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Primal and dual first order methods for SVM: applications to driver monitoring

Primal and dual first order methods for SVM: applications to...

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international conference on System Theory, Control, and Computing (ICSTCC)

作者： Daniela Lupu Ion Necoara Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania

Machine learning tools are become recently very popular for solving real applications from many areas. Most of the learning problems are formulated as optimization problems with simple objective function but large number of constraints of order the number of training data. When considering the dual formulation, usually the objective function is difficult to minimize but the constraints are simple. One relevant application that fits into this pattern is the support vector machine (SVM). A popular approach for solving the primal SVM problem is based on first order methods due to their superior empirical performance. When considering the dual SVM formulation, which has simple constraints, coordinate descent schemes are typically the method of choice in practice due to their cheap iteration. In this paper we present a comparative study of several first order methods for solving primal or dual SVM problems. Numerical evidence on support vector machine classification for automatic detection of driver fatigue supports the effectiveness of such first order methods in real-world problems.

关键词： Support vector machines Linear programming Optimization Fatigue Machine learning Convergence Machine learning algorithms

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Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments

Towards an IOT Based System for Detection and Monitoring of ...

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IEEE international conference on intelligent engineering Systems

作者： Zenon Chaczko Anup Kale Jose Juan Santana-Rodriguez Carmen Paz Suarez-Araujo Global Big Data Technologies Centre University of Technology Sydney Ultimo Australia Instituto Universitario de Estudios Ambientales y Recursos Naturales (i-UNAT) Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain Instituto Universitario de Ciencias y Tecnologías Cibernéticas (IUCTC) Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain

Monitoring presence of micro-plastics in the ocean and fresh waters is an important research topic due to a need to preserve marine ecosystem. Microplastics represent threats to living organisms, producing harmful effects, ultimately also having an impact on humans through the food-chain. Use of laboratory-based and in situ techniques do help in investigating density and scale of this kind of pollutants. The in-situ sensing techniques are gaining popularity due to automation and continuous availability. These techniques though need an accurate hardware and efficient computing model to achieve desired success. Here, we propose an IoT based system called 'SmartIC' using specialized sensors and intelligent computing tools, specifically designed for in-situ monitoring of microplastics in natural aquatic environments. This paper is focused on system architecture, monitoring process and outline of experimental work. The initial research provides very promising results. A further course of the investigation with validation will be conducted in future to establish the proposed system completely.

关键词： Monitoring Sensors Plastics Computer architecture Wireless sensor networks Wireless communication Hardware

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Predictive models for Lettuce quality from Internet of Things-based hydroponic farm

Predictive models for Lettuce quality from Internet of Thing...

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international Computer Science and engineering conference (ICSEC)

作者： Sethavidh Gertphol Pariyanuj Chulaka Tanabut Changmai Department of Computer Science Kasetsart University Bangkok Thailand Department of Horticulture Kasetsart University Bangkok Thailand

ISBN: (纸本)9781538681657;9781538681640

As changes in the environment affect quality and quantity of crop, yield forecasting becomes very important for farmers. Thailand's economy depends on agriculture for a very long time, and lettuce is sold at high price. The authors had deployed smart hydroponic lettuce farms using the Internet of Things to collect environmental data and control the farm's operation in real time. The experiment generated large dataset which was used to create regression models using machine learning techniques in this study. Features used include environmental data such as the amount and intensity of light, humidity, temperature, together with weekly measurement of plant growth. Target variables are total fresh weight, nitrate content, number of leaf, and leaf area. RMSE was used as a measure for model selection. Models were created using several linear regression techniques, as well as newer techniques, such as, SVR, MLR, and ANN and the best ones were selected for each week prediction. Using only environmental data up to the 3rd week of planting, our predictive model performs 24.44% better than SVR when predicting total fresh weight, 13.93% better when predicting nitrate content, 0.47% better for number of leaves and 12.04% better for leaf area. When using additional plant growth data, our model is 13.09%, 5.52%, 19.47% and 5.06% better than SVR when predicting total fresh weight, nitrate content, number of leaf and leaf area, consecutively.

关键词： Predictive models Agriculture Machine learning data models Internet of Things Prediction algorithms Error analysis

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Block Bayesian Sparse Topical Coding

Block Bayesian Sparse Topical Coding

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international conference on Computer Supported Cooperative Work in Design

作者： Min Peng Hongliang Shi Qianqian Xie Yihan Zhang Hua Wang Zhaoyunfei Li Jianming Yong School of Computer Science Wuhan University Wuhan China School of Engineering National University of Singapore Kent Ridge Singapore Institute for Sustainable Industries & Liveable Cities Victoria University Melbourne Australia College of Information Science and Technology Jinan University Guangzhou China School of Management and Enterprise University of Southern Queensland Toowoomba Australia

ISBN: (纸本)9781538614839

learning low dimensional representations from a large number of short corpora has a profound practical significance but with vital challenge in content analysis and data mining applications. In this paper, we propose a novel topic model called Block Bayesian Sparse Topic Coding (Block-BSTC), which is capable of discovering the latent semantic representation of short texts. The Block-BSTC relaxes the normalization constraint of the inferred representations with word embeddings and block sparse Bayesian learning, which is convenient to directly control the sparsity of word codes with exploiting the intra-block correlations. Furthermore, the experimental results show that Block-BSTC achieves great performance on the sparsity ratio of word codes. Meanwhile, it can improve the accuracy of document classification.

关键词： Bayes methods Correlation Encoding Probabilistic logic Analytical models Semantics Dictionaries

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Towards automatic skill evaluation in microsurgery 22

Towards automatic skill evaluation in microsurgery

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22nd international conference on intelligent User Interfaces, IUI 2017

作者： Eivazi, Shahram Slupina, Michael Fuhl, Wolfgang Afkari, Hoorieh Hafez, Ahmad Kasneci, Enkelejda University of Tübingen Perception Engineering Sand 14 Tübingen72076 Germany University of Eastern Finland School of Computing Joensuu Finland Helsinki University Central Hospital Department of Neurosurgery Töölö Hospital Helsinki Finland

ISBN: (纸本)9781450348935

In the past decade, eye tracking has emerged as a promising answer to the increasing needs of understanding surgical expertise. The implicit desire is to design an intelligent user interface (IUI) to monitor and assess the competency of surgical trainees. In this paper, for the first time in microsurgery, we explore the potential for a surgical automatic skill assessment through a combination of machine learning techniques, computational modeling, and eye tracking. We present primary findings from a random forest classification method where we achieved about 70% recognition rate for the detection of expert and novice group. This leads us to a conclusion that prediction of the micro-surgeon performance is possible, can be automated, and that the eye movement data carry important information about the skills of micro-surgeons. Copyright held by the owner/author(s).

关键词： Eye tracking

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Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small data Sets 17

Interactive Elicitation of Knowledge on Feature Relevance Im...

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22nd international conference on intelligent User Interfaces (IUI)

作者： Micallef, Luana Sundin, Iiris Marttinen, Pekka Ammad-ud-din, Muhammad Peltola, Tomi Soare, Marta Jacucci, Giulio Kaski, Samuel Aalto Univ Dept Comp Sci HIIT Espoo Finland Univ Helsinki Dept Comp Sci HIIT Helsinki Finland

ISBN: (纸本)9781450343480

Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain.

关键词： interactive knowledge elicitation prediction user model

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learning Likely Invariants to Explain Why a Program Fails 22

Learning Likely Invariants to Explain Why a Program Fails

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22nd international conference on engineering of Complex Computer Systems (ICECCS)

作者： Pham, Long H. Sun, Jun Ly Ly Tran Thi Wang, Jingyi Peng, Xin Singapore Univ Technol & Design ISTD Pillar Singapore Singapore Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9781538624319

Debugging is difficult. Recent studies show that automatic bug localization techniques have limited usefulness. One of the reasons is that programmers typically have to understand why the program fails before fixing it. In this work, we aim to help programmers understand a bug by automatically generating likely invariants which are violated in the failed tests. Given a program with an initial assertion and at least one test case failing the assertion, we first generate random test cases, identify potential bug locations through bug localization, and then generate program state mutation based on active learning techniques to identify a predicate "explaining" the cause of the bug. The predicate is a classifier for the passed test cases and failed test cases. Our main contribution is the application of invariant learning for bug explanation, as well as a novel approach to overcome the problem of lack of test cases in practice. We apply our method to real-world bugs and show the generated invariants are often correlated to the actual bug fixes.

关键词： Computer bugs Testing Java Debugging Software engineering Writing Sun

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IUI'17 companion-workshop summary for HUMANIZE'17 22

IUI'17 companion-workshop summary for HUMANIZE'17

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22nd international conference on intelligent User Interfaces, IUI 2017

作者： Graus, Mark Ferwerda, Bruce Schedl, Markus Tkalcic, Marko Willemsen, Martijn Germanakos, Panagiotis Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Department of Computational Perception Johannes Kepler University Linz Austria Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano Italy Suite Engineering UX Products and Innovation SAP SE Walldorf Germany Department of Computer Science University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781450348935

The first workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE)1 took place in conjunction with the 22nd annual meeting of the intelligent user interfaces (IUI)2 community in Limassol, Cyprus on March 13, 2017. The goal of the workshop was to attract researchers from different fields by accepting contributions on the intersection of practical data mining methods and theoretical knowledge for personalization. A total of six papers were accepted for this edition of the workshop. Copyright is held by the author/owner(s).

关键词： data mining

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Brain Image Completion by Bayesian Tensor Decomposition 22

Brain Image Completion by Bayesian Tensor Decomposition

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2017 22nd international conference on Digital Signal Processing (DSP)

作者： Gui, Lihua Zhao, Qibin Cao, Jianting Graduate School of Engineering Saitama Institute of Technology Fukaya Japan Tensor Learning Unit RIKEN AIP Guangdong University of Technology

ISBN: (纸本)9781538618950

The MRI data is naturally represented by a three-order tensor. The reconstruction of MRI image from sparse observations is a challenging task, which has many potential applications for data compression, feature extraction and classifications. In this paper, we employ Bayesian Tucker decomposition to learn the low-rank representations of MRI data from partially observed voxels. By specifying the sparsity priors over factor matrices and core tensor, the multilinear ranks can be automatically determined via variational Bayesian inference, which thus avoids the difficulty in tuning parameters empirically. We apply the proposed method to MRI image with 50-80% missing voxels, the experimental results demonstrate that our method can effectively recover the whole MRI image with high predictive performance.

关键词： Tensor completion MRI completion Tucker decomposition

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An Effective Model for Edge-Side Collaborative Storage in data-Intensive Edge Computing

An Effective Model for Edge-Side Collaborative Storage in Da...

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international conference on Computer Supported Cooperative Work in Design

作者： Yunhao Li Junzhou Luo Jiahui Jin Runqun Xiong Fang Dong School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781538614839

Edge Computing is a new computing paradigm that performs data processing at the edge of the network (i.e., edge servers) to lower data processing latency. Existing research works have paid lots of attention to how to offload computation tasks from terminals to edge servers, but most of them ignored how to store tasks' necessary data like pretrained models or databases on edge servers. Recently, the data-intensive tasks like deep learning and augmented reality are becoming common, which need large data storages and powerful computation resources. This leads to a cumbersome challenge, since many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, it needs to offload the task to cloud data centers or download the necessary data from the cloud. Both cases could increase the data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework (ECS). In ECS, the edge servers collaboratively store and process data-intensive tasks' necessary data. Particularly, if an edge server does not have the necessary data, it will forward the task to the nearest servers that contain the data. An effective iterative data placement algorithm is also proposed to improve ECS's performance. The experimental results show that ECS is 2× better than the traditional non-shared storage framework in terms of the cache hit rate.

关键词： Servers Task analysis Collaboration Edge computing Computational modeling data centers Image edge detection

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