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Articles 61 - 90 of 175
Full-Text Articles in Data Storage Systems
Scaling Human Activity Recognition Via Deep Learning-Based Domain Adaptation, Md Abdullah Hafiz Khan, Nirmalya Roy, Archan Misra
Scaling Human Activity Recognition Via Deep Learning-Based Domain Adaptation, Md Abdullah Hafiz Khan, Nirmalya Roy, Archan Misra
Research Collection School Of Computing and Information Systems
We investigate the problem of making human activityrecognition (AR) scalable–i.e., allowing AR classifiers trainedin one context to be readily adapted to a different contextualdomain. This is important because AR technologies can achievehigh accuracy if the classifiers are trained for a specific individualor device, but show significant degradation when the sameclassifier is applied context–e.g., to a different device located ata different on-body position. To allow such adaptation withoutrequiring the onerous step of collecting large volumes of labeledtraining data in the target domain, we proposed a transductivetransfer learning model that is specifically tuned to the propertiesof convolutional neural networks (CNNs). Our model, …
Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak
Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak
Research Collection School Of Computing and Information Systems
To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed …
Constant-Size Ciphertexts In Threshold Attribute-Based Encryption Without Dummy Attributes, Willy Susilo, Guomin Yang, Fuchun Guo, Qiong Huang
Constant-Size Ciphertexts In Threshold Attribute-Based Encryption Without Dummy Attributes, Willy Susilo, Guomin Yang, Fuchun Guo, Qiong Huang
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) is an augmentation of public key encryption that allows users to encrypt and decrypt messages based on users' attributes. In a (t, s) threshold ABE, users who can decrypt a ciphertext must hold at least t attributes among the s attributes specified by the encryptor. At PKC 2010, Herranz, Laguillaumie and Raft& proposed the first threshold ABE with constant-size ciphertexts. In order to ensure the encryptor can flexibly select the attribute set and a threshold value, they use dummy attributes to satisfy the decryption requirement. The advantage of their scheme is that any addition or removal of …
Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Research Collection School Of Computing and Information Systems
The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE …
A Distributed Intrusion Detection Model Via Nondestructive Partitioning And Balanced Allocation For Big Data, Xiaonian Wu, Chuyun Zhang, Runlian Zhang, Yujue Wang, Jinhua Cui
A Distributed Intrusion Detection Model Via Nondestructive Partitioning And Balanced Allocation For Big Data, Xiaonian Wu, Chuyun Zhang, Runlian Zhang, Yujue Wang, Jinhua Cui
Research Collection School Of Computing and Information Systems
There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as …
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Research Collection School Of Computing and Information Systems
Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items. SourceVote models the endorsement relations among sources by quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects …
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Research Collection School Of Computing and Information Systems
Data fusion is a fundamental research problem of identifyingtrue values of data items of interest from conflicting multi-sourceddata. Although considerable research efforts have been conducted on thistopic, existing approaches generally assume every data item has exactlyone true value, which fails to reflect the real world where data items withmultiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items.SourceVote models the endorsement relations among sources by quantifyingtheir two-sided inter-source agreements. In particular, two graphs areconstructed to model inter-source relations. Then two aspects of sourcereliability are derived from these graphs and …
Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao
Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao
Research Collection School Of Computing and Information Systems
This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. Existing outlier detection methods work on a full data space or feature subspaces that are identified independently from subsequent outlier scoring. As a result, they are significantly challenged by overwhelming irrelevant features in high-dimensional data due to the noise brought by the irrelevant features and its huge search space. In contrast, SelectVC works on a clean and condensed data space spanned by selective …
Cross-Modal Recipe Retrieval With Rich Food Attributes, Jingjing Chen, Chong-Wah Ngo, Tat-Seng Chua
Cross-Modal Recipe Retrieval With Rich Food Attributes, Jingjing Chen, Chong-Wah Ngo, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Food is rich of visible (e.g., colour, shape) and procedural (e.g., cutting, cooking) attributes. Proper leveraging of these attributes, particularly the interplay among ingredients, cutting and cooking methods, for health-related applications has not been previously explored. This paper investigates cross-modal retrieval of recipes, specifically to retrieve a text-based recipe given a food picture as query. As similar ingredient composition can end up with wildly different dishes depending on the cooking and cutting procedures, the difficulty of retrieval originates from fine-grained recognition of rich attributes from pictures. With a multi-task deep learning model, this paper provides insights on the feasibility of …
Semantic Reasoning In Zero Example Video Event Retrieval, M. H. T. De Boer, Yi-Jie Lu, Hao Zhang, Klamer Schutte, Chong-Wah Ngo, Wessel Kraaij
Semantic Reasoning In Zero Example Video Event Retrieval, M. H. T. De Boer, Yi-Jie Lu, Hao Zhang, Klamer Schutte, Chong-Wah Ngo, Wessel Kraaij
Research Collection School Of Computing and Information Systems
Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneficial for the retrieval of high-level events using visual examples, but without examples it is still hard to (1) determine which concepts are useful to pre-train (Vocabulary challenge) and (2) which pre-trained concept detectors are relevant for a certain unseen high-level event (Concept Selection challenge). In our article, we present our Semantic Event Retrieval Systemwhich (1) shows the importance of high-level concepts …
Personalized Microtopic Recommendation On Microblogs, Yang Li, Jing Jiang, Ting Liu, Minghui Qiu, Xiaofei Sun
Personalized Microtopic Recommendation On Microblogs, Yang Li, Jing Jiang, Ting Liu, Minghui Qiu, Xiaofei Sun
Research Collection School Of Computing and Information Systems
Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. In Sina Weibo, these tags are called microtopics, and in Twitter, they are called hashtags. In Sina Weibo, each microtopic has a designate page and can be directly visited or commented on. Recommending these microtopics to users based on their interests can help users efficiently acquire information. However, it is non-trivial to recommend microtopics to users to satisfy their information needs. In this article, we investigate the task of personalized microtopic recommendation, which exhibits two challenges. First, users usually do not …
Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang
Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang
Research Collection School Of Computing and Information Systems
The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivot-based indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies …
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Research Collection School Of Computing and Information Systems
Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set …
Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
Research Collection School Of Computing and Information Systems
Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster …
Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw
Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a …
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Research Collection School Of Computing and Information Systems
The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize …
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision-making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Research Collection School Of Computing and Information Systems
We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Research Collection School Of Computing and Information Systems
This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors …
Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism, Qi Fan, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan Tan
Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism, Qi Fan, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan Tan
Research Collection School Of Computing and Information Systems
Automatic discovery of newsworthy themes from sequenced data can relieve journalists from manually poring over a large amount of data in order to find interesting news. In this paper, we propose a novel k -Sketch query that aims to find k striking streaks to best summarize a subject. Our scoring function takes into account streak strikingness and streak coverage at the same time. We study the k -Sketch query processing in both offline and online scenarios, and propose various streak-level pruning techniques to find striking candidates. Among those candidates, we then develop approximate methods to discover the k most representative …
Attribute-Based Encryption With Expressive And Authorized Keyword Search, Hui Cui, Robert H. Deng, Joseph K. Liu, Yingjiu Li
Attribute-Based Encryption With Expressive And Authorized Keyword Search, Hui Cui, Robert H. Deng, Joseph K. Liu, Yingjiu Li
Research Collection School Of Computing and Information Systems
To protect data security and privacy in cloud storage systems, a common solution is to outsource data in encrypted forms so that the data will remain secure and private even if storage systems are compromised. The encrypted data, however, must be pliable to search and access control. In this paper, we introduce a notion of attribute-based encryption with expressive and authorized keyword search (ABE-EAKS) to support both expressive keyword search and fine-grained access control over encrypted data in the cloud. In ABE-EAKS, every data user is associated with a set of attributes and is issued a private attribute-key corresponding to …
Mining Capstone Project Wikis For Knowledge Discovery, Swapna Gottipati, Venky Shankararaman, Melvrivk Goh
Mining Capstone Project Wikis For Knowledge Discovery, Swapna Gottipati, Venky Shankararaman, Melvrivk Goh
Research Collection School Of Computing and Information Systems
Wikis are widely used collaborative environments as sources of information and knowledge. The facilitate students to engage in collaboration and share information among members and enable collaborative learning. In particular, Wikis play an important role in capstone projects. Wikis aid in various project related tasks and aid to organize information and share. Mining project Wikis is critical to understand the students learning and latest trends in industry. Mining Wikis is useful to educationists and academicians for decision-making about how to modify the educational environment to improve student's learning. The main challenge is that the content or data in project Wikis …
Is The Whole Greater Than The Sum Of Its Parts?, Liangyue Li, Hanghang Tong, Yong Wang, Conglei Shi, Nan Cao, Norbou Buchler
Is The Whole Greater Than The Sum Of Its Parts?, Liangyue Li, Hanghang Tong, Yong Wang, Conglei Shi, Nan Cao, Norbou Buchler
Research Collection School Of Computing and Information Systems
The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), …
Sap: Improving Continuous Top-K Queries Over Streaming Data, Rui Zhu, Bin Wang, Xiaochun Yang, Baihua Zheng, Guoren Wang
Sap: Improving Continuous Top-K Queries Over Streaming Data, Rui Zhu, Bin Wang, Xiaochun Yang, Baihua Zheng, Guoren Wang
Research Collection School Of Computing and Information Systems
Continuous top-k query over streaming data is a fundamental problem in database. In this paper, we focus on the sliding window scenario, where a continuous top-k query returns the top-k objects within each query window on the data stream. Existing algorithms support this type of queries via incrementally maintaining a subset of objects in the window and try to retrieve the answer from this subset as much as possible whenever the window slides. However, since all the existing algorithms are sensitive to query parameters and data distribution, they all suffer from expensive incremental maintenance cost. In this paper, we propose …
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Research Collection School Of Computing and Information Systems
Cloud computing offers a new way of services by re-arranging various resources and providing them to users based on their demands. It also plays an important role in the next generation mobile networks and services (5G) and Cyber-Physical and Social Computing (CPSC). Storing data in the cloud greatly reduces storage burden of users and brings them access convenience, thus it has become one of the most important cloud services. However, cloud data security, privacy and trust become a crucial issue that impacts the success of cloud computing and may impede the development of 5G and CPSC. First, storing data at …
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
Research Collection School Of Computing and Information Systems
This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …
Unsupervised Feature Selection For Outlier Detection By Modelling Hierarchical Value-Feature Couplings, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Unsupervised Feature Selection For Outlier Detection By Modelling Hierarchical Value-Feature Couplings, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Research Collection School Of Computing and Information Systems
Proper feature selection for unsupervised outlier detection can improve detection performance but is very challenging due to complex feature interactions, the mixture of relevant features with noisy/redundant features in imbalanced data, and the unavailability of class labels. Little work has been done on this challenge. This paper proposes a novel Coupled Unsupervised Feature Selection framework (CUFS for short) to filter out noisy or redundant features for subsequent outlier detection in categorical data. CUFS quantifies the outlierness (or relevance) of features by learning and integrating both the feature value couplings and feature couplings. Such value-to-feature couplings capture intrinsic data characteristics and …
Indoor Localization Via Multi-Modal Sensing On Smartphones, Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, Yunhao Liu
Indoor Localization Via Multi-Modal Sensing On Smartphones, Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, Yunhao Liu
Research Collection School Of Computing and Information Systems
Indoor localization is of great importance to a wide range ofapplications in shopping malls, office buildings and publicplaces. The maturity of computer vision (CV) techniques andthe ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pureCV-based solutions usually involve hundreds of photos andpre-calibration to construct image database, a labor-intensiveoverhead for practical deployment. We present ClickLoc, anaccurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted insemantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the …
Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie
Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie
Research Collection School Of Computing and Information Systems
In large-scale distributed file systems, efficient metadata operations are critical since most file operations have to interact with metadata servers first. In existing distributed hash table (DHT) based metadata management systems, the lookup service could be a performance bottleneck due to its significant CPU overhead. Our investigations showed that the lookup service could reduce system throughput by up to 70%, and increase system latency by a factor of up to 8 compared to ideal scenarios. In this paper, we present MetaFlow, a scalable metadata lookup service utilizing software-defined networking (SDN) techniques to distribute lookup workload over network components. MetaFlow tackles …
Outlier Detection In Complex Categorical Data By Modeling The Feature Value Couplings, Guansong Pang, Longbing Cao, Ling Chen
Outlier Detection In Complex Categorical Data By Modeling The Feature Value Couplings, Guansong Pang, Longbing Cao, Ling Chen
Research Collection School Of Computing and Information Systems
This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial …