Article
Computer Science, Hardware & Architecture
Mayur Wankhade, Chandra Sekhara Rao Annavarapu, Ajith Abraham
Summary: This study proposes a framework called MAPA BiLSTM-BERT model to address unresolved issues in fine-grained sentiment categorization. By introducing explicit multiple aspect position-aware attention and BERT aspect-specific attention, the model achieves significant performance improvement in the evaluation and attention of different aspects.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Bo Huang, Ruyan Guo, Yimin Zhu, Zhijun Fang, Guohui Zeng, Jin Liu, Yini Wang, Hamido Fujita, Zhicai Shi
Summary: In recent years, researchers have paid more attention to aspect-level sentiment analysis in natural language processing. A fine-grained sentiment analysis distinguishes each aspect of the text and makes separate judgments on the sentiment polarity. This paper proposes an aspect-level sentiment analysis model with aspect-specific contextual location information, adjusting the weight of contextual words and extracting the influence of contextual association on individual sentence sentiment polarity.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Psychology, Multidisciplinary
Xianyong Li, Li Ding, Yajun Du, Yongquan Fan, Fashan Shen
Summary: This article introduces a PMHSAT-BiGRU model that utilizes position-enhanced multi-head self-attention network to enhance aspect-level sentiment classification task. Experimental results demonstrate significant performance improvement with increased accuracy values on multiple datasets.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
MeiZhen Liu, FengYu Zhou, Ke Chen, Yang Zhao
Summary: This study introduces a co-attention mechanism to comprehensively analyze semantic correlations and improve the performance of aspect-level sentiment analysis. Experimental results show that the proposed method achieves state-of-the-art performance on the Restaurant and Twitter datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yanxia Lv, Fangna Wei, Lihong Cao, Sancheng Peng, Jianwei Niu, Shui Yu, Cuirong Wang
Summary: Sentiment analysis has become a hot topic in NLP, especially aspect-level sentiment analysis. The CAMN method proposed in this paper combines deep memory network and multi-attention mechanism, achieving better performance than baseline models.
Article
Computer Science, Artificial Intelligence
Dong Tian, Jia Shi, Jianying Feng
Summary: This paper introduces an attention-based multi-level feature aggregation (AMFA) network for aspect category-based sentiment analysis. The proposed approach demonstrates efficiency in extracting precise semantics and sentiments related to specific feature categories. Experimental results on multiple datasets validate the effectiveness and practicality of the model.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Guixian Xu, Zixin Zhang, Ting Zhang, Shaona Yu, Yueting Meng, Sijin Chen
Summary: In this paper, a aspect-level sentiment classification model based on Attention-BiLSTM model and transfer learning is proposed. The methods of pre-training and multi-task learning are used to improve the training of neural network models on small datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lei Jiang, Yuan Li, Jing Liao, Ziwei Zou, Caoqing Jiang
Summary: As a popular research field of sentiment analysis, aspect-based sentiment analysis aims to analyze emotional expressions in different aspects. However, current research lacks precision in dividing aspects, leading to semantic overlap issues. To address these problems, we propose the concept of non-dependent aspects and a method for their division based on analyzing aspect dependencies. Theoretical analysis and real-world experiments demonstrate that sentiment analysis based on non-dependent aspects provides more accurate results compared to traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaodi Wang, Mingwei Tang, Tian Yang, Zhen Wang
Summary: This paper proposes a novel network with multiple attention mechanisms for aspect-level sentiment analysis, utilizing multi-head self-attention and global attention mechanisms to improve performance compared to baseline models.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics
Zhouxin Lan, Qing He, Liu Yang
Summary: This paper proposes a dual-channel interactive graph convolutional network (DC-GCN) model for aspect-level sentiment analysis, which improves the model performance by considering both syntactic structure information and multi-aspect sentiment dependencies.
Article
Computer Science, Artificial Intelligence
Kai Zhang, Qi Liu, Hao Qian, Biao Xiang, Qing Cui, Jun Zhou, Enhong Chen
Summary: This paper proposes a novel model called EATN for accurately classifying sentiment polarities towards aspects in multiple domains in sentiment analysis tasks. The model incorporates a Domain Adaptation Module (DAM) to learn common features and uses multiple-kernel selection method to reduce feature discrepancy among domains. Additionally, EATN includes an aspect-oriented multi-head attention mechanism to capture the direct associations between aspects and contextual sentiment words. Extensive experiments on six public datasets demonstrate the effectiveness and universality of the proposed method compared to current state-of-the-art methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Weizhi Liao, Jiarui Zhou, Yu Wang, Yanchao Yin, Xiaobing Zhang
Summary: This study aims to address the performance deterioration issue in aspect-level sentiment classification by introducing a phrase-aware neural network based on fine-grained attention (FAPN) to improve accuracy. FAPN utilizes a convolutional neural network to extract phrase representations and designs a fine-grained attention module to capture word-level interactions between the aspect and the sentence, achieving aspect-specific representations.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Huyen Trang Phan, Ngoc Thanh Nguyen, Dosam Hwang
Summary: Aspect-level sentiment analysis (ALSA) is crucial in social networks, especially in e-commerce. This study provides a comprehensive survey on GCN-based ALSA methods, proposing a novel taxonomy and discussing benchmark datasets and text representations commonly used. The study also highlights future research directions and challenges in this field.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yan, Benshun Yi, Huixin Li, Danqing Wu
Summary: In this paper, a sentiment knowledge-based bidirectional encoder representation from transformers (SK-BERT) is proposed to overcome the limitations of existing models in sentiment analysis. Experimental results show that the proposed SK-BERT model outperforms other state-of-the-art models in accuracy.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chao Wu, Qingyu Xiong, Zhengyi Yang, Min Gao, Qiude Li, Yang Yu, Kaige Wang, Qiwu Zhu
Summary: Aspect-based sentiment analysis (ABSA) is a task focused on predicting sentiment polarity of each aspect term in text, with recent research utilizing neural networks and attention mechanisms. Proposed models RA-CNN and RAO-CNN aim to improve feature representation by incorporating specially designed residual attention mechanism and handling interference from sentiment information of other aspect terms in multi-aspect text. Experimental results demonstrate the effectiveness of the models in achieving state-of-the-art results in ABSA tasks.
Article
Computer Science, Theory & Methods
Lidia Fotia, Flavia Delicato, Giancarlo Fortino
Summary: The Internet of Things (IoT) enables smart objects to provide smart services inserted into information networks for human beings. The introduction of edge computing in IoT reduces decision-making latency, saves bandwidth resources, and expands cloud services at the network's edge. However, decentralized trust management poses challenges for edge-based IoT systems. Trust management is crucial for reliable mining and data fusion, improved user privacy and data security, and context-aware service provisioning.
ACM COMPUTING SURVEYS
(2023)
Article
Engineering, Electrical & Electronic
Alessandro Sabato, Shweta Dabetwar, Nitin Nagesh Kulkarni, Giancarlo Fortino
Summary: Engineering structures and infrastructure are still being used beyond their design lifetime. Noncontact methods, such as photogrammetry and infrared thermography, provide accurate and continuous spatial information to assess the condition of these structures. The incorporation of artificial intelligence algorithms expedites and improves the assessment process. This article summarizes the recent efforts in utilizing AI-aided noncontact sensing techniques, particularly image-based methods, for structural health monitoring (SHM) and discusses future directions to advance AI-aided image-based SHM techniques for engineering structures.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Ke Wang, Zicong Chen, Mingjia Zhu, Siu-Ming Yiu, Chien-Ming Chen, Mohammad Mehedi Hassan, Stefano Izzo, Giancario Fortino
Summary: Artificial intelligence-driven automation is becoming the technical trend in the new automation era. Convolutional neural network (CNN) technology has been widely used in industrial automation for defect detection and machine vision-driven automation for robot-human tracking. However, the high dependence on neural networks can lead to potential failures in defect detection system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Peng Xu, Ke Wang, Mohammad Mehedi Hassan, Chien-Ming Chen, Weiguo Lin, Md Rafiul Hassan, Giancarlo Fortino
Summary: This paper employs a One-Shot Neural Architecture Search (NAS) to generate derivative models with different scales and studies the relationship between network sizes and model robustness. The experimental results show that reducing model parameters can increase model robustness under maximum adversarial attacks, while increasing model parameters can enhance model robustness under minimum adversarial attacks. This analysis helps to understand the adversarial robustness of models with different scales for edge AI transportation systems.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Kai Lin, Jian Gao, Yihui Li, Claudio Savaglio, Giancarlo Fortino
Summary: This paper investigates the quality and real-time assurance problem of collaborative decision-making in large-scale intelligent transportation systems during multi-task parallel execution. It develops a collaborative decision architecture with cognitive networking and proposes an AI-driven cognitive networking collaborative decision-making algorithm.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Analytical
Diego Avellaneda, Diego Mendez, Giancarlo Fortino
Summary: Positioning systems are important in many different sectors, but traditional systems like GPS are not accurate or scalable for indoor positioning. Fingerprinting is an alternative solution that uses RF signals to recognize location characteristics. This project uses a machine learning approach to classify RSSI information from scanning stations. The implementation uses TinyML, a growing technological paradigm for ML on resource-constrained embedded devices. The deployed system achieves a classification accuracy of 88%, which can be increased to 94% with post-processing.
Review
Chemistry, Analytical
Roohallah Alizadehsani, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H. M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny, Abbas Khosravi, U. Rajendra Acharya, Saeid Nahavandi, Giancarlo Fortino
Summary: Continuous advancements in technologies like the internet of things and big data analysis have enabled information sharing and smart decision-making using everyday devices. Swarm intelligence algorithms facilitate constructive interaction among individuals regardless of their intelligence level to address complex nonlinear problems. This paper examines the application of swarm intelligence algorithms in the internet of medical things, with a focus on wearable devices in healthcare. It reviews existing works on utilizing swarm intelligence in tackling IoMT problems such as disease prediction, data encryption, and resource allocation. The paper concludes with research perspectives and future trends.
Article
Chemistry, Analytical
Alaa Menshawi, Mohammad Mehedi Hassan, Nasser Allheeib, Giancarlo Fortino
Summary: A generic framework has been developed for heart problem diagnosis using a hybrid of machine learning and deep learning techniques. The framework utilizes a novel voting technique based on the prediction probabilities of multiple models to eliminate bias. Experimental results show that the framework outperforms single machine learning models, classical stacking techniques, and traditional voting techniques, achieving an accuracy of 95.6%.
Review
Chemistry, Analytical
Amira Bourechak, Ouarda Zedadra, Mohamed Nadjib Kouahla, Antonio Guerrieri, Hamid Seridi, Giancarlo Fortino
Summary: Given its advantages, edge computing has emerged as key support for intelligent applications and 5G/6G IoT networks. However, there are concerns about its capabilities to handle the computational complexity of machine learning techniques for big IoT data analytics. This paper aims to explore the confluence of AI and edge computing in various application domains to leverage existing research and identify new perspectives.
Editorial Material
Computer Science, Artificial Intelligence
David B. Kaber, Andreas Nuernberger, Giancarlo Fortino, David Mendonca
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2023)
Article
Computer Science, Information Systems
Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
Summary: This study proposes a novel approach for human activity monitoring and recognition that combines multihead convolutional neural networks and long short-term memory techniques, and enhances activity detection accuracy and feature extraction through attention mechanism. The results show that the proposed method performs well in real-time human activity recognition.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Aitizaz Ali, Muhammad Fermi Pasha, Antonio Guerrieri, Antonella Guzzo, Xiaobing Sun, Aamir Saeed, Amir Hussain, Giancarlo Fortino
Summary: This paper proposes a hybrid deep learning model for Industrial Internet of Medical Things (IIoMT) that addresses security challenges using homomorphic encryption (HE) and blockchain technology, providing higher privacy and security. By deploying a pre-trained model on edge devices and utilizing a consortium blockchain for data sharing and updating, the model can effectively classify and train local models while delivering higher efficiency and low latency.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Zhihan Lv, Chen Cheng, Antonio Guerrieri, Giancarlo Fortino
Summary: More data are generated through mobile network technology, giving birth to the cyber-physical social intelligent ecosystem (C & P-SIE). This survey studies the development of physical social intelligence, discussing its applications in various domains such as intelligent transportation, healthcare, public service, economy, and social networking. It also explores the future prospects of behavior modeling in C & P-SIE under information security, data-driven techniques, and cooperative artificial intelligence technologies. This research provides a theoretical foundation and new opportunities for the digital and intelligent development of smart cities and social systems.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Giancarlo Fortino, Lidia Fotia, Fabrizio Messina, Domenico Rosaci, Giuseppe M. L. Sarne
Summary: This article introduces a multi-agent SIoT architecture that incorporates a reputation system based on clustering of smart objects, providing reliability for transactions in SIoT scenarios. By enabling feedback between smart objects, and communication between edge servers and the cloud, reputation values are updated, enhancing the trustworthiness of objects.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Syed Tauhidun Nabi, Md. Rashidul Islam, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Salman A. AlQahtani, Gianluca Aloi, Giancarlo Fortino
Summary: This research utilizes 6.2 million real network time series LTE data traffic and other associated parameters to build a traffic forecasting model using multivariate feature inputs and deep learning algorithms, which can forecast traffic at a granular eNodeB-level and provide eNodeB-wise forecasted PRB utilization.
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)