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34th Canadian Conference on Computational Geometry, CCCG 2022

作者： Abam, Mohammad Ali Lavasani, Ali Mohammad Pankratov, Denis Department of Computer Engineering Sharif University of Technology Iran Department of Computer Science and Software Engineering Concordia University Canada

We study the maximum weight convex polytope problem, in which the goal is to find a convex polytope maximizing the total weight of enclosed points. Prior to this work, the only known result for this problem was an O(n3) algorithm for the case of 2 dimensions due to Bautista et al. We show that the problem becomes NP-hard to solve exactly in 3 dimensions, and NPhard to approximate within n1/2-Ε for any Ε > 0 in 4 or more dimensions. We also give a new algorithm for 2 dimensions, albeit with the same O(n3) running time complexity as that of the algorithm of Bautsita et al. © 2022 Yeganeh Bahoo and Konstantinos Georgiou.

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Application of a class of density peak clustering algorithm in short-term smart grid 8

Application of a class of density peak clustering algorithm ...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Wang, Chenyu Zhang, Zhao Zhu, Zhipeng Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the operation of power system. Density peaks clustering algorithm with k-nearest neighbors and weighted similarity can reduce the negative impact of global data on local data, avoid the collateral effect of errors and improve the accuracy of the model. It has better effect on local peak power change and global power forecast. In this paper, the density peaks clustering algorithm with k-nearest neighbors and weighted similarity are compared with DPC clustering method through simulation examples, and the effectiveness of this method is verified. © 2024 SPIE.

关键词： Clustering algorithms

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer Systems science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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Empirical Investigation of role of Meta-learning approaches for the Improvement of software Development Process via software Fault Prediction 22

Empirical Investigation of role of Meta-learning approaches ...

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26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Hussain, Shahid Ibrahim, Naseem Department of Computer Science and Software Engineering Penn State University United States

ISBN: (纸本)9781450396134

Context: software engineering (SE) community has empirically investigated software defect prediction as a proxy to benchmark it as a process improvement activity to assure software quality. In the domain of software fault prediction, the performance of classification algorithms is highly provoked with the residual effects attributed to feature irrelevance and data redundancy issues. Problem: The meta-learning-based ensemble methods are usually carried out to mitigate these noise effects and boost the software fault prediction performance. However, there is a need to benchmark the performance of meta-learning ensemble methods (as fault predictor) to assure software quality control and aid developers in their decision making. Method: We conduct an empirical and comparative study to evaluate and benchmark the improvement in the fault prediction performance via meta-learning ensemble methods as compared to their component base-level fault predictors. In this study, we perform a series of experiments with four well-known meta-level ensemble methods Vote, StackingC (i.e., Stacking), MultiScheme, and Grading. We also use five high-performance fault predictors Logistic (i.e., Logistic Regression), J48 (i.e., Decision Tree), IBK (i.e. k-nearest neighbor), NaiveBayes, and Decision Table (DT). Subsequently, we performed these experiments on public defect datasets with k-fold (k=10) cross-validation. We used F-measure and ROC-AUC (Receiver Operating Characteristic-Area Under Curve) performance measures and applied the four non-parametric tests to benchmark the fault prediction performance results of meta-learning ensemble methods. Results and Conclusion: we conclude that meta-learning ensemble methods, especially Vote could outperform the base-level fault predictors to tackle the feature irrelevance and redundancy issues in the domain of software fault prediction. Having said that, their performance is highly related to the number of base-level classifiers and the set of softwa

关键词： Metrics Ensemble method Performance Fault Prediction Classification

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Event-Triggered Control for Obstacle Avoidance in Second-Order Nonlinear Multi-Agent Systems with Directed Graph 8

Event-Triggered Control for Obstacle Avoidance in Second-Ord...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhu, Zhipeng Zhang, Zhao Wang, Chenyu Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

In this paper, we study the obstacle avoidance problem of second-order nonlinear multi-agent systems (MASs) with directed graph based on event-triggered control. Firstly, the consensus requirement is accomplished by using the event-triggered consensus algorithm, and experiments are given for verification. Secondly, an improved artificial potential field (IAPF) method is proposed to solve the obstacle avoidance problem, and comparison experiments are made, from which the experimental results show that the IAPF method is significantly better than the traditional artificial potential field (TAPF) method under the same conditions. © 2024 SPIE.

关键词： Directed graphs

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On designing an unaided authentication service with threat detection and leakage control for defeating opportunistic adversaries

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Frontiers of computer science 2021年第2期15卷 161-179页

作者： Nilesh CHAKRABORTY Samrat MONDAL College of Computer Science and Software Engineering Shenzhen UniversityShenzhen518060China Department of Computer Science Indian Institute of Technology PatnaBihar801106India

Unaided authentication services provide the flexibility to login without being dependent on any additional *** power of recording attack resilient unaided authentication services(RARUAS)is undeniable as,in some aspects,they are even capable of offering better security than the biometric based authentication ***,high login complexity of these RARUAS makes them far from usable in *** adopted information leakage control strategies have often been identified as the primary cause behind such high login *** recent proposals have made some significant efforts in designing a usable RARUAS by reducing its login complexity,most of them have failed to achieve the desired usability *** this paper,we have introduced a new notion of controlling the information leakage *** maintaining a good security standard,the introduced idea helps to reduce the login complexity of our proposed mechanism—named as Textual-Graphical Password-based Mechanism or TGPM,by a significant *** with resisting the recording attack,TGPM also achieves a remarkable property of threat *** the best of our knowledge,TGPM is the first RARUAS,which can both prevent and detect the activities of the opportunistic recording attackers who can record the complete login activity of a genuine user for a few login *** study reveals that TGPM assures much higher session resiliency compared to the existing authentication services,having the same or even higher login ***,TGPM stores the password information in a distributed way and thus restricts the adversaries to learn the complete secret from a single compromised server.A thorough theoretical analysis has been performed to prove the strength of our proposal from both the security and usability *** have also conducted an experimental study to support the theoretical argument made on the usability standard of TGPM.

关键词： authentication recording attack premature attack opportunistic adversary leakage control threat prevention threat detection

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Collaborative Multi-Agent Proximal Policy Based Resource Allocation in Clustered IoV Networks 10

Collaborative Multi-Agent Proximal Policy Based Resource All...

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10th IEEE Smart World Congress, SWC 2024

作者： Raza, Arif Xiang, Junfan Khan, Salabat Shrivastava, Shivanshu Ashraf, M. Wasim Abbas Wang, Lu Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Qilu Institute of Technology College of Computer and Information Engineering Jinan China Rajiv Gandhi Institute of Petroleum Technology Department of Electrical and Electronics Engineering Amethi India Hanjiang Normal University School of Mathematics and Computer Science Shiyan China

ISBN: (纸本)9798331520861

The Internet of Vehicles (IoV) necessitates efficient resource management to meet the growing demands for high data rates, low latency, and real-time communication in Intelligent Transportation Systems (ITS). This paper presents a novel multiagent reinforcement learning framework based on the Proximal Policy Optimization (PPO) algorithm for optimizing resource allocation in clusters of cells within IoV networks. The framework dynamically allocates transmission power and bandwidth across base stations, each acting as a collaborative agent. Extensive simulations demonstrate that the multi-agent PPO approach outperforms other reinforcement learning algorithms, including Soft Actor-Critic (SAC) and Advantage Actor-Critic (A2C), regarding throughput, power efficiency, dynamic bandwidth allocation, and overall network performance. © 2024 IEEE.

关键词： Bandwidth

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Time-Division Online Speculative Inference for Cost-Effective Intelligent Internet of Things 10

Time-Division Online Speculative Inference for Cost-Effectiv...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Du, Yao Wang, Zehua Leung, Cyril Leung, Victor C.M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9798350373011

Intelligent Internet of Things (IIoT), a network paradigm, is an interconnection of intelligent edge devices, empowered by machine learning models. The recent emergence of large language models (LLMs) opens a new path towards IIoT. Although device models are generally lightweight and suitable for edge devices with limited processing power, their performance is less impressive compared to LLMs running on large servers. Server models offer superior performance with the cost of significant computational resources for model inference. To realize cost-effective IIoT, we introduce a novel collaborative inference framework: the online speculative inference enabled by device collaboration and time divisions. We aim to find the minimum number of servers required for the immediate processing of arrived inference requests. To solve the problem, we propose an evolving directed acyclic graph along with a Proof-of-Deletion mechanism for cost reduction and privacy protection. Based on computer simulations using heterogeneous LLMs, it is found that time-division online speculative inference is a promising approach towards cost-effective IIoT. © 2024 IEEE.

关键词： Adversarial machine learning

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT 10

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Munoz-Lara, Rodrigo Cespedes, Sandra Diaz, Marcos University of Chile Dept.of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng. MontrealQC Canada

ISBN: (纸本)9798350373011

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites. © 2024 IEEE.

关键词： Internet of things

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SEM-F1: an Automatic Way for Semantic Evaluation of Multi-Narrative Overlap Summaries at Scale

SEM-F1: an Automatic Way for Semantic Evaluation of Multi-Na...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Bansal, Naman Akter, Mousumi Karmaker, Shubhra Kanti Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

Recent work has introduced an important yet relatively under-explored NLP task called Semantic Overlap Summarization (SOS) that entails generating a summary from multiple alternative narratives which conveys the common information provided by those narratives. Previous work also published a benchmark dataset for this task by collecting 2, 925 alternative narrative pairs from the web and manually annotating 411 different reference summaries by engaging human annotators. In this paper, we exclusively focus on the automated evaluation of the SOS task using the benchmark dataset. More specifically, we first use the popular ROUGE metric from text-summarization literature and conduct a systematic study to evaluate the SOS task. Our experiments discover that ROUGE is not suitable for this novel task and therefore, we propose a new sentence-level precision-recall style automated evaluation metric, called SEM-F1 (Semantic F1). It is inspired by the benefits of the sentence-wise annotation technique using overlap labels reported by the previous work. Our experiments show that the proposed SEM-F1 metric yields a higher correlation with human judgment and higher inter-rater agreement compared to the ROUGE metric. © 2022 Association for Computational Linguistics.

关键词： Semantics

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