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arXiv 2023年

作者： Zhan, Huixin Zhang, Kun Lu, Keyi Sheng, Victor S. Department of Computer Science Texas Tech University Box 43104 LubbockTX79409-3104 United States Department of Computer Science Xavier University of Louisiana New OrleansLA70125 United States Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States

In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA). We propose a GRA that recovers a graph’s adjacency matrix from the representations via a graph decoder that minimizes the reconstruction loss between the partial graph and the reconstructed graph. We study three types of representations that are trained on the graph, i.e., representations output from graph convolutional network (GCN), graph attention network (GAT), and our proposed simplicial neural network (SNN) via a higher-order combinatorial laplacian. Unlike the first two types of representations that only encode pairwise relationships, the third type of representation, i.e., SNN outputs, encodes higher-order interactions (e.g., homological features) between nodes. We find that the SNN outputs reveal the lowest privacy-preserving ability to defend the GRA, followed by those of GATs and GCNs, which indicates the importance of building more private representations with higher-order node information that could defend the potential threats, such as *** Codes 51Hxx © 2023, CC BY.

关键词： Encoding (symbols)

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Deep-learning assisted Cross-layer Routing in Multi-hop Wireless Network 7

Deep-learning assisted Cross-Layer Routing in Multi-hop Wire...

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Regis, Paulo Alexandre Bhunia, Suman Patra, Amar Nath Sengupta, Shamik Southeastern Louisiana University Department of Computer Science HammondLA United States Miami University Department of Computer Science and Software Engineering OxfordOH United States Radford University School of Computing and Information Sciences RadfordVA United States University of Nevada Department of Computer Science and Engineering RenoNV United States

ISBN: (纸本)9781665444316

Wireless ad hoc networks rely on exchanging information among the nodes to find a feasible route between source and destination. Routing protocols require periodically flooding the entire system with the updated state of the network. Routing protocols define the rules and semantics of how this information is spread throughout the network and can be classified into proactive and reactive protocols, both of which eventually sends out control packets to the network. However, with the increase of the scale of the network, the amount of packets exchanged uses resources that could be otherwise used for application messages. In this paper, we explore the use of machine learning techniques to restrict the flooding radius of control packets. We define the problem objective as a regression problem and use neural networks to model it. The model can be used in networks that share similar attributes, suggesting that transfer learning can be used to re-purpose the initial model. The results show that using machine learning reduces the amount of resources used to maintain the network state up to date, which could be subsequently used to improve the overall performance of the networked system. We anticipate machine learning to be the starting point for more sophisticated models of cross-layer routing mechanisms. © 2021 IEEE.

关键词： Routing protocols

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Cross-Channel and Regional Node Relation Enhanced Hybrid Graph Convolution Transformer for Ct Segmentation

SSRN

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SSRN 2023年

作者： Cui, Hui Jin, Qiangguo Wu, Xixi Wang, Linlin Zhang, Tiangang Nakaguchi, Toshiya Xuan, Ping Feng, David Dagan Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Software Northwestern Polytechnical University Shaanxi China School of Computer Science and Technology Heilongjiang University Harbin China Department of Radiation Oncology Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan China School of Mathematical Science Heilongjiang University Harbin China Center for Frontier Medical Engineering Chiba University Chiba Japan Department of Computer Science School of Engineering Shantou University Shantou China School of Computer Science The University of Sydney Sydney Australia

Accurate and robust organ and tumour segmentation from CT scans are critical for precision diagnosis and prognosis of cancer, and the development of personalized treatment planning. However, automatic segmentation of tumours and organs they have invaded is challenging due to significant variations, abnormalities, and unclear boundaries. While graph convolutional networks can propagate knowledge and correlations in a flexible feature space, they suffer from information saturation during deep learning which can limit their effectiveness. To overcome this issue, we proposed a hybrid graph convolution transformer (HCGT) model. The HCGT has a channel transformer (CTrans) component, which helps to learn contextual relationships across different feature channels. Furthermore, we developed a convolutional graph transformer (convG-Trans) that can simultaneously and interactively learn enhanced relationships between different parts of the image by aggregating knowledge propagation from graph convolution and cross-node similarities from the transformer. Finally, we incorporated category-level attention to understand the significance of the two representations from the CTrans and convG-Trans, which helped adjust the fusion process before generating the segmentation output. We evaluated the HCGT on kidney and tumour and lung and non-small cell lung cancer datasets. Our evaluations included comparisons with other three-dimensional (3D) medical image segmentation benchmarks, as well as graph-based and transformer-based segmentation models. The results demonstrated improved performance in abdominal and thorax organ and tumour segmentation tasks. Additionally, ablation studies showed that the major technical innovations were effective and consistent when using different 3D medical image segmentation backbones. © 2023, The Authors. All rights reserved.

关键词： Convolution

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No-Regret Linear Bandits under Gap-Adjusted Misspecification

arXiv

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arXiv 2025年

作者： Liu, Chong Qiao, Dan Yin, Ming Bogunovic, Ilija Wang, Yu-Xiang Department of Computer Science University at Albany State University of New York AlbanyNY12222 United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA92093 United States Department of Computer Science University of California Santa Barbara Santa BarbaraCA93106 United States Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States

This work studies linear bandits under a new notion of gap-adjusted misspecification and is an extension of Liu et al. (2023). When the underlying reward function is not linear, existing linear bandits work usually relies on a uniform misspecification parameter ϵ that measures the sup-norm error of the best linear approximation. This results in an unavoidable linear regret whenever ϵ > 0. We propose a more natural model of misspecification which only requires the approximation error at each input x to be proportional to the suboptimality gap at x. It captures the intuition that, for optimization problems, near-optimal regions should matter more and we can tolerate larger approximation errors in suboptimal regions. Quite surprisingly, we show that the classical LinUCB algorithm — designed for the realizable case — is automatically robust against such ρ-gap-adjusted misspecification with parameter ρ diminishing at O(1/(d√log T)). It achieves a near-optimal O(√T) regret for problems that the best-known regret is almost linear in time horizon T. We further advance this frontier by presenting a novel phased elimination-based algorithm whose gap-adjusted misspecification parameter ρ = O(1/√d) does not scale with T. This algorithm attains optimal O(√T) regret and is deployment-efficient, requiring only log T batches of exploration. It also enjoys an adaptive O(log T) regret when a constant suboptimality gap exists. Technically, our proof relies on a novel self-bounding argument that bounds the part of the regret due to misspecification by the regret itself, and a new inductive lemma that limits the misspecification error within the suboptimality gap for all valid actions in each batch selected by G-optimal design. Copyright © 2025, The Authors. All rights reserved.

关键词： Errors

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Design and Implementing of Innovative Joystick for Shooting Games

Design and Implementing of Innovative Joystick for Shooting ...

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Smart Systems and Power Management (IC2SPM), International Conference on

作者： Charbel Akiki Jeanpierre EL Zanety Gaby ABOU Haidar Roy ABI ZEID Daou Polytech Biomedical Engineering Department Furn El Chebbak Faculty of Engineering Université La Sagesse Lebanon Faculty of Engineering & Computer Science Zahle American University of Science and Technology Lebanon Mechatronics Engineering department Furn El Chebbak Faculty of Engineering Université La Sagesse Beirut Lebanon

ISBN: (数字)9798331529796

ISBN: (纸本)9798331529802

The gaming landscape has undergone significant evolution over the years, with technological advancements consistently transforming how players engage with virtual environments. One pivotal aspect of this evolution lies in the interface between the player and the game - the controller. Traditional controllers, while effective, often present challenges such as high costs and limited life cycles. The proposed device is characterized by its low-cost, accuracy, and customization as it can adhere to the user motion. Shooting games, characterized by dynamic and fast-paced interactions, require precise and responsive control mechanisms for an immersive player experience. Therefore, this paper presents a comprehensive exploration of implementing sensor-based controls in shooting games. The selected sensors, coupled with the Arduino-based microcontroller integration, form the backbone of a system designed for accuracy, responsiveness, and adaptability. By providing players with a tangible connection to the virtual realm, the system aims to redefine the boundaries of player interaction, elevating the game beyond a mere gaming experience to a visceral and captivating journey where the line between player and character becomes delightfully blurred. The challenge lies in seamlessly translating physical gestures into in-game actions, redefining the player's relationship with the digital battlefield. Following a successful implementation, the system was subjected to extensive testing by a group of 20 users, with a total testing time exceeding 15 hours. This thorough evaluation yielded overwhelmingly positive feedback, affirming the system's functionality, reliability, and performance.

关键词： Accuracy Microcontrollers Power system management Virtual environments Games Sensor phenomena and characterization Smart systems Sensor systems Integrated circuit reliability Testing

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lawson-Norm Based Adaptive Filter for Channel Estimation and in-Car Echo Cancellation

TechRxiv

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TechRxiv 2022年

作者： Li, Yingsong Huang, Xinqi Han, Xiao Tu, Huawei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia

We proposed a lawson-norm-based adaptive estimation algorithm within the affine-project theory framework and give a name of lawson-norm adaptive filter (LNAF) algorithm. The LNAF algorithm is derived, analyzed, and simulated for echo cancellation when background noise is impulsive, which is realized and implemented via using lawson norm of past errors to take a sliding window on cost function to speed up convergence and achieve robustness for impulsive noises for colored signal. Simulations based on measured data are used for in-car echo cancellation and channel estimation to verify the LNAF algorithm’s performance with different inputs, which prove that the LNAF algorithm is superior to the popular AP algorithms under differnt impulsive interferences. © 2022, CC BY.

关键词： Adaptive filtering

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Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

arXiv

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arXiv 2023年

作者： Zheng, Yu Koh, Huan Yee Jin, Ming Chi, Lianhua Phan, Khoa T. Pan, Shirui Phoebe Chen, Yi-Ping Xiang, Wei Department of Computer Science and Information Technology La Trobe University Melbourne Australia The Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia

Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the non-linear relations well or conventional deep learning models (e.g., CNN and LSTM) that do not explicitly learn the pairwise correlations among variables. To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time-series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module based on which a spatial-temporal graph neural network (STGNN) can be developed. Then, by employing a graph convolution network that exploits one- and multi-hop neighbor information, our STGNN component can encode rich spatial information from complex pairwise dependencies between variables. With a temporal module that consists of dilated convolutional functions, the STGNN can further capture long-range dependence over time. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner. Experimental results demonstrate that CST-GL can detect and diagnose anomalies effectively in general settings as well as enable early detection across different time delays. Our code is available: https://***/huankoh/CST-GL © 2023, CC BY.

关键词： Anomaly detection

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Computing maximum likelihood thresholds using graph rigidity

Algebraic Statistics

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Algebraic Statistics 2024年第2期14卷 287-305页

作者： Daniel Irving Bernstein Sean Dewar Steven J. Gortler Anthony Nixon Meera Sitharam Louis Theran Department of Mathematics Tulane University New Orleans LA United States School of Mathematics University of Bristol Bristol United Kingdom Department of Computer Science Harvard University Cambridge MA United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Computer and Information Science and Engineering University of Florida Gainesville FL United States School of Mathematics and Statistics University of St Andrews St Andrews United Kingdom

The maximum likelihood threshold (MLT) of a graph ☆G☆ is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. We recently proved a new characterization of the MLT in terms of rigidity-theoretic properties of ☆G☆. This characterization was then used to give new combinatorial lower bounds on the MLT of any graph. We continue this line of research by exploiting combinatorial rigidity results to compute the MLT precisely for several families of graphs. These include graphs with at most nine vertices, graphs with at most 24 edges, every graph sufficiently close to a complete graph and graphs with bounded degrees.

关键词： maximum likelihood threshold Gaussian graphical model combinatorial rigidity generic completion rank graph rigidity

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Short Wave Infrared Upconversion Imager with Electron Blocking layer to Screen Electron Injection into Photosensitive layer

Short Wave Infrared Upconversion Imager with Electron Blocki...

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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Chanho Shin Ning Li Tse Nga Ng Material Science and Engineering Program University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

In this work, we demonstrated upconversion imagers integrated with shortwave infrared photodetectors paired with an electron blocking layer. The use of electron blocking layer screened charge injection to prevent recombination in photosensitive layer. The characteristics of each electron blocking layer were analyzed in aspects of noise and detectivity. For the optimized device, the parasitic luminance in the dark was efficiently suppressed, and the photon-to-photon efficiency was increased. The electron blocking layer used in this work is generally applicable for upconversion imagers using different absorption and emitting materials.

关键词：

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Multirate Audiometric Filter Bank for Hearing Aid Devices 55

Multirate Audiometric Filter Bank for Hearing Aid Devices

引用

55th Asilomar Conference on Signals, Systems and computers, ACSSC 2021

作者： Sokolova, Alice Sengupta, Dhiman Chen, Kuan-Lin Gupta, Rajesh Aksanli, Baris Harris, Fredric Garudadri, Harinath UC San Diego Department of Electrical and Computer Engineering La Jolla CA United States San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States UC San Diego Department of Computer Science and Engineering La Jolla CA United States

ISBN: (纸本)9781665458283

The frequency-dependent nature of hearing loss poses many challenges for hearing aid design. In order to compensate for a hearing aid user's unique hearing loss pattern, an input signal often needs to be separated into frequency bands, or channels, through a process called sub-band decomposition. In this paper, we present a real-time filter bank for hearing aids. Our filter bank features 10 channels uniformly distributed on the logarithmic scale, located at the standard audiometric frequencies used for the characterization and fitting of hearing aids. We obtained filters with very narrow passbands in the lower frequencies by employing multi-rate signal processing. Our filter bank offers a 9.1× reduction in complexity as compared to conventional signal processing. We implemented our filter bank on Open Speech Platform, an open-source hearing aid, and confirmed real-time operation. © 2021 IEEE.

关键词： Filter banks

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