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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： A. Sivasundari Pravin R. Kshirsagar Tan Kuan Tak Shrikant V. Sonekar Department of Computer Science and Engineering Misrimal Navajee Munoth Jain Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore J D College of Engineering & Management Nagpur India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The "Internet of Things (IoT)" sector has grown in recent years, penetrating many different markets. The diversity and diminutive size of Internet-connected gadgets render them vulnerable to attacks. Improving and managing network forensic processes to avoid the various attacks, key logging, and denial of service becomes challenging in big and heterogeneous networks. Implementing high-end safety systems is challenging in IoT networks due to their variety and resource restrictions. Using deep learning, botnet detection is made better. It often requires a great deal of internet engagement. Internet of Things devices with little RAM have difficulties when using deep learning. This study use Convolutional Neural Networks (CNNs) for model training and Similarity Associated Feature Selection (SAFS) to decrease the dimensionality of smaller Internet of Things (IoT) traffic networks in order to effectively classify attacks in devices with limited memory. The effectiveness of botnet attacks is evaluated by first collecting datasets from the internet. By improving attack detection data from acquired datasets, data cleaning aids in eliminating unnecessary information. To enhance attack detection, SAFS cleans the data and then extracts efficient deep features. To classify risks to Internet of Things devices based on retrieved deep data, an efficient Recurrent Neural Network method is employed. Research evaluations compare proposed methods against existing ones in order to determine whether one is more effective.

关键词： Deep learning Training Recurrent neural networks Botnet Random access memory Feature extraction Safety Internet of Things Security Informatics

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Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems

Co-Design of Out-of-Distribution Detectors for Autonomous Em...

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International Conference on Intelligent Transportation

作者： Michael Yuhas Arvind Easwaran Energy Research Institute @ NTU Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs require heavy utilization of embedded hardware typically found in AVs. For both components, there is a tradeoff between non-functional and functional performance, and both impact a vehicle's safety. For instance, giving an OOD detector a longer response time can increase its accuracy at the expense of the LEC. We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety. We formulate a co-design methodology that uses this risk model to find the design parameters for an OOD detector and LEC that decrease risk below that of the baseline system and demonstrate it on a vision based AEBS. Using our methodology, we achieve a 42.3% risk reduction while maintaining equivalent resource utilization.

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Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization 37

Trusted-Maximizers Entropy Search for Efficient Bayesian Opt...

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37th Conference on Uncertainty in Artificial Intelligence, UAI 2021

作者： Nguyen, Quoc Phong Wu, Zhaoxuan Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute of Data Science National University of Singapore Singapore NUSGS Integrative Sciences and Engineering Programme National University of Singapore Singapore

Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require several approximations or simplifying assumptions (without clearly understanding their effects on the BO performance) and/or their generalization to batch BO is computationally unwieldy, especially with an increasing batch size. To alleviate these issues, this paper presents a novel trusted-maximizers entropy search (TES) acquisition function: It measures how much an input query contributes to the information gain on the maximizer over a finite set of trusted maximizers, i.e., inputs optimizing functions that are sampled from the Gaussian process posterior belief of the objective function. Evaluating TES requires either only a stochastic approximation with sampling or a deterministic approximation with expectation propagation, both of which are investigated and empirically evaluated using synthetic benchmark objective functions and real-world optimization problems, e.g., hyperparameter tuning of a convolutional neural network and synthesizing 'physically realizable' faces to fool a black-box face recognition system. Though TES can naturally be generalized to a batch variant with either approximation, the latter is amenable to be scaled to a much larger batch size in our experiments. © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.

关键词： Entropy

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Time-Domain Heart Sound Classification Using Neural Audio Codecs

Time-Domain Heart Sound Classification Using Neural Audio Co...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Ansh Mishra Jia Qi Yip Eng Siong Chng Department of Physics IIT Delhi New Delhi India Interdisciplinary Graduate Programme Nanyang Technological University (NTU) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Heart auscultation remains a valuable approach for early detection of valvular heart disease, but its efficacy depends on physician skill. The rise of telehealth services necessitates consideration of heart sound transmission and classification through compressed formats. While neural audio codecs offer impressive compression rates, they may introduce distortions that could impact classification accuracy. We propose a novel approach to perform heart sound classification directly on the continuous representations of the codec, rather than on reconstructed audio. Our method demonstrates good performance on the heart sound classification task while leveraging the encoder of the audio codec, potentially improving robustness and efficiency in telehealth applications. Using the Descript Audio Codec encoder combined with a modified M5 convolutional neural network, our approach achieves a Classification Error Rate of 0.78 on the Yaseen Dataset, outperforming models trained on transmitted audio and comparing favorably with state-of-the-art methods. This performance, coupled with the method's inherent robustness to transmission artifacts, highlights its potential for improving remote cardiac diagnostics in telehealth settings.

关键词： Heart Codecs Error analysis Telemedicine Computational modeling Medical services Robustness Real-time systems Time-domain analysis Informatics

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Hybrid Heuristic Optimization of Deep Learning Models for Robust Plant Disease Detection

Hybrid Heuristic Optimization of Deep Learning Models for Ro...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： K. Ananthajothi Pravin R. Kshirsagar Tan Kuan Tak Kailash Kumar Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.

关键词： Deep learning Plant diseases Image segmentation Computational modeling Neural networks Convolutional neural networks Particle swarm optimization Informatics Optimization Monitoring

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Intent-Based Service Graph Selection for Cost-Effective Cloud Deployment

Intent-Based Service Graph Selection for Cost-Effective Clou...

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Sasan Sabour Amin Ebrahimzadeh Mbarka Soualhia Fetahi Wuhib Roch. H. Glitho Telecommunication Service Engineering Research Lab Concordia University Montréal QC Canada Ericsson Research Montréal QC Canada Computer Science Programme University of Western Cape Capetown South Africa

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Cloud computing offers a diverse range of virtualized services for users. However, selecting and composing these services to generate optimal service graphs that meet user requirements is complex because it requires an expert with extensive knowledge of the cloud applications and their service composition, to manually generate service graphs. This, however, cannot often guarantee that all the requirements are met. A potentially promising approach to address this complexity is to develop a system that enables cloud users to specify their intent and then to automatically select a service graph that meets all their requirements. In this paper, we address the problem of service graph selection while considering both functional and non-functional requirements derived from user intent. Our objective is to minimize the total deployment cost of the service graph in a data center network and to determine its placement within the distributed data center network. We formulate the problem as an Integer Linear Programming (ILP), taking into account the bandwidth and latency requirements of user intent. To solve this, we propose our Service Graph Selection (SGS) solution, which aims to achieve a near-optimal solution in a computationally efficient manner. Our results demonstrate that the proposed solution achieves a deployment cost that is only 4–6% larger than the lower bound of the optimal deployment cost.

关键词： Cloud computing Data centers Technological innovation Lower bound Costs Bandwidth Integer linear programming Computational efficiency Complexity theory

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Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

arXiv

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arXiv 2023年

作者： Guo, Xiaobao Selvaraj, Nithish Muthuchamy Yu, Zitong Kong, Adams Wai-Kin Shen, Bingquan Kot, Alex Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Computer Science and Engineering NTU Singapore School of Electrical & Electronic Engineering NTU Singapore DSO National Laboratories Singapore

Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively. To address this issue, we introduce DOLOS1, the largest gameshow deception detection dataset with rich deceptive conversations. DOLOS includes 1,675 video clips featuring 213 subjects, and it has been labeled with audio-visual feature annotations. We provide train-test, duration, and gender protocols to investigate the impact of different factors. We benchmark our dataset on previously proposed deception detection approaches. To further improve the performance by fine-tuning fewer parameters, we propose Parameter-Efficient Crossmodal Learning (PECL), where a Uniform Temporal Adapter (UT-Adapter) explores temporal attention in transformer-based architectures, and a crossmodal fusion module, Plug-in Audio-Visual Fusion (PAVF), combines crossmodal information from audio-visual features. Based on the rich fine-grained audio-visual annotations on DOLOS, we also exploit multi-task learning to enhance performance by concurrently predicting deception and audiovisual features. Experimental results demonstrate the desired quality of the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the source codes are available at here. © 2023, CC BY.

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Provably Efficient Algorithm for Nonstationary Low-Rank MDPs

arXiv

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arXiv 2023年

作者： Cheng, Yuan Yang, Jing Liang, Yingbin Integrative Sciences and Engineering Programme NUS Graduate School Singapore School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering The Ohio State University OH43210 United States

Reinforcement learning (RL) under changing environment models many real-world applications via nonstationary Markov Decision Processes (MDPs), and hence gains considerable interest. However, theoretical studies on nonstationary MDPs in the literature have mainly focused on tabular and linear (mixture) MDPs, which do not capture the nature of unknown representation in deep RL. In this paper, we make the first effort to investigate nonstationary RL under episodic low-rank MDPs, where both transition kernels and rewards may vary over time, and the low-rank model contains unknown representation in addition to the linear state embedding function. We first propose a parameter-dependent policy optimization algorithm called PORTAL, and further improve PORTAL to its parameter-free version of Ada-PORTAL, which is able to tune its hyper-parameters adaptively without any prior knowledge of nonstationarity. For both algorithms, we provide upper bounds on the average dynamic suboptimality gap, which show that as long as the nonstationarity is not significantly large, PORTAL and Ada-PORTAL are sample-efficient and can achieve arbitrarily small average dynamic suboptimality gap with polynomial sample complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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A Two-stage Anomaly Detection System for Fog Computing Services

A Two-stage Anomaly Detection System for Fog Computing Servi...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Sunday Oyinlola Ogundoyin Ismaila Adeniyi Kamil Isaac Adewale Ojedokun John Oluwaseun Babalola Vincent Omollo Nyangaresi Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya Department of Applied Electronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10 −5 . The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively.

关键词： Performance evaluation Cloud computing Accuracy Computational modeling Mission critical systems Machine learning Time factors Long short term memory Anomaly detection Edge computing

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

Spin-UP: Spin Light for Natural Light Uncalibrated Photometr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zongrui Li Zhan Lu Haojie Yan Boxin Shi Gang Pan Qian Zheng Xudong Jiang Rapid-Rich Object Search (ROSE) Lab Interdisciplinary Graduate Programme Nanyang Technological University School of Electrical and Electronic Engineering Nanyang Technological University The State Key Lab of Brain-Machine Intelligence Zhejiang University College of Computer Science and Technology Zhejiang University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised / unsupervised NaUPS meth-ods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP.

关键词： Reflectivity Training Shape Supervised learning Anisotropic Rendering (computer graphics) Structural engineering

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