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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yu Han Ziwei Long Yanting Zhang Jin Wu Zhijun Fang Rui Fan School of Computer Science and Technology Donghua University Shanghai P. R. China Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Machine Intelligence & Autonomous Systems (MIAS) Group the College of Electronics & Information Engineering Tongji University Shanghai P. R. China Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology P. R. China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM.

关键词： Performance evaluation Accuracy Semantic segmentation Source coding Image matching Supervised learning Pose estimation

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Prompt Learning on Temporal Interaction Graphs

arXiv

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arXiv 2024年

作者： Chen, Xi Zhang, Siwei Xiong, Yun Wu, Xixi Zhang, Jiawei Sun, Xiangguo Zhang, Yao Zhao, Feng Kang, Yulin Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China IFM Lab Department of Computer Science University of California DavisCA United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Ant Group Shanghai Hangzhou China

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems, such as e-commerce and social networks. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their "pre-train, predict" training paradigm. First, there is a temporal gap that exhibits limited accommodation ability to their timely predictions. This shortcoming severely undermines their applicability in distant future predictions on the dynamically evolving TIG data. Second, there is a semantic gap due to the lack of versatility in these pre-trained models to effectively cater to diverse downstream tasks. This hinders their practical applications, as they struggle to align with their learning and prediction capabilities across various application scenarios. Recently, the "pre-train, prompt" paradigm has emerged as a lightweight mechanism for model generalization. Therefore, applying this paradigm within TIGs is a potential solution to solve the aforementioned challenges. However, the adaptation of this paradigm to TIGs is not straightforward. The prevalent application of prompting in static graph contexts falls short in temporal settings due to a lack of consideration for time-sensitive dynamics and a deficiency in expressive power. To address this issue, we introduce Temporal Interaction Graph Prompting (TIGPrompt), a versatile framework that seamlessly integrates with existing TIG models, bridging both the temporal and semantic gaps mentioned above. In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks. These prompts stand out for their minimalistic design, relying solely on the fine-tuning of the prompt generator with very little supervision data, which is extremely efficient. To cater to varying computational resource demands, we propose an extended "pre-train, prompt-based fine

关键词： Forecasting

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To Impute or Not: Recommendations for Multibiometric Fusion

To Impute or Not: Recommendations for Multibiometric Fusion

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IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Melissa R Dale Elliot Singer Bengt J. Borgström Arun Ross Dept of Computer Science and Engineering Michigan State University East Lansing MI USA Artificial Intelligence Technology and Systems Group MIT Lincoln Laboratory Lexington MA USA

Combining match scores in multibiometrics via fusion is a well-established approach to improving recognition performances. However, when scores are missing, this can degrade performance, as well as limit the possible fusion techniques that can be applied. Imputation is a technique for estimating reasonable values to replace missing data and an approach that has previously shown promise in addressing missing scores in multibiometrics. In this paper, we evaluate various approaches to imputation methods on three multimodal biometric score datasets: NIST BSSR1, BIOCOP2008, and MIT LL TRIMODAL, and investigate the factors which might influence the effectiveness of imputation. Our studies reveal three key observations: (1) Imputation is preferable to not imputing missing scores, even when the fusion rule does not necessitate complete score data. (2) Balancing the classes in the training data is crucial to mitigate biases in the imputation technique and prevent favoritism towards the overrepresented class, even if it involves dropping a substantial number of score vectors. (3) Multivariate imputation approaches exhibit better estimation for genuine scores, while univariate imputation approaches yield stronger results for imputed imposter scores.

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Distributed Bidding Strategy for an Aggregator Valuing Uncertain Prosumers in Energy and Ancillary Service Markets

Distributed Bidding Strategy for an Aggregator Valuing Uncer...

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International Conference on European Electricity Market, EEM

作者： Amir Hamidi Mokhtar Bozorg Mohsen Hamzeh Rachid Cherkaoui Power Systems Group EPFL Lausanne Switzerland School of Electrical and Computer Engineering University of Tehran Tehran Iran HES-SO University of Applied Sciences of Western Switzerland Switzerland

Aggregators have an influential intermediary role to exploit the flexibility of prosumers like smart homes for procuring the required capacity of ancillary services to support power systems. Local generation and consumption uncertainties of prosumers affect their cost reduction objectives which in turn have a significant impact on the flexibility capacity provided by them. So, towards boosting the operational flexibility capacity in the market, the aggregator’s bidding and remuneration frameworks should devise a valuing scheme of prosumers considering this impact to induce positive incentives relieving capacity uncertainties. This scheme, simultaneously, should observe data privacy concerns. To this end, this paper proposes a day-ahead distributed bidding strategy framework for an aggregator to participate in regulation and energy markets using a new rewarding scheme derived from a flexibility certainty valuing approach. This framework is developed within the Benders decomposition method, in which the aggregator performs valuing of prosumers in the master problem through their implicit information extracted from home energy management systems modeled in the subproblems. The performance of the framework to appropriately reward prosumers is presented through numerical results for an illustrative case study.

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

arXiv

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arXiv 2024年

作者： Poe, Kyle Mallada, Enrique Vidal, René Applied Mathematics and Computational Science Group University of Pennsylvania PA19104 United States Department of Electrical and Computer Engineering Johns Hopkins University MD21218 United States Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA19104 United States

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparse-input setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024, CC BY.

关键词： Linear systems

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A Novel Low Power and High Speed 9- Transistors Dynamic Full-Adder Cell Simulation and Design

A Novel Low Power and High Speed 9- Transistors Dynamic Full...

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IEEE Symposium on computers and Communications (ISCC)

作者： Parisa Rahimi Myasar Tabany Seyedali Pourmoafi Networks Security and Systems Research Group School of Physics Engineering and Computer Science. University of Hertfordshire ALI0 9AB Hatfield Hertfordshire UK

In this paper, a novel Full-Adder cell, named pseudo dynamic has been proposed and designed through an intensive simulation. The circuit has only 9 transistors and no internal nodes connected to ground. The circuit has been designed based on a “floating full adder cell,” which has 3 inputs and a clock signal. Simulations have been performed using HSPICE 2008.03, based on 90nm technology, BSIM4 (level 54) version 4.4 model. The cell layout was designed and extracted by Tanner Research's L-Edit Version 13.00. Results show that the proposed new Full-Adder cell has considerable low power-delay product (PDP). The resulting PDP of a Multi-Output design with a 1.2 V power supply, 10 fF load capacitors, and a maximum input frequency of 200 MHz was 15.3405fJ. The maximum propagation delay was 25 pS, which shows the cell would work properly at high speeds.

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P4toNFV: Offloading from P4 switches to NFV in programmable data planes

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International Journal of Communication systems 2024年第5期37卷

作者： Pekar, Adrian Makara, Laszlo A. Lai, Yuan-Cheng Lin, Ying-Dar Seah, Winston Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary HUN-REN-BME Information Systems Research Group Budapest University of Technology and Economics Budapest Hungary Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

P4 combines the benefits of hardware-based networking with the adaptability of software-based network operations. However, when faced with intricate network functions, P4 switches reveal constraints in memory and processing primitives. To address these, we advocate offloading traffic demanding intricate processing from the programmable data plane to network function virtualization (NFV). By leveraging this approach, P4 switches handle the core data plane, ensuring maximum performance, whereas virtualized network functions (VNF) cater to the intricate processing. Central to our research is the optimization of this offloading process, specifically considering delay constraints. We developed an analytical model that examines a P4 switch overseen by an SDN controller, integrating an offloading capability to NFV. The principal objective was to determine an offloading rate that minimizes packet processing delay. To this end, we employed a Bounded method, an advancement from Brent's method, to determine this optimal rate. The findings indicate that offloading approximately 66% of packets to the VNF achieves the lowest total delay, registering at 0.1505 (Formula presented.) s. This strategy of optimal offloading can notably reduce the system's average delay as the demand for network functions increases. The optimization technique we adopted exhibited rapid convergence in our experiments, reflecting the method's efficacy. Furthermore, a rigorous parametric sensitivity analysis spanning no offloading, full offloading, and optimal offloading strategies underscores that optimal offloading to NFV consistently augments system performance, particularly in terms of delay reduction. Conclusively, our study furnishes valuable insights into offloading strategies, augmenting the narrative on resource allocation in both PNFs and VNFs. © 2023 The Authors. International Journal of Communication systems published by John Wiley & Sons Ltd.

关键词： Network function virtualization

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The graphene squeeze-film microphone

arXiv

引用

arXiv 2024年

作者： Abrahams, M.P. Martinez, J. Steeneken, P.G. Verbiest, G.J. Department of Precision and Microsystems Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Multimedia Computing Group Intelligent Systems Department Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands

Most microphones operate by detecting the sound-pressure induced motion of a membrane. In contrast, here we introduce a microphone that operates by monitoring the sound-pressure-induced modulation of the compressibility of air. By driving a graphene membrane at its resonance frequency, the gas, that is trapped in a squeeze-film beneath it, is compressed at high frequency. Since the stiffness of the gas film depend on the air pressure, the resonance frequency of the graphene is modulated by variations in sound pressure. We demonstrate that this squeeze-film microphone principle can be used to detect sound and music by tracking the membrane's resonance frequency using a phase-locked loop (PLL). Since the sound detection principle is different from conventional devices, the squeeze-film microphone potentially offers advantages like increased dynamic range, and a lower susceptibility to pressure-induced failure and vibration-induced noise. Moreover, it might be made much smaller, as demonstrated by the microphone in this work that operates using a circular graphene membrane with an area that is more than a factor 1000 smaller than that of MEMS microphones. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphene

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Collaborative Hybrid Human AI Learning through Conceptual Exploration 3

Collaborative Hybrid Human AI Learning through Conceptual Ex...

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3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2024

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes Obiedkov, Sergei Ernst-Schröder-Zentrum Karolinenpl. 5 Darmstadt64289 Germany TU Dresden Dresden01062 Germany Intelligent Information Systems Universisty of Hildesheim Universitätsplatz 1 Hildesheim31141 Germany Knowledge & Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Faculty of Computer Science cfaed ScaDS.AI TU Dresden Dresden01062 Germany

Conceptual Exploration is a sophisticated method for the interactive and structured acquisition of knowledge from experts. It is therefore particularly suitable for the use in hybrid settings where both humans and AIs act as experts. This article provides a brief summary of how conceptual exploration can be used in the context of Hybrid Human AI systems, as discussed within a tutorial during the third HHAI conference in Malmö, Sweden. We will recapitulate two small experiments that were carried out with the participants of the tutorial and their results. Finally, we give some pointers on how this promising link can be further researched. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

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PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones

PupilRec: Leveraging Pupil Morphology for Recommending on Sm...

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作者： Shen, Xiangyu Jiang, Hongbo Liu, Daibo Yang, Kehua Deng, Feiyang Lui, John C. S. Liu, Jiangchuan Dustdar, Schahram Luo, Jun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China The City University of Hong Kong School of Graduate Studies Hong Kong The Chinese University of Hong Kong Computer Science and Engineering Department Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada Vienna University of Technology Distributed Systems Group Vienna1040 Austria Nanyang Technological University School of Computer Engineering 639798 Singapore

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users' inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users' inner feelings, we consider using the change of pupil size when browsing to model users' preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users' pupil size change information through the front camera of smartphones;it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user-product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users' preference and can recommend products users interested in. © 2014 IEEE.

关键词： Smartphones

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