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22nd international conference on Information and Software Technologies, ICIST 2016

22nd International Conference on Information and Software Te...

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22nd international conference on Information and Software Technologies, ICIST 2016

ISBN: (纸本)9783319462530

The proceedings contain 61 papers. The special focus in this conference is on Information Systems, Special Session on Innovative Applications for Knowledge Transfer Support, Special Session on e-Health Information Systems, Special Session on Information and Software Technologies for intelligent Power Systems, Business Intelligence for Information, Software engineering and Special Session on Smart e-learning Technologies and Applications. The topics include: Modelling of adequate costs of utilities services;views to legal information systems and legal sublevels;a model of the tacit knowledge transfer support tool;cloud-based enterprise information systems;semi-supervised learning approach for ontology mapping problem;rule-based canonicalization of arbitrary tables in spreadsheets;algorithm for colorblindness detection sets generation;towards a comprehensive formal model for business processes;shared resource model for allocating resources to activities in BP simulation;parallel computing for dimensionality reduction;lessons learned from tool integration with OSLC;novel recursive fast sort algorithm;a modified electromagnetic-like mechanism for rough set attribute reduction;a new classifier based on the dual indiscernibility matrix;novel image correction method based on swarm intelligence approach;a comparison of mining incomplete and inconsistent data;affective engagement to virtual and live lectures;topic classification problem solving for morphologically complex languages;text type differentiation based on the structural properties of language networks;an algorithm for phonetic string matching in Russian language;quality and importance of Wikipedia articles in different languages and change impact in product lines.

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Accelerating Online CP Decompositions for Higher Order Tensors 16

Accelerating Online CP Decompositions for Higher Order Tenso...

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Zhou, Shuo Nguyen Xuan Vinh Bailey, James Jia, Yunzhe Davidson, Ian Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

ISBN: (纸本)9781450342322

Tensors are a natural representation for multidimensional data. In recent years, CAndECOMP/PARAFAC (CP) decomposition, one of the most popular tools for analyzing multi-way data, has been extensively studied and widely applied. However, today's datasets are often dynamically changing over time. Tracking the CE decomposition for such dynamic tensors is a crucial but challenging task, due to the large scale of the tensor and the velocity of new data arriving. Traditional techniques, such as Alternating Least Squares (ALS), cannot be directly applied to this problem because of their poor scalability in terms of time and memory. Additionally, existing online approaches have only partially addressed this problem and can only be deployed on third-order tensors. To fill this gap, we propose an efficient online algorithm that can incrementally track the CP decompositions of dynamic tensors with an arbitrary number of dimensions. In terms of effectiveness, our algorithm demonstrates comparable results with the most accurate algorithm, ALS, whilst being computationally much more efficient. Specifically, on small and moderate datasets, our approach is tens to hundreds of times faster than ALS, while for large-scale datasets, the speedup can be more than 3,000 times. Compared to other state-of-tine-art online approaches, our method shows not only significantly better decomposition quality, but also better performance in terms of stability, efficiency and scalability.

关键词： Tensor Decomposition CP Decomposition Online learning

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Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features 16

Deep Crossing: Web-Scale Modeling without Manually Crafted C...

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Shan, Ying Hoens, T. Ryan Jiao, Jian Wang, Haijing Yu, Dong Mao, J. C. Microsoft Res Bing Ads One Microsoft Way Redmond WA USA Microsoft Corp One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781450342322

Manually crafted combinatorial features have been the "secret sauce" behind many successful models. For web-scale applications, however, the variety and volume of features make these manually crafted features expensive to create, maintain, and deploy. This paper proposes the Deep Crossing model which is a deep neural network that automatically combines features to produce superior models. The input of Deep Crossing is a set of individual features that can be either dense or sparse. The important crossing features are discovered implicitly by the networks, which are comprised of an embedding and stacking layer, as well as a cascade of Residual Units. Deep Crossing is implemented with a modeling tool called the Computational Network Tool Kit (CNTK), powered by a multi-GPU platform. It was able to build, from scratch, two web-scale models for a major paid search engine, and achieve superior results with only a sub-set of the features used in the production models. This demonstrates the potential of using Deep Crossing as a general modeling paradigm to improve existing products, as well as to speed up the development of new models with a fraction of the investment in feature engineering and acquisition of deep domain knowledge.

关键词： Neural Networks Deep learning Convolutional Neural Network (CNN) Deep Crossing Combinatorial Features DSSM Residual Net CNTK GPU Multi-GPU Platform

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Towards Robust and Versatile Causal Discovery for Business Applications 16

Towards Robust and Versatile Causal Discovery for Business A...

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Borboudakis, Giorgos Tsamardinos, Ioannis Univ Crete Dept Comp Sci Gnosis Data Anal IKE Iraklion Greece

ISBN: (纸本)9781450342322

Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however, all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above. The code is available on the *** website.

关键词： Causal Discovery Semi-Markov Causal Models Latent Variables Selection Bias Multiple datasets Bayes-Ball Algorithm Answer Set Programming Interventional data

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Contextual Intent Tracking for Personal Assistants 16

Contextual Intent Tracking for Personal Assistants

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Sun, Yu Yuan, Nicholas Jing Wang, Yingzi Xie, Xing McDonald, Kieran Zhang, Rui Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia Microsoft Res Redmond WA USA Microsoft Corp Redmond WA 98052 USA Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781450342322

A new paradigm of recommendation is emerging in intelligent personal assistants such as Apple's Siri, Google Now, and Microsoft Cortana, which recommends "the right information at the right time" and proactively helps you "get things done". This type of recommendation requires precisely tracking users' contemporaneous intent, i.e., what type of information (e.g., weather, stock prices) users currently intend to know, and what tasks (e.g., playing music, getting taxis) they intend to do. Users' intent is closely related to context, which includes both external environments such as time and location, and users' internal activities that can be sensed by personal assistants. The relationship between context and intent exhibits complicated co-occurring and sequential correlation, and contextual signals are also heterogeneous and sparse, which makes modeling the context intent relationship a challenging task. To solve the intent tracking problem, we propose the Kalman filter regularized PARAFAC2 (KP2) nowcasting model, which compactly represents the structure and co-movement of context and intent. The KP2 model utilizes collaborative capabilities among users, and learns for each user a personalized dynamic system that enables efficient nowcasting of users' intent. Extensive experiments using real-world data sets from a commercial personal assistant show that the KP2 model significantly outperforms various methods, and provides inspiring implications for deploying large-scale proactive recommendation systems in personal assistants.

关键词： Recommendation nowcasting multi-task learning

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METU-MMDS: An intelligent multimedia database system for multimodal content extraction and querying 22nd

METU-MMDS: An intelligent multimedia database system for mul...

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22nd international conference on MultiMedia Modeling, MMM 2016

作者： Yazici, Adnan Sattari, Saeid Yilmaz, Turgay Sert, Mustafa Koyuncu, Murat Gulen, Elvan Multimedia Database Laboratory Department of Computer Engineering METU Ankara Turkey Command Control and Combat Systems HAVELSAN Inc Ankara Turkey Department of Computer Engineering Baskent University Ankara Turkey Department of Information System Engineering Atilim University Ankara Turkey C + E Management Microsoft Corporation RedmondWA United States

ISBN: (纸本)9783319276731

Managing a large volume of multimedia data, which contain various modalities (visual, audio, and text), reveals the need for a specialized multimedia database system (MMDS) to efficiently model, process, store and retrieve video shots based on their semantic content. This demo introduces METU-MMDS, an intelligent MMDS which employs both machine learning and database techniques. The system extracts semantic content automatically by using visual, audio and textual data, stores the extracted content in an appropriate format and uses this content to efficiently retrieve video shots. The system architecture supports various multimedia query types including unimodal querying, multimodal querying, query-by-concept, query-by-example, and utilizes a multimedia index structure for efficiently querying multi-dimensional multimedia data. We demonstrate METU-MMDS for semantic data extraction from videos and complex multimedia querying by considering content and concept-based queries containing all modalities. © Springer international Publishing Switzerland 2016.

关键词： Semantics

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Ubiquitous Robot: A New Paradigm for Intelligence 17th

Ubiquitous Robot: A New Paradigm for Intelligence

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17th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Zhang, Tiantian Yuan, Bo Meng, Tao Ren, Yinghao Liu, Houde Wang, Xueqian Tsinghua Univ Intelligent Comp Lab Div Informat Grad Sch Shenzhen Shenzhen 518055 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Lab Space Robot & Telesci Shenzhen 518055 Peoples R China

ISBN: (纸本)9783319462578;9783319462561

This paper presents a systematic review of the recent development in ubiquitous robotics, with a focus on its implication on intelligence. Ubiquitous robot (Ubibot), the third generation robot, is characterized by its unprecedented power to sense environments and provide sophisticated services autonomously. Based on the architectures of existing Ubibot projects, we describe a general framework containing the perception, intelligence and execution modules as well as the middleware layer used to integrate the three modules to make them collaborate seamlessly. Two representative projects are introduced to exemplify the state-of-the-art progress in Ubibot, along with a brief discussion of various underlying communication techniques. In the perspective of intelligence, we point out that Ubibot opens up new horizons for investigating and applying popular AI techniques such as computer vision and pattern recognition, compared to traditional robots. Furthermore, a list of new challenging research topics is identified that deserve full consideration in the future to make Ubibot more robust, effective and adaptive.

关键词： Ubiquitous robot Middleware Intelligence Communication

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22nd international conference on Conceptual Structures, ICCS 2016

22nd International Conference on Conceptual Structures, ICCS...

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22nd international conference on Conceptual Structures, ICCS 2016

ISBN: (纸本)9783319409849

The proceedings contain 19 papers. The special focus in this conference is on Time Representation, Graphs, Networks, Formal Concept Analysis and Ontologies. The topics include: Exploring the usefulness of formal concept analysis for robust detection of spatio-temporal spike patterns in massively parallel spike trains;extracting hierarchies of closed partially-ordered patterns using relational concept analysis;the interpretation of branching time diagrams;extending GWAPS for building profile aware associative networks;transforming UML models to and from conceptual graphs to identify missing requirements;a semantic network for software design and development domain via graph database;a semiotic-conceptual analysis of conceptual learning;organised crime and social media;detecting and corroborating weak signals of human trafficking online;distilling conceptual structures from weblog data using polyadic FCA;a multi-scale and multi-step ontology for transformation processes;knowledge engineering method based on consensual knowledge and trust computation;web-mining defeasible knowledge from concessional statements;key discovery for numerical data;a model for linked open data acquisition and SPARQL query generation and dealing with incompatibilities during a knowledge bases fusion process.

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22nd international conference on MultiMedia Modeling, MMM 2016

22nd International Conference on MultiMedia Modeling, MMM 20...

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22nd international conference on MultiMedia Modeling, MMM 2016

ISBN: (纸本)9783319276731

The proceedings contain 44 papers. The special focus in this conference is on MultiMedia Modeling. The topics include: Transfer nonnegative matrix factorization for image representation;sentiment analysis on multi-view social data;single image super-resolution via convolutional neural network and total variation regularization;an effective face verification algorithm to fuse complete features in convolutional neural network;driver fatigue detection system based on DSP platform;real-time grayscale-thermal tracking via laplacian sparse representation;efficient perceptual region detector based on object boundary;1d barcode region detection based on the Hough transform and support vector machine;a hashing scheme for approximate similarity search with early stop condition;learning Hough transform with latent structures for joint object detection and pose estimation;consensus guided multiple match removal for geometry verification in image retrieval;locality constrained sparse representation for cat recognition;image retrieval using color-aware tag on progressive image search and recommendation system;advancing iterative quantization hashing using isotropic prior;an improved RANSAC image stitching algorithm based similarity degree;a novel emotional saliency map to model emotional attention mechanism;automatic endmember extraction using pixel purity index for hyperspectral imagery;smart ambient sound analysis via structured statistical modeling;a very deep sequences learning approach for human action recognition;ten research questions for scalable multimedia analytics;informed perspectives on human annotation using neural signals and an intelligent multimedia database system for multimodal content extraction and querying.

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Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test 16

Fast Unsupervised Online Drift Detection Using Incremental K...

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22nd ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： dos Reis, Denis Flach, Peter Matwin, Stan Batista, Gustavo Univ Sao Paulo ICMC Sao Carlos SP Brazil Univ Bristol Bristol Avon England Polish Acad Sci Inst Comp Sci Warsaw Poland

ISBN: (纸本)9781450342322

data stream research has grown rapidly over the last decade. Two major features distinguish data stream from batch learning: stream data are generated on the fly, possibly in a fast and variable rate;and the underlying data distribution can be non-stationary, leading to a phenomenon known as concept drift. Therefore, most of the research on data stream classification focuses on proposing efficient models that can adapt to concept drifts and maintain a stable performance over time. However, specifically for the classification task, the majority of such methods rely on the instantaneous availability of true labels for all already classified instances. This is a strong assumption that is rarely fulfilled in practical applications. Hence there is a clear need for efficient methods that can detect concept drifts in an unsupervised way. One possibility is the well-known Kolmogorov-Smirnov test, a statistical hypothesis test that checks whether two samples differ. This work has two main contributions. The first one is the Incremental Kolmogorov-Smirnov algorithm that allows performing the Kolmogorov-Smirnov hypothesis test instantly using two samples that change over time, where the change is an insertion and/or removal of an observation. Our algorithm employs a randomized tree and is able to perform the insertion and removal operations in O(log N) with high probability and calculate the Kolmogorov-Smirnov test in O(1), where N is the number of sample observations. This is a significant speed-up compared to the O(N log N) cost of the non-incremental implementation. The second contribution is the use of the Incremental Kolmogorov-Smirnov test to detect concept drifts without true labels. Classification algorithms adapted to use the test rely on a limited portion of those labels just to update the classification model after a concept drift is detected.

关键词： Kolmogorov-Smirnov data Stream Concept Drift Cartesian Tree Treap Lazy Propagation

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