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SSRN 2024年

作者： Saleh, Alaa Tarkoma, Sasu Pirttikangas, Susanna Lovén, Lauri Center for Ubiquitous Computing University of Oulu Finland Department of Computer Science University of Helsinki Finland

The growth of the Internet of Things (IoT) has been exponential, resulting in billions of devices generating vast amounts of data. Given the varied and resource-limited nature of these IoT devices, there's a significant dependence on the device-edge-cloud computing continuum for *** data exchange paradigm typically used for these transmissions is the publish/subscribe (pub/sub) model, primarily because of its scalability and flexibility. In this review, we offer a comprehensive examination of pub/sub systems in the computing continuum, outlining their advantages, highlighting pivotal discoveries, identifying gaps, and suggesting future improvements that align with modern application demands. Our process starts with an analysis of existing surveys to pinpoint areas that require deeper exploration. Subsequently, we delve into diverse pub/sub paradigms, protocols, and message brokers, addressing their pros, cons, and potential enhancements. Unique to our paper is an in-depth exploration of the role of pub/sub systems in EdgeOps and their applications in real-time scenarios — topics previously untouched in other surveys. Additionally, we provide a summary of applications that have reaped the benefits of pub/sub systems. In conclusion, we highlight several unresolved challenges that could shape the future of the pub/sub model. We also suggest leveraging artificial intelligence and machine learning to introduce more adaptability and autonomy. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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Communicative robot signals: Presenting a new typology for human-robot interaction 23

Communicative robot signals: Presenting a new typology for h...

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18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023

作者： Holthaus, Patrick Schulz, Trenton Lakatos, Gabriella Soma, Rebekka University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom Norwegian Computing Center Department of Applied Research in Ict Oslo Norway University of Oslo Department of Informatics Oslo Norway

ISBN: (纸本)9781450399647

We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal's reference, the genuineness of the signal, and its clarity (i.e., how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours. © 2023 Copyright held by the owner/author(s).

关键词： Human robot interaction

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

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IEEE Annual International Symposium Virtual Reality

作者： Leonardo Pavanatto Alexander Giovannelli Brian Giera Timo Bremer Haichao Miao Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

关键词： Human computer interaction Location awareness Three-dimensional displays Navigation Virtual reality Inspection

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A Battery Swapping Service Management System Reliability-Based Design Concerning Station Information Delay

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1063-1078页

作者： Li, Xinyu Cao, Yue Hu, Ziyi Zhang, Xu Liu, Zhi Lei, Xinhai Wuhan University School of Cyber Science and Engineering Wuhan430073 China Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430250 China School of Computing Sciences Norwich United Kingdom University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Wuhan Public Resources Trading Center Wuhan China

Since the long duration associated with traditional plug-in charging modes inevitably causes range anxiety to electric vehicle (EV) drivers, the battery swapping technology has emerged as a promising alternative. It thus becomes imperative to develop a reliable battery swapping service system, efficiently managing the large-scale energy replenishing demands from EVs. Firstly, this paper considers drivers' trip destination to minimize total trip duration, and the expense paid for battery swapping service to save users' service cost. By optimizing the requirement of battery swapping service during peak-price period, the expenditure for energy supplement can be reduced. Secondly, concerning in practice that the transmission delay is a crucial factor impacting the overall network and system reliability, this paper further investigates the impact of station information delay between the global controller and battery swapping stations (BSSs). By characterizing the delay into two types, namely, low and large delays, the scenario based on a known delay type are investigated. Thirdly, delay cases with multiple BSSs are also taken into account and a mixed decision-making method is developed. Finally, simulation results indicate the desirable performance of proposed scheme in satisfying user demand and alleviating impact of station information delay. © 1975-2011 IEEE.

关键词： Decision making

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Pareto smoothed importance sampling

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3491-3548页

作者： Aki Vehtari Daniel Simpson Andrew Gelman Yuling Yao Jonah Gabry Department of Computer Science Aalto University Normal Computing Departments of Statistics and Political Science Columbia University Center for Computational Mathematics Flatiron Institute Department of Statistics Columbia University

Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution, in which case more stable estimates can be obtained by modifying extreme importance ratios. We present a new method for stabilizing importance weights using a generalized Pareto distribution fit to the upper tail of the distribution of the simulated importance ratios. The method, which empirically performs better than existing methods for stabilizing importance sampling estimates, includes stabilized effective sample size estimates, Monte Carlo error estimates, and convergence diagnostics. The presented Pareto $\hat{k}$ finite sample convergence rate diagnostic is useful for any Monte Carlo estimator.

关键词： importance sampling Monte Carlo Bayesian computation diagnostics

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Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems

Trace-based Multi-Dimensional Root Cause Localization of Per...

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International Conference on Software Engineering (ICSE)

作者： Chenxi Zhang Zhen Dong Xin Peng Bicheng Zhang Miao Chen Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multidimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multidimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multidimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution.

关键词： Location awareness Runtime environment Accuracy Microservice architectures Benchmark testing Data mining Spectral analysis

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Multi-criteria rating conversion without relation loss for recommender systems

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International Journal of computers and Applications 2022年第2期44卷 130-138页

作者： Sitkrongwong, Padipat Maneeroj, Saranya Takasu, Atsuhiro Department of Informatics School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tokyo Japan Advanced Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand National Institute of Informatics Tokyo Japan

Traditional recommender systems let users provide a single rating indicating their overall preferences toward items. Beside overall rating, multi-criteria recommender systems let users rate on multiple aspects of items with multi-criteria ratings. Most methods in recommender systems are based on collaborative filtering, which makes recommendations by exploiting ratings from neighbor users. However, most of them ignore the fact that rating behaviors of users vary due to their personal preference biases. Thus, exploiting ratings from neighbors directly might result in a poor recommendation. To solve this, the rating conversion techniques have been applied in some single criterion recommendations. For multi-criteria ratings, converting each criterion rating independently might result in loss of relation among criteria ratings. We propose a novel method that simultaneously converts all criteria ratings between users to maintain their implicit relations. The multi-criteria ratings are first normalized by variances of users and principle component analysis is applied to extract user preference patterns. Such patterns are then used for multi-criteria rating conversion. The experiment results show that our method outperforms both current single and multi-criteria rating conversion techniques with RMSEs of 1.1082 and 3.5574 on TripAdvisor and Yahoo datasets respectively, while maintaining considerably high level of prediction coverages. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Recommender systems

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Data-Free Network Debiasing for Long-Tailed visual Recognition

Data-Free Network Debiasing for Long-Tailed Visual Recogniti...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Cai, Jinmian Wang, Zheng Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Biren Technology IHPC A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods. © 2022 IEEE.

关键词： Classification (of information)

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Guoshan Liu Hailong Yin Bin Zhu Jingjing Chen Chong-Wah Ngo Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Singapore Management University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset.

关键词： Training computer vision Accuracy Computational modeling Stochastic processes Predictive models Reliability Faces

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