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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Samera Obaid Barraood Haslina Mohd Fauziah Baharom Abdullah Almogahed Department of Computer Science College of Computers and Information Technology Hadhramout University Hadhramout Yemen School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia

Companies are constantly optimizing their processes to increase the efficiency of software development, which heavily depends on software testing to fulfil stringent business requirements and with high-quality software released. The detection of software defects and the assurance of software quality are facilitated by high-quality test cases. However, the current metrics used to measure test case quality (TCQ) lack clarity. Therefore, this study aims to address this gap by identifying metrics and the corresponding characteristics that measure the TCQ. This paper presents a systematic literature review (SLR) focused on TCQ characteristics and metrics. By analyzing empirical papers published between 2010 and June 2018, a total of 23 papers were identified for inclusion in the review. Through this process, 16 characteristics and 50 metrics were identified. These characteristics and metrics were further analyzed, categorized, and finalized as the evaluation metrics for TCQ characteristics. The outcomes of this study contribute to the development of a comprehensive model for assessing TCQ.

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An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

arXiv

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arXiv 2022年

作者： Li, Lingjie Xuan, Manlin Lin, Qiuzhen Jiang, Min Ming, Zhong Tan, Kay Chen The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Informatics Xiamen University Xiamen361005 China The Department of Computing The Hong Kong Polytechnic University Hong Kong

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature Selection

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Comparative Study of Noises Over Quantum Key Distribution Protocol 7th

Comparative Study of Noises Over Quantum Key Distribution ...

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7th International Conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Bhattacharyya, Sawan Das, Ajanta Banerjee, Anindita Chakrabarti, Amlan Department of Computer Science Ramakrishna Mission Vivekananda Centenary College West Bengal Kolkata India Amity Institute of Information Technology Amity University Kolkata Kolkata India Corporate Research and Development Centre for Development of Advanced Computing Pune India A. K. Choudhury School of Information Technology University of Calcutta West Bengal Kolkata India

ISBN: (纸本)9789819914135

The security of modern cryptographic systems mostly depends on mathematical hardness to solve a particular problem manually. The most popular and widely used classical cryptosystem, RSA (Rivest, Shamir, Alderman) is based on the mathematical hardness of prime factorization of the product of two large prime by any of the present classical processors. But the advancement in technology particularly in quantum information provides a threat to the present communication protocols. It motivates the researchers to move to new technologies that are fundamentally more secure through the principle of quantum mechanics. Such protocols rely upon quantum bits or qubits for data transmissions securely. The application of qubits instead of bits in cryptography using quantum key distribution protocols makes encryption robust. But the quantum communication protocols are prone to noise due to environmental factors, fragile and traverse’ a long fiber optical path. Hence, the states of qubits are easily tampered with by external factors. Noise alters the information;eventually, the message may not be meaningful to receivers. Except for bit flip and phase flip noises, depolarization, amplitude damping, decoherence, thermal relaxation, and phase damping are obstacles to achieving long-distance communication. The objective of this paper is to present a comparative study of quantum communication protocols in a noisy environment with different classes of noise. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quantum entanglement

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Using the Hurwicz Criterion to Optimize Selection Queries Under Partial Ignorance

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International Journal of Semantic computing 2025年

作者： Helmer, Sven Wood, Peter T. Stuefer, Manuel Department of Informatics University of Zurich Zurich8050 Switzerland School of Computing and Mathematical Sciences Birkbeck University of London LondonWC1E 7HX United Kingdom Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy

Optimizing queries in real-world situations under imperfect conditions is still a problem that has not been fully solved. We consider finding the optimal order in which to execute a given set of selection operators under partial ignorance of their selectivities. The selectivities are modeled as intervals rather than exact values and we apply various methods from decision theory in order to measure optimality. On the one hand, we evaluate the different techniques theoretically, showing which axioms of decision theory they satisfy and for which cases they efficiently provide an optimal solution. On the other hand, we also conduct an empirical evaluation, illustrating the differences of the approaches in terms of the relative error to the optimal solution. We demonstrate that a hybrid algorithm combining optimistic and pessimistic aspects performs best. © World Scientific Publishing Company.

关键词： Database query optimization Hurwicz criterion selection ordering

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Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning

arXiv

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arXiv 2024年

作者： Lv, Fengling Shang, Xinyi Zhou, Yang Zhang, Yiqun Li, Mengke Lu, Yang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University China University College London United Kingdom A*STAR Institute of High Performance Computing Singapore School of Computer Science and Technology Guangdong University of Technology Guangzhou China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China

Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. However, the data collected in real-world scenarios is likely to follow a long-tailed distribution. For example, in the medical domain, it is more common for the number of general health notes to be much larger than those specifically related to certain diseases. The presence of long-tailed data can significantly degrade the performance of PFL models. Additionally, due to the diverse environments in which each client operates, data heterogeneity is also a classic challenge in federated learning. In this paper, we explore the joint problem of global long-tailed distribution and data heterogeneity in PFL and propose a method called Expert Collaborative Learning (ECL) to tackle this problem. Specifically, each client has multiple experts, and each expert has a different training subset, which ensures that each class, especially the minority classes, receives sufficient training. Multiple experts collaborate synergistically to produce the final prediction output. Without special bells and whistles, the vanilla ECL outperforms other state-of-the-art PFL methods on several benchmark datasets under different degrees of data heterogeneity and long-tailed distribution. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion 2

Multimodal E-Commerce Product Classification Using Hierarchi...

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2nd IEEE Conference on Information Technology and Data science, CITDS 2022

作者： Tashu, Tsegaye Misikir Fattouh, Sara Kiss, Peter Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 IEEE.

关键词： Neural network models

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Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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Large Language Models as Evolutionary Optimizers

Large Language Models as Evolutionary Optimizers

引用

Congress on Evolutionary Computation

作者： Shengcai Liu Caishun Chen Xinghua Qu Ke Tang Yew-Soon Ong A*STAR Department of Computer Science and Engineering Centre for Frontier AI Research Southern University of Science and Technology Centre for Frontier AI Research A*STAR Tianqiao & Chrissy Chen Institute Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Centre for Frontier AI Research A*STAR College of Computing and Data Science Nanyang Technological University

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self- adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges.

关键词： Training Large language models Computational modeling Sociology Evolutionary computation Traveling salesman problems Temperature control

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images

Identification of Diabetic Retinopathy by Transfer Learning ...

引用

International Conference on Communication and Electronics Systems (ICCES)

作者： Sreenivas Reddy Sagili Chandrashekhar Goswami V.C. Bharathi S. Ananthi K Rani R. Sathya Department of computer science Rochester Institute Of Technology Rochester New York Department of Computer Science & Engineering MIT School of Computing MIT Art Design and Technology University Pune Maharashtra School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Information Technology M. Kumarasamy College of Engineering Karur Tamilnadu India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported.

关键词： Diabetic retinopathy Pathology Image color analysis Transfer learning Retina Feature extraction Prediction algorithms Classification algorithms Convolutional neural networks Object recognition

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Deep Learning and CNN-Based Approach for Efficient Detection of Potato Leaf Diseases

Deep Learning and CNN-Based Approach for Efficient Detection...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Muhammad Salah-ud-Din Iqbal Waqas Iqbal Munir Ahmad Muhammad Saleem Faculty of Information Science and Technology College of Arts & Science Applied Science University Manama Bahrain & Center for Cyber Security Universiti Kebangsaan Malaysia Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain School of Computer Science Minhaj University Lahore Pakistan Department of forestry and Range Management University of Agriculture Faisalabad Pakistan College of Informatics Korea University Seoul Republic of Korea

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

Potato is a maj or food crop worldwide, providing essential nutrients to millions. However, various diseases can severely affect potato crops, leading to reduced yield and quality. Early disease detection is crucial for effective disease management and control. This study presents a deep learning-based approach using CNN to autonomously diagnose potato leaf diseases. The model leverages the Plant Village dataset, which contains images of potato leaves in both healthy and diseased states. The proposed method demonstrates high accuracy in identifying multiple potato leaf diseases, offering promising results for application in precision agriculture. By utilizing CNN s, the model effectively detects and classifies disease symptoms, providing a reliable tool for early intervention in agricultural practices. The approach's potential impact on improving crop yield and minimizing losses due to diseases is highlighted, making it a significant contribution to smart farming techniques. The study underscores the importance of deep learning in modern agricultural practices and its role in enhancing disease management strategies.

关键词： Deep learning Precision agriculture Smart agriculture Plants (biology) Geoscience Nutrients Reliability Remote sensing Diseases Testing

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