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Representing object categories by connections: Evidence from a mutivariate connectivity pattern classification approach

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HUMAN BRAIN MAPPING 2016年第10期37卷 3685-3697页

作者： Wang, Xiaosha Fang, Yuxing Cui, Zaixu Xu, Yangwen He, Yong Guo, Qihao Bi, Yanchao Beijing Normal Univ Natl Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Beijing Normal Univ IDG McGovern Inst Brain Res Beijing 100875 Peoples R China Fudan Univ Dept Neurol Huashan Hosp Shanghai Peoples R China

The representation of object categories is a classical question in cognitive neuroscience and compelling evidence has identified specific brain regions showing preferential activation to categories of evolutionary significance. However, the potential contributions to category processing by tuning the connectivity patterns are largely unknown. Adopting a continuous multicategory paradigm, we obtained whole-brain functional connectivity (FC) patterns of each of four categories (faces, scenes, animals and tools) in healthy human adults and applied multivariate connectivity pattern classification analyses. We found that the whole-brain FC patterns made high-accuracy predictions of which category was being viewed. The decoding was successful even after the contributions of regions showing classical category-selective activations were excluded. We further identified the discriminative network for each category, which span way beyond the classical category-selective regions. Together, these results reveal novel mechanisms about how categorical information is represented in large-scale FC patterns, with general implications for the interactive nature of distributed brain areas underlying high-level cognition. Hum Brain Mapp 37:3685-3697, 2016. (c) 2016 Wiley Periodicals, Inc.

关键词： functional connectivity machine learning algorithm support vector machine visual categories

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Mass-Spectrometry-Based Proteomics Reveals Organ-Specific Expression Patterns To Be Used as Forensic Evidence

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JOURNAL OF PROTEOME RESEARCH 2016年第1期15卷 182-192页

作者： Dammeier, Sascha Nahnsen, Sven Veit, Johannes Wehner, Frank Ueffing, Marius Kohlbacher, Oliver Univ Hosp Tuebingen Inst Ophthalm Res Med Proteome Ctr D-72074 Tubingen Germany Eberhard Karls Univ Tuebingen Quantitat Biol Ctr D-72076 Tubingen Germany Eberhard Karls Univ Tuebingen Ctr Bioinformat D-72076 Tubingen Germany Eberhard Karls Univ Tuebingen Dept Comp Sci D-72076 Tubingen Germany Eberhard Karls Univ Tuebingen Inst Forens Med D-72074 Tubingen Germany

Standard forensic procedures to examine bullets after an exchange of fire include a mechanical or ballistic reconstruction of the event. While this is routine to identify which projectile hit a subject by DNA analysis of biological material on the surface of the projectile, it is rather difficult to determine which projectile caused the lethal injury often the crucial point with regard to legal proceedings. With respect to fundamental law it is the duty of the public authority to make every endeavor to solve every homicide case. To improve forensic examinations, we present a forensic proteomic method to investigate biological material from a projectile's surface and determine the tissues traversed by it. To obtain a range of relevant samples, different major bovine organs were penetrated with projectiles experimentally. After tryptic "on-surface" digestion, mass-spectrometry-based proteome analysis, and statistical data analysis, we were able to achieve a cross-validated organ classification accuracy of >99%. Different types of anticipated external variables exhibited no prominent influence on the findings. In addition, shooting experiments were performed to validate the results. Finally, we show that these concepts could be applied to a real case of murder to substantially improve the forensic reconstruction.

关键词： bottom-up proteomics forensic medicine projectile machine learning algorithm data classification PCA

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Sentiment Analysis in A Cross-Media Analysis Framework

Sentiment Analysis in A Cross-Media Analysis Framework

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Woldemariam, Yonas Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9781467395915

This paper introduces the implementation and integration of a sentiment analysis pipeline into the ongoing open source cross-media analysis framework. The pipeline includes the following components;chat room cleaner, NLP and sentiment analyzer. Before the integration, we also compare two broad categories of sentiment analysis methods, namely lexicon-based and machine learning approaches. We mainly focus on finding out which method is appropriate to detect sentiments from forum discussion posts. In order to conduct our experiments, we use the apache-hadoop framework with its lexicon-based sentiment prediction algorithm and Stanford coreNLP library with the Recursive Neural Tensor Network (RNTN) model. The lexicon-based uses sentiment dictionary containing words annotated with sentiment labels and other basic lexical features, and the later one is trained on Sentiment Treebank with 215,154 phrases, labeled using Amazon Turk. Our overall performance evaluation shows that RNTN outperforms the lexicon-based by 9.88% accuracy on variable length positive, negative, and neutral comments. How-ever, the lexicon-based shows better performance on classifying positive comments. We also found out that the F1-score values of the Lexicon-based is greater by 0.16 from the RNTN.

关键词： sentiment analysis cross-media machine learning algorithm lexicon-based neural network

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Genetic algorithm for Energy Harvesting-Wireless Sensor Networks 1

Genetic Algorithm for Energy Harvesting-Wireless Sensor Netw...

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IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Darji, Harsh Shah, Hitesh B. GCET E&C Dept Anand Gujarat India

ISBN: (纸本)9781509007745

Traditional routing protocols such as LEACH, PEGASIS, TEEN etc. are no longer appropriate for the Energy Harvesting-Wireless Sensing element Networks (EH-WSN). Requirement is that WSN must have low energy consumption. machine learning algorithms can be used for minimizing energy consumption. Thus our main objective is to develop machine learning based routing protocol, which is having energy harvested from environment instead of batteries. Research work done on LEACH and modified LEACH algorithms in order to achieve energy efficiency in WSN. Majorly these approach concentrate on clustering of sensor nodes and/or modifying the routing protocol. Finally we compared our proposed machine learning algorithm with LEACH to show energy efficient network with improved network lifetime.

关键词： WSN Energy Harvesting-WSN LEACH machine learning algorithm Genetic algorithm fuzzy

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Data-Driven Modeling Method for Analyzing Grade Crossing Safety 20

Data-Driven Modeling Method for Analyzing Grade Crossing Saf...

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20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Trudel, Eric Liu, Yan Yang, Chunsheng Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9781509019151

A grade crossing is defined as an intersection between a roadway and a railway at the same elevation or grade. Multiple new prevention measures have been implemented to reduce the number of train-vehicle collisions;however, crossing safety is still a major issue as accidents still frequently occur. The push for data-driven models to evaluate risks at grade crossings has also increased to keep up with the changing technologies. There are many different protection types (gates with bells, cross-buck, stop-sign, mirrors and etc.) that serve to warn or stop oncoming traffic. Many attributes have an inherent impact on accident frequency;including the protection type, train speed, traffic volume and e.t.c. To address which factors are most important, we propose a data-driven modeling method to effectively analyze the impact of multiple factors that affect crossing safety and subsequently provide scientific insight for key factors for enhancing crossing safety. In this work, the Canadian crossing accident database for the years of 2004 - 2013 was used with additional generated features to enhance the scope of the study. These include features that were computed using GIS and sightline measurements. Data-driven modeling using RandomForests were used to rank and analyze 21 attributes for each protection type. From the analysis results it is possible to identify which key factors have the highest influence on improving safety and collision prediction at grade crossings.

关键词： Grade crossing data-driven modeling feature generation machine learning algorithm RandomForest regression crossing safety GIS

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Predicting Maintainability of Open Source Software using Gene Expression Programming and Bad Smells 5

Predicting Maintainability of Open Source Software using Gen...

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5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

作者： Tarwani, Sandhya Chug, Anuradha Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol New Delhi India

ISBN: (纸本)9781509014897

Software maintenance phase of Software Development Lifecycle (SDLC) is the most expensive and complex phase that requires nearly 60-70% of the total project cost. Due to this, many software fails to get repair within real time constraint. Ascribe to technology advancements and changing requirements, software must be well developed and maintained to get adapted. Hence, it is necessary to predict software maintainability in the early phases of the lifecycle so that optimization of resources can be possible and cost can be reduced. Software Maintainability is the quality attribute of software product that explains the ease with which modifications can be performed. The main focus in this study is to propose the use of Gene Expression Programming (GEP) for the software maintainability prediction and measure its performance with various machine leaning techniques such as Decision Tree Forest, Support Vector machine, Linear regression, Multilayer Perceptron and Radial basis function neural network. The empirical study is conducted with the help of four open source datasets. Eleven bad smells are identified and is considered as maintenance effort. Results of this study show that GEP algorithm performs better than machine learning classifiers;hence it can be used as sound alternative in the prediction of software maintainability. This study would be helpful in achieving better resource allocation hence it will be useful for developers and maintainers.

关键词： Software Maintainability Bad smell Gene Expression Programming machine learning algorithm Prediction accuracy measures Software Metrics

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COMPARING SUPERVISED algorithmS IN LAND USE AND LAND COVER CLASSIFICATION OF A LANDSAT TIME-SERIES 36

COMPARING SUPERVISED ALGORITHMS IN LAND USE AND LAND COVER C...

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36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Nery, Thayse Sadler, Rohan Solis-Aulestia, Maria White, Ben Polyakov, Maksym Chalak, Morteza Univ Western Australia Sch Agr & Resource Econ Nedlands WA 6009 Australia CAPES Fdn Minist Educ Brazil Brasilia DF Brazil Pink Lake Analyt 90 Meriwa St Nedlands WA Australia

ISBN: (纸本)9781509033324

machine learning algorithms (MLAs) are often applied to identify Land Use and Land Cover (LULC) changes, but typically to only a limited set of imagery. This leaves the consistency of MLAs performance through time poorly understood. The research objective was therefore to compare the performance of six MLAs across a time-series of Landsat imagery (1979, 1992, 2003, 2014), all processed in the same manner. Here Support Vector machines (SVM), K-Nearest Neighbours (KNN), Random Forests (RF), learning Vector Quantization (LVQ), Recursive Partitioning, Regression Trees (RPART) and Stochastic Gradient Boosting (GBM) were evaluated. The results demonstrated that SVM achieved higher overall accuracies and kappa coefficients, and a slightly improved fit at individual class level, than the second best classifier RF. Both classifiers clearly outperformed the other algorithms. These results suggest that SVMs (or RFs) should be prioritised when classifying time-series imagery for LULC change detection.

关键词： machine learning algorithm time-series imagery Landsat LULC classification

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Introducing a Novel Smart Design Framework for a Reconfigurable Multi-Processor Systems-on-Chip (MPSoC) Architecture 2

Introducing a Novel Smart Design Framework for a Reconfigura...

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2nd IEEE International Conference on Smart Computing (SMARTCOMP)

作者： Dutta, Anandi Bayoumi, Magdy Univ Louisiana Lafayette Lafayette LA 70504 USA

ISBN: (纸本)9781509008988

In this work, a smart designing framework for a Systems-on-Chip has been proposed. We examined a reconfigurable MPSoC architecture which incorporates one Processor-FPGA core to ensure flexibility and better design parameters. The objective of this work is to propose and develop a smart framework which initiates this philosophy: the system would build a better system by itself. The system would learn about usage statistics by using an android application. Then the system would form a decision function and classify the user with the help of support vector machine-based machine learning algorithm. According to user's preference, it would re-design the reconfigurable MPSoC to ensure customized and superior user experience. The machine learning algorithm runs on cloud for saving computing power and resources. An image processing task has been performed as a case-study on a FPGA-SoC platform and on GPU as a proof of concept to ensure current standards. As of our knowledge, this is a novel and competent approach of designing system for hand-held devices which enables customization of the device after the manufacturer's end.

关键词： Reconfigurable MPSoC machine learning algorithm Android Application Hand-held Device

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Newsvendor Problems with Demand Shocks and Unknown Demand Distributions

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DECISION SCIENCES 2016年第1期47卷 125-156页

作者： O'Neil, Shawn Zhao, Xuying Sun, Daewon Wei, Jerry C. Oregon State Univ Ctr Genome Res & Biocomp ALS 3135 Corvallis OR 97331 USA Univ Notre Dame Dept Management Mendoza Coll Business Notre Dame IN 46556 USA

In today's competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range;no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithm's performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta-algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta-algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent

关键词： Demand Forecasting Demand Shocks Inventory Management machine learning algorithm Newsvendor Problem and Stochastic Demands

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Optimized Classification Based on Particle Swarm Optimization algorithm for Tamil Sign Language Digital Images 1

Optimized Classification Based on Particle Swarm Optimizatio...

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1st IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS)

作者： Krishnaveni, M. Subashini, P. Dhivyaprabha, T. T. Priya, A. Sathiya Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9781509010226

Sign is the way of communication when there is no appropriate verbal communication for deaf and dump persons. Sign Language Recognition System (SLRS) acts as a contrivance to perform the conversion of sign language into text or voice. Many classification techniques and algorithms with a variety of methods already exist in SLRS. But still only a few considerable contributions are there in Tamil Sign Language Recognition. The objective of this paper is to extract the features from the pre-processed image and propose an optimization based classifier to improve the classification accuracy in Tamil Sign Language images. The experimental result of the proposed work demonstrated that BPN-PSO outperforms existing classification techniques.

关键词： Tamil Sign Language Hand Gesture Feature Extraction Classification machine learning algorithm Particle Swarm Optimization

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