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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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Improving Mall Traffic Flow with IoT and Reinforcement Learning Techniques in Footfall Analytics

Improving Mall Traffic Flow with IoT and Reinforcement Learn...

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Visual Analytics and data Visualization (ICVADV), International Conference on

作者： S. Muthumarilakshmi A. Sairam N. Mohankumar D. Hemalatha S. Velmurugan T. R. GaneshBabu Department of CSE Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence & Data Science Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

An innovative method for improving mall traffic flow utilizing IoT devices and reinforcement learning (RL) methods in footfall analytics is presented. Inefficiencies, congestion, and less-than-ideal consumer experiences are common outcomes of the conventional wisdom about managing mall foot traffic. Our proposed system uses Internet of Things (IoT) sensors to track shoppers' flow and density in real time. This data is then input into an RL model to optimize and predict traffic flow patterns. Aiming to enhance the shopping experience and distribute foot traffic more equitably, the system automatically adjusts parameters, including digital signage, escalator functioning, and store promotions. Simulation and pilot deployment results show substantial improvement in lowering congestion and boosting mall visitors' time spent there. By integrating IoT with powerful machine learning (ML) algorithms, this integrated technique demonstrates how smarter and more responsive retail environments may be created. IoT and RL improves mall traffic flow by analyzing footfall trends, optimizing routing, mitigating congestion, and enhancing customer experience.

关键词： Technological innovation Smart cities Visual analytics Scalability Reinforcement learning Routing Real-time systems Sensor systems Internet of Things Intelligent sensors

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Specific-Modal Spatial Guidance and Feature Enhancement for Multi-modal Brain Tumor Segmentation

Specific-Modal Spatial Guidance and Feature Enhancement for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junyang Han Cheng Xue Hongzhi Liu Shiyu Zhu Jiacheng Nie Weixuan Wan Chunfeng Yang Wenxue Yu Xiaoying Tang Yang Chen Shijie Wang Pinzheng Zhang Jean-Louis Coatrieux School of Computer Science and Engineering Southeast University Nanjing China College of Engineering Southern University of Science and Technology Shenzhen China Ministry of Education The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Nanjing China Centre de Recherche en Information Biomedicale Sino-Francais Inserm University of Rennes 1 Rennes France

Multi-modal information plays a pivotal role in the segmentation of brain tumors. However, previous studies have largely overlooked the distinctive characteristics of individual modalities, which are correlated with the target tumor region due to distinct imaging principles. In this paper, we harness the distinctive traits of individual modalities and introduce a brain tumor segmentation model called specific modality guided brain tumor segmentation model (SMG-BTS). Our SMG-BTS adopts a three-branch encoder-decoder architecture. The main branch utilizes full modalities fused at input-level, while the two affiliated branches operate in parallel to provide guidance to the main branch in acquiring a robust representation. We propose a specific modality spatial guidance (SMSG) module to guide the process of feature extraction. Spatial information is obtained from selected modalities and utilized to enhance features extracted from the main branch. A shared-specific feature enhancement(SSFE) module is proposed to enhance the shared features across modalities and utilizes modality-specific features to further supplement specific information of modalities. Experimental results on the BraTS2021 benchmark dataset demonstrate the effectiveness of our proposed SMG-BTS over state-of-the-art brain tumor segmentation methods.

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An Improved GPS Assisted Fishermen Tracking System Using Internet of Things with Smart Sensors Association

An Improved GPS Assisted Fishermen Tracking System Using Int...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： G.M. Karthik Samuthira Pandi V Gayathri S Anni Princy B Kiran Kumar Bhadavath S. Sujatha Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of EEE Sri Sairam College of Engineering Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

This research proposes a border alert system for fisherman that is based on the Internet of Things. As a result of their survival tactics, fisherman often unknowingly crosses international boundaries, putting themselves in danger of being seized or killed. The inability to reliably determine international maritime borders is the root source of this problem. This system aims to prevent such incidents and provide prior warning to fisherman about border regions by utilizing cutting-edge GPS and GSM technologies along with Internet of Things (IoT) monitoring. Protecting fishermen from harm is an implanted gadget that uses GPS to notify them to the country's boundaries. In this research, a novel approach to tracking fisherman using the Internet of Things (IoT) is presented; the IoTGPSTM model is cross-validated with the standard AGPST model to assess the effectiveness of the suggested scheme. The ESP8266 Wi-Fi module is used to transfer the obtained GPS position data to the IoT cloud. The navy may then track the data. If the boat's GPS coordinates are within the danger zone, an alarm will be heard through the buzzer, and the motor speed will be decreased to 25%. The fundamental concept is to use a GPS receiver module and a WiFi-enabled NodeMCU microcontroller to notify the fisherman when they are out at sea.

关键词： Technological innovation Receivers Safety Internet of Things Reliability Relays Monitoring Wireless fidelity Standards Global Positioning System

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A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

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Applied Soft Computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

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One-Step Cu-Assisted Chemical Etching at Room Temperature of Inverted Pyramid Array for High-Performance Crystalline Silicon Solar Cells

SSRN

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SSRN 2023年

作者： Cai, Boyuan Yu, Zhiyong Li, Runze See, Khay Zhang, Yinan Hu, Jinming Ping, Peng Yue, Zengji Duo, Shuwang Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Gongqing Institute of Science and Technology Jiangxi Province Jiujiang332020 China Institute for Superconducting and Electronic Materials University of Wollongong North Wollongong2522 Australia Jiangxi Key Laboratory of Surface Engineering Jiangxi Science and Technology Normal University Jiangxi Nanchang330013 China

Previous studies on the one-step Cu-assisted texturization of silicon wafers have predominantly focused on etching conditions at relatively high temperatures, which are unsuitable for the mass production of solar cells in industrial settings. In this study, we introduced the one-step Cu-assisted chemical etching (Cu-ACE) technique at room temperature to obtain large-area silicon inverted pyramid (IP) array structures. Based on this structure, diamond wire saw (DWS) mc-Si solar cells can achieve a balance between enhanced light trapping and lower carrier recombination by optimizing the concentration of the component Cu(NO3)2/HF/H2O2 in the etchant solution and the etching time during the Cu-ACE process. As a result, these solar cells deliver a power conversion efficiency (PCE) of 18.20%, which is 1.99% absolute higher than solar cells using traditional etching techniques. This work paves a new pathway toward the mass production of high-efficiency crystalline Si solar cells, offering a cost-effective and simple texturization approach. © 2023, The Authors. All rights reserved.

关键词： Silicon wafers

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Adversarial Attacks on Large Language Model-Based System and Mitigating Strategies: A Case Study on ChatGPT

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Security and Communication Networks 2023年第1期2023卷

作者： Liu, Bowen Xiao, Boao Jiang, Xutong Cen, Siyuan He, Xin Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China School of Computer Science & Technology Nanjing University of Posts and Telecommunications Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

Machine learning algorithms are at the forefront of the development of advanced information systems. The rapid progress in machine learning technology has enabled cutting-edge large language models (LLMs), represented by GPT-3 and ChatGPT, to perform a wide range of NLP tasks with a stunning performance. However, research on adversarial machine learning highlights the need for these intelligent systems to be more robust. Adversarial machine learning aims to evaluate attack and defense mechanisms to prevent the malicious exploitation of these systems. In the case of ChatGPT, adversarial induction prompt can cause the model to generate toxic texts that could pose serious security risks or propagate false information. To address this challenge, we first analyze the effectiveness of inducing attacks on ChatGPT. Then, two effective mitigating mechanisms are proposed. The first is a training-free prefix prompt mechanism to detect and prevent the generation of toxic texts. The second is a RoBERTa-based mechanism that identifies manipulative or misleading input text via external detection models. The availability of this method is demonstrated through experiments. © 2023 Bowen Liu et al.

关键词： Machine learning

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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Blockchain-Based Information Supervision Model for Rice Supply Chains

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Computational intelligence and Neuroscience 2022年第0期2022卷 2914571页

作者： Wang, Jian Zhang, Xin Xu, Jiping Wang, Xiaoyi Li, Haisheng Zhao, Zhiyao Kong, Jianlei School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China

Rice is a major food crop around the world, and its various quality and safety problems are closely related to human health. As an important area of food safety research, the rice supply chain has attracted increasing attention. Based on blockchain technology, this study investigated problems of data privacy and circulation efficiency caused by complex rice supply networks, long circulation cycles, and various risk factors in each link. First, we deconstructed the quality and safety of each link of the rice supply chain at the information level and established a key information classification table for each link. On that basis, we built a rice supply chain information supervision model based on blockchain. Various encryption algorithms are used to secure the sensitive data of enterprises in the supply chain to meet regulators' needs for efficient supervision. Moreover, we propose a practical Byzantine fault-tolerant consensus algorithm that scores the credit of enterprise nodes, optimizes the selection strategy of master nodes, and ensures high efficiency and low cost. Then, we built a prototype system based on the open-source framework of hyperledger fabric, analyzed the model's viability, and implemented the system using cases. The results indicated that the proposed system can optimize the information supervision process of rice supply chain regulators and provide a feasible solution for the quality and safety supervision of grain and oil. © 2022 Jian Wang et al.

关键词： Supply chains

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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