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IEEE Computer Society D&I Fund: Drive Diversity & Inclusion in Computing IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-28
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Embracing LLMs for Point-of-Interest Recommendations IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-28 Tianxing Wang, Can Wang
A point-of-interest (POI) recommendation becomes the core function of location-based services. Unlike a traditional item recommendation, a POI recommendation has distinct features, such as geographical influences, complex mobility patterns, and a balance between local and global user preferences. Past POI recommendation system research has focused mainly on integrating deep learning models like convolutional
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The Rise and Design of Enterprise Large Language Models IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-28 Daniel E. O’Leary
This article investigates a new phenomenon of enterprise large language models (ELLMs) focusing on what they are, why they are being developed, and what are some key capabilities. In addition, the article drills down on issues associated with integrating retrieval augmented generation approaches into ELLMs, including emerging research issues.
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Integrating Graphs With Large Language Models: Methods and Prospects IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-28 Shirui Pan, Yizhen Zheng, Yixin Liu
Large language models (LLMs) such as Generative Pre-trained Transformer 4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications including answering queries, code generation, and more. Parallelly, graph-structured data, intrinsic data types, are pervasive in real-world scenarios. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest
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Responsible AI: An Urgent Mandate IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-28 Ricardo Baeza-Yates, Usama M. Fayyad
AI is rapidly becoming essential in various industries, raising societal expectations. AI’s societal consequences include impacts on mental health; misinformation; workforce displacement; and economic, regulatory, and law enforcement challenges. Indeed, the regulation of AI usage is on the horizon, with the European Union and China already taking big steps, while the United States drafted its first
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Neurosymbolic Value-Inspired Artificial Intelligence (Why, What, and How) IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-28 Amit Sheth, Kaushik Roy
The rapid progression of artificial intelligence (AI) systems, facilitated by the advent of large language models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as a part of human society. Toward this end
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EEG Emotion Recognition Based on Manifold Geomorphological Features in Riemannian Space IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-02-09 Yanbing Wang, Hong He
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From EEG Data to Brain Networks: Graph Learning Based Brain Disease Diagnosis IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-01-11 Ke Sun, Ciyuan Peng, Shuo Yu, Zhuoyang Han, Feng Xia
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Reflecting on Algorithmic Bias with Design Fiction: the MiniCoDe Workshops IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-01-11 T. Turchi, A. Malizia, S. Borsci
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Multi-agent Cooperative Search Learning with Intermittent Communication IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-01-08 Ruixue Zhang, Jiao Wang, Jun Ge, Qiyuan Huang
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Overcoming the Challenges of Long-tail Distribution in Night-time Vehicle Detection IEEE Intell. Syst. (IF 6.4) Pub Date : 2024-01-08 Houwang Zhang, Leanne Lai Hang Chan
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Seven Pillars for the Future of Artificial Intelligence IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-12-08 Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho
In recent years, artificial intelligence (AI) research has showcased tremendous potential to positively impact humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision making, sense disambiguation, sarcasm detection, and narrative understanding
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Smart Decentralized Autonomous Organizations and Operations for Smart Societies: Human–Autonomous Organizations for Industry 5.0 and Society 5.0 IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-12-08 Xiao Wang, Yutong Wang, Mariana Netto, Larry Stapleton, Zhe Wan, Fei-Yue Wang
This article explores the concept of human–autonomous organizations (HAOs) based on decentralized autonomous organizations (DAOs) and operations as well as human, artificial, natural, and organizational intelligence and their roles in shaping smart societies in the context of Industry 5.0 and Society 5.0. It discusses the potential of AI-generated content and prompt engineering in specific goal-guided
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Parallel Intelligence in CPSSs: Being, Becoming, and Believing IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-12-08 Jing Yang, Xiao Wang, Yonglin Tian, Xiao Wang, Fei-Yue Wang
The recent debut and success of ChatGPT have brought up renewed debates and desires for artificial general intelligence (AGI) amid fears and anxieties of potential disruptions to our humanity and social values, as witnessed by the call from tech celebrities for a pause in the development of ChatGPT-style AGI tools. At the IEEE IS’ AI and CPSS Department, we would like to initiate cautious, balanced
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Artificial Intelligence Ethics and Trust: From Principles to Practice IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-12-08 Fang Chen, Jianlong Zhou, Andreas Holzinger, Kenneth R. Fleischmann, Simone Stumpf
Despite the proliferation of ethical frameworks of artificial intelligence (AI) from different organizations such as government agencies, large corporations, and academic institutions, it is still a challenge to implement and operationalize ethical and legal frameworks for AI in practice due to its complexities. The implementation and operationalization involve different aspects in original theoretical
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An Operational Framework for Guiding Human Evaluation in Explainable and Trustworthy Artificial Intelligence IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-11-20 Roberto Confalonieri, Jose Maria Alonso-Moral
The assessment of explanations by humans presents a significant challenge within the context of explainable and trustworthy artificial intelligence. This is attributed not only to the absence of universal metrics and standardized evaluation methods but also to the complexities tied to devising user studies that assess the perceived human comprehensibility of these explanations. To address this gap
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UCRI: A Unified Conversational Recommender System Based on Item-Guided Conditional Generation IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-11-06 Xi Chen, Yuehai Wang, Jianyi Yang
In recent years, great efforts have been made to develop a conversational recommender system (CRS). However, existing works always ignore the incorporation of the recommended items and the generated replies. This causes the performance of the recommendation to degrade in the conversations. To solve this problem, we propose a novel framework called unified conversational recommender system based on
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Whom to Trust, How and Why: Untangling Artificial Intelligence Ethics Principles, Trustworthiness, and Trust IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-10-09 Andreas Duenser, David M. Douglas
In this article, we present an overview of the literature on trust in artificial intelligence (AI) and AI trustworthiness and argue for distinguishing these concepts more clearly and gathering more empirically evidence on what contributes to people’s trusting behaviors. We discuss that trust in AI involves not only reliance on the system itself but also trust in the system’s developers. AI ethics principles
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Can ChatGPT’s Responses Boost Traditional Natural Language Processing? IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-10-03 Mostafa M. Amin, Erik Cambria, Björn W. Schuller
The employment of foundation models is steadily expanding, especially with the launch of ChatGPT and the release of other foundation models. These models have shown the potential of emerging capabilities to solve problems without being particularly trained to solve them. A previous work demonstrated these emerging capabilities in affective computing tasks; the performance quality was similar to that
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Why Do We Need Neurosymbolic AI to Model Pragmatic Analogies? IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-10-03 Thilini Wijesiriwardene, Amit Sheth, Valerie L. Shalin, Amitava Das
A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning. In this article, we delve into the performance of large language models (LLMs) in dealing with progressively complex analogies expressed in unstructured text. We discuss analogies at four distinct levels of complexity: lexical, syntactic, semantic, and pragmatic
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If Our Aim Is to Build Morality Into an Artificial Agent, How Might We Begin to Go About Doing So? IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-10-02 Reneira Seeamber, Cosmin Badea
As AI becomes pervasive in most fields, from health care to autonomous driving, it is essential that we find successful ways of building morality into our machines, especially for decision making. However, the question of what it means to be moral is still debated, particularly in the context of AI. In this article, we highlight the different aspects that should be considered when building moral agents
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Unjustified Sample Sizes and Generalizations in Explainable Artificial Intelligence Research: Principles for More Inclusive User Studies IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-09-29 Uwe Peters, Mary Carman
Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what
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The Building Blocks of a Responsible Artificial Intelligence Practice: An Outlook on the Current Landscape IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-09-29 Maryem Marzouk, Cyrine Zitoun, Oumaima Belghith, Sabri Skhiri
For artificial intelligence (AI)-driven companies, awareness of the urgency of the responsible application of AI became essential with increased interest from different stakeholders. Responsible AI (RAI) has emerged as a practice to guide the design, development, deployment, and use of AI systems to ensure a benefit to users and those impacted by the systems’ outcomes. This benefit is achieved through
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The Importance of an Ethical Framework for Trust Calibration in AI IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-09-29 Amelie Schmid, Manuel Wiesche
The transformative power of AI raises serious concerns about ethical issues within organizations and implicates the need for trust. To cope with that, numerous ethical frameworks are generally published, but only on a theoretical level. Furthermore, proper trust calibration in AI is of high relevance for the workers. Up to now, only limited studies have been carried out to investigate how an ethical
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Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible Artificial Intelligence Engineering Approach IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-09-29 Qinghua Lu, Yuxiu Luo, Liming Zhu, Mingjian Tang, Xiwei Xu, Jon Whittle
The recent release of ChatGPT has gained huge attention and discussion worldwide, with responsible artificial intelligence (RAI) being a crucial topic of discussion. One key question is, “How can we ensure that AI systems, like ChatGPT, are developed and adopted in a responsible way?” To tackle RAI challenges, various ethical principles have been released by governments, organizations, and companies
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Sentiment Classification of Cryptocurrency-Related Social Media Posts IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-08-17 Mikolaj Kulakowski, Flavius Frasincar
Many researchers agree that sentiment analysis can improve the performance of quantitative trading models. We develop two off-the-shelf solutions for analyzing the sentiments of cryptocurrency-related social media posts. First, we posttrain and fine-tune a Twitter-oriented model based on the bidirectional encoder representations from transformers (BERT) architecture, BERTweet, on the cryptocurrency
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Machine Ethics Research: Promises and Potential Pitfalls IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-08-17 Jahna Otterbacher, Yannis Manolopoulos
How should machines treat people? What would it take to develop an ethical machine? These questions were the focus of a 2006 special issue, as well as the Association for the Advancement of Artificial Intelligence 2005 Fall Symposium on Machine Ethics. Since then, ethical issues surrounding AI and data analytics—lately referred to by the umbrella term FATE: Fairness, Accountability, Transparency and
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Integrated Circuit Mask–Generative Adversarial Network for Circuit Annotation With Targeted Data Augmentation IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-08-17 Yee-Yang Tee, Deruo Cheng, Yiqiong Shi, Tong Lin, Bah-Hwee Gwee
In recent years, deep-learning-based segmentation techniques have been applied to circuit annotation for the hardware assurance of integrated circuits (ICs). However, imperfections in circuit images often cause incorrectly segmented pixels, which result in critical circuit connection errors that are detrimental to subsequent circuit analysis. To mitigate such circuit connection errors, this article
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Effectively Modeling Sentence Interactions With Factorization Machines for Fact Verification IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-08-03 Zhendong Chen, Fuzhen Zhuang, Lejian Liao, Meihuizi Jia, Jiaqi Li, Heyan Huang
Fact verification is a very challenging task that requires retrieving multiple evidence sentences from a reliable corpus to authenticate claims. Many claims require the simultaneous integration and reasoning of several pieces of evidence for verification. Existing models exhibit limitations in two aspects: 1) during the sentence selection stage, they only consider the interaction between the claim
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CRule: Category-Aware Symbolic Multihop Reasoning on Knowledge Graphs IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-07-04 Zikang Wang, Linjing Li, Jinlin Li, Pengfei Zhao, Daniel Zeng
Multihop reasoning is essential in knowledge graph (KG) research and applications. Current methods rely on specific KG entities, while human cognition operates at a more abstract level. This article proposes a category-aware rule-based (CRule) approach for symbolic multihop reasoning. Specifically, given a KG, CRule first categorizes entities and constructs a category-aware KG; it then uses rules retrieved
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The SRVM: A Similarity-Based Relevance Vector Machine for Remaining Useful Lifetime Prediction in the Industrial Internet of Things IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-06-23 Guorui Li, Yajun Wu, Cong Wang, Sancheng Peng, Jianwei Niu, Shui Yu
With the continuous advancement of Industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for equipment in the Industrial Internet of Things
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Big Data Analytics and Mental Health: Would Ethics Be the Only Safeguard Against the Risks of Identifying “Potential Patients”? IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-06-19 Kevin Palomino, Carmen Berdugo
Despite all the prospects for data growth, sharing, and processing, and all the benefits that big data can bring, this revolution is not exempt from risks. Even if, at some point, computers may be able to provide diagnoses with greater accuracy than medical professionals, would ethics be the only safeguard against the possible risks? In this article, we implement a pragmatic approach to answer this
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COVID-19 Sentiment Analysis Based on Tweets IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-06-12 Valerio La Gatta, Vincenzo Moscato, Marco Postiglione, Giancarlo Sperlí
In this work, based on sentiment analysis of tweets, we investigate how individuals in Italy perceived the COVID-19 outbreak and its implications in real life. We unveil the most discussed narratives on Twitter and measure how users’ interests, sentiments, and emotions have evolved over time and across the several aspects of the pandemic. Our analysis shows that while the overall sentiment is negative
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Neurosymbolic Artificial Intelligence (Why, What, and How) IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-06-12 Amit Sheth, Kaushik Roy, Manas Gaur
Humans interact with the environment using a combination of perception—transforming sensory inputs from their environment into symbols, and cognition—mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of artificial intelligence (AI), refers to large-scale pattern recognition
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From Stochastic Parrots to Intelligent Assistants—The Secrets of Data and Human Interventions IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-06-12 Usama M. Fayyad
Generative AI is all the rage nowadays—primarily driven by ChatGPT capturing the public imagination and attracting hundreds of millions of users in record time, reaching 100 million users in two months. However, there is much ambiguity from the providers about the technology, the methodology, and the way OpenAI makes it work. This compounds the mystique and speculation. I focus on what we know, with
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New User Intent Discovery With Robust Pseudo Label Training and Source Domain Joint Training IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-06-08 Wenbin An, Feng Tian, Ping Chen, Qinghua Zheng, Wei Ding
Discovering new user intents based on existing intents from constantly incoming unlabeled data is an important task in many intelligent systems deployed in the real world (e.g., dialogue systems). Since data with new intents are completely unlabeled, most current approaches employ clustering methods to generate pseudo labels to train their models. However, due to intent gaps between existing and new
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A Fault Diagnosis of Rotating Machinery Based on a Mutual Dimensionless Index and a Convolution Neural Network IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-05-12 Naiquan Su, Qinghua Zhang, Lingmeng Zhou, Xiaoxiao Chang, Ting Xu
For the fault diagnosis process of petrochemical rotating machinery, it is difficult to accurately identify faults by relying only on dimensionless index methods. Therefore, a fault diagnosis of rotating machinery based on mutual dimensionless index and a convolution neural network is proposed. First, it collects the rotating machinery fault signal of the petrochemical large unit and mutual dimensionless
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AI’s 10 to Watch, 2022 IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-28 Jürgen Dix, Zhongfei Zhang
IEEE Intelligent Systems is promoting young and aspiring artificial intelligence (AI) scientists and recognizing the rising stars as “AI‘s 10 Watch.” This biennial 2022 edition is slightly different from the previous editions: We solicited submissions from individuals who had obtained their Ph.D. up to 10 years prior (as opposed to 5 years in all of the previous editions). This led to more applications
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Will Affective Computing Emerge From Foundation Models and General Artificial Intelligence? A First Evaluation of ChatGPT IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-28 Mostafa M. Amin, Erik Cambria, Björn W. Schuller
ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks. In this work, we evaluate the capabilities of ChatGPT to perform text classification on three affective computing problems, namely, big-five personality prediction, sentiment analysis, and suicide tendency detection. We
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Rethinking Homework in the Age of Artificial Intelligence IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-28 Hazem Ibrahim, Rohail Asim, Fareed Zaffar, Talal Rahwan, Yasir Zaki
The evolution of natural language processing techniques has led to the development of advanced conversational tools such as ChatGPT, capable of assisting users with a variety of activities. Media attention has centered on ChatGPT’s potential impact, policy implications, and ethical ramifications, particularly in the context of education. As such tools become more accessible, students across the globe
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On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-28 Nikolaos Mylonas, Ioannis Mollas, Bin Liu, Yannis Manolopoulos, Grigorios Tsoumakas
Multilabel data comprise instances associated with multiple binary target variables. The main learning task from such data is multilabel classification, where the goal is to output a bipartition of the target variables into relevant and irrelevant ones for a given instance. Other tasks involve ranking the target variables from the most to the least relevant one or even outputting a full joint distribution
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Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-28 Feng Xia, Leman Akoglu, Charu Aggarwal, Huan Liu
Deep anomaly analytics is a rapidly evolving field that leverages the power of deep learning to identify anomalies in various datasets. The use of deep anomaly analytics has increased significantly in recent years due to the growing need to detect anomalies in complex data that traditional methods struggle to handle. Deep anomaly analytics has the potential to transform various industries, including
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DCAT: Combining Multisemantic Dual-Channel Attention Fusion for Text Classification IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-19 Kaifang Dong, Yifan Liu, Fuyong Xu, Peiyu Liu
Text classification is a fundamental and central position in natural language processing. There are many solutions to the text classification problem, but few use the semantic combination of multiple perspectives to improve the classification performance. This article proposes a dual-channel attention network model called DCAT, which uses the complementarity between semantics to refine the understanding
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Effective Interpretable Policy Distillation via Critical Experience Point Identification IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-10 Xiao Liu, Shuyang Liu, Bo An, Yang Gao, Shangdong Yang, Wenbin Li
Interpretable policy distillation aims to imitate a deep reinforcement learning (DRL) policy into a self-explainable model. However, the distilled policy usually does not generalize well to complex tasks. To investigate this phenomenon, we examine the experience pools of DRL tasks and find that these interactive experience distributions are heavy tailed. However, this critical issue is largely ignored
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A Multiview Text Imagination Network Based on Latent Alignment for Image-Text Matching IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-04-10 Heng Shang, Guoshuai Zhao, Jing Shi, Xueming Qian
In image-text matching fields, one of the keys to improving performance is to extract features with more semantic information. Existing works demonstrate that semantic enrichment through knowledge expansion can improve performance. Most of them expand image features, however, the shortage of semantic information in text modality and the unilateral character of the view are often bottlenecks that limit
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Generating Emotion Descriptions for Fine Art Paintings Via Multiple Painting Representations IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-03-23 Yue Lu, Chao Guo, Xingyuan Dai, Fei-Yue Wang
The task of generating emotion descriptions for fine art paintings using machine learning is gaining increasing attention. However, captioning the emotions depicted in paintings is challenging due to the artistic and subtle nature of the relied-upon visual clues. Previous studies on painting emotion captioning mainly focus on content-oriented semantic features, resulting in limited performance. Recognizing
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Simulation Driven AI: From Artificial to Actual and Vice Versa IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-03-06 Li Li, Yilun Lin, Yutong Wang, Fei-Yue Wang
In this perspective, we discuss the important role of simulations in building state-of-the-art artificial intelligence (AI) systems. We first explain why simulations become vital in building complex AI systems. Then, we study some challenges and candidate solutions related to simulation-based AI systems. Finally, we discuss future research directions in this field.
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A Semantic Web Approach to Fault Tolerant Autonomous Manufacturing IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-03-06 Fadi El Kalach, Ruwan Wickramarachchi, Ramy Harik, Amit Sheth
The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future factories are required to be agile, allowing for more customized production and resistance to disturbances. Such production lines would be able to reallocate resources as needed and minimize downtime while keeping up with market demands. These systems must be capable of complex
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Affect Detection From Wearables in the “Real” Wild: Fact, Fantasy, or Somewhere In between? IEEE Intell. Syst. (IF 6.4) Pub Date : 2023-03-06 Sidney K. D’Mello, Brandon M. Booth
Affect detection from wearables in the “real” wild—where people go about their daily routines in heterogeneous contexts—is a different problem than affect detection in the lab or in the “quasi” wild (e.g., curated or restricted contexts). The U.S. government recently supported a program to develop and evaluate the performance of contemporary affect detection systems in the real-wild along the dimensions