ACM Transactions on Intelligent Systems and Technology (TIST)最新文献

{"title":"Gray-Box Shilling Attack: An Adversarial Learning Approach","authors":"Zongwei Wang, Min Gao, Jundong Li, Junwei Zhang, Jiang Zhong","doi":"10.1145/3512352","DOIUrl":"https://doi.org/10.1145/3512352","url":null,"abstract":"Recommender systems are essential components of many information services, which aim to find relevant items that match user preferences. Several studies have shown that shilling attacks can significantly weaken the robustness of recommender systems by injecting fake user profiles. Traditional shilling attacks focus on creating hand-engineered fake user profiles, but these profiles can be detected effortlessly by advanced detection methods. Adversarial learning, which has emerged in recent years, can be leveraged to generate powerful and intelligent attack models. To this end, in this article we explore potential risks of recommender systems and shed light on a gray-box shilling attack model based on generative adversarial networks, named GSA-GANs. Specifically, we aim to generate fake user profiles that can achieve two goals: unnoticeable and offensive. Toward these goals, there are several challenges that we need to address: (1) learning complex user behaviors from user-item rating data, and (2) adversely influencing the recommendation results without knowing the underlying recommendation algorithms. To tackle these challenges, two essential GAN modules are respectively designed to make generated fake profiles more similar to real ones and harmful to recommendation results. Experimental results on three public datasets demonstrate that the proposed GSA-GANs framework outperforms baseline models in attack effectiveness, transferability, and camouflage. In the end, we also provide several possible defensive strategies against GSA-GANs. The exploration and analysis in our work will contribute to the defense research of recommender systems.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116895179","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}

{"title":"CLC: A Consensus-based Label Correction Approach in Federated Learning","authors":"Bixiao Zeng, Xiaodong Yang, Yiqiang Chen, Hanchao Yu, Yingwei Zhang","doi":"10.1145/3519311","DOIUrl":"https://doi.org/10.1145/3519311","url":null,"abstract":"Federated learning (FL) is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants’ side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a Consensus-based Label Correction approach (CLC) in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is https://github.com/bixiao-zeng/CLC.git.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134507340","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}

{"title":"A Review on Source Code Documentation","authors":"Sawan Rai, R. Belwal, Atul Gupta","doi":"10.1145/3519312","DOIUrl":"https://doi.org/10.1145/3519312","url":null,"abstract":"Context: Coding is an incremental activity where a developer may need to understand a code before making suitable changes in the code. Code documentation is considered one of the best practices in software development but requires significant efforts from developers. Recent advances in natural language processing and machine learning have provided enough motivation to devise automated approaches for source code documentation at multiple levels. Objective: The review aims to study current code documentation practices and analyze the existing literature to provide a perspective on their preparedness to address the stated problem and the challenges that lie ahead. Methodology: We provide a detailed account of the literature in the area of automated source code documentation at different levels and critically analyze the effectiveness of the proposed approaches. This also allows us to infer gaps and challenges to address the problem at different levels. Findings: (1) The research community focused on method-level summarization. (2) Deep learning has dominated the past five years of this research field. (3) Researchers are regularly proposing bigger corpora for source code documentation. (4) Java and Python are the widely used programming languages as corpus. (5) Bilingual Evaluation Understudy is the most favored evaluation metric for the research persons.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696229","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}

{"title":"Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning","authors":"Lin Zhang, Lixin Fan, Yongliang Luo, Ling-Yu Duan","doi":"10.1145/3523059","DOIUrl":"https://doi.org/10.1145/3523059","url":null,"abstract":"The rapid development of modern artificial intelligence technique is mainly attributed to sufficient and high-quality data. However, in the data collection, personal privacy is at risk of being leaked. This issue can be addressed by federated learning, which is proposed to achieve efficient model training among multiple data providers without direct data access and aggregation. To encourage more parties owning high-quality data to participate in the federated learning, it is important to evaluate and reward the participant contribution in a reasonable, robust, and efficient manner. To achieve this goal, we propose a novel contribution estimation method: Intrinsic Performance Influence-based Contribution Estimation (IPICE). In particular, the class-level intrinsic performance influence is adopted as the contribution estimation criteria in IPICE, and a neural network is employed to exploit the non-linear relationship between the performance change and estimated contribution. Extensive experiments are conducted on various datasets, and the results demonstrate that IPICE is more accurate and stable than the counterpart in various data distribution settings. The computational complexity is significantly reduced in our IPICE, especially when a new party joins the federation. IPICE assigns small contributions to bad/garbage data and thus prevent them from participating and deteriorating the learning ecosystem.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122804110","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}

{"title":"Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform","authors":"Ke Li, Bin Guo, Jiaqi Liu, Jiangtao Wang, Hao Ren, Fei Yi, Zhiwen Yu","doi":"10.1145/3523060","DOIUrl":"https://doi.org/10.1145/3523060","url":null,"abstract":"Recently, fake news has been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about the dynamic evolution of crowd opinions, and usually neglect the issue of uneven arrival of data in the real world. To address these issues, in this article, we focus on a kind of approach for fake news detection, namely progressive detection, which can be achieved by the dynamic Probabilistic Graphical Model. Based on the observation on real-world datasets, we adaptively improve the Kalman Filter to the Labeled Variable Dimension Kalman Filter (LVDKF) that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly. It can take sequential data as input, distill the dynamic evolution knowledge regarding a post, and utilize crowd wisdom from users’ responses to achieve progressive detection. Then we derive the formulas using the Forward, Backward, and EM Algorithm, and we design a dynamic detection algorithm using Bayes’ theorem. Finally, we design experimental scenarios simulating progressive detection and evaluate LVDKF on two public datasets. It outperforms the baseline methods in these experimental scenarios, which indicates that it is adequate for progressive detection.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122284391","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}

{"title":"Bayesian Attribute Bagging-Based Extreme Learning Machine for High-Dimensional Classification and Regression","authors":"Yulin He, Xuan Ye, J. Huang, Philippe Fournier-Viger","doi":"10.1145/3495164","DOIUrl":"https://doi.org/10.1145/3495164","url":null,"abstract":"This article presents a Bayesian attribute bagging-based extreme learning machine (BAB-ELM) to handle high-dimensional classification and regression problems. First, the decision-making degree (DMD) of a condition attribute is calculated based on the Bayesian decision theory, i.e., the conditional probability of the condition attribute given the decision attribute. Second, the condition attribute with the highest DMD is put into the condition attribute group (CAG) corresponding to the specific decision attribute. Third, the bagging attribute groups (BAGs) are used to train an ensemble learning model of extreme learning machines (ELMs). Each base ELM is trained on a BAG which is composed of condition attributes that are randomly selected from the CAGs. Fourth, the information amount ratios of bagging condition attributes to all condition attributes is used as the weights to fuse the predictions of base ELMs in BAB-ELM. Exhaustive experiments have been conducted to compare the feasibility and effectiveness of BAB-ELM with seven other ELM models, i.e., ELM, ensemble-based ELM (EN-ELM), voting-based ELM (V-ELM), ensemble ELM (E-ELM), ensemble ELM based on multi-activation functions (MAF-EELM), bagging ELM, and simple ensemble ELM. Experimental results show that BAB-ELM is convergent with the increase of base ELMs and also can yield higher classification accuracy and lower regression error for high-dimensional classification and regression problems.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117101879","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}

{"title":"Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System","authors":"Renhe Jiang, Z. Cai, Zhaonan Wang, Chuang Yang, Z. Fan, Quanjun Chen, Xuan Song, R. Shibasaki","doi":"10.1145/3472300","DOIUrl":"https://doi.org/10.1145/3472300","url":null,"abstract":"Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g., police) and public service operators (e.g., subway/bus operator) to protect people’s safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent, which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115071510","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}

{"title":"Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction","authors":"Zhilong Lu, Weifeng Lv, Zhipu Xie, Bowen Du, Guixi Xiong, Leilei Sun, Haiquan Wang","doi":"10.1145/3470889","DOIUrl":"https://doi.org/10.1145/3470889","url":null,"abstract":"Recent years have witnessed the emerging success of Graph Neural Networks (GNNs) for modeling graphical data. A GNN can model the spatial dependencies of nodes in a graph based on message passing through node aggregation. However, in many application scenarios, these spatial dependencies can change over time, and a basic GNN model cannot capture these changes. In this article, we propose a Graph Sequence neural network with an Attention mechanism (GSeqAtt) for processing graph sequences. More specifically, two attention mechanisms are combined: a horizontal mechanism and a vertical mechanism. GTransformer, which is a horizontal attention mechanism for handling time series, is used to capture the correlations between graphs in the input time sequence. The vertical attention mechanism, a Graph Network (GN) block structure with an attention mechanism (GNAtt), acts within the graph structure in each frame of the time series. Experiments show that our proposed model is able to handle information propagation for graph sequences accurately and efficiently. Moreover, results on real-world data from three road intersections show that our GSeqAtt outperforms state-of-the-art baselines on the traffic speed prediction task.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123804970","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}

{"title":"Algorithms for Trajectory Points Clustering in Location-based Social Networks","authors":"Nan Han, Shaojie Qiao, Kun Yue, Jianbin Huang, Qiang He, Tingting Tang, Faliang Huang, Chunlin He, Chang-an Yuan","doi":"10.1145/3480972","DOIUrl":"https://doi.org/10.1145/3480972","url":null,"abstract":"Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents. This has further increased the popularity of location-based social networks (LBSNs) and produces a huge amount of trajectories composed of continuous and complex spatio-temporal points from people’s daily lives. How to accurately aggregate large-scale trajectories is an important and challenging task. Conventional clustering algorithms (e.g., k-means or k-mediods) cannot be directly employed to process trajectory data due to their serialization, triviality and redundancy. Aiming to overcome the drawbacks of traditional k-means algorithm and k-mediods, including their sensitivity to the selection of the initial k value, the cluster centers and easy convergence to a locally optimal solution, we first propose an optimized k-means algorithm (namely OKM) to obtain k optimal initial clustering centers based on the density of trajectory points. Second, because k-means is sensitive to noisy points, we propose an improved k-mediods algorithm called IKMD based on an acceptable radius r by considering users’ geographic location in LBSNs. The value of k can be calculated based on r, and the optimal k points are selected as the initial clustering centers with high densities to reduce the cost of distance calculation. Thirdly, we thoroughly analyze the advantages of IKMD by comparing it with the commonly used clustering approaches through illustrative examples. Last, we conduct extensive experiments to evaluate the performance of IKMD against seven clustering approaches including the proposed optimized k-means algorithm, k-mediods algorithm, traditional density-based k-mediods algorithm and the state-of-the-arts trajectory clustering methods. The results demonstrate that IKMD significantly outperforms existing algorithms in the cost of distance calculation and the convergence speed. The methods proposed is proved to contribute to a larger effort targeted at advancing the study of intelligent trajectory data analytics.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"10 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":"131580581","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}

{"title":"Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms","authors":"Jianbin Huang, Longji Huang, Meijuan Liu, He Li, Qinglin Tan, Xiaoke Ma, Jiangtao Cui, De-Shuang Huang","doi":"10.1145/3474841","DOIUrl":"https://doi.org/10.1145/3474841","url":null,"abstract":"Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"102 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":"122053899","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}