ACM/IMS transactions on data science最新文献_第4页

Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm 多用户远程实验室:使用单纯形非支配排序遗传算法的时间表调度

ACM/IMS transactions on data science Pub Date : 2020-03-26 DOI: 10.1145/3437260

S. M. Zandavi, Yuk Ying Chung, Ali Anaissi

引用次数: 1

Group-Based Recurrent Neural Networks for POI Recommendation 基于组的递归神经网络POI推荐

ACM/IMS transactions on data science Pub Date : 2020-03-12 DOI: 10.1145/3343037

Guohui Li, Qi Chen, Bolong Zheng, Hongzhi Yin, Quoc Viet Hung Nguyen, Xiaofang Zhou

引用次数: 19

A Survey on Privacy in Social Media 社交媒体中的隐私调查

ACM/IMS transactions on data science Pub Date : 2020-03-12 DOI: 10.1145/3343038

Ghazaleh Beigi, Huan Liu

{"title":"A Survey on Privacy in Social Media","authors":"Ghazaleh Beigi, Huan Liu","doi":"10.1145/3343038","DOIUrl":"https://doi.org/10.1145/3343038","url":null,"abstract":"The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. These data provide opportunities for researchers and service providers to study and better understand users’ behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals’ privacy. Users privacy in social media is an emerging research area and has attracted increasing attention recently. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users’ privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups: (1) social graphs and privacy, (2) authors in social media and privacy, (3) profile attributes and privacy, (4) location and privacy, and (5) recommendation systems and privacy. We also discuss open problems and future research directions regarding user privacy issues in social media.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"9 4","pages":"1 - 38"},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3343038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72428813","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: 36

Explaining Financial Uncertainty through Specialized Word Embeddings 通过专业词嵌入解释金融不确定性

ACM/IMS transactions on data science Pub Date : 2020-03-12 DOI: 10.1145/3343039

Kilian Theil, Sanja Štajner, H. Stuckenschmidt

{"title":"Explaining Financial Uncertainty through Specialized Word Embeddings","authors":"Kilian Theil, Sanja Štajner, H. Stuckenschmidt","doi":"10.1145/3343039","DOIUrl":"https://doi.org/10.1145/3343039","url":null,"abstract":"The detection of vague, speculative, or otherwise uncertain language has been performed in the encyclopedic, political, and scientific domains yet left relatively untouched in finance. However, the latter benefits from public sources of big financial data that can be linked with extracted measures of linguistic uncertainty as a mean of extrinsic model validation. Doing so further helps in understanding how the linguistic uncertainty of financial disclosures might induce financial uncertainty to the market. To explore this field, we use term weighting methods to detect linguistic uncertainty in a large dataset of financial disclosures. As a baseline, we use an existing dictionary of financial uncertainty triggers; furthermore, we retrieve related terms in specialized word embedding models to automatically expand this dictionary. Apart from an industry-agnostic expansion, we create expansions incorporating industry-specific jargon. In a set of cross-sectional event study regressions, we show that the such enriched dictionary explains a significantly larger share of future volatility, a common financial uncertainty measure, than before. Furthermore, we show that—different to the plain dictionary—our embedding models are well suited to explain future analyst forecast uncertainty. Notably, our results indicate that enriching the dictionary with industry-specific vocabulary explains a significantly larger share of financial uncertainty than an industry-agnostic expansion.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"52 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76030378","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: 8

Interval-based Queries over Lossy IoT Event Streams 基于时间间隔的有损物联网事件流查询

ACM/IMS transactions on data science Pub Date : 2020-03-10 DOI: 10.1145/3385191

Nimrod Busany, Han van der Aa, Arik Senderovich, A. Gal, M. Weidlich

{"title":"Interval-based Queries over Lossy IoT Event Streams","authors":"Nimrod Busany, Han van der Aa, Arik Senderovich, A. Gal, M. Weidlich","doi":"10.1145/3385191","DOIUrl":"https://doi.org/10.1145/3385191","url":null,"abstract":"Recognising patterns that correlate multiple events over time becomes increasingly important in applications that exploit the Internet of Things, reaching from urban transportation through surveillance monitoring to business workflows. In many real-world scenarios, however, timestamps of events may be erroneously recorded, and events may be dropped from a stream due to network failures or load shedding policies. In this work, we present SimpMatch, a novel simplex-based algorithm for probabilistic evaluation of event queries using constraints over event orderings in a stream. Our approach avoids learning probability distributions for time-points or occurrence intervals. Instead, we employ the abstraction of segmented intervals and compute the probability of a sequence of such segments using the notion of order statistics. The algorithm runs in linear time to the number of lost events and shows high accuracy, yielding exact results if event generation is based on a Poisson process and providing a good approximation otherwise. We demonstrate empirically that SimpMatch enables efficient and effective reasoning over event streams, outperforming state-of-the-art methods for probabilistic evaluation of event queries by up to two orders of magnitude.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"31 1","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83154938","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: 3

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles 物理引导的机器学习用于科学发现:在模拟湖泊温度剖面中的应用

ACM/IMS transactions on data science Pub Date : 2020-01-28 DOI: 10.1145/3447814

X. Jia, J. Willard, A. Karpatne, J. Read, J. Zwart, M. Steinbach, Vipin Kumar

{"title":"Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles","authors":"X. Jia, J. Willard, A. Karpatne, J. Read, J. Zwart, M. Steinbach, Vipin Kumar","doi":"10.1145/3447814","DOIUrl":"https://doi.org/10.1145/3447814","url":null,"abstract":"Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"2 1","pages":"1 - 26"},"PeriodicalIF":0.0,"publicationDate":"2020-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3447814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42131364","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: 138

Toward Translating Raw Indoor Positioning Data into Mobility Semantics 将原始室内定位数据转换为移动语义

ACM/IMS transactions on data science Pub Date : 2020-01-20 DOI: 10.1145/3385190

Huan Li, Hua Lu, Gang Chen, Ke Chen, Q. Chen, L. Shou

{"title":"Toward Translating Raw Indoor Positioning Data into Mobility Semantics","authors":"Huan Li, Hua Lu, Gang Chen, Ke Chen, Q. Chen, L. Shou","doi":"10.1145/3385190","DOIUrl":"https://doi.org/10.1145/3385190","url":null,"abstract":"Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into mobility semantics that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the cleaning layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the annotation layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the complementing layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution’s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"51 1","pages":"1 - 37"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76693257","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: 11

Measuring Female Representation and Impact in Films over Time 衡量女性在电影中的表现和影响

ACM/IMS transactions on data science Pub Date : 2020-01-10 DOI: 10.1145/3411213

Luoying Yang, Zhou Xu, Jiebo Luo

{"title":"Measuring Female Representation and Impact in Films over Time","authors":"Luoying Yang, Zhou Xu, Jiebo Luo","doi":"10.1145/3411213","DOIUrl":"https://doi.org/10.1145/3411213","url":null,"abstract":"Women have always been underrepresented in movies and not until recently has the representation of women in movies improved. To investigate the improvement of female representation and its relationship with a movie’s success, we propose a new measure, the female cast ratio, and compare it to the commonly used Bechdel test result. We employ generalized linear regression with L1 penalty and a Random Forest model to identify the predictors that influence female representation, and evaluate the relationship between female representation and a movie’s success in three aspects: revenue/budget ratio, rating, and popularity. Three important findings in our study have highlighted the difficulties women in the film industry face both upstream and downstream. First, female filmmakers, especially female screenplay writers, are instrumental for movies to have better female representation, but the percentage of female filmmakers has been very low. Second, movies that have the potential to tell insightful stories about women are often provided with lower budgets, and this usually causes the films to in turn receive more criticism. Finally, the demand for better female representation from moviegoers has also not been strong enough to compel the film industry to change, as movies that have poor female representation can still be very popular and successful in the box office.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"26 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91084269","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

Inaugural Issue Editorial 创刊号社论

ACM/IMS transactions on data science Pub Date : 2020-01-01 DOI: 10.1145/3368254

B. Ooi

引用次数: 0

Utility Mining across Multi-Sequences with Individualized Thresholds 基于个性化阈值的多序列效用挖掘

ACM/IMS transactions on data science Pub Date : 2019-12-25 DOI: 10.1145/3362070

Wensheng Gan, Jerry Chun‐wei Lin, Jiexiong Zhang, Philip S. Yu

{"title":"Utility Mining across Multi-Sequences with Individualized Thresholds","authors":"Wensheng Gan, Jerry Chun‐wei Lin, Jiexiong Zhang, Philip S. Yu","doi":"10.1145/3362070","DOIUrl":"https://doi.org/10.1145/3362070","url":null,"abstract":"Utility-oriented pattern mining is an emerging topic, since it can reveal high-utility patterns from different types of data, which provides more information than the traditional frequency/confidence-based pattern mining models. The utilities of various items/objects are not exactly equal in realistic situations; each item/object has its own utility or importance. In general, the user considers a uniform minimum utility (minutil) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while the minutil is set extremely high or low. We first design a new utility mining framework namely USPT for mining high-Utility Sequential Patterns across multi-sequences with individualized Thresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results show that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.","PeriodicalId":93404,"journal":{"name":"ACM/IMS transactions on data science","volume":"277 1-2 1","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2019-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78465865","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: 13