title: Interactive Urban Synthesis Computational Methods for Fast Prototyping of Urban Design Proposals

abstract: In this paper, we present a method for generating fast conceptual urban design prototypes. We synthesize spatial configurations for street networks, parcels and building volumes. Therefore, we address the problem of implementing custom data structures for these configurations and how the generation process can be controlled and parameterized. We exemplify our method by the development of new components for Grasshopper/Rhino3D and their application in the scope of selected case studies. By means of these components, we show use case applications of the synthesis algorithms. In the conclusion, we reflect on the advantages of being able to generate fast urban design prototypes, but we also discuss the disadvantages of the concept and the usage of Grasshopper as a user interface.


title: Fast High Voltage Switching Using Stacked MOSFETs

abstract: Fast switching of high voltage using stacked MOSFETs has been studied. It is shown that the effective drain-source capacitance has a negative influence in the turn-off process, especially when the load impedance is relatively high. In order to solve this problem, an alternative circuit configuration is tested where additional switching modules are used to deal with the drain-source capacitance. The experimental results have demonstrated fast switching performance, even for a high impedance load.


title: Creativity Is a Key to the Future and to Education: The Importance of Creative Visioning.

abstract: A study was conducted to better understand creative visioning, the human ability to make connections. Interviews were conducted with 20 Seattle (Washington) residents, each of whom was involved in creativity or creative visioning. The sample defined creative visioning as a process which encourages the pursuit of new possibilities, dimensions, and connections. The inquiry revealed support for an open system that is flexible and relaxed wherein discovery flourishes; a system not bound by rules nor boxed in but that is helpful in developing the self-discipline, skills, and familiarity with history that will enable connections to be made. Extrapolations from the interviews led to recommendations regarding the role schools can play in developing the skills of creative visioning. Teachers and administrators must create an environment that is free of fear, structured but not rigid, tied to history but not chained to one answer, open to new discoveries and connections, respectful of the individual, and touched by the arts, creativity, and imagination. Schools need to move to do all that is possible to promote, nurture, develop, and celebrate creative visioning, risk taking, and the development of courage to see differently, in an atmosphere where new connections can easily occur. (LL) **** ************ *4* **** ** ' e************** * * ******* ** ** ** ******* *** ** **** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** CREATIVITY IS A KEY TO THE FUTURE AND TO EDUCATION The Importance of Creative Visioning U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) cl This document has been reproduced as received from the person or organtzzhon ongmettng t CI Mthor changes have been made to improve reproduction quality Points of view or opinions stated in this document do not necessarily represent official OERI position or policy "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC1." Dr. Ronald Cromwell Director of Teacher Education Marist College Poughkeepsie, NY 12601 914-575-3000-2994 Paper presented at the Annual Meeting of the American Association of Colleges for Teacher Education (San Diego, CA, February 24-27, 1993).


The LTTng tracer: A low impact performance and behavior monitor for GNU/Linux Mathieu Desnoyers


title: Introduction to quantum information theory and outline of two applications to physics: the black hole information paradox and the renormalization group information flow

abstract: This review paper is intended for scholars with different backgrounds, possibly in only one of the subjects covered, and therefore little background knowledge is assumed. The first part is an introduction to classical and quantum information theory (CIT, QIT): basic definitions and tools of CIT are introduced, such as the information content of a random variable, the typical set, and some principles of data compression. Some concepts and results of QIT are then introduced, such as the qubit, the pure and mixed states, the Holevo theorem, the no-cloning theorem, and the quantum complementarity. In the second part, two applications of QIT to open problems in theoretical physics are discussed. The black hole (BH) information paradox is related to the phenomenon of the Hawking radiation (HR). Consid- ering a BH starting in a pure state, after its complete evaporation only the Hawking radiation will remain, which is shown to be in a mixed state. This either describes a non-unitary evolution of an isolated system, contradicting the evolution postulate of quantum mechanics and violating the no-cloning theorem, or it implies that the initial information content can escape the BH, therefore contradicting general relativity. The progress toward the solution of the paradox is discussed. The renormalization group (RG) aims at the extraction of the macroscopic description of a physical system from its microscopic description. This passage from microscopic to macroscopic can be described in terms of several steps from one scale to another, and is therefore formalized as the action of a group. The c-theorem proves the existence, under certain conditions, of a function which is monotonically decreasing along the group transformations. This result suggests an interpretation of this function as entropy, and its use to study the information flow along the RG transformations.


title: Hacking and cybercrime

abstract: This paper explores the fast growing Cyberworld and its components. It starts with definitions of who is the hacker, and what is a cybercrime. Types and offenses of cybercrime are addressed as well. The paper concentrates on the possibilities to protect ourselves from the cybercrime, and guard Cyberworld from us. Therefore, it emphasizes the importance of users' education, starting from the early age, creation and enforcement of policies, and awareness training. The paper presents laws, applicable to the computer related crime, highlights the U.S. Department of Homeland Security involvement, and investigates on the fact why businesses do not report hackers' attacks and why is it important.


title: Design of Vivaldi antenna array with end-fire beam steering function for 5G mobile terminals

abstract: This manuscript proposes a new design of phased array antenna for future fifth generation (5G) cellular communications. The proposed phased array antenna is designed on a low-cost N9000 PTFE substrate with overall size of 60×130×0.8 mm3. It consists of eight 28-GHz Vivaldi antenna elements used to form a linear phased array in the edge region (top-side) on a mobile phone PCB. The simulated results show that the antenna has the reflection coefficient (S11) less than −10 dB in the frequency range of 27.4 to 28.6 GHz. The proposed phased array antenna has good gain, efficiency, and 3D beam steering characteristics in the entire operation band, which makes it suitable for millimeter-wave 5G communications. In addition, the performance of the antenna in the vicinity of user's hand has been investigated in this study.


A systematic review of attention deficit hyperactivity disorder (ADHD) and mathematical ability: current findings and future implications


Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field


Why the Diagnosis of Attention Deficit Hyperactivity Disorder Matters


An experimental field study in elementary health education methods classes comparing four training procedures on the acquisition of three specifically defined teacher behaviors


title: Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices

abstract: While indoor portable navigation using portable devices is becoming increasingly required, it faces many challenges in obtaining accurate positioning performance. One of the methods to improve navigation results is to recognize the mode of motion of the user carrying the portable device containing low-cost microelectromechanical systems (MEMS) motion sensors. Pattern recognition methodology has been employed to detect a group of motion modes that are common indoors, namely, walking, stationary, going up/down stairs, standing on an escalator, walking on an escalator, standing on a moving walkway, and walking on a moving walkway. The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance. The module does not require satellite or wireless positioning signals, and depends only on MEMS sensors.


title: An online version of the Mooney Face Test: phenotypic and genetic associations

abstract: The Mooney Face Test is a widely used test of face perception, but was originally designed to be administered by personal interview. We have developed a three-alternative forced-choice version for online testing. We tested 397 healthy adults between the ages of 18 and 42 (M=24 years). There was a wide range of performance (64-100% correct; M=89.6%). We observed a significant sex difference favoring males (.31 standard deviation; p =.004). In addition, independently of sex, higher 2D:4D digit ratios were significantly associated with higher scores (ρ=.14, p=.006). A genome-wide association study (GWAS) for a subset of 370 participants identified an association between Mooney performance and a polymorphism in the RAPGEF5 gene (rs1522280; p=9.68×10(-8)). This association survives a permutation test (p=.031).


title: Hidden Markov Model Implementation for Prediction of Stock Prices with TF-IDF features

abstract: Stock data analysis is challenging research area. The proposed analysis works on online news data for prediction of stock market states such as high, low etc. The Hidden Markov Model along with features extracted such as TF-IDF is used to find out next day's stock market value for group of companies. The method can be further extended to adjustment of probability values to calculate tuned model for prediction.


title: Power converters modeling in Matlab/Simulink for microgrid simulations

abstract: Smart grid is a developing field for energy researchers. It offers technological improvements to have more controllable, reliable and sustainable power grids. Especially, the decreasing trend of fuels has pressurized to pay researchers’ attention to renewable energy sources such as solar systems and wind turbines. The renewable energy sources generate electric power in different characteristics. Therefore, power converters are needed to integrate these sources in a grid to provide power flows among the sources and loads. This study presents Matlab/Simulink models of power converters and dynamic load structure, which are useful for smart grid simulations. These simulations can be used for research and educational purposes. Characteristic parameters of the models are suitable for existing devices. For power converters, Pulse-width Modulation technique is utilized that it is widely used in industry. The designed power converters and dynamic load models are tested in a microgrid application. The simulation results show that the designed power components are useful for smart grid simulation studies.


title: Strength of evidence in systematic reviews in software engineering

abstract: Systematic reviews are only as good as the evidence they are based on. It is important, therefore, that users of systematic reviews know how much confidence they can place in the conclusions and recommendations arising from such reviews. In this paper we present an overview of some of the most influential systems for assessing the quality of individual primary studies and for grading the overall strength of a body of evidence. We also present an example of the use of such systems based on a systematic review of empirical studies of agile software development. Our findings suggest that the systems used in other disciplines for grading the strength of evidence for and reporting of systematic reviews, especially those that take account of qualitative and observational studies are of particular relevance for software engineering.


title: XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

abstract: In this paper, we present the system we have developed for the SemEval-2014 Task 4 dedicated to Aspect-Based Sentiment Analysis. The system is based on a robust parser that provides information to feed different classifiers with linguistic features dedicated to aspect categories and aspect categories polarity classification. We mainly present the work which has been done on the restaurant domain 1 for the four subtasks, aspect term and category detection and aspect term and category polarity.


title: Vertical vibration suppression for a position controlled biped robot

abstract: A controller design to suppress vertical vibration of a position controlled biped walking robot is presented. The system model of structural vibration is estimated from a measurement of frequency response. By using the model, an optimal feedback controller with full state observer is designed. With a consideration of leg support phase, the vibration controller is combined into our walking controller. The effectiveness of our controller is experimentally demonstrated.


title: Aesthetics and Ethics in Gadamer, Levinas, and Romanticism: Problems of Phronesis and Techne

abstract: hermeneutics in Coleridge. HE EFFORT TO READ literature in the context of ethics raises A the question of how the kind of knowledge used in approaching ethical life is related to the kind of knowledge involved in the production and reception of literary works. Although recent criticism, striving to situate literature among other forms of cultural activity, is rightly suspicious of claims that grant special status to aesthetic or ethical knowledge, the relation between the often conflicting claims of artistic activity and ethical life was of vital importance to British Romanticism and continues to inform modem hermeneutical and ethical thought. This relation can be illuminated by considering the fate of Aristotle's distinction between phronesis, or ethical knowledge, and techne, or productive knowledgea distinction still relevant both to an understanding of the Romantic struggle with the ethical implications of aesthetic experience and to an understanding of the relation between ethical and interpretive issues in literary criticism.


title: Students' self-presentation on Facebook: An examination of personality and self-construal factors

abstract: The present research seeks to extend existing theory on self-disclosure to the online arena in higher educational institutions and contribute to the knowledge base and understanding about the use of a popular social networking site (SNS), Facebook, by college students. We conducted a non-experimental study to investigate how university students (N=463) use Facebook, and examined the roles that personality and culture play in disclosure of information in online SNS-based environments. Results showed that individuals do disclose differently online vs. in-person, and that both culture and personality matter. Specifically, it was found that collectivistic individuals low on extraversion and interacting in an online environment disclosed the least honest and the most audience-relevant information, as compared to others. Exploratory analyses also indicate that students use sites such as Facebook primarily to maintain existing personal relationships and selectively used privacy settings to control their self-presentation on SNSs. The findings of this study offer insight into understanding college students' self-disclosure on SNS, add to the literature on personality and self-disclosure, and shape future directions for research and practice on online self-presentation.


title: Metamaterial-Based Antennas: Research and Developments

abstract: A brief review of metamaterials and their applications to antenna systems is given. Artificial magnetic conductors and electrically small radiating and scattering systems are emphasized. Single negative, double negative, and zero-index metamaterial systems are discussed as a means to manipulate their size, efficiency, bandwidth, and directivity characteristics.


title: Deep Networks With Large Output Spaces

abstract: Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, they are still prohibitively expensive to train and apply for problems containing millions of classes in the output layer. Based on the observation that the key computation common to most neural network layers is a vector/matrix product, we propose a fast locality-sensitive hashing technique to approximate the actual dot product enabling us to scale up the training and inference to millions of output classes. We evaluate our technique on three diverse large-scale recognition tasks and show that our approach can train large-scale models at a faster rate (in terms of steps/total time) compared to baseline methods.


title: An Efficient Deep Learning Technique for the Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics

abstract: We present an efficient deep learning technique for the model reduction of the Navier-Stokes equations for unsteady flow problems. The proposed technique relies on the Convolutional Neural Network (CNN) and the stochastic gradient descent method. Of particular interest is to predict the unsteady fluid forces for different bluff body shapes at low Reynolds number. The discrete convolution process with a nonlinear rectification is employed to approximate the mapping between the bluff-body shape and the fluid forces. The deep neural network is fed by the Euclidean distance function as the input and the target data generated by the full-order Navier-Stokes computations for primitive bluff body shapes. The convolutional networks are iteratively trained using the stochastic gradient descent method with the momentum term to predict the fluid force coefficients of different geometries and the results are compared with the full-order computations. We attempt to provide a physical analogy of the stochastic gradient method with the momentum term with the simplified form of the incompressible Navier-Stokes momentum equation. We also construct a direct relationship between the CNN-based deep learning and the Mori-Zwanzig formalism for the model reduction of a fluid dynamical system. A systematic convergence and sensitivity study is performed to identify the effective dimensions of the deep-learned CNN process such as the convolution kernel size, the number of kernels and the convolution layers. Within the error threshold, the prediction based on our deep convolutional network has a speed-up nearly four orders of magnitude compared to the full-order results and consumes an insignificant fraction of computational resources. The proposed CNN-based approximation procedure has a profound impact on the parametric design of bluff bodies and the feedback control of separated flows.


title: On the Analysis and Design of Low-Loss Single-Pole Double-Throw W-Band Switches Utilizing Saturated SiGe HBTs

abstract: This paper describes the analysis and design of saturated silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) switches for millimeter-wave applications. A switch optimization procedure is developed based on detailed theoretical analysis and is then used to design multiple switch variants. The switches utilize IBM's 90-nm 9HP technology, which features SiGe HBTs with peak f T/ fmax of 300/350 GHz. Using a reverse-saturated configuration, a single-pole double-throw switch with a measured insertion loss of 1.05 dB and isolation of 22 dB is achieved at 94 GHz after de-embedding pad losses. The switch draws 5.2 mA from a 1.1-V supply, limiting power consumption to less than 6 mW. The switching speed is analyzed and the simulated turn-on and turn-off times are found to be less than 200 ps. A technique is also introduced to significantly increase the power-handling capabilities of saturated SiGe switches up to an input-referred 1-dB compression point of 22 dBm. Finally, the impact of RF stress on this novel configuration is investigated and initial measurements over a 48-h period show little performance degradation. These results demonstrate that SiGe-based switches may provide significant benefits to millimeter-wave systems.


title: Single Layer Chebyshev Neural Network Model for Solving Elliptic Partial Differential Equations

abstract: The purpose of the present study is to solve partial differential equations (PDEs) using single layer functional link artificial neural network method. Numerical solution of elliptic PDEs have been obtained here by applying Chebyshev neural network (ChNN) model for the first time. Computations become efficient because the hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. Feed forward neural network model with unsupervised error back propagation principle is used for modifying the network parameters and to minimize the computed error function. Numerical efficiency and accuracy of the ChNN model are investigated by three test problems of elliptic partial differential equations. The results obtained by this method are compared with the existing methods and are found to be in good agreement.


title: Baseband analysis of Tx leakage in WCDMA Zero-IF-receivers

abstract: Frequency division duplex transceivers employing the zero-if principle suffers from Tx leakage. In this contribution a baseband representation of the Tx leakage impact in zero-if receivers is derived and the statistical properties of the involved baseband signals are analysed. Additionally requirements on the digital compensation of Tx leakage are discussed, based on measurement results of the Tx-Rx isolation of the duplexer as well as properties of the receive branch. Thus, a framework for the digital compensation of Tx leakage at the discrete baseband is presented. Simulation results are used to validate the theoretically predicted spectral shape of the Tx leakage impact and to evaluate the influence of Tx leakage on the system performance.


title: The Influence of Task-Irrelevant Music on Language Processing: Syntactic and Semantic Structures

abstract: Recent research has suggested that music and language processing share neural resources, leading to new hypotheses about interference in the simultaneous processing of these two structures. The present study investigated the effect of a musical chord’s tonal function on syntactic processing (Experiment 1) and semantic processing (Experiment 2) using a cross-modal paradigm and controlling for acoustic differences. Participants read sentences and performed a lexical decision task on the last word, which was, syntactically or semantically, expected or unexpected. The simultaneously presented (task-irrelevant) musical sequences ended on either an expected tonic or a less-expected subdominant chord. Experiment 1 revealed interactive effects between music-syntactic and linguistic-syntactic processing. Experiment 2 showed only main effects of both music-syntactic and linguistic-semantic expectations. An additional analysis over the two experiments revealed that linguistic violations interacted with musical violations, though not differently as a function of the type of linguistic violations. The present findings were discussed in light of currently available data on the processing of music as well as of syntax and semantics in language, leading to the hypothesis that resources might be shared for structural integration processes and sequencing.


title: Internal Coupled-Fed Dual-Loop Antenna Integrated With a USB Connector for WWAN/LTE Mobile Handset

abstract: A coupled-fed dual-loop antenna capable of providing eight-band WWAN/LTE operation and suitable to integrate with a USB connector in the mobile handset is presented. The antenna integrates with a protruded ground, which is extended from the main ground plane of the mobile handset to accommodate a USB connector functioning as a data port of the handset. To consider the presence of the integrated protruded ground, the antenna uses two separate shorted strips and a T-shape monopole encircled therein as a coupling feed and a radiator as well. The shorted strips are short-circuited through a common shorting strip to the protruded ground and coupled-fed by the T-shape monopole to generate two separate quarter-wavelength loop resonant modes to form a wide lower band to cover the LTE700/GSM850/900 operation (704-960 MHz). An additional higher-order loop resonant mode is also generated to combine with two wideband resonant modes contributed by the T-shape monopole to form a very wide upper band of larger than 1 GHz to cover the GSM1800/1900/UMTS/LTE2300/2500 operation (1710-2690 MHz). Details of the proposed antenna are presented. For the SAR (specific absorption rate) requirement in practical mobile handsets to meet the limit of 1.6 W/kg for 1-g human tissue, the SAR values of the antenna are also analyzed.


title: Hierarchical Multiscale Recurrent Neural Networks

abstract: Learning both hierarchical and temporal representation has been among the long-standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural networks, which can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that our proposed multiscale architecture can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level language modelling and handwriting sequence modelling.


title: Real time volumetric shadows using polygonal light volumes

abstract: This paper presents a more efficient way of computing single scattering effects in homogeneous participating media for real-time purposes than the currently popular ray-marching based algorithms. These effects include halos around light sources, volumetric shadows and crepuscular rays. By displacing the vertices of a base mesh with the depths from a standard shadow map, we construct a polygonal mesh that encloses the volume of space that is directly illuminated by a light source. Using this volume we can calculate the airlight contribution for each pixel by considering only points along the eye-ray where shadow-transitions occur. Unlike previous ray-marching methods, our method calculates the exact airlight contribution, with respect to the shadow map resolution, at real time frame rates.


title: A Novel Artificial Bee Colony Algorithm for Numerical Function Optimization

abstract: Artificial Bee Colony(ABC) algorithm is a biological-inspired optimization algorithm, which has been shown to be compared with some conventional biological-inspired algorithms, such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Differential Evolution(DE). However, there exists problems such as premature convergence and trapping in local optimal. Inspired by DE, we propose an improved ABC algorithm called pbest-guided ABC (PABC) algorithm by incorporating the information of local best (pbest) solution into the solution search equation to improve the exploitation in the onlookers stage. Moreover, in each iteration, we modify the frequency of perturbation. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. The experimental results show that PABC algorithm can improve the performance of ABC algorithm.


title: Opinion Mining and Analysis: A survey

abstract: The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available in digital form. One important problem in sentiment analysis of product reviews is to produce summary of opinions based on product features. We have surveyed and analyzed in this paper, various techniques that have been developed for the key tasks of opinion mining. We have provided an overall picture of what is involved in developing a software system for opinion mining on the basis of our survey and analysis.


title: Implementation of STEAM Education to Improve Mastery Concept

abstract: Science Technology Engineering, Art, Mathematics (STEAM) is an integration of art into Science Technology Engineering, Mathematics (STEM). Connecting art to science makes learning more effective and innovative. This study aims to determine the increase in mastery of the concept of high school students after the application of STEAM education in learning with the theme of Water and Us. The research method used is one group Pretest-posttest design with students of class VII (n = 37) junior high school. The instrument used in the form of question of mastery of concepts in the form of multiple choices amounted to 20 questions and observation sheet of learning implementation. The results of the study show that there is an increase in conceptualization on the theme of Water and Us which is categorized as medium ( =0, 46) after the application of the STEAM approach. The conclusion obtained that by applying STEAM approach in learning can improve the mastery of concept


title: A.: Sequential deep learning for human action recognition

abstract: We propose in this paper a fully automated deep model, which learns to classify human actions without using any prior knowledge. The first step of our scheme, based on the extension of Convolutional Neural Networks to 3D, automatically learns spatio-temporal features. A Recurrent Neural Network is then trained to classify each sequence considering the temporal evolution of the learned features for each timestep. Experimental results on the KTH dataset show that the proposed approach outperforms existing deep models, and gives comparable results with the best related works.


title: Designing Algorithms To Aid Discovery by Chemical Robots

abstract: Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for k...


title: A Symbolic Approach for Explaining Errors in Image Classification Tasks

abstract: Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has fail ...


title: A Planning/Scheduling Methodology for the Constrained Resource Problem

abstract: This paper presents a new planning/scheduling methodology for the constrained resource problem (CRP), in which the amount of available resources is limited and usually monotonically diminishing as the planning process progresses. The tasks are tightly-coupled since they compete for the limited resources. Two domain independent policies -- most-constraint and least-tmpact help to make this planning/scheduling approach sensitive to dynamic interactions among tasks. The most-constraint policy selects a task dynamically according to the criticality, which measures how a task is constrained by task interaction. The least-impact policy dynamically chooses a solution for the selected task according to the cruciality of each possible solution, which expresses the impact on the rest of the unachieved tasks. These policies have enhanced the operability and measurability of problem solving by planning/scheduling. Hence, this method can provide realistic and executable planning/scheduling guidelines for CRP solvers. This model has been successfully applied to several CRPs in which the amount of backtracking has been reduced dramatically.


title: Motion Interaction Field for Accident Detection in Traffic Surveillance Video

abstract: This paper presents a novel method for modeling of interaction among multiple moving objects to detect traffic accidents. The proposed method to model object interactions is motivated by the motion of water waves responding to moving objects on water surface. The shape of the water surface is modeled in a field form using Gaussian kernels, which is referred to as the Motion Interaction Field (MIF). By utilizing the symmetric properties of the MIF, we detect and localize traffic accidents without solving complex vehicle tracking problems. Experimental results show that our method outperforms the existing works in detecting and localizing traffic accidents.


title: Cloud manufacturing: a new manufacturing paradigm

abstract: Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new manufacturing paradigm – cloud manufacturing CMfg – for solving the bottlenecks in the informatisation development and manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, manufacturing cloud service and manufacturing cloud are studied, and the constructing method for manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented.


title: TQM, innovation and the role of cultural change

abstract: Purpose – The purpose of this paper is to better understand the influence of total quality management (TQM) practices on incremental and radical innovation, examining the role of diverse cultural change as a mediator, particularly in firms where ever‐increasing competitive pressure demands a combination of quality and innovation.Design/methodology/approach – From previous research on the influence of TQM practices on innovation, the paper proposes a model which is tested through a survey carried out on a sample of 72 Spanish firms that have been drastically hit by competition from Asian companies, achieving a 51.42 percent valid return rate.Findings – None of the sets of TQM practices directly affects radical innovation, while all of them have a significant and positive relationship with incremental innovation. However, when the paper introduces cultural change as a mediating factor, the model's goodness of fit improves substantially, and all the relations are significant.Research limitations/implications...


title: Germanium Compounds Containing Ge═E Double Bonds (E = S, Se, Te) as Single-Source Precursors for Germanium Chalcogenide Materials.

abstract: New germanium chalcogenide precursors, S═Ge(dmamp)2 (3), S═Ge(dmampS)2 (4), Se═Ge(dmamp)2 (5), Se═Ge(dmampS)2 (6), Te═Ge(dmamp)2 (7), and Te═Ge(dmampS)2 (8), were synthesized from Ge(dmamp)2 (1) and Ge(dmampS)2 (2) using sulfur, selenium, and tellurium powders (dmamp = 1-dimethylamino-2-methyl-2-propanolate, dmampS = 1-dimethylamino-2-methylpropane-2-thiolate). Complexes 1 and 2 were synthesized from metathesis reactions of GeCl2·dioxane with 2 equiv of aminoalkoxide or aminothiolate ligands. Thermogravimetric analysis of complex 1 displayed good thermal stability and volatility. The molecular structures of complexes 2-8 from X-ray single crystallography showed distorted trigonal bipyramidal geometry at the germanium centers. Germanium chalcogenide materials (GeSe and GeTe) were obtained from the thermal decomposition of complexes 5, 6, and 8 in hexadecane. X-ray diffraction patterns exhibited that GeSe and GeTe had orthorhombic and rhombohedral phases, respectively. This study affords a facile method to easily prepare germanium chalcogenide materials using well-designed and stable complexes by thermal decomposition of single-source precursors in solution.


title: Hand gesture recognition with 3D convolutional neural networks

abstract: Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers' hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatio-temporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset.


title: Stacked U-Nets with Multi-output for Road Extraction

abstract: With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked UNets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.


CLaaS: Cybersecurity Lab as a Service


title: Phytochemical Adaptations to Stress

abstract: 1. Introductory Chapter.- 2. Putrescine and Inorganic Ions.- 3. Phytochemical Aspects of Osmotic Adaptation.- 4. Thigmomorphogenesis: Callose and Ethylene in the Hardening of Mechanically Stressed Plants.- 5. Stress and Secondary Metabolism in Cultured Plant Cells.- 6. Temperature Stress and Lipid Modification.- 7. Morphology, Chemistry, and Genetics of Gossypium Adaptations to Pests.- 8. Bioregulation of Plant Constituents.- 9. Production of Resins by Arid-Adapted Astereae.- 10. Changes in the Levels of Plant Secondary Metabolite Production Under Water and Nutrient Stress.- Species Index.


title: A review of kernels on covariance matrices for BCI applications

abstract: Recently, covariance matrices have been shown to be interesting features for signal classification and object detection. In this paper, we review and compare the existing kernels on covariance matrices and explore their use for EEG classification in Brain-Computer Interfaces (BCI). This study addresses both experimental and theoretical aspects of the problem. Beside the apparent complexity of the kernels, we show that this approach simplifies the whole BCI system. Finally, we empirically demonstrate that this simpler approach obtains state-of-the-art results on the BCI competition IV dataset 2a.


title: Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

abstract: Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n = 49) and healthy controls (n = 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia.


title: Picking pesky parameters: optimizing regular expression matching in practice

abstract: Network security systems inspect packet payloads for signatures of attacks. These systems use regular expression matching at their core. Many techniques for implementing regular expression matching at line rate have been proposed. Solutions differ in the type of automaton used (i.e., deterministic versus non-deterministic) and in the configuration of implementation-specific parameters. While each solution has been shown to perform well on specific rule sets and traffic patterns, there has been no systematic comparison across a large set of solutions, rule sets and traffic patterns. Thus, it is extremely challenging for a practitioner to make an informed decision within the plethora of existing algorithmic and architectural proposals. Moreover, as multi-core processors are becoming popular, many parameters need to be tuned to maximize the multi-core potential. To address this problem, we present a comprehensive evaluation of a broad set of regular expression matching techniques. We consider both algorithmic and architectural aspects. Specifically, we explore the performance, area requirements, and power consumption of implementations targeting multi-core processors and FPGAs using rule sets of practical size and complexity. We present detailed performance results and specific guidelines for determining optimal configurations based on a simple evaluation of the rule set. These guidelines can help significantlywhen implementing regular expression matching systems in practice.


title: Design, modeling and control of a 5-DoF light-weight robot arm for aerial manipulation

abstract: The design, modeling and control of a 5 degrees-of-freedom light-weight robot manipulator is presented in this paper. The proposed robot arm, named Prisma Ultra-Lightweight 5 ARm (PUL5AR), is employed to execute manipulation tasks equipped on board of a vertical take-off and landing unmanned aerial vehicle. The arm is compact and light-weight. Its mechanics is designed such that it can fold on itself during landing manoeuvres. Moreover, the design is conceived to constrain the center of gravity of the arm as close as possible to vehicle base, thus reducing the total inertia and static unbalancing of the system. Experimental tests have been carried out in order to validate the dynamic model, the communication library, the developed electronics, and the control schemes implemented for the designed robot arm.


title: Secure Split-Test for preventing IC piracy by untrusted foundry and assembly

abstract: Counterfeit ICs can have a major impact on the security and reliability of critical applications. This paper presents a method called Secure Split-Test (SST) for securing the manufacturing process to prevent counterfeits, allowing intellectual property (IP) owners to protect and meter their IPs. This is done by requiring test results to be verified by the IP owner and by requiring the IP owner to provide a “key” to unlock the IPs correct functionality. The results and analysis demonstrate that SST can effectively prevent counterfeited ICs from untrusted foundries or assemblies as well as its resilience to attacks and circumvention.


title: Architectural space planning using evolutionary computing approaches: a review

abstract: This paper presents various applications of evolutionary computing approach for architectural space planning problem. As such the problem of architectural space planning is NP-complete. Finding an optimal solution within a reasonable amount of time for these problems is impossible. However for architectural space planning problem we may not be even looking for an optimal but some feasible solution based on varied parameters. Many different computing approaches for space planning like procedural algorithms, heuristic search based methods, genetic algorithms, fuzzy logic, and artificial neural networks etc. have been developed and are being employed. In recent years evolutionary computation approaches have been applied to a wide variety of applications as it has the advantage of giving reasonably acceptable solution in a reasonable amount of time. There are also hybrid systems such as neural network and fuzzy logic which incorporates the features of evolutionary computing paradigm. The present paper aims to compare the various aspects and merits/demerits of each of these methods developed so far. Sixteen papers have been reviewed and compared on various parameters such as input features, output produced, set of constraints, scope of space coverage-single floor, multi-floor and urban spaces. Recent publications emphasized on energy aspect as well. The paper will help the better understanding of the Evolutionary computing perspective of solving architectural space planning problem. The findings of this paper provide useful insight into current developments and are beneficial for those who look for automating architectural space planning task within given design constraints.


title: A self-configurable geo-replicated cloud storage system

abstract: Reconfiguring a cloud storage system can improve its overall service. Tuba is a geo-replicated key-value store that automatically reconfigures its set of replicas while respecting application-defined constraints so that it adapts to changes in clients' locations or request rates. New replicas may be added, existing replicas moved, replicas upgraded from secondary to primary, and the update propagation between replicas adjusted. Tuba extends a commercial cloud-based service, Microsoft Azure Storage, with broad consistency choices (as in Bayou), consistency-based SLAs (as in Pileus), and a novel replication configuration service. Compared with a system that is statically configured, our evaluation shows that Tuba increases the reads that return strongly consistent data by 63%.


title: Inducing Multilingual Text Analysis Tools Via Robust Projection Across Aligned Corpora

abstract: This paper describes a system and set of algorithms for automatically inducing stand-alone monolingual part-of-speech taggers, base noun-phrase bracketers, named-entity taggers and morphological analyzers for an arbitrary foreign language. Case studies include French, Chinese, Czech and Spanish.Existing text analysis tools for English are applied to bilingual text corpora and their output projected onto the second language via statistically derived word alignments. Simple direct annotation projection is quite noisy, however, even with optimal alignments. Thus this paper presents noise-robust tagger, bracketer and lemmatizer training procedures capable of accurate system bootstrapping from noisy and incomplete initial projections.Performance of the induced stand-alone part-of-speech tagger applied to French achieves 96% core part-of-speech (POS) tag accuracy, and the corresponding induced noun-phrase bracketer exceeds 91% F-measure. The induced morphological analyzer achieves over 99% lemmatization accuracy on the complete French verbal system.This achievement is particularly noteworthy in that it required absolutely no hand-annotated training data in the given language, and virtually no language-specific knowledge or resources beyond raw text. Performance also significantly exceeds that obtained by direct annotation projection.


title: Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

abstract: Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization and Markov Chain methods have emerged as two separate but powerful paradigms for modeling the two respectively. Combining these ideas has led to unified methods that accommodate long- and short-term dynamics simultaneously by modeling pairwise user-item and item-item interactions. ::: In spite of the success of such methods for tackling dense data, they are challenged by sparsity issues, which are prevalent in real-world datasets. In recent years, similarity-based methods have been proposed for (sequentially-unaware) item recommendation with promising results on sparse datasets. In this paper, we propose to fuse such methods with Markov Chains to make personalized sequential recommendations. We evaluate our method, Fossil, on a variety of large, real-world datasets. We show quantitatively that Fossil outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.


title: Stochastic Variational Video Prediction

abstract: Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world. Many existing methods tackle this problem by making simplifying assumptions about the environment. One common assumption is that the outcome is deterministic and there is only one plausible future. This can lead to low-quality predictions in real-world settings with stochastic dynamics. In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables. To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video. We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned. We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods. Our SV2P implementation will be open sourced upon publication.


title: Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

abstract: Summary We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.


title: Fuzzy modeling of thermal process based on chaos genetic algorithm

abstract: In view of modeling problems of complicated nonlinear systems, a generalized T-S fuzzy modeling approach based on chaos genetic algorithm is presented. In this method, adaptive generalized Gaussian membership function is used, and its figure is optimized by chaos immigrant genetic algorithm, which can overcome shortcomings the traditional fuzzy cluster algorithm has, such as a large quantity of calculation, prone to dead center, local minimum and center redundancy in the iterative optimization of cluster center, and it shows great performance in antecedent parameters estimation. Finally, effectiveness and practicability of this method is demonstrated by the simulation results of the Box-Jenkins model and the overheated steam system.


title: Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

abstract: Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI’s were found. To test the system’s efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computeraided detection of lung nodules.


title: Open-set Plant Identification Using an Ensemble of Deep Convolutional Neural Networks

abstract: Open-set recognition, a challenging problem in computer vision, ::: is concerned with identification or verification tasks where queries ::: may belong to unknown classes. This work describes a fine-grained plant ::: identification system consisting of an ensemble of deep convolutional ::: neural networks within an open-set identification framework. Two wellknown ::: deep learning architectures of VGGNet and GoogLeNet, pretrained ::: on the object recognition dataset of ILSVRC 2012, are finetuned ::: using the plant dataset of LifeCLEF 2015. Moreover, GoogLeNet ::: is fine-tuned using plant and non-plant images for rejecting samples from ::: non-plant classes. Our systems have been evaluated on the test dataset ::: of PlantCLEF 2016 by the campaign organizers and our best proposed ::: model has achieved an official score of 0.738 in terms of the mean average ::: precision, while the best official score is 0.742.


title: Design of a multilink-articulated wheeled pipeline inspection robot using only passive elastic joints

abstract: AbstractThis paper presents a multilink-articulated robot with omni and hemispherical wheels (AIRo-2.1) for inspecting and exploring pipelines. To quickly adapt to winding pipes, holonomic rolling movement without moving forward and backward is useful. However, this requires the rolling actuators to replace the driving actuators at the expense of the driving force. Furthermore, so far the number of driving wheels and torsion springs, magnitude of driving forces, stiffness and natural angle of the spring that are required to adapt to various pipelines have not been clarified. In this paper, we investigate the possibility of high maneuverability of multilink-articulated robots in winding pipes with as few driving actuators as possible and only elastic joints (torsion springs) for body bending. We further validate its effectiveness by experimental verification.


title: Quantifying Information Overload in Social Media and its Impact on Social Contagions

abstract: Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users' information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.


title: 4–20 GHz GaAs True-Time Delay Amplifier MMIC

abstract: A wideband amplifier with a fine control of the true-time delay (TTD) has been successfully implemented using a commercial 0.5- $\mu \text{m}$ GaAs pHEMT monolithic microwave integrated circuit (MMIC) process. The proposed circuit effectively combines switching elements into an artificial transmission line structure in a distributed amplifier to achieve an adjustable TTD which enables time delay control with gain in a wider band than other GaAs-based TTD circuits. The TTD amplifier MMIC achieves a controllable delay of 9 ps, a gain of 9 to 2 dB, and a typical return loss of more than 10 dB from 4 to 20 GHz. The proposed TTD amplifier can be effectively integrated into a multifunction chip based on GaAs semiconductors for wideband active electronically scanned array or phased array systems.


title: Building an Extensible Open vSwitch Datapath

abstract: The virtual switch is the cornerstone of the today's virtualized data center. As all traffic to and from virtual machines or containers must pass through a vSwitch, it is the ideal location for network configuration and policy enforcement. The bulk of Open vSwitch functionality is platform-agnostic and portable. However the datapath, which touches every packet, is unique to each supported platform. Maintaining each datapath requires duplicated effort and the result has been inconsistent support of features across platforms. Even on a single platform, the features supported by a particular kernel version can vary. Further, datapath functionality must be broadly useful which prevents having application-specific features in the fast path. eBPF, extended Berkeley Packet Filter, enables userspace applications to customize and extend the Linux kernel's functionality. It provides flexible platform abstractions for network functions, and is being ported to a variety of platforms. This paper describes the design, implementation, and evaluation of an eBPF-based extensible OVS datapath. The eBPF OVS datapath delivers the equivalent functionality of the existing OVS kernel datapath, while significantly reducing development pain points around maintainability and extensibility. We demonstrate that these benefits don't necessarily have a trade off in regards to performance, with the eBPFbased datapath showing negligible overhead compared to the existing kernel datapath.


title: Twitter sentiment analysis using deep learning methods

abstract: The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.


title: Towards Semantic Query Segmentation

abstract: Query Segmentation is one of the critical components for understanding users' search intent in Information Retrieval tasks. It involves grouping tokens in the search query into meaningful phrases which help downstream tasks like search relevance and query understanding. In this paper, we propose a novel approach to segment user queries using distributed query embeddings. Our key contribution is a supervised approach to the segmentation task using low-dimensional feature vectors for queries, getting rid of traditional hand tuned and heuristic NLP features which are quite expensive. ::: We benchmark on a 50,000 human-annotated web search engine query corpus achieving comparable accuracy to state-of-the-art techniques. The advantage of our technique is its fast and does not use external knowledge-base like Wikipedia for score boosting. This helps us generalize our approach to other domains like eCommerce without any fine-tuning. We demonstrate the effectiveness of this method on another 50,000 human-annotated eCommerce query corpus from eBay search logs. Our approach is easy to implement and generalizes well across different search domains proving the power of low-dimensional embeddings in query segmentation task, opening up a new direction of research for this problem.


title: Applications of data mining in retail business

abstract: The proliferation of computer in today's dynamic business environment has increased the demand and urgency of successful business organization to be able to react rapidly to the changing market demands both locally and globally, by utilizing the latest data mining techniques of extracting previously unknown and potentially useful information from vast resources of raw data. We highlight the application of data mining in retail business, and its pros and cons on consumers.


title: Bad subsequences of well-known linear congruential pseudorandom number generators

abstract: We present a spectral test analysis of full-period subsequences with small step sizes generated by well-known linear congruential pseudorandom number generators. Subsequences may occur in certain simulation problems or as a method to get parallel streams of pseudorandom numbers. Applying the spectral test, it is possible to find bad subsequences with small step sizes for almost all linear pseudorandom number generators currently in use.


title: Earlier Web Usage Statistics as Predictors of Later Citation Impact

abstract: Abstract: The use of citation counts to assess the impact of research articles is well established. However, the citation impact of an article can only be measured several years after it has been published. As research articles are increasingly accessed through the Web, the number of times an article is downloaded can be instantly recorded and counted. One would expect the number of times an article is read to be related both to the number of times it is cited and to how old the article is. This paper analyses how short-term Web usage impact predicts medium-term citation impact. The physics e-print archive -- arXiv.org -- is used to test this.


title: Dealing with feeling: a meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation

abstract: The present meta-analysis investigated the effectiveness of strategies derived from the process model of emotion regulation in modifying emotional outcomes as indexed by experiential, behavioral, and physiological measures. A systematic search of the literature identified 306 experimental comparisons of different emotion regulation (ER) strategies. ER instructions were coded according to a new taxonomy, and meta-analysis was used to evaluate the effectiveness of each strategy across studies. The findings revealed differences in effectiveness between ER processes: Attentional deployment had no effect on emotional outcomes (d+ = 0.00), response modulation had a small effect (d+ = 0.16), and cognitive change had a small-to-medium effect (d+ = 0.36). There were also important within-process differences. We identified 7 types of attentional deployment, 4 types of cognitive change, and 4 types of response modulation, and these distinctions had a substantial influence on effectiveness. Whereas distraction was an effective way to regulate emotions (d+ = 0.27), concentration was not (d+ = −0.26). Similarly, suppressing the expression of emotion proved effective (d+ = 0.32), but suppressing the experience of emotion or suppressing thoughts of the emotion-eliciting event did not (d+ = −0.04 and −0.12, respectively). Finally, reappraising the emotional response proved less effective (d+ = 0.23) than reappraising the emotional stimulus (d+ = 0.36) or using perspective taking (d+ = 0.45). The review also identified several moderators of strategy effectiveness including factors related to the (a) to-be-regulated emotion, (b) frequency of use and intended purpose of the ER strategy, (c) study design, and (d) study characteristics. (PsycINFO Database Record (c) 2012 APA, all rights reserved)


title: When Loading Working Memory Reduces Distraction: Behavioral and Electrophysiological Evidence from an Auditory-Visual Distraction Paradigm

abstract: The sensitivity of involuntary attention to top-down modulation was tested using an auditory-visual distraction task and a working memory (WM) load manipulation in subjects performing a simple visual classification task while ignoring contingent auditory stimulation. The sounds were repetitive standard tones (80%) and environmental novel sounds (20%). Distraction caused by the novel sounds was compared across a 1-back WM condition and a no-memory control condition, both involving the comparison of two digits. Event-related brain potentials (ERPs) to the sounds were recorded, and the N1/MMN (mismatch negativity), novelty-P3, and RON components were identified in the novel minus standard difference waveforms. Distraction was reduced in the WM condition, both behaviorally and as indexed by an attenuation of the late phase of the novelty-P3. The transient/change detection mechanism indexed by MMN was not affected by the WM manipulation. Sustained, slow frontal and parietal waveforms related to WM processes were found on the standard ERPs. The present results indicate that distraction caused by irrelevant novel sounds is reduced when a WM component is involved in the task, and that this modulation by WM load takes place at a late stage of the orienting response, all in all confirming that involuntary attention is under the control of top-down mechanisms. Moreover, as these results contradict predictions of the load theory of selective attention and cognitive control, it is suggested that the WM load effects on distraction depend on the nature of the distractor-target relationships.


title: A Novel Method for Accurate and Efficient Barcode Detection with Morphological Operations

abstract: Barcode technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several methods for barcode localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need automatic barcode localization, e.g. conveyor belts, automated production, where missed detections cause loss of profit. In this paper, we mainly deal with segmentation of images with 1D barcode, but also analyze the operation of different methods for 2D barcode images as well. Our goal is to detect automatically, rapidly and accurately the barcode location by the help of extracted features. We compare some published method from the literature, which basically rely on the contrast between the background and the shape that represent the code. We also propose a novel algorithm, that outperforms the others in both accuracy and efficiency in detecting 1D codes.


title: Sex Differences and Individual Differences in Cognitive Performance and Their Relationship to Endogenous Gonadal Hormones and Gonadotropins

abstract: Sexually dimorphic cognitive performance in men (n = 42) and women (n = 42) was related to testosterone, estradiol, progesterone, luteinizing hormone, follicle-stimulating hormone, and sex hormone binding globulin, measured in 10-ml blood samples collected between 0900 and 1030 and, among women, during the follicular phase of the menstrual cycle. Significant sex differences favored men on spatial tasks (Mental Rotation and Judgment of Line Orientation) and on an inhibition task and favored women on a verbal task (category fluency). However, there were no significant relationships between any of the hormones and cognitive performance, suggesting that there are few, if any, consistent, substantial relationships between endogenous, nonfluctuating levels of gonadal hormones or gonadotropins and these cognitive abilities in men or women.


title: Complications of primary and revision functional endoscopic sinus surgery for chronic rhinosinusitis

abstract: OBJECTIVES/HYPOTHESIS ::: The goal of this study was to determine the incidence of major complications following primary and revision functional endoscopic sinus surgery (FESS). In addition, this study aimed to determine factors associated with the occurrence of complications including patient and provider characteristics and the use of image guidance system (IGS) technology. ::: ::: ::: STUDY DESIGN ::: Retrospective cohort analysis of California and Florida all-payer databases from 2005 to 2008. ::: ::: ::: METHODS ::: The rates of major surgical complications (skull base, orbital, and hemorrhagic) after primary and revision FESS were calculated, and bivariate analyses were performed to investigate relationships of complications with demographic and clinical characteristics. A multivariate model was used to determine risk factors for the occurrence of major complications. ::: ::: ::: RESULTS ::: Among 78,944 primary FESS cases, 288 major complications were identified representing a complication rate of 0.36% (95% CI 0.32%-0.40%). The major complication rate following revision cases (n = 19; 0.46%) and primary cases (n = 288; 0.36%) was similar (OR = 1.26; 95% CI 0.79-2.00). Multivariate analysis showed that patients who were >40 years old, had a primary payer of Medicaid, had surgery involving the frontal sinus, or had image guidance during surgery were at higher risk for major complications. ::: ::: ::: CONCLUSION ::: The rate of major complications (0.36%) associated with primary FESS is lower than earlier reports. The rate of major complications following revision FESS (0.46%) was found to be similar to primary cases. IGS, insurance status, age, and extent of surgery were found to be associated with an increased risk of major complications following FESS. ::: ::: ::: LEVEL OF EVIDENCE ::: 2C.


title: meshmixer: an interface for rapid mesh composition

abstract: In recent years a wide variety of mesh editing techniques have been developed for tasks such as smoothing, deformation, and parameterization. Comparatively fewer alternatives are available for composition tasks, such as combining parts of existing meshes. As designs often evolve from a combination of existing ideas and models, rapid composition techniques could significantly improve the workflow of mesh-based modeling tools. In our work we are exploring shape reuse and composition problems in 3D mesh modeling.


title: Common-Centroid FinFET Placement Considering the Impact of Gate Misalignment

abstract: The FinFET technology has been regarded as a better alternative among different device technologies at 22nm node and beyond due to more effective channel control and lower power consumption. However, the gate misalignment problem resulting from process variation based on the FinFET technology becomes even severer compared with the conventional planar CMOS technology. Such misalignment may increase the threshold voltage and decrease the drain current of a single transistor. When applying the FinFET technology to analog circuit design, the variation of drain currents will destroy the current matching among transistors and degrade the circuit performance. In this paper, we present the first FinFET placement technique for analog circuits considering the impact of gate misalignment together with systematic and random mismatch. Experimental results show that the proposed algorithms can obtain an optimized common-centroid FinFET placement with much better current matching.


title: Presupposed Content and Entailments in Natural Language Inference

abstract: Previous work has presented an accurate natural logic model for natural language inference. Other work has demonstrated the effectiveness of computing presuppositions for solving natural language inference problems. We extend this work to create a system for correctly computing lexical presuppositions and their interactions within the natural logic framework. The combination allows our system to properly handle presupposition projection from the lexical to the sentential level while taking advantage of the accuracy and coverage of the natural logic system. To solve an inference problem, our system computes a sequence of edits from premise to hypothesis. For each edit the system computes an entailment relation and a presupposition entailment relation. The relations are then separately composed according to a syntactic tree and the semantic properties of its nodes. Presuppositions are projected based on the properties of their syntactic and semantic environment. The edits are then composed and the resulting entailment relations are combined with the presupposition relation to yield an answer to the inference problem.


title: A novel gait-based synthesis procedure for the design of 4-bar exoskeleton with natural trajectories

abstract: Abstract Background/Objective Human walking involves the coordination of brain, nerves, and muscles. A disturbance in their coordination may result in gait disorder. The gait disorder may be treated through manually assisted gait training or with the aid of assistive devices/robotic devices. These robotic devices involve mechanisms which are synthesized using complex conventional procedures. Therefore, in this study, a new gait-based synthesis procedure is proposed, which simplifies the mechanism synthesis and helps to develop a mechanism which can be used in rehabilitation devices, bipeds, etc. Methods This article presents a novel procedure for the synthesis of 4-bar linkage using the natural gait trajectories. As opposed to the conventional synthesis procedures, in this procedure, a global reference frame is considered, which allows the use of hip trajectory while moving. Moreover, this method is divided into two stages, and five precision points are considered on the hip trajectory in each stage. In the first stage, the 4-bar linkage is designed, thereafter, the configurations of the linkage for the remaining precision points are determined in the second stage. The proposed synthesis procedure reduces the complexity involved in the synthesis and helps in the simplification of the problem formulation. A two-stage optimization problem is formulated for minimizing the error between the generated and desired hip trajectories. Two nature-inspired algorithms are used for solving the optimization problem. The obtained best results are presented, and the designed linkage is simulated in MATLAB. Results The best design of the linkage is obtained using particle swarm optimization. The trajectories generated by the designed linkage using the proposed methodology can accurately track the desired path, which indicates that designed linkage can achieve all the orientations required during walking. The positions of a whole lower limb at all the desired precision points are demonstrated by stick diagram for one gait. Conclusion The proposed methodology has reduced the complexity of synthesis procedures and used optimization techniques to obtain a feasible design of the mechanism. The stick diagram of the designed mechanism obtained using the proposed method indicates that the designed mechanism can walk smoothly. Hence, the designed mechanism can be used in the rehabilitation devices. Furthermore, a conceptual design of an exoskeleton knee is also presented. The Translational Potential of this Article Many hospitals and individuals have used the immobile and portable rehabilitation devices. These devices involve mechanisms, and the design of mechanism plays a vital role in the functioning of these devices; therefore, we have developed a new synthesis procedure for the design of the mechanism. Besides synthesis procedure, a mechanism is developed that can be used in the rehabilitation devices, bipeds, exoskeletons, etc., to benefit the society.


title: Environmental context-dependent memory: A review and meta-analysis

abstract: To address questions about human memory’s dependence on the coincidental environmental contexts in which events occur, we review studies of incidental environmental context-dependent memory in humans and report a meta-analysis. Our theoretical approach to the issue stems from Glenberg’s (1997) contention that introspective thought (e.g., remembering, conceptualizing) requires cognitive resources normally used to represent the immediate environment. We propose that if tasks encourage processing of noncontextual information (i.e., introspective thought) at input and/or at test, then both learning and memory will be less dependent on the ambient environmental contexts in which those activities occur. The meta-analysis showed that across all studies, environmental context effects were reliable, and furthermore, that the use of noncontextual cues during learning (overshadowing) and at test (outshining), as well as mental reinstatement of appropriate context cues at test, all reduce the effect of environmental manipulations. We conclude that environmental context-dependent memory effects are less likely to occur under conditions in which the immediate environment is likely to be suppressed.


title: Detection and mitigation of Denial of Service (DoS) attacks using performance aware Software Defined Networking (SDN)

abstract: Software Defined Networking (SDN) stands to transmute our modern networks and data centers, opening them up into highly agile frameworks that can be reconfigured depending on the requirement. Denial of Service (DoS) attacks are considered as one of the most destructive attacks. This paper, is about DoS attack detection and mitigation using SDN. DoS attack can minimize the bandwidth utilization, leaving the network unavailable for legitimate traffic. To provide a solution to the problem, concept of performance aware Software Defined Networking is used which involves real time network monitoring using sFlow as a visibility protocol. So, OpenFlow along with sFlow is used as an application to fight DoS attacks. Our analysis and results demonstrate that using this technique, DoS attacks are successfully defended implying that SDN has promising potential to detect and mitigate DoS attacks.


title: Anaphora as an Indicator of Elaboration: A Corpus Study

abstract: This article describes an investigation of the relationship between anaphora and relational discourse structure, notably the Elaboration relation known from theories like RST. A corpus was annotated on the levels of anaphoric structure and rhetorical structure. The statistical analysis of interrelations between the two annotation layers revealed correlations between specific subtypes of anaphora and Elaboration, indicating that anaphora can function as a cue for Elaboration. 1


title: Error propagation analysis for file systems

abstract: Unchecked errors are especially pernicious in operating system file management code. Transient or permanent hardware failures are inevitable, and error-management bugs at the file system layer can cause silent, unrecoverable data corruption. We propose an interprocedural static analysis that tracks errors as they propagate through file system code. Our implementation detects overwritten, out-of-scope, and unsaved unchecked errors. Analysis of four widely-used Linux file system implementations (CIFS, ext3, IBM JFS and ReiserFS), a relatively new file system implementation (ext4), and shared virtual file system (VFS) code uncovers 312 error propagation bugs. Our flow- and context-sensitive approach produces more precise results than related techniques while providing better diagnostic information, including possible execution paths that demonstrate each bug found.


title: A Mention-Ranking Model for Abstract Anaphora Resolution

abstract: Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence--antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and -- if disregarding syntax -- discriminates candidates using deeper features.


title: Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

abstract: Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks in order to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech as well as normal, prepared speech. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by Mild Cognitive Impairment and healthy elderly patients.


title: A Survey on Resilient Machine Learning

abstract: Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (eg, training data collection, training, operation). All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs. Maliciously created input samples can affect the learning process of a ML system by either slowing down the learning process, or affecting the performance of the learned mode, or causing the system make error(s) only in attacker's planned scenario. Because of these developments, understanding security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey of this emerging area in machine learning.


title: Past, present, and future of decision support technology

abstract: Since the early 1970s, decision support systems (DSS) technology and applications have evolved significantly. Many technological and organizational developments have exerted an impact on this evolution. DSS once utilized more limited database, modeling, and user interface functionality, but technological innovations have enabled far more powerful DSS functionality. DSS once supported individual decision-makers, but later DSS technologies were applied to workgroups or teams, especially virtual teams. The advent of the Web has enabled inter-organizational decision support systems, and has given rise to numerous new applications of existing technology as well as many new decision support technologies themselves. It seems likely that mobile tools, mobile e-services, and wireless Internet protocols will mark the next major set of developments in DSS. This paper discusses the evolution of DSS technologies and issues related to DSS definition, application, and impact. It then presents four powerful decision support tools, including data warehouses, OLAP, data mining, and Web-based DSS. Issues in the field of collaborative support systems and virtual teams are presented. This paper also describes the state of the art of optimization-based decision support and active decision support for the next millennium. Finally, some implications for the future of the field are discussed.


title: Dynamic modeling of Mckibben pneumatic artificial muscles for antagonistic actuation

abstract: This paper presents the dynamic modeling of Mckibben pneumatic artificial muscles. The air flow model of a valve orifice and the air volume model of a pneumatic muscle are incorporated into the proposed dynamic model to estimate precisely the pressure variance of a pneumatic muscle when Mckibben muscles are in inflating and deflating. Coefficient parameters of the proposed model are determined optimally through well-coordinated experiments. Frequency response test is performed on an antagonistic structure consisting of Mckibben muscles manufactured in our laboratory and a pneumatic circuit with fast pneumatic control valves. Comparing with experimental results, simulations revealed that the proposed model gave good performance in estimating motions of the antagonistic actuation as well as the pressure variance of the Mckibben muscles.


title: DEVELOPMENT OF AN EFFICIENT DATA MINING CLASSIFIER WITH MICROARRAY DATA SET FOR GENE SELECTION AND CLASSIFICATION

abstract: Microarray sample classification has been studied extensively using classification techniques in machine learning and pattern recognition. In a microarray chip, the number of genes available is far greater than that of samples, which is a serious problem and the gene expression reduction, is important one. Prior to sample classification, it is important to perform gene selection and more interpretable genes to be identified as biomarkers, so that a more efficient, accurate, and reliable performance in classification can be achieved. For this purpose, a hybrid scheme was proposed and this scheme is called as Single Filter – Single Wrapper Classifier (SFSW). In this technique, the Filter approach is used to select the data sets from the large Microarray and the Wrapper approach is used to classify the gene expressions from the selected data sets. From the available statistical report, it is revealed that the Filter method has a fast dimensionality reduction step for selecting a small set of genes at the cost of accuracy and the Wrapper method used for improving classification accuracy on this small set of genes at the cost of computational delay. However this approach also has some problems such as different filtered subset leads to complex evaluation. Hence an Efficient Hybrid Classifier called “Multiple-Filter-Multiple-Wrapper Technique” has been proposed with improved performance of SFSW. The use of Multiple Filters with different filter metrics ensures that useful biomarkers are unlikely to be screened out in the initial filter stage. The use of Multiple Wrappers is intended to optimize the reliability of the classification by establishing consensus among several classifiers. However, in this research work, we have identified that the Classification Accuracy of MFMW is poor due to the consideration of Indecisive Prediction Status. Hence, this Research Work is introduced an efficient classifier called ICS4-MFMW, which is focusing both the dimensionality reduction and Indecisive Status. This work is demonstrated its efficiency in terms of classification accuracy with Sensitivity and Specificity. From the result, it is revealed that our proposed work perform better as compared with the existing MFMW Classifier.


title: Secondary cleft rhinoplasty rejuvenates the nose: a suggestion from a panel survey.

abstract: BACKGROUND ::: Cleft-lip nasal deformity (CLND) affects the overall facial appearance and attractiveness. The CLND nose shares some features in part with the aging nose. ::: ::: ::: OBJECTIVES ::: This questionnaire survey examined: 1) the panel perceptions of the role of secondary cleft rhinoplasty in nasal rejuvenation; and 2) the influence of a medical background in cleft care, age and gender of the panel members on the estimated age of the CLND nose. ::: ::: ::: STUDY DESIGN ::: Using a cross-sectional study design, we enrolled a random sample of adult laypersons and health care providers. The predictor variables were secondary cleft rhinoplasty (before/after) and a medical background in cleft care (yes/no). The outcome variable was the estimated age of nose in photographs derived from 8 German nonsyndromic CLND patients. Other study variables included age, gender, and career of the assessors. Appropriate descriptive and univariate statistics were computed, and a P value of <.05 was considered to be statistically significant. ::: ::: ::: RESULTS ::: The sample consisted of 507 lay volunteers and 51 medical experts (407 [72.9%] were female; mean age ± SD = 24.9 ± 8.2 y). The estimated age of the CLND noses was higher than their real age. The rhinoplasty decreased the estimated age to a statistically significant degree (P < .0001). A medical background, age, and gender of the participants were not individually associated with their votes (P > .05). ::: ::: ::: CONCLUSIONS ::: The results of this study suggest that CLND noses lack youthful appearance. Secondary cleft rhinoplasty rejuvenates the nose and makes it come close to the actual age of the patients.


title: AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning

abstract: Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users' public data. ::: In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user's private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user's public data, then the attacker's classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user's public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection.


title: Automatic differentiation in machine learning: a survey

abstract: Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply “auto-diff”, is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until ::: very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other’s results. Despite its ::: relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names “dynamic computational ::: graphs” and “differentiable programming”. We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main imple- ::: mentation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms “autodiff”, “automatic differentiation”, and “symbolic differentiation” as these are encountered more and more in machine learning settings.


title: Text mining for product attribute extraction

abstract: We describe our work on extracting attribute and value pairs from textual product descriptions. The goal is to augment databases of products by representing each product as a set of attribute-value pairs. Such a representation is beneficial for tasks where treating the product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include demand forecasting, assortment optimization, product recommendations, and assortment comparison across retailers and manufacturers. We deal with both implicit and explicit attributes and formulate both kinds of extractions as classification problems. Using single-view and multi-view semi-supervised learning algorithms, we are able to exploit large amounts of unlabeled data present in this domain while reducing the need for initial labeled data that is expensive to obtain. We present promising results on apparel and sporting goods products and show that our system can accurately extract attribute-value pairs from product descriptions. We describe a variety of application that are built on top of the results obtained by the attribute extraction system.