title: Self-compassion and relationship maintenance: The moderating roles of conscientiousness and gender

abstract: Should intimates respond to their interpersonal mistakes with self-criticism or with self-compassion? Although it is reasonable to expect self-compassion to benefit relationships by promoting self-esteem, it is also reasonable to expect self-compassion to hurt relationships by removing intimates’ motivation to correct their interpersonal mistakes. Two correlational studies, 1 experiment, and 1 longitudinal study demonstrated that whether self-compassion helps or hurts relationships depends on the presence versus absence of dispositional sources of the motivation to correct interpersonal mistakes. Among men, the implications of self-compassion were moderated by conscientiousness. Among men high in conscientiousness, self-compassion was associated with greater motivation to correct interpersonal mistakes (Studies 1 and 3), observations of more-constructive problem-solving behaviors (Study 2), reports of more accommodation (Study 3), and fewer declines in marital satisfaction that were mediated by decreases in interpersonal problem severity (Study 4); among men low in conscientiousness, self-compassion was associated with these outcomes in the opposite direction. Among women, in contrast, likely because women are inherently more motivated than men to preserve their relationships for cultural and/or biological reasons, self-compassion was never harmful to the relationship. Instead, women’s self-compassion was positively associated with the motivation to correct their interpersonal mistakes (Study 1) and changes in relationship satisfaction (Study 4), regardless of conscientiousness. Accordingly, theoretical descriptions of the implications of self-promoting thoughts for relationships may be most complete to the extent that they consider the presence versus absence of other sources of the motivation to correct interpersonal mistakes.


title: Big data framework for students' academic performance prediction: A systematic literature review

abstract: Big Data is becoming an integral part of education system worldwide, bringing in so much of prediction potential and therefore opportunities to improve learning and teaching methodologies. In fact, it has become the digital policy instrument for policy makers to make strategic decisions supported by big data analytics. This paper uses Systematic Literature Review (SLR) to establish a general overview and background in establishing gaps that need to be addressed in big data analytics for education landscape. A total of 59 research papers from 473 papers were chosen and analyzed. There is an increased trend in incorporating big data in education however, it is not sufficient and need more research in this area particularly predictive models and frameworks for educational settings.


title: ANT and After: What’s New for IS Research? Actor Network Theory and After: What’s New For IS Research?

abstract: The basic argument presented in the paper is that actor network theory has often been forced to adopt the ontology of interpretivism and thus suppress its own ontology. Interpretivism is socio-constructivist in nature but ANT is not. Therein lies the gap, a gap that is traversed by using an assumed flexibility of ANT which thus allows it to be used as a lens within the interpretivist ontology. The ontology of interpretvism deems the interpreter to be in a position to construct reality in his/her mind whereas, ANT accentuates that reality is actually constructed by the interplay of more actors (both human and non human) and this reality emerges from this interaction. Therefore for interpretivism, reality is created in the mind and for ANT, reality emerges ‘out there’. These differences in the ontological foundation of ANT and interpretivis must be taken into consideration when ANT is used to inform research in IS. These differences are in fact defining the object of the analysis and hence its outcome.


title: Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

abstract: Traditional graph-based semi-supervised learning (SSL) approaches, even though widely applied, are not suited for massive data and large label scenarios since they scale linearly with the number of edges $|E|$ and distinct labels $m$. To deal with the large label size problem, recent works propose sketch-based methods to approximate the distribution on labels per node thereby achieving a space reduction from $O(m)$ to $O(\log m)$, under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that captures the sparsity of the label distribution and ensures the algorithm propagates labels accurately, and further reduces the space complexity per node to $O(1)$. We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. We also study different graph construction mechanisms for natural language applications and propose a robust graph augmentation strategy trained using state-of-the-art unsupervised deep learning architectures that yields further significant quality gains.


title: Learning Multiagent Communication with Backpropagation

abstract: Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.


title: VEGF receptor kinase inhibitors: phthalazines, anthranilamides and related structures.

abstract: Inhibition of vascular endothelial growth factor receptor (VEGFR) signalling, using either antibodies or small molecule inhibitors of the VEGFR kinase domain, has become a major area of research in oncology. The phthalazine PTK787/ZK222584, first published in the literature in 1998, is one of the most advanced VEGFR inhibitors in the clinic. This paper provides an update on the patenting activity related to the phthalazine class. In addition, newer kinase inhibitor pharmacophores derived from this class (e.g., anthranilamides) will be reviewed.


title: Learning to Learn from Weak Supervision by Full Supervision

abstract: In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.


title: Political hashtag trends

abstract: Political Hashtag Trends (PHT) is an analysis tool for political left-vs.-right polarization of Twitter hashtags. PHT computes a leaning for trending, political hashtags in a given week, giving insights into the polarizing U.S. American issues on Twitter. The leaning of a hashtag is derived in two steps. First, users retweeting a set of "seed users" with a known political leaning, such as Barack Obama or Mitt Romney, are identified and the corresponding leaning is assigned to retweeters. Second, a hashtag is assigned a fractional leaning corresponding to which retweeting users used it. Non-political hashtags are removed by requiring certain hashtag co-occurrence patterns. PHT also offers functionality to put the results into context. For example, it shows example tweets from different leanings, it shows historic information and it links to the New York Times archives to explore a topic in depth. In this paper, we describe the underlying methodology and the functionality of the demo.


title: Deep Learning-Based Fast Hand Gesture Recognition Using Representative Frames

abstract: In this paper, we propose a vision-based hand gesture recognition system for intelligent vehicles. Vision-based gesture recognition systems are employed in automotive user interfaces to increase the driver comfort without compromising their safety. In our algorithm, the long-term recurrent convolution network is used to classify the video sequences of hand gestures. In the standard long-term recurrent convolution network-based action classifier, multiple frames sampled from the video sequence are given as an input to the network, to perform classification. However, the use of multiple frames increases the computational complexity, apart from reducing the classification accuracy of the classifier. We propose to address these issues by extracting a fewer representative frames from the video sequence, and inputting them to the long-term recurrent convolution network. To extract the representative frames, we propose to use novel tiled image patterns and tiled binary pattern within a semantic segmentation- based deep learning framework, the deconvolutional neural network. The novel tiled image patterns contain multiple non-overlapping blocks and represent the entire gesture-based video sequence within a single tiled image. These image patterns represent the input to the deconvolution network and are generated from the video sequence. The novel tiled binary pattern also contain multiple non-overlapping blocks and represent the representative frames of the video sequence. These binary patterns represent the output of the deconvolution network. The training binary patterns are generated from the training video sequences using the dictionary learning and sparse modeling framework. We validate our proposed algorithm on the public Cambridge gesture recognition dataset. A comparative analysis is performed with baseline algorithms and an improved classification accuracy is observed. We also perform a detailed parametric analysis of the proposed algorithm. We report a gesture classification accuracy of 91% and report a near real-time computational complexity of $110$~ms per video sequence.


title: Interfacing of differential-capacitive biomimetic hair flow-sensors for optimal sensitivity

abstract: Biologically inspired sensor-designs are investigated as a possible path to surpass the performance of more traditionally engineered designs. Inspired by crickets, artificial hair sensors have shown the ability to detect minute flow signals. This paper addresses developments in the design, fabrication, interfacing and characterization of biomimetic hair flow-sensors towards sensitive high-density arrays. Improvement of the electrode design of the hair sensors has resulted in a reduction of the smallest hair movements that can be measured. In comparison to the arrayed hairs-sensor design, the detection-limit was arguably improved at least twelve-fold, down to 1 mm s–1 airflow amplitude at 250 Hz as measured in a bandwidth of 3 kHz. The directivity pattern closely resembles a figure-of-eight. These sensitive hair-sensors open possibilities for high-resolution spatio-temporal flow pattern observations.


title: Gesture Generation by Imitation: From Human Behavior to Computer Character Animation

abstract: In an e ort to extend traditional human-computer interfaces research has introduced embodied agents to utilize the modalities of everyday human-human communication, like facial expression, gestures and body postures. However, giving computer agents a human-like body introduces new challenges. Since human users are very sensitive and critical concerning bodily behavior the agents must act naturally and individually in order to be believable. This dissertation focuses on conversational gestures. It shows how to generate conversational gestures for an animated embodied agent based on annotated text input. The central idea is to imitate the gestural behavior of a human individual. Using TV show recordings as empirical data, gestural key parameters are extracted for the generation of natural and individual gestures. The gesture generation task is solved in three stages: observation, modeling and generation. For each stage, a software module was developed. For observation, the video annotation research tool ANVIL was created. It allows the eAEcient transcription of gesture, speech and other modalities on multiple layers. ANVIL is application-independent by allowing users to de ne their own annotation schemes, it provides various import/export facilities and it is extensible via its plugin interface. Therefore, the tool is suitable for a wide variety of research elds. For this work, selected clips of the TV talk show \Das Literarische Quartett" were transcribed and analyzed, arriving at a total of 1,056 gestures. For the modeling stage, the NOVALIS module was created to compute individual gesture pro les from these transcriptions with statistical methods. A gesture pro le models the aspects handedness, timing and function of gestures for a single human individual using estimated conditional probabilities. The pro les are based on a shared lexicon of 68 gestures, assembled from the data. Finally, for generation, the NOVA generator was devised to create gestures based on gesture pro les in an overgenerate-andlter approach. Annotated text input is processed in a graph-based representation in multiple stages where semantic data is added, the location of potential gestures is determined by heuristic rules, and gestures are added and ltered based on a gesture pro le. NOVA outputs a linear, player-independent action script in XML.


title: Resource constrained real-time lane-vehicle detection for advanced driver assistance on mobile devices

abstract: Recently, advanced driver assistance system (ADAS) has attracted a lot of attention due to the fast growing industry of smart cars, which is believed to be the next human-computer interaction after smart phones. As ADAS is a critical enabling component in a human-in-the-loop cyber-physical system (CPS) involving complicated physical environment, it has stringent requirements on reliability, accuracy as well as latency. Lane and vehicle detections are the basic functions in ADAS, which provide lane departure warning (LDW) and forward collision warning (FCW) to predict the dangers and warn the drivers. While extensive literature exists on this topic, none of them considers the important fact that many vehicles today do not have powerful embedded electronics or cameras. It will be costly to upgrade the vehicle just for ADAS enhancement. To address this issue, we demonstrate a new framework that utilizes microprocessors in mobile devices with embedded cameras for advanced driver assistance. The main challenge that comes with this low cost solution is the dilemma between limited computing power and tight latency requirement, and uncalibrated camera and high accuracy requirement. Accordingly, we propose an efficient, accurate, flexible yet light-weight real-time lane and vehicle detection method and implement it on Android devices. Real road test results suggest that an average latency of 15 fps can be achieved with a high accuracy of 12.58 average pixel offset for each lane in all scenarios and 97+ precision for vehicle detection. To the best of the authors' knowledge, this is the very first implementation of both lane and vehicle detections on mobile devices with un-calibrated embedded camera.


Introduction To Literature: A guide for the Understanding of Basic Literature


title: Enabling Elastic Stream Processing in Shared Clusters

abstract: Distributed data stream processing has become an increasingly popular computational framework due to many emerging applications which require real-time processing of data such as dynamic content delivery and security event analysis. These distributed data stream processing applications are often run on shared, multi-tenant clusters as companies try to consolidate from dedicated clusters for each application (batch and streaming) to a single cluster using a global cluster manager such as Hadoop YARN. In shared cluster environments, guaranteeing the quality of service constraints for throughput and response time for both stream processing applications and batch applications is a significant challenge. Stream processing applications often face an elastic demand where the input rate can vary drastically. The typical solution to solve workload elasticity is to guarantee enough resources to the application, but this solution is not possible when resources are being shared among multiple applications. In this paper, we present an approach for supporting elastic scaling of distributed data stream processing applications and efficiently scheduling and coordinating stream processing with batch processing in shared clusters. Our solution consists of a congestion detection monitor which detects bottlenecks in the streaming system and a global state manager that performs non-disruptive, stateful scaling of streaming applications. We implemented our solution using Storm, a popular stream processing framework, and tested our implementation on a Hadoop YARN cluster using a real-time security event processing workload. Our experimental results show that our solution improves stream processing application throughput by 49% over default Storm while decreasing average request response times by 58%.


title: High Speed Differential Drive Mobile Robot Path Following Control With Bounded Wheel Speed Commands

abstract: The great majority of path following control laws for either kinematical or dynamical mobile robot models are designed assuming ideal actuators, i.e. assuming that any commanded velocity or torque (in the kinematical and dynamical cases respectively) will be instantly implemented regardless of its value. Real actuators are far from being ideal. In particular, only bounded velocities and torques can be realized for any given command. With reference to the kinematical model of a differential drive mobile robot, a known path following control law is modified to account for actuator velocity saturation. The proposed solution is experimentally shown to be particularly useful for high speed applications where accounting for actuator velocity saturation may have a large influence on performance.


title: Method for stakeholder identification in interorganizational environments

abstract: Stakeholders are the first emerging challenge in any software project. Their identification is a critical task for success. Nevertheless, many authors consider them as a default product of a non-explained identification process. Several aspects must be considered when the project is carried out in environments where multiple organizations interact. These complex contexts demand extremely hard efforts. Stakeholders must be identified taking into account their attributes (types, roles), which must be extended and defined for these environments. In general, there are no methodologies that allow performing this task in a systematic way for the development of interorganizational information systems. The paper proposes a method for carrying out stakeholder identification considering the diverse dimensions involved in interorganizational environments: organizational, interorganizational and external. It allows the systematic specification of all the people, groups and organizations whose interests and needs can affect or are affected by the interorganizational system. Also diverse stakeholders’ attributes such as types, roles, influence and interest are defined, analyzed, and included in the method. They are all important in later stages of any software project.


title: On the Sensor Design of Torque Controlled Actuators: A Comparison Study of Strain Gauge and Encoder-Based Principles

abstract: Joint torque sensing represents one of the foundations and vital components of modern robotic systems that target to match closely the physical interaction performance of biological systems through the realization of torque controlled actuators. However, despite decades of studies on the development of different torque sensors, the design of accurate and reliable torque sensors still remains challenging for the majority of the robotics community preventing the use of the technology. This letter proposes and evaluates two joint torque sensing elements based on strain gauge and deflection-encoder principles. The two designs are elaborated and their performance from different perspectives and practical factors are evaluated including resolution, nonaxial moments load crosstalk, torque ripple rejection, bandwidth, noise/residual offset level, and thermal/time dependent signal drift. The letter reveals the practical details and the pros and cons of each sensor principle providing valuable contributions into the field toward the realization of higher fidelity joint torque sensing performance.


title: CAR: A Compression-Aware Refresh Approach to Improve Memory Performance and Energy Efficiency

abstract: DRAM memory is suffering increasingly aggravating refresh penalty, which no longer causes trivial performance degradation and power consumption. As memory capacity increases, refresh penalty has become increasingly worse as more rows have to be refreshed. In this work, we propose a simple, practical, and effective refresh approach called CAR (Compression-Aware Refresh) to efficiently mitigate refresh overheads. We apply data compression technique to store data in compressed format so that data blocks which are originally distributed across all the constituent chips of a rank only need to be stored in a subset of those chips, leaving banks in the remaining chips not fully occupied. As a result, the memory controller can safely skip refreshing memory rows which contain no useful data without compromising data integrity. Such a compression-aware refresh scheme can result in significant refresh savings and thus improve overall memory performance and energy efficiency. Moreover, to further take advantage of data compression, we adopt the rank subsetting technique to enable accesses to only those occupied chips for memory requests accessing compressed data blocks. Evaluations using benchmarks from SPEC CPU 2006 and the PARSEC 3.0 on the recent DDR4 memory systems have shown that CAR can achieve up to 1.66x performance improvement (11.7% on average).


title: UWB 4-element ridge-SIW vivaldi antenna array

abstract: A 4-element uniform Vivaldi antenna array fed by a novel ridge SIW (RSIW) feed network has been developed using a two-layer substrate. The low cost planar RSIW divider is designed for wide band operation to sustain the inherent wide bandwidth of the Vivaldi array that is fed by a very wide band GCPW to RSIW transition as well. The proposed antenna structure has been fabricated and showed good radiation characteristics and less than 1.5:1 VSWR over a wide bandwidth exceeding 55% with a maximum gain of 6-10 dB and over 87% efficiency over 6 to 10 GHz. Measured results are in good agreement with simulated ones.


title: The cognitive organization of music knowledge: a clinical analysis

abstract: Despite much recent interest in the clinical neuroscience of music processing, the cognitive organization of music as a domain of non-verbal knowledge has been little studied. Here we addressed this issue systematically in two expert musicians with clinical diagnoses of semantic dementia and Alzheimer’s disease, in comparison with a control group of healthy expert musicians. In a series of neuropsychological experiments, we investigated associative knowledge of musical compositions (musical objects), musical emotions, musical instruments (musical sources) and music notation (musical symbols). These aspects of music knowledge were assessed in relation to musical perceptual abilities and extra-musical neuropsychological functions. The patient with semantic dementia showed relatively preserved recognition of musical compositions and musical symbols despite severely impaired recognition of musical emotions and musical instruments from sound. In contrast, the patient with Alzheimer’s disease showed impaired recognition of compositions, with somewhat better recognition of composer and musical era, and impaired comprehension of musical symbols, but normal recognition of musical emotions and musical instruments from sound. The findings suggest that music knowledge is fractionated, and superordinate musical knowledge is relatively more robust than knowledge of particular music. We propose that music constitutes a distinct domain of non-verbal knowledge but shares certain cognitive organizational features with other brain knowledge systems. Within the domain of music knowledge, dissociable cognitive mechanisms process knowledge derived from physical sources and the knowledge of abstract musical entities.


title: A face detection algorithm based on Adaboost and new Haar-Like feature

abstract: This paper uses a new face detection method based on Haar-Like feature. New Haar-Like feature is an extension of the Haar-Like feature basis. This article use four new Haar-Like feature, and these features with existing Haar-Like feature are input Adaboost classifier together to select feature, finally constructed classification performance and powerful cascade classifier for face detection. After detection experiments we can see, the algorithm can get better results compared with other traditional face detection classifiers like Haar-Like.


title: RaspiReader: Open Source Fingerprint Reader

abstract: We open source an easy to assemble, spoof resistant, high resolution, optical fingerprint reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the fingerprint research community a seamless and simple method for quickly prototyping new ideas involving fingerprint reader hardware. In particular, we posit that this open source fingerprint reader will facilitate the exploration of novel fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical readers. Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.


title: Speed Control for a Pseudo Direct Drive Permanent-Magnet Machine With One Position Sensor on Low-Speed Rotor

abstract: The paper describes a novel approach to operate a Pseudo Direct Drive (PDD) permanent magnet (PM) machine with a single sensor on the low-speed rotor. Due to the magnetic coupling between its two rotors, the PDD machine exhibits torsional oscillation when a conventional drive is employed. While full state feedback control can effectively supress the torsional oscillation, its implementation requires two position/speed sensors: one on the high-speed rotor for electronic commutation, and the other on the low-speed rotor for speed/position control of the payload. This not only increases the drive system cost, but the need for fitting a position/speed sensor on the high-speed rotor imposes great constraints on the PDD mechanical design. A novel technique has been developed in order to control the PDD machine using a single sensor mounted on the low-speed rotor while the position and speed of the high-speed rotor needed for electronic commutation and state feedback control is estimated with an observer. The commutation signal is generated with a specially designed hardware to mimic a hardware resolver.


title: GALE: Geometric Active Learning for Search-Based Software Engineering

abstract: Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.


title: A recurrent neural network for solving nonlinear convex programs subject to linear constraints

abstract: In this paper, we propose a recurrent neural network for solving nonlinear convex programming problems with linear constraints. The proposed neural network has a simpler structure and a lower complexity for implementation than the existing neural networks for solving such problems. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution within a finite time under the condition that the objective function is strictly convex. Compared with the existing convergence results, the present results do not require Lipschitz continuity condition on the objective function. Finally, examples are provided to show the applicability of the proposed neural network.


title: Secure and efficient protocols for iris and fingerprint identification

abstract: Recent advances in biometric recognition and the increasing use of biometric data prompt significant privacy challenges associated with the possible misuse, loss, or theft of biometric data. Biometric matching is often performed by two mutually distrustful parties, one of which holds one biometric image while the other owns a possibly large biometric collection. Due to privacy and liability considerations, neither party is willing to share its data. This gives rise to the need to develop secure computation techniques over biometric data where no information is revealed to the parties except the outcome of the comparison or search. To address the problem, in this work we develop and implement the first privacy-preserving identification protocol for iris codes. We also design and implement a secure protocol for fingerprint identification based on FingerCodes with a substantial improvement in the performance compared to existing solutions. We show that new techniques and optimizations employed in this work allow us to achieve particularly efficient protocols suitable for large data sets and obtain notable performance gain compared to the state-of-the-art prior work.


title: Controller-free exploration of medical image data: Experiencing the Kinect

abstract: In this paper, an open-source system for a controller-free, highly interactive exploration of medical images is presented. By using a Microsoft Xbox Kinect™ as the only input device, the system's user interface allows users to interact at a distance through hand and arm gestures. The paper also details the interaction techniques we have designed specifically for the deviceless exploration of medical imaging data. Since the user interface is touch-free and does not require complex calibration steps, it is suitable for use in operating rooms, where non-sterilizable devices cannot be used.


title: Estimating coverage holes and enhancing coverage in mixed sensor networks

abstract: Area coverage is one of the most fundamental problems in ad hoc wireless sensor networks because it directly relates to optimization of resources in a sensing field. Maximizing the coverage area while maintaining a lower cost of deployment has always been a challenge, especially when the monitoring region is unknown and possibly hazardous. In this paper, we present a method to deterministically estimate the exact amount of coverage holes under random deployment using Voronoi diagrams and use the static nodes to collaborate and estimate the number of additional mobile nodes needed to be deployed and relocated to optimal positions to maximize coverage. We follow a two-step deployment process in a mixed sensor network and we argue by simulation and analysis that our collaborative coverage enhancing algorithm (COVEN) can achieve a tradeoff between the cost of deployment and percentage of area covered.


title: C.S.: Automatic white balance for digital still cameras

abstract: In recent years digital cameras have captured the camera market. Although the factors that consumer consider is the quality of the photographs produced. White balance is one of the methods used to improve the quality of the photographs. The basic white balance methods are, however powerless to handle all possible situations in the captured scene. In order to improve the white balance we divide this problem in three steps-white object purification, white point detection, and white point adjustment. The proposed method basically solves the problem of detecting the white point very well. The experimental results show that the proposed method can achieve superior result in both objective and subjective evaluative terms. The complexity of the proposed system is acceptable. The propose method can be easily applied to digital cameras to obtain good results.


title: UNDER EMBARGO UNTIL AUGUST 18, 2009, 12:01 AM LOCAL TIME Health-Risk Correlates of Video-Game Playing Among Adults

abstract: BACKGROUND ::: Although considerable research suggests that health-risk factors vary as a function of video-game playing among young people, direct evidence of such linkages among adults is lacking. ::: ::: ::: PURPOSE ::: The goal of this study was to distinguish adult video-game players from nonplayers on the basis of personal and environmental factors. It was hypothesized that adults who play video games, compared to nonplayers, would evidence poorer perceptions of their health, greater reliance on Internet-facilitated social support, more extensive media use, and higher BMI. It was further hypothesized that different patterns of linkages between video-game playing and health-risk factors would emerge by gender. ::: ::: ::: METHODS ::: A cross-sectional, Internet-based survey was conducted in 2006 with a sample of adults from the Seattle-Tacoma area (n=562), examining health risks; media use behaviors and perceptions, including those related to video-game playing; and demographics. Statistical analyses conducted in 2008 to compare video-game players and nonplayers included bivariate descriptive statistics, stepwise discriminant analysis, and ANOVA. ::: ::: ::: RESULTS ::: A total of 45.1% of respondents reported playing video games. Female video-game players reported greater depression (M=1.57) and poorer health status (M=3.90) than female nonplayers (depression, M=1.13; health status, M=3.57). Male video-game players reported higher BMI (M=5.31) and more Internet use time (M=2.55) than male nonplayers (BMI, M=5.19; Internet use, M=2.36). The only determinant common to female and male video-game players was greater reliance on the Internet for social support. ::: ::: ::: CONCLUSIONS ::: A number of determinants distinguished video-game players from nonplayers, and these factors differed substantially between men and women. The data illustrate the need for further research among adults to clarify how to use digital opportunities more effectively to promote health and prevent disease.


title: Deep Brain Stimulation for Treatment-Resistant Depression

abstract: Treatment-resistant depression is a severely disabling disorder with no proven treatment options once multiple medications, psychotherapy, and electroconvulsive therapy have failed. Based on our preliminary observation that the subgenual cingulate region (Brodmann area 25) is metabolically overactive in treatment-resistant depression, we studied whether the application of chronic deep brain stimulation to modulate BA25 could reduce this elevated activity and produce clinical benefit in six patients with refractory depression. Chronic stimulation of white matter tracts adjacent to the subgenual cingulate gyrus was associated with a striking and sustained remission of depression in four of six patients. Antidepressant effects were associated with a marked reduction in local cerebral blood flow as well as changes in downstream limbic and cortical sites, measured using positron emission tomography. These results suggest that disrupting focal pathological activity in limbic-cortical circuits using electrical stimulation of the subgenual cingulate white matter can effectively reverse symptoms in otherwise treatment-resistant depression.


title: The Evidence-Based Policing Matrix

abstract: The next phase of evidence-based policing requires both scholars and practitioners to move from lists of specific studies about “what works” to using that information strategically. This requires developing generalizations or principles on the nature of effective police strategies and translating the field of police evaluation research into digestible forms that can be used to alter police tactics, strategies, accountability systems, and training. In this article, we present a tool intended for such use: the Evidence-Based Policing Matrix. The Matrix is a consistently updated, research-to-practice translation tool that categorizes and visually bins all experimental and quasi-experimental research on police and crime reduction into intersections between three common dimensions of crime prevention—the nature of the target, the extent to which the strategy is proactive or reactive, and the specificity or generality of the strategy. Our mapping and visualization of 97 police evaluation studies conducted through December 31, 2009, indicate that proactive, place-based, and specific policing approaches appear much more promising in reducing crime than individual-based, reactive, and general ones. We conclude by discussing how the Matrix can be used to guide future research and facilitate the adoption of evidence-based policing.


title: Accuracy studies for TDOA and TOA localization

abstract: In sensor networks, passive localization can be performed by exploiting the received signals of unknown emitters. In this paper, the Time of Arrival (TOA) measurements are investigated. Often, the unknown time of emission is eliminated by calculating the difference between two TOA measurements where Time Difference of Arrival (TDOA) measurements are obtained. In TOA processing, additionally, the unknown time of emission is to be estimated. Therefore, the target state is extended by the unknown time of emission. A comparison is performed investigating the attainable accuracies for localization based on TDOA and TOA measurements given by the Cramer-Rao Lower Bound (CRLB). Using the Maximum Likelihood estimator, some characteristic features of the cost functions are investigated indicating a better performance of the TOA approach. But counterintuitive, Monte Carlo simulations do not support this indication, but show the comparability of TDOA and TOA localization.


title: Impedance Control of an aerial-manipulator: Preliminary results

abstract: In this paper, an impedance control scheme for aerial robotic manipulators is proposed, with the aim of reducing the end-effector interaction forces with the environment. The proposed control has a multi-level architecture, in detail the outer loop is composed by a trajectory generator and an impedance filter that modifies the trajectory to achieve a complaint behaviour in the end-effector space; a middle loop is used to generate the joint space variables through an inverse kinematic algorithm; finally the inner loop is aimed at ensuring the motion tracking. The proposed control architecture has been experimentally tested.


Pricing Digital Marketing: Information, Risk Sharing and Performance


title: Creative Teaching: Collaborative Discussion as Disciplined Improvisation

abstract: Teaching has often been thought of as a creative performance. Although comparisons with performance were originally intended to emphasize teacher creativity, they have become associated instead with contemporary reform efforts toward scripted instruction that deny the creativity of teachers. Scripted instruction is opposed to constructivist, inquiry-based, and dialogic teaching methods that emphasize classroom collaboration. To provide insight into these methods, the “teaching as performance” metaphor must be modified: Teaching is improvisational performance. Conceiving of teaching as improvisation highlights the collaborative and emergent nature of effective classroom practice, helps us to understand how curriculum materials relate to classroom practice, and shows why teaching is a creative art.


title: A Behavioral Model of Rational Choice

abstract: Introduction, 99. — I. Some general features of rational choice, 100.— II. The essential simplifications, 103. — III. Existence and uniqueness of solutions, 111. — IV. Further comments on dynamics, 113. — V. Conclusion, 114. — Appendix, 115.


title: Changing Ethical Attitudes: The Case of the Enron and ImClone Scandals*

abstract: We analyze the process of changing ethical attitudes over time by focusing on a specific set of "natural experiments" that occurred over an 18-month period, namely, the accounting scandals that occurred involving Enron/Arthur Andersen and insider-trader allegations related to ImClone. Copyright (c) 2006 by the Southwestern Social Science Association.


title: Sourcing of internal auditing : An empirical study

abstract: This paper studies the factors associated with organizations' internal audit sourcing decisions, building from a previous study by Widener & Selto (1999; henceforth W&S). In their study, W&S used Transaction Cost Economics (TCE) to explain the governance of internal auditing. Our study seeks to replicate their results, using newly collected data from 66 companies headquartered in the Netherlands. Our findings are supportive of W&S. Like W&S, we find asset specificity and frequency (both individually and in interaction) to be significantly associated with sourcing decisions. These findings are robust against different model specifications, and they hold across variously defined samples. We conclude that the W&S results are reproducible in different conditions, enhancing the credibility of the TCE-based explanation of organizations' internal audit sourcing practices.


title: MedRec: Using Blockchain for Medical Data Access and Permission Management

abstract: Years of heavy regulation and bureaucratic inefficiency have slowed innovation for electronic medical records (EMRs). We now face a critical need for such innovation, as personalization and data science prompt patients to engage in the details of their healthcare and restore agency over their medical data. In this paper, we propose MedRec: a novel, decentralized record management system to handle EMRs, using blockchain technology. Our system gives patients a comprehensive, immutable log and easy access to their medical information across providers and treatment sites. Leveraging unique blockchain properties, MedRec manages authentication, confidentiality, accountability and data sharing -- crucial considerations when handling sensitive information. A modular design integrates with providers' existing, local data storage solutions, facilitating interoperability and making our system convenient and adaptable. We incentivize medical stakeholders (researchers, public health authorities, etc.) to participate in the network as blockchain "miners". This provides them with access to aggregate, anonymized data as mining rewards, in return for sustaining and securing the network via Proof of Work. MedRec thus enables the emergence of data economics, supplying big data to empower researchers while engaging patients and providers in the choice to release metadata. The purpose of this short paper is to expose, prior to field tests, a working prototype through which we analyze and discuss our approach.


title: Reward and reinforcement activity in the nucleus accumbens during learning

abstract: The nucleus accumbens core (NAcc) has been implicated in learning associations between sensory cues and profitable motor responses. However, the precise mechanisms that underlie these functions remain unclear. We recorded single-neuron activity from the NAcc of primates trained to perform a visual-motor associative learning task. During learning, we found two distinct classes of NAcc neurons. The first class demonstrated progressive increases in firing rates at the go-cue, feedback/tone and reward epochs of the task, as novel associations were learned. This suggests that these neurons may play a role in the exploitation of rewarding behaviors. In contrast, the second class exhibited attenuated firing rates, but only at the reward epoch of the task. These findings suggest that some NAcc neurons play a role in reward-based reinforcement during learning.


BURG: fast optimal instruction selection and tree parsing


title: EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing

abstract: Practical wearable gesture tracking requires that sensors align with existing ergonomic device forms. We show that combining EMG and pressure data sensed only at the wrist can support accurate classification of hand gestures. A pilot study with unintended EMG electrode pressure variability led to exploration of the approach in greater depth. The EMPress technique senses both finger movements and rotations around the wrist and forearm, covering a wide range of gestures, with an overall 10-fold cross validation classification accuracy of 96%. We show that EMG is especially suited to sensing finger movements, that pressure is suited to sensing wrist and forearm rotations, and their combination is significantly more accurate for a range of gestures than either technique alone. The technique is well suited to existing wearable device forms such as smart watches that are already mounted on the wrist.


title: Morphometric Analysis of the Working Zone for Posterolateral Endoscopic Lumbar Discectomy Based on Magnetic Resonance Neurography

abstract: STUDY DESIGN ::: A magnetic resonance neurography (MRN)-based morphometric analysis of the working zone for posterolateral endoscopic lumbar discectomy (PELD) procedures on 32 health volunteers. ::: ::: ::: OBJECTIVE ::: The purpose is to utilize MRN as a noninvasive evaluation of the Kambin's working zone, and further to analyze operative safety of the PELD procedures. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Intraoperative nerve root injuries of PELD procedures occur relative to the Kambin's triangular working zone, which has been described previously based on formalin-fixed cadaver studies. However, the investigation in living individuals is not known. Thus, it is necessary to evaluate the dimensions of the working zone on both coronal and sagittal plane by radiologic assessments. ::: ::: ::: MATERIALS AND METHODS ::: MRN images of 32 health volunteers (average age 26.8 y; 18 men, 14 women) were analyzed from L1-L2 to L5-S1. On the coronal plane, we measured the distance from the superior endplate to the nerve root exiting from the dura (distance a), the distance from lateral aspect of the dura to the medial aspect of the nerve root along the superior endplate (distance b), and the angle between the nerve root and plane of the corresponding disk (angle α). On the transversal plane, the vertical distance from the upper facet surface to the exiting nerve root at the lower/upper disk margin level (distance c/d) was also measured. ::: ::: ::: RESULTS ::: On the coronal plane, distance a was 16.69±5.07 mm (range, 6.60-26.10 mm), distance b was 13.64±2.52 mm (range, 9.30-21.20 mm), angle α was 55.45±7.14 degrees (range, 40.00-73.00 degrees). Distance c on the transversal plane was 5.01±2.66 mm (range, 1.30-13.10 mm) and distance d was 1.99±1.26 mm (range, 0.70-7.80 mm). All these measurements increased as the spine level went down. ::: ::: ::: CONCLUSIONS ::: The study indicated that MRN was a feasible noninvasive tool to evaluate the anatomic dimensions in the Kambin's working zone. Before PELD, radiologic measurements of this working zone were recommended to perform a safer procedure.


title: A cyber forensics needs analysis survey: Revisiting the domain's needs a decade later

abstract: Abstract The number of successful cyber attacks continues to increase, threatening financial and personal security worldwide. Cyber/digital forensics is undergoing a paradigm shift in which evidence is frequently massive in size, demands live acquisition, and may be insufficient to convict a criminal residing in another legal jurisdiction. This paper presents the findings of the first broad needs analysis survey in cyber forensics in nearly a decade, aimed at obtaining an updated consensus of professional attitudes in order to optimize resource allocation and to prioritize problems and possible solutions more efficiently. Results from the 99 respondents gave compelling testimony that the following will be necessary in the future: (1) better education/training/certification (opportunities, standardization, and skill-sets); (2) support for cloud and mobile forensics; (3) backing for and improvement of open-source tools (4) research on encryption, malware, and trail obfuscation; (5) revised laws (specific, up-to-date, and which protect user privacy); (6) better communication, especially between/with law enforcement (including establishing new frameworks to mitigate problematic communication); (7) more personnel and funding.


title: Prom Week: Designing past the game/story dilemma

abstract: New player experiences require new game designs – and some designs only become possible with new technology. Emergent narratives are part of existing story games but the game systems are not capable of understanding the stories their players create. If we could capture the enjoyable aspects of player-driven narratives while still keeping the complex dynamism of sandbox games, we could enable a new type of game experience. Creating this experience would require fundamental technical research to enable new kinds of authorial affordances. This paper presents the lessons learned while designing the game Prom Week and AI system Comme il Faut together to produce such an experience — a two and a half year process of researchdriven game design. Prom Week tightly combines player experiences of choice and consequence (as found, for example, in simulation games and combat systems) with experiences of specific characters, histories, language, and narrative conclusions (as found in the non-interactive, or minimally-interactive, fictions of many games). This combination was accomplished by creating novel technology informed by the design problem, experimenting with gameplay approaches appropriate to the technology, iterating on both using player feedback, and finally executing a unique design providing satisfying stories that reflect the player's choices in a wide possibility space.


title: Inflection points in sepsis biology: from local defense to systemic organ injury

abstract: Sepsis and septic shock lead to considerable morbidity and mortality in developed and developing countries. Despite advances in understanding the innate immune events that lead to septic shock, molecular therapies based on these advances have failed to improve sepsis mortality. The clinical failure of laboratory-derived therapies may be, in part, due to the pleiotropic consequences of the acute inflammatory response, which is the focus of this review. A brisk response to infecting organism is essential for pathogen containment and eradication. However, systemic spread of inflammation beyond a single focus leads to organ injury and higher mortality. The primary goal of this article is to discuss recent animal- and human-based scientific advances in understanding the host response to infection and to highlight how these defense mechanisms can be locally beneficial but systemically detrimental. There are other factors that determine the severity of sepsis that are beyond the scope of this review, including the virulence of the pathogen and regulation by Toll-like receptors. Specifically, this review focuses on how the effector mechanisms of platelets, mast cells, neutrophil extracellular traps (NETs), and the endothelium participate in combating local infections yet can induce organ injury during systemic infection.


title: Performance and Energy Aware Inhomogeneous 3D Networks-on-Chip Architecture Generation

abstract: Recently, Through-Silicon-Via (TSV) has been more popular to provide faster inter-layer communication in three-dimensional Networks-on-Chip (3D NoCs). However, the area overhead of TSVs reduces wafer utilization and yield which impact design of 3D architectures using a large number of TSVs such as homogeneous 3D NoCs topologies. Also, 3D routers require more memory and thus they are more power hungry than conventional 2D routers. Alternatively, hybrid 3D NoCs combine both the area and performance benefits of 2D and 3D router architectures by using a limited number of TSVs. Existing hybrid architectures suffer from higher packet delays as they do not consider the dynamic communication patterns of different application and their NoC resource usage. We propose a novel algorithm to systematically generate hybrid 3D NoC topologies for a given application such that the vertical connections are minimized while the NoC performance is not sacrificed. The proposed algorithm analyses the target application and generates hybrid architectures by efficiently redistributing the vertical links and buffer spaces based on their utilizations. Furthermore, the algorithm has been evaluated with synthetic and various real-world traffic patterns. Experimental results show that the proposed algorithm generates optimized architectures with lower energy consumption and a significant reduction in packet delay compared to the existing solutions.


title: Using Velocity, Acceleration, and Jerk to Manage Agile Schedule Risk

abstract: Stable velocity is paramount for agile planning and builds trust with stakeholders. What metric represents agile stability? Agile processes establish the principle the schedule is fixed and scope changes. Agile velocity is well defined. Velocity is how much technical scope a team of people can produce over a period of time. It ties the technical scope to the schedule. This research continues to develop the concept that a stable velocity is an important factor in the change management process and risk management process. Specifically, stable velocity can help prevent a chaotic relationship with the customer. If the velocity is stable from sprint to sprint, it means planning and the execution is well aligned. This research uses past data from software agile development projects to investigate whether a metric for change in technical scope provides a leading indicator for schedule risk. Specifically, this paper studies agile velocity and proposes two physics concepts derived from velocity, as new agile metrics: agile acceleration and agile jerk. This paper identifies agile acceleration as a trigger for change and agile jerk as an indicator for stability of a release. The process to incorporate the metrics into agile risk management is provided. This research is relevant to persons who participate and manage Agile development projects. This research can be used to understand potential schedule risk/opportunity associated with unfinished work returning to the product backlog for later planning.


title: STR: A Seasonal-Trend Decomposition Procedure Based on Regression

abstract: We propose new generic methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. Our new methods are much more general than any alternative time series decomposition methods. They allow for multiple seasonal and cyclic components, and multiple linear regressors with constant, flexible, seasonal and cyclic influence. Seasonal patterns (for both seasonal components and seasonal regressors) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. We also provide confidence intervals for the estimated components, and discuss how STR can be used for forecasting.


title: SMARTDIAB: A Communication and Information Technology Approach for the Intelligent Monitoring, Management and Follow-up of Type 1 Diabetes Patients

abstract: SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.


title: Aerial display of vibrotactile sensation with high spatial-temporal resolution using large-aperture airborne ultrasound phased array

abstract: We fabricated a tactile display which can generate vibrotactile sensation on human skin on which no equipment is mounted. It utilizes focused airborne ultrasound radiation pressure for stimulation. The workspace of our new tactile display is widened to a cube of 1 m × 1 m × 1 m, which allows users free motions in it. In order to widen the workspace, our new prototype integrates multiple ultrasound transducer units and achieves a large aperture airborne ultrasound phased array. As the workspace is widened, it has become possible to stimulate arbitrary regions all over a human body. The amplitude of imposed radiation pressure can be time-variant. The profiles of generated vibrotactile stimuli can be designed with a temporal resolution of 0.5 ms and 320-level quantization of radiation pressure amplitude. It is easy to choose a recorded waveform and reproduce it as vibrotactile stimuli at an arbitrary spatial point. This paper introduces how our new tactile display works and reports its performance evaluation.


title: Self-Capacitance of High-Voltage Transformers

abstract: The calculation of a transformer's parasitics, such as its self capacitance, is fundamental for predicting the frequency behavior of the device, reducing this capacitance value and moreover for more advanced aims of capacitance integration and cancellation. This paper presents a comprehensive procedure for calculating all contributions to the self-capacitance of high-voltage transformers and provides a detailed analysis of the problem, based on a physical approach. The advantages of the analytical formulation of the problem rather than a finite element method analysis are discussed. The approach and formulas presented in this paper can also be used for other wound components rather than just step-up transformers. Finally, analytical and experimental results are presented for three different high-voltage transformer architectures.


title: Sensory outcomes after reconstruction of lingual and inferior alveolar nerve discontinuities using processed nerve allograft--a case series.

abstract: PURPOSE ::: The present study describes the results of using a processed nerve allograft, Avance Nerve Graft, as an extracellular matrix scaffold for the reconstruction of lingual nerve (LN) and inferior alveolar nerve (IAN) discontinuities. ::: ::: ::: PATIENTS AND METHODS ::: A retrospective analysis of the neurosensory outcomes for 26 subjects with 28 LN and IAN discontinuities reconstructed with a processed nerve allograft was conducted to determine the treatment effectiveness and safety. Sensory assessments were conducted preoperatively and 3, 6, and 12 months after surgical reconstruction. The outcomes population, those with at least 6 months of postoperative follow-up, included 21 subjects with 23 nerve defects. The neurosensory assessments included brush stroke directional sensation, static 2-point discrimination, contact detection, pressure pain threshold, and pressure pain tolerance. Using the clinical neurosensory testing scale, sensory impairment scores were assigned preoperatively and at each follow-up appointment. Improvement was defined as a score of normal, mild, or moderate. ::: ::: ::: RESULTS ::: The neurosensory outcomes from LNs and IANs that had been microsurgically repaired with a processed nerve allograft were promising. Of those with nerve discontinuities treated, 87% had improved neurosensory scores with no reported adverse experiences. Similar levels of improvement, 87% for the LNs and 88% for the IANs, were achieved for both nerve types. Also, 100% sensory improvement was achieved in injuries repaired within 90 days of the injury compared with 77% sensory improvement in injuries repaired after 90 days. ::: ::: ::: CONCLUSIONS ::: These results suggest that processed nerve allografts are an acceptable treatment option for reconstructing trigeminal nerve discontinuities. Additional studies will focus on reviewing the outcomes of additional cases.


title: Textile antenna for RF energy harvesting fully embedded in clothing

abstract: In the context of Wireless Body Sensor Networks for healthcare and pervasive applications, textile antennas allow an ubiquitous monitoring, communication, energy harvesting and storage. This paper presents a smart coat with a dual-band textile antenna for Radio Frequency (RF) energy harvesting, operating at GSM 900 and DSC 1800 bands, which is fully embedded in the garment. Results obtained before and after the integration of the antenna into the garment are compared. The gain obtained in the simulation is about 1.8 dBi and 2.06 dBi, with radiation efficiency of 82% and 77,6% for the lowest and highest operating frequency bands, respectively.


title: TSDF Manifolds: A Scalable and Consistent Dense Mapping Approach

abstract: In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps. However, during operation over longer missions, reconstructions can easily become inconsistent due to accumulated camera tracking error and delayed loop closure. Without explicitly addressing the problem of map consistency, recovery from such distortions tends to be difficult. We present a novel system for dense 3D mapping which addresses the challenge of building consistent maps while dealing with scalability. Central to our approach is the representation of the environment as a manifold map, comprised of overlapping TSDF volumes. These volumes are localized through feature-based bundle adjustment. Our main contribution is to use a probabilistic measure to identify stable regions in the map, and fuse the contributing subvolumes. This approach allows us to reduce map growth while still maintaining consistency. We demonstrate the proposed system on publicly available datasets, as well as on a number of our own datasets, and demonstrate the efficacy of the proposed approach for building consistent and scalable maps.


title: Automatic Entity Recognition and Typing in Massive Text Data

abstract: In today's computerized and information-based society, individuals are constantly presented with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To extract value from these large, multi-domain pools of text, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their fine-grained types (e.g., people, product and food) in a scalable way. Since these methods do not rely on annotated data, predefined typing schema or hand-crafted features, they can be quickly adapted to a new domain, genre and language. We demonstrate on real datasets including various genres (e.g., news articles, discussion forum posts, and tweets), domains (general vs. bio-medical domains) and languages (e.g., English, Chinese, Arabic, and even low-resource languages like Hausa and Yoruba) how these typed entities aid in knowledge discovery and management.


title: Annotating Targets of Opinions in Arabic using Crowdsourcing

abstract: We present a method for annotating targets of opinions in Arabic in a two-stage process using the crowdsourcing tool Amazon Mechanical Turk. The first stage consists of identifying candidate targets “entities” in a given text. The second stage consists of identifying the opinion polarity (positive, negative, or neutral) expressed about a specific entity. We annotate a corpus of Arabic text using this method, selecting our data from online commentaries in different domains. Despite the complexity of the task, we find high agreement. We present detailed analysis.


title: SLSA: A Sentiment Lexicon for Standard Arabic

abstract: Sentiment analysis has been a major area of interest, for which the existence of highquality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies. The paper presents a large-scale Standard Arabic Sentiment Lexicon (SLSA) that is publicly available for free and avoids the deficiencies in the current resources. SLSA has the highest up-to-date reported coverage. The construction of SLSA is based on linking the lexicon of AraMorph with SentiWordNet along with a few heuristics and powerful back-off. SLSA shows a relative improvement of 37.8% over a state-of-theart lexicon when tested for accuracy. It also outperforms it by an absolute 3.5% of F1-score when tested for sentiment analysis.


title: Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions

abstract: Multilayer connectionist models of memory based on the encoder model using the backpropagation learning rule are evaluated. The models are applied to standard recognition memory procedures in which items are studied sequentially and then tested for retention. Sequential learning in these models leads to 2 major problems. First, well-learned information is forgotten rapidly as new information is learned. Second, discrimination between studied items and new items either decreases or is nonmonotonic as a function of learning. To address these problems, manipulations of the network within the multilayer model and several variants of the multilayer model were examined, including a model with prelearned memory and a context model, but none solved the problems. The problems discussed provide limitations on connectionist models applied to human memory and in tasks where information to be learned is not all available during learning. The first stage of the connectionist revolution in psychology is reaching maturity and perhaps drawing to an end. This stage has been concerned with the exploration of classes of models, and the criteria that have been used to evaluate the success of an application have been necessarily loose. In the early stages of development of a new approach, lax acceptability criteria are appropriate because of the large range of models to be examined. However, there comes a second stage when the models serve as competitors to existing models developed within other theoretical frameworks, and they have to be competitively evaluated according to more stringent criteria. A few notable connectionist models have reached these standards, whereas others have not. The second stage of development also requires that the connectionist models be evaluated in areas where their potential for success is not immediately obvious. One such area is recognition memory. The work presented in this article evaluates several variants of the multilayer connectionist model as accounts of empirical results in this area. I mainly discuss multilayer models using the error-correcting backpropagation algorithm and do not address other architectures such as adaptive resonance schemes (Carpenter & Grossberg, 1987). Before launching into the modeling of recognition memory, I need to specify the aims and rules under which this project was carried out. This is important in a new area of inquiry because there are many divergent views about what needs to be


title: Anthropomorphic Low-Inertia High-Stiffness Manipulator for High-Speed Safe Interaction

abstract: In this paper, a manipulator is proposed for safe human–robot interaction at high speed. The manipulator has both low mass and inertia and high stiffness and strength. It is basically a cable-driven manipulator; nevertheless, by using a unique lightweight tension-amplification mechanism, the manipulator retains high stiffness and strength. The joint stiffness, which is strongly related to the motion control performance, is amplified by the quadratic order. Both 1-degree of freedom (DOF) and 3-DOF joint mechanisms using the tension-amplification mechanism are presented and combined to develop a 7-DOF anthropomorphic manipulator named LIMS. The mass and inertia beyond the shoulder were 2.24 kg and 0.599 kg·m2, respectively, which are lower than those of a human. The stiffness of the developed elbow joint was 1410 N·m/rad, which is approximately seven times higher than that of a human. Considering the ratio of stiffness to inertia, the manipulator is expected to show a control performance that is comparable to those of conventional industrial manipulators. Comprehensive experiments, including joint stiffness tests and high-speed interaction tests, were conducted to verify the feasibility of the developed manipulator.


title: Direct marketing decision support through predictive customer response modeling

abstract: Decision support techniques and models for marketing decisions are critical to retail success. Among different marketing domains, customer segmentation or profiling is recognized as an important area in research and industry practice. Various data mining techniques can be useful for efficient customer segmentation and targeted marketing. One such technique is the RFM method. Recency, frequency, and monetary methods provide a simple means to categorize retail customers. We identify two sets of data involving catalog sales and donor contributions. Variants of RFM-based predictive models are constructed and compared to classical data mining techniques of logistic regression, decision trees, and neural networks. The spectrum of tradeoffs is analyzed. RFM methods are simpler, but less accurate. The effect of balancing cells, of the value function, and classical data mining algorithms (decision tree, logistic regression, neural networks) are also applied to the data. Both balancing expected cell densities and compressing RFM variables into a value function were found to provide models similar in accuracy to the basic RFM model, with slight improvement obtained by increasing the cutoff rate for classification. Classical data mining algorithms were found to yield better prediction, as expected, in terms of both prediction accuracy and cumulative gains. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are presented. Finally we discuss practical implications based on the empirical results.


title: Probabilistic Frame-Semantic Parsing

abstract: This paper contributes a formalization of frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. It finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two feature-based, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results.


title: Cross-Lingual Syntactic Transfer with Limited Resources

abstract: We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available. The method makes use of three steps: 1) a method for deriving cross-lingual word clusters, which can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins (2015). Experiments show improvements over the state-of-the-art in several languages used in previous work, in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work. Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015). We conclude with results on 38 datasets from the Universal Dependencies corpora.


title: The link between childhood trauma and depression: Insights from HPA axis studies in humans

abstract: Childhood trauma is a potent risk factor for developing depression in adulthood, particularly in response to additional stress. We here summarize results from a series of clinical studies suggesting that childhood trauma in humans is associated with sensitization of the neuroendocrine stress response, glucocorticoid resistance, increased central corticotropin-releasing factor (CRF) activity, immune activation, and reduced hippocampal volume, closely paralleling several of the neuroendocrine features of depression. Neuroendocrine changes secondary to early-life stress likely reflect risk to develop depression in response to stress, potentially due to failure of a connected neural circuitry implicated in emotional, neuroendocrine and autonomic control to compensate in response to challenge. However, not all of depression is related to childhood trauma and our results suggest the existence of biologically distinguishable subtypes of depression as a function of childhood trauma that are also responsive to differential treatment. Other risk factors, such as female gender and genetic dispositions, interfere with components of the stress response and further increase vulnerability for depression. Similar associations apply to a spectrum of other psychiatric and medical disorders that frequently coincide with depression and are aggravated by stress. Taken together, this line of evidence demonstrates that psychoneuroendocrine research may ultimately promote optimized clinical care and help prevent the adverse outcomes of childhood trauma.


title: Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys

abstract: Deep learning techniques are being rapidly applied to medical imaging tasks—from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys. In the study of PKD, segmentation of the kidneys is needed in order to measure total kidney volume (TKV). Automated methods to segment the kidneys and measure TKV are needed to increase measurement throughput and alleviate the inherent variability of human-derived measurements. We hypothesize that deep learning techniques can be leveraged to perform fast, accurate, reproducible, and fully automated segmentation of polycystic kidneys. Here, we describe a fully automated approach for segmenting PKD kidneys within MR images that simulates a multi-observer approach in order to create an accurate and robust method for the task of segmentation and computation of TKV for PKD patients. A total of 2000 cases were used for training and validation, and 400 cases were used for testing. The multi-observer ensemble method had mean ± SD percent volume difference of 0.68 ± 2.2% compared with the reference standard segmentations. The complete framework performs fully automated segmentation at a level comparable with interobserver variability and could be considered as a replacement for the task of segmentation of PKD kidneys by a human.


Home Is Where the Neurosis Is: A Topography of the Spatial Unconscious


title: Real time sentiment analysis of tweets using Naive Bayes

abstract: Twitter1 is a micro-blogging website which provides platform for people to share and express their views about topics, happenings, products and other services. Tweets can be classified into different classes based on their relevance with the topic searched. Various Machine Learning algorithms are currently employed in classification of tweets into positive and negative classes based on their sentiments, such as Baseline, Naive Bayes Classifier, Support Vector Machine etc. This paper contains implementation of Naive Bayes using sentiment140 training data using Twitter database and propose a method to improve classification. Use of SentiWordNet along with Naive Bayes can improve accuracy of classification of tweets, by providing positivity, negativity and objectivity score of words present in tweets. For actual implementation of this system python with NLTK and python-Twitter APIs are used.


title: Elsevier Foundations of AI: the big issues*

abstract: Abstract The objective of research in the foundations of AI is to explore such basic questions as: What is a theory in AI? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core AI is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding problem. (3) Cognition is nicely described in propositional terms. (4) We can study cognition separately from learning. (5) There is a single architecture underlying virtually all cognition. I explain what each of these implies and present arguments from both outside and inside AI why each has been seen as right or wrong.


title: A comparison of similarity measures for online social media Thai text classification

abstract: Social media is widely used as a channel of communication in general purposes, including the comment that are related to retail business. It is a highly effective communication tool for direct interacting with their customers. Growth rate of the users is rapidly increasing, because they use this channel to receive information and share something interesting. In this paper, we present a comparison experimental results on the similarity measurements on Thai social media text. We use the nearest neighbor classifier with the 10 distance functions to classify there data into 4 classes, which the classes were defined by the expert in retail business. The experiments were performed on two feature extractions with the well-known term weightings and compared the classification results with the experts. According to the experimental results, we found the feature extraction and the distance that can be employed with Thai social media text classification.


The Search for an Integrated KM Framework


title: Flexible spiral antenna with microstrip tapered infinite balun for wearable applications

abstract: In this paper we introduce a flexible, side-fed, ultra-wideband, spiral antenna with an integrated microstrip tapered infinite balun operating from 1–5GHz. It was fabricated using Rogers 5880 subtrate and specially coated to prevent oxidization. The simulation and measurements match quite well with S11 below −10dB throughout the frequency range and gain of close to 5dB and average efficiency of 65%. Most importantly, this antenna does not have an obtrusive center feed, and instead has a side feed, is flexible, compact, rendering many possible wearable antenna applications.


title: Characteristics of pressure-based input for mobile devices

abstract: We conducted a series of user studies to understand and clarify the fundamental characteristics of pressure in user interfaces for mobile devices. We seek to provide insight to clarify a longstanding discussion on mapping functions for pressure input. Previous literature is conflicted about the correct transfer function to optimize user performance. Our study results suggest that the discrepancy can be explained by different signal conditioning circuitry and with improved signal conditioning the user-performed precision relationship is linear. We also explore the effects of hand pose when applying pressure to a mobile device from the front, the back, or simultaneously from both sides in a pinching movement. Our results indicate that grasping type input outperforms single-sided input and is competitive with pressure input against solid surfaces. Finally we provide an initial exploration of non-visual multimodal feedback, motivated by the desire for eyes-free use of mobile devices. The findings suggest that non-visual pressure input can be executed without degradation in selection time but suffers from accuracy problems.


title: Timeline generation with social attention

abstract: Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.


title: The Importance of Sensory-Motor Control in Providing Core Stability Implications for Measurement and Training

abstract: Although the hip musculature is found to be very important in connecting the core to the lower extremities and in transferring forces from and to the core, it is proposed to leave the hip musculature out of consideration when talking about the concept of core stability. A low level of co-contraction of the trunk muscles is important for core stability. It provides a level of stiffness, which gives sufficient stability against minor perturbations. Next to this stiffness, direction-specific muscle reflex responses are also important in providing core stability, particularly when encountering sudden perturbations.


title: Variational Neural Machine Translation

abstract: Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoderdecoder model that can be trained end-to-end. Different from the vanilla encoder-decoder model that generates target translations from hidden representations of source sentences alone, the variational model introduces a continuous latent variable to explicitly model underlying semantics of source sentences and to guide the generation of target translations. In order to perform efficient posterior inference and large-scale training, we build a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on both Chinese-English and English- German translation tasks show that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machine translation baselines.


Programming a crystalline shape memory polymer network with thermo- and photo-reversible bonds toward a single-component soft robot


title: Code Reuse in Open Source Software Development: Quantitative Evidence, Drivers, and Impediments

abstract: The focus of existing open source software (OSS) research has been on how and why individuals and firms add to the commons of public OSS code - that is, on the “giving” side of this open innovation process. In contrast, research on the corresponding “receiving” side of the innovation process is scarce. We address this gap, studying how existing OSS code is reused and serves as an input to further OSS development. Our findings are based on a survey with 686 responses from OSS developers. As the most interesting results, our multivariate analyses of developers’ code reuse behavior point out that developers with larger personal networks within the OSS community and those who have experience in a greater number of OSS projects reuse more, presumably because both network size and a broad project experience facilitate local search for reusable artifacts. Moreover, we find that a development paradigm that calls for releasing an initial functioning version of the software early - as the “credible promise” in OSS - leads to increased reuse. Finally, we identify developers’ interest to tackle difficult technical challenges as detrimental to efficient reuse-based innovation. Beyond OSS, we discuss the relevance of our findings for companies developing software and for the receiving side of open innovation processes in general.


title: Online Unsupervised Multi-view Feature Selection

abstract: In this paper, we propose an Online unsupervised Multi-View Feature Selection method, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via nonnegative matrix factorization with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods.


title: A Neural Network Based Artificial Vision System for Licence Plate Recognition

abstract: This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%


title: Realistic Microwave Breast Models Through T1-Weighted 3-D MRI Data

abstract: In this paper we present an effective method for developing realistic numerical three-dimensional (3-D) microwave breast models of different shape, size, and tissue density. These models are especially convenient for microwave breast cancer imaging applications and numerical analysis of human breast–microwave interactions. As in the recent studies on this area, anatomical information of the breast tissue is collected from T1-weighted 3-D MRI data of different patients’ in prone position. The method presented in this paper offers significant improvements including efficient noise reduction and tissue segmentation, nonlinear mapping of electromagnetic properties, realistically asymmetric phantom shape, and a realistic classification of breast phantoms. Our method contains a five-step approach where each MRI voxel is classified and mapped to the appropriate dielectric properties. In the first step, the MRI data are denoised by estimating and removing the bias field from each slice, after which the voxels are segmented into two main tissues as fibro-glandular and adipose. Using the distribution of the voxel intensities in MRI histogram, two nonlinear mapping functions are generated for dielectric permittivity and conductivity profiles, which allow each MRI voxel to map to its proper dielectric properties. Obtained dielectric profiles are then converted into 3-D numerical breast phantoms using several image processing techniques, including morphologic operations, filtering. Resultant phantoms are classified according to their adipose content, which is a critical parameter that affects penetration depth during microwave breast imaging.


title: Server-based traffic shaping for stabilizing oscillating adaptive streaming players

abstract: Prior work has shown that two or more adaptive streaming players can be unstable when they compete for bandwidth. The root cause of the instability problem is that, in Steady-State, a player goes through an ON-OFF activity pattern in which it overestimates the available bandwidth. We propose a server-based traffic shaping method that can significantly reduce such oscillations without significant (or any) loss in bandwidth utilization. The shaper is only activated when oscillations are detected, and it dynamically adjusts the shaping rate so that the player should ideally receive the highest available video profile while being stable. We evaluate the proposed method experimentally in terms of instability and utilization comparing with the unshaped case, under several scenarios.


title: Architectural Data Flow Analysis

abstract: Quality properties including performance, security and compliance are crucial for a system's success but are hard to prove, especially for complex systems. Data flow analyses support this but often only consider source code and thereby introduce high costs of repair. Data flow analyses on the architectural design level use call-and-return semantics or event-based communication between components but do not define data flows as first class entities or consider important runtime or deployment configurations. We propose introducing data flows as first class entities on the architectural level. Analyses ensure that systems meet the quality requirements even after changes in e.g. runtime or deployment configurations. Having data flows modeled as first class entities allows analyzing compliance with privacy laws, requirements for external service providers, and throughput requirements in big data scenarios on architectural level. The results allow early, cost-efficient fixing of issues.


title: P2P model for distributed energy trading, grid control and ICT for local smart grids

abstract: This paper provides a view to Peer-to-Peer (P2P) approach for smart grid operation adopted in P2P-SmarTest project. It provides an overview on solutions proposed for distributed P2P energy trading, P2P grid control and wireless communication enabling the proposed P2P operation. The paper proposes some business models that can be adopted in a P2P setting. We also outline the barriers and enablers against and for adopting local or regional P2P based electricity operations.


title: Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media

abstract: Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5p of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any health-related issues, including adverse drug reactions they encounter. There is a large volume of health-consumer-contributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.


title: An Adjusted Boxplot for Skewed Distributions

abstract: The boxplot is a very popular graphical tool for visualizing the distribution of continuous unimodal data. It shows information about the location, spread, skewness as well as the tails of the data. However, when the data are skewed, usually many points exceed the whiskers and are often erroneously declared as outliers. An adjustment of the boxplot is presented that includes a robust measure of skewness in the determination of the whiskers. This results in a more accurate representation of the data and of possible outliers. Consequently, this adjusted boxplot can also be used as a fast and automatic outlier detection tool without making any parametric assumption about the distribution of the bulk of the data. Several examples and simulation results show the advantages of this new procedure.


title: Contrast and Assimilation in Consumer Software Selection Decisions - An Experimental Study

abstract: Software selection research has so far mainly focused on corporations and often assumed a rational evaluation of selection criteria. Research on consumers’ software selection is still scarce. However, findings from outside IS indicate that consumers’ software selection decisions might be biased by the choice alternatives among which software is selected. In an experiment, this study investigates if consumers’ selection decisions for software package variants are subject to contrast or assimilation and