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Reusability analysis of four standard object-oriented class libraries

2nd International Conference on software engineering Research and Applications, SERA 2004

作者： Araban, Saeed Sajeev, A.S.M. Department of Computer Science and Software Engineering University of Melbourne Carlton Vic. 3010 Australia School of Mathematics Statistics and Computer Science University of New England Australia

ISBN: (纸本)3540321330

Class libraries play a key role in object-oriented paradigm. They provide, by and large, the most commonly reused components in object-oriented environments. In this paper, we use a number of metrics to study reusability of four standard class libraries of two object-oriented languages;namely Java and Eiffel. The purpose of the study is to demonstrate how different design philosophies of the two languages have affected structural design and organization of their standard libraries that in turn might have affected their reusability with regards to Ease of Reuse and Design with Reuse. Our study concludes that within limits of our measurements, the Java libraries are easy to reuse whereas Eiffel libraries are better designed with reuse. We observe that whilst design with reuse may make class libraries extensible and maintainable, but it does not necessarily make them easy to reuse. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Digital libraries

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Particle swarm optimization with a modified sigmoid function for gene selection from gene expression data

Particle swarm optimization with a modified sigmoid function...

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15th International Symposium on Artificial Life and Robotics, AROB '10

作者： Mohamad, M.S. Omatu, S. Deris, S. Yoshioka, M. Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai Osaka 599-8531 Japan Department of Software Engineering Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia 81310 Skudai Johore Malaysia

ISBN: (纸本)9784990288044

In order to select a small subset of informative genes from gene expression data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an enhancement of binary particle swarm optimization to select a small subset of informative genes that is relevant for classifying cancer samples more accurately. In this proposed method, three approaches have been introduced to increase the probability of bits in particle's positions to be zero. By performing experiments on two gene expression data sets, we have found that the performance of the proposed method is superior to previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO. © 2010 ISAROB.

关键词： Particle swarm optimization (PSO)

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Using Category Theory to Verify Implementation Against Design in Concurrent Systems

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Procedia computer Science 2015年 52卷 530-537页

作者： Ming Zhu Peter Grogono Olga Ormandjieva Department of Computer Science and Software Engineering Concordia University Montreal Canada

The research has shown that process-oriented programming languages provide a suitable means for developing concurrent systems. However, in the development of a concurrent system, there is a challenge to manage consistency between design and implementation. To deal with such a challenge, we propose a new formal verification methodology and illustrate it by a running example. In this methodology, a concurrent system is designed using a process algebra, namely communicating sequential processes, and implemented in a process-oriented programming language, namely Erasmus. The consistency between the design and the implementation of such a concurrent system is verified formally using category theory.

关键词： category theory concurrent system CSP Erasmus process-oriented programming verification

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Teaching software testing skills: Metamorphic testing as vehicle for creativity and effectiveness in software testing 4

Teaching software testing skills: Metamorphic testing as veh...

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4th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2015

作者： Towey, Dave Chen, Tsong Yueh School of Computer Science University of Nottingham Ningbo China Zhejiang315100 China Department of Computer Science and Software Engineering Swinburne University of Technology VIC3122 Australia

ISBN: (纸本)9781467392266

In spite of its importance to software quality, software testing is often considered the "poor man" of software engineering processes, left to the end of many projects, and frequently omitted altogether. Compounding this is the reported perception of testing as a menial, low-level job, lacking any need for creativity or ingenuity, and of much lower status and attractiveness than others, such as design and implementation. When teaching about software testing, instructors often face very unmotivated students, professing little or no interest in becoming testers. This tutorial will address some testing misconceptions, and, through Metamorphic Testing - a new approach to testing which alleviates some of the major challenges in the field (including the Oracle Problem) - will provide a fresh and exciting new perspective on software testing. © 2015 IEEE.

关键词： software testing

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Sensor networks attacks classifications and mitigation

Annals of Emerging Technologies in Computing

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Annals of Emerging Technologies in Computing 2018年第4期2卷 28-43页

作者： Abu Daia, Ahmed S. Ramadan, Rabie A. Fayek, Magda B. Department of Computer Engineering Cairo University Egypt Department of Computer Science and Software Engineering University of Hail Saudi Arabia

Wireless Sensor Networks (WSNs) are exposed to many security attacks, and it can be easily compromised. One of the main reasons for these vulnerabilities is the deployment nature, where sensor nodes are deployed without physical guarding duty. That makes the network susceptible to physical attacks. The communication nature between sensor nodes is another reason, where intruders can easily send/receive information if they are located in the network communication range. In this paper, most of the possible WSN attacks are discussed, different security services expected in WSN are explained, and trust-based solutions proposed in the literature are listed. Moreover, the state-of-the-art of the attacks’ mitigation and avoidance techniques are presented. Besides, this paper is enriched with a new classification of the WSNs attacks regarding attacks’ characteristics. It will be beneficial to researchers in the field of WSNs security if they can distinguish between different attacks that have common characteristics. © 2018 by the author(s).

关键词： Sensor nodes

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Parallel intelligent education with ChatGPT

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07764USA Department of Electrical and Computer Engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning *** especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class *** address the strengths and weaknesses of learning with ***,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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ROS Supported Heterogeneous Multiple Robots Registration and Communication with User Instructions 2

ROS Supported Heterogeneous Multiple Robots Registration and...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Rajapaksha, U.U. Samantha Jayawardena, Chandimal MacDonald, Bruce A. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System and Engineering Malabe Sri Lanka University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9781665407410

Different types of heterogeneous multiple service robots are working in the same environment to help humans in many ways in a smart house. These service robots have different capabilities based on the different control and communication systems. The complexity of the programming of the robots is now reduced by using middleware like Robot Operating System (ROS). However, the communication and control of the heterogeneous service robots using very high-level instruction is still tricky because of differences in ROS topics. If a user has issued a high-level instruction to all multiple heterogeneous robots in the same environment, then all robots must complete the given task without considering the software differences in each robot. This research has developed a web-based interface where users can input high-level instruction to all multiple heterogeneous robots running in the same environment. We have used three levels of instructions. Level 01 moves robot forward for the given speed for some distance without obstacles. Level 02 is to move the robot to a specific location without obstacles. Level 03 is to navigate the robot to a goal with some obstacles in the environment. Initially, all robots need to register their software specifications and hardware specification Format (URDF) in the robot registration engine with the help of the ontology. Then all service robots act according to the instruction given by the interpreter. The proposed system was evaluated using a simulated environment with a gazebo using "Turtlebot"and "Tiago"robots. Time complexity analysis of all algorithms was completed using the Big O notation. © 2022 IEEE.

关键词： Ontology

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Automatic Leaf Recognition Based on Attention DenseNet 5th

Automatic Leaf Recognition Based on Attention DenseNet

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Wu, Huisi Shi, Zhouan Huang, Haiming Wen, Zhenkun Sun, Fuchun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science and Technology Tsinghua University Beijing100083 China

ISBN: (纸本)9789811623356

Automatic leaf recognition algorithm is widely used in plant taxonomy, horticulture teaching, traditional Chinese medicine research and plant protection, which is one of the research hotspots in information science. Due to the diversity of plant leaves, the variety of leaf forms, and the susceptibility to seasonal and other external factors, there is often a small inter-class variance and a large intra-class variance, which brings great challenges to the task of automatic leaf recognition. To solve this problem, we propose a leaf recognition algorithm base on the attention mechanism and dense connection. Firstly, base on dense connection, DenseNet is applied to realize the cross-layer learning of our model, which effectively improves the generalization ability of the network to the intra-class variance. At the same time, the learning ability of our model to the discriminative features such as the veins and textures of plant leaves is also improved. Secondly, we also employ the attention mechanism to further enhance the ability of our network in learning discriminative features of plant leaves. The experimental results show that our Attention DenseNet achieves a high accuracy of leaf recognition in our plant leaf database, including the challenging cases. Visual and statistical comparisons with state-of-the-art methods also demonstrate its effectiveness. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Horticulture

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A Pairwise Surrogate Model using GNN for Evolutionary Optimization

A Pairwise Surrogate Model using GNN for Evolutionary Optimi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gharavian, Vida Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Lab

ISBN: (纸本)9798350337020

Optimization problems widely arise in various science and engineering fields and can be computationally expensive in many real-world applications. Evaluation of the fitness function to assess a candidate solution is the main operation in all optimization procedures which can be heavily compute-intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to consequently reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison despite the fact that most of the surrogate models try to predict the exact fitness value. The proposed idea can significantly help the optimizer to reach better results in a shorter period of time. It seems comparing two candidate solutions for a greedy selection is much easier than approximating fitness values for both. We demonstrated Graph Neural Network (GNN) for this purpose to be trained on a limited number of pairwise ranks and then utilized to compare a pair of candidate solutions. In order to examine the efficacy of our model, we utilized different well-known single-objective optimization benchmarks in dimensions 10,20, and 30. Moreover, the results of the learning-based evaluation are compared with the results from the real fitness evaluation. The results, assessed in terms of the number of fitness calls and the best-found solution, showed that the proposed method is able to decrease the computing cost of fitness evaluation significantly while we achieve a comparable solution. Our model can be tested with any optimization algorithm which employs a comparison-based mechanism among its candidate solutions. © 2023 IEEE.

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