title: Psychometric evaluation of Persian Nomophobia Questionnaire: Differential item functioning and measurement invariance across gender

abstract: Background and aims Research examining problematic mobile phone use has increased markedly over the past 5 years and has been related to "no mobile phone phobia" (so-called nomophobia). The 20-item Nomophobia Questionnaire (NMP-Q) is the only instrument that assesses nomophobia with an underlying theoretical structure and robust psychometric testing. This study aimed to confirm the construct validity of the Persian NMP-Q using Rasch and confirmatory factor analysis (CFA) models. Methods After ensuring the linguistic validity, Rasch models were used to examine the unidimensionality of each Persian NMP-Q factor among 3,216 Iranian adolescents and CFAs were used to confirm its four-factor structure. Differential item functioning (DIF) and multigroup CFA were used to examine whether males and females interpreted the NMP-Q similarly, including item content and NMP-Q structure. Results Each factor was unidimensional according to the Rach findings, and the four-factor structure was supported by CFA. Two items did not quite fit the Rasch models (Item 14: "I would be nervous because I could not know if someone had tried to get a hold of me;" Item 9: "If I could not check my smartphone for a while, I would feel a desire to check it"). No DIF items were found across gender and measurement invariance was supported in multigroup CFA across gender. Conclusions Due to the satisfactory psychometric properties, it is concluded that the Persian NMP-Q can be used to assess nomophobia among adolescents. Moreover, NMP-Q users may compare its scores between genders in the knowledge that there are no score differences contributed by different understandings of NMP-Q items.


title: SPARQL over GraphX

abstract: The ability of the RDF data model to link data from heterogeneous domains has led to an explosive growth of RDF data. So, evaluating SPARQL queries over large RDF data has been crucial for the semantic web community. However, due to the graph nature of RDF data, evaluating SPARQL queries in relational databases and common data-parallel systems needs a lot of joins and is inefficient. On the other hand, the enormity of datasets that are graph in nature such as social network data, has led the database community to develop graph-parallel processing systems to support iterative graph computations efficiently. In this work we take advantage of the graph representation of RDF data and exploit GraphX, a new graph processing system based on Spark. We propose a subgraph matching algorithm, compatible with the GraphX programming model to evaluate SPARQL queries. Some experiments are performed to show the system scalability to handle large datasets.


title: The advantages of cyanoacrylate wound closure in circumcision

abstract: PURPOSE ::: To compare 2-octyl-cyanoacrylate (2-octyl-CA) glue with polyglytone 6211 sutures in circumcision wound closure on the basis of operative time, safeness, postoperative pain and cosmesis. ::: ::: ::: PATIENTS AND METHODS ::: The boys who would undergo circumcision in 18-month period (n = 137) were grouped into two. In Group 1 (n = 64), 6/0 polyglytone 6211 sutures, and in Group 2 (n = 73), 2-octyl-CA glue were used for wound closure. Durations of anesthesia, surgical procedures and postoperative pain, and pain scores on the 2nd and 48th postoperative hours were determined. A classification system was constructed to evaluate cosmesis, intraoperative and postoperative bleeding. Quantitative differences were assessed by Mann-Whitney U and Student's t tests, and qualitative analyses were performed by χ (2) test. p was considered as significant at less than 0.05. ::: ::: ::: RESULTS ::: The pain scores of the groups were comparable. Durations of anesthesia, surgical procedures and postoperative pain were significantly shorter (p < 0.05); and cosmesis, intraoperative and postoperative bleeding status were significantly better in Group 2 (p < 0.05). ::: ::: ::: CONCLUSION ::: 2-Octyl-CA seems to be a good alternative to the conventional suturing technique in circumcision wound closure with shorter operation time, ease of appliance, improved safeness, better cosmesis and shorter duration of postoperative pain.


title: Involvement of visual cortex in tactile discrimination of orientation

abstract: The primary sense modalities (vision, touch and so on) are generally thought of as distinct. However, visual imagery is implicated in the normal tactile perception of some object properties, such as orientation1, shape and size2. Furthermore, certain tactile tasks, such as discrimination of grating orientation1 and object recognition3, are associated with activity in areas of visual cortex. Here we show that disrupting function of the occipital cortex using focal transcranial magnetic stimulation (TMS) interferes with the tactile discrimination of grating orientation. The specificity of this effect is illustrated by its time course and spatial restriction over the scalp, and by the failure of occipital TMS to affect either detection of an electrical stimulus applied to the fingerpad or tactile discrimination of grating texture. In contrast, TMS over the somatosensory cortex blocked discrimination of grating texture as well as orientation. We also report that, during tactile discrimination of grating orientation, an evoked potential is recorded over posterior scalp regions with a latency corresponding to the peak of the TMS interference effect (about 180 ms). The findings indicate that visual cortex is closely involved in tactile discrimination of orientation. To our knowledge, this is the first demonstration that visual cortical processing is necessary for normal tactile perception.


title: Mobile Applications for Diabetics: A Systematic Review and Expert-Based Usability Evaluation Considering the Special Requirements of Diabetes Patients Age 50 Years or Older

abstract: Background: A multitude of mhealth (mobile health) apps have been developed in recent years to support effective self-management of patients with diabetes mellitus type 1 or 2. Objective: We carried out a systematic review of all currently available diabetes apps for the operating systems iOS and Android. We considered the number of newly released diabetes apps, range of functions, target user groups, languages, acquisition costs, user ratings, available interfaces, and the connection between acquisition costs and user ratings. Additionally, we examined whether the available applications serve the special needs of diabetes patients aged 50 or older by performing an expert-based usability evaluation. Methods: We identified relevant keywords, comparative categories, and their specifications. Subsequently, we performed the app review based on the information given in the Google Play Store, the Apple App Store, and the apps themselves. In addition, we carried out an expert-based usability evaluation based on a representative 10% sample of diabetes apps. Results: In total, we analyzed 656 apps finding that 355 (54.1%) offered just one function and 348 (53.0%) provided a documentation function. The dominating app language was English (85.4%, 560/656), patients represented the main user group (96.0%, 630/656), and the analysis of the costs revealed a trend toward free apps (53.7%, 352/656). The median price of paid apps was €1.90. The average user rating was 3.6 stars (maximum 5). Our analyses indicated no clear differences in the user rating between free and paid apps. Only 30 (4.6%) of the 656 available diabetes apps offered an interface to a measurement device. ::: ::: We evaluated 66 apps within the usability evaluation. On average, apps were rated best regarding the criterion “comprehensibility” (4.0 out of 5.0), while showing a lack of “fault tolerance” (2.8 out of 5.0). Of the 66 apps, 48 (72.7%) offered the ability to read the screen content aloud. The number of functions was significantly negative correlated with usability. The presence of documentation and analysis functions reduced the usability score significantly by 0.36 and 0.21 points. Conclusions: A vast number of diabetes apps already exist, but the majority offer similar functionalities and combine only one to two functions in one app. Patients and physicians alike should be involved in the app development process to a greater extent. We expect that the data transmission of health parameters to physicians will gain more importance in future applications. ::: ::: The usability of diabetes apps for patients aged 50 or older was moderate to good. But this result applied mainly to apps offering a small range of functions. Multifunctional apps performed considerably worse in terms of usability. Moreover, the presence of a documentation or analysis function resulted in significantly lower usability scores. The operability of accessibility features for diabetes apps was quite limited, except for the feature “screen reader”. [J Med Internet Res 2014;16(4):e104]


title: Diagnosis of canine monocytotropic ehrlichiosis (Ehrlichia canis): an overview.

abstract: Canine monocytotropic ehrlichiosis (CME), caused by the rickettsia Ehrlichia canis, an important canine disease with a worldwide distribution. Diagnosis of the disease can be challenging due to its different phases and multiple clinical manifestations. CME should be suspected when a compatible history (living in or traveling to an endemic region, previous tick exposure), typical clinical signs and characteristic hematological and biochemical abnormalities are present. Traditional diagnostic techniques including hematology, cytology, serology and isolation are valuable diagnostic tools for CME, however a definitive diagnosis of E. canis infection requires molecular techniques. This article reviews the current literature covering the diagnosis of infection caused by E. canis.


title: High-Performance and High-Data-Rate Quasi-Coaxial LTCC Vertical Interconnect Transitions for Multichip Modules and System-on-Package Applications

abstract: A new design of stripline transition structures and flip-chip interconnects for high-speed digital communication systems implemented in low-temperature cofired ceramic (LTCC) substrates is presented. Simplified fabrication, suitability for LTCC machining, suitability for integration with other components, and connection to integrated stripline or microstrip interconnects for LTCC multichip modules and system on package make this approach well suited for miniaturized, advanced broadband, and highly integrated multichip ceramic modules. The transition provides excellent signal integrity at high-speed digital data rates up to 28 Gbits/s. Full-wave simulations and experimental results demonstrate a cost-effective solution for a wide frequency range from dc to 30 GHz and beyond. Signal integrity and high-speed digital data rate performances are verified through eye diagram and time-domain reflectometry and time-domain transmissometry measurements over a 10-cm long stripline.


title: Literature Survey on Stereo Vision Disparity Map Algorithms

abstract: This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.


title: Context over Time: Modeling Context Evolution in Social Media

abstract: The rise of online social media has led to an explosion in user-generated content. However, user-generated content is difficult to analyze in isolation from its context. Accordingly, context detection and tracking its evolution is essential to understanding social media. This paper presents a statistical model that can detect interpretable topics along with their contexts. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags that frequently occur with a topic. The model combines a context with a related topic by jointly modeling words with hashtags and time. Experiments on real datasets demonstrate that the proposed model successfully discovers both meaningful topics and contexts, and tracks their evolution.


Prediction of Yelp Review Star Rating using Sentiment Analysis


title: Change agency in a primary health care context: The case of distributed leadership

abstract: BACKGROUND ::: Integration of services across disciplines and organizations has been pursued increasingly in the primary care sector. Successful integration requires adept leadership of change. There have been questions about the extent to which studies on change agency that focus on a stand-alone leader are applicable in the complex setting of health care. It has been suggested that a model of collective leadership is more appropriate to this setting. ::: ::: ::: PURPOSE ::: The objective is to understand the dynamics of collective or distributed leadership by attending to change agency roles in a context involving collaboration across health organizations. The study examines how change agency roles develop, evolve, interact, and complement each other. It also examines the bases of the change agents' ability to exercise influence. ::: ::: ::: METHODOLOGY ::: A qualitative, longitudinal case study allowed us to map the evolution of a successful model of leadership. We tracked changes and agents' roles by engaging in extensive observations and conducting 74 interviews over a period of 4 years. ::: ::: ::: FINDINGS ::: The findings point to the importance of the distributed change leadership model in contexts where legitimacy, authority, resources, and ability to influence complex change are dispersed across loci. Distributed leadership has both planned and emergent components, and its success in bringing about change is associated with the social capital prevalent in the site. ::: ::: ::: PRACTICE IMPLICATIONS ::: Change leaders need to build a winning coalition of agents with complementary skills and resources that support the change. Successful change leadership involves investing time in finding common ground across stakeholders and in building credibility and trust. Having an agent whose main responsibility is to manage the change process is likely to bring more success than asking busy health care practitioners to take on this charge because in the latter case, there is likelihood of dilution of change focus and momentum.


title: LOCALLY REFINED SPLINES REPRESENTATION FOR GEOSPATIAL BIG DATA

abstract: Abstract. When viewed from distance, large parts of the topography of landmasses and the bathymetry of the sea and ocean floor can be regarded as a smooth background with local features. Consequently a digital elevation model combining a compact smooth representation of the background with locally added features has the potential of providing a compact and accurate representation for topography and bathymetry. The recent introduction of Locally Refined B-Splines (LR B-splines) allows the granularity of spline representations to be locally adapted to the complexity of the smooth shape approximated. This allows few degrees of freedom to be used in areas with little variation, while adding extra degrees of freedom in areas in need of more modelling flexibility. In the EU fp7 Integrating Project IQmulus we exploit LR B-splines for approximating large point clouds representing bathymetry of the smooth sea and ocean floor. A drastic reduction is demonstrated in the bulk of the data representation compared to the size of input point clouds. The representation is very well suited for exploiting the power of GPUs for visualization as the spline format is transferred to the GPU and the triangulation needed for the visualization is generated on the GPU according to the viewing parameters. The LR B-splines are interoperable with other elevation model representations such as LIDAR data, raster representations and triangulated irregular networks as these can be used as input to the LR B-spline approximation algorithms. Output to these formats can be generated from the LR B-spline applications according to the resolution criteria required. The spline models are well suited for change detection as new sensor data can efficiently be compared to the compact LR B-spline representation.


title: Depth-Fighting Aware Methods for Multifragment Rendering

abstract: Many applications require operations on multiple fragments that result from ray casting at the same pixel location. To this end, several approaches have been introduced that process for each pixel one or more fragments per rendering pass, so as to produce a multifragment effect. However, multifragment rasterization is susceptible to flickering artifacts when two or more visible fragments of the scene have identical depth values. This phenomenon is called coplanarity or Z-fighting and incurs various unpleasant and unintuitive results when rendering complex multilayer scenes. In this work, we develop depth-fighting aware algorithms for reducing, eliminating and/or detecting related flaws in scenes suffering from duplicate geometry. We adapt previously presented single and multipass rendering methods, providing alternatives for both commodity and modern graphics hardware. We report on the efficiency and robustness of all these alternatives and provide comprehensive comparison results. Finally, visual results are offered illustrating the effectiveness of our variants for a number of applications where depth accuracy and order are of critical importance.


title: Evolution of Topologies, Modeling, Control Schemes, and Applications of Modular Multilevel Converters

abstract: Modular multilevel converter (MMC) is one of the most promising topologies for medium to high-voltage high-power applications. The main features of MMC are modularity, voltage and power scalability, fault tolerant and transformer-less operation, and high-quality output waveforms. Over the past few years, several research studies are conducted to address the technical challenges associated with the operation and control of the MMC. This paper presents the development of MMC circuit topologies and their mathematical models over the years. Also, the evolution and technical challenges of the classical and model predictive control methods are discussed. Finally, the MMC applications and their future trends are presented.


title: How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications

abstract: In this paper an analysis of the presence and possibilities of altmetrics for bibliometric and performance analysis is carried out. Using the web based tool Impact Story, we collected metrics for 20,000 random publications from the Web of Science. We studied both the presence and distribution of altmetrics in the set of publications, across fields, document types and over publication years, as well as the extent to which altmetrics correlate with citation indicators. The main result of the study is that the altmetrics source that provides the most metrics is Mendeley, with metrics on readerships for 62.6 % of all the publications studied, other sources only provide marginal information. In terms of relation with citations, a moderate spearman correlation (r = 0.49) has been found between Mendeley readership counts and citation indicators. Other possibilities and limitations of these indicators are discussed and future research lines are outlined.


title: The architecture framework design of dual cloud technology for C-learning

abstract: C-learning is an integration of the technology Combined with cloud computing, virtual desktop, multimedia network teaching, etc., C-learning provides informationized instruction with online education services with featured in great variety, constant access, capacity on demand, safety and reliability, quality and pricing competitiveness as well. In order to implement the C-Learning, we should build a cloud terminal network learning environment with low cost, low operation and maintenance, and low-power. After taking into account the construction costs and the effect, and the actual situation of cloud security and teaching resources on the cloud, in order to ensure the stability and reliability of the C-learning, we proposed using the framework of dual-cloud technology to build the C-learning environment, in support of dual-server protection mechanism and virtual desktop technical technology.


title: Social comparison on Facebook: Motivation, affective consequences, self-esteem, and Facebook fatigue

abstract: Abstract A growing body of research examines social comparison on Facebook, a social media environment where users can present optimized versions of themselves. To grasp the complexity of social comparison on Facebook, the researchers followed Helgeson and Mickelson’s (1995) functional approach, focusing on motives (i.e., self-enhancement, self-improvement, self-evaluation, and self-destruction) rather than fixed targets (i.e., downward, upward, and unilateral comparison) of social comparison. Social comparison motivations were explored in relation to self-esteem and affective consequences of comparison. A college-student sample ( N = 267) reported engaging in social comparison frequently on Facebook and low-self-esteem (LSE) individuals were more likely than those with higher self-esteem (HSE) to compare themselves to others on Facebook for self-evaluation, self-enhancement, self-improvement, and self-destruction motives. Moreover, LSEs perceived increased levels of social comparison on Facebook, although the relationship between self-esteem and actual social comparison behavior proved non-significant. Significant moderating effects of self-esteem were observed in the relationship between motivation and affect. A self-improvement motive produced greater positive affect among HSEs compared to LSEs, while self-enhancement motives engendered positive affect more prominently among LSEs compared to HSEs. The paper also begins to distill a popular phenomenon, Facebook fatigue, in social comparison empirical work.


title: Idea Generation Techniques among Creative Professionals

abstract: The creative process has been a key topic research over the past century, but it wasn't until the last decade that creativity became a hot topic of research in the HCI. It is an important commodity to businesses and individuals alike spawning numerous research studies in business, psychology and design. However, it wasn't until recently that researchers became interested in developing technologies to support creative behavior. This article outlines the role of creativity in design from the designer's perspective, provides a model for the creative process and provides a foundation and direction for future creativity support research by identifying nineteen idea generation techniques utilized by creative professionals.


title: Active stabilization of a humanoid robot for impact motions with unknown reaction forces

abstract: During heavy work, humans utilize whole body motions in order to generate large forces. In extreme cases, exaggerated weight shifts are used to impart large impact forces. There have been approaches to design stable whole body impact motions based on precise dynamic models of the robot and the target object, but they have practical limitations as the uncertainty in the ensuing reaction forces can lead to instability. In the current work, we describe a motion controller for a humanoid robot that generates impacts at an end effector while keeping the robot body balanced before and after the impact. Instead of relying on the accuracy of the impact dynamics model, we use a simplified model of the robot and biomechanically motivated push recovery controllers to reactively stabilize the robot against unknown perturbations from the impact. We demonstrate our approach in physically realistic simulations, as well as experimentally on a small humanoid robot platform.


title: Development of the servant leadership assessment instrument

abstract: Purpose – Building upon Patterson's servant leadership theory, this study aims to present an instrument to measure the constructs of this working theory (identified as agapao love, humanity, altruism, vision, trust, service, and empowerment).Design/methodology/approach – Specifically, the seven component concepts, as defined by Patterson, were used to build items for a servant leadership instrument. This study used DeVellis' “Guidelines in Scale Development” to develop an instrument for Patterson's new theory of servant leadership. The participants for the study consisted of a stratified sample taken from the study response data base. The surveys were created, and administered, using an online survey using surveysuite.Findings – Three separate data collections were used for the development of this instrument reducing the 71‐item scale to 42 items yielding five factors: empowerment, love, humility, trust, and vision.Research limitations/implications – Recommend that future research include surveys at compa...


1 Autonomous Real-time Vehicle Detection from a Medium-Level UAV


title: Learning Traffic Light Parameters with Floating Car Data

abstract: The knowledge of traffic light parameters, such as cycle plan or future signal phase and timing information (SPaT) of traffic lights is the base for a vast number of use scenarios. A few examples are traffic signal adaptive routing, green light optimal speed control, red light duration advisory or efficient start-stop control. The basis for all these functionalities is the knowledge on the correct traffic light cycle time, i.e. the periodicity of the traffic light's signaling sequence. With a correct cycle time given, green start and end times can be derived from periodically reoccurring movement patterns. In this paper, we propose a method to reconstruct a traffic light's cycle plan through the interpretation of the recorded information on a vehicle's movement pattern (trajectory) in the intersection area. The recorded trajectories are temporarily sparse and and the cycle plan changes frequently. Therefore, we propose a model that focuses on the performance on very limited available trajectory data and yet is robust with regard to estimation errors. We show that our approach is able to detect the correct cycle time with already 30 trajectories at an accuracy of 99%.


title: Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks

abstract: The detection of bad weather conditions is crucial for meteorological centers, specially with demand for air, sea and ground traffic management. In this article, a system based on computer vision is presented which detects the presence of rain or snow. To separate the foreground from the background in image sequences, a classical Gaussian Mixture Model is used. The foreground model serves to detect rain and snow, since these are dynamic weather phenomena. Selection rules based on photometry and size are proposed in order to select the potential rain streaks. Then a Histogram of Orientations of rain or snow Streaks (HOS), estimated with the method of geometric moments, is computed, which is assumed to follow a model of Gaussian-uniform mixture. The Gaussian distribution represents the orientation of the rain or the snow whereas the uniform distribution represents the orientation of the noise. An algorithm of expectation maximization is used to separate these two distributions. Following a goodness-of-fit test, the Gaussian distribution is temporally smoothed and its amplitude allows deciding the presence of rain or snow. When the presence of rain or of snow is detected, the HOS makes it possible to detect the pixels of rain or of snow in the foreground images, and to estimate the intensity of the precipitation of rain or of snow. The applications of the method are numerous and include the detection of critical weather conditions, the observation of weather, the reliability improvement of video-surveillance systems and rain rendering.


title: Reduced out-of-band radiation-based filter optimization for UFMC systems in 5G

abstract: Universal-filtered multi-carrier (UFMC) technique is considered as a potential candidate for future communication systems due to its robustness against inter-carrier interference (ICI), suitability for non-contiguous fragmented available spectrum resources and low latency scenario in 5G network. In this paper, we present a novel pulse shaping approach in UFMC to reduce the spectral leakage into nearby subbands used for same or other users with low complexity and high throughput. In the new scheme, we apply Bohman filter-based pulse shaping with combination of antipodal symbol-pairs to the edge-subcarriers of the subbands, and consequently reduce the out-of-band radiation. This scheme outperforms the current state-of-the art and offers better signal-to-interference ratio (SIR) to improve the robustness against carrier frequency offset (CFO) for energy saving in loosely synchronized scenario. We further validate the proposed scheme on field programmable gate array (FPGA) hardware prototype.


title: Ground-Shielded Dual-Type High-Voltage Electrode for Corona Charging Applications

abstract: Corona discharge generated by various electrode arrangements is commonly employed for several electrostatic applications, such as charging nonwoven fabrics for air filters and insulating granules in electrostatic separators. The aim of this paper is to analyze the effects of the presence of a grounded metallic shield in the proximity of a high-voltage corona electrode facing a grounded plate electrode. The metallic shield was found to increase the current intensity and decrease the inception voltage of the corona discharge generated by this electrode arrangement, both in the absence and in the presence of a layer of insulating particles at the surface of the plate electrode. With the shield, the current density measured at the surface of the collecting electrode is higher and distributed on a larger area. As a consequence, the charge acquired by millimeter-sized HDPE particles forming a monolayer at the surface of the grounded plate electrode is twice as high as in the absence of the shield. These experiments are discussed in relation with the results of the numerical analysis of the electric field generated by the wire-plate configuration with and without shield.


title: Performance Tradeoffs Posed by Moving Magnet Actuators in Flexure-Based Nanopositioning

abstract: Moving magnet actuators (MMAs) are direct-drive, single-phase electromagnetic linear actuators that provide frictionless and backlash-free motion over a range of several millimeters. This paper investigates the feasibility of using MMAs to simultaneously achieve large range, high speed, and high motion quality in flexure-based nanopositioning systems. Component and system level design challenges and associated tradeoffs in meeting the aforementioned nanopositioning performance are discussed and derived. In particular, it is shown that even as the overall size of a traditional MMA is varied, the actuation force remains directly proportional to the square root of the actuator's moving magnet mass and the square root of power consumed. This proportionality constant, identified as the dynamic actuator constant, serves as a figure of merit for MMAs. When an MMA is employed in a flexure-based nanopositioning system, this constant directly impacts the system-level positioning performance in terms of range, resolution, speed, and temperature rise. This quantitative determination highlights the significance of incorporating a thermal management system for heat dissipation, minimizing noise and harmonic distortion in the current driver, and improving the force-stroke uniformity of the actuator. Based on this understanding, a single-axis nanopositioning system, which simultaneously achieves 10 mm range, 4 nm resolution, open-loop natural frequency of 25 Hz, and temperature rise of less than 0.5 °C, is designed, fabricated, and tested. Preliminary controller design and closed-loop operation highlight the potential and limitations of MMAs in large-range, high-speed nanopositioning.


title: The Theory of Probabilistic Databases

abstract: ABS!l’RACT A theory of probabilistic databases is outlined. This theory is one component of an integrated approach to data-modelling that accomodates both probabilistic and relational data. In fact, many of the results presented here were developed in the context of a framework for structural modelling of systems. Much that is fundamental to relational database theory was also developed in this context, and previous to the introduction by Codd of the relational model of data. Probabilistic databases can store types of information that cannot be represented using the relational model. Probabilistic databases may also be viewed as generalisations of relational databases; any relational database can be represented without loss of information by a probabilistic database. A number of relational database concepts are shown to have probabilistic counterparts. In many cases, it is preferable to deal with the probabilistic formulation of a concept even when applying it to a relational database. For example, we define a new project-join mapping for relational databases that is based on transf orming a relational to a probabilistic database. This mapping is shown to have more fmed points than the standard one.


title: Self-Regulation and Personality: How Interventions Increase Regulatory Success, and How Depletion Moderates the Effects of Traits on Behavior

abstract: Self-regulation is a highly adaptive, distinctively human trait that enables people to override and alter their responses, including changing themselves so as to live up to social and other standards. Recent evidence indicates that self-regulation often consumes a limited resource, akin to energy or strength, thereby creating a temporary state of ego depletion. This article summarizes recent evidence indicating that regular exercises in self-regulation can produce broad improvements in self-regulation (like strengthening a muscle), making people less vulnerable to ego depletion. Furthermore, it shows that ego depletion moderates the effects of many traits on behavior, particularly such that wide differences in socially disapproved motivations produce greater differences in behavior when ego depletion weakens the customary inner restraints.


title: Topology and Analysis of Three-Phalanx COSA Finger Based on Linkages for Humanoid Robot Hands

abstract: The design of a novel linkage three-phalanx finger was proposed in this paper. The finger is based on the concept of COSA (coupling and self-adaptation) and also two-phalanx COSA finger. As a combination of rigid coupled finger and self-adaptive under-actuated finger, the COSA finger has comprehensive grasping ability with human-like motion. Available topology designs were discussed in this paper and then mechanical design in details is given. Motion and grasping patterns of the finger were illustrated and compared with traditional coupled finger and under-actuated finger. Moreover, stability of grasp was simulated by static analysis. The three-phalanx finger with advantages of coupling and self-adaptation can be widely applied in hands for prosthetic limbs or humanoid robots.


title: Bayesian Optimization in a Billion Dimensions via Random Embeddings

abstract: Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high-dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple, has important invariance properties, and applies to domains with both categorical and continuous variables. We present a thorough theoretical analysis of REMBO. Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low. They also show that REMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver.


title: DyVEDeep: Dynamic Variable Effort Deep Neural Networks

abstract: Deep Neural Networks (DNNs) have advanced the state-of-the-art on a variety of machine learning tasks and are deployed widely in many real-world products. However, the compute and data requirements demanded by large-scale DNNs remains a significant challenge. In this work, we address this challenge in the context of DNN inference. We propose Dynamic Variable Effort Deep Neural Networks (DyVEDeep), which exploit the heterogeneity in the characteristics of inputs to DNNs to improve their compute efficiency while maintaining the same classification accuracy. DyVEDeep equips DNNs with dynamic effort knobs, which in course of processing an input, identify how critical a group of computations are to classify the input. DyVEDeep dynamically focuses its compute effort only on the critical computations, while the skipping/approximating the rest. We propose 3 effort knobs that operate at different levels of granularity viz. neuron, feature and layer levels. We build DyVEDeep versions for 5 popular image recognition benchmarks on 3 image datasets---MNIST, CIFAR and ImageNet. Across all benchmarks, DyVEDeep achieves 2.1X-2.6X reduction in number of scalar operations, which translates to 1.9X-2.3X performance improvement over a Caffe-based sequential software implementation, for negligible loss in accuracy.


title: Psychological Predictors of Problem Mobile Phone Use

abstract: Mobile phone use is banned or illegal under certain circumstances and in some jurisdictions. Nevertheless, some people still use their mobile phones despite recognized safety concerns, legislation, and informal bans. Drawing potential predictors from the addiction literature, this study sought to predict usage and, specifically, problematic mobile phone use from extraversion, self-esteem, neuroticism, gender, and age. To measure problem use, the Mobile Phone Problem Use Scale was devised and validated as a reliable self-report instrument, against the Addiction Potential Scale and overall mobile phone usage levels. Problem use was a function of age, extraversion, and low self-esteem, but not neuroticism. As extraverts are more likely to take risks, and young drivers feature prominently in automobile accidents, this study supports community concerns about mobile phone use, and identifies groups that should be targeted in any intervention campaigns.


title: Complexity of Culture: The Role of Identity and Context in Bicultural Individuals' Body Ideals

abstract: Culture plays an important role in shaping body image, and people from different cultures have different beliefs about what constitutes the “ideal” body type. This study examines the relationship between culture and body ideals in Asian-American and Black-American women. Results from two studies show that subjective cultural identity and situational cultural cues had different relationships with body ideals. Among Asian-American women, identification with Asian culture was related to a thinner body ideal, but exposure to Asian cultural cues (relative to American cultural cues) was related to a thicker body ideal. Among Black-American women, identification with Black culture was related to a thicker body ideal, but exposure to Black cultural cues (relative to American cultural cues) was related to a thinner body ideal. These results have theoretical and practical implications for understanding how internal and external manifestations of culture can differentially influence body image.


title: A Breadth-First Algorithm for Mining Frequent Patterns from Event Logs

abstract: Today, event logs contain vast amounts of data that can easily overwhelm a human. Therefore, the mining of frequent patterns from event logs is an important system and network management task. This paper discusses the properties of event log data, analyses the suitability of popular mining algorithms for processing event log data, and proposes an efficient algorithm for mining frequent patterns from event logs.


Portfolio Selection with Predicted Returns Using Neural Networks


title: Management Accounting Practices in ERP Environments: A Research Agenda

abstract: Today’s rapidly changing and highly competitive business environment, in conjunction with the technology expansion, put pressure on enterprises to search for new ways to survive and succeed. In order to ensure both their position in the market and a competitive advantage, a large number of companies -perhaps the majority- all around the world, has adopted or are in the process of implementing Enterprise resource planning (ERP hereafter) systems. ERPs have fundamentally re-shaped the operating practices of companies. The significant changes ERPs brought in enterprises have important implications for Management accounting (MA hereafter) and Management accountants as well, who may need to adjust their methods and procedures when working in such an environment. This study is motivated by the mixed results of recent academic works examining the effects of ERP systems on MA and by the desire to shed light on this subject matter. The principal objectives of the paper are firstly, identify major factors affecting the effectiveness of an ERP system based on an extensive search of the literature and secondly, provide a comprehensive framework for researching management accounting change in an ERP environment. Research questions are postulated and an appropriate research model is developed in order to test the hypothesized relationships. A number of radical changes on MA derived from ERPs, are focused on the following dimensions: accounting information quality, reports, innovative practices, methods, and advanced techniques, decision making process and accountants’ role and working tasks. User satisfaction with the ERP system, successful implementation of the ERP system, the extent of Business Process Re-engineering as well as of the Post Implementation Review have been identified as important dimensions of ERP systems’ impact on management accounting practices.


title: User Acceptance of the E-Government Services in Malaysia: Structural Equation Modelling Approach

abstract: This paper identifies the factors that determine users’ acceptance of e-Government services and its causal relationships using a theoretical model based on the Technology Acceptance Model. Data relating to the constructs were collected from 200 respondents in Malaysia and subjected to Structural Equation Modeling analysis. The proposed model fits the data well. Results indicate that the important determinants of user acceptance of the e-Government services are perceived usefulness, ease of use, compatibility, interpersonal influence, external influence, self efficacy, facilitating conditions, attitude, subjective norms, perceived behavioral control, and intention to use e-Government services/system. Finally, implications and recommendations of these finding are discussed.


Understanding organizational diversity management programs : A theoretical framework and directions for future research


title: Seeing, thinking, and feeling: Emotion-regulating effects of gaze-directed cognitive reappraisal.

abstract: Cognitive reappraisal (CR) is an emotion-regulatory (ER) process that is theorized to operate via changes in appraisals. CR is distinct from attentional deployment (AD), an ER process that is theorized to operate via changes in attention. However, a recent neuroimaging study has suggested that the ER effects of CR might largely be explained by AD. In this study, I manipulated CR while holding visual AD constant across CR conditions. In a randomized within-subjects design, 54 participants used CR to increase and decrease emotion in response to unpleasant pictures. This was compared with simply viewing the pictures. On all trials, gaze was directed to a circumscribed area of the pictures. Even with gaze held constant across conditions, increase reappraisals led to higher ratings of emotional intensity, greater corrugator activity, and greater autonomic arousal. In addition, decrease reappraisals led to lower ratings of emotional intensity and lower corrugator activity, the latter of which held only when gaze was directed to arousing information. Overall, the results suggest that changes in appraisal are the likely mechanism for the ER effects of CR.


title: A Neural Network Architecture for Detecting Grammatical Errors in Statistical Machine Translation

abstract: In this paper we present a Neural Network (NN) architecture for detecting grammatical er- rors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word rep- resentations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting over- all post-editing e ort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting over- all post-editing e ort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages.


title: Auditory Stimuli as Environmental Enrichment Tool for Family Dogs

abstract: Ten healthy pet dogs with an average maximum resting heart rate of 92 bpm that had never been used in studies of auditory stimulation were exposed randomly to one of three sound conditions on each of three occasions within a 5-day period. Posture and behaviour were recorded continuously by video for a total of 20 min over three phases: 5 min before sound exposure, 10 min during sound exposure, and 5 min after sound exposure. Each dog wore a Polar HR monitor throughout testing, and heart rate was recorded by using R–R interval data. Maximum heart rate was significantly greater during heartbeat sound exposure than afterwards, and average heart rate with both heartbeat sound and classical sound showed decrease tendencies.The time spent in dynamic (e.g. movement-related) postures was significantly higher before treatment than during or after under all three conditions. These data suggest that auditory stimuli in dogs may affect physiological responses without necessarily affecting behaviour, and specifically that heart rate may be elevated by auditory exposure to a faster heartbeat.


Linking Geospatial Data With GeoSPARQL


title: Bidirectional PWM Converter Integrating Cell Voltage Equalizer Using Series-Resonant Voltage Multiplier for Series-Connected Energy Storage Cells

abstract: In conventional energy storage systems using series-connected energy storage cells such as lithium-ion battery cells and supercapacitors (SCs), an interface bidirectional converter and cell voltage equalizer are separately required to manage charging/discharging and ensure years of safe operation. In this paper, a bidirectional PWM converter integrating cell voltage equalizer is proposed. This proposed integrated converter can be derived by combining a traditional bidirectional PWM converter and series-resonant voltage multiplier (SRVM) that functionally operates as an equalizer and is driven by asymmetric square wave voltage generated at the switching node of the converter. The converter and equalizer can be integrated into a single unit without increasing the switch count, achieving not only system-level but also circuit-level simplifications. Open-loop control is feasible for the SRVM when operated in discontinuous conduction mode, meaning the proposed integrated converter can operate similarly to conventional bidirectional converters. An experimental charge–discharge cycling test for six SCs connected in series was performed using the proposed integrated converter. The cell voltage imbalance was gradually eliminated by the SRVM while series-connected SCs were cycled by the bidirectional converter. All the cell voltages were eventually unified, demonstrating the integrated functions of the proposed converter.


title: Model compression via distillation and quantization

abstract: Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher, into the training of a student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to full-precision teacher models, while providing order of magnitude compression, and inference speedup that is linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.


title: Fuzzy association rule mining for community crime pattern discovery

abstract: Current manual inspection of crime data by analysts is limited, primarily due to the amount of data that can be processed concurrently and in a reasonable time frame. Further, complex relationships between various crime attributes can be overlooked by human analysts. Providing automated knowledge discovery tools becomes attractive to accelerate the efforts of local law enforcement. In this paper, we study the application of fuzzy association rule mining for community crime pattern discovery. Discovered rules are presented and discussed at regional and national levels. Rules found to hold in all states, be consistent across all regions, and subsets of regions are also discussed. A relative support metric was defined to extract rare, novel rules from thousands of discovered rules. Such an approach relieves the need of law enforcement personnel to sift through uninteresting, obvious rules in order to find interesting and meaningful crime patterns of importance to their community.


title: Absorptive capacity, technological innovation, and product life cycle: a system dynamics model

abstract: While past research has recognized the importance of the dynamic nature of absorptive capacity, there is limited knowledge on how to generate a fair and comprehensive analytical framework. Based on interviews with 24 Chinese firms, this study develops a system-dynamics model that incorporates an important feedback loop among absorptive capacity, technological innovation, and product life cycle (PLC). The simulation results reveal that (1) PLC affects the dynamic process of absorptive capacity; (2) the absorptive capacity of a firm peaks in the growth stage of PLC, and (3) the market demand at different PLC stages is the main driving force in firms’ technological innovations. This study also explores a sensitivity simulation using the variables of (1) time spent in founding an external knowledge network, (2) research and development period, and (3) knowledge diversity. The sensitivity simulation results show that the changes of these three variables have a greater impact on absorptive capacity and technological innovation during growth and maturity stages than in the introduction and declining stages of PLC. We provide suggestions on how firms can adjust management policies to improve their absorptive capacity and technological innovation performance during different PLC stages.


title: The Scientific Attack on Brown v. Board of Education, 1954-1964.

abstract: Psychologists' work was cited in the Supreme Court case of Brown v. Board of Education (1954). One criticism of the citation was that psychology could be used to overturn the Brown decision and return the country to segregation. A historical examination of such an attempt to overturn Brown in the early 1960s on the basis of new psychological knowledge shows that psychology was not persuasive in the face of the civil rights movement. The failure of segregationists to overturn Brown with psychological experts underscores how psychology is ineluctably bound to the larger society.


title: Precuneus shares intrinsic functional architecture in humans and monkeys.

abstract: Evidence from macaque monkey tracing studies suggests connectivity-based subdivisions within the precuneus, offering predictions for similar subdivisions in the human. Here we present functional connectivity analyses of this region using resting-state functional MRI data collected from both humans and macaque monkeys. Three distinct patterns of functional connectivity were demonstrated within the precuneus of both species, with each subdivision suggesting a discrete functional role: (i) the anterior precuneus, functionally connected with the superior parietal cortex, paracentral lobule, and motor cortex, suggesting a sensorimotor region; (ii) the central precuneus, functionally connected to the dorsolateral prefrontal, dorsomedial prefrontal, and multimodal lateral inferior parietal cortex, suggesting a cognitive/associative region; and (iii) the posterior precuneus, displaying functional connectivity with adjacent visual cortical regions. These functional connectivity patterns were differentiated from the more ventral networks associated with the posterior cingulate, which connected with limbic structures such as the medial temporal cortex, dorsal and ventromedial prefrontal regions, posterior lateral inferior parietal regions, and the lateral temporal cortex. Our findings are consistent with predictions from anatomical tracer studies in the monkey, and provide support that resting-state functional connectivity (RSFC) may in part reflect underlying anatomy. These subdivisions within the precuneus suggest that neuroimaging studies will benefit from treating this region as anatomically (and thus functionally) heterogeneous. Furthermore, the consistency between functional connectivity networks in monkeys and humans provides support for RSFC as a viable tool for addressing cross-species comparisons of functional neuroanatomy.


title: Mind-sets matter: A meta-analytic review of implicit theories and self-regulation.

abstract: This review builds on self-control theory (Carver & Scheier, 1998) to develop a theoretical framework for investigating associations of implicit theories with self-regulation. This framework conceptualizes self-regulation in terms of 3 crucial processes: goal setting, goal operating, and goal monitoring. In this meta-analysis, we included articles that reported a quantifiable assessment of implicit theories and at least 1 self-regulatory process or outcome. With a random effects approach used, meta-analytic results (total unique N 28,217; k 113) across diverse achievement domains (68% academic) and populations (age range 5–42; 10 different nationalities; 58% from United States; 44% female) demonstrated that implicit theories predict distinct self-regulatory processes, which, in turn, predict goal achievement. Incremental theories, which, in contrast to entity theories, are characterized by the belief that human attributes are malleable rather than fixed, significantly predicted goal setting (performance goals, r .151; learning goals, r .187), goal operating (helpless-oriented strategies, r .238; masteryoriented strategies, r .227), and goal monitoring (negative emotions, r .233; expectations, r .157). The effects for goal setting and goal operating were stronger in the presence (vs. absence) of ego threats such as failure feedback. Discussion emphasizes how the present theoretical analysis merges an implicit theory perspective with self-control theory to advance scholarship and unlock major new directions for basic and applied research.


title: Self-referenced laser system for optical 3D seafloor mapping

abstract: Underwater high resolution 3D mapping implies a very accurate sensor pose estimation to fuse the sensor readings over time into one consistent model. In the case of optical mapping systems, the needed accuracy can easily lie outside of the specification of Doppler Velocity Logs normally used for pose estimation by remotely operated and autonomous underwater vehicles. This is especially the case for difficult terrains or for low altitude situations. Therefore, to improve performance in such situations, a self-referenced laser system is proposed performing micro navigation to significantly improve the resolution of optical underwater 3D seabed mapping.


title: Social Information-Processing Mechanisms in Reactive and Proactive Aggression.

abstract: Theories of aggressive behavior and ethological observations in animals and children suggest the existence of distinct forms of reactive (hostile) and proactive (instrumental) aggression. Toward the validation of this distinction, groups of reactive aggressive, proactive aggressive, and nonaggressive children were identified (n= 624 9–12-year-olds). Social information-processing patterns were assessed in these groups by presenting hypothetical vignettes to subjects. 3 hypotheses were tested: (1) only the reactive-aggressive children would demonstrate hostile biases in their attributions of peers' intentions in provocation situations (because such biases are known to lead to reactive anger); (2) only proactive-aggressive children would evaluate aggression and its consequences in relatively positive ways (because proactive aggression is motivated by its expected external outcomes); and (3) proactive-aggressive children would select instrumental social goals rather than relational goals more often than nonaggressive children. All 3 hypotheses were at least partially supported.


title: Cellular Automata based Decentralized Cooperative Collision Avoidance Control for Multiple Mobile Robots

abstract: One of the main challenges in the decentralized implementation of mobile multi-robotic systems is the avoidance of collisions among agents and other obstacles in cluttered environments as well as the convergence of each robot to its desired destination. This paper presents a novel decentralized, cooperative navigation algorithm for a team of mobile robots based on the concept of cellular automata that is proven to guarantee collision avoidance at all times even under the presence of static non-cooperative obstacles. The algorithm does not need to differentiate among agents and obstacles and use a time-varying localization-based priority rule to guarantee safe motion while reducing the occurrence of deadlocks. A Monte Carlo simulation is performed showcasing the performance of the algorithm under different densities of obstacles and agents.


title: How do software engineers understand code changes?: an exploratory study in industry

abstract: Software evolves with continuous source-code changes. These code changes usually need to be understood by software engineers when performing their daily development and maintenance tasks. However, despite its high importance, such change-understanding practice has not been systematically studied. Such lack of empirical knowledge hinders attempts to evaluate this fundamental practice and improve the corresponding tool support. To address this issue, in this paper, we present a large-scale quantitative and qualitative study at Microsoft. The study investigates the role of understanding code changes during software-development process, explores engineers' information needs for understanding changes and their requirements for the corresponding tool support. The study results reinforce our beliefs that understanding code changes is an indispensable task performed by engineers in software-development process. A number of insufficiencies in the current practice also emerge from the study results. For example, it is difficult to acquire important information needs such as a change's completeness, consistency, and especially the risk imposed by it on other software components. In addition, for understanding a composite change, it is valuable to decompose it into sub-changes that are aligned with individual development issues; however, currently such decomposition lacks tool support.


title: Deep neural networks are robust to weight binarization and other non-linear distortions

abstract: Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we show that this is just the tip of the iceberg: these same networks, during testing, also exhibit a remarkable robustness to distortions beyond quantization, including additive and multiplicative noise, and a class of non-linear projections where binarization is just a special case. To quantify this robustness, we show that one such network achieves 11% test error on CIFAR-10 even with 0.68 effective bits per weight. Furthermore, we find that a common training heuristic--namely, projecting quantized weights during backpropagation--can be altered (or even removed) and networks still achieve a base level of robustness during testing. Specifically, training with weight projections other than quantization also works, as does simply clipping the weights, both of which have never been reported before. We confirm our results for CIFAR-10 and ImageNet datasets. Finally, drawing from these ideas, we propose a stochastic projection rule that leads to a new state of the art network with 7.64% test error on CIFAR-10 using no data augmentation.


title: Microblog Sentiment Classification Using Parallel SVM in Apache Spark

abstract: In the information age, sentiment classification of internet topics is of great significance. This paper proposes a microblog sentiment classification approach with parallel support vector machine (SVM). The proposed method integrates the features of microblog with preprocessing to ensure the data suitable for sentiment classification. After the preprocessing process, Apache Spark parallel SVM is used to execute the classification. SVM is one of the most popular algorithms in text classification. It fits small scale and nonlinear problems. However, SVM takes very long when dealing with big data. We apply Spark to parallelize SVM with Radial Basis Function (RBF) kernel function. The introduction of Apache Spark results in outstanding performance in machine learning compared to Hadoop. The experiments show that Spark increases the execution speed of SVM significantly. At the same time the classification accuracy is also increased by information gain (IG) approach in the preprocessing and kernel function parameter selection.


title: An Amharic Stemmer : Reducing Words to their Citation Forms

abstract: Stemming is an important analysis step in a number of areas such as natural language processing (NLP), information retrieval (IR), machine translation(MT) and text classification. In this paper we present the development of a stemmer for Amharic that reduces words to their citation forms. Amharic is a Semitic language with rich and complex morphology. The application of such a stemmer is in dictionary based cross language IR, where there is a need in the translation step, to look up terms in a machine readable dictionary (MRD). We apply a rule based approach supplemented by occurrence statistics of words in a MRD and in a 3.1M words news corpus. The main purpose of the statistical supplements is to resolve ambiguity between alternative segmentations. The stemmer is evaluated on Amharic text from two domains, news articles and a classic fiction text. It is shown to have an accuracy of 60% for the old fashioned fiction text and 75% for the news articles.


CRANIOMETRIC ANALYSIS OF NIGERIAN POPULATION


Secure Digital Image Watermarking using DWT , SVD and Chaotic Encryption with Genetic Algorithm


title: Variational Inference for Policy Search in changing situations

abstract: Many policy search algorithms minimize the Kullback-Leibler (KL) divergence to a certain target distribution in order to fit their policy. The commonly used KL-divergence forces the resulting policy to be 'reward-attracted'. The policy tries to reproduce all positively rewarded experience while negative experience is neglected. However, the KL-divergence is not symmetric and we can also minimize the the reversed KL-divergence, which is typically used in variational inference. The policy now becomes 'cost-averse'. It tries to avoid reproducing any negatively-rewarded experience while maximizing exploration. ::: ::: Due to this 'cost-averseness' of the policy, Variational Inference for Policy Search (VIP) has several interesting properties. It requires no kernel-band with nor exploration rate, such settings are determined automatically by the inference. The algorithm meets the performance of state-of-the-art methods while being applicable to simultaneously learning in multiple situations. ::: ::: We concentrate on using VIP for policy search in robotics. We apply our algorithm to learn dynamic counterbalancing of different kinds of pushes with human-like 2-link and 4-link robots.


title: Towards 3D object recognition via classification of arbitrary object tracks

abstract: Object recognition is a critical next step for autonomous robots, but a solution to the problem has remained elusive. Prior 3D-sensor-based work largely classifies individual point cloud segments or uses class-specific trackers. In this paper, we take the approach of classifying the tracks of all visible objects. Our new track classification method, based on a mathematically principled method of combining log odds estimators, is fast enough for real time use, is non-specific to object class, and performs well (98.5% accuracy) on the task of classifying correctly-tracked, well-segmented objects into car, pedestrian, bicyclist, and background classes. We evaluate the classifier's performance using the Stanford Track Collection, a new dataset of about 1.3 million labeled point clouds in about 14,000 tracks recorded from an autonomous vehicle research platform. This dataset, which we make publicly available, contains tracks extracted from about one hour of 360-degree, 10Hz depth information recorded both while driving on busy campus streets and parked at busy intersections.


title: Performance-Oriented Coordinated Adaptive Robust Control for Four-Wheel Independently Driven Skid Steer Mobile Robot

abstract: Four-wheel independently driven skid-steered mobile robots are widely used in the fields of industrial automation and outdoor exploration. In most of existing controllers of skid-steered mobile robots, the wheel velocities are controlled independently to track the desired velocities from the high-level kinematic controller. However, this kind of control method may lead to chattering phenomenon of skid-steered mobile robots in practice, when the desired velocity commands of four wheels are not matched under different ground conditions. In this paper, the coordinated control problem is investigated for the four-wheel independently driven skid steer mobile robots, so as to solve the chattering phenomenon and also achieve good control performance under different ground conditions. Since the mobile robots are over-actuated and lack of suspension systems, a coordinated adaptive robust control scheme integrated with torque allocation technique is proposed. First, an adaptive robust control law is developed to attenuate the negative effects of load variations and uncertainties. Second, instead of directly giving the desired velocity commands, a torque control and allocation algorithm is developed to regulate the driving torque of each wheel motor. A coordinated control law with considering the wheel slip compensation is also proposed. Comparative experiments are carried out, and the results show the proposed scheme can avoid the chattering problem and achieve the excellent performance under different ground conditions.


title: Causal Reasoning from Meta-reinforcement Learning

abstract: Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether causal reasoning can emerge via meta-reinforcement learning. We train a recurrent network with model-free reinforcement learning to solve a range of problems that each contain causal structure. We find that the trained agent can perform causal reasoning in novel situations in order to obtain rewards. The agent can select informative interventions, draw causal inferences from observational data, and make counterfactual predictions. Although established formal causal reasoning algorithms also exist, in this paper we show that such reasoning can arise from model-free reinforcement learning, and suggest that causal reasoning in complex settings may benefit from the more end-to-end learning-based approaches presented here. This work also offers new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform -- and interpret -- experiments.


title: Noncespaces: Using Randomization to Enforce Information Flow Tracking and Thwart Cross-Site Scripting Attacks

abstract: Cross-site scripting (XSS) vulnerabilities are among the most common and serious web application vulnerabilities. Eliminating XSS is challenging because it is difficult for web applications to sanitize all user inputs appropriately. We present Noncespaces, a technique that enables web clients to distinguish between trusted and untrusted content to prevent exploitation of XSS vulnerabilities. Using Noncespaces, a web application randomizes the XML namespace prefixes of tags in each document before delivering it to the client. As long as the attacker is unable to predict the randomized prefixes, the client can distinguish between trusted content created by the web application and untrusted content provided by an attacker. To implement Noncespaces with minimal changes to web applications, we leverage a popular web application architecture to automatically apply Noncespaces to static content processed through a popular PHP template engine. We show that with simple policies Noncespaces thwarts popular XSS attack vectors.


title: Nonlinear model predictive control using Feedback Linearization and local inner convex constraint approximations

abstract: Model predictive control (MPC) is one of the most popular advanced control techniques and is used widely in industry. The main drawback with MPC is that it is fairly computationally expensive and this has so far limited its practical use for nonlinear systems. To reduce the computational burden of nonlinear MPC, Feedback Linearization together with linear MPC has been used successfully to control nonlinear systems. The main drawback is that this results in an optimization problem with nonlinear constraints on the control signal. In this paper we propose a method to handle the nonlinear constraints that arises using a set of dynamically generated local inner polytopic approximations. The main benefits of the proposed method is guaranteed recursive feasibility and convergence.


title: A clustering rule-based approach to predictive modeling

abstract: Recent discoveries using rule-based classifiers and pre-learning data clustering have helped improve classification accuracy in predictive modeling tasks. This research introduces a unique approach which combines the above techniques and studies its predictive effects. The algorithm presented in this research, a Clustering Rule-based Algorithm (CRA), first clusters the original training set using an Expectation Maximization (EM) algorithm. Then, a separate Classification and Regression Tree (CART) is trained on each individual cluster. To obtain an upper-bound on accuracy, each test instance is evaluated against all of the rules produced by each separate Tree, to determine if there exists a rule produced by one of the Trees which correctly classifies the test instance. This study reveals that a predictive accuracy of 100% was achievable. Moreover, this approach exploits the advantages of supervised and unsupervised learning to produce a more powerful and more accurate predictive model.


title: A Comparison of Class Imbalance Techniques for Real-World Landslide Predictions

abstract: Landslides cause lots of damage to life and property world over. There has been research in machine-learning that aims to predict landslides based on the statistical analysis of historical landslide events and its triggering factors. However, prediction of landslides suffers from a class-imbalance problem as landslides and land-movement are very rare events. In this paper, we apply state-of-the-art techniques to correct the class imbalance in landslide datasets. More specifically, to overcome the class-imbalance problem, we use different synthetic and oversampling techniques to a real-world landslide data collected from the Chandigarh - Manali highway. Also, we apply several machine-learning algorithms to the landslide data set for predicting landslides and evaluating our algorithms. Different algorithms have been assessed using techniques like the area under the ROC curve (AUC) and sensitivity index (d'). Results suggested that random forest algorithm performed better compared to other classification techniques like neural networks, logistic regression, support vector machines, and decision trees. Furthermore, among class-imbalance methods, the Synthetic Minority Oversampling Technique with iterative partitioning filter (SMOTE-IPF) performed better than other techniques. We highlight the implications of our results and methods for predicting landslides in the real world.


Named Entity Recognition using Word Embedding as a Feature


title: Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

abstract: The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.


title: An Optimization-Based DC-Network Reduction Method

abstract: Reduced order models of dc networks using Ward, REI, and bus-aggregation approaches have been used to advantage for some time. Proposed in this paper is an optimization-based framework that can easily be modified to generate a Ward reduction, a bus-aggregation-based reduction, or a range of hybrid reductions between these two extremes, generating reduced-order models that are tailored to the purpose of the study. The so-called optimization-based Ward reduction method is demonstrated to be numerically stable on practical examples and is specifically applied to the problem of large-reactance branch elimination. We show that it offers accuracy advantages over the traditional approach of removing high-reactance branches using a threshold criterion.


title: Walking on minimax paths for k-NN search

abstract: Link-based dissimilarity measures, such as shortest path or Euclidean commute time distance, base their distance on paths between nodes of a weighted graph. These measures are known to be better suited to data manifold with nonconvex-shaped clusters, compared to Euclidean distance, so that k-nearest neighbor (NN) search is improved in such metric spaces. In this paper we present a new link-based dissimilarity measure based on minimax paths between nodes. Two main benefits of minimax path-based dissimilarity measure are: (1) only a subset of paths is considered to make it scalable, while Euclidean commute time distance considers all possible paths; (2) it better captures nonconvex-shaped cluster structure, compared to shortest path distance. We define the total cost assigned to a path between nodes as Lp norm of intermediate costs of edges involving the path, showing that minimax path emerges from our Lp norm over paths framework. We also define minimax distance as the intermediate cost of the longest edge on the minimax path, then present a greedy algorithm to compute k smallest minimax distances between a query and N data points in O(logN + k log k) time. Numerical experiments demonstrate that our minimax k- NN algorithm reduce the search time by several orders of magnitude, compared to existing methods, while the quality of k-NN search is significantly improved over Euclidean distance.


title: Cooperative behavior control of robot group using stress antibody allotment reward

abstract: Lately, development in robotics for utilizing in both industry and home is in much progress. In this research, a group of robots is made to handle relatively complicated tasks. Cooperative action among robots is one of the research areas in robotics that is progressing remarkably well. Reinforcement learning is known as a common approach in robotics for deploying acquisition of action under dynamic environment. However, until recently, reinforcement learning is only applied to one agent problem. In multi-agent environment where plural robots exist, it was difficult to differentiate between learning of achievement of task and learning of performing cooperative action. This paper introduces a method of implementing reinforcement learning to induce cooperation among a group of robots where its task is to transport luggage of various weights to a destination. The general Q-learning method is used as a learning algorithm. Also, the switching of learning mode is proposed for reduction of learning time and learning area. Finally, grid world simulation is carried out to evaluate the proposed methods.


title: Integration of an On-line Kaldi Speech Recogniser to the Alex Dialogue Systems Framework

abstract: This paper describes the integration of an on-line Kaldi speech recogniser into the Alex Dialogue Systems Framework (ADSF). As the Kaldi OnlineLatgenRecogniser is written in C++, we first developed a Python wrapper for the recogniser so that the ADSF, written in Python, could interface with it. Training scripts for acoustic and language modelling were developed and integrated into ADSF, and acoustic and language models were build. Finally, optimal recogniser parameters were determined and evaluated. The dialogue system Alex with the new speech recogniser is evaluated on Public Transport Information (PTI) domain.


title: The o.d.e. method for convergence of stochastic approximation and reinforcement learning

abstract: It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergence of the algorithm. Several specific classes of algorithms are considered as applications. It is found that the results provide (i) a simpler derivation of known results for reinforcement learning algorithms; (ii) a proof for the first time that a class of asynchronous stochastic approximation algorithms are convergent without using any a priori assumption of stability; (iii) a proof for the first time that asynchronous adaptive critic and Q-learning algorithms are convergent for the average cost optimal control problem.


title: Turk J Elec Engin, VOL.15, NO.1 2007, c ○ TÜB˙ITAK Offline Signature Verification Using Graph Matching

abstract: In this paper, we present a simple and effective signature verification method that depends only on the raw binary pixel intensities and avoids using complex sets of features. The method looks at the signature verification problem as a graph matching problem. The method is tested using genuine and forgery signatures produced by five subjects. An equal error rate of 26.7% and 5.6% was achieved for skilled and random forgeries, respectively. A positive property of our algorithm is that the false acceptance rate of random forgeries vanishes at the point of equal false rejection and skilled forgery false acceptance rates. Keeping the normalization size at 32 × 64 pixels makes the verification time in the two seconds range.


title: Holding Phenomenon of Annual Ring in Sugi (Cryptomeria japonica D. Don.) Specimen under Large Compression with Closed-Die Forging Method.

abstract: The large increase in strength properties of wooden specimens arises from a large compression using the closed-die forging method. Upon compression of a wooden specimen with inclined annual rings, folding of the annual rings takes place. Despite the knowledge of a fundamental deformation mode of the wooden specimen that was subjected to large compression perpendicular to grain, conditions leading to folding of annual rings are still debated. Here we report the holding behavior of annual rings in wooden specimens cut out from Sugi (Japanese Cedar, Cryptorneria japonica D. DON.) log, under large compression using the closed-die forging method. Our experiments demonstrated that, when the mean ring angle between the loading direction and an annual ring is less than 45°, holding occurs. Observations by scanning electron microscopy showed that the holding of latewood in the annual ring propagates to that in a neighboring annual ring via the rays. From these observations, we inferred that holding of annual rings propagates to the center of log. The validity of a general criterion for the determination of conditions leading to holding based on the inferred result was confirmed experimentally.


Parallel big data processing system for security monitoring in Internet of Things networks


title: Deep temporal models and active inference

abstract: How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.


title: INVITED PAPER Special Issue on Measurements and Visualization Technology of Biological Information Fiber Tract Following in the Human Brain Using DT-MRI Data

abstract: SUMMARY In Vivo Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) can now be used to elucidate and investigate major nerve pathways in the brain. Nerve pathways are constructed bya) calculating a continuous diffusion tensor field from the discrete, noisy, measured DT-MRI data and then b) solving an equation describing the evolution of a fiber tract, in which the local direction vector of the trajectoryis identified with the direction of maximum apparent diffusivity. This approach has been validated previouslyusing sy nthesized, noisy DT-MRI data. Presently, it is possible to reconstruct large white matter structures in the brain, such as the corpus callosum and the pyramidal tracts. Several problems, however, still affect the method’s reliability. Its accuracy degrades where the fiber-tract directional distribution is non-uniform, and background noise in diffusion weighted MRIs can cause computed trajectories to jump to different tracts. Nonetheless, this method can provide quantitative information with which to visualize and studyconnectivity and continuityof neural pat hways in the central and peripheral nervous systems in vivo, and holds promise for elucidating archi


title: LSD: a Line Segment Detector

abstract: LSD is a linear-time Line Segment Detector giving subpixel accurate results. It is designed to work on any digital image without parameter tuning. It controls its own number of false detections: on average, one false alarm is allowed per image [1]. The method is based on Burns, Hanson, and Riseman’s method [2], and uses an a contrario validation approach according to Desolneux, Moisan, and Morel’s theory [3, 4]. The version described here includes some further


title: Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution

abstract: Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR). However, most CNN-based SR methods neglect the different importance among feature channels or fail to take full advantage of the hierarchical features. To address these issues, this paper presents a novel recursive unit. Firstly, at the beginning of each unit, we adopt a compact channel attention mechanism to adaptively recalibrate the channel importance of input features. Then, the multi-level features, rather than only deep-level features, are extracted and fused. Additionally, we find that it will force our model to learn more details by using the learnable upsampling method (i.e., transposed convolution) only on residual branch (instead of using it both on residual branch and identity branch) while using the bicubic interpolation on the other branch. Analytic experiments show that our method achieves competitive results compared with the state-of-the-art methods and maintains faster speed as well.


title: Deep regression for monocular camera-based 6-DoF global localization in outdoor environments

abstract: Precise localization of robots is imperative for their safe and autonomous navigation in both indoor and outdoor environments. In outdoor scenarios, the environment typically undergoes significant perceptual changes and requires robust methods for accurate localization. Monocular camera-based approaches provide an inexpensive solution to such challenging problems compared to 3D LiDAR-based methods. Recently, approaches have leveraged deep convolutional neural networks (CNNs) to perform place recognition and they turn out to outperform traditional handcrafted features under challenging perceptual conditions. In this paper, we propose an approach for directly regressing a 6-DoF camera pose using CNNs and a single monocular RGB image. We leverage the idea of transfer learning for training our network as this technique has shown to perform better when the number of training samples are not very high. Furthermore, we propose novel data augmentation in 3D space for additional pose coverage which leads to more accurate localization. In contrast to the traditional visual metric localization approaches, our resulting map size is constant with respect to the database. During localization, our approach has a constant time complexity of O(1) and is independent of the database size and runs in real-time at ∼80 Hz using a single GPU. We show the localization accuracy of our approach on publicly available datasets and that it outperforms CNN-based state-of-the-art methods.


title: Antecedents of perceived playfulness: an exploratory study on user acceptance of general information-searching websites

abstract: Understanding user acceptance of the Internet, in particular user acceptance of general information-searching sites, is important in assessing its effectiveness. Such sites provide users with various information options while navigating the Internet. A well-designed general information-searching site helps to ensure a positive user experience and therefore repeat usage. This study extended existing research into user acceptance of the Internet. It explored the antecedents of Playfulness. Results indicated that website characteristics play a dominant role in influencing a user's experience of Playfulness and that cognitive aspects and motivations for searching are also important. Implications are discussed.


title: Treatment planning of adhesive additive rehabilitations: the progressive wax-up of the three-step technique.

abstract: Abstract A full-mouth rehabilitation should be correctly planned from the start by using a diagnostic wax-up to reduce the potential for remakes, increased chair time, and laboratory costs. However, determining the clinical validity of an extensive wax-up can be complicated for clinicians who lack the experience of full-mouth rehabilitations. The three-step technique is a simplified approach that has been developed to facilitate the clinician's task. By following this technique, the diagnostic wax-up is progressively developed to the final outcome through the interaction between patient, clinician, and laboratory technician. This article provides guidelines aimed at helping clinicians and laboratory technicians to become more proactive in the treatment planning of full-mouth rehabilitations, by starting from the three major parameters of incisal edge position, occlusal plane position, and the vertical dimension of occlusion.


title: Big Data: Promises and Problems

abstract: Despite some key problems, big data could fundamentally change scientific research methodology and how businesses develop products and provide services.


title: A Tradeoff Analysis of FPGAs, GPUs, and Multicores for Sliding-Window Applications

abstract: The increasing usage of hardware accelerators such as Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) has significantly increased application design complexity. Such complexity results from a larger design space created by numerous combinations of accelerators, algorithms, and hw/sw partitions. Exploration of this increased design space is critical due to widely varying performance and energy consumption for each accelerator when used for different application domains and different use cases. To address this problem, numerous studies have evaluated specific applications across different architectures. In this article, we analyze an important domain of applications, referred to as sliding-window applications, implemented on FPGAs, GPUs, and multicore CPUs. For each device, we present optimization strategies and analyze use cases where each device is most effective. The results show that, for large input sizes, FPGAs can achieve speedups of up to 5.6× and 58× compared to GPUs and multicore CPUs, respectively, while also using up to an order of magnitude less energy. For small input sizes and applications with frequency-domain algorithms, GPUs generally provide the best performance and energy.


title: Ubiquitin C gene: Structure, function, and transcriptional regulation

abstract: Ubiquitin C (UbC) is one of the four genes encoding for ubiquitin in the mammalian genome. It has been described as the most responsive gene to cellular treats such as UV irradiation, heat shock, oxidative stress and translational impairment; it was also reported to contribute to maintaining ubiquitin steady state levels under physiological conditions. Despite the bulk of knowledge concerning its function, little is known about the molecular mechanisms modulating UbC expression. Here we review the state of the art of UbC structure, function and transcriptional regulation. Starting from the first evidences which circumscribed the genomic region, pointing out both basic promoter marks (such as transcription start site and TATA-like element), and transcript structure (exonintron boundaries) we go through more detailed molecular studies performed by Marinovic in 2002 and by Bianchi et al. in 2009 and 2013. Herein, the key players orchestrating UbC gene basal activity are underlined.


title: High quality compatible triangulations for 2D shape morphing

abstract: We propose a new method to compute compatible triangulations of two polygons in order to create a smooth geometric transformation between them. Compared with existing methods, our approach creates triangulations of better quality, that is, triangulations with fewer long thin triangles and Steiner points. This results in visually appealing morphing when transforming the shape from one to another. Our method consists of three stages. First, we compatibly decompose the target and source polygons into a set of sub-polygons, in which each source sub-polygon is triangulated. Second, we map the triangulation of a source sub-polygon onto the corresponding sub-polygon of the target polygon using linear transformation, thereby generating the compatible meshes between the source and the target. Third, we refine the compatible meshes, which can create better quality planar shape morphing with detailed textures. Experimental results show that our method can create compatible meshes of higher quality compared with existing methods, which facilitates smoother morphing process. The proposed algorithm is robust and computationally efficient. It can be applied to produce convincing transformations such as interactive 2D animation creation and special effects in movies.


title: Class H Power Amplifier for Power Saving in Fluxgate Current Transducers

abstract: This paper presents a new improvement in the design of a fluxgate-based current transducer in order to reduce the power consumption of control electronics. The proposed improvement involves the replacement of the output linear amplifier of the transducer by a class H amplifier. The output amplifier is devoted to the magnetic flux compensation and generates the transducer output current, which is proportional to the current to be measured. In this way, it is possible to reduce significantly the power consumption of these current transducers without affecting their performance in terms of linearity, accuracy, and bandwidth.


title: Sensed presence and mystical experiences are predicted by suggestibility, not by the application of transcranial weak complex magnetic fields

abstract: Transcranial magnetic stimulation (TMS) with weak (micro Tesla) complex waveform fields have been claimed to evoke the sensed presence of a sentient being in up to 80% in the general population. These findings have had a questionable neurophysiological foundation as the fields are approximately six orders of magnitude weaker than ordinary TMS fields. Also, no independent replication has been reported. To replicate and extend previous findings, we performed a double-blind experiment (N = 89), with a sham-field control group. Personality characteristics indicating suggestibility (absorption, signs of abnormal temporal lobe activity, and a "new age"-life-style orientation) were used as predictors. Sensed presence, mystical, and other somatosensory experiences previously reported from the magnetic field stimulation were outcome measures. We found no evidence for any effects of the magnetic fields, neither in the entire group, nor in individuals high in suggestibility. Because the personality characteristics significantly predicted outcomes, suggestibility may account for previously reported effects. Our results strongly question the earlier claims of experiential effects of weak magnetic fields. (Less)