Article
Computer Science, Artificial Intelligence
Vlad Pandelea, Edoardo Ragusa, Tom Young, Paolo Gastaldo, Erik Cambria
Summary: The use of transformer-based models has become increasingly popular in recent years, but their application to embedded devices, especially in retrieval-based dialogue systems, poses challenges. To reduce storage capacity and computational power requirements, a new framework based on the Dual-Encoder architecture for hardware-aware retrieval-based dialogue systems has been proposed.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Sung-Nien Yu, I-Mei Lin, San-Yu Wang, Yi-Cheng Hou, Sheng-Po Yao, Chun-Ying Lee, Chai-Jan Chang, Chih-Sheng Chu, Tsung-Hsien Lin
Summary: This study successfully distinguished emotional states in patients with hypertension using photoplethysmography waveform indices and affective computing, demonstrating high accuracy in categorizing PPG records into distinct emotional states.
Article
Computer Science, Artificial Intelligence
Geneva M. Smith, Jacques Carette
Summary: This study aims to assist in the design of computational models that can generate emotions in computer agents and interfaces. It provides an overview of 67 CMEs from various domains, analyzes the emotion theories used by these models, and summarizes how they generally utilize each theory.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Engineering, Civil
Taiyuan Gong, Li Zhu, F. Richard Yu, Tao Tang
Summary: Edge intelligence (EI) is a research hotspot that empowers intelligent transportation systems (ITS). By pushing AI to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network, and better use of edge big data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Fei Yan, Abdullah M. Iliyasu, Kaoru Hirota
Summary: This study aims to interpret and manipulate robots' emotions within the framework of quantum mechanics, encoding emotion information as superposition states and using unitary operators to manipulate emotion transitions. Fusion of multi-robots' emotions through quantum entanglement reduces the qubit requirements and quantum gate usage, demonstrating the feasibility and effectiveness of the proposed framework in transitioning emotional intelligence formulations to the quantum era.
Article
Computer Science, Artificial Intelligence
Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman
Summary: Affective Computing is a fast-growing field that proposes a probabilistic programming approach to translate psychological theories of emotion into computational models. Probabilistic programming languages offer flexibility, modularity, integration with deep learning libraries, and ease of adoption, providing a standardized platform for theory-building and experimentation.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Krzysztof Wolk, Agnieszka Wolk, Dominika Wnuk, Tomasz Grzes, Ida Skubis
Summary: This article conducts an empirical study on the state-of-the-art dialogue systems within Slavic languages, reviews the existing models, and identifies the current main challenges and potential research directions for practical and intelligent systems within low-resourced languages.
Article
Chemistry, Multidisciplinary
Chung-Hong Lee, Hsin-Chang Yang, Xuan-Qi Su, Yao-Xiang Tang
Summary: Successful investments not only require financial expertise and market information, but also individual personality traits. A multimodal personality-recognition system was developed to analyze investors' traits, showing more accuracy than unimodal models and a correlation between personality traits and risk tolerance. Experimental results demonstrated high performance of the system in predicting investors' personalities.
APPLIED SCIENCES-BASEL
(2022)
Review
Neurosciences
Jiajuan Shi, Zhongqiang Wang, Ye Tao, Haiyang Xu, Xiaoning Zhao, Ya Lin, Yichun Liu
Summary: A neuromorphic computing chip can significantly improve computer architecture, with memristive devices considered among the best hardware units for building neuromorphic intelligence systems. The emerging self-powered memristive system shows promise in solving high power consumption and complex circuit structures.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Computer Science, Information Systems
Hongwen Hui, Fuhong Lin, Lei Yang, Chao Gong, Haitao Xu, Zhu Han, Peng Shi
Summary: The combination of Internet of Things (IoT) and artificial intelligence (AI) technology is important in psychology and medical treatment. In this study, an affective robotics based on IoT and AI technology is developed to serve humans emotionally. The research introduces a human-robot interaction architecture that includes emotion recognition, affective computing, and emotion control. A mathematical formulation method is also provided to quantify emotional states.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Guillermo Alvarez-Pardo, Ernesto Fabregas
Summary: Most research in affective computing focuses on recognizing and classifying emotions, but little attention has been paid to aspects of human behavior and interaction like disputes and resolutions in the design of social and affective robots. This article introduces a non-intrusive, low-cost system that allows robots to recognize and simulate affections, personality, and relationships through human-robot interactions.
Article
Computer Science, Information Systems
Andrea Borghesi, Alessio Burrello, Andrea Bartolini
Summary: In recent years, the Industrial Internet of Things (IIoT) has made significant progress in various industries by utilizing technologies such as Big Data processing and artificial intelligence (AI). Large-scale data centers can benefit from adopting Big Data analytics and AI-driven approaches for effective predictive maintenance. However, existing off-the-shelf solutions are not well-suited for high-performance computing (HPC) environments, as they do not sufficiently address the heterogeneous data sources and privacy concerns, or fully utilize the computing capabilities in supercomputing facilities. This article proposes an IIoT holistic and vertical framework for predictive maintenance in supercomputers, which includes a lightweight data monitoring infrastructure, specialized databases for heterogeneous data, and high-level AI functionalities tailored to the specific needs of HPC actors. The deployment and usage of this framework in several in-production HPC systems are presented and evaluated.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Qian Zhang, Jie Lu, Yaochu Jin
Summary: Recommender systems use artificial intelligence to provide personalized services, improve prediction accuracy, and solve data sparsity and cold start issues. This paper discusses how AI can effectively enhance technological development in recommender systems and reviews current research problems and new directions in this field. It also examines the use of various AI techniques, such as fuzzy techniques, transfer learning, genetic algorithms, neural networks, and deep learning, in improving recommender systems.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Maha M. Althobaiti, K. Pradeep Mohan Kumar, Deepak Gupta, Sachin Kumar, Romany F. Mansour
Summary: Advanced developments in Industrial Cyber-Physical Systems (CPSs), including Internet of Things (IoT), provide practical use in various application areas but also pose threats to user security. Recently, cognitive computing and artificial intelligence techniques have opened new opportunities for the revolution of industrial CPSs. AI based intrusion detection systems are crucial for achieving security in industrial CPS environments.
Article
Computer Science, Theory & Methods
Haochen Hua, Yutong Li, Tonghe Wang, Nanqing Dong, Wei Li, Junwei Cao
Summary: In recent years, the widespread popularity of the Internet of Things (IoT) has greatly promoted the development of Artificial Intelligence (AI). However, the traditional cloud computing model may face difficulties in independently handling the massive data generated by IoT. In response, the new computing model of Edge Computing (EC) has gained extensive attention. Scholars have found that traditional methods have limitations in enhancing the performance of EC, leading to the exploration of AI as a solution. This article serves as a guide to explore new research ideas in optimizing EC using AI and applying AI to other fields under the EC architecture.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Tian-Hui You, Ling-Ling Tao, Erik Cambria
Summary: This study proposes a hotel ranking model based on online textual reviews, considering the differences in the number of reviews on different aspects. The model utilizes sentiment analysis to assist tourists in making desirable decisions on hotel selection.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
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
Computer Science, Artificial Intelligence
Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Summary: Human coders assign standardized medical codes to clinical documents, but it is prone to errors and requires significant effort. Automated medical coding methods using machine learning, such as deep neural networks, have been developed. However, challenges still exist due to code association complexity, noise in lengthy documents, and imbalanced class problem. In this study, we propose a novel neural network model called the Multitask Balanced and Recalibrated Neural Network to address these issues. Experiments on a real-world clinical dataset called MIMIC-III demonstrate that our model outperforms competitive baselines.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Xulang Zhang, Rui Mao, Erik Cambria
Summary: Computational syntactic processing is a fundamental technique in natural language processing that transforms natural language into structured texts with syntactic features. This work surveys low-level syntactic processing techniques such as normalization, sentence boundary disambiguation, part-of-speech tagging, text chunking, and lemmatization, categorizes widely used methods, investigates challenges, and proposes future research directions.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria
Summary: Sentiment analysis, a research hotspot in natural language processing, has attracted significant attention and resulted in a growing number of research papers. Despite numerous literature reviews on sentiment analysis, there has been no dedicated survey examining the evolution of research methods and topics. This study fills this gap by conducting a comprehensive survey that combines keyword co-occurrence analysis and community detection algorithm. The survey compares and analyzes the connections between research methods and topics over the past two decades and uncovers hotspots and trends over time, providing valuable guidance for researchers. Furthermore, the paper offers practical insights, technical directions, limitations, and future research prospects in sentiment analysis.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Qika Lin, Rui Mao, Jun Liu, Fangzhi Xu, Erik Cambria
Summary: Knowledge graph completion (KGC) is crucial for many downstream applications. Existing language model-based methods for KGC often overlook the importance of modeling the deeper semantic information, such as topology contexts and logical rules. In this paper, we propose a unified framework FTL-LM that effectively incorporates topology contexts and logical rules in language models, and experimental results demonstrate its superiority over the state-of-the-art methods.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani
Summary: The field of explainable artificial intelligence (XAI) has gained increasing importance in recent years. However, existing research often overlooks the role of natural language in generating explanations. This survey reviews 70 XAI papers published between 2006 and 2021 and evaluates their readiness in terms of natural language explanations. The results show that only a few recent studies have considered using natural language for communication with end users or implemented methods for generating natural language explanations.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Kelvin Du, Frank Xing, Erik Cambria
Summary: Combining symbolic and subsymbolic methods has emerged as a promising strategy in tackling increasingly complex AI research tasks. This study presents a targeted aspect-based financial sentiment analysis hybrid model that incorporates multiple lexical knowledge sources into the fine-tuning process of pre-trained transformer models. Experimental results demonstrate that knowledge-enabled models systematically improve aspect sentiment analysis performance and even outperform state-of-the-art results.
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Luna Ansari, Shaoxiong Ji, Qian Chen, Erik Cambria
Summary: Changes in human lifestyle have led to an increase in depression cases. Automated detection methods are effective in identifying depressed individuals. Ensemble models outperform hybrid models for depression detection.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ruicheng Liu, Rui Mao, Anh Tuan Luu, Erik Cambria
Summary: The task of resolving repeated objects in natural languages, known as coreference resolution, is an important part of modern natural language processing. It is classified into entity coreference resolution and event coreference resolution based on the resolved objects. Predicting coreference connections and identifying mentions/triggers are the major challenges in coreference resolution due to the difficulty of implicit relationships in natural language understanding. In this survey, we review the current employed evaluation metrics, datasets, and methods, investigating 10 widely used metrics, 18 datasets, and 4 main technical trends. We believe that this work provides a comprehensive roadmap for understanding the past and the future of coreference resolution.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan
Summary: This paper proposes a new explainable fine-grained multi-class sentiment analysis method called MiMuSA, which mimics human language understanding processes. It builds multiple knowledge bases to support sentiment understanding and can identify fine-grained multi-class sentiments. Experimental results show that MiMuSA outperforms other existing multi-class sentiment analysis methods in terms of accuracy and F1-Score.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Summary: This research proposes a 3-phase hybrid model that utilizes both technical indicators and social media text sentiments as influence factors for stock trending prediction. The result shows that the proposed method has an accuracy of 73.41% and F1-score of 84.19%. The research not only demonstrates the merits of the proposed method, but also indicates that integrating social opinions with technical indicators is a right direction for enhancing the performance of learning-based stock market trending analysis methods.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Mostafa M. Amin, Erik Cambria, Bjoern W. Schuller
Summary: The employment of foundation models is expanding and ChatGPT has the potential to enhance existing NLP techniques with its novel knowledge.
IEEE INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Li, Yang Li, Vlad Pandelea, Mengshi Ge, Luyao Zhu, Erik Cambria
Summary: The paper introduces a new task called emotion-cause pair extraction in conversations (ECPEC), which aims to extract pairs of emotional utterances and corresponding cause utterances in conversations. The utterance-level ECPEC task is more challenging as the distance between emotion and cause utterances is greater. The experimental results on the proposed ConvECPE dataset demonstrate the feasibility of the ECPEC task and the effectiveness of the framework.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
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
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
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, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
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
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
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
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
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(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
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)
