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A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-18 Xiaojin Zhang, Yan Kang, Lixin Fan, Kai Chen, Qiang Yang
Trustworthy Federated Learning (TFL) typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency reduction while protecting data privacy. Therefore, protection mechanisms and their parameters should be carefully chosen to strike an optimal trade-off between privacy leakage, utility loss, and efficiency reduction. To
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Perceiving Actions via Temporal Video Frame Pairs ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-17 Rongchang Li, Tianyang Xu, Xiao-Jun Wu, Zhongwei Shen, Josef Kittler
Video action recognition aims to classify the action category in given videos. In general, semantic-relevant video frame pairs reflect significant action patterns such as object appearance variation and abstract temporal concepts like speed, rhythm, etc. However, existing action recognition approaches tend to holistically extract spatiotemporal features. Though effective, there is still a risk of neglecting
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Multimodal Dialogue Systems via Capturing Context-aware Dependencies and Ordinal Information of Semantic Elements ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-12 Weidong He, Zhi Li, Hao Wang, Tong Xu, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Enhong Chen
The topic of multimodal conversation systems has recently garnered significant attention across various industries, including travel, retail, and others. While pioneering works in this field have shown promising performance, they often focus solely on context information at the utterance level, overlooking the context-aware dependencies of multimodal semantic elements like words and images. Furthermore
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Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-13 Cody Lewis, Vijay Varadharajan, Nasimul Noman, Uday Tupakula
With the increasing tension between conflicting requirements of the availability of large amounts of data for effective machine learning based analysis, and for ensuring their privacy, the paradigm of federated learning has emerged, a distributed machine learning setting where the clients provide only the machine learning model updates to the server rather than the actual data for decision making.
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Self-supervised Bipartite Graph Representation Learning: A Dirichlet Max-margin Matrix Factorization Approach ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-08 Shenghai Zhong, Shu Guo, Jing Liu, Hongren Huang, Lihong Wang, Jianxin Li, Chen Li, Yiming Hei
Bipartite graph representation learning aims to obtain node embeddings by compressing sparse vectorized representations of interactions between two types of nodes, e.g., users and items. Incorporating structural attributes among homogeneous nodes, such as user communities, improves the identification of similar interaction preferences, namely, user/item embeddings, for downstream tasks. However, existing
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Deconfounded Cross-modal Matching for Content-based Micro-video Background Music Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-06 Jing Yi, Zhenzhong Chen
Object-oriented micro-video background music recommendation is a complicated task where the matching degree between videos and background music is a major issue. However, music selections in user-generated content (UGC) are prone to selection bias caused by historical preferences of uploaders. Since historical preferences are not fully reliable and may reflect obsolete behaviors, over-reliance on them
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Analysing Utterances in LLM-based User Simulation for Conversational Search ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-05 Ivan Sekulić, Mohammad Aliannejadi, Fabio Crestani
Clarifying the underlying user information need by asking clarifying questions is an important feature of modern conversational search systems. However, evaluation of such systems through answering prompted clarifying questions requires significant human effort, which can be time-consuming and expensive. In our recent work, we proposed an approach to tackle these issues with a user simulator, USi.
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A Novel Blockchain-Based Responsible Recommendation System for Service Process Creation and Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-02 Tieliang Gao, Li Duan, Lufeng Feng, Wei Ni, Quan Z. Sheng
Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible
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Learning Cross-Modality Interaction for Robust Depth Perception of Autonomous Driving ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-01 Yunji Liang, Nengzhen Chen, Zhiwen Yu, Lei Tang, Hongkai Yu, Bin Guo, Daniel Dajun Zeng
As one of the fundamental tasks of autonomous driving, depth perception aims to perceive physical objects in three dimensions and to judge their distances away from the ego vehicle. Although great efforts have been made for depth perception, LiDAR-based and camera-based solutions have limitations with low accuracy and poor robustness for noise input. With the integration of monocular cameras and LiDAR
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FedCMD: A Federated Cross-Modal Knowledge Distillation for Drivers Emotion Recognition ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-01 Saira Bano, Nicola Tonellotto, Pietro Cassarà, Alberto Gotta
Emotion recognition has attracted a lot of interest in recent years in various application areas such as healthcare and autonomous driving. Existing approaches to emotion recognition are based on visual, speech, or psychophysiological signals. However, recent studies are looking at multimodal techniques that combine different modalities for emotion recognition. In this work, we address the problem
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Balanced Quality Score (BQS): Measuring Popularity Debiasing in Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-01 Erica Coppolillo, Marco Minici, Ettore Ritacco, Luciano Caroprese, Francesco Sergio Pisani, Giuseppe Manco
Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks quality measures that effectively enable their analyses and comparisons. In this paper, we first introduce a formal, data-driven, and parameter-free
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Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-29 Yongsu Ahn, Yu-Ru Lin
Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes,
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MHGCN+: Multiplex Heterogeneous Graph Convolutional Network ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-29 Chaofan Fu, Pengyang Yu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex networks between multi-typed nodes and the different importance of relations in meta-paths for node embedding, which can hardly
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Robust Recommender Systems with Rating Flip Noise ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-29 Shanshan Ye, Jie Lu
Recommender systems have become important tools in the daily life of human beings since they are powerful to address information overload, and discover relevant and useful items for users. The success of recommender systems largely relies on the interaction history between users and items, which is expected to accurately reflect the preferences of users on items. However, the expectation is easily
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Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-29 Vinayak Gupta, Srikanta Bedathur
Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such as cooking a four-course meal. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike
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CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-27 Luca Gherardini, Varun Ravi Varma, Karol Capała, Roger Woods, Jose Sousa
The availability of large data sets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small data sets, mainly due to practical and cost-effective deployment issues, as well as the opacity of deep learning models. To address this, the Comprehensive Abstraction and Classification Tool for Uncovering
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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-26 Davor Vukadin, Petar Afrić, Marin Šilić, Goran Delač
Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model transparency are crucial. Over the years, researchers proposed many algorithms to aid neural network understanding and provide additional information
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EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Ana Neacşu, Jean-Christophe Pesquet, Corneliu Burileanu
This article introduces a novel approach for building a robust Automatic Gesture Recognition system based on Surface Electromyographic (sEMG) signals, acquired at the forearm level. Our main contribution is to propose new constrained learning strategies that ensure robustness against adversarial perturbations by controlling the Lipschitz constant of the classifier. We focus on nonnegative neural networks
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Demand-driven Urban Facility Visit Prediction ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Yunke Zhang, Tong Li, Yuan Yuan, Fengli Xu, Fan Yang, Funing Sun, Yong Li
Predicting citizens’ visiting behaviors to urban facilities is instrumental for city governors and planners to detect inequalities in urban opportunities and optimize the distribution of facilities and resources. Previous works predict facility visits simply using observed visit behavior, yet citizens’ intrinsic demands for facilities are not characterized explicitly, causing potential incorrect learned
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Explainable Product Classification for Customs ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Eunji Lee, Sihyeon Kim, Sundong Kim, Soyeon Jung, Heeja Kim, Meeyoung Cha
The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most
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Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Quyang Pan, Junbo Zhang, Zeju Li, Qingxiang Liu
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from cli ents without assembling their private data. Constrained communication and personalization requirements pose severe challenges to FL. Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between
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Explainability for Large Language Models: A Survey ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Mengnan Du
Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this article, we introduce a taxonomy
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Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi, Nirvana Meratnia
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices, in reality, label noise can naturally occur in FL and is closely related to clients’ characteristics. Due to scarcity
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T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Wei Chen, Hongjun Wang, Yinghui Zhang, Ping Deng, Zhipeng Luo, Tianrui Li
Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute
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TS-Fastformer: Fast Transformer for Time-series Forecasting ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Sangwon Lee, Junho Hong, Ling Liu, Wonik Choi
Many real-world applications require precise and fast time-series forecasting. Recent trends in time-series forecasting models are shifting from LSTM-based models to Transformer-based models. However, the Transformer-based model has a limited ability to represent sequential relationships in time-series data. In addition, the transformer-based model suffers from slow training and inference speed due
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Fairness-Driven Private Collaborative Machine Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Dana Pessach, Tamir Tassa, Erez Shmueli
The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated
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Generating Daily Activities with Need Dynamics ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Yuan Yuan, Jingtao Ding, Huandong Wang, Depeng Jin
Daily activity data recording individuals’ various activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance. However
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Strengthening Cooperative Consensus in Multi-Robot Confrontation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Meng Xu, Xinhong Chen, Yechao She, Yang Jin, Guanyi Zhao, Jianping Wang
Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and preventing actions that often lead to failures on our side. This exacerbates the cooperation dilemma, ultimately resulting in our agents acting independently
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RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-22 Jin Han, Yun-Feng Ren, Alessandro Brighente, Mauro Conti
Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image
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MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Unit Detection ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-09 Xuri Ge, Joemon M. Jose, Songpei Xu, Xiao Liu, Hu Han
The Facial Action Coding System (FACS) encodes the action units (AUs) in facial images, which has attracted extensive research attention due to its wide use in facial expression analysis. Many methods that perform well on automatic facial action unit (AU) detection primarily focus on modelling various AU relations between corresponding local muscle areas or mining global attention-aware facial features;
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Boosting Healthiness Exposure in Category-constrained Meal Recommendation Using Nutritional Standards ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-05 Ming Li, Lin Li, Xiaohui Tao, Zhongwei Xie, Qing Xie, Jingling Yuan
Food computing, as a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, as important user information, is often used by existing
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FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-03 Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items
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Internal Rehearsals for a Reconfigurable Robot to Improve Area Coverage Performance ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-02 S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara
Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore,
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Temporal Implicit Multimodal Networks for Investment and Risk Management ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-01 Gary Ang, Ee-Peng Lim
Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single task and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks
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Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-01 Simi Job, Xiaohui Tao, Lin Li, Haoran Xie, Taotao Cai, Jianming Yong, Qing Li
Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence
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Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-01 Paula G. Duran, Pere Gilabert, Santi Seguí, Jordi Vitrià
In today’s digital landscape, recommender systems have gained ubiquity as a means of directing users towards personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune to shortcomings. A significant challenge faced by recommender systems is the presence of biases, which produces various undesirable effects, prominently
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Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-01 Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Sang Woo Kim
L2 regularization for weights in neural networks is widely used as a standard training trick. In addition to weights, the use of batch normalization involves an additional trainable parameter γ, which acts as a scaling factor. However, L2 regularization for γ remains an undiscussed mystery and is applied in different ways depending on the library and practitioner. In this paper, we study whether L2
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SiG: A Siamese-based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-01 Mai Hao, Ming Cai, Minghui Fang, Linlin You
Domain knowledge is gradually renovating its attributes to exhibit distinct features in autonomy, propelled by the shift of modern transportation systems (TS) towards autonomous TS (ATS) comprising three progressive generations. Knowledge graph (KG) and its corresponding versions can help depict the evolving TS. Given that KG versions exhibit asymmetry primarily due to variations in evolved knowledge
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Bayesian Strategy Networks Based Soft Actor-Critic Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-02-01 Qin Yang, Ramviyas Parasuraman
A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system’s utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical
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AMT-CDR: A Deep Adversarial Multi-channel Transfer Network for Cross-domain Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-27 Kezhi Lu, Qian Zhang, Danny Hughes, Guangquan Zhang, Jie Lu
Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data
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Deep Learning in Single-Cell Analysis ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-26 Dylan Molho, Jiayuan Ding, Wenzhuo Tang, Zhaoheng Li, Hongzhi Wen, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang
Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance
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Reinforcement Learning for Solving Multiple Vehicle Routing Problem with Time Window ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-25 Zefang Zong, Tong Xia, Meng Zheng, Yong Li
Vehicle routing problem with time window (VRPTW) is of great importance for a wide spectrum of services and real-life applications, such as online take-out and car-hailing platforms. A promising method should generate high-qualified solutions within limited inference time, and there are three major challenges: a) directly optimizing the goal with several practical constraints; b) efficiently handling
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A Survey on Evaluation of Large Language Models ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-23 Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past
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Credit Card Fraud Detection via Intelligent Sampling and Self-supervised Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-23 Chiao-Ting Chen, Chi Lee, Szu-Hao Huang, Wen-Chih Peng
The significant increase in credit card transactions can be attributed to the rapid growth of online shopping and digital payments, particularly during the COVID-19 pandemic. To safeguard cardholders, e-commerce companies, and financial institutions, the implementation of an effective and real-time fraud detection method using modern artificial intelligence techniques is imperative. However, the development
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Decentralized Federated Recommendation with Privacy-Aware Structured Client-Level Graph ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-22 Zhitao Li, Zhaohao Lin, Feng Liang, Weike Pan, Qiang Yang, Zhong Ming
Recommendation models are deployed in a variety of commercial applications in order to provide personalized services for users. However, most of them rely on the users’ original rating records that are often collected by a centralized server for model training, which may cause privacy issues. Recently, some centralized federated recommendation models are proposed for the protection of users’ privacy
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Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-22 Ehsan Elahi, Sajid Anwar, Babar Shah, Zahid Halim, Abrar Ullah, Imad Rida, Muhammad Waqas
With the ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to
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VesNet: a Vessel Network for Jointly Learning Route Pattern and Future Trajectory ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-18 Fenyu Jiang, Huandong Wang, Yong Li
Vessel trajectory prediction is the key to maritime applications such as traffic surveillance, collision avoidance, anomaly detection, etc. Making predictions more precisely requires a better understanding of the moving trend for a particular vessel since the movement is affected by multiple factors like marine environment, vessel type, and vessel behavior. In this paper, we propose a model named VesNet
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Reconstructing Turbulent Flows Using Spatio-temporal Physical Dynamics ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Shengyu Chen, Tianshu Bao, Peyman Givi, Can Zheng, Xiaowei Jia
Accurate simulation of turbulent flows is of crucial importance in many branches of science and engineering. Direct numerical simulation (DNS) provides the highest fidelity means of capturing all intricate physics of turbulent transport. However, the method is computationally expensive because of the wide range of turbulence scales that must be accounted for in such simulations. Large eddy simulation
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Inferring Real Mobility in Presence of Fake Check-ins Data ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Qiang Gao, Hongzhu Fu, Kunpeng Zhang, Goce Trajcevski, Xu Teng, Fan Zhou
Understanding human mobility has become an important aspect of location-based services in tasks such as personalized recommendation and individual moving pattern recognition, enabled by the large volumes of data from geo-tagged social media (GTSM). Prior studies mainly focus on analyzing human historical footprints collected by GTSM and assuming the veracity of the data, which need not hold when some
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Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Shuo Ma, Yingwei Zhang, Yiqiang Chen, Tao Xie, Shuchao Song, Ziyu Jia
Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive sleep stages that collectively form a sleep cycle. Typically, this cycle repeats about five times, providing an insightful portrayal of the subject’s
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Nationwide Air Pollution Forecasting with Heterogeneous Graph Neural Networks ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Fernando Terroso-Saenz, Juan Morales-García, Andres Muñoz
Nowadays, air pollution is one of the most relevant environmental problems in most urban settings. Due to the utility in operational terms of anticipating certain pollution levels, several predictors based on Graph Neural Networks (GNN) have been proposed for the last years. Most of these solutions usually encode the relationships among stations in terms of their spatial distance, but they fail when
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A Survey on Graph Representation Learning Methods ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Shima Khoshraftar, Aijun An
Graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node
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E2Storyline: Visualizing the Relationship with Triplet Entities and Event Discovery ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Yunchao Wang, Guodao Sun, Zihao Zhu, Tong Li, Ling Chen, Ronghua Liang
The narrative progression of events, evolving into a cohesive story, relies on the entity-entity relationships. Among the plethora of visualization techniques, storyline visualization has gained significant recognition for its effectiveness in offering an overview of story trends, revealing entity relationships, and facilitating visual communication. However, existing methods for storyline visualization
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Hierarchical Pruning of Deep Ensembles with Focal Diversity ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Yanzhao Wu, Ka-Ho Chow, Wenqi Wei, Ling Liu
Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study and apply deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and
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Enabling Graph Neural Networks for Semi-Supervised Risk Prediction in Online Credit Loan Services ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-16 Hao Tang, Cheng Wang, Jianguo Zheng, Changjun Jiang
Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning
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Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-01-13 Jhih-Chen Liu, Chiao-Ting Chen, Chi Lee, Szu-Hao Huang
The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem
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Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2023-12-19 Aqsa Ashraf Makhdomi, Iqra Altaf Gillani
In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been
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One-step Multi-view Clustering with Consensus Graph and Data Representation Convolution ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2023-12-19 F. Dornaika
Multi-view clustering aims to partition unlabeled patterns into disjoint clusters using consistent and complementary information derived from features of patterns in multiple views. Downstream methods perform this clustering sequentially: estimation of individual or consistent similarity matrices, spectral embedding, and clustering. In this article, we present an approach that can address some of the
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Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph Mining ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2023-12-19 Thanh Toan Nguyen, Thanh Tam Nguyen, Thanh Hung Nguyen, Hongzhi Yin, Thanh Thi Nguyen, Jun Jo, Quoc Viet Hung Nguyen
Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e., subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent subgraph mining (FSM), as maximal frequent subgraphs lie in the middle of graph lattice, and FSM algorithms must explore an exponential space and an NP-hard
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Human Pose Transfer with Augmented Disentangled Feature Consistency ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2023-12-19 Kun Wu, Chengxiang Yin, Zhengping Che, Bo Jiang, Jian Tang, Zheng Guan, Gangyi Ding
Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring the poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve