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23rd International Conference on Cyberworlds, CW 2024

作者： Wang, Hanqin Sourin, Alexei Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331527174

Music visualization is presented in different formats. The mainstream music visualization remains focused on providing animated images for visual pleasure or for enhancing the perception of sheet music with colors. Static visualization of music, on the other hand, has the potential to provide quick insights into the music's attributes, particularly its mood. However, despite our previous work exploring several approaches to music mood visualization, a major problem still exists in how to objectively evaluate the mood of music pieces and the generated images. Thus, this paper proposes IMEMNextan improvement to the existing framework IMEMNet [1], which serves as a novel and objective metric for music mood visualization. Not only does it allow for a user study-free evaluation of music mood visualization, but the new network also enables for creation of datasets of music-image pairs with the same mood. The new IMEMNext network achieved a significant performance improvement compared to its original implementation. © 2024 IEEE.

关键词： Visualization

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Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA

Diversify, Rationalize, and Combine: Ensembling Multiple QA ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Miaoyu Li, Haoxin Du, Zilin Li, Boyang College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Knowledge-based Visual Qustion-answering (K-VQA) often requires the use of background knowledge beyond the image. However, we discover that a single knowledge generation strategy is often insufficient for all K-VQA questions. To this end, we propose Diversification, Evidence Truncation, and Combination for Knowledge-based Elucidation (DIETCOKE), which utilizes a bundle of complementary question-answering tactics and aggregates their answers using textual rationales. DIETCOKE comprises of three stages: diversification, rationalization, and ensemble. The diversification stage generates three distinctive decision contexts, each leading to its own answer candidate. The rationalization stage generates two rationales, the automatic rationale and the mechanistic rationale, for each answer candidate using decorrelated techniques. Finally, in the ensemble stage, an LLM informed by the rationales selects one answer from the three candidates. Experiments show that DIETCOKE significantly outperforms state-of-the-art LLM-based baselines by 2.8% on OK-VQA and 4.7% on A-OKVQA and that the strategies in the ensembles are highly complementary. Code is available at: https://***/limiaoyu/DietCoke. © 2024 Association for Computational Linguistics.

关键词： Question answering

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A Survey on Physical Event Verification in User-centric Smart Home Systems 35

A Survey on Physical Event Verification in User-centric Smar...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Huang, Bing Lam, Kwok-Yan Nanyang Technological University School of Computing and Data Science Singapore

ISBN: (纸本)9798350367041

Recent advances in IoT technologies have led to the rise of home automation (HA) systems, greatly improving users' daily life quality. With the growing use of smart devices in homes, event sensors play a key role by reporting physical events like doors opening or lights turning on, automatically or manually. However, these sensor reports can be unreliable due to faults or active attackers, leading to false events (event spoofing). This is critical, as smart homes can trigger event chains without user involvement. Therefore, verifying the authenticity of physical events is important in securing smart home automation systems. Despite increasing attention from academia and industry, no survey has yet summarized HA systems' security research on physical event verification. This paper provides a comprehensive review of physical event verification and future research directions. © 2024 IEEE.

关键词： Smart homes

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Impulsive Control of Nonlinear Multiagent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1889-1898页

作者： Han, Fang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China

In this article, we present a hybrid control approach that integrates an adaptive fuzzy mechanism with an event-triggered impulse strategy to address consensus control challenges in nonlinear multiagent systems (MASs) with uncertain information, both in leader and leaderless scenarios. First, a fuzzy logic-based adaptive control mechanism is proposed to handle system nonlinearity and uncertainty. This mechanism dynamically adjusts fuzzy control rules in real time, enabling the system to adapt to changing conditions and enhancing its robustness against nonlinear disturbances. Second, to alleviate communication overhead and reduce control frequency, an event-triggered impulse strategy is implemented. This strategy activates impulse control only under specific conditions, effectively minimizing control actions while maintaining system consensus. Using Lyapunov stability theory, sufficient conditions for achieving system consensus are established, demonstrating the global consensus and robustness of the MASs under certain criteria. Finally, numerical simulations and experimental evaluations validate the proposed method's effectiveness and its practical advantages. © 1993-2012 IEEE.

关键词： Multi agent systems

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Fuzzy knowledge based intelligent decision support system for ground based air defence

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Journal of Ambient Intelligence and Humanized computing 2024年第4期15卷 2317-2340页

作者： Ahmad, Adnan Amjad, Rawan Basharat, Amna Farhan, Asma Ahmad Abbas, Ali Ezad Department of Computer Science School of Computing National University of Computer and Emerging Sciences Islamabad Pakistan Department of Data Science School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Islamabad Pakistan

This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A Fire Control Officer is responsible for assessing threats and assigning the most appropriate weapon to neutralize them. However, the decision-making process can be prone to errors, risking resource wastage and endangering DA protection. To address this problem, this research proposes a hybrid approach that combines a knowledge-driven fuzzy inference system with machine learning models to optimize resource allocation while incorporating expert knowledge in the decision-making process. Since sensory data obtained from multiple radars may be incomplete or incorrect, a fuzzy knowledge graph-based system is used for data fusion and providing it to the connected modules. Feature selection is optimized by including the most important parameters, such as the vitality of defended assets and threat score, in the threat evaluation. The results from these subsystems are visualized using a Geographical Information System, allowing for real-time mapping of the GBAD environment and displaying the results in a user-friendly web interface. The proposed system has undergone rigorous testing and evaluation, resulting in an efficient and accurate weapon assignment model with a low RMSE value of 0.037. Overall, this Intelligent Decision Support System provides an effective solution for optimizing decision-making processes in GBAD environments and can significantly improve DA protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graphic methods

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ReCSA:a dedicated sort accelerator using ReRAM-based content addressable memory

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Frontiers of Computer science 2023年第2期17卷 1-13页

作者： Huize LI Hai JIN Long ZHENG Yu HUANG Xiaofei LIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardware's *** the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the *** this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array *** this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort *** hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting *** sorting algorithm in ReCSA can process various data types,such as integer,float,double,and *** also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort *** experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data *** also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.

关键词： ReCAM parallel sorting architecture design processing-in-memory

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A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

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Computer Systems science & Engineering 2023年第7期46卷 337-356页

作者： N.Vasudevan T.Karthick Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurTamilnadu603203India

Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious *** diseases diminish the quality of crop *** detect disease spots on grape leaves,deep learning technology might be *** the other hand,the precision and efficiency of identification remain *** quantity of images of ill leaves taken from plants is often *** an uneven collection and few images,spotting disease is *** plant leaves dataset needs to be expanded to detect illness accurately.A novel hybrid technique employing segmentation,augmentation,and a capsule neural network(CapsNet)is used in this paper to tackle these *** proposed method involves three ***,a graph-based technique extracts leaf area from a plant *** second step expands the dataset using an Efficient Generative Adversarial Network ***,a CapsNet identifies the illness and *** proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf *** proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.

关键词： Feature extraction neural network disease segmentation pattern analysis

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Attention boosted Individualized Regression 38

Attention boosted Individualized Regression

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Guang Cao, Yuan Feng, Long Department of Data Science College of Computing City University of Hong Kong Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model. © 2024 Neural information processing systems foundation. All rights reserved.

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An automatic lung nodule detection and classification using an optimized convolutional neural network and enhanced k-means clustering

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Journal of Ambient Intelligence and Humanized computing 2023年第12期14卷 16973-16984页

作者： Lydia, M. Dhasny Prakash, M. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

Lung cancer can be lethal if it is not found in the initial phases. Lung cancer, nevertheless, is challenging to identify early due to the dimensions and form of the nodules. Imaging specialists require the assistance of automated instruments for accurate interpretation. Therefore, in this paper, automatic lung nodule classification is proposed. In the proposed methodology, there are four phases, namely pre-processing, segmentation, classification, and severity analysis. As a first step, lung nodule images are collected from the dataset and pre-processed. After pre-processing, segmentation is carried out. For segmentation enhanced k-means clustering algorithm is applied. To identify the object as cancerous or benign, the segmented region is then fed into an optimized convolution neural network (OCNN). Here, adaptive sunflower optimization (ASFO) algorithm is used to pick the hyperparameters effectively to improve the effectiveness of the CNN classifier. Finally, from the segmented region, the severity of the patient is evaluated. The efficiency of the presented technique is analyzed based on various metrics and research work compared with different state-of-art-works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Human Stress Recognition by Correlating Vision and EEG data

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Computer Systems science & Engineering 2023年第6期45卷 2417-2433页

作者： S.Praveenkumar T.Karthick SRM Institute of Science and Technology Department of Data Science and Business SystemsSchool of ComputingKattankulathur603203TamilnaduIndia

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday *** human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human *** the multimodal dataset DEAP(database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human *** combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when *** on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress *** the stress identification test,we utilized the DEAP dataset,which included video and EEG *** also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate *** the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG *** Level(FL)fusion that combines the features extracted from video and EEG *** use XGBoost as our classifier model to predict stress,and we put it into *** stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

关键词： Mental stress physiological data XGBoost feature fusion DEAP video data EEG CNN HAR

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