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Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation 14th

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Efficient Evolutionary Deep Neural Architecture Search (NAS)...

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Chen, Yiming Pan, Tianci He, Cheng Cheng, Ran Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789811534140

Deep learning has achieved enormous breakthroughs in the field of image recognition. However, due to the time-consuming and error-prone process in discovering novel neural architecture, it remains a challenge for designing a specific network in handling a particular task. Hence, many automated neural architecture search methods are proposed to find suitable deep neural network architecture for a specific task without human experts. Nevertheless, these methods are still computationally/economically expensive, since they require a vast amount of computing resource and/or computational time. In this paper, we propose several network morphism mutation operators with extra noise, and further redesign the macro-architecture based on the classical network. The proposed methods are embedded in an evolutionary algorithm and tested on CIFAR-10 classification task. Experimental results indicate the capability of our proposed method in discovering powerful neural architecture which has achieved a classification error 2.55% with only 4.7M parameters on CIFAR-10 within 12 GPU-hours. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Abdulla, Srwa Hasan Toufiq, Dalia Mohammad Maghdid, Halgurd S. Rashid, Tarik A. Farho, Pakshan F. Sabr, Shadan Sh. Taher, Akar H. Hamad, Darya S. Veisi, Hadi Asaad, Aras T. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Department of Horticulture College of Agricultural Engineering Sciences University of Sulaimani- Kurdistan Region Iraq Branch of Basic Medical Science College of medicine University of Sulaimani- Kurdistan Region Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Kurdistan Region Iraq Department of Kurdish Language Faculty of Education Koya University Kurdistan Region Iraq Department of Kurdish Faculty of Education Koya University Kurdistan Region Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran

Nowadays natural Language Processing (NLP) is an important tool for most people’s daily life routines, ranging from understating speech, translation, name entity recognition (ENR), and text categorizing, to the generative text models such as ChatGPT. Due to existing big data and consequently big corpora for the most usable languages including English, Spanish, Turkish, Persia, and many more, these applications have been utilized accurately. However, the Kurdish language still needs more corpora and big datasets to be included in the NLP applications. This is because the Kurdish language has a rich linguistic structure, varied dialects, and a poor dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several types of research have been conducted in KNLP for various applications, the Kurdish ENR (KNER) remained a challenge for most of the KNLP applications including text analysis and text classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER1. To this end, first, a Kurdish corpus is created, followed by designing a modified model architecture, and the training procedures are implemented. To evaluate the trained model, a set of experiments is conducted to show the performance of the KNER model using different tokenization methods and trained models. The experimental results showed that fine-tuned RoBERTa with sentence-piece tokenization method substantially improves KNER performance by 12.8% (F1-score improvement) in comparison to traditional models and consequently establishes a new benchmark for KNLP. © 2024, CC BY.

关键词： Natural language processing systems

Adaptive Optimal Electrical Resistance Tomography for Large-Area Tactile Sensing

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Adaptive Optimal Electrical Resistance Tomography for Large-...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Wendong Zheng Huaping Liu Di Guo Wuqiang Yang Department of Computer Science and Technology Tsinghua University Beijing China the State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Electronic Engineering The University of Manchester Manchester U.K.

It is critical to perceive physical contact for intelligent robots to safely interact in dynamic, unstructured environments. As physical contacts can occur at any location, a well-performing tactile sensing system should be able to deploy a large area on robotic surface. Some researchers have implemented large-area tactile sensors by using sensing arrays, but it is challenging to deploy many sensing elements. Electrical resistance tomography (ERT) has recently been introduced into tactile sensing to overcome some of the limitations with conventional tactile sensing arrays, and good results have been achieved for some robotic applications. However, a particular challenge is that spatial resolution is low. Although various attempts have been made to improve the performance of ERT-based tactile sensors, the intrinsic resolution issue remains unsolved. In this paper, we propose a novel adaptive optimal drive strategy for efficient ERT-based large-area tactile sensing for robotic applications, which can adaptively select the current injection and voltage measurement pattern for optimal tactile stimulus. In particular, regions of tactile contacts are preliminarily detected and localized by a base scanning pattern with only a few measurement data. According to this detected region, the adaptive strategy can select the optimal current injection and voltage measurement pattern to improve the sensing performance by maximizing the current density. To verify the effectiveness of the proposed strategy, the proposed method is comprehensively evaluated by simulation and experiments. The results revealed that the optimal strategy can effectively improve both spatial and temporal resolution.

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

Design and Analysis of the Comb Gripper of the Saffron Flowers Harvesting Robot

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Design and Analysis of the Comb Gripper of the Saffron Flowe...

RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Shima Shademani Shahriyar Kouravand Payam Zarafshan Khalil Aalipour Amir Ghalamzan Esfahani Department of Agro-Technology College of Aburaihan University of Tehran Teltran Iran Department of Mechatronics Engineering AGRicultural INtelligent Systems (AGRINS) Faculty of New Sciences and Technologies University of Tehran Teltran Iran Department of Mechatronics Engineering Advanced Service Robots (ASR) Laboratory Faculty of New Sciences and Technologies University of Tehran Teltran Iran School of Computer Science University of Lincoln United Kingdom

Saffron is one of the most valuable spices in the world, whose origin is attributed to Iran. Iran produces about 90% of the world's saffron annually, however, the harvesting of this product in Iran is mostly done manually. Manual harvest of saffron brings problems. Decreasing the quality of saffron and contamination of saffron with microorganisms are among the problems that arise during manual harvesting. For this purpose, a gripper is recommended for harvesting saffron flowers. One of the special features of the saffron plant is that several flowers may come out from each corm at different time intervals, for this reason, the proposed gripper uses the plucking method to harvest the flowers and separate flowers from the stem so that the next flowers can grow easily. Design, simulation, and control of the gripper have been conducted using conventional software such as SOLIDWORKS, ADAMS, and MATLAB.

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning

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arXiv 2023年

作者： Li, Heng Liu, Haofeng Fu, Huazhu Xu, Yanwu Shu, Hai Niu, Ke Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Singapore School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Department of Biostatistics School of Global Public Health New York University New YorkNY United States Computer School Beijing Information Science and Technology University Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

TEXTOIR: an integrated and visualized platform for text open intent recognition

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arXiv 2021年

作者： Zhang, Hanlei Li, Xiaoteng Xu, Hua Zhang, Panpan Zhao, Kang Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University School of Information Science and Engineering Hebei University of Science and Technology

TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each module integrates most of the state-of-the-art algorithms and benchmark intent datasets. It also contains an overall framework connecting the two modules in a pipeline scheme. In addition, this platform has visualized tools for data and model management, training, evaluation and analysis of the performance from different aspects. TEXTOIR provides useful toolkits and convenient visualized interfaces for each sub-module1, and designs a framework to implement a complete process to both identify known intents and discover open intents2 Copyright © 2021, The Authors. All rights reserved.

关键词： Character recognition

Permutation polynomials x~(2k+1+3)+ax2k+2+bx over F_(22k) and their differential uniformity

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science China(Information sciences) 2020年第10期63卷 255-257页

作者： Jie PENG Lijing ZHENG Chunsheng WU Haibin KAN Mathematics and Science College Shanghai Normal University School of Mathematics and Physics University of South China Department of Mathematics Lianyungang Normal University Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Blockchain Engineering Research Center Shanghai Institute for Advanced Communication and Data Science Shanghai Institute of Intelligent Electronics & Systems

Dear editor,A polynomial f（x）∈Fq[x] is called a permutation polynomial （PP） over the finite field Fq if the associated mapping f:c （?） f（c） from Fq to itself is bijective.A PP f is called a complete permutation polynomial if f（x）+x is a *** over finite fields of even characteristic have wide applications,including cryptography,coding theory,and communication *** many block ciphers with substitution-permutation network structure,the substitution box is usually a PP over F_(22k)for some positive integer *** resist the differential attacks,the differential uniformity of this

关键词： Finite fields Permutation polynomial Binomial Trinomial Differential uniformity