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Attention Enhanced Feature Fusion for Mental Stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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Insights into the Future: XAI Integration in O-RAN and Space Communication systems

Insights into the Future: XAI Integration in O-RAN and Space...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Tahir, H. Ahmed Nabi, Fahad Tariq, Muhammad Ziad Khan, Ahmad Fasih Mahmud, Azwan Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Kiel Department of Electrical Engineering & Information Technology Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (纸本)9798331530747

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as 'black boxes,' making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Radio access networks

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A New Technique of Soluble NPK Nutrient Detection system Using Deep Learning-Based Recurrent Neural Networks (RNN) 12

A New Technique of Soluble NPK Nutrient Detection System Usi...

引用

12th International Electrical engineering Congress, iEECON 2024

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University Of Technology Krungthep Department Of Electrical And Telecommunication Engineering Bangkok Thailand Thammasat School Of Engineering Thammasat University Faculty Of Engineering Department Of Electrical And Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department Of Robotic Engineering And Automation System Bangkok Thailand

ISBN: (纸本)9798350383591

Modern agriculture relies heavily on effective process monitoring, control, and remote data collection, making accurate and cost-effective sensors crucial for innovation. This research focuses on the development of a spectroscopy-based device for real-time and remote monitoring of nutrient levels in hydroponic systems. The device utilizes IoT architecture, incorporating principles of light absorbance to quantify concentrations of essential nutrients during crop growth. Three deep learning techniques, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) were adopted for data analysis. Results demonstrate that tailored implementation of deep learning can significantly enhance the performance of sensor network system (SNS). Specifically, the RNN format achieved an impressive 98% model accuracy during both training and testing phases. The technological advancements in addressing contract variation and photometric levels contribute to increased accuracy as more sensors become available. The findings serve as a comprehensive guide for collecting extensive agricultural data, encompassing environmental factors throughout the entire cultivation period, using state-of-the-art remote control sensor equipment. This work provides a practical, efficient, and cost-effective solution for precise nutrient control and continuous data monitoring in agricultural production. © 2024 IEEE.

关键词： Cost effectiveness

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Multi-Task Learning with Convolutional Neural Network Approach for Packet Collision Avoidance in 802.11 WLAN 9

Multi-Task Learning with Convolutional Neural Network Approa...

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9th NAFOSTED Conference on Information and computer Science, NICS 2022

作者： Putra, Dody Ichwana Pratama, Harry Bintang Nakashima, Tomoki Nagao, Yuhei Kurosaki, Masayuki Ochi, Hiroshi Grad. School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (纸本)9781665454223

Packet collision can degrade wireless network performance. The IEEE 802.11 Wireless Local Area Network (WLAN) uses the Clear Channel Assessment (CCA) mechanism to monitor channel availability to avoid interference of the presence signal. CCA successfully detects 802.11 signals if it obtains the packet preamble information or detects the threshold ambient power on the channel to determine the channel state. This paper proposes multi-task learning (MTL) with convolutional neural network (CNN) approach to detect WLAN packet formats and modulation types without preamble part information as a supplement to enhance CCA sensitivity. The main advantages of this method over single-task training are high classification accuracy and rapid learning with a lightweight neural network model. Shared knowledge of representation layers, such as model weights or gradients, improves the efficiency of training data and reduces redundancy. WLAN signals generated by the Matlab waveform simulator are used to verify the accuracy of the proposed method, which is then implemented on a real-time SDR-based hardware testbed. Although different time offsets affect the classifications, the proposed method proves superior in classifying the packet format and modulation of WLAN signals with an accuracy of 98.93% and 88.53% at SNR = 24 dB, respectively. The proposed method improves channel utilization and throughput of the WLAN network, as demonstrated by an NS-3 simulation. © 2022 IEEE.

关键词： MATLAB

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

引用

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Honeypot Comparison of Attack Detection and Mitigation of SSH Attack 3

Honeypot Comparison of Attack Detection and Mitigation of SS...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ali, Wasif Sajid, Ahthasham Ghodke, Tanvi Ajit Malik, Rida Malik, Neeru Kaushik, Keshav Riphah Institute of System Engineering Riphah International University Department of Cyber Security Islamabad Pakistan Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra India School of Engineering & Technology Pimpri Chinchwad University Maharashtra Pune India Amity School of Engineering and Technology Amity University Mohali India

ISBN: (纸本)9798331518592

This research study addresses the importance of honeypots at multiple levels, which includes but not limited to placement in network or group of networks working alongside in an organization. This paper talks about the usage of multiple honeypots working alongside to log useful data for the mitigation of ongoing bot or actual attack this approach helps the organization better understand the threat they are dealing and the techniques which they can use to overcome the gap of the unseen security risks. The comparison will shed better light to the working and attack patterns of the ongoing attacks, gaining more confidence on using the honeypot for better protection. This allows strategic, optimized and critical insight to the evolving attack patterns. © 2024 IEEE.

关键词： Network intrusion

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Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities 59

Operating-Envelopes-Aware Decentralized Welfare Maximization...

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59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

作者： Alahmed, Ahmed S. Cavraro, Guido Bernstein, Andrey Tong, Lang Cornell University School of Electrical and Computer Engineering IthacaNY United States National Renewable Energy Laboratory Power System Engineering Center GoldenCO United States

ISBN: (纸本)9798350328141

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance. © 2023 IEEE.

关键词： Dynamics

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking 12

Adaptive Confidence Threshold for ByteTrack in Multi-Object ...

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12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023

作者： Ma, Linh Van Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Korea Republic of Pusan National University Department of Mechanical-IT Convergence System Engineering Busan Korea Republic of

ISBN: (纸本)9798350328783

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. © 2023 IEEE.

关键词： Object detection

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Robust Network For Segmenting Roof Planes From Sparse Point Clouds 36

Robust Network For Segmenting Roof Planes From Sparse Point ...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Fu, Hang Zeng, Cheng Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China School of Computer and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China

ISBN: (纸本)1891706594

The plane is the fundamental characteristic for describing the shape of polyhedral buildings. Roof plane segmentation of airborne LiDAR point clouds is an important step in 3D building model reconstruction. Existing methods are either non-automatic and efficient traditional methods, or hindered by the lack of datasets for roof plane segmentation and therefore fail to achieve good training for deep learning methods. To address the above issues, we proposes an end-to-end network called PUTr for roof plane segmentation of sparse point clouds. This network integrates a Point-cloud Upsampling module and Transformer to ensure effective generalization even when trained on low-density datasets, while also capturing dependency relationships within point cloud neighborhoods. Our model overcomes the low automation and manual intervention of existing methods, and the limited generalization of deep learning due to scarce high-quality datasets. Evaluations also demonstrate its effectiveness in roof plane segmentation of laser point cloud data. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： 3D reconstruction

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