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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Moguel, Enrique Garcia-Alonso, Jose Galan-Jimenez, Jaime Berrocal, Javier Computing and Advanced Technologies Foundation of Extremadura Carretera Nacional 521 Km 418 Cáceres10071 Spain Universidad de Estremadura Quercus Software Engineering Group Avda. de la Universidad s/n Cáceres10004 Spain

ISBN: (纸本)9789893347928

The world's population is showing an aging trend. So much so that this will become one of the most significant social transformations of the 21st century. Its consequences affect almost all sectors of society, from the labor and financial markets to family structures and tourism. There is no doubt that this demographic change is a major challenge, but also an opportunity that seeks to take advantage of this new market niche, the so-called Silver Economy. In this line, the tourism sector has to adapt its offer to the Silver Economy, and more specifically to the Silver Tourists, by offering an aging-friendly offer adapted to the environment and taking advantage of new technologies. Today, one of the best ways to access updated tourist information is through chatbots and voice assistants that can offer a new intelligent interface that has all the tourist offers updated in real-time. However, there are large rural areas where there is no Internet connectivity. This circumstance limits tourists' access to the Internet and, consequently, to all information. In order to favor the access to tourist information and associated services to this new target of tourists, this work aims to carry out research tasks with the purpose of designing a new technological solution based on voice assistants without Internet connection that allows providing all the relevant tourist information in an updated way to the tourists in question. © 2023 ITMA.

关键词： Silver

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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Deep Learning Approaches for Protein Secondary Structure Prediction

Deep Learning Approaches for Protein Secondary Structure Pre...

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International Conference on Computer and Information Technology (ICCIT)

作者： Umme Israt Afroz Shreshthajit Das Nazmul Siddique Department of Computer Science and Engineering Khulna University of Engineering & Technology(KUET) Khulna Bangladesh Department of Software Engineering Shahjalal University of Science & Technology(SUST) Sylhet Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry UK

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper presents a novel hybrid model comprising Evolutionary Scale Modeling (ESM), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) network for prediction of protein secondary structures from coil (C), helix (H), and sheet (E)—from amino acid sequences using deep learning techniques. Each architecture leverages unique strengths, with LSTMs capturing long-range dependencies, CNNs extracting local spatial patterns, and ESM enhancing contextual understanding of sequences. The hybrid model was trained and tested using two key datasets: the UniProt dataset and the pdb-intersect-pisces dataset, which provide a rich source of protein sequences and structural information. The proposed model achieved an accuracy of 89.22%, demonstrating robust performance in protein secondary structure prediction.

关键词： Proteins Deep learning Accuracy Computational modeling Neural networks Computer architecture Predictive models Transformers Information technology Long short term memory

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Resource provisioning in collaborative fog computing for multiple delay-sensitive users

Resource provisioning in collaborative fog computing for mul...

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作者： Lu, Shuaibing Wu, Jie Wang, Ning Duan, Yubin Liu, Haiming Zhang, Jiayue Fang, Juan Faculty of Information Technology Beijing University of Technology Beijing China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer Science Rowan University GlassboroNJ United States School of Software Engineering Beijing Jiaotong University Beijing China

Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay-sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited-processor fog nodes (UPFN) and limited-processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP-hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near-optimal solution bounded by (Formula presented.) has been found. m is the number of fog nodes, and (Formula presented.) is the upper bound on the Lipschitz constant of the delay function. Another one is the application-driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms. © 2021 John Wiley & Sons, Ltd.

关键词： Fog

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GoMatching: a simple baseline for video text spotting via long and short term matching 24

GoMatching: a simple baseline for video text spotting via lo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haibin He Maoyuan Ye Jing Zhang Juhua Liu Bo Du Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science at Nanyang Technological University

ISBN: (纸本)9798331314385

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets.

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Case-based Explanation of Classification Models for the Detection of SQL Injection Attacks 31

Case-based Explanation of Classification Models for the Dete...

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Workshops at the 31st International Conference on Case-Based Reasoning, ICCBR-WS 2023

作者： Recio-Garcia, Juan A. Orozco-Del-Castillo, Mauricio G. Soladrero, Jose A. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

This paper investigates the interpretability of various machine learning models in the context of detecting SQL injection attacks. The models under investigation include decision trees, multi-layer perceptron, random forests, support vector machines, ADA Boost, naive Bayes, and quadratic discriminant analysis. Our main objective is to gain insights into how these models make decisions when classifying SQL commands as either legitimate or potentially malicious. To achieve this, we employ a set of commonly used features from the literature to train and evaluate the models. By prioritizing explainability, our aim is to uncover the underlying factors and decision rules that drive the models’ predictions. Through this research, we seek to bridge the gap between model accuracy and human understanding, thereby facilitating the practical application of machine learning models in real-world security scenarios. The findings of this study contribute to the development of a case-based explanation system that provides valuable insights for cybersecurity practitioners and researchers. © 2023 CEUR-WS. All rights reserved.

关键词： Decision trees

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Studying Risks of Hybrid Reengineering on Air-Transportation Systems Management 'A Case Study on Jordanian Civil Aviation Regulatory Commission (CARC)' 24

Studying Risks of Hybrid Reengineering on Air-Transportation...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Refai Mohammed, N. Taani, M.K. Otoom, Ahmed Ali Samara, Ghassan Hanandeh, S.E. Khafajeh, Hayel Zarqa University Software Engineering Dep. Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Dep. Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Artificial Intelligence Dep. Zarqa Jordan

ISBN: (纸本)9798350384307

This study examines decision-making in software re-engineering within businesses, focusing on the challenges and considerations involved. Financial constraints, time, and staff efforts are critical factors in these decisions. The study identifies difficulties faced by top-level management with legacy systems, existing systems, underused systems, and packaged software due to user resistance and customer dissatisfaction. The primary goal of the study is to assess the importance of software quality factors, especially from the users' perspective, in making re-engineering decisions. It uses the ISO 9126 Model to analyze quality elements in the case study software, aiming to provide guidance for deciding whether to proceed with re-engineering. The study explores functionality, reliability, usability, efficiency, maintainability, and portability as factors influencing re-engineering decisions, based on the ISO 9126 model. To understand user interactions and program performance, the study used unstructured participant observation in the case study software, highlighting the challenges faced by top management due to user resistance. These findings were further confirmed through face-to-face interviews with software users and department employees, offering insights into their perspectives on software quality elements. The results reveal that, despite some flaws, the system has valuable features and capabilities. Most employees express a desire to undergo re-engineering to enhance service quality, align with ISO 9126 standards, and leverage the expertise of competent staff for re-engineering activities. © 2023 IEEE.

关键词： customer dissatisfaction ISO 9126 model decision-making software quality factors software re-engineering User acceptance

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangding Fu, Shaobin Feng, Hao Zeng, Yangyang Wang, Jinghao Jiang, Zhihao Zhang, Lvyun Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University School of Information Science and Engineering Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China Hechi University School of Big Data and Computer Science Guangxi China

ISBN: (纸本)9798350312492

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive. © 2023 IEEE.

关键词： Recommender systems

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