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A Trustworthy and Responsible Decision-Making Framework for Resource Management in Food-Energy-Water Nexus: A Control-Theoretical Approach ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-23 Suleyman Uslu, Davinder Kaur, Samuel J. Rivera, Arjan Durresi, Meghna Babbar-Sebens, Jenna H. Tilt
This paper introduces a hybrid framework for trustworthy and responsible natural resource management, aimed at building bottom-up trust to enhance cooperation among decision makers in the Food, Energy, and Water sectors. Cooperation is highly critical for the adoption and application of resource management alternatives (solutions), including those generated by AI-based recommender systems, in communities
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Teacher-Student Framework for Polyphonic Semi-supervised Sound Event Detection: Survey and Empirical Analysis ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-23 Zhor Diffallah, Hadjer Ykhlef, Hafida Bouarfa
Polyphonic sound event detection refers to the task of automatically identifying sound events occurring simultaneously in an auditory scene. Due to the inherent complexity and variability of real-world auditory scenes, building robust detectors for polyphonic sound event detection poses a significant challenge. The task becomes further more challenging without sufficient annotated data to develop sound
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An Explore-Exploit Workload-bounded Strategy for Rare Event Detection in Massive Energy Sensor Time Series ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-17 Lo Pang-Yun Ting, Rong Chao, Chai-Shi Chang, Kun-Ta Chuang
With the rise of Internet-of-Things devices, the analysis of sensor-generated energy time series data has become increasingly important. This is especially crucial for detecting rare events like unusual electricity usage or water leakages in residential and commercial buildings, which is essential for optimizing energy efficiency and reducing costs. However, existing detection methods on large-scale
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CGKPN: Cross-Graph Knowledge Propagation Network with Adaptive Connection for Reasoning-Based Machine Reading Comprehension ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-17 Zhuo Zhao, Guangyou Zhou, Zhiwen Xie, Lingfei Wu, Jimmy Xiangji Huang
The task of machine reading comprehension (MRC) is to enable machine to read and understand a piece of text, and then answer the corresponding question correctly. This task requires machine to not only be able to perform semantic understanding, but also possess logical reasoning capabilities. Just like human reading, it involves thinking about the text from two interacting perspectives of semantics
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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 : 2024-04-15 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 and retail, among 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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CACTUS: A Comprehensive Abstraction and Classification Tool for Uncovering Structures ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-15 Luca Gherardini, Varun Ravi Varma, Karol Capała, Roger Woods, Jose Sousa
The availability of large datasets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small datasets, 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 : 2024-04-15 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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Learning Cross-modality Interaction for Robust Depth Perception of Autonomous Driving ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-15 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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Tapestry of Time and Actions: Modeling Human Activity Sequences Using Temporal Point Process Flows ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-15 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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Deconfounded Cross-modal Matching for Content-based Micro-video Background Music Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-15 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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MHGCN+: Multiplex Heterogeneous Graph Convolutional Network ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-15 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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A Game-theoretic Framework for Privacy-preserving Federated Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-10 Xiaojin Zhang, Lixin Fan, Siwei Wang, Wenjie Li, Kai Chen, Qiang Yang
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of privacy leakage cannot be ignored in the presence of semi-honest adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and attackers seems never-ending, we are concerned with one critical
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HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-05 Chance DeSmet, Diane J Cook
Generative adversarial networks have become a de facto approach to generate synthetic data points that resemble their real counterparts. We tackle the situation where the realism of individual samples is not the sole criterion for synthetic data generation. Additional constraints such as privacy preservation, distribution realism, and diversity promotion may also be essential to optimize. To address
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GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-03 Giacomo Medda, Francesco Fabbri, Mirko Marras, Ludovico Boratto, Gianni Fenu
Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly related to biased recommendations. In this paper
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Popularity Bias in Correlation Graph based API Recommendation for Mashup Creation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-02 Chao Yan, Weiyi Zhong, Dengshuai Zhai, Arif Ali Khan, Wenwen Gong, Yanwei Xu, Baogui Xin
The explosive growth of the API economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation
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A Survey on Evaluation of Large Language Models ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-29 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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Deep Learning in Single-cell Analysis ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-29 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, and 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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Bayesian Strategy Networks Based Soft Actor-Critic Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-29 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 article proposes a novel hierarchical
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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 : 2024-03-29 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 article, we study whether
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Score-based Graph Learning for Urban Flow Prediction ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-01 Pengyu Wang, Xucheng Luo, Wenxin Tai, Kunpeng Zhang, Goce Trajcevski, Fan Zhou
Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and temporal graph neural networks (GNNs) to deal with the complex dependence between the traffic in adjacent areas. However, existing GNN-based approaches
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Counterfactual Graph Convolutional Learning for Personalized Recommendation ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-04-01 Meng Jian, Yulong Bai, Xusong Fu, Jingjing Guo, Ge Shi, Lifang Wu
Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced interaction clue and produce suboptimal representations to users and items for recommendation. Towards the issue, this work is dedicated to bias-aware
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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 : 2024-03-29 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 modeling various AU relations between corresponding local muscle areas or mining global attention–aware facial features;
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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 : 2024-03-29 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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Reinforcement Learning for Solving Multiple Vehicle Routing Problem with Time Window ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-28 Zefang Zong, Xia Tong, 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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Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-28 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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VesNet: A Vessel Network for Jointly Learning Route Pattern and Future Trajectory ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-28 Fenyu Jiang, Huandong Wang, Yong Li
Vessel trajectory prediction is the key to maritime applications such as traffic surveillance, collision avoidance, anomaly detection, and so on. 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
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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 : 2024-03-28 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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Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-28 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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SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-28 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) toward autonomous TS (ATS) comprising three progressive generations. The 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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Temporal Implicit Multimodal Networks for Investment and Risk Management ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-28 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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Discovering Expert-Level Air Combat Knowledge via Deep Excitatory-Inhibitory Factorized Reinforcement Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-27 Haiyin Piao, Shengqi Yang, Hechang Chen, Junnan Li, Jin Yu, Xuanqi Peng, Xin Yang, Zhen Yang, Zhixiao Sun, Yi Chang
Artificial Intelligence (AI) has achieved a wide range of successes in autonomous air combat decision-making recently. Previous research demonstrated that AI-enabled air combat approaches could even acquire beyond human-level capabilities. However, there remains a lack of evidence regarding two major difficulties. First, the existing methods with fixed decision intervals are mostly devoted to solving
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Quintuple-based Representation Learning for Bipartite Heterogeneous Networks ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-26 Cangqi Zhou, Hui Chen, Jing Zhang, Qianmu Li, Dianming Hu
Recent years have seen rapid progress in network representation learning, which removes the need for burdensome feature engineering and facilitates downstream network-based tasks. In reality, networks often exhibit heterogeneity, which means there may exist multiple types of nodes and interactions. Heterogeneous networks raise new challenges to representation learning, as the awareness of node and
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Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-26 Hussein Al-Bazzaz, Muhammad Azam, Manar Amayri, Nizar Bouguila
We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum message length (MML) model selection criterion to discover the optimal number of clusters for the unsupervised approach of our proposed model. Given
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Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting Items ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-26 Chenhao Zhang, Weitong Chen, Wei Emma Zhang, Miao Xu
Dynamic Learning-to-Rank (DLTR) is a method of updating a ranking policy in real-time based on user feedback, which may not always be accurate. Although previous DLTR work has achieved fair and unbiased DLTR under inaccurate feedback, they face the trade-off between fairness and user utility and also have limitations in the setting of feeding items. Existing DLTR works improve ranking utility by eliminating
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Federated Momentum Contrastive Clustering ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-26 Runxuan Miao, Erdem Koyuncu
Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a central entity, considering data privacy requirements and computational resources. Federated Learning (FL) has been developed successfully to aggregate
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Deep Causal Reasoning for Recommendations ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-26 Yaochen Zhu, Jing Yi, Jiayi Xie, Zhenzhong Chen
Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders
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Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-26 Xu Chen
Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning solutions suffers from prohibitive computational burdens thus cannot be computed for streaming data. Motivated by that, we present a novel unified framework
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Empowering Predictive Modeling by GAN-based Causal Information Learning ACM Trans. Intell. Syst. Technol. (IF 5.0) Pub Date : 2024-03-20 Jinwei Zeng, Guozhen Zhang, Jian Yuan, Yong Li, Depeng Jin
Generally speaking, we can easily specify many causal relationships in the prediction tasks of ubiquitous computing, such as human activity prediction, mobility prediction, and health prediction. However, most of the existing methods in these fields failed to take advantage of this prior causal knowledge. They typically make predictions only based on correlations in the data, which hinders the prediction
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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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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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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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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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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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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