title: Research on a Skin Color Detection Algorithm Based on Self-adaptive Skin Color Model

abstract: Skin color detection is an important subject in computer vision research. Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. To aim at existing skin color algorithms considering the luminance information not enough, a reliable color modeling approach was proposed. It is based on the fact that color distribution of a single-colored object is not invariant with respect to luminance variations even in the Cb-Cr plane and does not ignore the influence on luminance Y component in YCbCr color space. Firstly, according to statistics of skin color pixels, we take the luminance Y by ascending order, divide the total range of Y into finite number of intervals, collect pixels whose luminance belongs to the same luminance interval, calculate the covariance and the mean value of Cb and Cr with respect to Y, and use the above data to train the BP neural network, then we get the self-adaptive skin color model and design a Gaussian model classifier. The experimental results have indicated that this algorithm can effectively fulfill the skin-color detection for images captured under different environmental condition and the performance of the skin color segmentation is significantly improved.


title: PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci

abstract: Statistical analysis system (SAS) is the most comprehensive statistical analysis software package in the world. It offers data analysis for almost all experiments under various statistical models. Each analysis is performed using a particular subroutine, called a procedure (PROC). For example, PROC ANOVA performs analysis of variances. PROC QTL is a user-defined SAS procedure for mapping quantitative trait loci (QTL). It allows users to perform QTL mapping for continuous and discrete traits within the SAS platform. Users of PROC QTL are able to take advantage of all existing features offered by the general SAS software, for example, data management and graphical treatment. The current version of PROC QTL can perform QTL mapping for all line crossing experiments using maximum likelihood (ML), least square (LS), iteratively reweighted least square (IRLS), Fisher scoring (FISHER), Bayesian (BAYES), and empirical Bayes (EBAYES) methods.


title: To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance

abstract: This study aimed to verify whether achieving a distinctive academic performance is unlikely for students at high risk of smartphone addiction. Additionally, it verified whether this phenomenon was equally applicable to male and female students. After implementing systematic random sampling, 293 university students participated by completing an online survey questionnaire posted on the university's student information system. The survey questionnaire collected demographic information and responses to the Smartphone Addiction Scale-Short Version (SAS-SV) items. The results showed that male and female university students were equally susceptible to smartphone addiction. Additionally, male and female university students were equal in achieving cumulative GPAs with distinction or higher within the same levels of smartphone addiction. Furthermore, undergraduate students who were at a high risk of smartphone addiction were less likely to achieve cumulative GPAs of distinction or higher. Students who are at high risk of smartphone addiction are less likely to achieve high GPAs.Male and female university students are equally susceptible to smartphone addiction.Every other university student was identified as high risk for smartphone addiction.Males and females are equal in achieving high GPAs within same levels of smartphone addiction.


title: A high input dynamic range, low voltage cascode current mirror and enhanced phase-margin folded cascode amplifier

abstract: A new adaptive biased cascode current mirror is presented. The proposed cascode current mirror has advantage of simplicity with low voltage operation without using any other bias currents or voltages. With proposed self-biasing configuration, large dynamic input current with high accuracy can be maintained without increasing input resistance or limiting output swing. Also, the proposed low voltage cascode mirror is incorporated in folded cascode amplifier in order to enhance its phase margin (PM). The Enhanced PM Folded Cascode amplifier has unity gain bandwidth of 84 MHz and consumes 720 μW @ 1.8 V with phase margin of 88˚. The proposed circuits are designed and simulated in standard 0.18μm CMOS technology.


title: Parametric cost estimation based on activity-based costing: A case study for design and development of rotational parts

abstract: Modern manufacturing systems are facing a globally competitive market to an extent not experienced before. This competitive pressure forces manufacturers to produce more products with shorter life span and better quality, yet at a lower cost. To succeed in this environment, manufacturing firms need to have an accurate estimate of product design and development costs. This is especially important since the shorter life span of products accentuates design and development stages. This paper presents a cost-estimation model that links activity-based costing (ABC) with parametric cost representations of the design and development phases of machined rotational parts. It also presents several parametric models applicable at design time by using parts' feature geometry. A comparison of various parametric cost models is provided, reflecting on the accuracy of the various approaches. There is an evident trade-off between the details embedded in the parametric representation and the cost-estimation accuracy. Thus, more detailed methods, which can be used later in the life cycle of the product, are more precise, while these methods that can be used as early as design time have a higher but acceptable error rate.


title: Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

abstract: Vulnerabilities need to be detected and removed from software. Although previous studies demonstrated the usefulness of employing prediction techniques in deciding about vulnerabilities of software components, the accuracy and improvement of effectiveness of these prediction techniques is still a grand challenging research question. This paper proposes a hybrid technique based on combining N-gram analysis and feature selection algorithms for predicting vulnerable software components where features are defined as continuous sequences of token in source code files, i.e., Java class file. Machine learning-based feature selection algorithms are then employed to reduce the feature and search space. We evaluated the proposed technique based on some Java Android applications, and the results demonstrated that the proposed technique could predict vulnerable classes, i.e., software components, with high precision, accuracy and recall.


title: Logic BIST for large industrial designs: real issues and case studies

abstract: This paper discusses practical issues involved in applying logic built-in self-test (BIST) to four large industrial designs. These multi-clock designs, ranging in size from 200 K to 800 K gates, pose significant challenges to logic BIST methodology, flow, and tools. The paper presents the process of generating a BIST-compliant core along with the logic BIST controller for at-speed testing. Comparative data on fault grades and area overhead between automatic test pattern generation (ATPG) and logic BIST are reported. The experimental results demonstrate that with automation of the proposed solutions, logic BIST can achieve test quality approaching that of ATPG with minimal area overhead and few changes to the design flow.


title: The Amputee Mobility Predictor: An instrument to assess determinants of the lower-limb amputee's ability to ambulate

abstract: OBJECTIVES ::: To describe the development of the Amputee Mobility Predictor (AMP) instrument designed to measure ambulatory potential of lower-limb amputees with (AMPPRO) and without (AMPnoPRO) the use of a prosthesis, and to test its reliability and validity. ::: ::: ::: DESIGN ::: Measurement study using known groups method and concurrence with existing measures. ::: ::: ::: SETTING ::: Academic medical center. ::: ::: ::: PARTICIPANTS ::: A convenience sample of 191 lower-limb amputee subjects who had completed prosthetic training, 24 in the reliability study (mean age +/- standard deviation, 68.3+/-17.9y, range, 28-99y) and 167 in the validity study (mean age, 54.8+/-18.6y; range, 18-100y). ::: ::: ::: INTERVENTIONS ::: Not applicable. ::: ::: ::: MAIN OUTCOME MEASURES ::: Intra- and interrater reliability; construct validity by known groups method; concurrent validity by comparisons with 6-minute walk test, Comorbidity Index, age, and time since amputation; predictive validity by comparison with 6-minute walk test after controlling for other factors. ::: ::: ::: RESULTS ::: Interrater reliability was.99 for subjects tested with and without their prosthesis; intrarater reliability was.96 and.97. Both the AMPnoPRO (P<.0001) and the AMPPRO scores (P<.0001) distinguished among the 4 Medicare functional classification levels. The AMP correlated strongly with 6-minute walk scores (AMPnoPRO r=.69, P<.0001; AMPPRO r=.82, P<.0001) and the amputee activity survey (AMPnoPRO r=.67, P<.0001; AMPPRO r=.77, P<.0001), and negatively correlated with age (AMPnoPRO r=-.69, P<.0001; AMPPRO r=.56, P<.0001) and comorbidity (AMPnoPRO r=-.43, P<.0001; AMPPRO r=.38, P<.0001). ::: ::: ::: CONCLUSION ::: The AMP with and without a prosthesis are reliable and valid measures for the assessment of functional ambulation in lower-limb amputee subjects.


title: Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms

abstract: Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.


title: Fast In-Memory Triangle Listing for Large Real-World Graphs

abstract: Triangle listing, or identifying all the triangles in an undirected graph, is a very important graph problem that serves as a building block of many other graph algorithms. The compute-intensive nature of the problem, however, necessitates an efficient method to solve this problem, especially for large real-world graphs. In this paper we propose a fast and precise in-memory solution for the triangle listing problem. Our solution includes fast common neighborhoods finding methods that consider power law degree distribution of real-word graphs. We prove how theoretic lower bound can be achieved by sorting the nodes in the graph by their degree and applying pruning. We explain how our techniques can be applied automatically by an optimizing DSL compiler. Our experiments show that hundreds of billions of triangles in a five billion edge graph can be enumerated in about a minute with a single server-class machine.


title: Quantum Computability

abstract: In this paper some theoretical and (potentially) practical aspects of quantum computing are considered. Using the tools of transcendental number theory it is demonstrated that quantum Turing machines (QTM) with rational amplitudes are sufficient to define the class of bounded error quantum polynomial time (BQP) introduced by Bernstein and Vazirani [Proc. 25th ACM Symposium on Theory of Computation, 1993, pp. 11--20, SIAM J. Comput., 26 (1997), pp. 1277--1339]. On the other hand, if quantum Turing machines are allowed unrestricted amplitudes (i.e., arbitrary complex amplitudes), then the corresponding BQP class has uncountable cardinality and contains sets of all Turing degrees. In contrast, allowing unrestricted amplitudes does not increase the power of computation for error-free quantum polynomial time (EQP). Moreover, with unrestricted amplitudes, BQP is not equal to EQP. The relationship between quantum complexity classes and classical complexity classes is also investigated. It is shown that when quantum Turing machines are restricted to have transition amplitudes which are algebraic numbers, BQP, EQP, and nondeterministic quantum polynomial time (NQP) are all contained in PP, hence in ${\rm P}^{\#{\rm P}}$ and PSPACE. A potentially practical issue of designing "machine independent" quantum programs is also addressed. A single ("almost universal") quantum algorithm based on Shor's method for factoring integers is developed which would run correctly on almost all quantum computers, even if the underlying unitary transformations are unknown to the programmer and the device builder.


Magma-assisted continental rift margins : the Ethiopian rift


title: Automatic Hyperparameter Tuning in Deep Convolutional Neural Networks Using Asynchronous Reinforcement Learning

abstract: Major gains have been made in recent years in object recognition due to advances in deep neural networks. One struggle with deep learning, however, revolves around the fact that currently it is unknown what network architecture is best for a given problem. Consequently, different configurations are tried until one is identified that gives acceptable results. This paper proposes an asynchronous reinforcement learning algorithm that finds an optimal network configuration by automatically adjusting parameters for a given problem. It is shown that asynchronous reinforcement learning is able to converge on an optimal solution for the MNIST data set.


title: Unsupervised Controllable Text Formalization

abstract: We propose a novel framework for controllable natural language transformation. Realizing that the requirement of parallel corpus is practically unsustainable for controllable generation tasks, an unsupervised training scheme is introduced. The crux of the framework is a deep neural encoder-decoder that is reinforced with text-transformation knowledge through auxiliary modules (called scorers). These scorers, based on off-the-shelf language processing tools, decide the learning scheme of the encoder-decoder based on its actions. We apply this framework for the text-transformation task of formalizing an input text by improving its readability grade; the degree of required formalization can be controlled by the user at run-time. Experiments on public datasets demonstrate the efficacy of our model towards: (a) transforming a given text to a more formal style, and (b) varying the amount of formalness in the output text based on the specified input control. Our code and datasets are released for academic use.


title: Pouring Skills with Planning and Learning Modeled from Human Demonstrations

abstract: We explore how to represent, plan and learn robot pouring. This is a case study of a complex task that has many variations and involves manipulating non-rigid materials such as liquids and granular substances. Variations of pouring we consider are the type of pouring (such as pouring into a glass or spreading a sauce on an object), material, container shapes, initial poses of containers and target amounts. The robot learns to select appropriate behaviors from a library of skills, such as tipping, shaking and tapping, to pour a range of materials from a variety of containers. The robot also learns to select behavioral parameters. Planning methods are used to adapt skills for some variations such as initial poses of containers. We show using simulation and experiments on a PR2 robot that our pouring behavior model is able to plan and learn to handle a wide variety of pouring tasks. This case study is a step towards enabling humanoid robots to perform tasks of daily living.


title: Motor Abilities in Autism: A Review Using a Computational Context

abstract: Altered motor behaviour is commonly reported in Autism Spectrum Disorder, but the aetiology remains unclear. Here, we have taken a computational approach in order to break down motor control into different components and review the functioning of each process. Our findings suggest abnormalities in two areas—poor integration of information for efficient motor planning, and increased variability in basic sensory inputs and motor outputs. In contrast, motor learning processes are relatively intact and there is inconsistent evidence for deficits in predictive control. We suggest future work on motor abilities in autism should focus on sensorimotor noise and on higher level motor planning, as these seem to have a significant role in causing motor difficulties for autistic individuals.


title: Exploring topic models in software engineering data analysis: A survey

abstract: Topic models are shown to be effective to mine unstructured software engineering (SE) data. In this paper, we give a simple survey of exploring topic models to support various SE tasks between 2003 and 2015. The survey results show that there is an increasing concern in this area. Among the SE tasks, source code comprehension and software history comprehension are the mostly studied, followed by software defects prediction. However, there is still only a few studies on other SE tasks, such as feature location and regression testing.


title: Potential-based bounded-cost search and Anytime Non-Parametric A*

abstract: This paper presents two new search algorithms: Potential Search (PTS) and Anytime Potential Search/Anytime Non-Parametric A? (APTS/ANA?). Both algorithms are based on a new evaluation function that is easy to implement and does not require user-tuned parameters. PTS is designed to solve bounded-cost search problems, which are problems where the task is to find as fast as possible a solution under a given cost bound. APTS/ANA? is a non-parametric anytime search algorithm discovered independently by two research groups via two very different derivations. In this paper, co-authored by researchers from both groups, we present these derivations: as a sequence of calls to PTS and as a non-parametric greedy variant of Anytime Repairing A?. ::: ::: We describe experiments that evaluate the new algorithms in the 15-puzzle, KPP-COM, robot motion planning, gridworld navigation, and multiple sequence alignment search domains. Our results suggest that when compared with previous anytime algorithms, APTS/ANA?: (1) does not require user-set parameters, (2) finds an initial solution faster, (3) spends less time between solution improvements, (4) decreases the suboptimality bound of the current-best solution more gradually, and (5) converges faster to an optimal solution when reachable.


title: Analysis, Classification, and Design of Tendon-Driven Mechanisms

abstract: This paper analyzes tendon-driven mechanisms (TDMs) with active and passive tendons and proposes a method for designing TDMs. First, we group TDMs into six classes according to their controllability and the number of driving degrees of freedom. In this classification system, the conventional underactuated mechanisms are grouped into three classes, two of which have often previously been grouped together although they have different manipulation abilities. Next, we analyze bias forces to separate and decouple a given TDM into several smaller TDMs. Finally, we propose a design method for combining smaller TDMs into an appropriate TDM. Using this method, we can easily determine an appropriate tendon transmission that meets the requirements for the number of tendons, the hardware of the tendon routing, and arbitrary joint constraint that has useful applications in prosthetic and biomimetic hands. Numerical examples show that the proposed method guarantees a nonsingular series actuation transmission and that the designed underactuated TDMs could achieve arbitrary stiffness independent of the actuation effort compared with the conventional underactuated TDM with torsion springs.


title: Performance Analysis of IEEE 802.15.6 MAC Protocol under Non-Ideal Channel Conditions and Saturated Traffic Regime

abstract: Recently, the IEEE 802.15.6 Task Group introduced a new wireless communication standard that provides a suitable framework specifically to support the requirements of wireless body area networks (WBANs). The standardization dictates the physical (PHY) layer and medium access control (MAC) layer protocols for WBAN-based communications. Unlike the pre-existing wireless communication standards, IEEE 802.15.6 standardization supports short-range, extremely low power wireless communication with high quality of service and support for high data rates upto 10 Mbps in the vicinity of living tissues. In this work, we construct a discrete-time Markov chain (DTMC) that efficiently depicts the states of an IEEE 802.15.6 CSMA/CA-based WBAN. Following this, we put forward a thorough analysis of the standard in terms of reliability, throughput, average delay, and power consumption. The work concerns non-ideal channel characteristics and a saturated network traffic regime. The major shortcoming of the existing literature on Markov chain-based analysis of IEEE 802.15.6 is that the authors did not take into consideration the time spent by a node awaiting the acknowledgement frame after transmission of a packet, until time-out occurs. Also, most of the work assume that ideal channel characteristics persist for the network which is hardly the case in practice. This work remains distinctive as we take into account the waiting time of a node after it transmits a packet while constructing the DTMC. Based on the DTMC, we perform a user priority (UP)-wise analysis, and justify the importance of the standard from a medical perspective.


title: A Two-Wheeled Self-Balancing Robot with the Fuzzy PD Control Method

abstract: A two-wheeled self-balancing robot with a fuzzy PD control method is described and analyzed as an example of a high-order, multiple-variable, nonlinear, strong-coupling, and unstable system. Based on a system structure model, a kinetic equation is constructed using Newtonian dynamics and mechanics. After a number of simulation experiments, we get the best Q, R, and state-feedback matrices. Then a fuzzy PD controller is designed for which the position and speed of the robot are inputs and for which the angle and angle rate of the robot are controlled by a PD controller. Finally, this paper describes a real-time control platform for the two-wheeled self-balancing robot that controls the robot effectively, after some parameter debugging. The result indicates that the fuzzy PD control algorithm can successfully achieve self-balanced control of the two-wheeled robot and prevent the robot from falling.


title: A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises

abstract: A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise. The new algorithm shows significantly better image quality than a standard median filter (SMF), adaptive median filters (AMF), a threshold decomposition filter (TDF), cascade, and recursive nonlinear filters. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The proposed algorithm (PA) is tested on different images and is found to produce better results in terms of the qualitative and quantitative measures of the image


title: A low cost wireless sensor network for water quality monitoring in natural water bodies

abstract: Water is one of the precious factors in human life. It plays a vital role in the survival and existence of flora and fauna. But, now a days it is been exploited heavily by dumping of human waste, industrial and chemical waste in to the water bodies which is the resultant of water being contaminated with toxic chemicals. Water scarcity and water contamination has been one of the major issues faced by the society around the world. In many developing countries like India, the main focus is given to industrialization and urbanization. But this has caused an increased water usage and water contamination. This paper deals with the monitoring of water quality by mainly considering the physical and chemical properties of water. For this purpose, a Wireless Sensor node is designed which will contain several sensors to sense the chemical contents of water. The highlight of sensor node is that it is water proof. The main components of WSN include microcontroller for processing the system, communication system for inter and intra node communication and several sensors. The power for the sensor nodes are obtained from the solar panels connected to the sensor nodes. Though, in earlier times, there were water quality monitoring systems, they were power hungry and not real time. Apart from acting as a single system, if this system integrates with other innovative solutions like Smart Home or Smart Building, the application of this system would increase leading to an increased demand for it. This paper highlights on how a real time monitoring system can be made generic, using Network Virtualization. Also, new technologies like Internet of Things (IoT) could be incorporated in this system to make it advanced.


title: Exploring the Effect of Teaching Methods on Students' Learning of School Informatics

abstract: School informatics is a relatively new subject in comparison with mathematics. As a result, there is much less research in informatics education. Likewise, there is lack of theoretical grounding of didactics of informatics in general and school informatics in particular. The lack of research in the field of school informatics makes it difficult to identify which methods are effective to teach the subject. In addition, despite the emphasis on a set of well-defined topics, the New National Curriculum in Norway makes few suggestions as to which didactical skills teachers need to acquire in order to teach informatics-related subjects. In fact, school informatics is still in its infancy. It lacks an extensive research base of materials that can be used to achieve educational goals and learning outcomes. The main research objective of this work is to investigate the effectiveness of teaching methods and their impact on learning school informatics.


title: Classification-Driven Watershed Segmentation

abstract: This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing.


title: Graph-based Dependency Parsing with Bidirectional LSTM

abstract: In this paper, we propose a neural network model for graph-based dependency parsing which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual information instead of using high-order factorization, and enable our model to use much fewer features than previous work. In addition, we propose an effective way to learn sentence segment embedding on sentence-level based on an extra forward LSTM network. Although our model uses only first-order factorization, experiments on English Peen Treebank and Chinese Penn Treebank show that our model could be competitive with previous higher-order graph-based dependency parsing models and state-of-the-art models.


Deep Learning in Intrusion Detection System: An Overview


title: Asymmetric Inequality Aversion and Noisy Behavior in Alternating-Offer Bargaining

abstract: In two-stage bargaining games with alternating offers, the amount of the pie that remains after a rejection is what the first player should offer to the second player, since the second player can capture this remainder in the final (ultimatum) stage. Fairness considerations will reduce the correlation between first-stage offers and the size of the remaining pie, but randomness in behavior will have the same "flattening" effect. This paper reports an experiment designed to separate these considerations, by introducing asymmetric fixed money payments to each player. These endowments do not affect the perfect positive correlation between initial Nash offers and the remaining pie, but are selected to induce a perfectly negative relationship between the remaining pie size and the first-stage offer that would equalize final earnings of the two players. This negative relationship is apparent in the data, which suggests the importance of fairness considerations. A theoretical model of asymmetric inequality aversion and stochastic choice is used to provide maximum likelihood estimates of utility and logit error parameters. The parameters representing "envy," "guilt," and logit errors are all significant, and the resulting model produces the observed negative relationship between initial offers and residual pie size.(This abstract was borrowed from another version of this item.)


title: featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

abstract: Motivation: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. Results: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. Availability and implementation: featureCounts is available under GNU General Public License as part of the Subread (http://subread. sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.


title: Best Practices and Lessons Learned from Deploying and Operating Large-Scale Data-Centric Parallel File Systems

abstract: The Oak Ridge Leadership Computing Facility (OLCF) has deployed multiple large-scale parallel file systems (PFS) to support its operations. During this process, OLCF acquired significant expertise in large-scale storage system design, file system software development, technology evaluation, benchmarking, procurement, deployment, and operational practices. Based on the lessons learned from each new PFS deployment, OLCF improved its operating procedures, and strategies. This paper provides an account of our experience and lessons learned in acquiring, deploying, and operating large-scale parallel file systems. We believe that these lessons will be useful to the wider HPC community.


title: A new collaborative filtering algorithm using K-means clustering and neighbors' voting

abstract: The Collaborative Filtering is the most successful algorithm in the recommender systems' field. A recommender system is an intelligent system can help users to come across interesting items. It uses data mining and information filtering techniques. The collaborative filtering creates suggestions for users based on their neighbors' preferences. But it suffers from its poor accuracy and scalability. This paper considers the users are m (m is the number of users) points in n dimensional space (n is the number of items) and represents an approach based on user clustering to produce a recommendation for active user by a new method. It uses k-means clustering algorithm to categorize users based on their interests. Then it uses a new method called voting algorithm to develop a recommendation. We evaluate the traditional collaborative filtering and the new one to compare them. Our results show the proposed algorithm is more accurate than the traditional one, besides it is less time consuming than it.


title: A decentralized approach for programming interactive applications with JavaScript and blockly

abstract: We present a decentralized-control methodology and a tool-set for developing interactive user interfaces. We focus on the common case of developing the client side of Web applications. Our approach is to combine visual programming using Google Blockly with a single-threaded implementation of behavioral programming in JavaScript. We show how the behavioral programming principles can be implemented with minimal programming resources, i.e., with a single-threaded environment using coroutines. We give initial, yet full, examples of how behavioral programming is instrumental in addressing common issues in this application domain, e.g., that it facilitates separation of graphical representation from logic and handling of complex inter-object scenarios. The implementation in JavaScript and Blockly (separately and together) expands the availability of behavioral programming capabilities, previously implemented in LSC, Java, Erlang and C++, to audiences with different skill-sets and design approaches.


title: IoT, cloud services, and big data: A comprehensive pricing solution

abstract: This paper shows a proposal for pricing cloud services tailored to IoT applications that generate big data. The basic assumption of our analysis is that the emerging IoT applications do not simply make use of the information collected by few sensors. The expected volume of information generated by sensors, the different nature of sensors, the different information delivery techniques, and the variable nature of applications make the system management a real 3V big data problem. In this paper we identify the key feature that characterizes a cloud-based, sensing-as-a-service IoT application, map each feature into a specific cost function, and suitably combine these cost functions. This way, we obtain a pricing strategy sufficiently simple to be applied in operation, dependent on all the identified features, and flexible enough for being updated for any new introduced IoT service in the cloud infrastructure.


title: Born Too Soon: Preterm birth matters

abstract: Urgent action is needed to address preterm birth given that the first country-level estimates show that globally 15 million babies are born too soon and rates are increasing in most countries with reliable time trend data. As the first in a supplement entitled "Born Too Soon", this paper focuses on the global policy context. Preterm birth is critical for progress on Millennium Development Goal 4 (MDG) for child survival by 2015 and beyond, and gives added value to maternal health (MDG 5) investments also linking to non-communicable diseases. For preterm babies who survive, the additional burden of prematurity-related disability may affect families and health systems. Prematurity is an explicit priority in many high-income settings; however, more attention is needed especially in low- and middle-income countries where the invisibility of preterm birth as well as its myths and misconceptions have slowed action on prevention and care. Recent global attention to preterm birth hit a tipping point in 2012, with the May 2 publication of Born Too Soon: The Global Action Report on Preterm Birth and with the 2nd annual World Prematurity Day on November 17 which mobilised the actions of partners in many countries to address preterm birth and newborn health. Interventions to strengthen preterm birth prevention and care span the continuum of care for reproductive, maternal, newborn and child health. Both prevention of preterm birth and implementation of care of premature babies require more research, as well as more policy attention and programmatic investment.


title: Statistical Models for Text Segmentation

abstract: This paper introduces a new statistical approach to automatically partitioning text into coherent segments. The approach is based on a technique that incrementally builds an exponential model to extract features that are correlated with the presence of boundaries in labeled training text. The models use two classes of features: topicality features that use adaptive language models in a novel way to detect broad changes of topic, and cue-word features that detect occurrences of specific words, which may be domain-specific, that tend to be used near segment boundaries. Assessment of our approach on quantitative and qualitative grounds demonstrates its effectiveness in two very different domains, Wall Street Journal news articles and television broadcast news story transcripts. Quantitative results on these domains are presented using a new probabilistically motivated error metric, which combines precision and recall in a natural and flexible way. This metric is used to make a quantitative assessment of the relative contributions of the different feature types, as well as a comparison with decision trees and previously proposed text segmentation algorithms.


title: A low cost environment monitoring system using raspberry Pi and arduino with Zigbee

abstract: Emerging environmental related problems have greater impact on technical, scientific, social and economical fields, which is why this issue needs attention. On a global scale this issue are being sort out using satellite based instruments. This paper discusses a monitoring system which gives information about environmental conditions on a more local level, the implementation area are categorize by industrial, home and office applications and briefly touches the technological advancements in monitoring the environment and bringing out the new scope in monitoring the current environment problems. This paper intends to provide information using wireless sensor technology which comprises of raspberry pi, Arduino Nano, Zigbee, wireless sensor network(WSN) and sensors. Realization of data gathered by sensors based on embedded raspbian Linux is displayed on Graphical User Interface (GUI). The system is developed using open source hardware raspberry pi and Zigbee which proves to be cost effective and having low power consumption. The sensors will gather the data of various environmental parameters and provide it to raspberry pi which act as a base station. Some sensors will directly process the data and provide it to the raspberry pi while some sensors will provide the data through Arduino Nano to raspberry pi using serial interface. The raspberry pi will then transmit the data using Zigbee and the processed data will be displayed on GUI through Zigbee that is on the receiver side.


title: Constraint-based recommender systems: technologies and research issues

abstract: Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.


title: Localizing anomalous changes in time-evolving graphs

abstract: Given a time-evolving sequence of undirected, weighted graphs, we address the problem of localizing anomalous changes in graph structure over time. In this paper, we use the term `localization' to refer to the problem of identifying abnormal changes in node relationships (edges) that cause anomalous changes in graph structure. While there already exist several methods that can detect whether a graph transition is anomalous or not, these methods are not well suited for localizing the edges which are responsible for a transition being marked as an anomaly. This is a limitation in applications such as insider threat detection, where identifying the anomalous graph transitions is not sufficient, but rather, identifying the anomalous node relationships and associated nodes is key. To this end, we propose a novel, fast method based on commute time distance called CAD (Commute-time based Anomaly detection in Dynamic graphs) that detects node relationships responsible for abnormal changes in graph structure. In particular, CAD localizes anomalous edges by tracking a measure that combines information regarding changes in graph structure (in terms of commute time distance) as well as changes in edge weights. For large, sparse graphs, CAD returns a list of these anomalous edges and associated nodes in O(n\log n) time per graph instance in the sequence, where $n$ is the number of nodes. We analyze the performance of CAD on several synthetic and real-world data sets such as the Enron email network, the DBLP co-authorship network and a worldwide precipitation network data. Based on experiments conducted, we conclude that CAD consistently and efficiently identifies anomalous changes in relationships between nodes over time.


title: The Emergence of a Circuit Model for Addiction

abstract: Addiction is a disease of altered behavior. Addicts use drugs compulsively and will continue to do so despite negative consequences. Even after prolonged periods of abstinence, addicts are at risk of relapse, particularly when cues evoke memories that are associated with drug use. Rodent models mimic many of the core components of addiction, from the initial drug reinforcement to cue-associated relapse and continued drug intake despite negative consequences. Rodent models have also enabled unprecedented mechanistic insight into addiction, revealing plasticity of glutamatergic synaptic transmission evoked by the strong activation of mesolimbic dopamine—a defining feature of all addictive drugs—as a neural substrate for these drug-adaptive behaviors. Cell type–specific optogenetic manipulations have allowed both identification of the relevant circuits and design of protocols to reverse drug-evoked plasticity and to establish links of causality with drug-adaptive behaviors. The emergence of a circuit model f...


title: A Survey on Learning to Hash

abstract: Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.


title: Secure Seamless Bluetooth Low Energy Connection Migration for Unmodified IoT Devices

abstract: At present, Bluetooth Low Energy (BLE) is dominantly used in commercially available Internet of Things (IoT) devices – such as smart watches, fitness trackers, and smart appliances. Compared to classic Bluetooth, BLE has been simplified in many ways that include its connection establishment, data exchange, and encryption processes. Unfortunately, this simplification comes at a cost. For example, only a star topology is supported in BLE environments and a peripheral (an IoT device) can communicate with only one gateway (e.g. a smartphone, or a BLE hub) at a set time. When a peripheral goes out of range, it loses connectivity to a gateway, and cannot connect and seamlessly communicate with another gateway without user interventions. In other words, BLE connections are not automatically migrated or handed-off to another gateway. In this paper, we propose SeamBlue , which brings seamless connectivity to BLE-capable mobile IoT devices in an environment that consists of a network of gateways. Our framework ensures that unmodified, commercial off-the-shelf BLE devices seamlessly and securely connect to a nearby gateway without any user intervention.


title: Device-to-device communications in cellular networks

abstract: Device-to-device communications enable two proximity users to transmit signal directly without going through the base station. It can increase network spectral efficiency and energy efficiency, reduce transmission delay, offload traffic for the BS, and alleviate congestion in the cellular core networks. However, many technical challenges need to be addressed for D2D communications to harvest the potential benefits, including device discovery and D2D session setup, D2D resource allocation to guarantee QoS, D2D MIMO transmission, as well as D2D-aided BS deployment in heterogeneous networks. In this article, the basic concepts of D2D communications are first introduced, and then existing fundamental works on D2D communications are discussed. In addition, some potential research topics and challenges are also identified.


title: Effects and mechanisms of actions of phytochemicals on Alzheimer's disease neuropathology.

abstract: Alzheimer's disease affects millions of people, yet, there are only a limited number approaches for it pharmacological treatment. Thus, identifying factors that decrease the risk of developing Alzheimer's disease is of paramount importance. A growing body of epidemiological and experimental evidence suggests that dietary fruits and vegetables have neuroprotective effects against the harmful effects of oxidative stress, neuroinflammation, and aging. These effects are mediated by various phytochemical compounds found in plants that exhibit antioxidant, anti-inflammatory, and other beneficial properties. This review addresses epidemiological and experimental evidence for the effects and potential mechanisms of several commonly consumed phytochemicals on neuropathology and outcomes of Alzheimer's disease. Based on available evidence, we suggest that regular consumption of bioactive phytochemicals from a variety of fruits and vegetables attenuates age- and insult-related neuropathology in Alzheimer's disease.


title: Velocity and position control of a wheeled inverted pendulum by partial feedback linearization

abstract: In this paper, the dynamic model of a wheeled inverted pendulum (e.g., Segway, Quasimoro, and Joe) is analyzed from a controllability and feedback linearizability point of view. First, a dynamic model of this underactuated system is derived with respect to the wheel motor torques as inputs while taking the nonholonomic no-slip constraints into considerations. This model is compared with the previous models derived for similar systems. The strong accessibility condition is checked and the maximum relative degree of the system is found. Based on this result, a partial feedback linearization of the system is obtained and the internal dynamics equations are isolated. The resulting equations are then used to design two novel controllers. The first one is a two-level velocity controller for tracking vehicle orientation and heading speed set-points, while controlling the vehicle pitch (pendulum angle from the vertical) within a specified range. The second controller is also a two-level controller which stabilizes the vehicle's position to the desired point, while again keeping the pitch bounded between specified limits. Simulation results are provided to show the efficacy of the controllers using realistic data.


title: Ocular Dermoids: 116 Consecutive Cases

abstract: PURPOSE ::: The aim was to describe the demographic and clinical data of 116 consecutive cases of ocular dermoids. ::: ::: ::: METHODS ::: This was a retrospective case series and a review of clinical records of all the patients diagnosed with ocular dermoids. Both demographic and clinical data were recorded. Statistical analysis was performed with SPSS v. 18. Descriptive statistics are reported. ::: ::: ::: RESULTS ::: The study included 116 consecutive patients with diagnosis consistent with ocular dermoids: corneal 18% (21), dermolipomas 38% (44), and orbital 44% (51). Sixty-five percent (71) were female, and 46% (54) were detected at birth. Secondary manifestations: amblyopia was present in 14% (3), and strabismus was detected in 6.8% (8). The Goldenhar syndrome was the most frequent syndromic entity in 7.5% (12) of the patients. Surgical resection was required on 49% (25) of orbital dermoids, 24% (5) of corneal dermoids, and 13% (6) of dermolipomas. ::: ::: ::: CONCLUSIONS ::: Orbital dermoids were the most frequent variety, followed by conjunctival and corneal. In contrast to other reports, corneal dermoids were significantly more prevalent in women. Goldenhar syndrome was the most frequent syndromatic entity.


title: Eyelid and eyelash detection method based on morphology

abstract: The reliable detection of the occlusion iris image in the polar domain is required to improve the performance of iris recognition. Estimated the occluding and refine a much more accurate iris mask before iris feature extraction is essential step in the iris recognition. In this paper, we propose a method for detecting occlusion and refining mask accurately based on morphology. Compared with traditional method which detected sequence is eyelid -eyelash and distinguish eyelash based on threshold alone. Out method segment image by using the connectivity of the eyelash which is the base of the region grow. It guards against the noise in the process of the thresholding and decreases the computer load significantly. This method is applied to the CASAI version 3 databases and shows good performance.


title: A Course on Programming and Problem Solving

abstract: At its core, Computer Science is the study of algorithmic problem solving. Although it is necessary to teach programming, data structures, computer organization, etc., students should ultimately learn to use these things to solve problems, understand what is good and bad about their solutions, and share their solutions with others. This paper describes a course that focuses on the four steps of the problem solving process: algorithmic thinking, implementation, analysis, and communication. This course, based on Knuth's popular seminar at Stanford, has been extremely successful at the authors' three institutions. In addition to discussing the course's objectives and methodology, we present sample problems, summarize the outcomes and feedback from students, and give advice to other educators looking to create a similar course.


title: Ensuring Data Integrity in Cloud Computing Venkatesa Kumar V

abstract: Cloud computing provides convenient on-demand network access to a shared pool of configurable computing resources. The resources can be rapidly deployed with great efficiency and minimal management overhead. Cloud is an insecure computing platform from the view point of the cloud users, the system must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect users from malicious behaviours by enabling the validation of the computation result. In this paper, we propose a new data encoding scheme called layered interleaving, designed for time-sensitive packet recovery in the presence of bursty loss. It is high-speed data recovery scheme with minimal loss probability and using a forward error correction scheme to handle bursty loss. The proposed approach is highly efficient in recovering the singleton losses almost immediately and from bursty data losses.


title: Microfluidic Microwave Sensor for Simultaneous Dielectric and Magnetic Characterization

abstract: Herein, we demonstrate a method for simultaneously quantifying the electric and magnetic properties of liquid systems confined within a capillary. This is based upon an optimized perturbation of a microwave-frequency coaxial resonator, chosen to maximize the spatial separation of the two fields and to minimize the depolarization of the liquid. A capillary is passed through the center of the resonator so the sample occupies either maximum electric field (zero magnetic field) or maximum magnetic field (zero electric field) depending on whether an odd or even TEM mode is interrogated. This allows electric and magnetic effects to be distinguished at multiple discrete frequencies. We demonstrate this capability through the quantification of varying ionic content of saline solutions, which interact with both the electric and magnetic fields via several polarization mechanisms. The distinction of different cations and anions is also demonstrated.


COBIT 5 as IT Governance Framework and Implementation Method - A Literature Mapping


title: Tiresias: Black-Box Failure Prediction in Distributed Systems

abstract: Faults in distributed systems can result in errors that manifest in several ways, potentially even in parts of the system that are not collocated with the root cause. These manifestations often appear as deviations (or "errors") in performance metrics. By transparently gathering, and then identifying escalating anomalous behavior in, various node-level and system-level performance metrics, the Tiresias system makes black-box failure-prediction possible. Through the trend analysis of performance metrics, Tiresias provides a window of opportunity (look-ahead time) for system recovery prior to impending crash failures. We empirically validate the heuristic rules of the Tiresias system by analyzing fault-free and faulty performance data from a replicated middleware-based system.


title: Geometric morphometrics of vole (Microtus californicus) dentition as a new paleoclimate proxy: Shape change along geographic and climatic clines

abstract: Quaternary small-mammal fossils are widespread and have served as valuable paleoclimatic proxies through niche-modeling or otherwise relating presence-absence data to climatic data. This paper examines the potential for a new, more resolved type of paleoclimate proxy in the form of Microtus tooth shape, using an abundant California species, Microtus californicus. Geometric morphometrics and partial least squares analyses are used to find geographic signals in the shape of the first lower molar (m1) of this species. M. californicus m1s are relatively straight in the northwest, cooler, moister portion of California and more curved in the southeast, hotter, drier portion of the state. These tooth shape changes may be a result of selection related to different vegetation ultimately controlled by climate, and therefore diet, within the species' range. The pattern in m1 shape persists when phylogeographic hypotheses are taken into account, indicating that the climate signal is significant independent of intraspecific groupings. This method adds an important new tool to the suite of proxies available to reconstruct past climates at fine spatiotemporal scales and reveals a geographic/climatic signal that correlates with morphological variation across the range of M. californicus.


title: Evolutionary Processes as Models for Exploratory Design

abstract: Biological evolution drives morphological diversity via genetic variation and results in a high level of adaptation, performance and resource efficiency. However, “biological design” arising from evolution is often counterintuitive and unexpected in a non-linear way. Evolutionary processes are undirected and very good at exploring novel design possibilities in an open-ended manner. Biological evolution thus differs profoundly from the gradualistic and constantly converging character of technical optimization with defined and static fitness functions. Evolutionary algorithms based on Darwinian principles are mainly developed for solving multi-criteria problems in technology. Technical goals are defined as fitness functions and the evolutionary mechanisms of selection, heredity, reproduction and mutation are employed as stochastic optimization processes. These metaheuristic algorithms do not include recent insights into micro- and macro-evolutionary mechanisms derived from genomics, phylogenomics and population genomics. Similar to natural evolution, the architectural design process is an open-ended process exploring possible solutions. However, in order to navigate this vast and dynamic design space, most design methodologies in architecture are based on a typological approach. The designers, based on their knowledge and understanding of the problems, usually limit the solution space to a particular structural, constructional, spatial or programmatic type that is iteratively adapted to the particular design requirements. The constraints inherent in typology-based design methodologies exclude a vast range of potentially more effective and better design variants. In contrast, the dynamics of biological evolution suggest ways of continuously expanding the design space towards new and unexplored possibilities, that can potentially in a new set of typologies that still satisfy the constraints. Thus, in architecture, evolutionary processes are more relevant as exploratory processes than as optimization tools.


title: H-shaped Dielectric Resonator Antenna for future 5G application

abstract: This paper presents the design of H-shaped Dielectric Resonator Antenna (DRA) of high permittivity (er = 30) for future 5G application especially above 6 GHz. The antenna is consisted with a rectangular shaped dielectric on a ground plane and it is excited by a coaxial probe. The proposed antenna has impedance bandwidth of 58%. The average gain within the band is 5.5dBi. The result shows the ability of this antenna to be used for future 5G application


title: Evaluating geo-social influence in location-based social networks

abstract: The emerging location-based social network (LBSN) services not only allow people to maintain cyber links with their friends, but also enable them to share the events happening on them at different locations. The geo-social correlations among event participants make it possible to quantify mutual user influence for various events. Such a quantification of influence could benefit a wide spectrum of real-life applications such as targeted advertising and viral marketing. In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two problems: user influence evaluation and influential events discovery. To capture the geo-social closeness between LBSN users, we propose a unified influence metric. This metric combines a novel social proximity measure named penalized hitting time, with a geographical weight function modeled by power law distribution. We propose two approximate algorithms, namely global iteration (GI) and dynamic neighborhood expansion (DNE), to efficiently evaluate user influence with tight theoretical error bounds. We then adopt the sampling technique and the threshold algorithm to support efficient retrieval of top-K influential events. Extensive experiments on both real-life and synthetic LBSN data sets confirm that the proposed algorithms are effective, efficient, and scalable.


title: Impact-Driven Process Model Repair

abstract: The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this paper assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness, i.e., the fraction of behavior in the log not possible according to the model is minimized. To compute fitness we need to align the model and log and this can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed-up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs.


title: Penile length and circumference: an Indian study

abstract: Apprehension about the normal size of penis is a major concern for men. Aim of the present investigation is to estimate the penile length and circumference of Indian males and to compare the results with the data from other countries. Results will help in counseling the patients worried about the penile size and seeking penis enlargement surgery. Penile length in flaccid and stretched conditions and circumference were measured in a group of 301 physically normal men. Erected length and circumference were measured for 93 subjects. Mean flaccid length was found to be 8.21 cm, mean stretched length 10.88 cm and circumference 9.14 cm. Mean erected length was found to be 13.01 cm and erected circumference was 11.46 cm. Penile dimensions are found to be correlated with anthropometric parameters. Insight into the normative data of penile size of Indian males obtained. There are significant differences in the mean penile length and circumference of Indian sample compared to the data reported from other countries. Study need to be continued with a large sample to establish a normative data applicable to the general population.


title: Prediction of potential for organ donation after cardiac death in patients in neurocritical state: a prospective observational study

abstract: BACKGROUND ::: Successful donation of organs after cardiac death (DCD) requires identification of patients who will die within 60 min of withdrawal of life-sustaining treatment (WLST). We aimed to validate a straightforward model to predict the likelihood of death within 60 min of WLST in patients with irreversible brain injury. ::: ::: ::: METHODS ::: In this multicentre, observational study, we prospectively enrolled consecutive comatose patients with irreversible brain injury undergoing WLST at six medical centres in the USA and the Netherlands. We assessed four clinical characteristics (corneal reflex, cough reflex, best motor response, and oxygenation index) as predictor variables, which were selected on the basis of previous findings. We excluded patients who had brain death or were not intubated. The primary endpoint was death within 60 min of WLST. We used univariate and multivariable logistic regression analyses to assess associations with predictor variables. Points attributed to each variable were summed to create a predictive score for cardiac death in patients in neurocritical state (the DCD-N score). We assessed performance of the score using area under the curve analysis. ::: ::: ::: FINDINGS ::: We included 178 patients, 82 (46%) of whom died within 60 min of WLST. Absent corneal reflexes (odds ratio [OR] 2·67, 95% CI 1·19-6·01; p=0·0173; 1 point), absent cough reflex (4·16, 1·79-9·70; p=0·0009; 2 points), extensor or absent motor responses (2·99, 1·22-7·34; p=0·0168; 1 point), and an oxygenation index score of more than 3·0 (2·31, 1·10-4·88; p=0·0276; 1 point) were predictive of death within 60 min of WLST. 59 of 82 patients who died within 60 min of WLST had DCD-N scores of 3 or more (72% sensitivity), and 75 of 96 of those who did not die within this interval had scores of 0-2 (78% specificity); taking into account the prevalence of death within 60 min in this population, a score of 3 or more was translated into a 74% chance of death within 60 min (positive predictive value) and a score of 0-2 translated into a 77% chance of survival beyond 60 min (negative predictive value). ::: ::: ::: INTERPRETATION ::: The DCD-N score can be used to predict potential candidates for DCD in patients with non-survivable brain injury. However, this score needs to be tested specifically in a cohort of potential donors participating in DCD protocols. ::: ::: ::: FUNDING ::: None.


title: Sexual and Physical Health After Sex Reassignment Surgery

abstract: A long-term follow-up study of 55 transsexual patients (32 male-to-female and 23 female-to-male) post-sex reassignment surgery (SRS) was carried out to evaluate sexual and general health outcome. Relatively few and minor morbidities were observed in our group of patients, and they were mostly reversible with appropriate treatment. A trend toward more general health problems in male-to-females was seen, possibly explained by older age and smoking habits. Although all male-to-females, treated with estrogens continuously, had total testosterone levels within the normal female range because of estrogen effects on sex hormone binding globulin, only 32.1% reached normal free testosterone levels. After SRS, the transsexual person's expectations were met at an emotional and social level, but less so at the physical and sexual level even though a large number of transsexuals (80%) reported improvement of their sexuality. The female-to-males masturbated significantly more frequently than the male-to-females, and a trend to more sexual satisfaction, more sexual excitement, and more easily reaching orgasm was seen in the female-to-male group. The majority of participants reported a change in orgasmic feeling, toward more powerful and shorter for female-to-males and more intense, smoother, and longer in male-to-females. Over two-thirds of male-to-females reported the secretion of a vaginal fluid during sexual excitation, originating from the Cowper's glands, left in place during surgery. In female-to-males with erection prosthesis, sexual expectations were more realized (compared to those without), but pain during intercourse was more often reported.


title: A comparative study of spectrum awareness techniques for cognitive radio oriented wireless networks

abstract: Abstract Spectrum scarcity is impeding practical implementations of emerging wireless multimedia applications requiring significantly more frequency spectrum. Cognitive radio (CR) has emerged as a promising solution to the current spectral congestion problem by imparting intelligence to the conventional software defined radio that allows spectrum sharing through opportunistic spectrum access. The principal objective of CR is to optimize the use of under-utilized spectrum through robust and efficient spectrum sensing (SS). This paper introduces cognitive functionality and provides an in-depth comparative survey of various spectrum awareness techniques in terms of their sensing accuracy and computational complexities along with their merits and demerits. Specifically, key challenges in SS are highlighted and possible solutions are discussed. A classification of SS is presented to address the sensing method selection criterion. Both non-cooperative and cooperative sensing schemes are reviewed and open research problems are highlighted to identify future research directions.


title: Modeling of underwater snake robots

abstract: Increasing efficiency by improving the locomotion methods is a key issue for underwater robots. Hence, an accurate dynamic model is important for both controller design and efficient locomotion methods. This paper presents a model of the kinematics and dynamics of a planar, underwater snake robot aimed at control design. Fluid contact forces and torques are modeled using analytical fluid dynamics. The model is derived in a closed form and can be utilized in modern model-based control schemes. The proposed model is easily implemented and simulated, regardless of the number of robot links. Simulation results with a ten link robotic system are presented.


Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning


title: Off-the-Shelf Unsupervised NMT

abstract: We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions. We leverage off-the-shelf neural MT architectures to train unsupervised MT models with no parallel data and show that such models can achieve reasonably good performance, competitive with models purpose-built for unsupervised MT. Finally, we propose improvements that allow us to apply our models to English-Turkish, a truly low-resource language pair.


title: Mock objects for testing java systems Why and how developers use them , and how they evolve

abstract: When testing software artifacts that have several dependencies, one has the possibility of either instantiating these dependencies or using mock objects to simulate the dependencies’ expected behavior. Even though recent quantitative studies showed that mock objects are widely used both in open source and proprietary projects, scientific knowledge is still lacking on how and why practitioners use mocks. An empirical understanding of the situations where developers have (and have not) been applying mocks, as well as the impact of such decisions in terms of coupling and software evolution can be used to help practitioners adapt and improve their future usage. To this aim, we study the usage of mock objects in three OSS projects and one industrial system. More specifically, we manually analyze more than 2,000 mock usages. We then discuss our findings with developers from these systems, and identify practices, rationales, and challenges. These results are supported by a structured survey with more than 100 professionals. Finally, we manually analyze how the usage of mock objects in test code evolve over time as well as the impact of their usage on the coupling between test and production code. Our study reveals that the usage of mocks is highly dependent on the responsibility and the architectural concern of the class. Developers report to frequently mock dependencies that make testing difficult (e.g., infrastructure-related dependencies) and to not mock classes that encapsulate domain concepts/rules of the system. Among the key challenges, developers report that maintaining the behavior of the mock compatible with the behavior of original class is hard and that mocking increases the coupling between the test and the production code. Their perceptions are confirmed by our data, as we observed that mocks mostly exist since the very first version of the test class, and that they tend to stay there for its whole lifetime, and that changes in production code often force the test code to also change.


From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles.


title: Developmental and behavioral characteristics of individuals with Pallister-Killian syndrome

abstract: Pallister–Killian syndrome is a sporadic disorder caused by the presence of mosaic tetrasomy of the short arms of chromosome 12. Case reports of children with Pallister–Killian syndrome have described a range of developmental and behavioral outcomes, but no systematic studies of these outcomes exist. The objective of this study was to describe developmental and behavioral characteristics of individuals with Pallister–Killian syndrome participating in a national meeting of families and their affected children. Sixteen individuals with Pallister–Killian syndrome, ages 16 months to 19 years, were studied using questionnaires and direct interview. Among the 16 patients enrolled in the study, 3 probands were between 16 and 19 months, and had severe developmental delay. Among the rest of the 13 probands older than 24 months, 11 children had a developmental level of less than 8 months age equivalent. They were non-ambulatory, nonverbal, and passive, requiring extensive assistance in daily living. There were two higher functioning children who were ambulatory, and verbal. One of these children met criteria for Autism on the Autism Diagnostic Interview-Revised. Thus, although most individuals with Pallister–Killian syndrome studied showed profound intellectual disability and sensory impairments, individuals with Pallister–Killian syndrome can have mild to moderate intellectual disability. Therefore, in individuals with physical examination findings of Pallister–Killian syndrome, formal diagnostic testing should be considered, even in individuals with mild to moderate intellectual disability. Further studies will be needed to determine if these higher functioning children with Pallister–Killian syndrome are at increased risk for autism. 2012 Wiley Periodicals, Inc.


title: Fuzzy k-Nearest Neighbors applied to phoneme recognition

abstract: In this work, the Fuzzy kNN (FkNN), an alternative of the standard kNN algorithm, is used for Timit phoneme recognition. Phoneme is the smallest unit that composes speech. For this reason, if phoneme recognition is performed, it can achieve a significant word and text recognition. Thus, the main idea consists on assigning phoneme membership to the data phonemes by measuring the distance to its kNN. FkNN compute the fuzzy distances between the data phonemes that define the cluster fuzziness. Mel Frequency Cepstral Coefficients (MFCC) associated with their first and second derivatives and energy coefficient were extracted from the speech signals as an input of the recognition system. A comparison of a crisp and fuzzy kNN was performed. Experiments show that FkNN algorithm not only can lead to significant recognition rates, but also may supersede in some ways Hidden Markov Model (HMM) the reference of speech recognition.


title: Neural Activity Relating to Generation and Representation of Galvanic Skin Conductance Responses: A Functional Magnetic Resonance Imaging Study

abstract: Central feedback of peripheral states of arousal influences motivational behavior and decision making. The sympathetic skin conductance response (SCR) is one index of autonomic arousal. The precise functional neuroanatomy underlying generation and representation of SCR during motivational behavior is undetermined, although it is impaired by discrete brain lesions to ventromedial prefrontal cortex, anterior cingulate, and parietal lobe. We used functional magnetic resonance imaging to study brain activity associated with spontaneous fluctuations in amplitude of SCR, and activity corresponding to generation and afferent representation of discrete SCR events. Regions that covaried with increased SCR included right orbitofrontal cortex, right anterior insula, left lingual gyrus, right fusiform gyrus, and left cerebellum. At a less stringent level of significance, predicted areas in bilateral medial prefrontal cortex and right inferior parietal lobule covaried with SCR. Generation of discrete SCR events was associated with significant activity in left medial prefrontal cortex, bilateral extrastriate visual cortices, and cerebellum. Activity in right medial prefrontal cortex related to afferent representation of SCR events. Activity in bilateral medial prefrontal lobe, right orbitofrontal cortex, and bilateral extrastriate visual cortices was common to both generation and afferent representation of discrete SCR events identified in a conjunction analysis. Our results suggest that areas implicated in emotion and attention are differentially involved in generation and representation of peripheral SCR responses. We propose that this functional arrangement enables integration of adaptive bodily responses with ongoing emotional and attentional states of the organism.


title: A parser generator using the Grammar Flow Graph

abstract: Recently, a new way to represent context-free grammars (CFG) has been put forward. The representation uses a directed graph called the Grammar Flow Graph (GFG). The GFG provides a framework to parse all context-free languages (CFL) based on Earley's algorithm that is easier to understand than the original Earley's presentation. In addition, the GFG connects two seemingly distant concepts for regular language parsing and parsing of CFLs. It shows that simulating all the moves through a non-deterministic finite-state automaton (NFA) can be generalized to moving along all appropriate GFG paths. This paper introduces a GFG parser generator whose functionalities are equivalent to popular parser generators like Yacc or Bison. However, it works on all CFGs-ambiguous or unambiguous. Our parser generator takes a grammar file that represents strings in CFL as an input and outputs a parser program to parse those strings. We evaluate it against two other parser generators, one based on the original Earley's representation and the other based on the Cocke-Younger-Kasami (CYK) algorithm. The result indicates that the performance of our parser generator is on a par with that based on the original Earley's scheme and superior to the CYK's parser generator. The code for our parser generator can be downloaded from the following link: https://bitbucket.org/kramatk/earleyparser.


title: HYPERPARAMETER SELECTION IN KERNEL PRINCIPAL COMPONENT ANALYSIS

abstract: In kernel methods, choosing a suitable kernel is indispensable for favorable results. No well-founded methods, however, have been established in general for unsupervised learning. We focus on kernel Principal Component Analysis (kernel PCA), which is a nonlinear extension of principal component analysis and has been used electively for extracting nonlinear features and reducing dimensionality. As a kernel method, kernel PCA also suffers from the problem of kernel choice. Although cross-validation is a popular method for choosing hyperparameters, it is not applicable straightforwardly to choose a kernel in kernel PCA because of the incomparable norms given by different kernels. It is important, thus, to develop a well-founded method for choosing a kernel in kernel PCA. This study proposes a method for choosing hyperparameters in kernel PCA (kernel and the number of components) based on cross-validation for the comparable reconstruction errors of pre-images in the original space. The experimental results on synthesized and real-world datasets demonstrate that the proposed method successfully selects an appropriate kernel and the number of components in kernel PCA in terms of visualization and classification errors on the principal components. The results imply that the proposed method enables automatic design of hyperparameters in kernel PCA.


title: A Threat Model Driven Approach for Security Testing

abstract: In this paper, we propose a novel threat model-driven security testing approach for detecting undesirable threat behavior at runtime. Threats to security policies are modelled with UML (unified modeling language) sequence diagrams. From a design-level threat model we extract a set of threat traces, each of which is an event sequence that should not occur during the system execution. The same threat model is also used to decide what kind of information should be collected at runtime and to guide the code instrumentation. The instrumented code is recompiled and executed using test cases randomly generated. The execution traces are collected and analyzed to verify whether the aforementioned undesirable threat traces are matched. If an execution trace is an instance of a threat trace, security violations are reported and actions should be taken to mitigate the threat in the system. Thus the linkage between models, code implementations, and security testing are extended to form a systematic methodology that can test certain security policies.


title: Federated Identity Management Challenges

abstract: Federated Identity Management is considered a promising approach to facilitate secure resource sharing between collaborating partners. The adoption rate of identity federation technologies in the industrial domain, however, has not been as expected. A structured survey provides the basis for this paper, which reports on challenges related to Federated Identity Management. This paper presents a narrative of the main challenges that are reported in existing FIdM research, and provide a starting point to those who seek to learn more about these concepts.


title: Treatment outcomes using CyberKnife for brain metastases from lung cancer

abstract: We investigated the clinical outcomes following treatment using stereotactic radiosurgery (SRS) and fractionated stereotactic radiotherapy (SRT) for brain metastases from lung cancer. A total of 67 patients with 109 brain metastases from lung cancer treated using CyberKnife between 1998 and 2011 were retrospectively analyzed. SRS (median dose, 24 Gy) was used to treat 79 lesions, and 3-fraction SRT (median dose, 30 Gy) was used to treat 30 lesions. The median follow-up time was 9.4 months (range, 0.4–125 months). The 1-year local control rate was 83.3%, and the 1-year distant brain failure rate was 30.1%. The median survival time was 13.1 months, and the 1- and 3-year overall survival (OS) rates were 54.8% and 25.9%, respectively. On multivariate analysis, three factors were found to be statistically significant predictors of OS: (i) presence of uncontrolled primary disease [hazard ratio (HR) = 3.04; P = 0.002]; (ii) Brinkman index (BI) ≥ 1000 (HR = 2.75; P = 0.007); and (iii) pulmonary metastases (HR = 3.54; P = 0.009). Radionecrosis and worsening of neurocognitive function after radiosurgery were observed in 5 (7%) and 3 (4%) patients, respectively. Our results indicated that SRS/SRT for brain metastases from lung cancer was effective. Uncontrolled primary disease, high BI, and pulmonary metastases at treatment were significant risk factors for OS.


Team CASE and the 2007 DARPA Urban Challenge


title: Dynamic analysis and PID control for a quadrotor

abstract: In order to analyze the dynamic characteristics and PID controller performance of a quadrotor, this paper firstly describes the architecture of the quadrotor and analyzes the dynamic model of it. Then, based on the classic scheme of PID control, this paper designs a controller, which aims to regulate the posture (position and orientation) of the 6 d.o.f. quadrotor. Thirdly, the dynamic model is implemented in Matlab/Simulink simulation, and the PID control parameters are obtained according to the simulation results. Finally, a quadrotor with PID controllers is designed and made. In order to do the experiment, a flying experiment for the quadrotor has been done. The results of flying experiment show that the PID controllers robustly stabilize the quadrotor.


title: Efficient and Effective Clustering Methods for Spatial Data Mining

abstract: Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLAHANS which is based on randomized search. We also develop two spatial data mining algorithms that use CLAHANS. Our analysis and experiments show that with the assistance of CLAHANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms. Furthermore, experiments conducted to compare the performance of CLAHANS with that of existing clustering methods show that CLAHANS is the most efficient.


title: Bring-Your-Own-Device (BYOD): An Evaluation of Associated Risks to Corporate Information Security

abstract: This study evaluates the cyber-risks to Business Information Assets posed by the adoption of Bring-Your-Own-Device (BYOD) to the workplace. BYOD is an emerging trend where employees bring and use personal computing devices on the companys network to access applications and sensitive data like emails, calendar and scheduling applications, documents, etc. Employees are captivated by BYOD because they can have access to private items as well as perform certain job functions while being unrestricted to their desks. This is however usually done on the blind side of management or the system administrator; a situation that tends to expose vital and sensitive corporate information to various threats like unwanted network traffic, unknown applications, malwares, and viruses. Expert opinions were elicited in this exploratory study. The study evaluated the characteristics of BYOD, assessed associated risks, threats and vulnerabilities. The findings indicate that little or no security measures were instituted to mitigate risks associated with BYOD. Though, profound benefits abound with BYOD adoption, they could be eroded by security threats and costs of mitigation in curing breaches. The most significant risk was found to be Data Loss which was in consonance with similar studies on Smartphone security risks. Some mitigation measures are then recommended.


title: Implementation of FDK Reconstruction Algorithm in Cone-Beam CT Based on the 3D Shepp-Logan Model

abstract: Cone-beam CT system has become a hot issue in current CT technique. Compared with the traditional 2D CT, cone beam CT can greatly reduce the scanning time, improve the utilization ratio of X-ray, and enhance the spatial resolution. In the article, simulation data based on the 3D Shepp-Logan Model was obtained by tracing the X-ray and applying the radial attenuation theory. FDK (Feldkamp, Davis and Kress) reconstruction algorithm was then adopted to reconstruct the 3D Shepp-Logan Mode. The reconstruction results indicate that for the central image the spatial resolution can reach 8linepairs/mm. Reconstructed images truthfully reveal the archetype.


title: Software Cost Estimation with Incomplete Data

abstract: The construction of software cost estimation models remains an active topic of research. The basic premise of cost modeling is that a historical database of software project cost data can be used to develop a quantitative model to predict the cost of future projects. One of the difficulties faced by workers in this area is that many of these historical databases contain substantial amounts of missing data. Thus far, the common practice has been to ignore observations with missing data. In principle, such a practice can lead to gross biases and may be detrimental to the accuracy of cost estimation models. We describe an extensive simulation where we evaluate different techniques for dealing with missing data in the context of software cost modeling. Three techniques are evaluated: listwise deletion, mean imputation, and eight different types of hot-deck imputation. Our results indicate that all the missing data techniques perform well with small biases and high precision. This suggests that the simplest technique, listwise deletion, is a reasonable choice. However, this will not necessarily provide the best performance. Consistent best performance (minimal bias and highest precision) can be obtained by using hot-deck imputation with Euclidean distance and a z-score standardization.


title: Knowledge transfer between speakers for personalised dialogue management

abstract: Model-free reinforcement learning has been shown to be a promising data driven approach for automatic dialogue policy optimization, but a relatively large amount of dialogue interactions is needed before the system reaches reasonable performance. Recently, Gaussian process based reinforcement learning methods have been shown to reduce the number of dialogues needed to reach optimal performance, and pre-training the policy with data gathered from different dialogue systems has further reduced this amount. Following this idea, a dialogue system designed for a single speaker can be initialised with data from other speakers, but if the dynamics of the speakers are very different the model will have a poor performance. When data gathered from different speakers is available, selecting the data from the most similar ones might improve the performance. We propose a method which automatically selects the data to transfer by defining a similarity measure between speakers, and uses this measure to weight the influence of the data from each speaker in the policy model. The methods are tested by simulating users with different severities of dysarthria interacting with a voice enabled environmental control system.


title: Universal Adversarial Perturbations Against Semantic Image Segmentation

abstract: While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise.


title: Memory Barriers: a Hardware View for Software Hackers

abstract: So what possessed CPU designers to cause them to inflict memory barriers on poor unsuspecting SMP software designers? In short, because reordering memory references allows much better performance, and so memory barriers are needed to force ordering in things like synchronization primitives whose correct operation depends on ordered memory references. Getting a more detailed answer to this question requires a good understanding of how CPU caches work, and especially what is required to make caches really work well. The following sections:


title: Multi-view reconstruction preserving weakly-supported surfaces

abstract: We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time.


title: A CNN-Based Supermarket Auto-Counting System

abstract: Deep learning has made significant breakthrough in the past decade. In certain application domain, its detection accuracy has surpassed human being in the same task, e.g., voice recognition and object detection. Various novel applications has been developed and achieved good performance by leveraging the latest advances in deep learning. In this paper, we propose to utilize deep learning based technique, specifically, Convolutional Neural Network (CNN), to develop an auto-counting system for supermarket scenario. Given a picture, the system can automatically detect the specified categories of goods (e.g., Head & Shoulders bottles) and their respective numbers. To improve detection accuracy of the system, we propose to combine hard example mining and multi-scale feature extraction to the Faster R-CNN framework. Experimental results demonstrate the efficacy of the proposed system. Specifically, our system achieves an mAP of 92.1%, which is better than the state-of-the-art, and the response time is about 250 ms per image, including all steps on a GTX 1080 GPU.


title: Opioid-sparing effects of ketorolac and its correlation with the recovery of postoperative bowel function in colorectal surgery patients: a prospective randomized double-blinded study.

abstract: OBJECTIVES ::: Postoperative ileus (PI) is one of many common complications in major abdominal surgery. PI results in patient discomfort, increased gastrointestinal leakage, prolonged hospital stay, and increased medical expenses. In this study, we have investigated the morphine-sparing effects of ketorolac and its correlation with the duration of PI in patients with colorectal surgeries. ::: ::: ::: METHODS ::: We collected data from 102 patients who had received elective colorectal resection. The patients were randomly allocated into 2 groups and received intravenous patient-controlled analgesia (IVPCA) morphine (M group) or IVPCA morphine plus ketorolac (M+K group). Time-scale morphine consumption (per 12 h), recovery of bowel functions (the first bowel movement and passage of flatus), pain scores, and opioid-related side effects were then recorded. ::: ::: ::: RESULTS ::: Patients in the M+K group received 18.3% less morphine than those in the M group within 72 postoperative hours. The maximal opioid-sparing effects of ketorolac appeared in 12 to 24 postoperative hours. The onset of the first bowel movement and passage of flatus was significantly less in the M+K group than in the M group. The M group showed a 5.25 times greater risk of inducing PI, a result comparable with the M+K group in colorectal surgery patients. ::: ::: ::: DISCUSSION ::: The addition of ketorolac to IVPCA morphine has demonstrated a clear opioid-sparing effect and benefits in regards to the shortening of the duration of bowel immobility. We suggest that adding ketorolac to morphine IVPCA be included in the multimodal postoperative rehabilitation program for the early restoration of normal bowel function.


Peculiarities of seasonal development and influence of ecological factors on the growth of thuja giant (Thuja plicata Donn Ex D. Don) in the conditions of introduction in the right-bank forest-steppe of Ukraine.


title: Insole Pedometer With Piezoelectric Energy Harvester and 2 V Organic Circuits

abstract: A shoe insole pedometer, which consists of a piezoelectric energy harvester and a 2 V organic pedometer circuit, has been developed as a first step toward the application of flexible large-area energy harvesting. A pseudo-CMOS 14 bit step counter records the number of steps up to 16383 steps using the harvested power. To increase the noise margin of the pseudo-CMOS logic circuits, a negative voltage is generated by an organic charge pump circuit and is applied to the pseudo-CMOS inverters and transmission gates in the flip-flops in the step counter. A pseudo-CMOS Schmitt trigger inverter used to feed clean square pulses to the step counter is presented. This paper describes the details of the insole pedometer and provides measurement results and some discussion.


title: Distributed Analogical Idea Generation with Multiple Constraints

abstract: Previous work has shown the promise of crowdsourcing analogical idea generation, where distributing the stages of analogical processing across many people can reduce fixation, identify inspirations from more diverse domains, and lead to more creative ideas. However, prior work has only considered problems with a single constraint, while many real-world problems involve multiple constraints. This paper contributes a systematic crowdsourcing approach for eliciting multiple constraints inherent in a problem and using those constraints to find inspirations useful in solving it. To do so we identify methods to elicit useful constraints at different levels of abstraction, and empirical results that identify how the level of abstraction influences creative idea generation. Our results show that crowds find the most useful inspirations when the problem domain is represented abstractly and constraints are represented more concretely.