title: DeepSketch 3 Analyzing deep neural networks features for better sketch recognition and sketch-based image retrieval

abstract: Freehand sketches are a simple and powerful tool for communication. They are easily recognized across cultures and suitable for various applications. In this paper, we use deep convolutional neural networks (ConvNets), state-of-the-art in the field of sketch recognition, to address several applications of automatic sketch processing: complete and partial sketch recognition, sketch retrieval using query-by-example (QbE), and sketch-based image retrieval (SBIR) i.e the retrieval of images using a QbE paradigm but where the query is a sketch. We first focus on improving sketch recognition. For this purpose we compare different ConvNet architectures, training paradigms and data fusion schemes. This enabled us to outperform previous state-of-the-art in two large scale benchmarks for sketch classification. We achieved a mean average accuracy of 79.18% for the TU-Berlin sketch benchmark and 93.02% for the sketchy database. For partial sketch recognition, we were able to produce a system that achieves a mean average accuracy of 52.58% with only 40% of the strokes. We then conduct a comprehensive study of ConvNets features to enhance sketch retrieval and image retrieval, using a kNN similarity search paradigm in the ConvNet feature space. For the sketch retrieval tasks, we compare the performance obtained with features extracted from various depths (ConvNet layers) using one of the best performing model from the previous work. For the sketch-based image retrieval (SBIR), a sketch query is used to retrieve images of objects that belong to the same category, or even with a shape and pose close to the sketch query. The main challenge in the field of SBIR is to obtain efficient cross-domain features for sketch-image similarity measure. For this, besides comparing features extracted from different depth, we additionally compare different training approaches (some novel) for the ConvNets applied to sketches and images. Eventually, our best SBIR system achieves state-of-the-art results on the sketchy database (close to 40% recall at k = 1).


title: Camera Pose Estimation Using Optical Flow and ORB Descriptor in SLAM-Based Mobile AR Game

abstract: A mobile augmented reality (AR) game application was developed based on simultaneous localization and mapping (SLAM). The SLAM-based AR game requires to estimate the pose from the camera input image in real time. Before running the game, point cloud data for a real-world game space is built. While the game is running, the camera pose is estimated by matching the prebuilt point cloud data and camera input image. To minimize errors in the matching, we present a hybrid method using an optical flow and ORB descriptor, where the optical flow accurately tracks the displacement of keypoints in consecutive images, and the ORB is a fast keypoint descriptor under a BSD license. The performance of the hybrid method is compared with a method using only the ORB descriptor matching. In addition, a mobile AR game embedding the hybrid method was tested in both indoor and outdoor environments.


title: Silicon microring modulator for 40 Gb/s NRZ-OOK metro networks in O-band

abstract: A microring-based silicon modulator operating at 40 Gb/s near 1310 nm is demonstrated for the first time to our knowledge. NRZ-OOK signals at 40 Gb/s with 6.2 dB extinction ratio are observed by applying a 4.8 Vpp driving voltage and biasing the modulator at 7 dB insertion loss point. The energy efficiency is 115 fJ/bit. The transmission performance of 40 Gb/s NRZ-OOK through 40 km of standard single mode fiber without dispersion compensation is also investigated. We show that the link suffers negligible dispersion penalty. This makes the modulator a potential candidate for metro network applications.


Crowdfunding : Why People Are Motivated to Post and Fund Projects on Crowdfunding Platforms


title: Energy-based anomaly detection for mixed data

abstract: Anomalies are those deviating significantly from the norm. Thus, anomaly detection amounts to finding data points located far away from their neighbors, i.e., those lying in low-density regions. Classic anomaly detection methods are largely designed for single data type such as continuous or discrete. However, real-world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Mixed data poses multiple challenges including (a) capturing the inter-type correlation structures and (b) measuring deviation from the norm under multiple types. These challenges are exaggerated under (c) high-dimensional regimes. In this paper, we propose a new scalable unsupervised anomaly detection method for mixed data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that estimates density of mixed data. We propose to use free energy derived from Mv.RBM as anomaly score as it is identical to data negative log-density up to an additive constant. We then extend this method to detect anomalies across multiple levels of data abstraction, an effective approach to deal with high-dimensional settings. The extension is dubbed \(\mathtt {MIXMAD}\), which stands for MIXed data Multilevel Anomaly Detection. In \(\mathtt {MIXMAD}\), we sequentially construct an ensemble of mixed-data Deep Belief Nets (DBNs) with varying depths. Each DBN is an energy-based detector at a predefined abstraction level. Predictions across the ensemble are finally combined via a simple rank aggregation method. The proposed methods are evaluated on a comprehensive suit of synthetic and real high-dimensional datasets. The results demonstrate that for anomaly detection, (a) a proper handling of mixed types is necessary, (b) free energy is a powerful anomaly scoring method, (c) multilevel abstraction of data is important for high-dimensional data, and (d) empirically Mv.RBM and \(\mathtt {MIXMAD}\) are superior to popular unsupervised detection methods for both homogeneous and mixed data.


title: Quality Attribute Workshops ( QAWs ) , Third Edition

abstract: Abstract : The Quality Attribute Workshop (QAW) is a facilitated method that engages system stake- holders early in the life cycle to discover the driving quality attributes of a software-intensive system. The QAW was developed to complement the Architecture Tradeoff Analysis Method(SM) (ATAM(SM)) and provides a way to identify important quality attributes and clarify system requirements before the software architecture has been created. This is the third edition of a technical report describing the QAW. We have narrowed the scope of a QAW to the creation of prioritized and refined scenarios. This report describes the newly revised QAW and describes potential uses of the refined scenarios generated during it.


title: Scanning Dynamic Competitive Landscapes: A Market-based and Resource-based Framework

abstract: Heterogeneity among rivals implies that each firm faces a unique competitive set, despite overlapping market domains. This suggests the utility of a firm-level approach to competitor identification and analysis, particularly under dynamic environmental conditions. We take such an approach in developing a market-based and resource-based framework for scanning complex competitive fields. By facilitating a search for functional similarities among products and resources, the framework reveals relevant commonalities in an otherwise heterogeneous competitive set. Beyond its practical contribution, the paper also advances resource-based theory as a theory of competitive advantage. Most notably, we show that resource substitution conditions not only the sustainability of a competitive advantage, but the attainment of competitive advantage as well. With equifinality among resources of different types, the rareness condition for even temporary competitive advantage must include resource substitutes. It is not rareness in terms of resource type that matters, but rareness in terms of resource functionality. Copyright © 2003 John Wiley & Sons, Ltd.


title: Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review

abstract: Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a model's choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling.


MarkovChainMonteCarloMaximumLikelihood CharlesJ.Geyer


title: Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013

abstract: This paper introduces an overview of Chinese Spelling Check task at SIGHAN Bake-off 2013. We describe all aspects of the task for Chinese spelling check, consisting of task description, data preparation, performance metrics, and evaluation results. This bake-off contains two subtasks, i.e., error detection and error correction. We evaluate the systems that can automatically point out the spelling errors and provide the corresponding corrections in students’ essays, summarize the performance of all participants’ submitted results, and discuss some advanced issues. The hope is that through such evaluation campaigns, more advanced Chinese spelling check techniques will be emerged.


title: Fast Algorithms for Convolutional Neural Networks

abstract: Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64.


title: Different Pre-Processing Models for Financial Accounts when Using Neural Networks for Auditing

abstract: The aim of this study is to investigate the impact of various pre-processing models on the forecast capability of artificial neural network (ANN) when auditing financial accounts. Hence, the focus of this paper is on the pre-processing of the data. ANNs are selected for auditing purposes because they are capable of learning complex, non-linear underlying relationships. Therefore, they are used to model the dynamics and the relationships between account values in order to find unexpected fluctuations. This study uses a multi-layered neural network with the backpropagation algorithm. The artificial neural network model used in this study was built by using the financial statements of 31 manufacturing companies over four years. The values of the accounts were regarded as a timeseries. The data were pre-processed in four different ways. Firstly, all the data were scaled linearly. Secondly, the data were pre-processed linearly on a yearly basis. Thirdly, the data were pre-processed linearly on a company basis. And fourthly, the data were pre-processed on a yearly and company basis. The best results were achieved when all the data were scaled either linearly or linearly on a yearly basis.


title: Fake fur rendering

abstract: A probabilistic lighting model is presented for thin coats of fur over skin. Previous methods for rendering furry objects and creatures have addressed the case where individual strands or tufts of hair may be resolvable at the pixel level. These methods are often computationally intensive. However, a large class of realworld cases where individual hairs are much smaller than the size of a pixel can be addressed using a probabilistic model for the expected value of reflected light within a small surface area. Under the assumption that hair parameters are slowly varying across the skin, lighting calculations are performed on a reference hair with prefiltered parameters. The reflected light from individual hairs and from the skin below is blended using the expectation of a ray striking a hair in that area as the opacity of the fur coating. Approximations for hair-to-hair shadowing and hair-to-skin shadowing can be made using the same hit-expectation model. Our system can be implemented in existing commercial surfacerendering software at a much lower computational cost than typical resolvable-hair methods. CR


DDoS Attack Detection Using Flow Entropy and Packet Sampling on Huge Networks


title: Auditory Expectation: The Information Dynamics of Music Perception and Cognition

abstract: Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond.


title: The Application of the Cascaded Multilevel Converters in Grid Connected Photovoltaic Systems

abstract: Unique features of multi-level converters have recently nominated them as significant alternatives for solid-state power converting units, even in the low and medium power range. The fact that multilevel converters need several DC sources in the DC side, makes them attractive for photovoltaic (PV) applications. This paper presents a new control strategy to control cascaded multilevel converters in a multi-string configuration for single phase grid connected systems. Eventually, simulation results are provided to validate the control system under various insolation conditions.


title: Robust Subspace Clustering via Smoothed Rank Approximation

abstract: Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this letter, we propose a rank approximation based on Logarithm-Determinant. We consider using this rank approximation for subspace clustering application. Our framework can model different kinds of errors and noise. Effective optimization strategy is developed with theoretical guarantee to converge to a stationary point. The proposed method gives promising results on face clustering and motion segmentation tasks compared to the state-of-the-art subspace clustering algorithms.


title: Design and development of automatic weed detection and smart herbicide sprayer robot

abstract: The conventional way of killing weeds in a crop plantation is to spray herbicides throughout the plantation. This results in contamination of the food crops and also the yield becomes less as some of the crop plants die along with the weeds. Thus there is a need for a smart weed control system. In this paper, an image processing algorithm is used to take images of the plantation rows at regular intervals and upon identifying the weeds in the image, the herbicide is sprayed directly and only on the weeds. The algorithm predominantly uses an Erosion and Dilation approach to detect weeds. The colour image is converted to binary by extracting the green parts of the image. The amount of white pixels present in the region of interest is determined and regions with higher white pixel count than the predefined threshold are considered as weeds. The herbicide is stored in a container fitted with water pump motors attached to spray nozzles. Once the weeds are identified, a signal is sent from Raspberry-Pi to the motor driver IC controlling the water pump motors to spray the chemicals over the weeds.


title: Annual Research Review: On the relations among self‐regulation, self‐control, executive functioning, effortful control, cognitive control, impulsivity, risk‐taking, and inhibition for developmental psychopathology

abstract: Background ::: Self-regulation (SR) is central to developmental psychopathology, but progress has been impeded by varying terminology and meanings across fields and literatures. ::: ::: Methods ::: The present review attempts to move that discussion forward by noting key sources of prior confusion such as measurement-concept confounding, and then arguing the following major points. ::: ::: Results ::: First, the field needs a domain-general construct of SR that encompasses SR of action, emotion, and cognition and involves both top-down and bottom-up regulatory processes. This does not assume a shared core process across emotion, action, and cognition, but is intended to provide clarity on the extent of various claims about kinds of SR. Second, top-down aspects of SR need to be integrated. These include (a) basic processes that develop early and address immediate conflict signals, such as cognitive control and effortful control (EC), and (b) complex cognition and strategies for addressing future conflict, represented by the regulatory application of complex aspects of executive functioning. Executive function (EF) and cognitive control are not identical to SR because they can be used for other activities, but account for top-down aspects of SR at the cognitive level. Third, impulsivity, risk-taking, and disinhibition are distinct although overlapping; a taxonomy of the kinds of breakdowns of SR associated with psychopathology requires their differentiation. Fourth, different aspects of the SR universe can be organized hierarchically in relation to granularity, development, and time. Low-level components assemble into high-level components. This hierarchical perspective is consistent across literatures. ::: ::: Conclusions ::: It is hoped that the framework outlined here will facilitate integration and cross-talk among investigators working from different perspectives, and facilitate individual differences research on how SR relates to developmental psychopathology.


title: Infusing Climate Change Content into Kenya’s Secondary School Taught Curriculum: Successes and Challenges

abstract: Despite the great role played by education in creating knowledge, developing skills and attitudes among people, little climate change content exists in taught curricula especially in most developing countries. This study therefore aimed at undertaking a survey on Kenya’s situation to establish steps made in infusing such content into the taught curriculum and challenges that come with such efforts. The study targeted heads of selected subjects (Agriculture, Biology, Chemistry, Physics, History, Christian Religious Education, Geography and Business Studies) from Kenya Institute of Curriculum Development. The study employed a survey research design. Purposive sampling technique was employed to select subject heads that the researcher felt that that their subjects were directly or indirectly related to climate change. The study conducted interviews from sampled respondents to ascertain the research problem and answer the research questions. Views from the sampled respondents were then analyzed and the findings are presented and discussed in this paper.


A Low-Power Text-Dependent Speaker Verification System with Narrow-Band Feature Pre-Selection and Weighted Dynamic Time Warping


title: MEMS-based embedded sensor virtual components

abstract: The design and implementation of a monolithic micro electro mechanical systems (MEMS)-based gas sensor virtual component is described. The gas sensor virtual component encloses the sensor and its associated analog circuitry into a digital shell so that all interface connections to the virtual component are digital. The system architecture supports an array of gas sensor elements. System response time is limited by the sensor and is complex. Example gas sensor characterization results are presented showing isothermal response to carbon monoxide molecules with detection


title: Algorithmic trading and information

abstract: We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is cheap and supply liquidity when it is expensive. AT contribute more to the efficient price by placing more efficient quotes and AT demanding liquidity to move the prices towards the efficient price.


title: A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization

abstract: Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.


title: Economia Aziendale: a missing connection between the theory and academic syllabi

abstract: The paper is focused on an interesting aspect of accounting education: based on the analysis of the syllabi of Economia Aziendale (EA) from all 65 Italian universities teaching economics or business administration, strong inconsistencies emerge among themselves and between syllabi and theory. The choice of the analysed subject is very relevant in Italian universities of Economics and Business Administration, since it represents a fundamental subject of the curricula and deeply impacts on financial accounting. EA is strongly related to the specific Italian approach to financial accounting compared with many other countries, and in particular with the ‘Anglo-Saxon’ and International Financial Reporting Standard system. Our findings are the following. First of all, the analysed syllabi are generally not fully consistent with the theory as it was stated at its origin, and how it is still considered today. In particular, the unitary approach of the discipline specifically stated as one of the main characteristics of this science is missing. One of the pillars of EA is, in this way, omitted or deleted. Our paper analyses the reasons of these inconsistencies and finds an important connection with these three elements: the career needs, the working constraints and the legal system of rules.


title: Siding Mode Control for PMSG based Wind Energy Conversion System connected to the grid

abstract: This paper presents a non-linear Sliding Mode Control (SMC) strategy based on continuous switching law for large-power (10MW) Permanent Magnet Synchronous Generator (PMSG) based Wind-Energy Conversion System (WECS). The WECS model consists of a wind turbine, PMSG, rectifier fed boost converter, PWM inverter and the filter. The controller is used to maintain the DC-link voltage constant and to generate controlled active and reactive power for grid. For verification of validity of proposed control scheme and model, the proposed system's dynamic model is simulated in MATLAB/Simulink. SMC has the promising features of: fast switching, insensitivity to parameter uncertainty, disturbance rejection quality, and more robustness than the conventional linear controllers. The proposed continuous control compelled the states trajectories of the system onto the surface in error space (sliding surface) for the system behavior to obtain design specifications. Simulation results demonstrate that the proposed SMC is effective and gives superior performance than conventionally tuned PI controller.


title: Phase Coherence Imaging

abstract: A new method for grating and side lobes suppression in ultrasound images is presented. It is based on an analysis of the phase diversity at the aperture data. Two coherence factors, namely the phase coherence factor (PCF) and the sign coherence factor (SCF), are proposed to weight the coherent sum output. Different from other approaches, phase rather than amplitude information is used to perform the correction action. Besides achieving the main goal, the method obtains improvements in lateral resolution and SNR. Implementation of the SCF technique is quite straightforward, operating in realtime, and can be added to any virtually existing beamformer to improve the resolution, contrast, SNR, and dynamic range of the images. A programmable parameter allows adjusting the sensitivity of the method to out-of-phase signals, from zero to a strict coherence criterion. The theoretical basis for the 2 methods are given and their performances evaluated by simulation. Then, experiments are conducted to provide results that are in good agreement with those expected from theory and simulation.


title: Analysis of first-order anti-aliasing integration sampler

abstract: Performance of the first-order anti-aliasing integration sampler used in software-defined radio (SDR) receivers is analyzed versus all practical nonidealities. The nonidealities that are considered in this paper are transconductor finite output resistance, switch resistance, nonzero rise and fall times of the sampling clock, charge injection, clock jitter, and noise. It is proved that the filter is quite robust to all of these nonidealities except for transconductor finite output resistance. Furthermore, linearity and noise performances are all limited to design of a low-noise and highly linear transconductor.


title: High-fidelity facial and speech animation for VR HMDs

abstract: Significant challenges currently prohibit expressive interaction in virtual reality (VR). Occlusions introduced by head-mounted displays (HMDs) make existing facial tracking techniques intractable, and even state-of-the-art techniques used for real-time facial tracking in unconstrained environments fail to capture subtle details of the user's facial expressions that are essential for compelling speech animation. We introduce a novel system for HMD users to control a digital avatar in real-time while producing plausible speech animation and emotional expressions. Using a monocular camera attached to an HMD, we record multiple subjects performing various facial expressions and speaking several phonetically-balanced sentences. These images are used with artist-generated animation data corresponding to these sequences to train a convolutional neural network (CNN) to regress images of a user's mouth region to the parameters that control a digital avatar. To make training this system more tractable, we use audio-based alignment techniques to map images of multiple users making the same utterance to the corresponding animation parameters. We demonstrate that this approach is also feasible for tracking the expressions around the user's eye region with an internal infrared (IR) camera, thereby enabling full facial tracking. This system requires no user-specific calibration, uses easily obtainable consumer hardware, and produces high-quality animations of speech and emotional expressions. Finally, we demonstrate the quality of our system on a variety of subjects and evaluate its performance against state-of-the-art real-time facial tracking techniques.


title: SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

abstract: Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and not being scalable. In this paper, we aim to overcome these drawbacks. Toward this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then, SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real-world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.


title: Gravity anomalies and segmentation of the Eastern USA passive continental margin

abstract: A compilation of high quality post-rift sediment isopach data has been used in conjunction with the observed free-air gravity anomaly to determine segmentation of the long term mechanical properties of the lithosphere at the Eastern USA passive margin. This segmentation is represented by a process-oriented analysis in which the flexural response of the margin to post-rift sediment loading is controlled by spatial variations in effective elastic thickness ( T e ) of the underlying lithosphere. Existing Eastern USA margin T e estimates range from less than 10 km to more than 30 km . In this study it is shown that high strengths of 10 - 40 km T e are confined to structural arches dividing the broadest marginal basins, while low strengths of less than 10 km T e are typically found in structural embayments and beneath the deep basins. The hinge zone, across which the degree of continental thinning increases rapidly, marks the transition between high and low strength. Yield strength envelope models support an argument that regions of low strength were created by lithospheric thinning during rifting, and sustained by thermal insulation and flexural curvature associated with voluminous post-rift sediment deposition. Along-strike T e variations - reflected in the alternation of basement platforms and embayments - are attributed to inheritance of lithospheric segmentation from earlier tectonic events. Along-strike segmentation of the margin has previously been observed as a 300 - 500 km wavelength spectral energy peak in the shelf break Airy isostatic gravity anomaly (IGA) high. That this segmentation is explained by variations in the underlying lithospheric strength is demonstrated by a flexural IGA high in which the equivalent spectral peak is absent. The spectral energy of the along-strike T e distribution peaks in the same waveband. Removal of process-oriented components from the observed free-air gravity anomaly reveals other contributions that were not resolved in earlier studies. In particular, the (previously unknown) Carolina Trough Isostatic Gravity Anomaly, has been identified and attributed to an extrusive (syn-rift) volcanic source. Detailed study of this anomaly suggests that the margin is segmented in terms of its volcanic character, and argues against recent estimates of the volume of new igneous material emplaced during rifting.


title: On the closed-form solution of the rotation matrix arising in computer vision problems

abstract: We show the closed-form solution to the maximization of trace(A'R), where A is given and R is unknown rotation matrix. This problem occurs in many computer vision tasks involving optimal rotation matrix estimation. The solution has been continuously reinvented in different fields as part of specific problems. We summarize the historical evolution of the problem and present the general proof of the solution. We contribute to the proof by considering the degenerate cases of A and discuss the uniqueness of R.


title: R.: On effectively finding maximal quasi-cliques in graphs

abstract: The problem of finding a maximum clique in a graph is prototypical for many clustering and similarity problems; however, in many real-world scenarios, the classical problem of finding a complete subgraph needs to be relaxed to finding an almost complete subgraph, a so-called quasi-clique . In this work, we demonstrate how two previously existing definitions of quasi-cliques can be unified and how the resulting, more general quasi-clique finding problem can be solved by extending two state-of-the-art stochastic local search algorithms for the classical maximum clique problem. Preliminary results for these algorithms applied to both, artificial and real-world problem instances demonstrate the usefulness of the new quasi-clique definition and the effectiveness of our algorithms.


title: Screen Unlocking by Spontaneous Flick Reactions with One-Class Classification Approaches

abstract: Physical biometrics technologies are introduced to the login process on smart devices. However, many of them have several disadvantages: requirement of embedding special sensor, limited environment to use and copy of key information for authentication. In this research, we proposed a new biometrics technique which can capture user's inimitable behavioral features in his/her spontaneous flick reactions on a touch-screen display for unlocking the device when it wakes up. For practical use of the technique, we adopted one-class classification approaches and they achieved about 1-2% EERs for 2500 samples from 50 subjects.


title: AirShare: Distributed coherent transmission made seamless

abstract: Distributed coherent transmission is necessary for a variety of high-gain communication protocols such as distributed MIMO and creating codes over the air. Unfortunately, however, distributed coherent transmission is intrinsically difficult because different nodes are driven by independent clocks, which do not have the exact same frequency. This causes the nodes to have frequency offsets relative to each other, and hence their transmissions fail to combine coherently over the air. This paper presents AirShare, a primitive that makes distributed coherent transmission seamless. AirShare transmits a shared clock on the air and feeds it to the wireless nodes as a reference clock, hence eliminating the root cause for incoherent transmissions. The paper addresses the challenges in designing and delivering such a shared clock. It also implements AirShare in a network of USRP software radios, and demonstrates that it achieves tight phase coherence. Further, to illustrate AirShare's versatility, the paper uses it to deliver a coherent-radio abstraction on top of which it demonstrates two cooperative protocols: distributed MIMO, and distributed rate adaptation.


title: The X-ray spectra of galaxies. II - Average spectral properties and emission mechanisms

abstract: The Imaging Proportional Counter data in the Einstein database is used to study the X-ray spectra of normal galaxies. The X-ray emission temperature of spirals is found to be higher on the average than that of ellipticals. This supports the idea that accreting binaries are a major source of X-rays in spirals, while a hot interstellar medium (ISM) is present in ellipticals. The X-ray spectra of Sa galaxies are intermediate between those of ellipticals and spirals, suggesting that these galaxies contain hot gaseous emission as well as emission from accreting binaries. In E and SO galaxies the emission temperature becomes higher with a decreasing X-ray to optical luminosity ratio, which suggests that the emission of X-ray faint early-type galaxies consists of a large evolved stellar component, while the gaseous emission becomes dominant in X-ray brighter galaxies. The group with the lowest X-ray to optical ratio does not follow this trend; in these galaxies a very soft X-ray component, amounting to about half the total X-ray emission, is found in addition to the hard X-ray component. Possible explanations are integrated emission of M stars and a relatively cool ISM. A very soft component is also found in several spiral galaxies. This may indicate that some spirals contain hot gaseous components similar to those seen in NGC 253 and M82.


title: AutoMashUpper: Automatic Creation of Multi-Song Music Mashups

abstract: In this paper we present a system, AutoMashUpper, for making multi-song music mashups. Central to our system is a measure of "mashability" calculated between phrase sections of an input song and songs in a music collection. We define mashability in terms of harmonic and rhythmic similarity and a measure of spectral balance. The principal novelty in our approach centres on the determination of how elements of songs can be made fit together using key transposition and tempo modification, rather than based on their unaltered properties. In this way, the properties of two songs used to model their mashability can be altered with respect to transformations performed to maximize their perceptual compatibility. AutoMashUpper has a user interface to allow users to control the parameterization of the mashability estimation. It allows users to define ranges for key shifts and tempo as well as adding, changing or removing elements from the created mashups. We evaluate AutoMashUpper by its ability to reliably segment music signals into phrase sections, and also via a listening test to examine the relationship between estimated mashability and user enjoyment.


title: Information extraction for knowledge base construction in the music domain

abstract: The rate at which information about music is being created and shared on the web is growing exponentially. However, the challenge of making sense of all this data remains an open problem. In this paper, we present and evaluate an Information Extraction pipeline aimed at the construction of a Music Knowledge Base. Our approach starts off by collecting thousands of stories about songs from the songfacts.com website. Then, we combine a state-of-the-art Entity Linking tool and a linguistically motivated rule-based algorithm to extract semantic relations between entity pairs. Next, relations with similar semantics are grouped into clusters by exploiting syntactic dependencies. These relations are ranked thanks to a novel confidence measure based on statistical and linguistic evidence. Evaluation is carried out intrinsically, by assessing each component of the pipeline, as well as in an extrinsic task, in which we evaluate the contribution of natural language explanations in music recommendation. We demonstrate that our method is able to discover novel facts with high precision, which are missing in current generic as well as music-specific knowledge repositories. A system that constructs a Music Knowledge Base entirely from scratch.A method for clustering and scoring relations in a Relation Extraction pipeline.Reveals music facts absent from knowledge repositories (e.g. Wikipedia).Explains music recommendations in natural language.


title: Quantitative Serial MRI of the Treated Fibroid Uterus

abstract: Objective ::: There are no long-term medical treatments for uterine fibroids, and non-invasive biomarkers are needed to evaluate novel therapeutic interventions. The aim of this study was to determine whether serial dynamic contrast-enhanced MRI (DCE-MRI) and magnetization transfer MRI (MT-MRI) are able to detect changes that accompany volume reduction in patients administered GnRH analogue drugs, a treatment which is known to reduce fibroid volume and perfusion. Our secondary aim was to determine whether rapid suppression of ovarian activity by combining GnRH agonist and antagonist therapies results in faster volume reduction.


title: Clustering appearances of objects under varying illumination conditions

abstract: We introduce two appearance-based methods for clustering a set of images of 3D (three-dimensional) objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity, which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.


title: The advanced data acquisition model (ADAM): A process model for digital forensic practice

abstract: As with other types of evidence, the courts make no presumption that digital evidence is reliable without some evidence of empirical testing in relation to the theories and techniques associated with its production. The issue of reliability means that courts pay close attention to the manner in which electronic evidence has been obtained and in particular the process in which the data is captured and stored. Previous process models have tended to focus on one particular area of digital forensic practice, such as law enforcement, and have not incorporated a formal description. We contend that this approach has prevented the establishment of generally accepted standards and processes that are urgently needed in the domain of digital forensics. This paper presents a generic process model as a step towards developing such a generally-accepted standard for a fundamental digital forensic activity-the acquisition of digital evidence.


title: Analysis of standard elliptic curves for the implementation of elliptic curve cryptography in resource-constrained E-commerce applications

abstract: In the public key cryptography infrastructure of electronic commerce (E-commerce), use of the Rivest, Shamir, and Adleman (RSA) algorithm is very common. Due to the security requirements, the necessary key size of RSA is increasing exponentially. Because of this large key size, it is not convenient to use RSA in resource-constrained applications where computational speed, storage and bandwidth are limited. Elliptic Curve Cryptography (ECC) is the best alternative to the traditional RSA. ECC provides the same level of security as RSA but with reduced key size. The most important factor in the use of ECC is the selection of the correct elliptic curve. There are different types of elliptic curves suggested by various standards. The selection of the curve is dependent on various parameters, such as prime field size, the shape of the curve, etc. In this paper, sets of elliptic curves recommended by different standards for cryptography are selected, and the selected curves are analysed, focusing on the performance and security features. The performance of each curve is measured in terms of computation time. The analysis is performed by considering each curve for the implementation of the Elliptic Curve Diffie-Hellman (ECDH) algorithm and the Elliptic Curve Digital Signature Algorithm (ECDSA). The comparative table of selected curves is arranged according to the computation time taken by each curve to perform various operations when used for the ECC algorithms.


title: Design of Reversible/Quantum Ternary Multiplexer and Demultiplexer

abstract: In this paper, we show realization of macro- level ternary reversible 2-qudit Feynman gate, 3-qudit controlled Feynman gate, and 3-qudit Toffoli gates using ternary reversible 1-qudit gates and 2-qudit Muthukrishnan-Stroud gates, which are theoretically realizable using quantum technology such as liquid ion trap (10). Then we show the design of ternary reversible multiplexer and demultiplexer using the macro-level gates.


title: A fair protocol for data trading based on Bitcoin transactions

abstract: Abstract On-line commercial transactions involve an inherent mistrust between participant parties since, sometimes, no previous relation exists between them. Such mistrust may be a deadlock point in a trade transaction where the buyer does not want to perform the payment until the seller sends the goods and the seller does not want to do so until the buyer pays for the purchase. In this paper we present a fair protocol for data trading where the commercial deal, in terms of delivering the data and performing the payment, is atomic, since the seller cannot redeem the payment unless the buyer obtains the data and the buyer cannot obtain the data without performing the payment. The protocol is based on Bitcoin scripting language and the fairness of the protocol can be probabilistically enforced.


title: D$^2$: Decentralized Training over Decentralized Data

abstract: While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from different workers can be {\em unique} and {\em different}. Ironically, recent analysis of decentralized parallel stochastic gradient descent (D-PSGD) relies on the assumption that the data hosted on different workers are {\em not too different}. In this paper, we ask the question: {\em Can we design a decentralized parallel stochastic gradient descent algorithm that is less sensitive to the data variance across workers?} In this paper, we present D$^2$, a novel decentralized parallel stochastic gradient descent algorithm designed for large data variance \xr{among workers} (imprecisely, "decentralized" data). The core of D$^2$ is a variance blackuction extension of the standard D-PSGD algorithm, which improves the convergence rate from $O\left({\sigma \over \sqrt{nT}} + {(n\zeta^2)^{\frac{1}{3}} \over T^{2/3}}\right)$ to $O\left({\sigma \over \sqrt{nT}}\right)$ where $\zeta^{2}$ denotes the variance among data on different workers. As a result, D$^2$ is robust to data variance among workers. We empirically evaluated D$^2$ on image classification tasks where each worker has access to only the data of a limited set of labels, and find that D$^2$ significantly outperforms D-PSGD.


title: Segmental acoustic indexing for zero resource keyword search

abstract: The task of zero resource query-by-example keyword search has received much attention in recent years as the speech technology needs of the developing world grow. These systems traditionally rely upon dynamic time warping (DTW) based retrieval algorithms with runtimes that are linear in the size of the search collection. As a result, their scalability substantially lags that of their supervised counterparts, which take advantage of efficient word-based indices. In this paper, we present a novel audio indexing approach called Segmental Randomized Acoustic Indexing and Logarithmic-time Search (S-RAILS). S-RAILS generalizes the original frame-based RAILS methodology to word-scale segments by exploiting a recently proposed acoustic segment embedding technique. By indexing word-scale segments directly, we avoid higher cost frame-based processing of RAILS while taking advantage of the improved lexical discrimination of the embeddings. Using the same conversational telephone speech benchmark, we demonstrate major improvements in both speed and accuracy over the original RAILS system.


title: Visualization of large hierarchical data by circle packing

abstract: In this paper a novel approach is described for tree visualization using nested circles. The brother nodes at the same level are represented by externally tangent circles; the tree nodes at different levels are displayed by using 2D nested circles or 3D nested cylinders. A new layout algorithm for tree structure is described. It provides a good overview for large data sets. It is easy to see all the branches and leaves of the tree. The new method has been applied to the visualization of file systems.


Master Data Management Roles - Their Part in Data Quality Implementation.


Performance of the “if in doubt, cut it out” rule for the management of nodular melanoma


The rôle of oxyhemoglobin and its derivatives in the pathogenesis of experimental hemoglobinuric nephrosis.


title: Illustrating the Power of fsQCA in Explaining Paradoxical Consumer Environmental Orientations

abstract: Prior research on proenvironmental and prosocial behavior focuses primarily on explaining consistent rather than paradoxical tendencies. Even though this field receives wide attention from different scientific disciplines, findings for many causal factors of such proenvironmental orientation are contradictory. Nevertheless, knowing who those individuals are who think and behave in a pro-/antienvironmental way or show a paradoxical behavior in this regard becomes useful for many different parties in human societies including public policy makers, governmental and nongovernmental environmental protection organizations, and for-profit firms. Therefore, this study identifies those individuals who show neither consistent proenvironmental nor consistent antienvironmental tendencies as the “walkers-only” and “talkers-only” (i.e., for short, “walkers” and “talkers”). The former are defined as persons who put much effort into the recycling of waste materials but do not support pollution standards, whereas the latter term describes individuals who have a strong opinion with regard to the support of pollution standards yet do not engage in recycling efforts. The present study reports evidence of the existence of walkers and talkers. Further, this research is the first study to employ “fuzzy-set qualitative comparative analysis” to identify the complex antecedent conditions for some individuals’ paradoxical belief–behaviors in the field of socially and environmentally directed behaviors and orientations. The findings yield valuable insights both into the applicability and benefits of configural analysis and for public policy makers and managers in waste management and recycling industries.


title: Detecting kernel-level rootkits through binary analysis

abstract: A rootkit is a collection of tools used by intruders to keep the legitimate users and administrators of a compromised machine unaware of their presence. Originally, root-kits mainly included modified versions of system auditing programs (e.g., ps or netstat on a Unix system). However, for operating systems that support loadable kernel modules (e.g., Linux and Solaris), a new type of rootkit has recently emerged. These rootkits are implemented as kernel modules, and they do not require modification of user-space binaries to conceal malicious activity. Instead, these rootkits operate within the kernel, modifying critical data structures such as the system call table or the list of currently-loaded kernel modules. This paper presents a technique that exploits binary analysis to ascertain, at load time, if a module's behavior resembles the behavior of a rootkit. Through this method, it is possible to provide additional protection against this type of malicious modification of the kernel. Our technique relies on an abstract model of module behavior that is not affected by small changes in the binary image of the module. Therefore, the technique is resistant to attempts to conceal the malicious nature of a kernel module.


title: Implementing computer-based assessment - A web-based mock examination changes attitudes

abstract: Interest in the educational use of information and communication technologies (ICT) in higher education is increasing. For successful implementation, it is important to know students' attitudes and reservations and how they can be positively influenced. The objective of the present study was to examine possible attitudinal changes towards computer-based assessment (CBA) in students, after undergoing one such assessment. A web-based mock examination was provided to all fourth year medical students at Leipzig Medical School in 2008 and 2009. Before and after the web test, students were asked to document their agreement with statements concerning CBA. A large number of students made use of the offered web-based assessment. 383 participants could be analysed for the pre-post comparisons. The majority of the students rated their computer self-efficacy as high. In summary, students' attitudes towards CBA in higher education tended to be positive. Gender differences seemed to be substantially influenced by differences in computer self-efficacy and were reduced considerably after only one practical experience. The actual experience had a positive influence on attitudes towards CBA. Nevertheless there were strong reservations about technical problems influencing the test performance when used for summative assessment. These concerns should not be ignored when trying to implement CBA. Optional formative CBA, perhaps early in higher education, seems to be a promising possibility of attracting students to computer- or web-based examination and learning methods, and may be a useful component of a successful implementation strategy.


title: Why CSCW applications fail: problems in the design and evaluationof organizational interfaces

abstract: Many systems, applications, and features that support cooperative work share two characteristics: A significant investment has been made in their development, and their successes have consistently fallen far short of expectations. Examination of several application areas reveals a common dynamic: 1) A factor contributing to the application’s failure is the disparity between those who will benefit from an application and those who must do additional work to support it. 2) A factor contributing to the decision-making failure that leads to ill-fated development efforts is the unique lack of management intuition for CSCW applications. 3) A factor contributing to the failure to learn from experience is the extreme difficulty of evaluating these applications. These three problem areas escape adequate notice due to two natural but ultimately misleading analogies: the analogy between multi-user application programs and multi-user computer systems, and the analogy between multi-user applications and single-user applications. These analogies influence the way we think about cooperative work applications and designers and decision-makers fail to recognize their limits. Several CSCW application areas are examined in some detail. Introduction. An illustrative example: automatic meeting


title: Programming models and HW-SW interfaces abstraction for multi-processor SoC

abstract: For the design of classic computers the Parallel programming concept is used to abstract HW/SW interfaces during high level specification of application software. The software is then adapted to an existing multiprocessor platforms using a low level software layers that implement the programming model. Unlike classic computers, the design of heterogeneous MPSoC includes also building the processors and other kind of hardware components required to execute the software. In this case, the programming model hides both hardware and software refinements. This paper deals with parallel programming models to abstract both hardware and software Interfaces in the case of heterogeneous MPSoC design. Different abstraction levels will be needed. For the long term, the use of higher level programming models will open new vistas for optimization and architecture exploration like CPU/RTOS tradeoffs.


title: A Pilot Study of the DBT Coach: An Interactive Mobile Phone Application for Individuals with Borderline Personality Disorder and Substance Use Disorder

abstract: Abstract Dialectical behavior therapy (DBT) has received strong empirical support and is practiced widely as a treatment for borderline personality disorder (BPD) and BPD with comorbid substance use disorders (BPD-SUD). Therapeutic success in DBT requires that individuals generalize newly acquired skills to their natural environment. However, there have been only a limited number of options available to achieve this end. The primary goal of this research was to develop and test the feasibility of the DBT Coach, a software application for a smartphone, designed specifically to enhance generalization of a specific DBT skill (opposite action) among individuals with BPD-SUD. We conducted a quasiexperimental study in which 22 individuals who were enrolled in DBT treatment programs received a smartphone with the DBT Coach for 10 to 14 days and were instructed to use it as needed. Participants used the DBT Coach an average of nearly 15 times and gave high ratings of helpfulness and usability. Results indicate that both emotion intensity and urges to use substances significantly decreased within each coaching session. Furthermore, over the trial period, participants reported a decrease in depression and general distress. Mobile technology offering in vivo skills coaching may be a useful tool for reducing urges to use substances and engage in other maladaptive behavior by directly teaching and coaching in alternative, adaptive coping behavior.


title: Parkinson's Disease Motor Symptoms in Machine Learning: A Review

abstract: This paper reviews related work and state-of-the-art publications for recognizing motor symptoms of Parkinson's Disease (PD). It presents research efforts that were undertaken to inform on how well traditional machine learning algorithms can handle this task. In particular, four PD related motor symptoms are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia) and their details summarized. Thus the primary objective of this research is to provide a literary foundation for development and improvement of algorithms for detecting PD related motor symptoms.


title: A Simple Yet Effective Baseline for 3d Human Pose Estimation

abstract: Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3- dimensional positions.,,With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feedforward network outperforms the best reported result by about 30% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (i.e., using images as input) yields state of the art results – this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.


title: A survey and taxonomy of approaches for mining software repositories in the context of software evolution

abstract: A comprehensive literature survey on approaches for mining software repositories (MSR) in the context of software evolution is presented. In particular, this survey deals with those investigations that examine multiple versions of software artifacts or other temporal information. A taxonomy is derived from the analysis of this literature and presents the work via four dimensions: the type of software repositories mined (what), the purpose (why), the adopted/invented methodology used (how), and the evaluation method (quality). The taxonomy is demonstrated to be expressive (i.e., capable of representing a wide spectrum of MSR investigations) and effective (i.e., facilitates similarities and comparisons of MSR investigations). Lastly, a number of open research issues in MSR that require further investigation are identified.


title: DisARM: Mitigating Buffer Overflow Attacks on Embedded Devices

abstract: Security of embedded devices today is a critical requirement for the Internet of Things (IoT) as these devices will access sensitive information such as social security numbers and health records. This makes these devices a lucrative target for attacks exploiting vulnerabilities to inject malicious code or reuse existing code to alter the execution of their software. Existing defense techniques have major drawbacks such as requiring source code or symbolic debugging information, and high overhead, limiting their applicability. In this paper we propose a novel defense technique, DisARM, that protects against both code-injection and code-reuse based buffer overflow attacks by breaking the ability for attackers to manipulate the return address of a function. Our approach operates on arbitrary executable binaries and thus does not require compiler support. In addition it does not require user interactions and can thus be automatically applied. Our experimental results show that our approach incurs low overhead and significantly increases the level of security against both code-injection and code-reuse based attacks.


Title: Code Smell Detection in Eclipse


title: Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

abstract: In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality


title: Shared-Memory Parallelism Can be Simple, Fast, and Scalable

abstract: Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many different settings. This book, a revised version of the thesis that won the 2015 ACM Doctoral Dissertation Award, addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. It provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice. The results serve to ease the transition into the multicore era. ::: ::: The book starts by introducing tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The book then introduces Ligra, the first high-level shared-memory framework for parallel graph traversal algorithms. The framework enables short and concise implementations that deliver performance competitive with that of highly optimized code and up to orders of magnitude faster than previous systems designed for distributed memory. Finally, the book bridges the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are both practical and theoretically efficient.


title: Bahurupi: A polymorphic heterogeneous multi-core architecture

abstract: Computing systems have made an irreversible transition towards parallel architectures with the emergence of multi-cores. Moreover, power and thermal limits in embedded systems mandate the deployment of many simpler cores rather than a few complex cores on chip. Consumer electronic devices, on the other hand, need to support an ever-changing set of diverse applications with varying performance demands. While some applications can benefit from thread-level parallelism offered by multi-core solutions, there still exist a large number of applications with substantial amount of sequential code. The sequential programs suffer from limited exploitation of instruction-level parallelism in simple cores. We propose a reconfigurable multi-core architecture, called Bahurupi, that can successfully reconcile the conflicting demands of instruction-level and thread-level parallelism. Bahurupi can accelerate the performance of serial code by dynamically forming coalition of two or more simple cores to offer increased instruction-level parallelism. In particular, Bahurupi can efficiently merge 2-4 simple 2-way out-of-order cores to reach or even surpass the performance of more complex and power-hungry 4-way or 8-way out-of-order core. Compared to baseline 2-way core, quad-core Bahurupi achieves up to 5.61 speedup (average 4.08 speedup) for embedded workloads. On an average, quad-core Bahurupi achieves 17p performance improvement and 43p improvement in energy consumption compared to 8-way out-of-order baseline core on a diverse set of embedded benchmark applications.


Climate Control in Mediterranean Greenhouses


The Impact of Newsroom Philosophy on Story Ideation and Story Narration


SUT System Description for Anti-Spoofing 2017 Challenge.


title: Automatically Evaluating Text Coherence Using Discourse Relations

abstract: We present a novel model to represent and assess the discourse coherence of text. Our model assumes that coherent text implicitly favors certain types of discourse relation transitions. We implement this model and apply it towards the text ordering ranking task, which aims to discern an original text from a permuted ordering of its sentences. The experimental results demonstrate that our model is able to significantly outperform the state-of-the-art coherence model by Barzilay and Lapata (2005), reducing the error rate of the previous approach by an average of 29% over three data sets against human upper bounds. We further show that our model is synergistic with the previous approach, demonstrating an error reduction of 73% when the features from both models are combined for the task.


title: A Framework and Architecture for Multi-Robot Coordination

abstract: In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.


title: Prevalence and Risk Factors of Video Game Dependency in Adolescence: Results of a German Nationwide Survey

abstract: Abstract In this article, results of a German nationwide survey (KFN schools survey 2007/2008) are presented. The controlled sample of 44,610 male and female ninth-graders was carried out in 2007 and 2008 by the Criminological Research Institute of Lower Saxony (KFN). According to a newly developed screening instrument (KFN-CSAS-II), which was presented to every third juvenile participant (N = 15,168), 3% of the male and 0.3% of the female students are diagnosed as dependent on video games. The data indicate a clear dividing line between extensive gaming and video game dependency (VGD) as a clinically relevant phenomenon. VGD is accompanied by increased levels of psychological and social stress in the form of lower school achievement, increased truancy, reduced sleep time, limited leisure activities, and increased thoughts of committing suicide. In addition, it becomes evident that personal risk factors are crucial for VGD. The findings indicate the necessity of additional research as well as the respecti...


title: Improving Search Result Clustering by Enriching Snippets with Word2Vec Model

abstract: Search Result Clustering (SRC) is an approach to solve the problems of web search engines under user's broad or ambiguous queries and no clues to find exact information in a long returned list. SRC groups the returned list and outputs a semantic structured organization to help users to find the desired information quickly. In a general way, the search engine results consist of concise information(snippet) about matching page documents. Because the snippet is short and carries few information, the clustering performance based on traditional TF-IDF weight is very low. An effective way to solve this problem is to enrich snippets according the semantic relationship from external text corpus. In this paper, we propose a new snippet enriching approach throughout word2vec model. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various Natural Language Processing(NLP) tasks. We propose to use the top-n similar words in word2vec model to enrich snippets and we still use traditional TF-IDF weights schema to select features. In order to demonstrate the effectiveness of our method, we design intensive experiments to evaluate new method and baseline methods. The result of the analysis shows that proposed method outperforms baseline approach significantly.


title: Generative Visual Rationales

abstract: Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique that addresses both these issues by leveraging large unlabelled datasets to encode and decode images into a dense latent representation. Using chest radiography as an example, we apply this encoder to other labelled datasets and apply simple models to the latent vectors to learn algorithms to identify heart failure. ::: For each prediction, we generate visual rationales by optimizing a latent representation to minimize the prediction of disease while constrained by a similarity measure in image space. Decoding the resultant latent representation produces an image without apparent disease. The difference between the original decoding and the altered image forms an interpretable visual rationale for the algorithm's prediction on that image. We also apply our method to the MNIST dataset and compare the generated rationales to other techniques described in the literature.


title: Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion

abstract: This study draws upon affective events theory, research regarding funders' perceptions, and research regarding expectation alignment between products and their presenters to develop and test an indirect effects model of crowdfunding resource allocation decisions. To test our hypothesized relationships, we drew upon a sample of 102 participants who each assessed ten different product pitches made by ten different entrepreneurs. Results from the study indicate that perceived product creativity is positively related to crowdfunding performance, both directly and indirectly, via positive affective reactions of prospective funders. Moreover, we find the indirect effect of product creativity is contingent upon the extent to which funders perceive an entrepreneur to be passionate, such that perceived entrepreneurial passion increases the positive nature of the indirect effect. Implications for future theory development, empirical research and implications for practitioners are discussed as well.


title: Error-backpropagation in temporally encoded networks of spiking neurons

abstract: Abstract For a network of spiking neurons that encodes information in the timing of individual spike times, we derive a supervised learning rule, SpikeProp, akin to traditional error-backpropagation. With this algorithm, we demonstrate how networks of spiking neurons with biologically reasonable action potentials can perform complex non-linear classification in fast temporal coding just as well as rate-coded networks. We perform experiments for the classical XOR problem, when posed in a temporal setting, as well as for a number of other benchmark datasets. Comparing the (implicit) number of spiking neurons required for the encoding of the interpolated XOR problem, the trained networks demonstrate that temporal coding is a viable code for fast neural information processing, and as such requires less neurons than instantaneous rate-coding. Furthermore, we find that reliable temporal computation in the spiking networks was only accomplished when using spike response functions with a time constant longer than the coding interval, as has been predicted by theoretical considerations.


title: Physico-chemical characterization of anhydrous D-mannitol

abstract: In this work the solid-state characterization of anhydrous D-mannitol has been performed: α and β modifications can be distinguished only by XRPD and FTIR as they show melting temperature and enthalpy that are the same within the standard deviation. The understanding of the thermal behaviour of the δ form (obtained by re-crystallization in acetone) has required XRPD experiments performed at variable temperature. This form during heating undergoes a solid phase transition to α modification. By cooling a melted sample, under a wide range of experimental conditions, a very fast crystallization occurs. Independently of the starting crystal form (β or δ form), the re-crystallization of D-mannitol from melt always leads to α form.


title: Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

abstract: Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors. This oncoming flood of data begs the question of how we will extract useful information from it. In this paper we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in high resolution, multichannel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.


title: Analytics to Literacies: The Development of a Learning Analytics Framework for Multiliteracies Assessment.

abstract: The rapid advances in information and communication technologies, coupled with increased access to information and the formation of global communities, have resulted in interest among researchers and academics to revise educational practice to move beyond traditional ‘literacy’ skills towards an enhanced set of “multiliteracies” or “new media literacies”. Measuring the literacy of a population, in the light of its linkage to individual and community wealth and wellbeing, is essential to determining the impact of compulsory education. The opportunity now is to develop tools to assess individual and societal attainment of these new literacies. Drawing on the work of Jenkins and colleagues (2006) and notions of a participatory culture, this paper proposes a conceptual framework for how learning analytics can assist in measuring individual achievement of multiliteracies and how this evaluative process can be scaled to provide an institutional perspective of the educational progress in fostering these fundamental skills.


title: Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback.

abstract: In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing works. First, most works perform deep content feature learning and resort to matrix factorization, which cannot effectively model the highly complex user-item interaction function. Second, due to the difficulty on training deep neural networks, existing models utilize a shallow architecture, and thus limit the expressiveness potential of deep learning. Third, neural network models are easy to overfit on the implicit setting, because negative interactions are not taken into account. To tackle these issues, we present a novel recommender framework called Deep Collaborative Autoencoder (DCAE) for both explicit feedback and implicit feedback, which can effectively capture the relationship between interactions via its non-linear expressiveness. To optimize the deep architecture of DCAE, we develop a three-stage pre-training mechanism that combines supervised and unsupervised feature learning. Moreover, we propose a popularity-based error reweighting module and a sparsity-aware data-augmentation strategy for DCAE to prevent overfitting on the implicit setting. Extensive experiments on three real-world datasets demonstrate that DCAE can significantly advance the state-of-the-art.


title: The Problem with Kappa

abstract: It is becoming clear that traditional evaluation measures used in Computational Linguistics (including Error Rates, Accuracy, Recall, Precision and F-measure) are of limited value for unbiased evaluation of systems, and are not meaningful for comparison of algorithms unless both the dataset and algorithm parameters are strictly controlled for skew (Prevalence and Bias). The use of techniques originally designed for other purposes, in particular Receiver Operating Characteristics Area Under Curve, plus variants of Kappa, have been proposed to fill the void. ::: ::: This paper aims to clear up some of the confusion relating to evaluation, by demonstrating that the usefulness of each evaluation method is highly dependent on the assumptions made about the distributions of the dataset and the underlying populations. The behaviour of a number of evaluation measures is compared under common assumptions. ::: ::: Deploying a system in a context which has the opposite skew from its validation set can be expected to approximately negate Fleiss Kappa and halve Cohen Kappa but leave Powers Kappa unchanged. For most performance evaluation purposes, the latter is thus most appropriate, whilst for comparison of behaviour, Matthews Correlation is recommended.


title: Interpretable and Informative Explanations of Outcomes

abstract: In this paper, we solve the following data summarization problem: given a multi-dimensional data set augmented with a binary attribute, how can we construct an interpretable and informative summary of the factors affecting the binary attribute in terms of the combinations of values of the dimension attributes? We refer to such summaries as explanation tables. We show the hardness of constructing optimally-informative explanation tables from data, and we propose effective and efficient heuristics. The proposed heuristics are based on sampling and include optimizations related to computing the information content of a summary from a sample of the data. Using real data sets, we demonstrate the advantages of explanation tables compared to related approaches that can be adapted to solve our problem, and we show significant performance benefits of our optimizations.


Numerical guidelines for selection of implant supported prostheses for completely edentulous patients


title: A Quantitative Meta-Analysis of Functional Imaging Studies of Social Rejection

abstract: Early neuroimaging studies using Cyberball suggested that social rejection activated the pain matrix, as identified in studies of physical pain. However, these early studies were characterized by small sample sizes. Our statistical multi-level kernel density analysis (MKDA) of Cyberball neuroimaging studies with 244 participants fails to support the claim that social rejection operates on the same pain matrix as nociceptive stimuli, questioning whether social pain is more figurative or literal. We also performed an MKDA of the neuroimaging studies of reliving a romantic rejection to test whether the pain matrix was activated if the rejection were more meaningful. Results again failed to support the notion that rejection activates the neural matrix identified in studies of physical pain. Reliving an unwanted rejection by a romantic partner was significantly characterized by activation within and beyond the ‘‘Cyberball’’ brain network, suggesting that the neural correlates of social pain are more complex than previously thought.


title: Who Are You? A Statistical Approach to Measuring User Authenticity

abstract: Passwords are used for user authentication by almost every Internet service today, despite a number of wellknown weaknesses. Numerous attempts to replace passwords have failed, in part because changing users’ behavior has proven to be difficult. One approach to strengthening password-based authentication without changing user experience is to classify login attempts into normal and suspicious activity based on a number of parameters such as source IP, geo-location, browser configuration, and time of day. For the suspicious attempts, the service can then require additional verification, e.g., by an additional phone-based authentication step. Systems working along these principles have been deployed by a number of Internet services but have never been studied publicly. In this work, we perform the first public evaluation of a classification system for user authentication. In particular: (i) We develop a statistical framework for identifying suspicious login attempts. (ii) We develop a fully functional prototype implementation that can be evaluated efficiently on large datasets. (iii) We validate our system on a sample of real-life login data from LinkedIn as well as simulated attacks, and demonstrate that a majority of attacks can be prevented by imposing additional verification steps on only a small fraction of users. (iv) We provide a systematic study of possible attackers against such a system, including attackers targeting the classifier itself.


title: Optical Detection of the Electromechanical Response of MEMS Micromirrors Designed for Scanning Picoprojectors

abstract: Single-axis rotational micromirrors actuated by comb finger structures have been designed in view of their application in reflective scanning picoprojectors for laser beam displacement along two perpendicular directions to obtain a raster scan scheme. A resonant mirror operating at a frequency around 25 kHz, suitable for horizontal scans, as well as a linear mirror, suitable for vertical scan at the typical video refresh rate (60 Hz), have been fabricated by Silicon-on-Insulator technology and are illustrated in this paper. We have in particular exploited the potentialities of semiconductor laser self-mixing interferometry, a powerful technique for characterizing the dynamic response of MEMS, for detecting the electromechanical response of both kinds of micromirrors. We report the results of the spot optical measurements performed on resonant and linear mirrors aimed at detecting the frequency of the fundamental rotational mode as well as of the in-plane and out-of-plane modes, close in frequency to the fundamental mode. We have experimentally demonstrated that the fabricated devices are suitable for high-resolution miniaturized projectors, in terms of frequency response and scanning angle.


title: Approximation methods for gaussian process regression

abstract: A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying �


title: An Evaluation of Space-Filling Information Visualizations for Depicting Hierarchical Structures

abstract: A variety of information visualization tools have been developed recently, but relatively little effort has been made to evaluate the effectiveness and utility of the tools. This article describes results from two empirical studies of two visualization tools for depicting hierarchies, in particular, computer file and directory structures. The two tools examined implement space-filling methodologies, one rectangular, the Treemap method, and one circular, the Sunburst method. Participants performed typical file/directory search and analysis tasks using the two tools. In general, performance trends favored the Sunburst tool with respect to correct task performance, particularly on initial use. Performance with Treemap tended to improve over time and use, suggesting a greater learning cost that was partially recouped over time. Each tool afforded somewhat different search strategies, which also appeared to influence performance. Finally, participants strongly preferred the Sunburst tool, citing better ability to convey structure and hierarchy.


title: Incorporating intersectionality into psychology: An opportunity to promote social justice and equity.

abstract: Intersectionality is receiving increasing attention in many fields, including psychology. This theory or framework has its roots in the work of Black feminist scholar-activists, and it focuses on interlocking systems of oppression and the need to work toward structural-level changes to promote social justice and equity. Thus, the current interest in intersectionality in psychology presents an opportunity to draw psychologists' attention more to structural-level issues and to make social justice and equity more central agendas to the field. The large, ever-growing bodies of research demonstrating the wide-ranging adverse consequences of structural- and interpersonal-level oppression, inequality, and stigma for the health and well-being of many diverse groups of people support that these issues are central to the field of psychology. We as individual psychologists and the field as a whole can work to fully incorporate the insights of intersectionality and therefore contribute to making social justice and equity more central across the varied subfields and realms of our work. Specific ways that we can do this are to (a) engage and collaborate with communities, (b) address and critique societal structures, (c) work together/build coalitions, (d) attend to resistance in addition to resilience, and (e) teach social justice curricula. There are important examples both within and outside of psychology that can guide us in achieving these goals. These suggestions are meant to foster conversation and consideration by psychologists across all subfields and areas of focus. (PsycINFO Database Record


title: A Comparative Study of a New ZCS DC–DC Full-Bridge Boost Converter With a ZVS Active-Clamp Converter

abstract: Pulse width modulation (PWM) current-fed full-bridge dc-dc boost converters are typically used in applications where the output voltage is considerably higher than the input voltage. In this paper, a comparison is made between two converter topologies of this type-the standard zero-voltage switching (ZVS) active-clamp topology and a new zero-current switching (ZCS) topology. This paper begins with a review of the operation of the ZVS active-clamp converter and that of ZCS converters in general; the advantages and disadvantages of each approach are stated. A new ZCS-PWM current-fed dc-dc boost full-bridge converter is then introduced. The operation of the new converter is explained and analyzed, and a procedure for the design of its key components is given and demonstrated with an example. Experimental results obtained from a prototype of a ZVS active-clamp converter and the new ZCS converter are presented. Finally, a comparison of the performance of the two converters is made and conclusion based on this comparison is stated.


title: SWAT: A Spiking Neural Network Training Algorithm for Classification Problems

abstract: This paper presents a synaptic weight association training (SWAT) algorithm for spiking neural networks (SNNs). SWAT merges the Bienenstock-Cooper-Munro (BCM) learning rule with spike timing dependent plasticity (STDP). The STDP/BCM rule yields a unimodal weight distribution where the height of the plasticity window associated with STDP is modulated causing stability after a period of training. The SNN uses a single training neuron in the training phase where data associated with all classes is passed to this neuron. The rule then maps weights to the classifying output neurons to reflect similarities in the data across the classes. The SNN also includes both excitatory and inhibitory facilitating synapses which create a frequency routing capability allowing the information presented to the network to be routed to different hidden layer neurons. A variable neuron threshold level simulates the refractory period. SWAT is initially benchmarked against the nonlinearly separable Iris and Wisconsin Breast Cancer datasets. Results presented show that the proposed training algorithm exhibits a convergence accuracy of 95.5% and 96.2% for the Iris and Wisconsin training sets, respectively, and 95.3% and 96.7% for the testing sets, noise experiments show that SWAT has a good generalization capability. SWAT is also benchmarked using an isolated digit automatic speech recognition (ASR) system where a subset of the TI46 speech corpus is used. Results show that with SWAT as the classifier, the ASR system provides an accuracy of 98.875% for training and 95.25% for testing.


Cross-Site Scripting Attacks in Social Network APIs


title: A mixed-method systematic review: support for ethical competence of nurses.

abstract: AIM ::: The aim was to appraise and synthesize evidence of empirical studies of how nurses' ethical competence can be supported. ::: ::: ::: BACKGROUND ::: Ethical competence is an essential element of nursing practice. Nurses increasingly need support in competence when carrying out their responsibilities towards their patients. ::: ::: ::: DESIGN ::: A mixed-method systematic review of quantitative and qualitative studies was undertaken according to the University of York's Centre for Reviews and Dissemination guidelines. ::: ::: ::: DATA SOURCES ::: Searches of MEDLINE, Nursing Database and British Nursing Index databases were conducted, yielding 512 citations between 1985-2012. ::: ::: ::: METHODS ::: After a two-stage application of inclusion and exclusion criteria, 34 articles were included. The quality of the studies was assessed using STROBE or COREQ criteria. Data were analysed by content analysis. ::: ::: ::: RESULTS ::: Nurses' ethical competence has been studied from different viewpoints: ethical decision-making, ethical sensitivity, ethical knowledge and ethical reflection. There was little empirical evidence of provided support, but studies offered recommendations on how to support ethical competence. The most common strategies to support ethical competence were ethics education, ethics rounds, ethics committee and consultation. Nurse leaders and colleagues have a key role in providing opportunities for nurses to gain ethical competence. ::: ::: ::: CONCLUSIONS ::: There is a need to develop evidence-based support at the organizational and individual level to support nurses' ethical competence. Barriers for multiprofessional cooperation in ethical issues should be recognized and addressed as part of the development of organizational ethical practices. Research should pay more attention to the conceptual, theoretical and practical perspectives of ethical competence.


title: State of the Art of Finite Control Set Model Predictive Control in Power Electronics

abstract: This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.


title: A Two-Phase Plagiarism Detection Method

abstract: In this paper we describe a two-phase plagiarism detection method, which we have used in the learning management system of SCUT to detect plagiarism in the network engineering related courses. And we also used this method to detect external plagiarism in the PAN-10 competition. The method is divided into two steps: the first step, called pre-selecting, is to narrow the scope of detection using the successive same fingerprint; the second step, called locating, is to find and merge all fragments between two documents using cluster method. Our method ran 19 hours in the PAN-10 competition, and the result ranked the second place, which met our expectation.


title: Metal-Insulator-Semiconductor Photodetectors

abstract: The major radiation of the Sun can be roughly divided into three regions: ultraviolet, visible, and infrared light. Detection in these three regions is important to human beings. The metal-insulator-semiconductor photodetector, with a simpler process than the pn-junction photodetector and a lower dark current than the MSM photodetector, has been developed for light detection in these three regions. Ideal UV photodetectors with high UV-to-visible rejection ratio could be demonstrated with III-V metal-insulator-semiconductor UV photodetectors. The visible-light detection and near-infrared optical communications have been implemented with Si and Ge metal-insulator-semiconductor photodetectors. For mid- and long-wavelength infrared detection, metal-insulator-semiconductor SiGe/Si quantum dot infrared photodetectors have been developed, and the detection spectrum covers atmospheric transmission windows.


title: Fuzzy logic based robot path planning in unknown environment

abstract: This paper proposes a new method, minimum risk approach, to address the local path planning to escape from local minimum during goal-oriented robot navigation in unknown environments. This approach is theoretically proved to guarantee global convergence even in the long-wall, unstructured, cluttered, maze-like, and modified environments. The approach adopts a strategy of multi-behavior coordination, in which a novel path-searching behavior is developed to recommend the regional direction with minimum risk. The paper provides a fuzzy logic framework to implement the behavior design and coordination. It is verified by the simulated and real world tests.