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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction

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China Communications 2022年第2期19卷 62-72页

作者： Xuelin Gu Banghua Yang Shouwei Gao Honghao Gao Linfeng Yan Ding Xu Wen Wang School of Mechanical and Electrical Engineering and Automation Shanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Department of Radiology&Functional and Molecular Imaging Key Lab of Shaanxi Province Tangdu HospitalFourth Military Medical UniversityXi’anShaanxi 710038China Shanghai Drug Rehabilitation Administration Bureau Shanghai 200080China

After abusing drugs for long,drug users will experience deteriorated self-control cognitive ability,and poor emotional *** paper designs a closed-loop virtual-reality(VR),motorimagery(MI)rehabilitation training system based on brain-computer interface(BCI)(MI-BCI+VR),aiming to enhance the self-control,cognition,and emotional regulation of drug addicts via personalized rehabilitation *** paper is composed of two *** the first part,data of 45 drug addicts(mild:15;moderate:15;and severe:15)is tested with electroencephalogram(EEG)and near-infrared spectroscopy(NIRS)equipment(EEG-NIRS)under the dual-mode,synchronous signal collection *** these data sets,a dual-modal signal convolutional neural network(CNN)algorithm is then designed based on decision fusion to detect and classify the addiction *** the second part,the MIBCI+VR rehabilitation system is designed,optimizing the Filter Bank Common Spatial Pattern(FBCSP)algorithm used in MI,and realizing MI-EEG intention *** VR rehabilitation scenes are devised,achieving the communication between MI-BCI and VR scene *** subjects are selected to test the rehabilitation system offline and online,and the test accuracy verifies the feasibility of the *** future,it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

关键词： drug addiction degree brain-computer interface,motor imagery virtual reality drug addiction rehabilitation

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Thermometer: Towards Universal Calibration for Large Language Models

arXiv

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arXiv 2024年

作者： Shen, Maohao Das, Subhro Greenewald, Kristjan Sattigeri, Prasanna Wornell, Gregory Ghosh, Soumya Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States MIT-IBM Watson AI Lab IBM Research United States

We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational requirements of LLMs as from their versatility, which allows them to be applied to diverse tasks. Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs. THERMOMETER learns an auxiliary model, given data from multiple tasks, for calibrating a LLM. It is computationally efficient, preserves the accuracy of the LLM, and produces better-calibrated responses for new tasks. Extensive empirical evaluations across various benchmarks demonstrate the effectiveness of the proposed method. © 2024, CC BY.

关键词： Calibration

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Southern University of Science and Technology Department of Electrical and Electronic Engineering China Wireless Technology Lab 2012 Laboratories Huawei China Weizmann Institute of Science Faculty of Mathematics and Computer Science Israel

ISBN: (纸本)9781665459754

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. © 2022 IEEE.

关键词： Beamforming

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Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture

arXiv

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arXiv 2024年

作者： Kong, Chenqi Luo, Anwei Bao, Peijun Li, Haoliang Wan, Renjie Zheng, Zengwei Rocha, Anderson Kot, Alex C. Lab School of Electrical and Electronic Engineering Nanyang Technology University 639798 Singapore The School of Computer Science and Engineering Sun Yatsen University Guangzhou China The Department of Computer Science Hong Kong Baptist University Hong Kong The Department of Electrical Engineering City University of Hong Kong Hong Kong The Department of Computer Science and Computing Zhejiang University City College Zhejiang China The University of Campinas Campinas13084-851 Brazil

Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations: they cannot generalize across unknown forgery domains and inefficiently adapt to new data. To address these issues, we introduce an approach that is both general and parameter-efficient for face forgery detection. It builds on the assumption that different forgery source domains exhibit distinct style statistics. Previous methods typically require fully fine-tuning pretrained networks, consuming substantial time and computational resources. In turn, we design a forgery-style mixture formulation that augments the diversity of forgery source domains, enhancing the model's generalizability across unseen domains. Drawing on recent advancements in vision transformers (ViT) for face forgery detection, we develop a parameter-efficient ViT-based detection model that includes lightweight forgery feature extraction modules and enables the model to extract global and local forgery clues simultaneously. We only optimize the inserted lightweight modules during training, maintaining the original ViT structure with its pre-trained ImageNet weights. This training strategy effectively preserves the informative pre-trained knowledge while flexibly adapting the model to the task of Deepfake detection. Extensive experimental results demonstrate that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters, representing an important step toward open-set Deepfake detection in the wild. The code is available at https://***/ChenqiKONG/OSDFD. © 2024, CC BY.

关键词： Feature extraction

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MIMO-NOMA Enabled Sectorized Cylindrical Massive Antenna Array for HAPS with Spatially Correlated Channels

arXiv

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arXiv 2024年

作者： Shafie, Rozita Omidi, Mohammad Javad Abbasi, Omid Yanikomeroglu, Halim Lab Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha35003 Kuwait

The high altitude platform station (HAPS) technology is garnering significant interest as a viable technology for serving as base stations in communication networks. However, HAPS faces the challenge of high spatial correlation among adjacent users' channel gains which is due to the dominant line-of-sight (LoS) path between HAPS and terrestrial users. Furthermore, there is a spatial correlation among antenna elements of HAPS that depends on the propagation environment and the distance between elements of the antenna array. This paper presents an antenna architecture for HAPS and considers the mentioned issues by characterizing the channel gain and the spatial correlation matrix of the HAPS. We propose a cylindrical antenna for HAPS that utilizes vertical uniform linear array (ULA) sectors. Moreover, to address the issue of high spatial correlation among users, the non-orthogonal multiple access (NOMA) clustering method is proposed. An algorithm is also developed to allocate power among users to maximize both spectral efficiency and energy efficiency while meeting quality of service (QoS) and successive interference cancellation (SIC) conditions. Finally, simulation results indicate that the spatial correlation has a significant impact on spectral efficiency and energy efficiency in multiple antenna HAPS systems. © 2024, CC BY-NC-SA.

关键词： Spectrum efficiency

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Towards Robust Online Domain Adaptive Semantic Segmentation under Adverse Weather Conditions

arXiv

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arXiv 2024年

作者： Liu, Taorong Xiao, Jing Liao, Liang Lin, Chia-Wen School of Computer Science Wuhan University China S-lab Nanyang Technological University Singapore Department of Electrical Engineering National Tsing Hua University Taiwan

Online Domain Adaptation (OnDA) is designed to handle unforeseeable domain changes at minimal cost that occur during the deployment of the model, lacking clear boundaries between the domain, such as sudden weather events. However, existing OnDA methods that rely solely on the model itself to adapt to the current domain often misidentify ambiguous classes amidst continuous domain shifts and pass on this erroneous knowledge to the next domain. To tackle this, we propose RODASS, a Robust Online Domain Adaptive Semantic Segmentation framework, which dynamically detects domain shifts and adjusts hyper-parameters to minimize training costs and error propagation. Specifically, we introduce the Dynamic Ambiguous Patch Mask (DAP Mask) strategy, which dynamically selects highly disturbed regions and masks these regions, mitigating error accumulation in ambiguous classes and enhancing the model’s robustness against external noise in dynamic natural environments. Additionally, we present the Dynamic Source Class Mix (DSC Mix), a domain-aware mix method that augments target domain scenes with class-level source buffers, reducing the high uncertainty and noisy labels, thereby accelerating adaptation and offering a more efficient solution for online domain adaptation. Our approach outperforms state-of-the-art methods on widely used OnDA benchmarks while maintaining approximately 40 frames per second (FPS). Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Towards an Integrated Framework for Fugitive Methane Source Localization and Monitoring in a Wireless Sensor Network

Towards an Integrated Framework for Fugitive Methane Source ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Amira Al Jawhari Yasmine Souissi Jawhar Ghommam Yassine Jamoussi Faisal Mnif Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Department of Engineering GUtech University Muscat Oman Laboratory of Control Energy Management (Cem-Lab) ENIS Tunisa Departement of Computer Science College of Science SQU

Gas leaks are the main cause of industrial fires and accidents. These cause countless fatalities, equipment damage, and other severe environmental effects. In this paper, we provide a framework for the monitoring and detection of methane leakage using a diffusion model based on the gas diffusion theory. Given that centralized Least Square methods are not efficient and robust as they require the gathering and processing of large-scale measurements on a central node. We propose a detection technique which makes use of the distributed (Non-linear) least squares method to overcome this problem. Then, a network of connected methane sensors is used to detect gas leaks. In order to estimate the parameters of the diffusive model for the gas leakage on each sensor node, a distributed recursive estimator of the consensus plus an innovation type technique is used. The characteristics being estimated include the gas source’s distance, which will be effectively triangulated to determine the source’s precise location. The targeted location is subsequently estimated using a location dispersed algorithm-based LS.

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Utilizing Deep Incomplete Classifiers to Implement Semantic Clustering for Killer Whale Photo Identification Data 27th

Utilizing Deep Incomplete Classifiers to Implement Semantic ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Barnhill, Alexander Towers, Jared R. Nöth, Elmar Maier, Andreas Bergler, Christian Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Bay Cetology 257 Fir Street Alert BayBCV0N 1A0 Canada Department of Electrical Engineering Media and Computer Science Technical University of Applied Sciences Amberg-Weiden Amberg Germany

ISBN: (纸本)9783031781063

Machine-assisted photo identification processes require significant amounts of data for each member of a population of interest but offer the possibility to alleviate a significant amount of manual effort. Gathering such data is time consuming and opportunistic, leading to imbalanced datasets ill-suited for traditional machine (deep) learning efforts. Incomplete classifiers, trained on a subset of classes in a population, can be initially useful to identify the most commonly seen individuals. This study investigates the use of incomplete classifiers trained on a subset of often-observed individual killer whales to generate latent space representations of the larger population containing unseen individuals. These semantically relevant representations are subsequently clustered to investigate the efficacy of this method as a secondary identification mechanism. This method proves to be robust to a significant amount of noise while being able to isolate individuals unknown to the classifier when applying limited expert knowledge to the approximate size of the population. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： labeled data

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Data-Driven Examination of the Impact Energy Efficiency has on Demand Response Capabilities in Institutional Buildings

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Journal of engineering for Sustainable Buildings and Cities 2022年第2期3卷 024501页

作者： Andrews, Abigail Roth, Jonathan Jain, Rishee K. Mathieu, Johanna L. Urban Informatics Lab. Department of Civil and Environmental Engineering Stanford University 473 Via Ortega Stanford 94305 CA United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor 48109 MI United States

Growing concerns over climate change and grid reliability have led to widespread adoption of energy efficiency (EE) and demand response (DR) programs at utilities. Despite such adoption, numerous questions exist regarding the interactions between EE and DR, including whether EE diminishes a building’s DR potential. In this brief, we empirically examine the impact a building’s EE level (quantified by traditional EE benchmarking metrics) has on its DR capabilities (quantified by a building’s normalized load shed) for 194 K-12 institutional school buildings in California, USA. We found inconclusive statistical evidence that a building’s EE level has an impact on its DR load shed capabilities. We provide initial evidence countering concerns that EE diminishes DR potential and thus pave the path for future work than can further support synergistic EE and DR strategies which can enhance demand-side management programs. Copyright © 2022 by ASME.

关键词： benchmarking demand response demand-side management energy efficiency

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