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A reinforcement learning-based approach to testing GUI of moblie applications 基于强化学习的移动应用程序图形用户界面测试方法

World Wide Web Pub Date : 2024-02-22 DOI: 10.1007/s11280-024-01252-9

Chuanqi Tao, Fengyu Wang, Yuemeng Gao, Hongjing Guo, Jerry Gao

{"title":"A reinforcement learning-based approach to testing GUI of moblie applications","authors":"Chuanqi Tao, Fengyu Wang, Yuemeng Gao, Hongjing Guo, Jerry Gao","doi":"10.1007/s11280-024-01252-9","DOIUrl":"https://doi.org/10.1007/s11280-024-01252-9","url":null,"abstract":"With the popularity of mobile devices, the software market of mobile applications has been booming in recent years. Android applications occupy a vast market share. However, the applications inevitably contain defects. Defects may affect the user experience and even cause severe economic losses. This paper proposes ATAC and ATPPO, which apply reinforcement learning to Android GUI testing to mitigate the state explosion problem. The article designs a new reward function and a new state representation. It also constructs two GUI testing models (ATAC and ATPPO) based on A2C and PPO algorithms to save memory space and accelerate training speed. Empirical studies on twenty open-source applications from GitHub demonstrate that: (1) ATAC performs best in 16 of 20 apps in code coverage and defects more exceptions; (2) ATPPO can get higher code coverage in 15 of 20 apps and defects more exceptions; (3) Compared with state-of-art tools Monkey and ARES, ATAC, and ATPPO shows higher code coverage and detects more errors. ATAC and ATPPO can not only cover more code coverage but also can effectively detect more exceptions. This paper also introduces Finite-State Machine into the reinforcement learning framework to avoid falling into the local optimal state, which provides high-level guidance for further improving the test efficiency.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949548","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

Sampling hypergraphs via joint unbiased random walk 通过联合无偏随机漫步对超图谱进行采样

World Wide Web Pub Date : 2024-02-19 DOI: 10.1007/s11280-024-01253-8

Qi Luo, Zhenzhen Xie, Yu Liu, Dongxiao Yu, Xiuzhen Cheng, Xuemin Lin, Xiaohua Jia

{"title":"Sampling hypergraphs via joint unbiased random walk","authors":"Qi Luo, Zhenzhen Xie, Yu Liu, Dongxiao Yu, Xiuzhen Cheng, Xuemin Lin, Xiaohua Jia","doi":"10.1007/s11280-024-01253-8","DOIUrl":"https://doi.org/10.1007/s11280-024-01253-8","url":null,"abstract":"Hypergraphs are instrumental in modeling complex relational systems that encompass a wide spectrum of high-order interactions among components. One prevalent analysis task is the properties estimation of large-scale hypergraphs, which involves selecting a subset of nodes and hyperedges while preserving the characteristics of the entire hypergraph. This paper aims to sample hypergraphs via random walks and is the first to perform unbiased random walks for sampling of nodes and hyperedges simultaneously in large-scale hypergraphs to the best of our knowledge. Initially, we analyze the stationary distributions of nodes and hyperedges for the simple random walk, and show that there is a high bias in both nodes and hyperedges. Subsequently, to eliminate the high bias of the simple random walk, we propose unbiased random walk strategies for nodes and hyperedges, respectively. Finally, a single joint walk schema is developed for sampling nodes and hyperedges simultaneously. To accelerate the convergence process, we employ delayed acceptance and history-aware techniques to assist our algorithm in achieving fast convergence. Extensive experimental results validate our theoretical findings, and the unbiased sampling algorithms for nodes and hyperedges have their complex hypergraph scenarios for which they are applicable. The joint random walk algorithm balanced the sampling applicable to both nodes and hyperedges.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139902170","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

Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method 为位置预测建立动态时空用户偏好模型：一种相互增强的方法

World Wide Web Pub Date : 2024-02-13 DOI: 10.1007/s11280-024-01245-8

Jiawei Cai, Dong Wang, Hongyang Chen, Chenxi Liu, Zhu Xiao

{"title":"Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method","authors":"Jiawei Cai, Dong Wang, Hongyang Chen, Chenxi Liu, Zhu Xiao","doi":"10.1007/s11280-024-01245-8","DOIUrl":"https://doi.org/10.1007/s11280-024-01245-8","url":null,"abstract":"As the cornerstone of location-based services, location prediction aims to predict user’s next location through modeling user’s personal preference or travel sequential pattern. However, most existing methods only consider one of them and extremely sparse data makes it difficult to dynamically and comprehensively characterize user preference. In this paper, we propose a novel Dynamic Spatiotemporal User Preference (DSUP) model to characterize dynamic spatiotemporal user preference and integrate it with user’s travel sequential pattern for location prediction. Specifically, we design an interaction-aware graph attention network to learn the embeddings of locations and timeslots, and infer dynamic spatiotemporal user preference from the history travel locations and timeslots. Then, we combine user’s current travel preference with the impact of history travel sequential pattern to predict user’s next location. In addition, we predict user’s next travel timeslot and combine it with the temporal pattern of locations to enhance the location and timeslot prediction results mutually. We conduct extensive experiments on two public datasets Gowalla, Foursquare and our own Private Car dataset. The results on three datasets show that our method improves the accuracy and mean reciprocal rank of location prediction by 3%-11% and 7%-10% respectively.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756313","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

LAMEE: a light all-MLP framework for time series prediction empowering recommendations LAMEE：用于时间序列预测的轻型全 MLP 框架授权建议

World Wide Web Pub Date : 2024-02-12 DOI: 10.1007/s11280-024-01251-w

Yi Xie, Yun Xiong, Xiaofeng Gao, Jiadong Chen, Yao Zhang, Xian Wu, Chao Chen

{"title":"LAMEE: a light all-MLP framework for time series prediction empowering recommendations","authors":"Yi Xie, Yun Xiong, Xiaofeng Gao, Jiadong Chen, Yao Zhang, Xian Wu, Chao Chen","doi":"10.1007/s11280-024-01251-w","DOIUrl":"https://doi.org/10.1007/s11280-024-01251-w","url":null,"abstract":"Exogenous variables, unrelated to the recommendation system itself, can significantly enhance its performance. Therefore, integrating these time-evolving exogenous variables into a time series and conducting time series predictions can maximize the potential of recommendation systems. We refer to this task as Time Series Prediction Empowering Recommendations (TSPER). However, as a subtask within the recommendation system, TSPER faces unique challenges such as computational and data constraints, system evolution, and the need for performance and interpretability. To meet these unique needs, we propose a lightweight Multi-Layer Perceptron architecture with joint Time-Frequency information, named Light All-MLP with joint TimE-frEquency information (LAMEE). LAMEE utilizes a lightweight MLP architecture to achieve computing efficiency and adaptive online learning. Moreover, various strategies have been employed to improve the model, ensuring stable performance and model interpretability. Across multiple time series datasets potentially related to recommendation systems, LAMEE balances performance, efficiency, and interpretability, overall surpassing existing complex methods.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756452","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

EMPNet: An extract-map-predict neural network architecture for cross-domain recommendation EMPNet：用于跨域推荐的提取-映射-预测神经网络架构

World Wide Web Pub Date : 2024-02-03 DOI: 10.1007/s11280-024-01240-z

Jinpeng Chen, Fan Zhang, Huan Li, Hua Lu, Xiongnan Jin, Kuien Liu, Hongjun Li, Yongheng Wang

{"title":"EMPNet: An extract-map-predict neural network architecture for cross-domain recommendation","authors":"Jinpeng Chen, Fan Zhang, Huan Li, Hua Lu, Xiongnan Jin, Kuien Liu, Hongjun Li, Yongheng Wang","doi":"10.1007/s11280-024-01240-z","DOIUrl":"https://doi.org/10.1007/s11280-024-01240-z","url":null,"abstract":"Cross-domain recommendation leverages a user’s historical interactions in the auxiliary domain to suggest items within the target domain, particularly for cold-start users with no prior activity in the target domain. Existing cross-domain recommendation models often overlook key aspects such as the complexities of transferring user interests between domains and the biases inherent in user behavior patterns. In contrast, our Extract-Map-Predict Neural Network Architecture (EMPNet) employs a disentanglement approach to map fine-grained user interests and utilize the biases inherent in the cross-domain recommendation. In feature extraction, we use the Bidirectional Encoder Representations from Transformers (BERT) and Identity-Enhanced Multi-Head Attention Mechanism to obtain the user and item feature vectors. In cross-domain user mapping, we disentangle the user feature vector into domain-shared and domain-specific interests for fine-grained cross-domain mapping to obtain the feature vector of cold-start users in the target domain. In rating prediction, we design a biased Attentional Factorization Machine (AFM) to utilize biases extracted from user and item features. We experimentally evaluate EMPNet on the Amazon dataset. The results show that it clearly outperforms the selected baselines.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139679518","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

Path-based approximate matching of fuzzy spatiotemporal RDF data 基于路径的模糊时空 RDF 数据近似匹配

World Wide Web Pub Date : 2024-02-03 DOI: 10.1007/s11280-024-01247-6

Lin Zhu, Jiajia Lu, Luyi Bai

{"title":"Path-based approximate matching of fuzzy spatiotemporal RDF data","authors":"Lin Zhu, Jiajia Lu, Luyi Bai","doi":"10.1007/s11280-024-01247-6","DOIUrl":"https://doi.org/10.1007/s11280-024-01247-6","url":null,"abstract":"As fuzzy spatiotemporal information continuously increases in RDF database, it is challenging to model and query fuzzy spatiotemporal RDF data efficiently and effectively. However, various researches are studied in temporal RDF database, spatial RDF database, and spatiotemporal RDF database. Querying fuzzy spatiotemporal RDF data has received relatively little attention, especially approximate matching of fuzzy spatiotemporal RDF data. To accomplish this, we first study fuzzy spatiotemporal RDF data graph, spatiotemporal RDF query graph, and path of fuzzy spatiotemporal RDF data graph. Then, we propose a scoring function for approximate evaluation of fuzzy spatiotemporal RDF data graph and spatiotemporal RDF query graph. After dividing the fuzzy spatiotemporal RDF data graphs into five categories based on their structure, we propose the decomposition algorithm, matching algorithm, and combination algorithm for approximate matching of fuzzy spatiotemporal RDF data. Our approach adopts path-based matching so that it is easy to discover the relations between two vertices in fuzzy spatiotemporal RDF data graph. Finally, the experimental results demonstrate the performance advantages of our approach.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139679432","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

PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection PriMonitor：多模态情感检测的自适应调整隐私保护方法

World Wide Web Pub Date : 2024-02-02 DOI: 10.1007/s11280-024-01246-7

{"title":"PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection","authors":"","doi":"10.1007/s11280-024-01246-7","DOIUrl":"https://doi.org/10.1007/s11280-024-01246-7","url":null,"abstract":"<h3>Abstract</h3> The proliferation of edge computing and the Internet of Vehicles (IoV) has significantly bolstered the popularity of deep learning-based driver assistance applications. This has paved the way for the integration of multimodal emotion detection systems, which effectively enhance driving safety and are increasingly prevalent in our daily lives. However, the utilization of in-vehicle cameras and microphones has raised concerns regarding the extensive collection of driver privacy data. Applying privacy-preserving techniques to a single modality alone proves insufficient in preventing privacy re-identification when correlated with other modalities. In this paper, we introduce PriMonitor, an adaptive tuning privacy-preserving approach for multimodal emotion detection. PriMonitor tackles these challenges by proposing a generalized random response-based differential privacy method that not only enhances the speed and data availability of text privacy protection but also ensures privacy preservation across multiple modalities. To determine suitable weight assignments within a given privacy budget, we introduce pre-aggregator and iterative mechanisms. Our PriMonitor effectively mitigates privacy re-identification due to modal correlation while maintaining a high level of accuracy in multimodal models. Experimental results validate the efficiency and competitiveness of our approach.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667631","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

Invariant representation learning to popularity distribution shift for recommendation 为推荐而学习流行度分布偏移的不变表示法

World Wide Web Pub Date : 2024-02-02 DOI: 10.1007/s11280-024-01242-x

{"title":"Invariant representation learning to popularity distribution shift for recommendation","authors":"","doi":"10.1007/s11280-024-01242-x","DOIUrl":"https://doi.org/10.1007/s11280-024-01242-x","url":null,"abstract":"<h3>Abstract</h3> Recommender systems often suffer from severe performance drops due to popularity distribution shift (PDS), which arises from inconsistencies in item popularity between training and test data. Most existing methods aimed at mitigating PDS focus on reducing popularity bias, but they usually require inaccessible information or rely on implausible assumptions. To solve the above problem, in this work, we propose a novel framework called Invariant Representation Learning (IRL) to PDS. Specifically, for simulating diverse popularity environments where popular items and active users become even more popular and active, or conversely, we apply perturbations to the user-item interaction matrix by adjusting the weights of popular items and active users in the matrix, without any prior assumptions or specialized information. In different simulated popularity environments, dissimilarities in the distribution of representations for items and users occur. We further utilize contrastive learning to minimize the dissimilarities among the representations of users and items under different simulated popularity environments, resulting in invariant representations that remain consistent across varying popularity distributions. Extensive experiments on three real-world datasets demonstrate that IRL outperforms state-of-the-art baselines in effectively alleviating PDS for recommendation.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667673","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

Multi-stage dynamic disinformation detection with graph entropy guidance 利用图熵引导进行多阶段动态虚假信息检测

World Wide Web Pub Date : 2024-01-31 DOI: 10.1007/s11280-024-01243-w

Xiaorong Hao, Bo Liu, Xinyan Yang, Xiangguo Sun, Qing Meng, Jiuxin Cao

{"title":"Multi-stage dynamic disinformation detection with graph entropy guidance","authors":"Xiaorong Hao, Bo Liu, Xinyan Yang, Xiangguo Sun, Qing Meng, Jiuxin Cao","doi":"10.1007/s11280-024-01243-w","DOIUrl":"https://doi.org/10.1007/s11280-024-01243-w","url":null,"abstract":"Online disinformation has become one of the most severe concerns in today’s world. Recognizing disinformation timely and effectively is very hard, because the propagation process of disinformation is dynamic and complicated. The existing newest research leverage uniform time intervals to study the multi-stage propagation features of disinformation. However, uniform time intervals are unrealistic in the real world, cause the process of information propagation is not regular. In light of these facts, we propose a novel and effective framework Multi-stage Dynamic Disinformation Detection with Graph Entropy Guidance(MsDD) to better analyze multi-stage propagation patterns. Instead of traditional snapshots, we analyze the dynamic propagation network via graph entropy, which can work effectively in finding the dynamic and variable-length stages. In this way, we can explicitly learn the changing pattern of propagation stages and support timely detection even at the early stages. Based on this effective multi-stage analysis framework, we further propose a novel dynamic analysis model to model both the structural and sequential evolving features. Extensive experiments on two real-world datasets prove the superiority of our model. We open the datasets and source code at https://github.com/researchxr/MsDD.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"155-156 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139658012","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

Adaptive retrofitting for industrial machines: utilizing webassembly and peer-to-peer connectivity on the edge 工业机器的自适应改造：利用网络组装和边缘点对点连接

World Wide Web Pub Date : 2024-01-25 DOI: 10.1007/s11280-024-01237-8

Otoya Nakakaze, István Koren, Florian Brillowski, Ralf Klamma

{"title":"Adaptive retrofitting for industrial machines: utilizing webassembly and peer-to-peer connectivity on the edge","authors":"Otoya Nakakaze, István Koren, Florian Brillowski, Ralf Klamma","doi":"10.1007/s11280-024-01237-8","DOIUrl":"https://doi.org/10.1007/s11280-024-01237-8","url":null,"abstract":"Leveraging previously untapped data sources offers significant potential for value creation in the manufacturing sector. However, asset-heavy shop floors, extended machine replacement cycles, and equipment diversity necessitate considerable investments for achieving smart manufacturing, which can be particularly challenging for small businesses. Retrofitting presents a viable solution, enabling the integration of low-cost sensors and microcontrollers with older machines to collect and transmit data. In this paper, we introduce a concept and a prototype for retrofitting industrial environments using lightweight web technologies at the edge. Our approach employs WebAssembly as a novel bytecode standard, facilitating a consistent development environment from the cloud to the edge by operating on both browsers and bare-metal hardware. By attaining near-native performance and modularity reminiscent of container-based service architectures, we demonstrate the feasibility of our approach. Our prototype was evaluated with an actual industrial robot within a showcase factory, including measurements of data exchange with a cutting-edge data lake system. We further extended the prototype to incorporate a peer-to-peer network that facilitates message routing and WebAssembly software updates. Our technology establishes a foundational framework for the transition towards Industry 4.0. By integrating considerations of sustainability and human factors, it further extends this groundwork to facilitate progression into Industry 5.0.","PeriodicalId":501180,"journal":{"name":"World Wide Web","volume":"123 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139554030","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