ACM Transactions on Intelligent Systems and Technology (TIST)最新文献_第5页

A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams 从移动和可穿戴数据流建模生物行为节律的计算框架

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-03-03 DOI: 10.1145/3510029

Runze Yan, Xinwen Liu, Janine M. Dutcher, Michael J. Tumminia, Daniella K. Villalba, Sheldon Cohen, David Creswell, Kasey G. Creswell, Jennifer Mankoff, A. Dey, Afsaneh Doryab

{"title":"A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams","authors":"Runze Yan, Xinwen Liu, Janine M. Dutcher, Michael J. Tumminia, Daniella K. Villalba, Sheldon Cohen, David Creswell, Kasey G. Creswell, Jennifer Mankoff, A. Dey, Afsaneh Doryab","doi":"10.1145/3510029","DOIUrl":"https://doi.org/10.1145/3510029","url":null,"abstract":"This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evaluate the framework with two case studies using datasets of smartphone, Fitbit, and OURA smart ring to evaluate the framework’s ability to (1) detect cyclic biobehavior, (2) model commonality and differences in rhythms of human participants in the sample datasets, and (3) predict their health and readiness status using models of biobehavioral rhythms. Our evaluation demonstrates the framework’s ability to generate new knowledge and findings through rigorous micro- and macro-level modeling of human rhythms from mobile and wearable data streams collected in the wild and using them to assess and predict different life and health outcomes.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126552403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data 交通运输者问题:基于异构非单调城市数据的公共交通系统环形路线安排

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-03-03 DOI: 10.1145/3510034

Fandel Lin, Hsun-Ping Hsieh

{"title":"Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data","authors":"Fandel Lin, Hsun-Ping Hsieh","doi":"10.1145/3510034","DOIUrl":"https://doi.org/10.1145/3510034","url":null,"abstract":"Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as heuristics based on neural networks, and thereafter formulate the traveling transporter problem (TTP). TTP is a multi-criteria optimization problem and may be applied to the circular route deployment in public transportation. In particular, TTP aims to find an optimized route that maximizes passenger flow according to a neural-network-based inference model and minimizes the length of the route given several constraints, including must-visit stations and the requirement for additional ones. As a variation of the traveling salesman problem (TSP), we propose a framework that first recommends new stations’ location while considering the herding effect between stations, and thereafter combines state-of-the-art TSP solvers and a metaheuristic named Trembling Hand, which is inspired by self-efficacy for solving TTP. Precisely, the proposed Trembling Hand enhances the spatial exploration considering the structural patterns, previous actions, and aging factors. Evaluation conducted on two real-world mass transit systems, Tainan and Chicago, shows that the proposed framework can outperform other state-of-the-art methods by securing the Pareto-optimal toward the objectives of TTP among comparative methods under various constrained settings.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132928504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion 基于双注意交叉分支融合的弱监督视频目标分割

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-03-03 DOI: 10.1145/3506716

Lili Wei, Congyan Lang, Liqian Liang, Songhe Feng, Tao Wang, Shidi Chen

{"title":"Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion","authors":"Lili Wei, Congyan Lang, Liqian Liang, Songhe Feng, Tao Wang, Shidi Chen","doi":"10.1145/3506716","DOIUrl":"https://doi.org/10.1145/3506716","url":null,"abstract":"Recently, concerning the challenge of collecting large-scale explicitly annotated videos, weakly supervised video object segmentation (WSVOS) using video tags has attracted much attention. Existing WSVOS approaches follow a general pipeline including two phases, i.e., a pseudo masks generation phase and a refinement phase. To explore the intrinsic property and correlation buried in the video frames, most of them focus on the later phase by introducing optical flow as temporal information to provide more supervision. However, these optical flow-based studies are greatly affected by illumination and distortion and lack consideration of the discriminative capacity of multi-level deep features. In this article, with the goal of capturing more effective temporal information and investigating a temporal information fusion strategy accordingly, we propose a unified WSVOS model by adopting a two-branch architecture with a multi-level cross-branch fusion strategy, named as dual-attention cross-branch fusion network (DACF-Net). Concretely, the two branches of DACF-Net, i.e., a temporal prediction subnetwork (TPN) and a spatial segmentation subnetwork (SSN), are used for extracting temporal information and generating predicted segmentation masks, respectively. To perform the cross-branch fusion between TPN and SSN, we propose a dual-attention fusion module that can be plugged into the SSN flexibly. We also pose a cross-frame coherence loss (CFCL) to achieve smooth segmentation results by exploiting the coherence of masks produced by TPN and SSN. Extensive experiments demonstrate the effectiveness of proposed approach compared with the state-of-the-arts on two challenging datasets, i.e., Davis-2016 and YouTube-Objects.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"79 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133719031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings FairSR:基于偏好图嵌入的多任务学习的公平感知顺序推荐

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-07 DOI: 10.1145/3495163

Cheng-te Li, Cheng-Mao Hsu, Yang Zhang

{"title":"FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings","authors":"Cheng-te Li, Cheng-Mao Hsu, Yang Zhang","doi":"10.1145/3495163","DOIUrl":"https://doi.org/10.1145/3495163","url":null,"abstract":"Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This article aims at bringing a marriage between SR and algorithmic fairness. We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups. We propose a multi-task learning-based deep end-to-end model, FairSR, which consists of two parts. One is to learn and distill personalized sequential features from the given user and her item sequence for SR. The other is fairness-aware preference graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the knowledge of users’ and items’ attributes and their correlation into entity representations, and alleviating the unfair distributions of user attributes on items. Extensive experiments conducted on three datasets show FairSR can outperform state-of-the-art SR models in recommendation performance. In addition, the recommended items by FairSR also exhibit promising interaction fairness.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124251466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Mining Willing-to-Pay Behavior Patterns from Payment Datasets 从支付数据集中挖掘支付意愿行为模式

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-07 DOI: 10.1145/3485848

Y. Wen, Hui-Kuo Yang, Wen-Chih Peng

{"title":"Mining Willing-to-Pay Behavior Patterns from Payment Datasets","authors":"Y. Wen, Hui-Kuo Yang, Wen-Chih Peng","doi":"10.1145/3485848","DOIUrl":"https://doi.org/10.1145/3485848","url":null,"abstract":"The customer base is the most valuable resource to E-commerce companies. A comprehensive understanding of customers’ preferences and behavior is crucial to developing good marketing strategies, in order to achieve optimal customer lifetime values (CLVs). For example, by exploring customer behavior patterns, given a marketing plan with a limited budget, a set of potential customers is able to be identified to maximize profit. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Moreover, effective future purchase estimation and recommendation help guide the customer to the up-selling stage. The proposed willing-to-pay prediction model (W2P) exploits the transaction data to predict customer payment behavior based on a probabilistic graphical model, which provides semantic explanation of the estimated results and deals with the sparsity of payment data from each customer. Existing work in this domain ranks the customers by their probabilities of purchase in different conditions. However, the customer with the highest purchase probability does not necessarily spend the most. Therefore, we propose a CLV maximization algorithm based on the prediction results. In addition, we improve the model by behavioral segmentation wherein we group the customers by payment behaviors to reduce the size of the offline models and enhance the accuracy for low-frequency customers. The experiment results show that our model outperforms the state-of-the-art methods in purchase behavior prediction.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128991155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data FedCTR:基于跨平台用户行为数据的联合原生广告点击率预测

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-04 DOI: 10.1145/3506715

Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie

{"title":"FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data","authors":"Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie","doi":"10.1145/3506715","DOIUrl":"https://doi.org/10.1145/3506715","url":null,"abstract":"Native ad is a popular type of online advertisement that has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms that can provide complementary information for user-interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this article, we propose a federated native ad CTR prediction method named FedCTR, which can learn user-interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy and differential privacy to the local and aggregated user embeddings, respectively, for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127604662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance 基于安全聚合的基于视频的分布式监控联邦动态图神经网络

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-04 DOI: 10.1145/3501808

Meng Jiang, Taeho Jung, Ryan Karl, Tong Zhao

{"title":"Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance","authors":"Meng Jiang, Taeho Jung, Ryan Karl, Tong Zhao","doi":"10.1145/3501808","DOIUrl":"https://doi.org/10.1145/3501808","url":null,"abstract":"Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. Can we exploit the structural and dynamic information without storing the spatiotemporal video data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets as well as simulation demonstrate that Feddy achieves great effectiveness and security.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133923241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Federated Learning for Healthcare: Systematic Review and Architecture Proposal 医疗保健的联邦学习:系统回顾和架构建议

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-04 DOI: 10.1145/3501813

Rodolfo Stoffel Antunes, Cristiano André da Costa, A. Küderle, Imrana Abdullahi Yari, B. Eskofier

{"title":"Federated Learning for Healthcare: Systematic Review and Architecture Proposal","authors":"Rodolfo Stoffel Antunes, Cristiano André da Costa, A. Küderle, Imrana Abdullahi Yari, B. Eskofier","doi":"10.1145/3501813","DOIUrl":"https://doi.org/10.1145/3501813","url":null,"abstract":"The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 110

FedBERT: When Federated Learning Meets Pre-training FedBERT:当联邦学习遇到预训练

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-04 DOI: 10.1145/3510033

Yuanyishu Tian, Yao Wan, Lingjuan Lyu, Dezhong Yao, Hai Jin, Lichao Sun

引用次数: 39

Dynamic-Aware Federated Learning for Face Forgery Video Detection 基于动态感知的人脸伪造视频检测联合学习

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2022-02-04 DOI: 10.1145/3501814

Ziheng Hu, Hongtao Xie, Lingyun Yu, Xingyu Gao, Zhihua Shang, Yongdong Zhang

{"title":"Dynamic-Aware Federated Learning for Face Forgery Video Detection","authors":"Ziheng Hu, Hongtao Xie, Lingyun Yu, Xingyu Gao, Zhihua Shang, Yongdong Zhang","doi":"10.1145/3501814","DOIUrl":"https://doi.org/10.1145/3501814","url":null,"abstract":"The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130558907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6