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  • 1. Chen, Ye
    et al.
    Wang, Aiguo
    Ding, Huitong
    Que, Xia
    Li, Yabo
    An, Ning
    Jiang, Lili
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A global learning with local preservation method for microarray data imputation2016Ingår i: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 77, s. 76-89Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Microarray data suffer from missing values for various reasons, including insufficient resolution, image noise, and experimental errors. Because missing values can hinder downstream analysis steps that require complete data as input, it is crucial to be able to estimate the missing values. In this study, we propose a Global Learning with Local Preservation method (GL2P) for imputation of missing values in microarray data. GL2P consists of two components: a local similarity measurement module and a global weighted imputation module. The former uses a local structure preservation scheme to exploit as much information as possible from the observable data, and the latter is responsible for estimating the missing values of a target gene by considering all of its neighbors rather than a subset of them. Furthermore, GL2P imputes the missing values in ascending order according to the rate of missing data for each target gene to fully utilize previously estimated values. To validate the proposed method, we conducted extensive experiments on six benchmarked microarray datasets. We compared GL2P with eight state-of-the-art imputation methods in terms of four performance metrics. The experimental results indicate that GL2P outperforms its competitors in terms of imputation accuracy and better preserves the structure of differentially expressed genes. In addition, GL2P is less sensitive to the number of neighbors than other local learning-based imputation. methods.

  • 2. Gonzalez, Roberto
    et al.
    Jiang, Lili
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ahmed, Mohamed
    Marciel, Miriam
    Cuevas, Ruben
    Metwalley, Hassan
    Niccolini, Saverio
    The cookie recipe: Untangling the use of cookies in the wild2017Ingår i: TMA Conference 2017: Proceedings of the 1st Network Traffic Measurement and Analysis Conference, IEEE, 2017, nr C 2014. Proceedings: LNCS 8783InformationSecurity 17th International Confe= nce, ISC 2014, 12-14 Oct. 2014, Hong Kong, China, P309 osh A., 2015, ACM Transactions on Economics and Computation, V3,=20 vakorn Suphannee, 2016, 2016 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP)I=Konferensbidrag (Refereegranskat)
    Abstract [en]

    Users online are commonly tracked using HTTP cookies when browsing on the web. To protect their privacy, users tend to use simple tools to block the activity of HTTP cookies. However, the "block all" design of tools breaks critical web services or severely limits the online advertising ecosystem. Therefore, to ease this tension, a more nuanced strategy that discerns better the intended functionality of the HTTP cookies users encounter is required. We present the first large-scale study of the use of HTTP cookies in the wild using network traces containing more than 5.6 billion HTTP requests from real users for a period of two and a half months. We first present a statistical analysis of how cookies are used. We then analyze the structure of cookies and observe that; HTTP cookies are significantly more sophisticated than the name=3Dvalue defined by the standard and assumed by researchers and developers. Based on our findings we present an algorithm that is able to extract the information included in 86% of the cookies in our dataset with an accuracy of 91.7%. Finally, we discuss the implications of our findings and provide solutions that can be used to improve the most promising privacy preserving tools.

  • 3.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Addi, Ait-Mlouk
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Graph-based Interactive Data Federation System for Heterogeneous Data Retrieval and Analytics2019Ingår i: Proceedings of The 30th TheWebConf'19 (formerly WWW), USA, New York, NY, USA: ACM Digital Library, 2019, s. 3595-3599Konferensbidrag (Refereegranskat)
    Abstract [en]

    Given the increasing number of heterogeneous data stored in relational databases, file systems or cloud environment, it needs to be easily accessed and semantically connected for further data analytic. The potential of data federation is largely untapped, this paper presents an interactive data federation system (https://vimeo.com/ 319473546) by applying large-scale techniques including heterogeneous data federation, natural language processing, association rules and semantic web to perform data retrieval and analytics on social network data. The system first creates a Virtual Database (VDB) to virtually integrate data from multiple data sources. Next, a RDF generator is built to unify data, together with SPARQL queries, to support semantic data search over the processed text data by natural language processing (NLP). Association rule analysis is used to discover the patterns and recognize the most important co-occurrences of variables from multiple data sources. The system demonstrates how it facilitates interactive data analytic towards different application scenarios (e.g., sentiment analysis, privacyconcern analysis, community detection).

  • 4.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Flekova, Lucie
    Amazon Research Germany.
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Gurevych, Iryna
    UKP Lab, Computer Science Department, Technische Universitat Darmstadt.
    Lexical-semantic resources: yet powerful resources for automatic personality classification2018Ingår i: Proceedings of the 9th Global WordNet Conference (GWC 2018) / [ed] Francis Bond, Takayuki Kuribayashi, Christiane Fellbaum, Piek Vossen, Singapore: Nanyang Technological University (NTU) , 2018, , s. 10s. 173-182Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.

  • 5.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jiang, Lili
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Brändström, Anders
    Umeå universitet, Samhällsvetenskapliga fakulteten, Enheten för demografi och åldrandeforskning (CEDAR).
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Personality-based Knowledge Extraction for Privacy-preserving Data Analysis2017Ingår i: K-CAP 2017 - Proceedings of the Knowledge Capture Conference, Austin, TX, USA: ACM Digital Library, 2017, artikel-id 45Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present a differential privacy preserving approach, which extracts personality-based knowledge to serve privacy guarantee data analysis on personal sensitive data. Based on the approach, we further implement an end-to-end privacy guarantee system, KaPPA, to provide researchers iterative data analysis on sensitive data. The key challenge for differential privacy is determining a reasonable amount of privacy budget to balance privacy preserving and data utility. Most of the previous work applies unified privacy budget to all individual data, which leads to insufficient privacy protection for some individuals while over-protecting others. In KaPPA, the proposed personality-based privacy preserving approach automatically calculates privacy budget for each individual. Our experimental evaluations show a significant trade-off of sufficient privacy protection and data utility.

  • 6.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Self-adaptive Privacy Concern Detection for User-generated Content2018Ingår i: Proceedings of the 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2018, 2018Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual’s sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).

  • 7.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Santra, Abhishek
    Chakravarthy, Sharma
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Generic Multilayer Network Data Analysis with the Fusion of Content and Structure2019Ingår i: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challenging especially if one wants to do it efficiently and retain the flexibility by choosing features of interest for analysis. Features (e.g., age, gender, relationship, political view etc.) can be explicitly given from datasets, but also can be derived from content (e.g., political view based on Facebook posts). Analysis from multiple perspectives is needed to understand the datasets (or subsets of it) and to infer meaningful knowledge. For example, the influence of age, location, and marital status on political views may need to be inferred separately (or in combination). In this paper, we adapt multilayer network (MLN) analysis, a nontraditional approach, to model the Facebook datasets, integrate content analysis, and conduct analysis, which is driven by a list of desired application based queries. Our experimental analysis shows the flexibility and efficiency of the proposed approach when modeling and analyzing datasets with multiple features.

  • 8.
    Vu, Xuan-Son
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tran N., Son
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    dpUGC: Learn Differentially Private Representation for User Generated Contents2019Ingår i: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and dataindependent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.

  • 9. Wang, Aiguo
    et al.
    Chen, Ye
    An, Ning
    Yang, Jing
    Li, Lian
    Lili, Jiang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Microarray Missing Value Imputation: A Regularized Local Learning Method2019Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics, ISSN 1545-5963, E-ISSN 1557-9964, Vol. 16, nr 3, s. 980-993Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Microarray experiments on gene expression inevitably generate missing values, which impedes further downstream biological analysis. Therefore, it is key to estimate the missing values accurately. Most of the existing imputation methods tend to suffer from the over-fitting problem. In this study, we propose two regularized local learning methods for microarray missing value imputation. Motivated by the grouping effect of L-2 regularization, after selecting the target gene, we train an L-2 Regularized Local Least Squares imputation model (RLLSimpute_L2) on the target gene and its neighbors to estimate the missing values of the target gene. Furthermore, RLLSimpute_L2 imputes the missing values in an ascending order based on the associated missing rate with each target gene. This contributes to fully utilizing the previously estimated values. Besides L-2, we further explore L-1 regularization and propose an L-1 Regularized Local Least Squares imputation model (RLLSimpute_L1). To evaluate their effectiveness, we conducted extensive experimental studies on six benchmark datasets covering both time series and non-time series cases. Nine state-of-the-art imputation methods are compared with RLLSimpute_L2 and RLLSimpute_L1 in terms of three performance metrics. The comparative experimental results indicate that RLLSimpute_L2 outperforms its competitors by achieving smaller imputation errors and better structure preservation of differentially expressed genes.

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