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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Collaborative Agents for Anxious Depression Diagnosis and Intervention Leverage Neuroimages in Human Brain-AI Interactions

Collaborative Agents for Anxious Depression Diagnosis and In...

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Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Yihang Cheng Hao Meng Wei Zhang Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan China

ISBN: (数字)9798331519827

ISBN: (纸本)9798331519834

Depressive disorders to some extent erode the mental and physical health of many individuals. There is a pressing need for new technologies to enhance the diagnosis as well as treatment of depression, which currently relies heavily on subjective reports and lacks precise classification for its subtypes. Anxiety-type depression is a subtype of depression that is clinically challenging to distinguish from non-anxiety-type depression. Statistical data indicates anxiety-type depression has a higher prevalence among university students, poses greater risks, and has more possibility of leading to suicidal tendencies. Research integrating neuroimaging with artificial intelligence for assisted diagnosis and treatment of anxiety-type depression holds significant clinical importance. This study collected fNIRS data from depression patients and evaluated classification performance using a deep learning measure based on the CNN framework, highlighting the extraction of the most significant feature associated with anxiety-type depression deficits. This work provides a valuable lens to understand the complexities of anxiety-type depression and design human-computer interaction treatment methods. Drawing on research in psychology and cognitive science, and recent advancements in cognitive intervention studies, we illustrate the demands of assisted diagnosis, requiring a high degree of intelligent treatment of depression. We propose addressing these demands by designing and integrating a social-assistive chatbot that employs CBT for treating anxiety-type depression into a comprehensive agent system.

关键词： Neuroimaging Deep learning Anxiety disorders Psychology Medical treatment Pressing Depression Feature extraction Functional near-infrared spectroscopy Lenses

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical Networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation Information security Optical fiber networks Routing

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Research on data value evaluation of railway construction period based on ahp-grey clustering method

Research on data value evaluation of railway construction pe...

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2022 International Workshop on Automation, Control, and Communication Engineering, IWACCE 2022

作者： Lian, Xiaoqin Yang, Kai Gao, Chao Wu, Yanhua Cheng, Zhibo Wu, Yelan Gong, Yonggang Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China

ISBN: (纸本)9781510660915

In the hierarchical storage system, the data value needed to be evaluated to formulate rules data for hierarchical storage and migration. To further research the data hierarchical storage system in railway construction period and how to allocate the resources of the storage media with different performances, the researches on evaluation of the data value in railway construction period were pursued in the paper. Based on the business and non-business characteristics of the railway construction period, the evaluation indicator system of data value in railway construction period were designed, which can provide a comprehensive reference for the model. Combined with the quantification standard of evaluation indicators of data value in railway construction period designed in this paper, the complexity of indicator measurement can be reduced. In order to further improve the accuracy of model evaluation and meet data storage requirements, a comprehensive data value evaluation of railway construction period based on analytic hierarchy process (AHP)-grey clustering method was proposed. A simulation was carried out based on the data actually stored in a railway construction period, and the applicability of the model in the hierarchical storage system was proved by the geometric distribution of the results. In order to further verify the accuracy of the data value evaluation model proposed in this paper, its evaluation results were compared with that of the traditional model based on the same group of data. Experimental results shown that the data value model proposed in this paper was more accurate and adaptable than the traditional model in railway construction period, and can better meet the application requirements of the actual railway hierarchical storage system. © 2022 SPIE. All rights reserved.

关键词： Analytic hierarchy process

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore Singapore The Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Fortemedia Singapore138637 Singapore Institute for Infocomm Research A*STAR Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The University of Bremen 28359 Germany

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named ‘MuSED’, which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. © 2024, CC BY.

关键词： Extraction

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization 13

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Li, Yongfeng Zhang, Yuze Ma, Lijia Ji, Junkai Liu, Dugang Leung, Victor C. M. Li, Jianqiang Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518123 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

ISBN: (纸本)9798350308365

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder

Improving Time-Series Classification Accuracy Based on Tempo...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Dohee Kim Sunghyun Sim Bori Yoon Ling Liu Hyerim Bae Dept. Industrial Engineering Major in Industrial Data Science & Engineering Pusan National University Busan South Korea Dept. Industrial Management & Big Data Engineering Dong-Eui University Busan South Korea College of Computing Georgia Institute of Technology Georgia USA

Time-series data consists of a sequence of observations recorded in chronological order, where the data changes over time. This type of data exhibits various characteristics, such as temporal volatility, trends, and seasonality. Recently, a new layer structure called Correlation Recurrent Units (CRU) has been proposed to capture not only temporal variability but also trends and seasonality in time-series data. In this study, we propose an end-to-end model that utilizes a CRU autoencoder to learn temporal feature representations and address time-series classification problems simultaneously. To validate the performance of our proposed model, we conducted comparative experiments using 30 time-series classification datasets from six different types. The experimental results showed that the proposed model outperformed the baseline models in 20 out of the 30 time-series datasets. This indicates that the proposed approach effectively captures various temporal features in time-series data and improves the performance of time-series classification tasks.

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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STSE-xLSTM: A Deep Learning Framework for Automated Seizure Detection in Long Video Sequences Using Spatio-Temporal and Attention Mechanisms 10

STSE-xLSTM: A Deep Learning Framework for Automated Seizure ...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Ding, Lihui Wang, Hongliang Fu, Lijun Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Laboratory of Big Data and Artificial Intelligence Technology Beijing China

A detailed analysis of seizure semiology, the symptoms and signs that occur during a seizure, is crucial for the management of epilepsy patients. The inter-rater reliability of qualitative visual analysis is often low, highlighting the need for automatic and quantitative analysis of video-recorded seizures for objective assessment. To enhance evaluation accuracy and consistency, computer-aided video analysis of seizures has emerged as a natural direction of development. In the field of medical applications, deep learning and computer vision methods have driven significant advancements. However, analyzing video data presents challenges due to high dimensionality, variations in lighting, posture, and video quality. Additionally, the duration of seizures can range from seconds to minutes. To effectively extract features from seizures, we propose a novel deep learning model, STSE-xLSTM, designed to learn deep representations of arbitrarily long seizure videos. This model integrates a Spatio-Temporal Convolutional Network (STCNN) for feature extraction, a Squeeze-and-Excitation (SE) attention mechanism to enhance relevant features, and an extended Long Short-Term Memory network (xLSTM) to process long video sequences. The SE mechanism adaptively recalibrates channel weights, improving recognition of minority classes and reducing information redundancy. The xLSTM introduces scalar and matrix memory units, optimizing memory capacity and multi-scale feature extraction, thereby enhancing detection accuracy. When evaluated on the GESTURES dataset, our model outperformed state-of-the-art epilepsy detection methods in classification performance. Key contributions include the integration of STCNN, SE attention mechanism, and xLSTM to address data imbalance issues and improve the handling of complex sequential data in epilepsy detection. © 2024 IEEE.

关键词： Video analysis

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