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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Two Birds with One Stone: A Dense Attentive GAN-based Model for Detecting Alzheimer's and Parkinson's Disorders 14

Two Birds with One Stone: A Dense Attentive GAN-based Model ...

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14th International Conference on Cloud computing, Data science and engineering, Confluence 2024

作者： Devika, K. Murthy Oruganti, V. Ramana Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350344837

This paper investigates two neurodegenerative disorders in adults - Alzheimer's (AD) and Parkinson's (PD). Most works in the research explored these diseases independently - AD vs. healthy subjects and PD vs. healthy subjects. We consider a one-class framework for capturing the characteristics of healthy subjects. This one-class model views any subject with AD or PD as an outlier. We employ Dense Attentive GAN for the one-class model. T1-weighted longitudinal structural magnetic resonance images (sMRI) are used as input. Furthermore, we use longitudinal samples to train the framework (two scans per subject over a 12-month gap). Most traditional methods relied on cross-sectional data (one scan per subject). The proposed method is analogous to doctor's diagnoses of the subject in different sessions to recognise the disorder. Extensive experimental studies have shown that our proposed method outperforms all other AD and PD-based works in the literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Crop-Chat: Pioneering Agricultural Paradigms

Crop-Chat: Pioneering Agricultural Paradigms

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2023 Innovations in Power and Advanced computing Technologies, i-PACT 2023

作者： Anantha Krishnan, S. Dayan, Hrishika Keerthana, A. Gopika Menon, M. Abhishek, S. Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350325188

India holds a prominent position as the second-largest global producer of agricultural output. As of 2023, the agriculture sector and its allied industries contribute 20 percent to the country's Gross Domestic Product (GDP). Despite the advancements witnessed during the digital era, farmers face a myriad of formidable challenges that hinder their progress. The proposed research work provides an ingenious solution by seamlessly employing the capabilities of Natural Language Processing (NLP) to support audio input and effectively assist farmers in various aspects of farming and market dynamics. CropChat demonstrates an exceptional comprehension and generate human-like text across an extensive range of over 95 languages. © 2023 IEEE.

关键词： Agriculture Artificial Intelligence Crop Farmers Land Natural Language Processing Soil

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DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2015-2033页

作者： Chandralekha, M. Jayadurga, N. Priyadharshini Chen, Thomas M. Sathiyanarayanan, Mithileysh Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India School of Science & Technology University of London London United Kingdom Research & Innovation MIT Square London United Kingdom

Accurate classification of eye state using Electroencephalogram (EEG) data reflects the high dimensionality and intricacy associated with the nature of the EEG signals. This state-of-the-art technique applies Discrete Cosine Transformation (DCT) for feature extraction, Cuckoo Search Algorithm (CSA) for optimal feature selection followed by a robust stacking algorithm integrating Adaptive Gradient Boosting Classifier (A-GBC) and k-Nearest Neighbors (k-NN) with Logistic Regression as meta-learner to improve the predictive accuracy and model performance. This model would strengthen the accuracy, reliability, and efficiency of eye state classification. DCT is used to compress the time series data into frequency domain for feature extraction, resulting in concentrating a vast amount of superficial signal energy. The next stage uses the Cuckoo Search Algorithm (CSA) in the selection of the optimal features. It thus optimizes the feature sub- set of the input in-order to get an optimal learning rate. The combination of these processes with a stacked ensemble using Adaptive Gradient Boosting Classifier (A-GBC) & k-Nearest Neighbors (k-NN) basal learners results in an immediate classification following the path of a Logistic Regression meta learner that achieves a remarkable classification accuracy of 96.96%. This strongly emphasizes the exceptional efficiency of the proposed framework when placed in context with existing approaches. It implies that the research not only reinforces the existing state of EEG eye state categorization but also provides for the optimization of its utility in other types of biomedical signal processing, thereby making it an example of a versatile medical tool. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Brain-computer Interface (BCI) Cuckoo Search Algorithm (CSA) DCTCS Stack Classifier Discrete Cosine Transformation (DCT) Electroencephalogram (EEG) Eye state Classification Stacked Machine Learning Model

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Klotski v2: Improved DNN Model Orchestration Framework for Dataflow Architecture Accelerators

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1045-1058页

作者： Bai, Chen Wei, Xuechao Zhuo, Youwei Cai, Yi Zheng, Hongzhong Yu, Bei Xie, Yuan The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Alibaba DAMO Academy Computing Technology Laboratory Hangzhou311121 China Peking University School of Integrated Circuits Beijing100871 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Dataflow architecture accelerators are a new kind of scalable DNN accelerators. For an instruction, the availability of input operands solely determines the beginning of executions. DNN model orchestration determines how to partition, schedule, and map the computation to the underlying hardware. In this article, we propose the Klotski v2 framework to solve DNN model orchestration for dataflow architecture accelerators. First, a Bayesian optimization-based entropy-directed partition algorithm is proposed to transform a DNN model into μ ops. Second, a unified formal formulation for μ ops scheduling and mapping is presented. Third, a two-stage methodology is proposed to decouple the scheduling and mapping. Fourth, a Hilbert curve-based mapping heuristic is proposed to enhance problem-solving efficiency, improving the tradeoff between solution quality and algorithm runtime. Extensive results show that Klotski v2 can achieve an average of 21.57% higher execution performance improvement than the previous methodologies. With the Hilbert curve-based mapping heuristic, we improve the algorithm efficiency by an average of 63.50% across different DNN workloads. © 1982-2012 IEEE.

关键词： Scheduling algorithms

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Exploring Variants of Extreme Learning Machines for Prediction of Mutual Fund NAV

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Journal of engineering science and Technology Review 2024年第2期17卷 97-118页

作者： Das, Smruti Rekha Mishra, Debahuti Rout, Minakhi Department of Computer Science and Engineering GITAM Deemed to be University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Siksha ‘O’Anusandhan Deemed to be University Odisha Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Odisha Bhuabaneswar India

Investing money through mutual fund benefits the small investors to access equities of big companies with a small amount of capital. It experiences the fluctuation of price along with the performance of stock, which is a major part in making the fund. Here, in this paper variant of Extreme Learning Machines (ELM) are applied to forecast the end-of-year net asset value (NAV) of mutual fund. Various types of ELM such as basic ELM, evolutionary ELM, online sequential ELM and error minimized ELM are explored and applied to historical data of four mutual funds such as SBI mutual fund, UTI mutual fund, Tata Mutual Fund and Kotak Mahindra Mutual Fund for the prediction of NAV. Along with the different ELM based prediction model, this paper has explored on different types of activation functions and the number of nodes in the hidden layer used in variants of ELM. Examining the simulation result of all the models, along with different activation functions and different number of nodes, it is observed that evolutionary ELM outperforms over the other variants of ELM used in this study. © 2024 school of science, DUTH. All Rights Reserved.

关键词： Forecasting

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meMIA: Multilevel Ensemble Membership Inference Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 93-106页

作者： Ullah, Najeeb Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing LincolnNE68588 United States

Leakage of private information in machine learning models can lead to breaches of confidentiality, identity theft, and unauthorized access to personal data. Ensuring the safe and trustworthy deployment of AI systems necessitates addressing privacy concerns to prevent unintentional disclosure and discrimination. One significant threat, membership inference (MI) attacks, exploit vulnerabilities in target learning models to determine if a given sample was part of the training set. However, the effectiveness of existing MI attacks is often limited by the number of classes in the dataset or the need for diverse multilevel adversarial features to exploit overfitted models. To enhance MI attack performance, we propose meMIA, a novel framework based on stacked ensemble learning. meMIA integrates embeddings from a neural network (NN) and a long short-term memory (LSTM) model, training a subsequent NN, termed the meta-model, on the concatenated embeddings. This method leverages the complementary strengths of NN and LSTM models;the LSTM captures order differences in confidence scores, while the NN discerns probability distribution differences between member and nonmember samples. We extensively evaluate meMIA on seven benchmark datasets, demonstrating that it surpasses current state-of-the-art MI attacks, achieving accuracy up to 94.6% and near-perfect recall. meMIA's superior performance, especially on datasets with fewer classes, underscores the urgent need for robust defenses against privacy attacks in machine learning, contributing to the safer and more ethical use of AI technologies. © 2024 IEEE.

关键词： Information leakage

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Optimized Learning Model for Brain-computer Interface Using Electroencephalogram (EEG) for Neuroprosthetics Robotic Arm Design for Society 5.0

Optimized Learning Model for Brain-Computer Interface Using ...

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2024 IEEE International Conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Arun Kumar, S. Anand, L. Kannur, Anil School of Computing SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Potheri SRM Nagar Kattankulathur India Dr. P.G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapura India

ISBN: (纸本)9798331517892

The brain activity can be represented by EEG, which does it by measuring voltage fluctuations in the brain. in this paper, the work intends to develop a Brain-computer Interface-based system that can identify and forecast the class of motor action corresponding to the left or right direction. There are two datasets used in this study, relating to motor movements - left/right-hand movement and eye open/close. The collected data is pre-processed by applying a Convolution Neuron Network to obtain a frequency domain matrix. The result computed by applying CNN provides a condensed representation of the data. The second dataset captured brain impulses corresponding to two states of the eyes opened and eyes closed, over a period of two minutes. This system is designed to effectively separate and utilize signals, with the adoption of AI techniques in these devices emerging as the most viable option for Society. The overall efficiency of this system ranges from 80% to 87%. © 2024 IEEE.

关键词： Frequency domain analysis

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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