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丛书名： Studies in Computational Intelligence

2020年

作者： Jyotsna Kumar Mandal Somnath Mukhopadhyay Paramartha Dutta Kousik Dasgupta

Individual risk profiling for portable devices using a neural network to process the cognitive reactions and the emotional responses to a multivariate situational risk assessment

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arXiv 2021年

作者： Jumelle, Frederic So, Kelvin Deng, Didan Bright Nation Limited Smart-Space 3F Cyberport Hong Kong Neuromorphic Interactive System Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In this paper, we are presenting a novel method and system for neuropsychological performance testing that can establish a link between cognition and emotion. It comprises a portable device used to interact with a cloud service which stores user information under username and is logged into by the user through the portable device;the user information is directly captured through the device and is processed by artificial neural network;and this tridimensional information comprises user cognitive reactions, user emotional responses and user chronometrics. The multivariate situational risk assessment is used to evaluate the performance of the subject by capturing the 3 dimensions of each reaction to a series of 30 dichotomous questions describing various situations of daily life and challenging the user's knowledge, values, ethics, and principles. In industrial application, the timing of this assessment will depend on the user's need to obtain a service from a provider such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work or securing online payments. © 2021, CC BY-NC-SA.

关键词： Risk assessment

Functional neural network for decision processing, a racing network of programmable neurons with fuzzy logic where the target operating model relies on the network itself

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arXiv 2021年

In this paper, we are introducing a novel model of artificial intelligence, the functional neural network for modeling of human decision-making processes. This neural network is composed of multiple artificial neurons racing in the network. Each of these neurons has a similar structure programmed independently by the users and composed of an intention wheel, a motor core and a sensory core representing the user itself and racing at a specific velocity. The mathematics of the neuron’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education and medicine including the opportunity to design an intelligent learning agent with application in business operations supervision. We believe that this functional neural network has a promising potential to transform the way we can compute decision-making and lead to a new generation of neuromorphic chips for seamless human-machine interactions. © 2021, CC BY-NC-SA.

关键词： Neurons

Group-wise inhibition based feature regularization for robust classification

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

Super-resolution compressive spectral imaging via two-tone adaptive coding

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Photonics Research 2020年第6期8卷 395-411页

作者： CHANG XU TINGFA XU GE YAN XU MA YUHAN ZHANG XI WANG FENG ZHAO GONZALO R.ARCE Key Laboratory of Photoelectronic Imaging Technology and System School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China Department of Electrical and Computer Engineering University of DelawareNewarkDelaware 19716USA

Coded apertures with random patterns are extensively used in compressive spectral imagers to sample the incident scene in the image *** samplings,however,are inadequate to capture the structural characteristics of the underlying signal due to the sparsity and structure nature of sensing matrices in spectral *** paper proposes a new approach for super-resolution compressive spectral imaging via adaptive *** this method,coded apertures are optimally designed based on a two-tone adaptive compressive sensing(CS)framework to improve the reconstruction resolution and accuracy of the hyperspectral imager.A liquid crystal tunable filter(LCTF)is used to scan the incident scene in the spectral domain to successively select different spectral *** output of the LCTF is modulated by the adaptive coded aperture patterns and then projected onto a lowresolution detector *** coded aperture patterns are implemented by a digital micromirror device(DMD)with higher resolution than that of the *** to the strong correlation across the spectra,the recovered images from previous spectral channels can be used as a priori information to design the adaptive coded apertures for sensing subsequent spectral *** particular,the coded apertures are constructed from the a priori spectral images via a two-tone hard thresholding operation that respectively extracts the structural characteristics of bright and dark regions in the underlying ***-resolution image reconstruction within a spectral channel can be recovered from a few snapshots of low-resolution *** no additional side information of the spectral scene is needed,the proposed method does not increase the system *** on the mutual-coherence criterion,the proposed adaptive CS framework is proved theoretically to promote the sensing efficiency of the spectral *** and experiments are provided to demonstrate and assess the proposed adaptive cod

关键词： resolution spectral image

Efficient and Non-Invasive Rice Moisture Quantification Using Coupled Lines Microwave Sensor Design

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Efficient and Non-Invasive Rice Moisture Quantification Usin...

International Conference on Information Technology (INCIT)

作者： Pratya Phanomupatum Ravee Phromloungsri Decha Wongpatsa Tiwat Pongthavornkamol Phatcharanat Saeng-On Somchat Sonasang Electronics Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Faculty of Agriculture and Technology Nakhon Phanom University Nakhon Phanom Thailand Location and Automatic Identification System Research Team Location and Automatic NECTEC National Science and Technology Development Agency Pathum Thani Thailand Electrical Elements and Wireless Devices Unit (EEWDU) Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This research presents the design, simulation, and experimental validation of a novel microwave sensor for noninvasive moisture measurement in rice. The sensor employs coupled microstrip lines fabricated on an FR4 substrate, designed to operate at a center frequency of 900 MHz with a quarter-wavelength configuration. The Advanced Design system software was utilized for the simulation, where key parameters such as S 21 and phase response were analyzed across a frequency range of 0.1 to 3 GHz. The sensor was fabricated and tested using a Vector Network Analyzer, where the moisture content in rice samples was varied incrementally, and the sensor’s performance was evaluated based on the changes in transmission characteristics. The results demonstrated the sensor’s potential for accurate moisture detection in rice, showcasing its feasibility as a noninvasive measurement technique in agricultural applications. This study contributes to the development of efficient, real-time moisture measurement technologies in the food processing industry.

关键词： Microwave measurement Sensitivity Moisture measurement Moisture Network analyzers Microwave sensors Vectors Real-time systems system software Substrates

A Comparative Study of Neural Network Techniques for Automatic Software Vulnerability Detection

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A Comparative Study of Neural Network Techniques for Automat...

Theoretical Aspects of Software engineering (TASE)

作者： Gaigai Tang Lianxiao Meng Huiqiang Wang Shuangyin Ren Qiang Wang Lin Yang Weipeng Cao School of computer science and technology Harbin Engineering University Harbin China National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering Chinese Academy of Military Science Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Software vulnerabilities are usually caused by design flaws or implementation errors, which could be exploited to cause damage to the security of the system. At present, the most commonly used method for detecting software vulnerabilities is static analysis. Most of the related technologies work based on rules or code similarity (source code level) and rely on manually defined vulnerability features. However, these rules and vulnerability features are difficult to be defined and designed accurately, which makes static analysis face many challenges in practical applications. To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. However, there are many types of neural networks, and different data preprocessing methods will have a significant impact on model performance. It is a great challenge for engineers and researchers to choose a proper neural network and data preprocessing method for a given problem. To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i.e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers. Specifically, we found that 1) the training speed of RVFL is always faster than Bi-LSTM, but the prediction accuracy of Bi-LSTM model is higher than RVFL; 2) using doc2vec for vector representation can make the model have faster training speed and generalization ability than using word2vec; and 3) multi-level symbolization is helpful to improve the precision of neural network models.

关键词： Training Neural networks Data preprocessing Static analysis Predictive models Feature extraction Software

Accelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine Learning

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SSRN

SSRN 2024年

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin Shanghai Institute of AI for Education East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100090 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100039 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the quality of the solution. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the DTLZ test suite and three real-world optimization problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach

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Expert systems with Applications 2024年 238卷

作者： Das, Soumen Kumar Yu, Vincent F. Roy, Sankar Kumar Weber, Gerhard Wilhelm Department of Pure and Applied Sciences Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei Taiwan Faculty of Engineering Management Poznan University of Technology Poznan Poland IAM METU Ankara Turkey

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers’ satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ-constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. © 2023

关键词： Sustainable development

Neuromodulated Meta-Learning

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning