title: Familicide: The killing of spouse and children

abstract: A familicide is a multiple-victim homicide incident in which the killer's spouse and one or more children are slain. National archives of Canadian and British homicides, containing 109 familicide incidents, permit some elucidation of the characteristic and epidemiology of this crime. Familicides were almost exclusively perpetrated by men, unlike other spouse-killings and other filicides. Half the familicidal men killed themselves as well, a much higher rate of suicide than among other uxoricidal or filicidal men. De facto unions were overrepresented, compared to their prevalence in the populations-at-large, but to a much lesser extent in familicides than in other uxoricides. Stepchildren were overrepresented as familicide victims, compared to their numbers in the populations-at-large, but to a much lesser extent than in other filicides; unlike killers of their genetic offspring, men who killed their stepchildren were rarely suicidal. An initial binary categorization of familicides as accusatory versus despondent is tentatively proposed. © 1995 Wiley-Liss, Inc. Language: en


title: Image Retrieval-Based Decision Support System for Dermatoscopic Images

abstract: This paper presents a content-based image retrieval system for dermatoscopic images as a diagnostic aid to the dermatologists for skin cancer recognition. In this context, the ultimate aim is to support decision making by locating, retrieving and displaying relevant past cases along with diagnostic reports. However, most challenging aspect in this domain is to extract local lesion specific image features and define the relevance between query and database images for retrieval. A fast and automatic segmentation method to detect the lesion from background healthy skin is proposed. This method first transforms a color image into an intensity image by utilizing domain specific image properties and NBS color distance in HVC color space. Lesion mask is detected by fusing individually segmented images based on iterative thresholding. Lesion specific local color and texture features are extracted and represented in the form of mean and variance-covariance of color channels and in a reduced PCA sub-space. Finally, for effective image retrieval, a similarity matching function is defined based on the fusion of a Bhattacharyya and Euclidean distance metric. The performance of the retrieval system is evaluated using average precision on a collection of 358 images, which demonstrates effectiveness of the proposed approach.


title: Lexical and syntactic ambiguity as a source of humor: The case of newspaper headlines

abstract: The paper analyzes some forms of linguistic ambiguity in English in a specific register, i.e. newspaper headlines. In particular, the focus of the research is on examples of lexical and syntactic ambiguity that result in sources of voluntary or involuntary humor. The study is based on a corpus of 135 verbally ambiguous headlines found on web sites presenting humorous bits of information. The linguistic phenomena that contribute to create this kind of semantic confusion in headlines will be analyzed and divided into the three main categories of lexical, syntactic, and phonological ambiguity, and examples from the corpus will be discussed for each category. The main results of the study were that, firstly, contrary to the findings of previous research on jokes, syntactically ambiguous headlines were found in good percentage in the corpus and that this might point to di¤erences in genre. Secondly, two new configurations for the processing of the disjunctor/connector order were found. In the first of these configurations the disjunctor appears before the connector, instead of being placed after or coinciding with the ambiguous element, while in the second one two ambiguous elements are present, each of which functions both as a connector and


title: Adversarial Domain Adaptation for Duplicate Question Detection

abstract: We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.


title: Glass dynamics probed by the long-lived stimulated photon echo

abstract: The dynamics in an ethanol glass at 1.5 K has been investigated from picoseconds to milliseconds by two-dimensional stimulated-photon-echo measurements on zinc porphin. In this time frame the distribution of relaxation rates exhibits a 1/R dependence except for a gap stretching from about 1 kHz to 1 MHz. Evidence is presented for a time evolution of the glass structure that is not accounted for in the standard two-level-system model.


title: DESIGN OF MULTIPLE BEAM FORMING ANTENNA SYSTEM USING SUBSTRATE INTEGRATED FOLDED WAVEGUIDE (SIFW) TECHNOLOGY

abstract: This paper introduces a novel structure of 4£4 multiple beam forming antenna system using substrate integrated folded waveguide technology. For high speed wireless communication it is necessary to minimize the interferences and multipath fading. Multiple beam forming antenna system is a good solution to these problems. The substrate integrated folded waveguide (SIFW) technology reduces the width of substrate integrated waveguide (SIW) by half. All the basic building blocks required for the antenna array system are designed and simulated individually. They are then combined to form the butler matrix fed antenna array system. The SIFW technology reduces the total width of butler matrix. The radiation performance of the multiple beam forming antenna system is realized by integrating the H-plane SIFW horn antennas with the output ports of the butler matrix. The system is practically realized and good directive multiple beams with symmetric gain (5.8dB, 5.63dB, 5.31dB and 5.9dB for the beams 1R, 2L, 2R and 1L) have been achieved.


title: Distance education via the Internet: the student experience

abstract: This is the second in a series of papers that describes the use of the Internet on a distance-taught undergraduate Computer Science course (Thomas et al., 1998). This paper examines students' experience of a large-scale trial in which students were taught using electronic communication exclusively. The paper compares the constitution and experiences of a group of Internet students to those of conventional distance learning students on the same course. Learning styles, background questionnaires, and learning outcomes were used in the comparison of the two groups. The study reveals comparable learning outcomes with no discrimination in grade as the result of using different communication media. The student experience is reported, highlighting the main gains and issues of using the Internet as a communication medium in distance education. This paper also shows that using the Internet in this context can provide students with a worthwhile experience.


title: Integrated and Gender-Affirming Transgender Clinical Care and Research

abstract: Transgender (trans) communities worldwide, particularly those on the trans feminine spectrum, are disproportionately burdened by HIV infection and at risk for HIV acquisition/transmission. Trans individuals represent an underserved, highly stigmatized, and under-resourced population not only in HIV prevention efforts but also in delivery of general primary medical and clinical care that is gender affirming. We offer a model of gender-affirmative integrated clinical care and community research to address and intervene on disparities in HIV infection for transgender people. We define trans terminology, briefly review the social epidemiology of HIV infection among trans individuals, highlight gender affirmation as a key social determinant of health, describe exemplar models of gender-affirmative clinical care in Boston MA, New York, NY, and San Francisco, CA, and offer suggested "best practices" for how to integrate clinical care and research for the field of HIV prevention. Holistic and culturally responsive HIV prevention interventions must be grounded in the lived realities the trans community faces to reduce disparities in HIV infection. HIV prevention interventions will be most effective if they use a structural approach and integrate primary concerns of transgender people (eg, gender-affirmative care and management of gender transition) alongside delivery of HIV-related services (eg, biobehavioral prevention, HIV testing, linkage to care, and treatment).


title: Masquer Hunter: Adversarial Occlusion-aware Face Detection

abstract: Occluded face detection is a challenging detection task due to the large appearance variations incurred by various real-world occlusions. This paper introduces an Adversarial Occlusion-aware Face Detector (AOFD) by simultaneously detecting occluded faces and segmenting occluded areas. Specifically, we employ an adversarial training strategy to generate occlusion-like face features that are difficult for a face detector to recognize. Occlusion mask is predicted simultaneously while detecting occluded faces and the occluded area is utilized as an auxiliary instead of being regarded as a hindrance. Moreover, the supervisory signals from the segmentation branch will reversely affect the features, aiding in detecting heavily-occluded faces accordingly. Consequently, AOFD is able to find the faces with few exposed facial landmarks with very high confidences and keeps high detection accuracy even for masked faces. Extensive experiments demonstrate that AOFD not only significantly outperforms state-of-the-art methods on the MAFA occluded face detection dataset, but also achieves competitive detection accuracy on benchmark dataset for general face detection such as FDDB.


title: The effect of inhalation of Citrus sinensis flowers and Mentha spicata leave essential oils on lung function and exercise performance: a quasi-experimental uncontrolled before-and-after study

abstract: Background ::: Recently, there has been an increased interest in the effects of essential oils on athletic performances and other physiological effects. This study aimed to assess the effects of Citrus sinensis flower and Mentha spicata leaves essential oils inhalation in two different groups of athlete male students on their exercise performance and lung function.


title: Exploring factors affecting undergraduate medical students’ study strategies in the clinical years: a qualitative study

abstract: The aim of this study is to explore the effects of clinical supervision, and assessment characteristics on the study strategies used by undergraduate medical students during their clinical rotations. We conducted a qualitative phenomenological study at King Saud Bin Abdulaziz University for Health Sciences, College of Medicine, Riyadh, Saudi Arabia during the period from November 2007 to December 2008. We conducted semi-structured focus groups interviews with students and conducted individual interviews with teachers and students to explore students’ and clinical teachers’ perceptions and interpretations of factors influencing students’ study strategies. Data collection was continued until saturation was reached. We used Atlas-ti Computer Software (Version 5.2) to analyse the data, apply the obtained themes to the whole dataset and rearrange the data according to the themes and sub-themes. Analysis of data from interviews with twenty-eight students and thirteen clinical supervisors yielded three major themes relating to factors affecting students’ study strategies: “clinical supervisors and supervision”, “stress and anxiety” and “assessment”. The three themes we identified played a role in students’ adoption of different study strategies in the “community of clinical practice”. It appeared that teachers played a key role, particularly as assessors, clinical supervisors and as a source of stress to students.


title: Automatic and topology-preserving gradient mesh generation for image vectorization

abstract: Gradient mesh vector graphics representation, used in commercial software, is a regular grid with specified position and color, and their gradients, at each grid point. Gradient meshes can compactly represent smoothly changing data, and are typically used for single objects. This paper advances the state of the art for gradient meshes in several significant ways. Firstly, we introduce a topology-preserving gradient mesh representation which allows an arbitrary number of holes. This is important, as objects in images often have holes, either due to occlusion, or their 3D structure. Secondly, our algorithm uses the concept of image manifolds, adapting surface parameterization and fitting techniques to generate the gradient mesh in a fully automatic manner. Existing gradient-mesh algorithms require manual interaction to guide grid construction, and to cut objects with holes into disk-like regions. Our new algorithm is empirically at least 10 times faster than previous approaches. Furthermore, image segmentation can be used with our new algorithm to provide automatic gradient mesh generation for a whole image. Finally, fitting errors can be simply controlled to balance quality with storage.


title: Photo-polymerization, swelling and mechanical properties of cellulose fibre reinforced poly(ethylene glycol) hydrogels

abstract: The application of hydrogels as load-bearing biomedical components is often limited by their mechanical properties. Often an attempt to improve a hydrogel's stiffness is accompanied by a loss of toughness and swelling properties. In this work, we show that the addition of nanofibrillated cellulose (NFC) provides a mean to tailor both the swelling and the mechanical properties of the hydrogel. Various volume fractions of NFC were added to poly(ethylene glycol) dimethacrylate (PEGDM) precursors with two different molecular weights (6 and 20 kDa). The viscosity measurements of the precursor solutions indicated that the dispersed NFCs form a network-like structure in the hydrogel precursor. Such a structure, as observed in the photo-rheology experiments, serves as a light-scattering source when the solution is illuminated by UV light, which provides a uniform polymerization of the hydrogel in three-dimension and reduces the curing time. Mechanical properties of the neat and composite hydrogels were characterized using monotonic and cyclic compression tests. NFC reinforcement increases the hydrogel's stiffness by a factor 2 and 3.5 for the PEGDM matrixes with molecular weights of 6 and 20 kDa respectively without compromising their toughness. Moreover, the desired stiffness and swelling properties can be simultaneously achieved by adapting the reinforcement concentration and the hydrogel cross-link density. The obtained composite hydrogels offer enhanced and tuneable properties and are proposed for injectable and photo-curable load-bearing implants.


title: The technology acceptance model and the World Wide Web

abstract: Abstract The technology acceptance model (TAM) proposes that ease of use and usefulness predict applications usage. The current research investigated TAM for work-related tasks with the World Wide Web as the application. One hundred and sixty-three subjects responded to an e-mail survey about a Web site they access often in their jobs. The results support TAM. They also demonstrate that (1) ease of understanding and ease of finding predict ease of use, and that (2) information quality predicts usefulness for revisited sites. In effect, the investigation applies TAM to help Web researchers, developers, and managers understand antecedents to users' decisions to revisit sites relevant to their jobs.


title: Attachment Theory and Research: Implications for Psychodynamic Psychotherapy

abstract: Though attachment research today is best conceptualized as integrationist and multidisciplinary, it is important to remember that attachment theory was born out of clinical process. Bowlby [1–3] was first and foremost a psychoanalyst, and he drew from clinical experiences with children and adults to conceptualize his theory. Many of his ideas developed in response to dissatisfaction with the prevailing perspectives of the time. Though Melanie Klein, his supervisor at the time, was quite influential in his thinking about object relations, her conceptualization of development focused almost exclusively on internal conflict rather than external events in the child’s family and environment [4, 5]. Contrary to Klein’s perspective, during the analysis of a 3-year-old boy, Bowlby observed direct links between disturbances in the mother and pathology in the child. Such experiences in analytic treatment formed the basis for his assertion that early attachment difficulties increase vulnerability to later psychopathology.


title: The effects of consuming a high protein diet (4.4 g/kg/d) on body composition in resistance-trained individuals

abstract: The consumption of dietary protein is important for resistance-trained individuals. It has been posited that intakes of 1.4 to 2.0 g/kg/day are needed for physically active individuals. Thus, the purpose of this investigation was to determine the effects of a very high protein diet (4.4 g/kg/d) on body composition in resistance-trained men and women. Thirty healthy resistance-trained individuals participated in this study (mean ± SD; age: 24.1 ± 5.6 yr; height: 171.4 ± 8.8 cm; weight: 73.3 ± 11.5 kg). Subjects were randomly assigned to one of the following groups: Control (CON) or high protein (HP). The CON group was instructed to maintain the same training and dietary habits over the course of the 8 week study. The HP group was instructed to consume 4.4 grams of protein per kg body weight daily. They were also instructed to maintain the same training and dietary habits (e.g. maintain the same fat and carbohydrate intake). Body composition (Bod Pod®), training volume (i.e. volume load), and food intake were determined at baseline and over the 8 week treatment period. The HP group consumed significantly more protein and calories pre vs post (p < 0.05). Furthermore, the HP group consumed significantly more protein and calories than the CON (p < 0.05). The HP group consumed on average 307 ± 69 grams of protein compared to 138 ± 42 in the CON. When expressed per unit body weight, the HP group consumed 4.4 ± 0.8 g/kg/d of protein versus 1.8 ± 0.4 g/kg/d in the CON. There were no changes in training volume for either group. Moreover, there were no significant changes over time or between groups for body weight, fat mass, fat free mass, or percent body fat. Consuming 5.5 times the recommended daily allowance of protein has no effect on body composition in resistance-trained individuals who otherwise maintain the same training regimen. This is the first interventional study to demonstrate that consuming a hypercaloric high protein diet does not result in an increase in body fat.


title: Source Task Creation for Curriculum Learning

abstract: Transfer learning in reinforcement learning has been an active area of research over the past decade. In transfer learning, training on a source task is leveraged to speed up or otherwise improve learning on a target task. This paper presents the more ambitious problem of curriculum learning in reinforcement learning, in which the goal is to design a sequence of source tasks for an agent to train on, such that final performance or learning speed is improved. We take the position that each stage of such a curriculum should be tailored to the current ability of the agent in order to promote learning new behaviors. Thus, as a first step towards creating a curriculum, the trainer must be able to create novel, agent-specific source tasks. We explore how such a space of useful tasks can be created using a parameterized model of the domain and observed trajectories on the target task. We experimentally show that these methods can be used to form components of a curriculum and that such a curriculum can be used successfully for transfer learning in 2 challenging multiagent reinforcement learning domains.


title: A Methodology for Determining the Reachable and Dexterous Workspace of Parallel Manipulators

abstract: A methodology is proposed for determining the reachable and dexterous workspace of parallel manipulators. The determination of two types of workspace is utmost important in optimal design and comparing performances of the manipulators because each configuration or point in this region has specified kinematic dexterity by designer. The method is based on a stratified workspace boundary search technique, which is referred to as stratified workspace boundary determining methodology (SWBDM), and considers the various physical and contrived constraints. After the two similar problems of the determination of two types of workspace boundaries were defined, we propose a unified method to solve these problems. The validity and efficiency of the proposed method are verified with two kinds of representative parallel manipulators, since their relational results were studied in literatures. Finally, we conclude some advantages of the proposed methodology by comparing with other methods.


From Flexible and Stretchable Meta-Atom to Metamaterial: A Wearable Microwave Meta-Skin with Tunable Frequency Selective and Cloaking Effects


title: Rapid response team and hospital mortality in hospitalized patients

abstract: PurposeAlthough rapid response systems are known to reduce in-hospital cardiac arrest rate, their effect on mortality remains debated. The present study aimed to evaluate the effect of implementing an intensivist-led rapid response team (RRT) on mortality in hospitalized patients.MethodsAn implementation of an intervention and a comparison with retrospective data analysis were performed in the four hospitals of Montpellier regional healthcare centre, in France. An intensivist-led RRT was implemented on a 24/7 basis along with educational modules, publicity and bedside simulation-based training in only one of the four hospitals from January 2012 to June 2012. A single activation criterion (heart rate below 40/min or above 140/min, systolic blood pressure below 80 mmHg, cardiac arrest, respiratory rate below 8/min or above 30/min, pulse oximetry below 90 % with O2 above 6 l/min, respiratory distress in a tracheotomised patient, respiratory arrest, coma or sudden change in level of consciousness, seizure) allowed any caregiver to directly contact the RRT using a dedicated cell phone number. Patients over 18 years admitted for more than 24 h in the medical-surgical wards from July 2010 to December 2011 (pre-RRT period) and from July 2012 to December 2013 (RRT period) were included. The main outcome was unexpected mortality. Analyses of data from one RRT hospital and three control hospitals (no RRT hospital) were performed.ResultsRRT implementation was associated with a decrease in unexpected mortality rate in the hospital that implemented RRT (from 21.9 to 17.4 per 1000 discharges; p = 0.002). Reduction in unexpected mortality associated with RRT implementation could be estimated at 1.5 lives saved per week in the RRT hospital. In the three other hospitals, mortality rate was not significantly modified (from 19.5 to 19.9 per 1000 discharges; p = 0.69). Overall mortality decreased from 39.6 to 34.6 per 1000 discharges between the pre-RRT and RRT period in the RRT hospital (p = 0.012), but did not significantly change in the other hospitals. Patients in the RRT hospital were more frequently admitted to the intensive care unit (ICU) during the RRT period (45.8 vs 52.9 per 1000; p = 0.002), and their sequential organ failure assessment (SOFA) score upon ICU admission significantly decreased from 7 (4–10) to 5 (2–9); p < 0.001.ConclusionsIn the present retrospective study, implementation of an intensivist-led RRT along with educational modules, publicity and bedside simulation-based training was associated with a significant decrease in unexpected and overall mortality of inpatients.


title: Iron and infection

abstract: Iron is an essential trace metal for nearly all infectious microorganisms, and host defense mechanisms target this dependence to deprive microbes of iron. This review highlights mechanisms that are activated during infections to restrict iron on mucosal surfaces, in plasma and extracellular fluid, and within macrophages. Iron overload disorders, such as hereditary hemochromatosis or β-thalassemia, interfere with iron-restrictive host responses, and thereby cause increased susceptibility to infections with microbes that can exploit this vulnerability. Anemia of inflammation (formerly known as anemia of chronic diseases) is an "off-target" effect of host defense wherein inflammatory cytokines shorten erythrocyte lifespan by activating macrophages, prioritize leukocyte production in the marrow, and induce hepcidin to increase plasma transferrin saturation and the concentration of non-transferrin-bound iron.


Causal conditions for loneliness: a set-theoretic analysis on an adult sample in the UK


title: Applying ethnography to the study of context in healthcare quality and safety

abstract: The effective export of ethnography into QI and safety research requires discussion and negotiation between social scientific and health services research perspectives, as well as creative approaches to producing self-reflexive data that will allow clinicians to understand their own context and so improve their own processes.


title: Is the Canadian childhood obesity epidemic related to physical inactivity?

abstract: OBJECTIVE ::: This study examined the relation among children's physical activity, sedentary behaviours, and body mass index (BMI), while controlling for sex, family structure, and socioeconomic status. ::: ::: ::: DESIGN ::: Epidemiological study examining the relations among physical activity participation, sedentary behaviour (video game use and television (TV)/video watching), and BMI on a nationally representative sample of Canadian children. ::: ::: ::: SUBJECTS ::: A representative sample of Canadian children aged 7-11 (N=7216) from the 1994 National Longitudinal Survey of Children and Youth was used in the analysis. ::: ::: ::: MEASUREMENTS ::: Physical activity and sport participation, sedentary behaviour (video game use and TV/video watching), and BMI measured by parental report. ::: ::: ::: RESULTS ::: Both organized and unorganized sport and physical activity are negatively associated with being overweight (10-24% reduced risk) or obese (23-43% reduced risk), while TV watching and video game use are risk factors for being overweight (17-44% increased risk) or obese (10-61% increased risk). Physical activity and sedentary behaviour partially account for the association of high socioeconomic status and two-parent family structure with the likelihood of being overweight or obese. ::: ::: ::: CONCLUSION ::: This study provides evidence supporting the link between physical inactivity and obesity of Canadian children.


title: NETGEN An advancing front 2D/3D-mesh generator based on abstract rules

abstract: In this paper, the algorithms of the automatic mesh generator NETGEN are described. The domain is provided by a Constructive Solid Geometry (CSG). The whole task of 3D mesh generation splits into four subproblems of special point calculation, edge following, surface meshing and finally volume mesh generation. Surface and volume mesh generation are based on the advancing front method. Emphasis is given to the abstract structure of the element generation rules. Several techniques of mesh optimization are tested and quality plots are presented.


title: The modality effect on reading literacy: perspectives from students’ online reading habits, cognitive and metacognitive strategies, and web navigation skills across regions

abstract: ABSTRACT This study tested the effects of the modality of reading formats (electronic vs. print), online reading habits (engagement in different online reading activities), use of cognitive strategies, metacognitive knowledge, and navigation skills on printed and electronic reading literacy across regions. Participants were 31,784 fifteen-year-old students (50.78% female) from 19 countries and economies in the 2009 Programme for International Student Assessment database. Results showed that students exhibited better reading literacy in the print environment. Moreover, information-seeking activities, control strategies, knowledge of metacognitive strategies, and navigation skills positively predicted reading literacy in both print and electronic formats for all regions, whereas social reading activities negatively predicted reading literacy in print and were most harmful for the Asian region in both formats. Memorization strategies were negatively associated with reading literacy in both formats for Australasian, Western and Eastern EU, and South American regions, but not for the Asian region. Online reading habits, regardless of types, had no impact on reading literacy in both formats for the South American region. The study findings provided suggestions for literacy instruction in the e-learning era across different regions.


title: CisSERS: Customizable In Silico Sequence Evaluation for Restriction Sites

abstract: High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated to enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERS enable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3’UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERS and results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.


An Agent-Based Approach for Dynamic Manufacturing Scheduling


title: A microservice based reference architecture model in the context of enterprise architecture

abstract: In the service oriented, customer experience centric and customer changing demand driven market environment, ICT is becoming the leading enabler and partner of the modern enterprise business. More and more companies are transformed into more or pure digital style and virtual organized enterprises that are enabled and supported by a group of business oriented and microservice based applications and modules that are new to most of the companies and enterprises, both business and technical solution staff. This paper presents a microservice based reference architecture model with clear understanding and definition of the concept of microservice and key architectural components and building blocks for implementing and managing enterprise microservices in the context of enterprise architecture. This reference architecture model can be used as the pragmatic guidance for both business and IT professionals when they develop the enterprise IT transformation architecture solutions so that the API and microservice relevant technologies could be used properly at enterprise level without confusion. This paper also highlights a set of key architectural issues and provides corresponding recommendations when APIs and microsevices are leveraged within a company at enterprise level.


title: Translucency of esthetic dental restorative CAD/CAM materials and composite resins with respect to thickness and surface roughness

abstract: Abstract Statement of problem Little information is available about the translucency of monolithic CAD/CAM materials. Purpose The purpose of this study was to evaluate the translucency of restorative CAD/CAM materials and direct composite resins with respect to thickness and surface roughness. Materials and methods In total, 240 disk-shaped specimens (12×14×1 mm and 12×14×2 mm) of 3 different CAD/CAM glass ceramics (CELTRA Duo, IPS e.max CAD, IPS Empress CAD), a fine-structure feldspathic ceramic (VITA Mark II), a hybrid ceramic (VITA Enamic), a resin nanoceramic composite resin (LAVA Ultimate), an experimental (CAD/CAM nanohybrid composite resin), 2 interim materials (Telio CAD; VITA CAD-Temp), and 3 direct composite resins (Tetric EvoCeram; Filtek Supreme XTE; Tetric EvoCeram Bulk Fill) were fabricated (n=10). After 3 different surface pretreatments (polished, rough SiC P1200, or SiC P500), absolute translucency and surface roughness were measured using spectrophotometry and tactile profilometry. The influence of material type, thickness, and roughness on absolute translucency was analyzed using a multivariate analysis, 1-way ANOVA, and the Tukey HSD post hoc test ( P P Results The effect of all tested parameters was significant among the materials ( P ηP² =.988), closely followed by material (.982), and the pretreatment method (.835). The surface roughness was strongly influenced by the pretreatment method (.975) and type of material (.941). Conclusion Thickness and surface roughness are major factors affecting the absolute translucency of adhesively luted restorations.


title: Monitoring of Drum-boiler Process Using Statistical Techniques

abstract: Present study addresses the monitoring of drum boiler process. Methodologies; based on clustering time series data and moving window based pattern matching have been proposed for the detection of fault in the chosen process. Design databases were created for the process by simulating the developed process model. A modified k-means clustering algorithm using similarity measure as a convergence criterion has been adopted for discriminating among time series data pertaining to various operating conditions. The proposed distance and PCA based combined similarity along with the moving window approach were used to discriminate among the normal operating conditions as well as detection of faults for the processes taken up. measures. The model has been derived using first principles and is characterized by few physical parameters. The parameters used in the model are those from a Swedish Power Plant. The values of all the parameters used were adapted from the work of K. J. Astrom & R. D. Bell. Sixteen (16) numbers of datasets belonging to four operating conditions including an abnormal operating condition were generated to evaluate the performance of the proposed techniques.


title: Repeatability of surface EMG variables in the sternocleidomastoid and anterior scalene muscles

abstract: In this study we examined the repeatability and reliability of the surface electromyographic (sEMG) signal mean frequency (MNF), average rectified value (ARV) and conduction velocity (CV) measured for the sternocleidomastoid (SCM) and the anterior scalene (AS) muscles in nine healthy volunteers during 15-s isometric cervical flexion contractions at 50% of the maximal voluntary contraction level over 3 non-consecutive days. Repeatability and reliability estimates were obtained for the initial values and rates of change of each sEMG variable by using both the Intraclass Correlation Coefficient (ICC) and the normalised standard error of the mean (nSEM). Results from SCM indicated good levels of repeatability for the initial value and slope of ARV (ICC>65%). For the AS, high levels of repeatability were identified for the initial value of MNF (ICC>70%) and the slope of ARV (ICC>75%). Values of nSEM in the range 2.8–7.2% were obtained for the initial values of MNF and CV for both SCM and AS, indicating clinically acceptable measurement precision. The low value obtained for the nSEM of the initial value of MNF for the AS, in combination with the high ICC, indicates that of all of the variables examined, this variable could offer the best normative index to distinguish between subjects with and without neck pain, and represents the sEMG variable of choice for future evaluation purposes.


title: A Feature-Pair-based Associative Classification Approach to Look-alike Modeling for Conversion-Oriented User-Targeting in Tail Campaigns

abstract: Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been further complemented by recent work in Look-alike Modeling (LAM) which helps in creating models which are customized according to each advertiser's requirements and each campaign's characteristics, and which show ads to users who are most likely to convert on them, not just click them. In Look-alike Modeling given data about converters and nonconverters, obtained from advertisers, we would like to train models automatically for each ad campaign. Such custom models would help target more users who are similar to the set of converters the advertiser provides. The advertisers get more freedom to define their preferred sets of users which should be used as a basis to build custom targeting models. In behavioral data, the number of conversions (positive class) per campaign is very small (conversions per impression for the advertisers in our data set are much less than 10-4), giving rise to a highly skewed training dataset, which has most records pertaining to the negative class. Campaigns with very few conversions are called as tail campaigns, and those with many conversions are called head campaigns. Creation of Look-alike Models for tail campaigns is very challenging and tricky using popular classifiers like Linear SVM and GBDT, because of the very few number of positive class examples such campaigns contain. In this paper, we present an Associative Classification (AC) approach to LAM for tail campaigns. Pairs of features are used to derive rules to build a Rule-based Associative Classifier, with the rules being sorted by frequency-weighted log-likelihood ratio (F-LLR). The top k rules, sorted by F-LLR, are then applied to any test record to score it. Individual features can also form rules by themselves, though the number of such rules in the top k rules and the whole rule-set is very small. Our algorithm is based on Hadoop, and is thus very efficient in terms of speed.


title: Lens ER-stress response during cataract development in Mip-mutant mice

abstract: Major intrinsic protein (MIP) is a functional water-channel (AQP0) that also plays a key role in establishing lens fiber cell architecture. Genetic variants of MIP have been associated with inherited and age-related forms of cataract; however, the underlying pathogenic mechanisms are unclear. Here we have used lens transcriptome profiling by microarray-hybridization and qPCR to identify pathogenic changes during cataract development in Mip-mutant (Lop/+) mice. In postnatal Lop/+ lenses (P7) 99 genes were up-regulated and 75 were down-regulated (>2-fold, p=<0.05) when compared with wild-type. A pathway analysis of up-regulated genes in the Lop/+ lens (P7) was consistent with endoplasmic reticulum (ER)-stress and activation of the unfolded protein response (UPR). The most up-regulated UPR genes (>4-fold) in the Lop/+ lens included Chac1>Ddit3>Atf3>Trib3>Xbp1 and the most down-regulated genes (>5-fold) included two anti-oxidant genes, Hspb1 and Hmox1. Lop/+ lenses were further characterized by abundant TUNEL-positive nuclei within central degenerating fiber cells, glutathione depletion, free-radical overproduction, and calpain hyper-activation. These data suggest that Lop/+ lenses undergo proteotoxic ER-stress induced cell-death resulting from prolonged activation of the Eif2ak3/Perk-Atf4-Ddit3-Chac1 branch of the UPR coupled with severe oxidative-stress.


title: System-level fault-tolerance in large-scale parallel machines with buffered coscheduling

abstract: Summary form only given. As the number of processors for multiteraflop systems grows to tens of thousands, with proposed petaflops systems likely to contain hundreds of thousands of processors, the assumption of fully reliable hardware has been abandoned. Although the mean time between failures for the individual components can be very high, the large total component count will inevitably lead to frequent failures. It is therefore of paramount importance to develop new software solutions to deal with the unavoidable reality of hardware faults. We will first describe the nature of the failures of current large-scale machines, and extrapolate these results to future machines. Based on this preliminary analysis we will present a new technology that we are currently developing, buffered coscheduling, which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency - requiring no changes to user applications. Preliminary results show that this is attainable with current hardware.


title: BotFinder: finding bots in network traffic without deep packet inspection

abstract: Bots are the root cause of many security problems on the Internet, as they send spam, steal information from infected machines, and perform distributed denial-of-service attacks. Many approaches to bot detection have been proposed, but they either rely on end-host installations, or, if they operate on network traffic, require deep packet inspection for signature matching. In this paper, we present BotFinder, a novel system that detects infected hosts in a network using only high-level properties of the bot's network traffic. BotFinder does not rely on content analysis. Instead, it uses machine learning to identify the key features of command-and-control communication, based on observing traffic that bots produce in a controlled environment. Using these features, BotFinder creates models that can be deployed at network egress points to identify infected hosts. We trained our system on a number of representative bot families, and we evaluated BotFinder on real-world traffic datasets -- most notably, the NetFlow information of a large ISP that contains more than 25 billion flows. Our results show that BotFinder is able to detect bots in network traffic without the need of deep packet inspection, while still achieving high detection rates with very few false positives.


title: On the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX)

abstract: The first energetically autonomous lower extremity exoskeleton capable of carrying a payload has been demonstrated at U.C. Berkeley. This paper summarizes the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). The anthropomorphically-based BLEEX has seven degrees of freedom per leg, four of which are powered by linear hydraulic actuators. The selection of the degrees of freedom and their ranges of motion are described. Additionally, the significant design aspects of the major BLEEX components are covered.


title: Learning to select data for transfer learning with Bayesian Optimization

abstract: Domain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. Inspired by work on curriculum learning, we propose to \emph{learn} data selection measures using Bayesian Optimization and evaluate them across models, domains and tasks. Our learned measures outperform existing domain similarity measures significantly on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We show the importance of complementing similarity with diversity, and that learned measures are -- to some degree -- transferable across models, domains, and even tasks.


title: The human mirror system: A motor resonance theory of mind-reading

abstract: Electrophysiological data confirm the existence of neurons that respond to both motor and sensory events in the macaque brain. These mirror neurons respond to execution and observation of goal-orientated actions. It has been suggested that they comprise a neural basis for encoding an internal representation of action. In this paper the evidence for a parallel system in humans is reviewed and the implications for human theory of mind processing are discussed. Different components of theory of mind are discussed; the evidence for mirror activity within subtypes is addressed. While there is substantial evidence for a human mirror system, there are weaknesses in the attempts to localize such a system in the brain. Preliminary evidence indicates that mirror neurons may be involved in theory of mind; however, these data by their very nature are reliant on the presence, and precise characterization, of the human mirror system.


title: Comparing corporate financial performance and qualitative information from annual reports using self-organizing maps

abstract: This paper develops a methodology to extract concepts containing qualitative information from corporate annual reports. The concepts are extracted from the corpus of U.S. corporate annual reports using WordNet ontology and singular value decomposition, and further visualized using self-organizing maps. The methodology makes it possible to easily compare the concepts with future financial performance. The results suggest that annual reports differ in terms of the concepts emphasized reflecting future financial performance.


title: A Pure Logic-Based Approach to Natural Reasoning

abstract: The paper presents a model for natural reasoning that combines theorem proving techniques with natural logic. The model is a tableau system for a higher-order logic the formulas of which resemble linguistic expressions. A textual entailment system LangPro, an implementation of the model, represents a tableau-based prover that directly operates on linguistic expressions. After training and evaluating on a textual entailment dataset, the prover shows accuracy comparable to the state-of-the-art results with almost perfect precision. Due to its reliable judgements, the system is also able to detect dubious problems in the dataset.


title: Accelerating the annotation of sparse named entities by dynamic sentence selection

abstract: BackgroundPrevious studies of named entity recognition have shown that a reasonable level of recognition accuracy can be achieved by using machine learning models such as conditional random fields or support vector machines. However, the lack of training data (i.e. annotated corpora) makes it difficult for machine learning-based named entity recognizers to be used in building practical information extraction systems.ResultsThis paper presents an active learning-like framework for reducing the human effort required to create named entity annotations in a corpus. In this framework, the annotation work is performed as an iterative and interactive process between the human annotator and a probabilistic named entity tagger. Unlike active learning, our framework aims to annotate all occurrences of the target named entities in the given corpus, so that the resulting annotations are free from the sampling bias which is inevitable in active learning approaches.ConclusionWe evaluate our framework by simulating the annotation process using two named entity corpora and show that our approach can reduce the number of sentences which need to be examined by the human annotator. The cost reduction achieved by the framework could be drastic when the target named entities are sparse.


title: Real time lane detection for autonomous vehicles

abstract: An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehiclepsilas heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.


title: Magnetic Levitation Over Large Translation and Rotation Ranges in All Directions

abstract: This paper describes a magnetic levitation system that uses a planar array of 16 cylindrical coils to levitate a platform containing two permanent disk magnets. Position and orientation feedback for closed-loop levitation control is provided by an optical rigid-body motion tracker and wireless LED markers. The actuation model for the levitation system across the full translation and rotation ranges was calculated using electromagnetic finite element analysis and validated with force and torque measurements between a single magnet and coil using motion stages and a six-axis force sensor. The transformation from the set of coil currents to the force and torque generated on the levitated body is recalculated in real time at each update of the levitated platform position and orientation obtained from the motion tracker using a precomputed single magnet and coil actuation model, and redundant kinematic control methods are used to find the least squares set of coil currents to generate the forces and torques needed to stabilize the levitated platform by proportional-derivative feedback control. The approximately 80 × 80 × 25 mm translation range of the implemented levitation system is comparable to the dimensions of the levitated magnet platform. The rotation ranges are ±40° in roll and ±15° in pitch, with unlimited yaw. The horizontal translation ranges can be extended by adding coils to the array, and the vertical translation and tilt rotation ranges are limited by heat dissipation in the coils. Analysis and control methods of the system are described, and large range translation and rotation trajectory following experimental results are given.


title: Deep Multi-Species Embedding

abstract: Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project \textit{eBird}, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.


title: Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques

abstract: Despite the fact that validating the measures of constructs is critical to building cumulative knowledge in MIS and the behavioral sciences, the process of scale development and validation continues to be a challenging activity. Undoubtedly, part of the problem is that many of the scale development procedures advocated in the literature are limited by the fact that they (1) fail to adequately discuss how to develop appropriate conceptual definitions of the focal construct, (2) often fail to properly specify the measurement model that relates the latent construct to its indicators, and (3) underutilize techniques that provide evidence that the set of items used to represent the focal construct actually measures what it purports to measure. Therefore, the purpose of the present paper is to integrate new and existing techniques into a comprehensive set of recommendations that can be used to give researchers in MIS and the behavioral sciences a framework for developing valid measures. First, we briefly elaborate upon some of the limitations of current scale development practices. Following this, we discuss each of the steps in the scale development process while paying particular attention to the differences that are required when one is attempting to develop scales for constructs with formative indicators as opposed to constructs with reflective indicators. Finally, we discuss several things that should be done after the initial development of a scale to examine its generalizability and to enhance its usefulness.


Review on recent advancements of bone regeneration in dental implantology


title: Image Sharpness Assessment Based on Local Phase Coherence

abstract: Sharpness is an important determinant in visual assessment of image quality. The human visual system is able to effortlessly detect blur and evaluate sharpness of visual images, but the underlying mechanism is not fully understood. Existing blur/sharpness evaluation algorithms are mostly based on edge width, local gradient, or energy reduction of global/local high frequency content. Here we understand the subject from a different perspective, where sharpness is identified as strong local phase coherence (LPC) near distinctive image features evaluated in the complex wavelet transform domain. Previous LPC computation is restricted to be applied to complex coefficients spread in three consecutive dyadic scales in the scale-space. Here we propose a flexible framework that allows for LPC computation in arbitrary fractional scales. We then develop a new sharpness assessment algorithm without referencing the original image. We use four subject-rated publicly available image databases to test the proposed algorithm, which demonstrates competitive performance when compared with state-of-the-art algorithms.


title: A Semantic Recommender System for Adaptive Learning

abstract: The ever-more complex labor world and the current economic crisis ask learners and workers to continuously update their qualification levels to stay relevant on the job. Hence, education and training providers need to adjust their offerings to cope with such evolving requirements. However, the huge number of variables to consider means that finding the right learning content that lets an individual fill his or her competency gap might be difficult. The authors' semantic-based recommender system crosses heterogeneous information about learners' and workers' backgrounds as well as advertised job positions with a catalog of online courses to identify the most appropriate learning resources. Experimental observations showed a good agreement between human and automatic recommendations, confirming the applicability of the emerging semantic technology to the generation of user-centered services that can adapt to individuals' learning needs. The authors discuss the details of their system in a video Web extra at http://youtu.be/A8c5MwnlGvA. A second Web extra shows the training booklet for evaluating the authors' system and the results of the automatic recommendation; it's available at https://s3.amazonaws.com/ieeecs.cdn.csdl.public/mags/it/2015/05/mit2015050050s.pdf.


title: t-PINE: Tensor-based Predictable and Interpretable Node Embeddings

abstract: Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspects, like efficacy, visualization, and interpretability. In this paper, we propose, t-PINE, a method that addresses these limitations. Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph, the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view, in order to learn explicit and implicit node representations, using the Canonical Polyadic (a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful, highly predictable, and gracefully interpretable representations. Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process. In addition, it helps us to exclude unrelated dimensions. Extensive experiments show that t-PINE drastically outperforms baseline methods by up to 158.6% with respect to Micro-F1, in several multi-label classification problems, while it has high visualization and interpretability utility.


title: Modelling language evolution: Examples and predictions

abstract: Abstract We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon–syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.


title: Experiences from introducing UML-based development in a large safety-critical project

abstract: UML and UML-based development methods have become de facto standards in industry, and there are many claims for the positive effects of modelling object-oriented systems using methods based on UML. However, there is no reported empirical evaluation of UML-based development in large, industrial projects. This paper reports a case study in ABB, a global company with 120,000 employees, conducted to identify immediate benefits as well as difficulties and their causes when introducing UML-based development in large projects. ABB decided to use UML-based development in the company's system development projects as part of an effort to enable certification according to the IEC 61508 safety standard. A UML-based development method was first applied in a large, international project with 230 system developers, testers and managers. The goal of the project was to build a new version of a safety-critical process control system. Most of the software was embedded. The project members were mostly newcomers to the use of UML. Interviews with 16 system developers and project managers at their sites in Sweden and Norway were conducted to identify the extent to which the introduction of UML-based development had improved their development process. The interviewees had experienced improvements with traceability from requirements to code, design of the code, and development of test cases as well as in communication and documentation. These results thus support claims in the literature regarding improvements that may be obtained through the use of UML. However, the results also show that the positive effects of UML-based development were reduced due to (1) legacy code that it was not feasible to reverse engineer into UML, (2) the distribution of requirements to development teams based on physical units and not on functionality, (3) training that was not particularly adapted to this project and considered too expensive to give to project members not directly involved in development with UML, and (4) a choice of modelling tools with functionality that was not in accordance with the needs of the project. The results from this study should be useful in enabling other UML adopters to have more realistic expectations and a better basis for making project management decisions.


title: GC-MS identification of volatile components of Galician (Northwestern Spain) white wines. Application to differentiate Rías Baixas wines from wines produced in nearby geographical regions

abstract: A capillary gas chromatoaraphy-ion trap mass spectrometry linked technique improved with a programmable temperature injector was applied to identify some volatile components (more than 100 compounds) in certified brand of origin (CBO) Rias Baixas white wines. Some of them have not been previously reported in young white wines. Forty of these components were determined by capillary gas chromatogtaphy with flame ionisation detection in Rias Baixas wines and in other Galician white wines. By means of muitivariate techniques of data exploration as hierarchical cluster analysis and principal components analysis two main groups of samples (Rias Baixas and non-Rias Baixas wines) were observed. However, some outliers were also pointed out among the two groups of wines. One sample elaborated with Albarino grapes out of the delimited Rias Baixas area showed the most anomalous behaviour. Two samples CBO Rias Baixas seemed to be produced by mixture of grape varieties not allowed by the CBO regulations. Exploratory analysis results were confirmed using supervised techniques of data classification as K-nearest neighbours and soft independent modelling of class analogies.


title: Psychopathic traits and conduct problems in community and clinic-referred samples of children: further development of the psychopathy screening device.

abstract: This study examined the structure of psychopathic traits in 2 samples of children. The nonreferred community sample included 1,136 children recruited from elementary schools in 2 school districts in the southeastern United States. The clinic sample included 160 children referred to an outpatient mental health clinic serving the same geographic region. In both samples, parent and teacher ratings of psychopathic traits were subjected to a principal-axis factor analysis, and the congruence of the factor structure across samples was examined using confirmatory factor analysis. In both samples, 1 dimension that consisted of the callous and unemotional traits that have been hallmarks of most clinical descriptions of psychopathy was isolated. Two other dimensions consisting of narcissistic traits and impulsivity emerged in the community sample. Both the narcissism and impulsivity dimensions were highly related to symptoms of oppositional defiant disorder, conduct disorder, and attention deficit hyperactivity disorder. However, the callous and unemotional traits were only weakly associated with these symptoms after controlling for the other dimensions of psychopathy.


title: Transformation Networks for Target-Oriented Sentiment Classification

abstract: Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer. Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer. Experiments show that our model achieves a new state-of-the-art performance on a few benchmarks.


title: Combustion properties of biomass

abstract: Properties of biomass relevant to combustion are briefly reviewed. The compositions of biomass among fuel types are variable, especially with respect to inorganic constituents important to the critical problems of fouling and slagging. Alkali and alkaline earth metals, in combination with other fuel elements such as silica and sulfur, and facilitated by the presence of chlorine, are responsible for many undesirable reactions in combustion furnaces and power boilers. Reductions in the concentrations of alkali metals and chlorine, created by leaching the elements from the fuel with water, yield remarkable improvements in ash fusion temperatures and confirm much of what is suggested regarding the nature of fouling by biomass fuels. Other influences of biomass composition are observed for the rates of combustion and pollutant emissions. Standardized engineering practices setting out protocols of analysis and interpretation may prove useful in reducing unfavorable impacts and industry costs, and further development is encouraged.


title: Applying centrality measures to impact analysis: A coauthorship network analysis

abstract: Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties, with the aim to apply centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of twenty years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness, betweenness, degree and PageRank) for authors in this network. We find out that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking, and suggest that centrality measures can be useful indicators for impact analysis.


title: Learning a Part-of-Speech Tagger from Two Hours of Annotation

abstract: Most work on weakly-supervised learning for part-of-speech taggers has been based on unrealistic assumptions about the amount and quality of training data. For this paper, we attempt to create true low-resource scenarios by allowing a linguist just two hours to annotate data and evaluating on the languages Kinyarwanda and Malagasy. Given these severely limited amounts of either type supervision (tag dictionaries) or token supervision (labeled sentences), we are able to dramatically improve the learning of a hidden Markov model through our method of automatically generalizing the annotations, reducing noise, and inducing word-tag frequency information.


title: Cross-Modal Retrieval via Deep and Bidirectional Representation Learning

abstract: Cross-modal retrieval emphasizes understanding inter-modality semantic correlations, which is often achieved by designing a similarity function. Generally, one of the most important things considered by the similarity function is how to make the cross-modal similarity computable. In this paper, a deep and bidirectional representation learning model is proposed to address the issue of image–text cross-modal retrieval. Owing to the solid progress of deep learning in computer vision and natural language processing, it is reliable to extract semantic representations from both raw image and text data by using deep neural networks. Therefore, in the proposed model, two convolution-based networks are adopted to accomplish representation learning for images and texts. By passing the networks, images and texts are mapped to a common space, in which the cross-modal similarity is measured by cosine distance. Subsequently, a bidirectional network architecture is designed to capture the property of the cross-modal retrieval—the bidirectional search. Such architecture is characterized by simultaneously involving the matched and unmatched image–text pairs for training. Accordingly, a learning framework with maximum likelihood criterion is finally developed. The network parameters are optimized via backpropagation and stochastic gradient descent. A great deal of experiments are conducted to sufficiently evaluate the proposed method on three publicly released datasets: IAPRTC-12, Flickr30k, and Flickr8k. The overall results definitely show that the proposed architecture is effective and the learned representations have good semantics to achieve superior cross-modal retrieval performance.


title: Simultaneous Penile–Vaginal Intercourse Orgasm is Associated with Satisfaction (Sexual, Life, Partnership, and Mental Health)

abstract: ABSTRACT Introduction Previous multivariate research found that satisfaction was associated positively with frequency of specifically penile–vaginal intercourse (PVI; as opposed to other sexual activities) as well as with vaginal orgasm. The contribution to satisfaction of simultaneous orgasm produced by PVI merited direct examination in a large representative sample. Aims To examine the associations of aspects of satisfaction (sexual, life, own mental health, partner relationship) with consistency of simultaneous orgasm produced by PVI (as well as with PVI frequency and vaginal orgasm consistency) . Methods A representative sample of Czechs (N = 1,570) aged 35–65 years completed a survey on aspects of satisfaction, PVI frequency, vaginal orgasm consistency, and consistency of simultaneous orgasm produced by PVI (the latter being a specially timed version of vaginal orgasm for women). Main Outcome Measures Analysis of variance of satisfaction components (LiSat scale items) from age and the sexual behaviors. Results For both sexes, all aspects of satisfaction were associated with simultaneous PVI orgasm consistency and with PVI frequency (except female life satisfaction). All aspects of satisfaction were also associated with vaginal orgasm consistency. Multivariate analyses indicated that PVI frequency and simultaneous orgasm consistency make independent contributions to the aspects of satisfaction for both sexes. Conclusions For both sexes, PVI frequency and simultaneous orgasm produced by PVI (as well as vaginal orgasm for women) are associated with greater life, sexual, partnership, and mental health satisfaction. Greater support for these specific aspects of sexual activity is warranted. Brody S and Weiss P. Simultaneous penile–vaginal intercourse orgasm is associated with satisfaction (sexual, life, partnership, and mental health). J Sex Med 2011;8:734–741.


title: An Efficient and Simple Multiple Criteria Model for a Grinding Circuit Selection Based on MOORA Method

abstract: Abstract. Thecomminutionprocess,particularlygrinding,isveryimportantinthemineralprocess-ingindustry.Somecharacteristicsoforeparticles,whichoccurasaproductofgrindingprocess,havea signiﬁcant impact on the eﬀects of further ore processing. At the same time, this process requiresa signiﬁcant amount ofenergy whichsigniﬁcantlyaﬀects theoverall processing costs. Therefore,inthis paper,wepropose new multiplecriteria decisionmakingmodel, basedonthe Ratiosystempartof the MOORA method, which should enable an eﬃcient selection of the adequate comminutioncircuit design. Keywords: MCDM,interval greynumber, grindingcircuitselection,mineral processing, ﬂotation,grinding, MOORA, ratio system. 1. Introduction Metallicoresminedinthe21stcenturygenerallyhavealowcontentofvaluableminerals.For example,a typical copperores that are now mined usually contain less than 0.5% Cufrom surface mining and 2% from undergroundmining (Davenport et al ., 2002).Thelow contentofvaluablemineralsin ores does notprovideanyeconomicjustiﬁca-tion fordirectore smelting.Valuablemineralsin minedores are inter-grownwith ganguematerials. Therefore, ores are crushed and ground into ﬁne particles to allow liberationof commerciallyvaluable minerals.Crushing and grindingare veryimportant operationsin the process of separating commerciallyvaluable minerals from ores. These operationsare also very expensive due to costs of comminutionequipments, used energy and main-tenance.


Personal Health Information Recommender: implementing a tool for the empowerment of cancer patients


Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder


Using AI to Make Predictions on Stock Market


title: Variable frequency and constant frequency modulation techniques for GaN based MHz H-bridge PFC

abstract: This paper explores two modulation techniques for power factor corrector (PFC) based on critical conduction mode (CRM) and proposes a new modulation technique which has the benefits of CRM and allows quasi constant switching frequency also. The converter is designed for MHz range switching frequency as high frequency reduces the size of the EMI filter. However at high frequency, the switching losses become the dominant losses making soft switching a necessity. CRM allows zero current switching (ZCS) turn-on but it's not able to achieve zero voltage switching (ZVS) turn-on when the input voltage is greater than half of the output voltage. To achieve ZVS turn-on over the entire mains cycle, triangular current mode (TCM) was proposed by Marxgut, C. et al.[1] but both these methods have the drawback of variable switching frequency. The new method proposed modifies TCM so that the switching frequency is quasi constant and ZVS turn-on is also achieved over the entire mains cycle. Based on analytical loss model of Cascode GaN transistor, the efficiency of the three modulation techniques is compared. Also, the parameters of the EMI filter required are compared based on simulated noise measurement. As variable switching frequency is not preferred in three phase systems, the quasi constant frequency approach finds its benefits in three phase PFC.


title: Analyzing and predicting not-answered questions in community-based question answering services

abstract: This paper focuses on analyzing and predicting not-answered questions in Community based Question Answering (CQA) services, such as Yahoo! Answers. In CQA, users express their information needs by submitting questions and await answers from other users. One of the key problems of this pattern is that sometimes no one helps to give answers. In this paper, we analyze the not-answered questions and give a first try of predicting whether questions will receive answers. More specifically, we first analyze the questions of Yahoo! Answers based on the features selected from different perspectives. Then, we formalize the prediction problem as supervised learning task and leverage the proposed features to make predictions. Extensive experiments are made on 76,251 questions collected from Yahoo! Answers.


Learning hierarchical category structure in deep neural networks


title: Intervening Sequences of Regularly Spaced Prokaryotic Repeats Derive from Foreign Genetic Elements

abstract: Prokaryotes contain short DN repeats known as CRISPR, recognizable by the regular spacing existing between the recurring units. They represent the most widely distributed family of repeats among prokaryotic genomes suggesting a biological function. The origin of the intervening sequences, at present unknown, could provide clues about their biological activities. Here we show that CRISPR spacers derive from preexisting sequences, either chromosomal or within transmissible genetic elements such as bacteriophages and conjugative plasmids. Remarkably, these extrachromosomal elements fail to infect the specific spacer-carrier strain, implying a relationship between CRISPR and immunity against targeted DNA. Bacteriophages and conjugative plasmids are involved in prokaryotic population control, evolution, and pathogenicity. All these biological traits could be influenced by the presence of specific spacers. CRISPR loci can be visualized as mosaics of a repeated unit, separated by sequences at some time present elsewhere in the cell.


title: Tweet2Vec: Character-Based Distributed Representations for Social Media

abstract: Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many out-of-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available.


title: A regulated charge pump with small ripple voltage and fast start-up

abstract: A regulated charge pump circuit is realized in a 3.3-V 0.13-/spl mu/m CMOS technology. The charge pump exploits an automatic pumping control scheme to provide small ripple output voltage and fast start-up by decoupling output ripple and start-up time. The automatic pumping control scheme is composed of two schemes, an automatic pumping current control scheme and an automatic pumping frequency control scheme. The former automatically adjusts the size of pumping driver to reduce ripple voltage according to output voltage. The latter changes the pumping period by controlling a voltage-controlled oscillator (VCO). The output frequency of the VCO varies from 400 kHz to 600 kHz by controlling the input bias voltage of the VCO. The prototype chip delivers regulated 4.5-V output voltage from a supply voltage of 3.3 V with a flying capacitor of 330 nF, while providing 30 mA of load current. The area is 0.25 mm/sup 2/ and the measured output ripple voltage is less than 33.8 mV with a 2-/spl mu/F load capacitor. The power efficiency is greater than 70% at the range of load current from 1 to 30 mA. An analytical model for ripple voltage and recovery time is proposed demonstrating a reasonable agreement with SPICE simulation results.


title: Clustered Support Vector Machines

abstract: In many problems of machine learning, the data are distributed nonlinearly. One way to address this kind of data is training a nonlinear classifier such as kernel support vector machine (kernel SVM). However, the computational burden of kernel SVM limits its application to large scale datasets. In this paper, we propose a Clustered Support Vector Machine (CSVM), which tackles the data in a divide and conquer manner. More specifically, CSVM groups the data into several clusters, followed which it trains a linear support vector machine in each cluster to separate the data locally. Meanwhile, CSVM has an additional global regularization, which requires the weight vector of each local linear SVM aligning with a global weight vector. The global regularization leverages the information from one cluster to another, and avoids over-fitting in each cluster. We derive a data-dependent generalization error bound for CSVM, which explains the advantage of CSVM over linear SVM. Experiments on several benchmark datasets show that the proposed method outperforms linear SVM and some other related locally linear classifiers. It is also comparable to a fine-tuned kernel SVM in terms of prediction performance, while it is more efficient than kernel SVM.


title: A Review of Rock Bolt Monitoring Using Smart Sensors

abstract: Rock bolts have been widely used as rock reinforcing members in underground coal mine roadways and tunnels. Failures of rock bolts occur as a result of overloading, corrosion, seismic burst and bad grouting, leading to catastrophic economic and personnel losses. Monitoring the health condition of the rock bolts plays an important role in ensuring the safe operation of underground mines. This work presents a brief introduction on the types of rock bolts followed by a comprehensive review of rock bolt monitoring using smart sensors. Smart sensors that are used to assess rock bolt integrity are reviewed to provide a firm perception of the application of smart sensors for enhanced performance and reliability of rock bolts. The most widely used smart sensors for rock bolt monitoring are the piezoelectric sensors and the fiber optic sensors. The methodologies and principles of these smart sensors are reviewed from the point of view of rock bolt integrity monitoring. The applications of smart sensors in monitoring the critical status of rock bolts, such as the axial force, corrosion occurrence, grout quality and resin delamination, are highlighted. In addition, several prototypes or commercially available smart rock bolt devices are also introduced.


title: Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems

abstract: Existing statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best for all problem instances. Many researchers are examining ensemble methods that combine the output of successful, separately developed modules to create more accurate solutions. This paper examines three merging rules for combining probability distributions: the well known mixture rule, the logarithmic rule, and a novel product rule. These rules were applied with state-of-the-art results to two problems commonly used to assess human mastery of lexical semantics|synonym questions and analogy questions. All three merging rules result in ensembles that are more accurate than any of their component modules. The dierences among the three rules are not statistically signicant, but it is suggestive that the popular mixture rule is not the best rule for either of the two problems.


title: First results on bistatic MTI activity at fraunhofer FHR

abstract: In this paper the preliminary results on bistatic MTI activities conducted at Fraunhofer FHR are presented. In particular, the bistatic experiment conducted in November 2010 is described which involved the use of a stationary transmitter and of the multi-channel system PAMIR [1] mounted on the Transall aircraft acting as moving receiver. Main challenges of the considered bistatic configuration such as local oscillators drift and clutter non-homogeneities are addressed. MTI results are presented both concerning cooperative and synthetic injected targets.


title: Implementation of Modified Twofish Algorithm using 128 and 192-bit keys on VHDL

abstract: Now-a-days internet is one of the most important sources of communication and thousands of people interact electronically. For sending sensitive messages over the internet, we need security. Hence for secure communication required the algorithms. Among these algorithms is Twofish, a promising 128-bit block cipher and one of the competitors in National Institute of Standards and Technology’s (NIST) AES competition, for the replacement of DES at the core of many encryption systems world-wide. In this paper the security algorithm, twofish has been explained with all of its modules (some modules has been modified) for both 128 and 192-bit key size. Implementation on VHDL using Xilinx – 6.1 xst software has been done taking delay as main constraint. General Terms Algorithms, Security, symmetric key, path delay


title: Aspects of the Safety and Health at the Workplace

abstract: Abstract The present paper approaches the problem of safety and health in work at European level. The paper aims to highlight the perception of the Romanian employees in Romania and abroad on the main aspects regarding the safety and health at work (information, training, quality of the working environment, occupational risks, satisfaction related to the working conditions). The methodology of research implies the online questioning on a random sample. The main results of the research show the occupational range, the employees’ perception on the workplace and on the risks.


title: 2007, Only: Meaning and implicature

abstract: Methods are disclosed for increasing ribozyme catalytic activity without reducing specificity, which methods comprise contacting an RNA molecule with a ribozyme and a facilitator oligonucleotide. The present invention further provides compositions comprising a ribozyme and an effective amount of a facilitator oligonucleotide.


title: Protein Turnover in Tissues of the Rat Fetus following Maternal Starvation

abstract: The effects of maternal starvation upon protein turnover in various tissues of the fetal rat were determined. Protein synthesis was determined in fetal tissues by the intravenous injection of "massive" amounts of 3H-phenylalanine into the maternal circulation, followed by measurements of tissue free and protein-bound phe specific radioactivity in fetal diaphragm, heart, liver, and brain. Rates of protein degradation in fetal tissues were assessed by subtracting protein accretion rates from protein synthesis. Fractional rates of protein synthesis were minimally affected by maternal starvation in diaphragm, heart, and brain. The major factor contributing to reduced fetal protein accretion in these fetal tissues during maternal starvation was enhanced protein breakdown. These findings differ from those previously reported in young adult rodents following fasting or malnutrition.


title: New Results on Multi-Step Traffic Flow Prediction

abstract: In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly more difficult than its single-step variant and is known to suffer from degradation in predictions as the time step increases. In this paper, two approaches to improve multi-step traffic flow prediction performance in recursive and multi-output settings are introduced. In particular, a model that allows recursive prediction approaches to take into account the temporal context in term of time-step index when making predictions is introduced. In addition, a conditional generative adversarial network-based data augmentation method is proposed to improve prediction performance in the multi-output setting. The experiments on a real-world traffic flow dataset show that the two methods improve on multi-step traffic flow prediction in recursive and multi-output settings, respectively.


Romancing leadership: Past, present, and future☆


title: Hewlett-Packard Laboratories A Semantic Web Services Architecture

abstract: The semantic Web services initiative architecture (SWSA) committee has created a set of architectural and protocol abstractions that serve as a foundation for semantic Web service technologies. This article summarizes the committee's findings, emphasizing its review of requirements gathered from several different environments. We also identify the scope and potential requirements for a semantic Web services architecture.


title: Survey on leaf recognization and classification

abstract: There are countless plant species available globally. To manage massive content, development of a fast and effective categorization methods has turned into a territory of dynamic research. As trees and plants are very important to ecology, accurate Identification and classification becomes necessary. Classification procedure is carried out through number of sub procedures. An identification or Classification issue is managed by mapping a input data with one of the unique classes. In this procedure, at first, database of a leaf images is created, that comprises of images of test leaf with their equivalent plant information. Essential features are extracted using image processing techniques. The features have to be stable in order to make the identification system robust. Subsequently the plant/leaf is recognized using machine learning techniques. In this paper a survey is presented on the various types of leaf identification process.


title: Advancement in the head pose estimation via depth-based face spotting

abstract: Head pose estimation is not only a crucial preprocessing task in applications such as facial expression and face recognition, but also the core task for many others, e.g. gaze; driver focus of attention; head gesture recognitions. In real scenarios, the fine location and scale of a processed face patch should be consistently and automatically obtained. To this end, we propose a depth-based face spotting technique in which the face is cropped with respect to its depth data, and is modeled by its appearance features. By employing this technique, the localization rate was gained. additionally, by building a head pose estimator on top of it, we achieved more accurate pose estimates and better generalization capability. To estimate the head pose, we exploit Support Vector (SV) regressors to map Histogram of oriented Gradient (HoG) features extracted from the spotted face patches in both depth and RGB images to the head rotation angles. The developed pose estimator compared favorably to state-of-the-art approaches on two challenging DRGB databases.


title: A framework for medical text mining using a feature weighted clustering algorithm

abstract: Text categorization is the task of deciding whether a document belongs to a set of pre specified classes of documents. Categorization of documents is challenging, as the number of discriminating words can be huge. Many existing text classification algorithms simply do not work with these many number of words. Traditional text classification algorithm uses all training samples for classification, thereby increasing the storage requirements and calculation complexity as the number of features increase. Mining medical records for relationships between living factors and the symptoms of a disease is an important task, however there has been relatively little research into this area. The proposed work evolves a text classification algorithm where al l cluster centers are taken as training samples there by reducing the sample size and introduces a weight factor to indicate the different importance of each training sample. A similarity measure function is used to classify a new patient document, based on the measure. Experiments on real life data show that the proposed algorithm outperforms the state of art classification algorithms such as k-nearest neighbor.


title: Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons.

abstract: Bacterial diversity among environmental samples is commonly assessed with PCR-amplified 16S rRNA gene (16S) sequences. Perceived diversity, however, can be influenced by sample preparation, primer selection, and formation of chimeric 16S amplification products. Chimeras are hybrid products between multiple parent sequences that can be falsely interpreted as novel organisms, thus inflating apparent diversity. We developed a new chimera detection tool called Chimera Slayer (CS). CS detects chimeras with greater sensitivity than previous methods, performs well on short sequences such as those produced by the 454 Life Sciences (Roche) Genome Sequencer, and can scale to large data sets. By benchmarking CS performance against sequences derived from a controlled DNA mixture of known organisms and a simulated chimera set, we provide insights into the factors that affect chimera formation such as sequence abundance, the extent of similarity between 16S genes, and PCR conditions. Chimeras were found to reproducibly form among independent amplifications and contributed to false perceptions of sample diversity and the false identification of novel taxa, with less-abundant species exhibiting chimera rates exceeding 70%. Shotgun metagenomic sequences of our mock community appear to be devoid of 16S chimeras, supporting a role for shotgun metagenomics in validating novel organisms discovered in targeted sequence surveys.


title: Room segmentation: Survey, implementation, and analysis

abstract: The division of floor plans or navigation maps into single rooms or similarly meaningful semantic units is central to numerous tasks in robotics such as topological mapping, semantic mapping, place categorization, human-robotinteraction, or automatized professional cleaning. Although many map partitioning algorithms have been proposed for various applications there is a lack of comparative studies on these different algorithms. This paper surveys the literature on room segmentation and provides four publicly available implementations of popular methods, which target the semantic mapping domain and are tuned to yield segmentations into complete rooms. In an attempt to provide new users of such technologies guidance in the choice of map segmentation algorithm, those methods are compared qualitatively and quantitatively using several criteria. The evaluation is based on a novel compilation of 20 challenging floor plans.


How a Cockpit Remembers Its Speeds


title: Quantitative and qualitative microbial analysis of raw milk reveals substantial diversity influenced by herd management practices

abstract: Abstract The quantitative ( n = 260) and qualitative ( n = 24) microbial compositions of raw milk samples were assessed using culture analyses and a molecular inventory based on the rDNA sequences of 1697 isolates. A statistical analysis was also performed to correlate farm practices with the microbial populations present in the milk samples. Ripening bacteria and Pseudomonas were the dominant groups. Milk samples were distinguishable by the different proportions of their microbial groups, which varied among producers. Individual practices influenced the levels of technologically important microorganisms, for example, pre-dipping of teats reduced levels of lactococci. Standard plate counts correlated with the levels of Gram-negative bacteria, Pseudomonas , lactococci and ripening bacteria, depending on the milking season. Lactic acid bacteria and yeasts, which were poorly represented, nevertheless seemed to constitute a resilient basal flora. Qualitative species diversity was considerable, with more than 100 bacterial species identified, dominated by Gram-positive bacteria, notably staphylococci and Corynebacterium .


title: Tohme: detecting curb ramps in google street view using crowdsourcing, computer vision, and machine learning

abstract: Building on recent prior work that combines Google Street View (GSV) and crowdsourcing to remotely collect information on physical world accessibility, we present the first 'smart' system, Tohme, that combines machine learning, computer vision (CV), and custom crowd interfaces to find curb ramps remotely in GSV scenes. Tohme consists of two workflows, a human labeling pipeline and a CV pipeline with human verification, which are scheduled dynamically based on predicted performance. Using 1,086 GSV scenes (street intersections) from four North American cities and data from 403 crowd workers, we show that Tohme performs similarly in detecting curb ramps compared to a manual labeling approach alone (F- measure: 84% vs. 86% baseline) but at a 13% reduction in time cost. Our work contributes the first CV-based curb ramp detection system, a custom machine-learning based workflow controller, a validation of GSV as a viable curb ramp data source, and a detailed examination of why curb ramp detection is a hard problem along with steps forward.