Article
Computer Science, Artificial Intelligence
Juan Boubeta-Puig, Jesus Rosa-Bilbao, Jan Mendling
Summary: This paper addresses the challenge of integrating blockchain with CEP, proposing an effective development environment for automatically transforming smart contracts, which can be deployed in both CEP engines and blockchain networks for execution.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
David Corral-Plaza, Guadalupe Ortiz, Inmaculada Medina-Bulo, Juan Boubeta-Puig
Summary: The system integrates stream processing and complex event processing technologies, providing a graphical editor for domain experts to define and infer data domains, with definitions automatically transformed into code for real-time data analysis and event detection.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Amir Masoud Rahmani, Zahra Babaei, Alireza Souri
Summary: IoT is transitioning to a smarter world by deploying a large number of sensors worldwide, with data analysis focusing on Complex Event Processing (CEP). In healthcare, CEP plays a crucial role in achieving reliable and cost-effective real-time event processing for improving healthcare quality.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jesus Rosa-Bilbao, Juan Boubeta-Puig, Adrian Rutle
Summary: This paper proposes an Event-Driven Application (EDA) that integrates Complex Event Processing (CEP) and blockchain through the low-code paradigm. This EDA allows for the development of user-friendly applications that can integrate IoT devices from multiple manufacturers and different data formats, and utilize CEP technology for complex event detection and blockchain for secure and accessible event storage.
INTERNET OF THINGS
(2023)
Article
Neurosciences
C. Fernandes, I. Macedo, A. R. Goncalves, R. Pasion, R. Mata, G. Danese, I. P. Martins, F. Barbosa, J. Marques-Teixeira
Summary: The study examined the neural correlates of age differences in decision-making using EEG recordings. The results indicate that aging affects affective processes, but integration and motivation processes remain preserved. However, further replication with larger samples is needed to confirm these findings.
Article
Computer Science, Information Systems
Fatima Boumahdi, Hadi Oqaibi, Rachid Chalal, Hamza Hentabli, Amina Madani
Summary: This paper proposes an integrated model-driven solution method for developing service-oriented architectures with decision requirements. The proposed approach offers a more comprehensive understanding of the system, platform-independent development, enhanced design reusability, simplified system evolution, and increased productivity.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Economics
Xileidys Parra, Xavier Tort-Martorell, Fernando Alvarez-Gomez, Carmen Ruiz-Vinals
Summary: The decision-making process (DMP) in organizations has undergone changes influenced by information technologies and computational science. This study provides a chronological review of the information-driven DMP evolution and discusses how technology has impacted information generation, storage, management, and its utilization for improved decision-making and knowledge acquisition.
JOURNAL OF THE KNOWLEDGE ECONOMY
(2022)
Review
Agriculture, Dairy & Animal Science
Thomas van Klompenburg, Ayalew Kassahun
Summary: This study conducted a systematic literature review on the applications of data-driven decision making in the pig sector, finding that various attributes of live pigs and slaughter data are used in analytics. The focus is mainly on disease occurrence and prevention, DNA-related analysis, and the effect of feeding strategies on growth. The application of machine learning algorithms, particularly random forest and neural network, has gained momentum since 2018. However, current studies mainly focus on isolated issues and future research should consider the complexity of real-life business circumstances and integrate data analytics within farm information management systems.
Article
Agronomy
Yunsong Jia, Xiang Li
Summary: The proposed complex event processing method effectively addresses the issue of complex pattern recognition in greenhouse conditions. With high applicability and low information coupling, it can be directly used by agricultural experts.
Article
Computer Science, Artificial Intelligence
Chonghui Zhang, Xiangyu Dong, Shouzhen Zeng, Llopis-Albert Carlos
Summary: This article presents a dual consistency driven group decision making method based on fuzzy preference relations. By introducing bidirectional adjustment and dynamic weight adjustment, it improves the level of consensus and efficiency in group decision making.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Automation & Control Systems
Fuyuan Xiao
Summary: Recent research has shown a growing interest in fuzzy complex event processing-based decision-making systems, which require well-managed operator distribution. However, dynamic input events bring intrinsic uncertainty, making operator distribution more challenging. The proposed CaFtR strategy utilizes TOpSIS to achieve cost-aware, fault-tolerant, and reliable operator scheduling, demonstrating efficiency through a case study on the StreamBase system.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Economics
Tanmoy Das, Priyodorshi Banerjee
Summary: Complex financial decisions often need to be made, and increased complexity can lead to social learning and reliance on observed decisions of peers. This study examines the relationship between decision complexity and peer effects on financial decisions through a field experiment. The experiment involves subjects making the same portfolio allocation decision twice, with the second decision influenced by unexpectedly observing a peer's choice. The study finds that increased complexity heightens revision activity, contributing to the understanding of peer effects in financial decision-making.
ECONOMIC MODELLING
(2023)
Article
Business
Li Xuan
Summary: With the continuous development of big data techniques, more and more group decision-making problems involve multiple decision-makers. This paper proposes a novel large-scale group decision-making (LSGDM) method to study the role of big data-driven decision-making. By collecting research standards and proposing a fuzzy LSGDM judgement matrix decision-making method based on group analysis, the method is applied to evaluate a takeout service platform.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Computer Science, Artificial Intelligence
Ke Li, Haifeng Nie, Huiru Gao, Xin Yao
Summary: This article presents a simple and effective knee point identification method that is attractive to decision makers in multicriterion decision making. The method validates whether a solution is a knee point by comparing its localized tradeoff utility with others within its neighborhood, and a solution is considered a knee point if it has the best-localized tradeoff utility among its neighbors. The GPU implementation reduces the worst-case complexity and the empirical results demonstrate the outstanding performance of the proposed method, especially on problems with many local knee points. The usefulness of the method in guiding evolutionary multiobjective optimization algorithms is also validated.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Mathematics
Carmen Maria Yago, Francisco Javier Diez
Summary: Cost-effectiveness analysis is increasingly used in medicine to determine the value of an intervention. Discrete event simulation is playing a role in this analysis, but artificial intelligence techniques have not been applied. The researchers aim to develop a tool that allows non-programmers to easily build simulation models for cost-effectiveness analysis.
Article
Computer Science, Hardware & Architecture
Guadalupe Ortiz, Meftah Zouai, Okba Kazar, Alfonso Garcia-de-Prado, Juan Boubeta-Puig
Summary: The Internet of Things has grown rapidly, with enhanced communication capabilities and reduced costs, as well as the development of new technologies such as big data and real-time data analysis. In order to fully utilize these resources, a software architecture is needed. Although various proposals have been made, finding a solution that simultaneously utilizes edge, fog, and cloud computing is not easy.
COMPUTER STANDARDS & INTERFACES
(2022)
Article
Computer Science, Software Engineering
Juan Marcelo Parra-Ullauri, Antonio Garcia-Dominguez, Nelly Bencomo, Changgang Zheng, Chen Zhen, Juan Boubeta-Puig, Guadalupe Ortiz, Shufan Yang
Summary: The increasing autonomy in modern software systems, especially in the context of Reinforcement Learning, raises concerns about the transparency of decision-making criteria, requiring solutions for explainability and trustworthiness in AI systems.
SOFTWARE AND SYSTEMS MODELING
(2022)
Article
Computer Science, Hardware & Architecture
Antonio Garcia-Dominguez, Francisco Palomo-Lozano, Inmaculada Medina-Bulo, Alfredo Ibias, Manuel Nunez
Summary: In modern web applications, in order to produce service compositions, both in-house and third-party web services are combined, and their performance is dependent on the services they integrate. The authors present algorithms that can compute the required performance for each service from a model of service composition in the early stages of development. These algorithms are useful for testing and selecting candidate web services, and can be integrated into service discovery as performance-driven recommendation systems.
COMPUTER STANDARDS & INTERFACES
(2023)
Article
Computer Science, Information Systems
Jose Roldan-Gomez, Jesus Martinez del Rincon, Juan Boubeta-Puig, Jose Luis Martinez
Summary: In recent years, the Internet of Things (IoT) has grown rapidly, leading to an increase in attacks against it. This paper proposes an architecture that can generate complex event processing (CEP) rules for real-time attack detection in an automatic and unsupervised manner. By integrating CEP technology with principal component analysis (PCA), Gaussian mixture models (GMM), and the Mahalanobis distance, the architecture is able to analyze and correlate large amounts of data in real time, making it suitable for IoT environments. The testing of this architecture in simulated attack scenarios shows that the generated rules achieve a high F1 score of .9890 in real-time detection of six different IoT attacks.
Article
Computer Science, Hardware & Architecture
Jesus Rosa-Bilbao, Juan Boubeta-Puig, Adrian Rutle
Summary: This paper proposes a low-code approach called EDALoCo to simplify the development of event-driven applications for smart contract management. The approach enhances the Node-RED low-code platform with blockchain technology, allowing non-expert developers to create user-friendly and lightweight applications that can compile, deploy, and monitor smart contracts. The approach was successfully applied in a case study of monitoring and tracing COVID-19 vaccine temperature in a blockchain network.
COMPUTER STANDARDS & INTERFACES
(2023)
Article
Computer Science, Software Engineering
Pedro Delgado-Perez, Aurora Ramirez, Kevin J. Valle-Gomez, Inmaculada Medina-Bulo, Jose Raul Romero
Summary: Automated test case generation has been proven useful in reducing software testing expenses. However, testers have shown skepticism towards the comprehension of generated test suites compared to manually designed ones. To address this, the paper proposes incorporating interactive readability assessments made by testers into EvoSuite, an evolutionary test generation tool.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Automation & Control Systems
Jose Roldan-Gomez, Juan Boubeta-Puig, Javier Carrillo-Mondejar, Juan Manuel Castelo Gomez, Jesus Martinez del Rincon
Summary: The Internet of Things (IoT) has rapidly grown, leading to the integration of sensors with IoT devices. However, the number of attacks against these devices has also increased as fast as the paradigm itself. Therefore, it is necessary to design, implement, and study new cybersecurity solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Computer Science, Cybernetics
Pablo Caballero, Guadalupe Ortiz, Inmaculada Medina-Bulo
Summary: Ambient assisted living (AAL) envisions a future where older people can stay in their homes with the help of intelligent systems enabled by the Internet of Things (IoT). This paper conducts a systematic literature review of AAL systems supported by IoT, exploring the types of systems, popular technologies used, and the challenges and opportunities in this field. The analysis reveals the synergy between AAL and IoT, highlighting the potential for creating more intelligent and user-friendly AAL systems.
UNIVERSAL ACCESS IN THE INFORMATION SOCIETY
(2023)
Article
Computer Science, Artificial Intelligence
Mariia Pokushko, Alena Stupina, Inmaculada Medina-Bulo, Svetlana Ezhemanskaya, Roman Kuzmich, Roman Pokushko
Summary: The aim of this study is to solve the problem of increasing the efficiency of fuel and energy complex enterprises by defining an algorithm using the data envelopment analysis (DEA) method and conducting experiments to improve the efficiency of a combined heat and power plant.
Article
Computer Science, Information Systems
Jesus Rosa-Bilbao, Juan Boubeta-Puig, Adrian Rutle
Summary: This paper proposes an Event-Driven Application (EDA) that integrates Complex Event Processing (CEP) and blockchain through the low-code paradigm. This EDA allows for the development of user-friendly applications that can integrate IoT devices from multiple manufacturers and different data formats, and utilize CEP technology for complex event detection and blockchain for secure and accessible event storage.
INTERNET OF THINGS
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Jesus Rosa-Bilbao, Juan Boubeta-Puig
Summary: Blockchain is a secure and distributed technology that is gaining popularity due to its ability to ensure traceability, immutability, and transparency of data. However, monitoring blockchain networks requires expertise in this field. To address this challenge, this paper proposes a low-code tool that allows inexperienced blockchain developers to define graphical flows for real-time monitoring of blockchain elements. This tool has been successfully used in a vaccine delivery scenario, facilitating the monitoring of temperature measurements stored in a smart contract. When a new transaction is added to the blockchain network, it is promptly notified and sent to specified data sinks as specified by non-expert blockchain developers.
SERVICE-ORIENTED COMPUTING - ICSOC 2022 WORKSHOPS
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Jesus Rosa-Bilbao, Juan Boubeta-Puig
Summary: Node4Chain is a novel extension tool that enables real-time monitoring of blockchain networks and integration with other systems and technologies, by defining graphical flows.
SERVICE-ORIENTED COMPUTING - ICSOC 2022 WORKSHOPS
(2023)
Article
Computer Science, Software Engineering
Jesus Rosa-Bilbao, Juan Boubeta-Puig
Summary: This paper provides a systematic literature review on existing approaches, frameworks, systems, and languages that integrate Model-Driven Engineering (MDE) with Complex Event Processing (CEP). The review also discusses the application domains and maturity levels of these proposals, as well as future research challenges in the CEP field.
JOURNAL OF OBJECT TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
