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International Workshop on Robot Motion and Control (RoMoCo)

作者： Somnath Kundu Yeganeh Bahoo Steven M. LaValle Department of Computer Science Toronto Metropolitan University (formerly Ryerson University) Toronto ON Canada Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

We consider a robot that can move in a straight line, until it hits any wall of a given 2D rectangle room. If the robot hits a corner of the room then it will stop, otherwise the robot bounces off the wall using the laws of reflection and again move in another straight line. The room may contain an unbroken opening of unit length on a wall, which is unknown to the robot. If the robot reaches any point of the opening with a non-zero angle, then it escapes through the opening. The objective of the problem is to devise an algorithm for the robot which enables it to find if there is an opening on the perimeter of any given rectangle or detect that the room contains no opening. This work is a continuation of our previous publication [1], where we presented an algorithm that enables the robot to find the opening or correctly declare that there is none, when any two adjacent sides of the rectangle are integer and co-prime. In this work, we study the problem for rectangles of any lengths and we have proposed a strategy where the robot is guaranteed to find the opening or correctly declare that there is none. Additionally, We provide other interesting results related to our proposed algorithm.

关键词： Robot motion Systematics Conferences Reflection Robots

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A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

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IEEE Networking Letters 2025年第2期7卷 83-87页

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

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Mining User Behavior in Decentralized Applications for Blockchain Trust and Security Analytics

Mining User Behavior in Decentralized Applications for Block...

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Blockchain computing and Applications (BCCA), International Conference on

作者： Shengwei You Aditya Joshi Andrey Kuehlkamp Jarek Nabrzyski Department of Computer Science and Engineering Center for Research Computing University of Notre Dame Notre Dame United States

This paper presents a cutting-edge data mining approach to investigate user trust in decentralized applications (dApps) on public blockchains. Our innovative data analysis methodology enables a comprehensive exploration of dApp user behaviors, uncovering crucial insights. Contrary to popular belief, large-scale hacking incidents on centralized exchanges (CEX) have a limited impact on decentralized exchange (DEX) migration and inactive user rates. Additionally, our results reveal a notable trend of increased user outflow in older dApp protocols compared to their newer counterparts and emphasize the significant role of government regulations on DEX user inactivity. This research advances the understanding of dApp scalability and user trust, laying the groundwork for future inquiries in this rapidly evolving field.

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Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings

Hubs and Hyperspheres: Reducing Hubness and Improving Transd...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Daniel J. Trosten Rwiddhi Chakraborty Sigurd Løksc Kristoffer Knutsen Wickstrøm Robert Jenssen Michael C. Kampffmeyer Department of Physics and Technology UiT The Arctic University of Norway UiT Machine Learning group (machine-learning.uit. no) and Visual Intelligence Centre (visual-intelligence.no) Norwegian Computing Center Department of Computer Science University of Copenhagen Pioneer Centre for AI (aicentre.dk)

Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur frequently in multiple nearest neighbour lists of other points. Hubness negatively impacts distance-based classification when hubs from one class appear often among the nearest neighbors of points from another class, degrading the classifier's performance. To address the hubness problem in FSL, we first prove that hubness can be eliminated by distributing representations uniformly on the hypersphere. We then propose two new approaches to embed representations on the hypersphere, which we prove optimize a tradeoff between uniformity and local similarity preservation - reducing hubness while retaining class structure. Our experiments show that the proposed methods reduce hubness, and significantly improves transductive FSL accuracy for a wide range of classifiers 1 1 Code available at https://***/uitml/noHub..

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The Surprising Benefits of Base Rate Neglect in Robust Aggregation

arXiv

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arXiv 2024年

作者： Kong, Yuqing Wang, Shu Wang, Ying Center on Frontiers of Computing Studies School of Computer Science Peking Univeristy China Department of Economics School of Economics and Management Tsinghua Univeristy China

Robust aggregation integrates predictions from multiple experts without knowledge of the experts' information structures. Prior work assumes experts are Bayesian, providing predictions as perfect posteriors based on their signals. However, real-world experts often deviate systematically from Bayesian reasoning. Our work considers experts who tend to ignore the base rate. We find that a certain degree of base rate neglect helps with robust forecast aggregation. Specifically, we consider a forecast aggregation problem with two experts who each predict a binary world state after observing private signals. Unlike previous work, we model experts exhibiting base rate neglect, where they incorporate the base rate information to degree λ ∈ [0, 1], with λ = 0 indicating complete ignorance and λ = 1 perfect Bayesian updating. To evaluate aggregators' performance, we adopt Arieli et al. (2018)'s worst-case regret model, which measures the maximum regret across the set of considered information structures compared to an omniscient benchmark. Our results reveal the surprising V-shape of regret as a function of λ. That is, predictions with an intermediate incorporating degree of base rate λ Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

arXiv

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arXiv 2024年

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-González, Cindy Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Department of Computer Science University of California Davis United States

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024, CC BY.

关键词： Software testing

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RES-EV: Identifying EV-Households under High AC Load Using a Residual-Based Model

RES-EV: Identifying EV-Households under High AC Load Using a...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Hussein Aly Abdulaziz Al-Ali Abdulla Al-Ali Qutaibah Malluhi Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

This paper introduces a novel residual-based model to identify households with Battery Electric Vehicles (EVs) under high Air Conditioning (AC) load. The considerable energy demands of AC units can obscure charging events for EVs. In this work we propose a residual based model which leverages the distinctive characteristics of EV charging patterns, marked by unpredictable spikes in energy consumption, and the more predictable nature of AC load. Our proposed approach involves training a lightweight forecasting model to predict overall house-hold consumption and utilizes the residuals of this model for iden-tifying household with EVs. The residual-based model, ResEV-AR, demonstrated a substantial advantage in F1 score (5.8% and 7.32%) compared to state-of-the-art models such as EVS and KBF, respectively. Additionally, a simpler residual model, ResEV-SRM, exhibited a 3.5% F1 score advantage over EVS, coupled with an impressive 11-fold reduction in computation time.

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Quorum sensing-based interactions among drugs,microbes,and diseases

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science China(Life sciences) 2023年第1期66卷 137-151页

作者： Shengbo Wu Shujuan Yang Manman Wang Nan Song Jie Feng Hao Wu Aidong Yang Chunjiang Liu Yanni Li Fei Guo Jianjun Qiao School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China School of Computer Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Key Laboratory of Systems Bioengineering Ministry of Education(Tianjin University)Tianjin 300072China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Department of Engineering Science University of OxfordOxford OX13PJUK Institute of Shaoxing Tianjin UniversityShaoxing 312300China

Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be *** existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate *** this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://***/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common *** interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental ***,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal *** QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse *** work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

关键词： quorum sensing drugs microbe-disease association microbe-host interaction gut microbiota personalized medicine

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Improving Energy Theft Detection through Time Series Segmentation and Ensemble Learning

Improving Energy Theft Detection through Time Series Segment...

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Annual Conference of Industrial Electronics Society

作者： Emran Altamimi Abdulaziz Al-Ali Abdulla Al-Ali Qutaibah Malluhi Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft.

关键词： Training Industrial electronics Electricity Time series analysis Power system stability Data models Stability analysis Robustness Smart grids Ensemble learning

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Design and development of intelligent well logging interpretation system 41

Design and development of intelligent well logging interpret...

引用

第41届中国控制会议

作者： Saifei Ma Jin Tang Zerui Li Kun Li Wenjun Lv School of Computer Science and Technology Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Key Laboratory of Multimodal Cognitive Computing Anhui University Institute of Advanced Technology University of Science and Technology of China Department of Automation University of Science and Technology of China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,the traditional well logging interpretation method manually operated by experts has shown some problems,such as low efficiency and poor results,facing the challenges of high dimension,diversity and complexity of current logging *** the progress of artificial intelligence,its effectiveness and efficiency in logging interpretation have gradually attracted researchers' ***,the uncertainty of logging process necessitates the preprocess of collected logging data before employing the artificial intelligence ***,an intelligent well logging interpretation system is designed and developed in this *** the logging data anomalies may include data repetition,null value,etc.,a specific preprocessing module is designed in the system to tackle the data anomalies,so as to ensure that the data input into the algorithms is correct and *** governance can effectively enhance the effectiveness of data,so it is used to guide the design and implementation of logging interpretation ***,user mode and debug mode are designed to solve the problem of cross functional conflict in data governance.

关键词： System Development Logging Interpretation Data Governance Artificial Intelligence

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