Article
Computer Science, Theory & Methods
Enda Yu, Dezun Dong, Xiangke Liao
Summary: This paper proposes a standard for systematically classifying communication optimization algorithms in distributed deep learning systems based on mathematical modeling, which is a novel contribution in the field. The authors categorize existing works into four categories based on communication optimization strategies and discuss potential future challenges and research directions.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Review
Physics, Multidisciplinary
Paolo Arena, Maide Bucolo, Arturo Buscarino, Luigi Fortuna, Mattia Frasca
Summary: This paper discusses the main guidelines for bioinspired technologies in the future, emphasizing the application to solve everyday problems in a low cost and sustainable way, as well as the need to change the paradigm for designing innovative bioinspired systems.
FRONTIERS IN PHYSICS
(2021)
Article
Computer Science, Information Systems
Ming Tang, Vincent W. S. Wong
Summary: In this paper, a model-free deep reinforcement learning-based distributed algorithm is proposed to address the load problem in mobile edge computing systems. The algorithm can effectively reduce the ratio of dropped tasks and average delay.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Review
Genetics & Heredity
Elisabetta Manduchi, Joseph D. Romano, Jason H. Moore
Summary: Parametric statistical methods have traditionally been used in genetic analysis of complex traits, but machine learning methods are better suited for detecting and modeling patterns in complex genetic architectures. The goal of AutoML is to eliminate guesswork by automatically identifying the right algorithms and hyperparameters for optimization. This review discusses the promises and challenges of AutoML in genetic analysis and emphasizes the potential for developing novel AutoML methods and software in the field of genetics and genomics.
Article
Chemistry, Applied
Fuqiang Wan, Hang Ping, Wenxuan Wang, Zhaoyong Zou, Hao Xie, Bao-Lian Su, Dabiao Liu, Zhengyi Fu
Summary: Biological materials possess excellent mechanical properties due to their organized structures at different scales. This study introduces a stress-induced method to fabricate anisotropic alginate fibers by incorporating aligned hydroxyapatite nanowires. The detailed structural characterization reveals a bone-like structure of the reinforced alginate fibers, showing promising mechanical properties.
CARBOHYDRATE POLYMERS
(2021)
Article
Chemistry, Analytical
Luis Mejias, Jean-Philippe Diguet, Catherine Dezan, Duncan Campbell, Jonathan Kok, Gilles Coppin
Summary: This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS) and proposes a method for defining autonomy levels based on task combinations, emphasizing the importance of embedded systems in system design.
Article
Computer Science, Interdisciplinary Applications
Florian Kromp, Lukas Fischer, Eva Bozsaky, Inge M. Ambros, Wolfgang Doerr, Klaus Beiske, Peter F. Ambros, Allan Hanbury, Sabine Taschner-Mandl
Summary: The study evaluates multiple deep learning architectures and conventional algorithms for complex fluorescence nuclear image segmentation, and introduces a novel strategy for creating artificial images. It shows that instance-aware segmentation architectures and Cellpose outperform U-Net architectures and conventional methods in terms of F1 scores, while U-Net architectures achieve higher mean Dice scores overall. Training with artificially generated images improves recall and F1 scores for complex images.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Materials Science, Multidisciplinary
Jinran Yu, Yifei Wang, Shanshan Qin, Guoyun Gao, Chong Xu, Zhong Lin Wang, Qijun Sun
Summary: This review discusses the development and applications of bioinspired interactive neuromorphic devices. By integrating various sensors and synaptic devices, these devices can sense, store, and process information from the external environment and perform functions of perception, learning, memory, and computation. The article presents the basic model and performance metrics of these devices and discusses the recent progress in materials, device architectures, synaptic plasticity, and potential applications. It also proposes devices that can fuse multiple sensing signals to address more complex problems. Finally, the pros and cons of computing neurons and integrating sensory neurons are discussed, along with the perspectives on future development at the material, device, network, and system levels.
Article
Computer Science, Artificial Intelligence
Saverio Giallorenzo, Fabrizio Montesi, Larisa Safina, Stefano Pio Zingaro
Summary: This article presents Jolie/Tquery, the first query framework designed for ephemeral data handling in microservices. It combines the advantages of Jolie, a technology-agnostic, microservice-oriented programming language, and the MongoDB aggregation framework, one of the most widely-used query languages for semi-structured data in microservices. The article introduces the Tquery theory and explains how Jolie/Tquery was implemented following Tquery. It also showcases the application of Jolie/Tquery through a use case of a medical algorithm. Microbenchmarks are conducted to validate the performance advantage of Jolie/Tquery in the ephemeral case compared to using an external database like MongoDB.
PEERJ COMPUTER SCIENCE
(2022)
Article
Energy & Fuels
Akshay Ajagekar, Fengqi You
Summary: The proposed hybrid QC-based deep learning framework combines the feature extraction capabilities of conditional restricted Boltzmann machine with an efficient classification of deep networks, demonstrating high computational efficiency and superior fault diagnosis performance. The framework shows faster response time and better diagnostic performance compared to state-of-the-art pattern recognition methods based on artificial neural networks and decision trees.
Article
Computer Science, Hardware & Architecture
Emanuel Di Nardo, Vincenzo Santopietro, Alfredo Petrosino
Summary: Deep Learning is widely applied in various research fields, but requires significant resources and specialized hardware support. Optimization of code, algorithms, numeric accuracy, and hardware is essential to improve efficiency and usability, leading to accurate and fast learning models.
MICROPROCESSORS AND MICROSYSTEMS
(2021)
Article
Engineering, Petroleum
G. E. Castaneda, J. Ferreira
Summary: The oil and gas industry uses expensive electrical submersible pumps (ESPs) to lift oil to the surface. However, the presence of gas inside the pump causes instabilities that can decrease the performance and even lead to production stoppage. To address this issue, a proposed adaptive controller based on monitoring the biphasic condition of the flow effectively keeps the ESP operating in stable conditions.
Letter
Computer Science, Interdisciplinary Applications
Hang Song, Kristen V. Matsuno, Jacob R. West, Akshay Subramaniam, Aditya S. Ghate, Sanjiva K. Lele
Summary: In this paper, a scalable algorithm for solving compact banded linear systems on distributed memory architectures is proposed. The algorithm reduces the communication footprint and can be applied to various types of compact banded systems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Abhinav Pandey, Vidit Gaur
Summary: ALGINEER is a novel algorithmic design framework that overcomes the limitations of traditional human-centered design methods. It utilizes genetic algorithms and machine learning to explore solution spaces and achieve trade-offs among multiple design objectives, demonstrating design behavior and learning akin to engineers.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Meng Ma, Weilan Lin, Disheng Pan, Ping Wang
Summary: This article discusses the challenges and implications of diagnosing root causes of anomalies in large-scale microservice architecture in the cloud. It proposes a novel framework called ServiceRank, which detects anomalies and identifies their root causes in a fast and accurate manner. ServiceRank includes an anomaly detector, a root cause analysis module, and various mechanisms to eliminate the impacts of cloud design patterns on anomaly diagnosis.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Javier Torregrosa, Sergio D'Antonio-Maceiras, Guillermo Villar-Rodriguez, Amir Hussain, Erik Cambria, David Camacho
Summary: Political tensions have increased in Europe since the beginning of the new century, leading to social movements and political changes in various countries. This study examines the political discourse and underlying tensions during Madrid's elections in May 2021, using a mixed methodology approach. The findings suggest that the electoral campaign is not as negative as perceived by the citizens, and that ideologically extreme parties tend to use more aggressive language.
COGNITIVE COMPUTATION
(2023)
Article
Automation & Control Systems
Francesco Piccialli, Fabio Giampaolo, David Camacho, Gang Mei
Summary: Deep learning technology is driving the in-depth development of industrial automation. Wang et al. interpret the decision process of convolutional neural networks (CNNs) using a percolation model from a statistical physics perspective. They introduce the concept of differentiation degree and present an empirical formula for quantifying it.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Review
Computer Science, Information Systems
Benoit Bossavit, Antonio J. Fernandez-Leiva
Summary: Motion-based technology (MBT) has been widely used in various applications with great success, especially in the health field. Recently, MBT has become more specialized according to the specific needs of different user groups, including children, teenagers, adults, and the elderly. This paper provides a comprehensive review of the application of MBT in different user groups and proposes a taxonomy for categorizing MBT applications. The focus of the review is on MBT applications for improving the health of elderly individuals. The findings of this paper contribute to a better understanding of MBT, especially when considering the elderly as end users.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jorge Duenas-Lerin, Raul Lara-Cabrera, Fernando Ortega, Jesus Bobadilla
Summary: This paper proposes an innovative strategy of aggregating user information in a multi-hot vector for group recommendations. Experiments show significant accuracy improvements compared to the state of the art.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Shrikant Upadhyay, Mohit Kumar, Aditi Upadhyay, Sahil Verma, Kavita, Maninder Kaur, Ruba A. Abu Khurma, Pedro A. Castillo
Summary: The use of IoT technology in healthcare and smart healthcare systems is growing rapidly, with applications in fitness programs, monitoring, data analysis, etc. Various studies have been conducted to improve the precision and efficiency of monitoring, and this article discusses the integration of IoT with cloud systems for better healthcare performance. The article also analyzes the use of IoT in healthcare, particularly in elderly care, and examines the limitations and challenges in terms of resources, power absorption, and security. Furthermore, the article explores the high-intensity applications of NB-IoT, such as blood pressure and heartbeat monitoring in pregnant women, and evaluates the performance of narrowband IoT in terms of delay and throughput.
Article
Chemistry, Analytical
Manuel Trinidad-Fernandez, Benoit Bossavit, Javier Salgado-Fernandez, Susana Abbate-Chica, Antonio J. Fernandez-Leiva, Antonio I. Cuesta-Vargas
Summary: This study validates the Meta Quest 2 HMD system as an alternative for screening neck movement in healthy people. The results demonstrate that the angles provided by the HMD system are valid to calculate the rotational angles of the neck in each of the three axes. This is significant for screening neck disorders in healthy individuals.
Article
Multidisciplinary Sciences
Mario Garcia-Valdez, Alejandra Mancilla, Oscar Castillo, Juan Julian Merelo-Guervos
Summary: In this work, a distributed and asynchronous bio-inspired algorithm is proposed to speed up the design process of a controller by executing simulations in parallel. The algorithm uses a multi-population multi-algorithmic approach with isolated populations interacting asynchronously using a distributed message queue. The results demonstrate the speedup benefit of the proposed algorithm and the advantages of mixing populations with distinct metaheuristics.
Article
Education & Educational Research
Juan J. Merelo, Pedro A. Castillo, Antonio M. Mora, Francisco Barranco, Noorhan Abbas, Alberto Guillen, Olia Tsivitanidou
Summary: This article examines the application of messaging platforms in higher education and the experiences and perceptions of teachers. A survey was conducted to gather teachers' preferences and opinions on messaging platforms and chatbots, as well as their views on how these tools can enhance student learning. The survey provides insights into teachers' needs and the various educational use cases where these tools could be valuable. The analysis also explores how teachers' opinions on tool usage vary based on gender, experience, and specialization. The key findings emphasize the factors that contribute to the adoption of messaging platforms and chatbots in higher education institutions to achieve desired learning outcomes.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Computer Science, Artificial Intelligence
Alvaro Huertas-Garcia, Alejandro Marin, Javier Huertas-Tato, David Camacho
Summary: Content moderation is crucial in stopping unacceptable behaviors in online platforms. This article presents an innovative approach involving the simulation and detection of content evasion techniques using a multilingual transformer model. The developed multilingual tool, pyleetspeak, allows for the generation and simulation of content evasion through word camouflage, while a multilingual NER model is designed for the detection of such evasion techniques.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Javier Huertas-Tato, Alejandro Martin, David Camacho
Summary: The emergence of complex attention-based language models like BERT, RoBERTa or GPT-3 has enabled the tackling of highly complex tasks in various scenarios. However, these models face significant difficulties when applied to specific domains, such as social networks like Twitter. In order to address the challenges of natural language processing in this domain, we present BERTuit, the largest transformer proposed for the Spanish language, pre-trained on a massive dataset of Spanish tweets. Our motivation is to provide a powerful resource for better understanding Spanish Twitter and combating the spread of misinformation. BERTuit is evaluated and compared against competitive multilingual transformers, showing its utility through applications like visualizing groups of hoaxes and profiling authors spreading disinformation.
Article
Computer Science, Artificial Intelligence
Victor Rodriguez-Fernandez, David Montalvo-Garcia, Francesco Piccialli, Grzegorz J. Nalepa, David Camacho
Summary: Deep Visual Analytics (DVA) is a field that aims to develop Visual Interactive Systems supported by deep learning for large-scale data processing and implementation across different data and domains. This paper presents DeepVATS, an open-source tool for time series data that uses a self-supervised masked time series autoencoder to discover patterns and anomalies.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Daniel Lopez-Fernandez, Aldo Gordillo, Raul Lara-Cabrera, Javier Alegre
Summary: This article fills the research gap by analyzing and comparing the instructional and motivational effectiveness of two different genres of educational video games: third-person shooter and endless runner. The results show that both games are highly effective in terms of knowledge acquisition and motivation.
ENTERTAINMENT COMPUTING
(2023)
Article
Education, Scientific Disciplines
Raul Lara-Cabrera, Fernando Ortega, Edgar Talavera, Daniel Lopez-Fernandez
Summary: Students' perception of excessive difficulty in STEM degrees lowers their motivation and, therefore, affects their performance. The use of badges, both physical and virtual, improves student performance and reduces dropout rates according to a case study involving 99 students enrolled in a Databases course of computer engineering degrees.
IEEE TRANSACTIONS ON EDUCATION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Eva Garcia-Soto, Alejandro Martin, Javier Huertas-Tato, David Camacho
Summary: This study utilizes CodeT5 pre-trained language model to generate context and semantic aware embeddings for a better representation of the behavior of Android applications. It shows how these embeddings can be used to train a recurrent neural network for malware detection tasks, and presents promising results.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022)
(2023)
Article
Computer Science, Artificial Intelligence
Helena Liz-Lopez, Mamadou Keita, Abdelmalik Taleb-Ahmed, Abdenour Hadid, Javier Huertas-Tato, David Camacho
Summary: Generative deep learning techniques have been widely discussed in the public, but the slow progress in applying these techniques to counter disinformation is concerning. With the ease and credibility of manipulating multimedia content, developing effective forensic techniques becomes invaluable. This survey comprehensively describes modern manipulation and forensic techniques, focusing on their applications in video, audio, and multimodal fusion. The classification of manipulation techniques and the generation of datasets using generative techniques are provided for forensic purposes. The review and comparative analysis of forensic techniques from 2018 to 2023, as well as the comparison of end-to-end forensic tools for end-users, are presented. Clear trends and challenges, such as multilinguality, multimodality, and improving data quality, are identified for future research in an ever-changing adversarial environment.
INFORMATION FUSION
(2024)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
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
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
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
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)