： Embeddings

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Accelerating Blockchain-Enabled Federated Learning with Clustered Clients

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IEEE Transactions on Big data 2024年 1-14页

作者： Cui, Laizhong Li, Yinghao Zhou, Yipeng Qu, Youyang Liu, Jiangchuan College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing FSE Macquarie University Macquare Park NSW Australia Data61 CSIRO VIC Australia School of Computing Science Simon Fraser University Burnaby BC Canada

With the rapid development of big data, Federated learning (FL) has found numerous applications, enabling machine learning (ML) on edge devices while preserving privacy. However, FL still faces crucial challenges, such as single point of failure and poisoning attacks, which motivate the integration of blockchain-enabled FL (BeFL). Beyond that, the efficiency issue still limits the further application of BeFL. To address these issues, we propose a novel decentralized framework: Accelerating Blockchain-Enabled Federated Learning with Clustered Clients (ABFLCC), who utilize actual training time for clustering clients to achieve hierarchical FL and solve the single point of failure problem through blockchain. Additionally, the framework clusters edge devices considering their actual training times, which allows for synchronous FL within clusters and asynchronous FL across clusters simultaneously. This approach guarantees that devices with a similar training time have a consistent global model version, improving the stability of the converging process, while the asynchronous learning between clusters enhances the efficiency of convergence. The proposed framework is evaluated through simulations on three real-world public datasets, demonstrating a training efficiency improvement of 30% to 70% in terms of convergence time compared to existing BeFL systems. IEEE

关键词： Big data

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Vanessa : Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis

Vanessa : Visual Connotation and Aesthetic Attributes Unders...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xiao, Luwei Mao, Rui Zhang, Xulang He, Liang Cambria, Erik School of Computer Science and Technology East China Normal University Shanghai China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Prevailing research concentrates on superficial features or descriptions of images, revealing a significant gap in the systematic exploration of their connotative and aesthetic attributes. Furthermore, the use of cross-modal relation detection modules to eliminate noise from comprehensive image representations leads to the omission of subtle contextual information. We present Vanessa, a visual connotation and aesthetic Attributes understanding network for multimodal aspect-based sentiment analysis. It incorporates a multi-aesthetic attributes aggregation (MA3) module that models intra- and inter-dependencies among bi-modal representations as well as emotion-laden aesthetic attributes. Moreover, we devise a self-supervised contrastive learning framework to explore the pairwise relevance between images and text via the Gaussian distribution of their CLIP scores. By dynamically clustering and merging multimodal tokens, Vanessa effectively captures both implicit and explicit sentimental cues. Extensive experiments on two widely adopted benchmarks verify Vanessa's effectiveness. © 2024 Association for Computational Linguistics.

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying datasets, Labelling, and Alertness Level Classification 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data

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Environmental science and Pollution Research 2025年第19期32卷 11931-11949页

作者： Jiang, Jingmin Zhang, Jiahua Wang, Xue Zhang, Shichao Kong, Delong Wang, Xiaopeng Ali, Shawkat Ullah, Hidayat Space Information and Big Earth Data Research Center School of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LSTM) and bidirectional long short-term memory (Bi-LSTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from Sentinel-2 data will be used as input features to extract the multi-crop planting area in Shandong province’s northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LSTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LSTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LSTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning High-resolution Monthly synthetic time-series NDVI Multi-crop classification Remote sensing Sentinel- 2

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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Multi-Objective Genetic-programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules 13

Multi-Objective Genetic-Programming Hyper-Heuristic for Evol...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Pang, Junwei Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

The job shop scheduling problem is an important combinatorial optimisation problem in the real world. Genetic programming hyper-heuristic has been successfully applied to automatically evolve effective dispatching rules to make a schedule in real time without much domain knowledge. However, the interpretability of GP-evolved rules has been largely neglected, which could lead to the lack of reliability and trustworthiness of the evolved rules in practice. Current work related to interpretable genetic programming algorithms primarily uses the model size as the interpretability metric. This could not fully reflect the interpretability of evolved rules. To overcome the limitation, we employ structural complexity and dimension gap as more comprehensive interpretability measures. In addition, a new multi-objective genetic programming algorithm, which applies the a non-dominated sorting method to solve the objective selection bias issue, is proposed to optimise the makespan (scheduling objective), structural complexity and dimension gap simultaneously. A variety of experiments demonstrates the competitive performance of our proposed algorithm based on effectiveness, convergence and diversity. Furthermore, the semantics of evolved dispatching rules are analysed to show their better interpretability. © 2024 IEEE.

关键词： Genetic programming

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset 9

Anatomization of Neural Networks based models for Semantic A...

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

作者： Bharath, B. Sai Bollineni, Jahnavi Mandala, Sandeep Preetham Ganguly, Tania Amudha, J. Shukla, Nikhil Joseph, Raj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Intellectyx Data Science Pvt Ltd India

ISBN: (纸本)9798350394474

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models' performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP. © 2024 IEEE.

关键词： Neural network models

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