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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Asim, Sherrjel Nayyer, Muhammad Ziad Ahmed, Usama Faheem, Muhammad GIFT University Department of Computer Science Gujranwala Pakistan Government College University Department of Software Engineering Faisalabad Pakistan

ISBN: (纸本)9781665476010

Rapidly increasing data requires a lot of time to execute different tasks by the systems at Cloudlet Federation (CF). The demand of today's computing is to exploit resource utilization up to maximum. Efficient resource allocation and scheduling of tasks are the significant characteristics of Cloudlet Federation (CF). Different tasks have diverse execution time. Some tasks require greater execution time whereas some need just a couple of seconds for their execution. Scheduling offers a feasible solution for handling these tasks with the help of different scheduling algorithms. To address task scheduling issue, this paper proposes a scheduling technique named Least Busy Time Scheduling (LBTS) for smart scheduling of tasks in Cloudlet Federation (CF). The proposed technique assists the scheduler to assign task to a system considering the parameter of least busy time to reduce waiting time and optimize the overall performance of the system. © 2023 IEEE.

关键词： Scheduling algorithms

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Hyperspectral Image Classification Using Multi-feature Fusion Residual Hypergraph Convolution Network 8

Hyperspectral Image Classification Using Multi-feature Fusio...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Zhu, Tianxing Liu, Qin Zhang, Lixiang Tongji University School of Software Engineering Shanghai China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350397932

Convolutional neural network (CNN) has been broadly adopted on hyperspectral image (HSI) processing due to its impressive feature extraction capabilities. Nevertheless, it is still a challenge for CNN-based hyperspectral image classification methods to extract more effective spectral-spatial features considering all spectral bands. The recent advances of graph convolutional neural networks (GCN), a combination of CNN and graph representation learning, have shown great potential in the HSI. However, the existing GCN-based methods have lower accuracy than the mature CNN methods due problems such as overdependence on the adjacency matrix, usage of a single modal feature. In this paper, we propose the Multi-Feature Fusion Residual Hypergraph Convolution Network (MF-RHCN) for HSI classification. We present a novel Hypergraph Convolution Network that fuses multimodal features effectively, and automatically updates the hyperedge weights, which solves the difficulties encountered by the previous GCN-based hyperspectral classification methods. MF-RHCN first generates hyperedges from features of different modalities to construct a hypergraph representing multimodal features in HSI. Then, the HSI and the extracted hypergraph are input into the hypergraph convolutional neural network for learning. We enhance the hypergraph convolutions with residual connection and input mapping, which helps reduce the over-smoothing issue associated with deep hypergraph models. Sufficient experiments on three datasets have proved the effectiveness of MF-RHCN. © 2023 IEEE.

关键词： Convolution

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ML-based Anomalies Detection in Wireless Network Link Layer of the Internet of Things (IoT) 5

ML-based Anomalies Detection in Wireless Network Link Layer ...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Goswami, Pankaj Kumar Goswami, Garima Mahdi, Hussain Falih Vaish, Arti Dewangan, Bhupesh Kumar Choudhury, Tanupriya Teerthanker Mahaveer University Moradabad India University of Diyala Department of Computer and Software Engineering Baquba Iraq OP Jindal University Department of Computer Science and Engineering Punjipathra Raigarh India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350337525

Internet of things is the most emergent technology, expanding interconnected device networks day by day to enhance the ease of device control and monitoring. IoTs are not covering the commercial utility but also providing emergency benefits to health care centres too. The extensive number of device connections over a network challenges a huge cost of operation management. The most recent research activity deals with anomaly detection in IoT networks over wide IoT networks. The automatic malfunctioning over the network is the most tedious task for researchers. The real-world IoT system analysis motivates this work to identify the anomalies in wireless networks. The link layer is selected to analyse, identifies and detect the wireless network anomalies. A comprehensive review on the performance threshold of machine learning-based algorithms is presented for automatic detection. © 2023 IEEE.

关键词： Internet of things

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Capabilities for Better ML engineering

Capabilities for Better ML Engineering

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2023 Workshop on Artificial Intelligence Safety, SafeAI 2023

作者： Yang, Chenyang Brower-Sinning, Rachel Lewis, Grace A. Kästner, Christian Wu, Tongshuang School of Computer Science Carnegie Mellon University United States Carnegie Mellon Software Engineering Institute United States

In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show the potential of capabilities for reflecting model generalizability, which can provide guidance for the ML engineering process. We discuss challenges and opportunities for the integration of capabilities into ML engineering. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Specifications

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A Framework for Multi-Grade Classification of Ulcerative-Colitis Using Deep Neural Networks 25

A Framework for Multi-Grade Classification of Ulcerative-Col...

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25th International Multi Topic Conference, INMIC 2023

作者： Noor, Muhammad Nouman Nazir, Muhammad Dilshad, Veena Haneef, Farah Hitec University Taxila Department of Computer Science Taxila Pakistan Cust University Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350317701

Endoscopic disease severity assessment is a critical component in the management of ulcerative colitis patients. Endoscopic evaluation, on the other hand, suffers from significant intra-observer and inter-observer differences, reducing the reliability of individual assessments. As a result, we set out to create a deep-learning model capable of distinguishing between distinct endoscopic disease severity levels. Initially, we preprocessed the dataset and then applied data augmentations on the images using various geometric transformations. Subsequently, we have utilized the transfer learning concept by applying modified ResNet-50 by stacking additional layers which further improves the classification performance. Our proposed model achieved an accuracy of 84.21%, 81.06% recall, and 88.33% precision. © 2023 IEEE.

关键词： Diseases

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software Systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation 28th

Self-Sampling Training and Evaluation for the Accuracy-Bi...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Liu, Dugang Qiao, Yang Tang, Xing Chen, Liang He, Xiuqiang Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China FIT Tencent Shenzhen China

ISBN: (纸本)9783031306778

Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Mathematical Modeling and Analysis of Patterns in Structured Collections of Big Data 13

Mathematical Modeling and Analysis of Patterns in Structured...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Borys Udovyk, Iryna Shvachych, Hennadii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

This paper addresses the issue of creating and applying mathematical models and methods for finding generalized solutions when working with structured collections of 'big data'. We reviewed the modern methodologies used to solve problems of this class. The mathematical model presented describes an ordered set of all subsets formed from a finite ordered base set of arbitrary size and data type. We explored a set of functional dependencies of five discrete input variables to work with this mathematical model. Some of these functional dependencies are derived for specific solutions with specified boundary conditions. The paper also presents examples of how the derived functional dependencies are applied in the implementation of mathematical methods using this model. This required us to conduct a comparative assessment of the search time for a solution with and without the use of these mathematical methods. Comparative graphs are demonstrated to show the rate of increase in the number of operations depending on the size of the original finite base set with and without the use of these mathematical methods. As a result of this, logical conclusions are drawn regarding the impact of mathematical methods for working with structured collections on minimizing time and computational resources. © 2023 IEEE.

关键词： Big data

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FEW-SHOT LEARNING AS CLUSTER-INDUCED VORONOI DIAGRAMS: A GEOMETRIC APPROACH 10

FEW-SHOT LEARNING AS CLUSTER-INDUCED VORONOI DIAGRAMS: A GEO...

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10th International Conference on Learning Representations, ICLR 2022

作者： Ma, Chunwei Huang, Ziyun Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo United States Computer Science and Software Engineering Penn State Erie United States

Few-shot learning (FSL) is the process of rapid generalization from abundant base samples to inadequate novel samples. Despite extensive research in recent years, FSL is still not yet able to generate satisfactory solutions for a wide range of real-world applications. To confront this challenge, we study the FSL problem from a geometric point of view in this paper. One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space. We retrofit it by making use of a recent advance in computational geometry called Cluster-induced Voronoi Diagram (CIVD). Starting from the simplest nearest neighbor model, CIVD gradually incorporates cluster-to-point and then cluster-to-cluster relationships for space subdivision, which is used to improve the accuracy and robustness at multiple stages of FSL. Specifically, we use CIVD (1) to integrate parametric and nonparametric few-shot classifiers;(2) to combine feature representation and surrogate representation;(3) and to leverage feature-level, transformation-level, and geometry-level heterogeneities for a better ensemble. Our CIVD-based workflow enables us to achieve new state-of-the-art results on mini-ImageNet, CUB, and tiered-ImagenNet datasets, with ∼2%-5% improvements upon the next best. To summarize, CIVD provides a mathematically elegant and geometrically interpretable framework that compensates for extreme data insufficiency, prevents overfitting, and allows for fast geometric ensemble for thousands of individual VD. These together make FSL stronger. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Computational geometry

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CSP-RM: Reputation Management Decision Support for Crowdsourcing Service Providers

CSP-RM: Reputation Management Decision Support for Crowdsour...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Shipeng Li, Qingzhong Cui, Lizhen Jiang, Yali Shen, Zhiqi Yu, Han Shandong University School of Software Jinan China China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350304855

The increasing popularity of crowdsourcing has resulted in the emergence of multiple crowdsourcing service providers (CSPs), such as Mechnical Turk and Crowdflower, which compete to attract crowd workers (CWs). CWs can share their experience working for various CSPs, which forms the basis of CSP reputation score. This information can be used for trust building and facilitating future CWs' decisions on which CSP to work for. Existing reputation management research in crowdsourcing has mainly focused on controlling task quality and improving revenue from the perspective of CSPs. Little attention has been paid to helping CSPs manage their reputation to attract and retain CWs. In this paper, we propose the Crowdsourcing Service Provider Reputation Management (CSP-RM) framework to bridge this important gap. Based on the current reputation of CSPs, it dynamically balances the trade-off between the reputation maintenance cost and the long-term profit for a given CSP. It performs dynamic commission allocation for a CSP based on Lyapunov optimization to guide the recruitment of CWs, while considering the revenue and the changes in the number of CWs. Extensive experiments based on highly competitive crowdsourcing market demonstrate that CSP-RM makes the most advantageous cost-benefit trade-off compared to existing approaches, outperforming the best baseline by 23.83%, 39.21% and 3.36% in terms of average cumulative revenue, average number of CWs and public reputation, respectively. To the best of our knowledge, it is the first decision support framework for enabling CSPs to recruit more CWs in a highly competitive market, while maintaining their reputation and ensuring long-term benefit. © 2023 IEEE.

关键词： Crowdsourcing

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