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IEEE International Conference on Image Processing

作者： Muhammad Ahmad Muhammad Usama Salvatore Distefano Manuel Mazzara Department of Computer Science National University of Computer and Emerging Sciences Pakistan Dipartimento di Matematica e Informatica MIFT University of Messina Messina Italy Institute of Software Development and Engineering Innopolis University Innopolis Russia

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Conventional active learning approaches for hyperspectral image classification (HSIC) have limitations such as incrementally growing training sets without considering class structure and heterogeneity within existing and new samples. Additionally, there is limited research leveraging both spectral and spatial information jointly, and stopping criteria are not well established. This study presents a novel fuzzybased spatial-spectral Within and Between method (FLG) for preserving local and global class discriminative information. The method first explores spatial fuzziness to identify misclassified samples. It then computes total within-class and between-class information locally and globally. This information is integrated into a discriminative objective function to selectively query heterogeneous samples, mitigating randomness among training data. Experimental results on benchmark Hyperspectral datasets demonstrate the FLG improves classification accuracy across generative, extreme learning machine, and sparse multinomial logistic regression models by jointly exploiting spectral and spatial information to expand labeled training sets strategically.

关键词： Training Logistic regression Accuracy Extreme learning machines Image processing Training data Linear programming

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Multimodal Difference Learning for Sequential Recommendation

arXiv

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arXiv 2024年

作者： Li, Changhong Guo, Zhiqiang Li, Guohui Zou, Li School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Tsinghua University Beijing China School of Software Engineering Huazhong University of Science and Technology Wuhan China

Sequential recommendations have drawn significant attention in modeling the user’s historical behaviors to predict the next item. With the booming development of multimodal data (e.g., image, text) on internet platforms, sequential recommendation also benefits from the incorporation of multimodal data. Most methods introduce modal features of items as side information and simply concatenates them to learn unified user interests. Nevertheless, these methods encounter the limitation in modeling multimodal differences. We argue that user interests and item relationships vary across different modalities. To address this problem, we propose a novel Multimodal Difference Learning framework for Sequential Recommendation, MDSRec for brevity. Specifically, we first explore the differences in item relationships by constructing modal-aware item relation graphs with behavior signal to enhance item representations. Then, to capture the differences in user interests across modalities, we design a interest-centralized attention mechanism to independently model user sequence representations in different modalities. Finally, we fuse the user embeddings from multiple modalities to achieve accurate item recommendation. Experimental results on five real-world datasets demonstrate the superiority of MDSRec over state-of-the-art baselines and the efficacy of multimodal difference learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Unifying Domain Gap in Federated Learning

SSRN

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SSRN 2023年

作者： Lei, Mingxi Ding, Meng Ma, Chunwei Huang, Ziyun Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo 338 Davis Hall BuffaloNY14260 United States Computer Science and Software Engineering Penn State Erie 4701 College Drive EriePA16563 United States

Federated Learning (FL) is a framework that aims to learn in a distributed and privacy preserving manner. It often works with a central server coordinating multiple clients to jointly train a global model, without the needs of sharing private data with each other. Recent studies revealed that such a parameter-only communicating strategy could suffer from non-independent and identically distributed (non-iid) data (i.e., statistical heterogeneity) across the clients. This necessitates Personalized Federated Learning (PFL), whose goal is to improve the performance of individual clients while it still allows a global model to be learned. Most of the existing work on PFL has focused on label shift in a supervised task, and not much attention has been paid on feature shift, which is another commonly encountered problem in real-world application of federated learning, e.g., client data may differ in illumination, angles, quality for natural images, or in imaging protocols for medical images. To address such an issue, we propose a general and yet powerful framework that benefits from the geometric interpretation of a deep neural network. Since, geometrically, a deep neural network partitions the feature space into a Power Diagram (PD) or Voronoi Diagram (VD), we introduce losses to alleviate feature shift from the viewpoint of PD and VD. Also, we formulate a personalized generator network for each client to rectify feature distribution and adapt the input data from its own domain to an ``averaged'' or unified domain that generalizes well across all clients. We show that our method achieves competitive performance compared to current state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions

Intelligent-Based Extraction of Business Capabilities from C...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Faisal Fahmi Siti Hafizah Ab Hamid Imam Yuadi Department of Library and Information Science Faculty of Political and Social Science Airlangga University Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya

In the era of Big Data, Information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This Information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia.

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Model-driven Multi-objective Optimization Approach to 6G Network Planning 15

Model-driven Multi-objective Optimization Approach to 6G Net...

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15th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2021

作者： Petrovic, Nenad Al-Azzoni, Issam Blank, Julian University of Nis Faculty of Electronic Engineering Aleksandra Medvedeva 14 Nis18000 Serbia Al Ain University Department of Software Engineering and Computer Science Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East Lansing United States

ISBN: (纸本)9781665444422

Ultra high-speed and reliable next-generation 6G mobile networks are recognized as key enablers for many innovative scenarios in smart cities - from vehicular use cases and surveillance to healthcare. However, deployment of such network requires tremendous amount of time and involves various costs. For that reason, optimal network planning is of utmost importance for development of 6G mobile networks in smart cities. In this paper, we explore the potential of multi-objective linear optimization in synergy with model-driven approach in order to achieve efficient network planning in smart cities. As outcome, a solution relying on pymoo is proposed and compared to previous works relying only on single objective implemented in AMPL. According to the achieved results, this approach speeds up the execution, while giving more flexibility when it comes to cost/performance trade-offs. © 2021 IEEE.

关键词： Multiobjective optimization

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Sakib, Nazmus Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480451

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2022 IEEE.

关键词： Supply chains

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading

The Impact of Transformer Models and Regression Algorithms o...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Abhijith. N. Nampoothiri M. G. Thushara Pradeesh. N Anand Krishna K. R Abhai Santhosh Abhijith Kumar S Suryan Kumar Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models—including BERT, Sentence-BERT, T5, Genism, and XLNet—are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations.

关键词： Support vector machines Measurement Accuracy Education Forestry Transforms Transformers Numerical models Reliability Random forests

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On Retrofitting Provenance for Transparent and Fair software - Drivers and Challenges

On Retrofitting Provenance for Transparent and Fair Software...

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Workshop on software Fairness (FairWare)

作者： Jens Dietrich Matthias Galster Markus Luczak-Roesch School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand School of Information Management Victoria University of Wellington Wellington New Zealand

There have been ongoing discussions about how to ensure transparency and fairness in software that utilise artificial intelligence (AI). However, transparency and fairness are not limited to AI. Modern (non-AI) software is often constructed in a black box fashion. This means, components and services provide some functionality, but details on how this is achieved are hidden. Common software development and design principles like encapsulation and information hiding promote this. Engineers often only look inside the black boxes when they need to fix problems, e.g., when tracing bugs or vulnerabilities. The demand for transparency has created a need to open those black boxes also to non-engineers. For instance, businesses need to demonstrate regulation compliance, and end users want to understand how systems make fair decisions that affect them. However, adding provenance (i.e., the ability to gather information about data and algorithms used in systems) to existing systems is invasive and costly, and current approaches to collect provenance data are not designed to expose data to end users. We argue that this requires “provenance retrofitting”, i.e., adding provenance capabilities to systems mechanically, and exposing provenance data through standard language and service application programming interfaces (APIs). This could facilitate an infrastructure that supports transparency, which then can in turn be used to create feedback mechanisms for users that in the long term can improve the fairness of software. In this paper we discuss drivers, objectives, key challenges and some possible approaches to provenance retrofitting.

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Stock Price Prediction using CuDNNLSTM and multiple CNN layers

Stock Price Prediction using CuDNNLSTM and multiple CNN laye...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Anika Kanwal Siva Chandrasekaran Adnan Akhunzada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

Academic and financial sectors are interested in research areas that focus on understanding the patterns of financial activities and predicting their future changes. The daily movement of financial data involves complex, insufficient, and ambiguous information, making its prediction an extremely difficult endeavour. The issues of forecasting and analysing financial data is therefore complex and time-dependent. Deep neural networks (DNNs) can be used to solve nonlinear problems more effectively than conventional machine learning techniques. This study proposes an intelligent and optimal model CuDNNLSTM-Multi(1dCNN) for stock market price prediction using a hybrid of CUDA based Long Short Term Memory (LSTM) and Multiple One Dimensional Convolution Neural Networks (CNNs). A variety of models, including Multiple CuDNNLSTM-CNN, Multi-CuDNNLSTM, Multi (1dCNN), are used to compare the prediction performance of the proposed model. In terms of RMSE, MAE, and MAPE, the results show that the suggested model outperforms all hybrid and individual models.

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