Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Nancy Abdallah, Hal Schenck
Summary: This article studies an AG ring with a non-unimodal H-vector and its relation to Lefschetz properties and Jordan type. By analyzing Stanley's example and other research results, we discuss the Lefschetz properties of AG rings under restrictions of codimension and regularity.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Luming Zhang, Ming Chen, Guifeng Wang, Zhigeng Pan, Roger Zimmerman
Summary: In this study, a bio-inspired CT image retargeting pipeline is proposed, which mimics human gaze behavior to achieve CT image retargeting. By extracting gaze shifting paths and using multi-attribute binary hashing to capture the semantics of CT images, a Gaussian mixture model is learned to guide the image shrinking process.
INFORMATION FUSION
(2024)
Article
Computer Science, Hardware & Architecture
Keqin Li
Summary: In this paper, we propose a computation offloading strategy to satisfy all UEs served by an MEC and develop an efficient method to find such a strategy. By using Markov chains to characterize UE mobility and calculating the joint probability distribution of UE locations, we can obtain the average response time of UEs and predict the overall average response time of tasks. Additionally, we solve the power constrained MEC speed setting problem.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Jiehao Tang, Zhuang Ma, Kaiyu Gan, Jianhua Zhang, Zhong Yin
Summary: The lack of complementary affective responses from both the central and peripheral nervous systems could limit the performance of emotion recognition with the single-modal physiological signal. However, direct fusion when integrating multimodalities may ignore the heterogeneous nature of multiple feature domains from one modality to another. Additionally, there is a risk of varying distribution of multimodal physiological responses across different affective scenarios, and the inter-individual variation may increase due to the superposition of biometric information from multimodal features. To address these issues, this article presents a hierarchical multimodal network for robust heterogeneous physiological representations (RHPRNet).
INFORMATION FUSION
(2024)
Article
Computer Science, Theory & Methods
Minh-Ngoc Tran, Younghan Kim
Summary: This article proposes a method for optimizing hybrid auto-scaling configurations on the Knative platform by using separate Kubernetes operators and custom resources. Compared to existing methods, this approach shows significant improvements in terms of resource usage.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Pedro Miguel Sanchez Sanchez, Alberto Huertas Celdran, Ning Xie, Gerome Bovet, Gregorio Martinez Perez, Burkhard Stiller
Summary: With the rapid expansion of the IoT and Edge Computing, centralized ML/DL methods face challenges due to distributed data silos and privacy concerns. The emerging solution of Federated Learning ensures data privacy while the need for trust in model predictions requires further research on trustworthy ML/DL. This paper introduces a comprehensive taxonomy with six pillars and over 30 metrics to evaluate the trustworthiness of Federated Learning models, and presents an algorithm named FederatedTrust for computing trustworthiness scores. Experimental results demonstrate the utility of FederatedTrust in real-world IoT security use cases.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Jiaru Bai, Kok Foong Lee, Markus Hofmeister, Sebastian Mosbach, Jethro Akroyd, Markus Kraft
Summary: This work develops a framework to annotate how information can be derived from others in a dynamic knowledge graph. It encodes this using the notion of a derivation and captures its metadata with a lightweight ontology. The framework provides an agent template for monitoring and standardizing the process, and implements synchronous and asynchronous communication modes for agents interacting with the knowledge graph. It is applied in the context of smart cities and demonstrates the ability to handle sequential events across different timescales.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Hardware & Architecture
Katharina T. Huber, Leo van Iersel, Remie Janssen, Mark Jones, Vincent Moulton, Yukihiro Murakami, Charles Semple
Summary: This paper investigates the relationship between undirected and directed phylogenetic networks, and provides corresponding algorithms. The study reveals that the directed phylogenetic network is unique under specific conditions. Additionally, an algorithm for directing undirected binary networks is described, applicable to certain classes of directed phylogenetic networks.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Mian Ahmad Jan, Wenjing Zhang, Fazlullah Khan, Sohail Abbas, Rahim Khan
Summary: Internet of Medical Things (IoMT) is a smart healthcare framework that uses wearable sensors to capture real-time physiological data. This paper proposes lightweight device and server-enabled data fusion approaches to improve the accuracy and precision of the captured data. The approaches involve in-node processing and knowledge of previously transmitted data values, as well as global data fusion to remove redundant data values. Simulation results show the exceptional performance of these approaches in terms of accuracy and precision ratio.
INFORMATION FUSION
(2024)
Article
Computer Science, Hardware & Architecture
Philipp Czerner, Roland Guttenberg, Martin Helfrich, Javier Esparza
Summary: This paper presents a construction method that produces population protocols with a small number of states, while achieving near-optimal expected number of interactions, for deciding Presburger predicates.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Theory & Methods
Emil Horobet
Summary: In this article, we explore the complexity involved in computing reach in arbitrary dimension and specifically the critical spherical curvature points of an arbitrary algebraic variety. We present the properties of these critical points and introduce an algorithm for their computation.
JOURNAL OF SYMBOLIC COMPUTATION
(2024)
