title: Ranking facial attractiveness

abstract: SUMMARY The fi rst aim of this investigation was to assemble a group of photographs of 30 male and 30 female faces representing a standardized spectrum of facial attractiveness, against which orthognathic treatment outcomes could be compared. The second aim was to investigate the infl uence of the relationship between ANB differences and anterior lower face height (ALFH) percentages on facial attractiveness. The initial sample comprised standardized photographs of 41 female and 35 male Caucasian subjects. From these, the photographs of two groups of 30 male and 30 female subjects were compiled. A panel of six clinicians and six non-clinicians ranked the photographs. The results showed there to be a good level of reliability for each assessor when ranking the photographs on two occasions, particularly for the clinicians (female subjects r = 0.76‐0.97, male subjects r = 0.72‐0.94). Agreement among individuals within each group was also high, particularly when ranking facial attractiveness in male subjects (female subjects r = 0.57‐0.84, male subjects r = 0.91‐0.94). Antero-posterior (AP) discrepancies, as measured by soft tissue ANB, showed minimal correlation with facial attractiveness. However, a trend emerged that would suggest that in faces where the ANB varies widely from 5 degrees, the face is considered less attractive. The ALFH percentage also showed minimal correlation with facial attractiveness. However, there was a trend that suggested that greater ALFH percentages are considered less attractive in female faces, while in males the opposite trend was seen. Either of the two series of ranked photographs as judged by clinicians and non-clinicians could be used as a standard against which facial attractiveness could be assessed, as both were in total agreement about the most attractive faces. However, to judge the outcome of orthognathic treatment, the series of ranked photographs produced by the non-clinician group should be used as the ‘standard’ to refl ect lay opinion.


title: An Online Sequential Learning Non-parametric Value-at-Risk Model for High-Dimensional Time Series

abstract: Online Value-at-Risk (VaR) analysis in high-dimensional space remains a challenge in the era of big data. In this paper, we propose an online sequential learning non-parametric VaR model called OS-GELM which is an autonomous cognitive system. This model uses a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process and an online sequential extreme learning machine (OS-ELM) to cognitively calculate VaR, which can be used for online risk analysis. The proposed model not only learns the data one-by-one or chunk-by-chunk but also calculates VaR in real time by extending OS-ELM from machine learning to the non-parametric GARCH process. The GARCH process is also extended to one-by-one and chunk-by-chunk mode. In OS-GELM, the parameters of hidden nodes are randomly selected. The output weights are analytically determined based on the sequentially arriving data. In addition, the generalization performance of the OS-GELM model attains a small training error and generates the smallest norm of weights. Experimentally obtained VaRs are compared with those given by GARCH-type models and conventional OS-ELM. The computational results demonstrate that the OS-GELM model obtains more accurate results and is better at forecasting the online VaR. OS-GELM model is an autonomous cognitive system to dynamically calculate Value-at-Risk, which can be used for online financial risk assessment about human being’s behavior. The OS-GELM model can calculate VaR in real time, which can be used as a tool for online risk management. OS-GELM can handle any bounded, non-constant, piecewise-continuous membership function to realize real-time VaR monitoring.


title: Spatial Diversity 24-GHz FMCW Radar With Ground Effect Compensation for Automotive Applications

abstract: In automotive radar applications, a realistic road surface can cause multipath reflection, degrade the received power, and reduce detection probability. This paper proposes a one-transmitter–two-receiver (1T2R) 24-GHz frequency-modulation continuous-wave (FMCW) radar architecture with spatial diversity and ground effect compensation for automotive applications. A ray-tracing technique was developed to evaluate the multipath effect of the road surface. In the proposed design, suitable spacing between the two receiving antennas compensated for the multipath effect of the road surface and improved the signal-to-ground clutter ratio of the receivers by a minimum of 20 dB at certain distances. The measurement and calculation results of the proposed radar are in close agreement.


title: Off-policy evaluation for slate recommendation

abstract: This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy's performance. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learning-to-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased---these conditions are weaker than prior heuristics for slate evaluation---and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.


Homicides of children and youth


title: Practical automated vulnerability monitoring using program state invariants

abstract: Despite the growing attention to security concerns and advances in code verification tools, many memory errors still escape testing and plague production applications with security vulnerabilities. We present RCORE, an efficient dynamic program monitoring infrastructure to perform automated security vulnerability monitoring. Our approach is to perform extensive static analysis at compile time to automatically index program state invariants (PSIs). At runtime, our novel dynamic analysis continuously inspects the program state and produces a report when PSI violations are found. Our technique retrofits existing applications and is designed for both offline and production runs. To avoid slowing down production applications, we can perform our dynamic analysis on idle cores to detect suspicious behavior in the background. The alerts raised by our analysis are symptoms of memory corruption or other-potentially exploitable-dangerous behavior. Our experimental evaluation confirms that RCORE can report on several classes of vulnerabilities with very low overhead.


title: Vibration and motion control design and trade-off for high-performance mechatronic systems

abstract: This paper discusses the H ∞ -based design of a vibration controller for an industrial pick-and-place machine. A vibration controller is added to the classical motion control scheme with the purpose of improving positioning behavior by reducing the vibration level and settling time. It is shown that a trade-off is required between vibration reduction and motion control. The approach is validated experimentally and the results clearly illustrate the benefit of the proposed method.


title: Proactive Supply Chain Performance Management with Predictive Analytics

abstract: Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.


title: Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization

abstract: This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.


title: The impact of late, non-balanced bilingualism on cognitive performance

abstract: We present a study examining cognitive functions in late non-balanced bilinguals with different levels of second language proficiency. We examined in two experiments a total of 193 mono- and bilingual university students. We assessed different aspects of attention (sustained, selective and attentional switching), verbal fluency (letter and category) as well as picture–word association as a measure of language proficiency. In Experiment 2 we also compared students in their first/initial (Y1) and fourth/final (Y4) year of either language or literature studies. There were no differences between both groups in category fluency. In selective attention, bilinguals outperformed monolinguals in Y1 and this difference remained significant in Y4 despite overall improvement in both groups. Contrasting results were found in attentional switching and letter fluency: while no differences were found in Y1 in both tasks, in Y4 there was an advantage for bilinguals in attentional switching and for monolinguals in letter fluency. We conclude that overall late-acquisition non-balanced bilinguals experience similar cognitive effects as their early-acquisition balanced counterparts. However, different cognitive effects may appear at different stages of adult second language acquisition.


title: Human Activity Recognition Using Inertial/Magnetic Sensor Units

abstract: This paper provides a comparative study on the different techniques of classifying human activities that are performed using bodyworn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost.


title: Modeling of intercellular connections in the sinoatrial node

abstract: Electrochemical activity of the sinoatrial node — the first natural heart pacemaker, relies on coordinated activity of sinoatrial cells and on signal transduction by intercellular connections. The modified Greenberg‐ Hastings automaton is used to model the electrochemical activity of a cell and basic intercellular interactions. A stochastic 2D lattice with preference set to lateral connections is a starting point in the construction of the network of intercellular connections. Then a flat structure of the network is carefully wrinkled by rewiring procedure. The rewiring is restricted to neighboring cells — local rewiring, and it favors densely connected cells — preferential rewiring. In simulations we find that if density of intercellular connections reaches d = 0:60 then spirals of activity clusters emerge robustly. However to observe strong spirals: oscillating with the shortest period possible and driving dynamics in the whole network, the intercellular connections have to be rewired locally and preferentially. The critical value of density corresponds very accurately to the known value at which the canine sinoatrial node works.


title: A compact UWB sub-nanosecond pulse generator for microwave radar sensor with ringing miniaturization

abstract: This paper presents a compact ultra-wideband (UWB) microwave radar transmitter. The developed design presents low-power and low-cost ultra-short pulse with a minimum of the ringing in the output waveform. Ultra-short pulse has been generated by avalanche pulse generator and sharpened with a step recovery diode (SRD). In order to improve the simulation response and to increase the rise-time of the pulses, a new model of a step recovery diode (SRD) was used as a sharpener circuit. Making the discharge path as short as possible, a minimum of the ringing has been obtained in the output pulse, around 3,6% in overshoot. Compact pulse generator circuit structure is completely fabricated using low-cost microstrip technology characteristic. The measurement results present generator Gaussian wave form pulses over 6.4 V in amplitude and 440 fall time at 100 KHz. Good Agreements between the measured and calculated result are achieved.


title: Adaptive Advertisement Recommender Systems for Digital Signage.

abstract: With the incorporation of new technologies, digital signages can adopt their content in real time to the audience demographic and temporal features. This research proposes an adaptive advertisement recommender system for digital signage. Our objective is to create a quantitative method for targeted advertising. After analyzing digital signage advertisement viewing data collected over the course of two months, our results show that learning-to-rank approach using Stochastic Gradient-Boosted Trees (SGBT) yields the best adaptive advertisement recommender system. Our system can identify the best sequence of advertisements to attract the most viewing. More importantly, we can use the same method for different business objectives like attracting the longest time of viewing or targeting a certain age groups or genders.


title: Data Programming: Creating Large Training Sets, Quickly

abstract: Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can "denoise" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.


title: A Topic-Based Search, Visualization, and Exploration System

abstract: From literature surveys to legal document collections, people need to organize and explore large amounts of documents. During these tasks, students and researchers will search for documents based on particular themes. In this paper, we use a popular topic modeling algorithm, Latent Dirichlet Allocation, to derive topic distributions for articles. We allow users to specify personal topic distribution to contextualize the exploration experience. We introduce three types of exploration: user model re-weighted keyword search, topic-based search, and topic-based exploration. We demonstrate these methods using a scientific citation data set and a Wikipedia article collection. We also describe the user interaction model.


title: Multiband Microstrip Antenna for 4G Mobile Application

abstract: In proposed work which has been analyzed and designed on IE3D tools, we cut a different types of slot like Lslot, Z-slot, U-slot and finally got the result at our desired frequency that is for 4G application at 2300MHz. In this research, we achieved multiband which is a essential parameter of wireless communication. The VSWR, Return loss, Bandwidth, Directivity, Gain is acceptable for this application. We have VSWR = 1.18 a Return loss = -21.5 dB at 2270MHZ. For this design we use RT Duroid as a substrate material with dielectric constant 2.2. These characteristics make the designed antenna suitable for 4 G applications.


title: Soft Locality Preserving Map (SLPM) for Facial Expression Recognition

abstract: For image recognition, an extensive number of methods have been proposed to overcome the high-dimensionality problem of feature vectors being used. These methods vary from unsupervised to supervised, and from statistics to graph-theory based. In this paper, the most popular and the state-of-the-art methods for dimensionality reduction are firstly reviewed, and then a new and more efficient manifold-learning method, named Soft Locality Preserving Map (SLPM), is presented. Furthermore, feature generation and sample selection are proposed to achieve better manifold learning. SLPM is a graph-based subspace-learning method, with the use of k-neighbourhood information and the class information. The key feature of SLPM is that it aims to control the level of spread of the different classes, because the spread of the classes in the underlying manifold is closely connected to the generalizability of the learned subspace. Our proposed manifold-learning method can be applied to various pattern recognition applications, and we evaluate its performances on facial expression recognition. Experiments on databases, such as the Bahcesehir University Multilingual Affective Face Database (BAUM-2), the Extended Cohn-Kanade (CK+) Database, the Japanese Female Facial Expression (JAFFE) Database, and the Taiwanese Facial Expression Image Database (TFEID), show that SLPM can effectively reduce the dimensionality of the feature vectors and enhance the discriminative power of the extracted features for expression recognition. Furthermore, the proposed feature-generation method can improve the generalizability of the underlying manifolds for facial expression recognition.


title: SiC-Based Z-Source Resonant Converter With Constant Frequency and Load Regulation for EV Wireless Charger

abstract: Traditional load regulation methods for a resonant converter mainly rely on frequency modulation. It is always a tradeoff between the design of the resonant network and the range of load. Especially for wireless power transfer (WPT) systems, the resonant network usually has a high quality factor. Small variation on frequency leads to huge drop in gain and efficiency. Due to this problem, many WPT systems are unregulated and they need one or two more front-end stages to regulate the dc bus voltage and perform power factor correction (PFC). In order to lower the cost and complexity of two- or three-stages structure, a single-stage solution with a silicon carbide (SiC) based Z-source resonant converter (ZSRC) was recently proposed. The Z-source network provides high reliability as being immune to shoot-through problems. Additionally, a ZSRC can boost the dc bus voltage while the traditional voltage-source inverter can only produce a lower voltage. However, the load regulation of this new topology has not been addressed. Two effective load regulation methods with constant frequency are presented for this SiC-based ZSRC specifically. Operation principle of the two load regulation methods are described in this paper. Experimental results based on a 200-W scale-down prototype with a full-bridge series resonant dc–dc converter are presented to illustrate the mechanism of these two methods.


title: Pulsed Power Switching of a 4 MM × 4 MM SIC Thyristor

abstract: While silicon carbide (SiC) is beginning to make its way into the low voltage (300-1200 V) commercial power diode market, its capabilities in pulse power applications have yet to be proven. A previous investigation by the U.S. Army Research Laboratory (ARL) of SiC GTOs suggested that this emerging technology could provide pulsed current densities 40 to 60 times greater than is obtainable in silicon-based switches [1]. This study continues that earlier work by examining 4 mm times 4 mm 4H-SiC thyristors designed by CREE Inc. to reach 1 kV and 4 kA. These devices were successfully switched up to 3.89 kA with a current density reaching 56.1 kA/cm2, a specific rate-of-current-rise of 49 kA/mus/cm2 (for peak rise-time 7.8 kA/mus) and a pulse-width ranging from 2.0 mus to 2.6 mus. The thyristors were tested at both single shot and repetitive switching rates up to 5 Hz. Device characteristics were mapped on a curve tracer at different stages of testing, and the failure of each thyristor was analyzed.


title: Applications of machine learning to cognitive radio networks

abstract: Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.


title: 3D cartoon face generation by local deformation mapping

abstract: We present a data-driven method for automatically generating a 3D cartoon of a real 3D face. Given a sparse set of 3D real faces and their corresponding cartoon faces modeled by an artist, our method models the face in each subspace as the deformation of its nearby exemplars and learn a mapping between the deformations defined by the real faces and their cartoon counterparts. To reduce the exemplars needed for learning, we regress a collection of linear mappings defined locally in both face geometry and identity spaces and develop a progressive scheme for users to gradually add new exemplars for training. At runtime, our method first finds the nearby exemplars of an input real face and then constructs the result cartoon face from the corresponding cartoon faces of the nearby real face exemplars and the local deformations mapped from the real face subspace. Our method greatly simplifies the cartoon generation process by learning artistic styles from a sparse set of exemplars. We validate the efficiency and effectiveness of our method by applying it to faces of different facial features. Results demonstrate that our method not only preserves the artistic style of the exemplars, but also keeps the unique facial geometric features of different identities.


title: Multiview 3D reconstruction based on vanishing points and homography

abstract: 3D reconstruction is one of the long lasting research topics in computer vision. It is the task of regenerating a model in three-dimensional (3D) space by images taken from the scene. An image-based method for 3D reconstruction based on intersection of planar visual hulls from projective images taken by multiview videos is proposed. Reconstruction can be performed without any further information related to camera calibration, feature extraction, and dense matching. This process computes the homography of reference plane along with one corresponding point off the reference plane, and vanishing points in all views. The 2D visual hulls along the object structure are generated. By stacking 2D visual hull slices, the 3D model of the object is obtained efficiently with least imposed constraints.


title: Neural basis of location-specific pupil luminance modulation

abstract: Spatial attention enables us to focus visual processing toward specific locations or stimuli before the next fixation. Recent evidence has suggested that local luminance at the spatial locus of attention or saccade preparation influences pupil size independent of global luminance levels. However, it remains to be determined which neural pathways produce this location-specific modulation of pupil size. The intermediate layers of the midbrain superior colliculus (SC) form part of the network of brain areas involved in spatial attention and modulation of pupil size. Here, we demonstrated that pupil size was altered according to local luminance level at the spatial location corresponding to a microstimulated location in the intermediate SC (SCi) map of monkeys. Moreover, local SCi inactivation through injection of lidocaine reversed this local luminance modulation. Our findings reveal a causal role of the SCi in preparing pupil size for local luminance conditions at the next saccadic goal.


title: Why scatter plots suggest causality, and what we can do about it

abstract: Scatter plots carry an implicit if subtle message about causality. Whether we look at functions of one variable in pure mathematics, plots of experimental measurements as a function of the experimental conditions, or scatter plots of predictor and response variables, the value plotted on the vertical axis is by convention assumed to be determined or influenced by the value on the horizontal axis. This is a problem for the public understanding of scientific results and perhaps also for professional scientists' interpretations of scatter plots. To avoid suggesting a causal relationship between the x and y values in a scatter plot, we propose a new type of data visualization, the diamond plot. Diamond plots are essentially 45 degree rotations of ordinary scatter plots; by visually jarring the viewer they clearly indicate that she should not draw the usual distinction between independent/predictor variable and dependent/response variable. Instead, she should see the relationship as purely correlative.


title: Hybrid Seven-Level Converter Based on T-Type Converter and H-Bridge Cascaded Under SPWM and SVM

abstract: This paper presents a hybrid seven-level converter based on T-type converter and H-bridge cascaded suitable for low-voltage and high-power-density applications. The operation principles and conduction paths are analyzed comprehensively. Voltage balance control of the floating capacitors is the key point of this paper. Mathematical expressions of capacitor voltage balance conditions are deduced based on sinusoidal pulse width modulation (SPWM). The results show that floating capacitor voltages are only balanced when the modulation index is below 0.82 under SPWM. In order to enlarge the modulation index ensuring the capacitor voltage balance, space vector modulation (SVM) is also applied to regulate floating capacitor voltages taking advantage of redundant switching vectors. Meanwhile, in SVM, there are no additional control requirements for the capacitor voltage. Taken both SPWM and SVM into consideration, the limit to the range of operation for this seven-level converter is presented, which is a function of the modulation index and the power factor. The merit of dc voltage utilization improvement in this topology is also analyzed based on simulation results. Simulations and experimental results under different modulation index and RL load are presented to verify the modulation strategies illustrated in this paper.


title: On Convergence and Stability of GANs

abstract: We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesirable local equilibria in this non-convex game to be responsible for mode collapse. We observe that these local equilibria often exhibit sharp gradients of the discriminator function around some real data points. We demonstrate that these degenerate local equilibria can be avoided with a gradient penalty scheme called DRAGAN. We show that DRAGAN enables faster training, achieves improved stability with fewer mode collapses, and leads to generator networks with better modeling performance across a variety of architectures and objective functions.


title: Research on Chinese text classification based on Word2vec

abstract: The set of features which the traditional feature selection algorithm of chi-square selected is not complete. This causes the low performance for the final text classification. Therefore, this paper proposes a method. The method utilizes word2vec to generate word vector to improve feature selection algorithm of the chi square. The algorithm applies the word vector generated by word2vec to the process of the traditional feature selection and uses these words to supplement the set of features as appropriate. Ultimately, the set of features obtained by this method has better discriminatory power. Because, the feature words with the better discriminatory power has the strong ability of distinguishing categories as its semantically similar words. On this base, multiple experiments have been carried out in this paper. The experimental results show that the performance of text classification can increase after extension of feature words.


Fault Detection of Drinking Water Treatment Process Using PCA and Hotelling’s T 2 Chart


title: Security problems in the TCP/IP protocol suite

abstract: The TCP/IP protocol suite, which is very widely used today, was developed under the sponsorship of the Department of Defense. Despite that, there are a number of serious security flaws inherent in the protocols, regardless of the correctness of any implementations. We describe a variety of attacks based on these flaws, including sequence number spoofing, routing attacks, source address spoofing, and authentication attacks. We also present defenses against these attacks, and conclude with a discussion of broad-spectrum defenses such as encryption.


title: Intelligent phase plane switching control of pneumatic artificial muscle manipulators with magneto-rheological brake

abstract: Abstract The pneumatic artificial muscle (PAM) is undoubtedly the most promising artificial muscle for the actuation of new types of industrial robots such as rubber actuator and PAM manipulators because it provides these advantages such as high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot. To overcome these problems a novel controller which harmonizes a phase plane switching control method (PPSC) with conventional PID controller and the adaptabilities of neural network, is newly proposed. In order to realize satisfactory control performance a variable damper, magneto-rheological brake (MRB), is equipped to the joint of the manipulator. Superb mixture of conventional PID controller and an intelligent phase plane switching control using neural network (IPPSC) brings us a novel controller. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with IPPSC and without regard for the changes of external inertia loads.


title: Peaches and almonds: phylogeny of Prunus subg. Amygdalus (Rosaceae) based on DNA sequences and morphology

abstract: Almonds and peaches are among the most economically important fruit crops in subtropical and temperate regions. Questions regarding the origin and domestication of the crop species, interspecific relationships, and evolutionary patterns of fruits have remained unanswered due to the lack of phylogenetic data of Prunus subgenus Amygdalus, in which both almond and peach are classified. Twenty-two species of subg. Amygdalus were included in phylogenetic analyses of plastid ndhF-rpl32, rpL16, trnH-psbA, trnL-trnF, trnQ-5′rps16, and trnS-trnG and nuclear s6pdh genes to investigate the circumscription of subg. Amygdalus, phylogenetic relationships among the species, and implication for mesocarp and endocarp evolution. Prunus subg. Amygdalus is best circumscribed to include both almonds and peaches, comprising two sections, Amygdalus and Persica. Species traditionally placed close to almonds, such as P. tenella, P. petunnikowii, P. triloba, and P. pedunculata, should be excluded from subg. Amygdalus. Characteristics used to diagnose subg. Amygdalus, such as dry, splitting mesocarp and the ornamentation of endocarp evolved multiple times in Prunus; thus classification based solely on these morphological characters produces a taxonomic scheme that might not represent common ancestry. A dry, splitting mesocarp is likely to be ancestral for the common ancestor of subg. Amygdalus, and the fleshy, non-splitting mesocarp found in peaches was derived from the dry, splitting mesocarp, presumably by domestication and selection for a fleshy fruit in China. Very low genetic divergence among cultivated almond and its close relatives might be associated with gene flow and/or rapid speciation within the group.


title: Nanosecond Delay Floating High Voltage Level Shifters in a 0.35 $\mu$m HV-CMOS Technology

abstract: We present novel circuits for high-voltage digital level shifting with zero static power consumption. The conventional topology is analysed, showing the strong dependence of speed and dynamic power on circuit area. Novel techniques are shown to circumvent this and speed up the operation of the conventional level-shifter architecture by a factor of 5-10 typically and 30-190 in the worst case. In addition, these circuits use 50% less silicon area and exhibit a factor of 20-80 lower dynamic power consumption typically. Design guidelines and equations are given to make the design robust over process corners, ensuring good production yield. The circuits were fabricated in a 0.35 high-voltage CMOS process and verified. Due to power and IO speed limitation on the test chip, a special ring oscillator and divider structure was used to measure inherent circuit speed.


title: Design Considerations for a Hyper-Redundant Pulleyless Rolling Joint With Elastic Fixtures

abstract: Compliant joints composed of slit tubes or springs are commonly utilized for steerable endoscopes and catheters. Because this type of joint is actuated by means of its elasticity, it is difficult to bend it with a small radius of curvature. In order to overcome this problem, a hyper-redundant Pulleyless Rolling joint with Elastic Fixtures (PREF joint) is proposed. To enable miniaturization, the PREF joint does not have a pulley, and the rolling contact motion aids the actuation cables to maintain the tension. The elastic fixtures guarantee a stable rolling motion at the center of the rolling surfaces. In this paper, the fundamental characteristics for the unit and the hyper-redundant PREF joints are presented. Moreover, design considerations are proposed for the PREF joint including the rolling tooth shape, structure of the elastic fixtures, and cable actuation method. The validity of the design is verified using finite-element analysis and a 10 mm diameter prototype composed of 12 units of PREF joints for a 1 degree-of-freedom motion. As a result, the 55 mm long joint can be bent up to ±180° with low actuation tension. The presented guidelines will assist in the development of various steerable instruments for medical applications.


title: Approximate Inference with Amortised MCMC

abstract: We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise MCMC using samples from an approximation network, apply the MCMC operator to improve these samples, and finally use the samples to update the approximation network thereby improving its quality. This provides a new generic framework for approximate inference, allowing us to deploy highly complex, or implicitly defined approximation families with intractable densities, including approximations produced by warping a source of randomness through a deep neural network. Experiments consider image modelling with deep generative models as a challenging test for the method. Deep models trained using amortised MCMC are shown to generate realistic looking samples as well as producing diverse imputations for images with regions of missing pixels.


title: Verdoheme formation in Proteus mirabilis catalase.

abstract: BACKGROUND ::: Heme oxidative degradation has been extensively investigated in peroxidases but not in catalases. The verdoheme formation, a product of heme oxidation which inactivates the enzyme, was studied in Proteus mirabilis catalase. ::: ::: ::: METHODS ::: The verdoheme was generated by adding peracetic acid and analyzed by mass spectrometry and spectrophotometry. ::: ::: ::: RESULTS ::: Kinetics follow-up of different catalase reactional intermediates shows that i) the formation of compound I always precedes that of verdoheme, ii) compound III is never observed, iii) the rate of compound II decomposition is not compatible with that of verdoheme formation, and iv) dithiothreitol prevents the verdoheme formation but not that of compound II, whereas NADPH prevents both of them. The formation of verdoheme is strongly inhibited by EDTA but not increased by Fe3+ or Cu2+ salts. The generation of verdoheme is facilitated by the presence of protein radicals as observed in the F194Y mutated catalase. The inability of the inactive variant (H54F) to form verdoheme, indicates that the heme oxidation is fully associated to the enzyme catalysis. ::: ::: ::: CONCLUSION ::: These data, taken together, strongly suggest that the verdoheme formation pathway originates from compound I rather than from compound II. ::: ::: ::: GENERAL SIGNIFICANCE ::: The autocatalytic verdoheme formation is likely to occur in vivo.


title: What do donations buy?: A model of philanthropy based on prestige and warm glow

abstract: Abstract Charities publicize the donations they receive, generally according to dollar categories rather than the exact amount. Donors in turn tend to give the minimum amount necessary to get into a category. These facts suggest that donors have a taste for having their donations made public. This paper models the effects of such a taste for “prestige” on the behavior of donors and charities. I show how a taste for prestige means that charities can increase donations by using categories. The paper also discusses the effect of a taste for prestige on competition between charities.


title: Chromatin assembly factor 1, subunit A (P150) facilitates cell proliferation in human hepatocellular carcinoma

abstract: Several studies have revealed that the abnormal expression of chromatin assembly factor 1, subunit A (P150) (CHAF1A) was involved in the development of some types of malignant tumors. However, CHAF1A expression and its role in hepatocellular carcinoma (HCC) remain poorly characterized. In this study, we first investigated CHAF1A expression in six cell lines and 116 pairs of HCC and matched normal tumor-adjacent tissues to evaluate the clinicopathological characteristics of CHAF1A in HCC. Then, we detected the proliferation and apoptosis in HCC cells. In addition, a subcutaneous tumor model in nude mice was performed to evaluate tumor growth in vivo. We found that the expression of CHAF1A was significantly higher in HCC tissues than that in adjacent nontumor tissues (P<0.01). Clinical analysis indicated that CHAF1A expression was significantly correlated with the tumor-node-metastasis stage, tumor number, and tumor differentiation in HCC tissues (P<0.05, respectively). We also found that CHAF1A may potentially function as a poor prognostic indicator for 5-year overall and disease-free survival in patients with HCC (P<0.05, respectively). The elevated expression of CHAF1A was also observed in HCC cell lines compared with that in normal LO2 hepatic cell line (P<0.01). HCC cancer cells exhibited inhibition of cell growth, reduction in colony-formation ability, increased cell apoptosis rate, and impaired tumorigenicity in nude mice after CHAF1A knockdown. Collectively, we propose that CHAF1A by potentially mediating cancer cell proliferation plays an important role in promoting the development of HCC and may serve as a potential therapeutic target in HCC.


title: A Review of False Data Injection Attacks Against Modern Power Systems

abstract: With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.


title: Optimizing charging station locations for urban taxi providers

abstract: Recently, electric vehicles are gaining importance which helps to reduce dependency on oil, increases energy efficiency of transportation, reduces carbon emissions and noise, and avoids tail pipe emissions. Because of short daily driving distances, high mileage, and intermediate waiting time, fossil-fuelled taxi vehicles are ideal candidates for being replaced by battery electric vehicles (BEVs). Moreover, taxi BEVs would increase visibility of electric mobility and therefore encourage others to purchase an electric vehicle. Prior to replacing conventional taxis with BEVs, a suitable charging infrastructure has to be established. This infrastructure consists of a sufficiently dense network of charging stations taking into account the lower driving ranges of BEVs.


title: R-Storm: Resource-Aware Scheduling in Storm

abstract: The era of big data has led to the emergence of new systems for real-time distributed stream processing, e.g., Apache Storm is one of the most popular stream processing systems in industry today. However, Storm, like many other stream processing systems lacks an intelligent scheduling mechanism. The default round-robin scheduling currently deployed in Storm disregards resource demands and availability, and can therefore be inefficient at times. We present R-Storm (Resource-Aware Storm), a system that implements resource-aware scheduling within Storm. R-Storm is designed to increase overall throughput by maximizing resource utilization while minimizing network latency. When scheduling tasks, R-Storm can satisfy both soft and hard resource constraints as well as minimizing network distance between components that communicate with each other. We evaluate R-Storm on set of micro-benchmark Storm applications as well as Storm applications used in production at Yahoo! Inc. From our experimental results we conclude that R-Storm achieves 30-47% higher throughput and 69-350% better CPU utilization than default Storm for the micro-benchmarks. For the Yahoo! Storm applications, R-Storm outperforms default Storm by around 50% based on overall throughput. We also demonstrate that R-Storm performs much better when scheduling multiple Storm applications than default Storm.


title: Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations

abstract: The determination of structural boundaries is a key task for understanding the structure of a musical piece, but it is also highly ambiguous. Recently, Convolutional Neural Networks (CNN) trained on spectrogram features and human annotations have been successfully used to tackle the problem, but still fall clearly behind human performance. We expand on the CNN approach by combining spectrograms with self-similarity lag matrices as audio features, thereby capturing more facets of the underlying structural information. Furthermore, in order to consider the hierarchical nature of structural organization, we explore different strategies to learn from the two-level annotations of main and secondary boundaries available in the SALAMI structural annotation dataset. We show that both measures improve boundary recognition performance, resulting in a significant improvement over the previous state of the art. As a side-effect, our algorithm can predict boundaries on two different structural levels, equivalent to the training data.


title: Textile Materials for the Design of Wearable Antennas: A Survey

abstract: In the broad context of Wireless Body Sensor Networks for healthcare and pervasive applications, the design of wearable antennas offers the possibility of ubiquitous monitoring, communication and energy harvesting and storage. Specific requirements for wearable antennas are a planar structure and flexible construction materials. Several properties of the materials influence the behaviour of the antenna. For instance, the bandwidth and the efficiency of a planar microstrip antenna are mainly determined by the permittivity and the thickness of the substrate. The use of textiles in wearable antennas requires the characterization of their properties. Specific electrical conductive textiles are available on the market and have been successfully used. Ordinary textile fabrics have been used as substrates. However, little information can be found on the electromagnetic properties of regular textiles. Therefore this paper is mainly focused on the analysis of the dielectric properties of normal fabrics. In general, textiles present a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. However, textile materials are constantly exchanging water molecules with the surroundings, which affects their electromagnetic properties. In addition, textile fabrics are porous, anisotropic and compressible materials whose thickness and density might change with low pressures. Therefore it is important to know how these characteristics influence the behaviour of the antenna in order to minimize unwanted effects. This paper presents a survey of the key points for the design and development of textile antennas, from the choice of the textile materials to the framing of the antenna. An analysis of the textile materials that have been used is also presented.


title: AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis

abstract: Most research on determining the strength of subjective expressions in a sentence or document uses single, specific parts of speech such as adjectives, adverbs, or verbs. To date, almost no research covers the development of a single comprehensive framework in which we can analyze sentiment that takes all three into account. The authors propose the AVA (adjective verb adverb) framework for identifying opinions on any given topic. In AVA, a user can select any topic t of interest and any document d. AVA will return a score that d expresses topic t. The score is expressed on a –1 (maximally negative) to +1 (maximally positive) scale.


title: Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants

abstract: Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44–77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.


title: Human facial expressions as adaptations: Evolutionary questions in facial expression research

abstract: The importance of the face in social interaction and social intelligence is widely recognized in anthropology. Yet the adaptive functions of human facial expression remain largely unknown. An evolutionary model of human facial expression as behavioral adaptation can be constructed, given the current knowledge of the phenotypic variation, ecological contexts, and fitness consequences of facial behavior. Studies of facial expression are available, but results are not typically framed in an evolutionary perspective. This review identifies the relevant physical phenomena of facial expression and integrates the study of this behavior with the anthropological study of communication and sociality in general. Anthropological issues with relevance to the evolutionary study of facial expression include: facial expressions as coordinated, stereotyped behavioral phenotypes, the unique contexts and functions of different facial expressions, the relationship of facial expression to speech, the value of facial expressions as signals, and the relationship of facial expression to social intelligence in humans and in nonhuman primates. Human smiling is used as an example of adaptation, and testable hypotheses concerning the human smile, as well as other expressions, are proposed.


title: Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition

abstract: To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract “Multi-Directional Multi-Level Dual-Cross Patterns” (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.


title: Platforms, People, and Perception: Using Affordances to Understand Self-Presentation on Social Media

abstract: The popularity of social media platforms today makes them an important venue for self-presentation, but the unique affordances of these platforms challenge our existing models for understanding self-presentation behavior. In particular, social media provide multiple platforms on which the self may be presented, expand the role other individuals can play in one's own self-presentation, and expand the audience while often simultaneously providing less information about who is in that audience. This paper presents an affordance-based approach to self-presentation on social media platforms rooted in these three challenges and presents a systematic taxonomy for considering aspects of platforms that affect self-presentation. Results from an exploratory study of 193 users suggest significant variation in user perception of our proposed affordances across social media platforms, participant experience levels, and participant personality traits.


title: BING: Binarized Normed Gradients for Objectness Estimation at 300fps

abstract: Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.


title: On the application of LoRa LPWAN technology in Sailing Monitoring System

abstract: Sailing Monitoring System operating in the sea area has the basic transmission requirement of low power and long range. LoRa Low Power Wide Area Network (LPWAN) technology can be seen as an alternative solution to address this problem. This study focuses on transmission performance of LoRa technology and attempts to apply LoRa technology to Sailing Monitoring System. Experiments of LoRa technology parameters' (e.g. spread factor and bandwidth) influences on data transmission time and coverage were set up. Then, the optimal parameters of LoRa technology are used in Sailing Monitoring System, and the evaluation of system's performances is given. The measurements were conducted in Brazil Olympics sailing venue for two cases, and system's performance of coverage and packet loss rate in sea area are analyzed. It shows that the system based on LoRa technology can achieve the intended purpose of system design and meet the basic requirement of system applications. In the concluding section, measurement results are summarized and the future research directions are presented.


Chapter # NON-FUNCTIONAL REQUIREMENTS ELICITATION


title: Decentralizing attribute-based encryption

abstract: We propose a Multi-Authority Attribute-Based Encryption (ABE) system. In our system, any party can become an authority and there is no requirement for any global coordination other than the creation of an initial set of common reference parameters. A party can simply act as an ABE authority by creating a public key and issuing private keys to different users that reflect their attributes. A user can encrypt data in terms of any boolean formula over attributes issued from any chosen set of authorities. Finally, our system does not require any central authority. ::: ::: In constructing our system, our largest technical hurdle is to make it collusion resistant. Prior Attribute-Based Encryption systems achieved collusion resistance when the ABE system authority "tied" together different components (representing different attributes) of a user's private key by randomizing the key. However, in our system each component will come from a potentially different authority, where we assume no coordination between such authorities. We create new techniques to tie key components together and prevent collusion attacks between users with different global identifiers. ::: ::: We prove our system secure using the recent dual system encryption methodology where the security proof works by first converting the challenge ciphertext and private keys to a semi-functional form and then arguing security. We follow a recent variant of the dual system proof technique due to Lewko and Waters and build our system using bilinear groups of composite order. We prove security under similar static assumptions to the LW paper in the random oracle model.


title: The formalism and matrix elements of a complete potential-harmonic scheme for directly solving the Schrödinger equation of the helium atom

abstract: Abstract By analyzing the wavefunctions and the precision of the calculated energy of several potential-harmonic (PH) expansion schemes previously developed by us, a complete PH scheme has been proposed which incorporates the electron-nucleus cusp as well as electron-electron correlation. The PH method including the coupled hyperradial ordinary differential equations and the secular equation for the energy has been formulated. The involved matrix elements of the two-body potentials and correlation function are analytically expressed through a sum of the grand angular momentums or beta functions. The new PH scheme has been applied to the helium atom, and the eigenenergies of the ground state as well as the low-lying singlet excited states are obviously improved over the previous CFPHGLF method.


title: Motion planning for an omnidirectional robot with steering constraints

abstract: Omnidirectional mobile robots, i.e., robots that can move in any direction without changing their orientation, offer better manoeuvrability in natural environments. Modeling the kinematics of such robots is a challenging problem and different approaches have been investigated. One of the best approaches for a nonholonomic robot is to model the robot's velocity state as the motion around its instantaneous center of rotation (ICR). In this paper, we present a motion planner designed to compute efficient trajectories for such a robot in an environment with obstacles. The action space is modeled in terms of changes of the ICR and the motion around it. Our motion planner is based on a Rapidly-Exploring Random Trees (RRT) algorithm to sample the action space and find a feasible trajectory from an initial configuration to a goal configuration. To generate fluid paths, we introduce an adaptive sampling technique taking into account constraints related to the ICR-based action space.


title: Prevalence of Anemia among Pregnant Women Attending Antenatal Care at Tikur Anbessa Specialized Hospital, Addis Ababa Ethiopia

abstract: Background: Anemia impairs cognitive development, reduces physical work capacity and in severe cases increases risk of mortality particularly during prenatal period. There are no previous studies done on anemia in pregnancy in the study area. Objective: To assess the prevalence and determinant factors of anemia among pregnant women attending antenatal care follow up at Tikur Anbessa specialized hospital. Methods: Health institutional based cross-sectional study design was conducted from November 23, 2013 - March8, 2013 among 395 pregnant mothers who had been attending antenatal care. Blood sample was taken to determine the hemoglobin concentration and haematocrit value. Interviewer administered structured questionnaire was used to assess different socio-demographic characteristics, obstetrics and dietary factors related to anaemia. Simple random sampling technique was used to select study subjects. Data were entered with EPI info version 3.5.1 Cleaning and analysis was done using SPSS version 17 and statistical tests were performed at the level of significance of 5%. Result: The overall prevalence of anemia in this study using a cut off level of hemoglobin <11 g/dl (<33% haematocrit) was 21.3% (84/395). Out of 84 anemic pregnant mothers, 80.95% (68/84) were mildly anemic, 17.86% (15/84) were moderately anemic and 1.19% (1/84) were severely anemic. The following variables were significantly associated with anemia: Age (39-45 yrs (AOR=1.21)), education status (illiterate (AOR=2.12)), family size (greater than four (AOR=2.04), gestational age (third trimester (AOR=2.04)), birth intervals (less than two years (AOR=1.21)), history of blood loss (AOR=1.67)), antenatal care (no ANC (AOR=1.11)), contraception (no contraception (AOR=2.31)), gravidity (multigravid (AOR=1.22)), parity (multiparous (AOR=2.19)). Conclusion: The present study showed high prevalence of anemia and the majority of them were of the mild type (hemoglobin: 10-10.9 g/dl).


title: IJ-OpenCV : Combining ImageJ and OpenCV for processing images in biomedicine

abstract: Background and Objective. The effective processing of biomedical images usually requires the interoperability of diverse software tools that have different aims but are complementary. The goal of this work is to develop a bridge to connect two of those tools: ImageJ, a program for image analysis in life sciences, and OpenCV, a computer vision and machine learning library.Methods. Based on a thorough analysis of ImageJ and OpenCV, we detected the features of these systems that could be enhanced, and developed a library to combine both tools, taking advantage of the strengths of each system. The library was implemented on top of the SciJava converter framework. We also provide a methodology to use this library.Results. We have developed the publicly available library IJ-OpenCV that can be employed to create applications combining features from both ImageJ and OpenCV. From the perspective of ImageJ developers, they can use IJ-OpenCV to easily create plugins that use any functionality provided by the OpenCV library and explore different alternatives. From the perspective of OpenCV developers, this library provides a link to the ImageJ graphical user interface and all its features to handle regions of interest.Conclusions. The IJ-OpenCV library bridges the gap between ImageJ and OpenCV, allowing the connection and the cooperation of these two systems.


title: Minimum operations and minimum parameters of the dynamic models of tree structure robots

abstract: An efficient method for the calculation of the inverse dynamic models of tree structure robots is presented. The given method reduces significantly the computational burden such that the inverse dynamics can be computed in real time at servo rate. The method leads almost directly to models with a minimum number of arithmetic operations. The method is based on a Newton-Euler formulation that is linear in the inertial parameters, on an iterative symbolic procedure, and on condensating the inertial parameters by regrouping and eliminating some of them. The description of the robot is carried out by a new notation inspired from Denavit and Hartenberg notation. A Fortran program has been developed to generate automatically the dynamic models of tree structure robots.


title: Nudging users towards privacy on mobile devices

abstract: By allowing individuals to be permanently connected to the Internet, mobile devices ease the way information can be accessed and shared online, but also raise novel privacy challenges for end users. Recent behavioral research on “soft” or “asymmetric” paternalism has begun exploring ways of helping people make better decisions in different aspects of their lives. We apply that research to privacy decision making, investigatinghowsoftpaternalisticsolutions(alsoknown as nudges) may be used to counter cognitive biases and ameliorate privacy-sensitive behavior. We present the theoretical background of our research, and highlight current industry solutions and research endeavors that could be classified as nudging interventions. We then describe our ongoing work on embedding soft paternalistic mechanisms in location sharing technologies and Twitter privacy agents. Author Keywords Nudge, Privacy, Security, LocationSharing, MobileDevices, Soft Paternalism


title: Distributed maximum power point tracking of photovoltaic arrays: Novel approach and system analysis

abstract: One of the major drawbacks of photovoltaic (PV) systems is represented by the effect of module mismatching and of partial shading of the PV field. Distributed maximum power point tracking (DMPPT) is a very promising technique that allows the increase of efficiency and reliability of such systems. Modeling and designing a PV system with DMPPT is remarkably more complex than implementing a standard MPPT technique. In this paper, a DMPPT system for PV arrays is proposed and analyzed. A dc and small-signal ac model is derived to analyze steady-state behavior, as well as dynamics and stability, of the whole system. Finally, simulation results are reported and discussed.


title: Steady-state analysis of an interleaved boost converter with coupled inductors

abstract: Boost converters are widely used as power-factor corrected preregulators. In high-power applications, interleaved operation of two or more boost converters has been proposed to increase the output power and to reduce the output ripple. A major design criterion then is to ensure equal current sharing among the parallel converters. In this paper, a converter consisting of two interleaved and intercoupled boost converter cells is proposed and investigated. The boost converter cells have very good current sharing characteristics even in the presence of relatively large duty cycle mismatch. In addition, it can be designed to have small input current ripple and zero boost-rectifier reverse-recovery loss. The operating principle, steady-state analysis, and comparison with the conventional boost converter are presented. Simulation and experimental results are also given.


title: The Impact of Consumer Product Package Quality on Consumption Satisfaction, Brand Perceptions, Consumer Investment and Behavior

abstract: Consumer product packaging can serve a critical role in the consumption experience, but marketing and packaging science researchers focus primarily on pre and post-consumption aspects of consumer product containers. Exhaustive research into packing ergonomics, logistics, safety, sustainability and promotional features are common across marketing and packaging disciplines, but research isolating the role of a packaging in consumption satisfaction and enduring consumer-brand relationships is rare. In addition to an undervalued role in product satisfaction, functional isolation between marketing and packaging scientists limits packaging’s overall impact on the bottom line. This research examines the role of bottle quality in bottled-water consumption satisfaction and its subsequent impact on brand attribute perceptions, consumer-brand relationship investment and behavioral intentions. We show that thicker water bottles are perceived to be of higher quality than thinner bottles, and that these perceptual differences impact how customers view a brand on aspects such as reliability and value offered by the brand’s products and ultimately intentions to re-purchase the brand’s products. We use qualitative, experimental and structural modeling analysis techniques to establish a fundamental role of packaging quality in consumer product satisfaction. We show that packaging characteristics are an indivisible component of the product and important to evaluation of the overall consumption experience. We finally conclude that packaging quality has a critical role to play in building profitable consumer-brand relationships, which should redefine the packaging cost-benefit equation to include the value of consumer loyalty as a balance to non-consumption packaging considerations.


title: Human-like walking patterns with pelvic rotation for a humanoid robot

abstract: Fast and stable walking is one of the important prerequisite for humanoid robots to serve the people. Pelvic rotations play a significant role when one walks fast. This paper proposes a method to generate human-like walking patterns with pelvic rotation in the transverse plane for a humanoid robot to effectively improve its quickness and stability. First, the regularities of pelvic rotation in the transverse plane during human walking are studied. Second, the mechanism design of a humanoid robot waist for pelvic rotations is presented. Then, a walking trajectory planning with pelvic rotation to improve the stability margin is proposed. Finally, the effectiveness of the method is demonstrated through simulations and experiments on BHR-5.


title: An Experimental Study of Emotion Regulation During Relationship Conflict Interactions: The Moderating Role of Attachment Orientations

abstract: Romantic couples (N 127) engaged in a relationship conflict interaction during which their autonomic physiology, emotional experience, and emotional behavior were recorded. Couples were assigned randomly to one of two interventions, or to a control condition: In the affective suppression condition, one partner was instructed to refrain from expressing emotions. In the positive mindset condition, one partner was instructed to think about the positive aspects of the relationship. Results revealed that emotion regulation interventions influenced the physiology, emotional behavior, and emotional experience of both the manipulated person and his or her partner, who was oblivious to regulation manipulations. Specifically, suppression increased, and positive mindset decreased cardiovascular arousal and negative affect. These effects were generally exacerbated among those high on attachment anxiety and attenuated among those high on attachment avoidance. The results of this research corroborate and extend the Temporal Interpersonal Emotion Systems model (Butler, 2011) in the context of relationship conflict interactions.


abstract: In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Experimental evaluation shows that our method outperforms subspace clustering on separate observations and the state of the art collaborative learning algorithm.


title: Policing the boundaries: the writing, representation and regulation of criminology

abstract: Writing has a central role in UK higher education as a technology for, and signifier of, the learning, teaching and assessment of students. The nature and quality of student writing has also become an important issue outside the academy, particularly in the context of a globalised neo-liberal knowledge economy discourse which emphasises the importance of transferable and employability skills. Although there is a considerable body of research relating to student writing, the work that I undertook for earlier professional doctorate assignments suggested that the role of academic staff in regulating student writing was under-researched and under-theorised. The research carried out for this thesis sought to address this gap in knowledge by focussing on two central questions. Firstly, what role do academic staff play in regulating student writing? Secondly, how is this role shaped by the specific departmental, disciplinary and institutional contexts in which they are located? ::: ::: The research was undertaken in a criminology department in a post-1992 university in the UK. It was positioned in an academic literacies framework which conceptualises writing as a social practice, and drew on linguistic ethnographic methodologies to explore the written feedback that staff give on student writing. The written feedback encounter is where staff and student expectations about academic writing practices intersect, and is therefore a telling site for the study of educational discourses relating to knowledge and how it is represented. Data were collected from three main sources: written feedback and comments given by academic staff on 120 pieces of student work; 18 interviews with staff about academic writing; and institutional policies and procedures relating to marking, assessment and feedback. Employing a range of theoretical perspectives, including those informed by feminist and poststructuralist analysis, these texts were analysed to explore the relationship between institutional discourses, pedagogical practices and identity construction. ::: ::: My research showed that there was a considerable disjuncture between the institutional discourses which governed marking, assessment and feedback and the actual feedback practices of staff. Despite the strong scientific and positivist discourse that pervaded institutional documentation on assessment and feedback, some staff drew on a range of alternative pedagogical discourses and engaged in assessment practices which were more subjective and localised in nature. This gap between the institutional discourse and the situated literacy practices was mediated to some extent by the assessment coversheet and marking procedures which worked to provide an appearance of consistency and agreement to external audiences. This promoted a technical rational approach to feedback which obscured the epistemological and gatekeeping functions of feedback. ::: ::: The thesis concludes that the effective theorisation and teaching of student writing rests on an understanding of how academic staff construct and police the boundaries of appropriate knowledge in their discipline. This approach draws on existing academic literacies theories but argues for a more holistic model which understands academic writing as co-constructed through the practices of both students who produce the written work and the academic staff who mark it.


title: Grounding Spatio-Semantic Referring Expressions for Human-Robot Interaction

abstract: The human language is one of the most natural interfaces for humans to interact with robots. This paper presents a robot system that retrieves everyday objects with unconstrained natural language descriptions. A core issue for the system is semantic and spatial grounding, which is to infer objects and their spatial relationships from images and natural language expressions. We introduce a two-stage neural-network grounding pipeline that maps natural language referring expressions directly to objects in the images. The first stage uses visual descriptions in the referring expressions to generate a candidate set of relevant objects. The second stage examines all pairwise relationships between the candidates and predicts the most likely referred object according to the spatial descriptions in the referring expressions. A key feature of our system is that by leveraging a large dataset of images labeled with text descriptions, it allows unrestricted object types and natural language referring expressions. Preliminary results indicate that our system outperforms a near state-of-the-art object comprehension system on standard benchmark datasets. We also present a robot system that follows voice commands to pick and place previously unseen objects.


title: Development and initial validation of a short three-dimensional inventory of character strengths

abstract: Purpose ::: Character strength is described as a positive and organized pattern of emotions, thoughts, and behaviors. It serves as a schema that organizes categories of information toward the self, others, and the world, and provides the self-aware knowledge that facilitates the pursuit of goals, values, and ethical principles. Recent research has suggested that three reliable factors emerge from the measures of character strengths: caring, inquisitiveness, and self-control. The goal of this paper is to develop a psychometrically sound short measure of character strength.


title: Learning Deep Sketch Abstraction

abstract: Human free-hand sketches have been studied in various contexts including sketch recognition, synthesis and fine-grained sketch-based image retrieval (FG-SBIR). A fundamental challenge for sketch analysis is to deal with drastically different human drawing styles, particularly in terms of abstraction level. In this work, we propose the first stroke-level sketch abstraction model based on the insight of sketch abstraction as a process of trading off between the recognizability of a sketch and the number of strokes used to draw it. Concretely, we train a model for abstract sketch generation through reinforcement learning of a stroke removal policy that learns to predict which strokes can be safely removed without affecting recognizability. We show that our abstraction model can be used for various sketch analysis tasks including: (1) modeling stroke saliency and understanding the decision of sketch recognition models, (2) synthesizing sketches of variable abstraction for a given category, or reference object instance in a photo, and (3) training a FG-SBIR model with photos only, bypassing the expensive photo-sketch pair collection step.


title: From Word Embeddings To Document Distances

abstract: We present the Word Mover's Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representations for words from local cooccurrences in sentences. The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document. We show that this distance metric can be cast as an instance of the Earth Mover's Distance, a well studied transportation problem for which several highly efficient solvers have been developed. Our metric has no hyperparameters and is straight-forward to implement. Further, we demonstrate on eight real world document classification data sets, in comparison with seven state-of-the-art baselines, that the WMD metric leads to unprecedented low k-nearest neighbor document classification error rates.


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title: UAV autopilot controllers test platform using Matlab/Simulink and X-Plane

abstract: Presently there is vast interest in UAV (Unmanned Aerial Vehicle) development given its civilian and military applications. One of the main UAV components is the autopilot system. Its development invariably demands several lab simulations and field tests. Generally after an UAV crash few parts remain usable. Thus, before embedding an autopilot system, it has to be exhaustively lab tested. With educational and research purposes in autopilot control systems development area, a test platform is herein proposed. It employs Matlab/Simulink to run the autopilot controller under test, the flight simulator X-Plane with the aircraft to be commanded, a microcontroller to command model aircraft flight control surfaces and a servo to drive these control surfaces. These resources are interconnected through data communication buses. So that, the autopilot controller designed on Matlab/Simulink is tested by controlling an aircraft on X-Plane. The inputs given to the aircraft flight control surfaces in the X-Plane are simultaneously sent to the microcontroller which translates these commands into effective servo movement control. Through this platform, designed autopilot systems can be applied into models similar to real aircraft minimizing risks and increasing flexibility for design changes. As study case, tests results from a roll attitude autopilot system are presented.


title: Internet use and the logics of personal empowerment in health.

abstract: OBJECTIVES ::: The development of personal involvement and responsibility has become a strategic issue in health policy. The main goal of this study is to confirm the coexistence of three logics of personal empowerment through health information found on the Internet. ::: ::: ::: METHODS ::: A theoretical framework was applied to analyze personal empowerment from the user's perspective. A well-established Canadian Web site that offers information on personal health was used as a case study. A close-ended questionnaire was completed online by 2275 visitors and members of the Web site. ::: ::: ::: RESULTS ::: The findings confirm that the development of feelings of competence and control through Internet use is structured around three different logics. This implies three types of aptitudes that are fostered when the Internet is used to seek health information: doing what is prescribed (the professional logic), making choices based on personal judgment (the consumer logic), and mutual assistance (the community logic). ::: ::: ::: CONCLUSIONS ::: A recurring issue in three logics is the balance of roles and responsibilities required between the individual and the health provider.


Chronic Constipation, Irritable Bowel Syndrome With Constipation and Constipation With Pain/Discomfort: Similarities and Differences


title: Rule extraction from neural networks via decision tree induction

abstract: Rule extraction from neural networks is the task for obtaining comprehensible descriptions that approximate the predictive behavior of neural networks. Rule-extraction algorithms are used for both interpreting neural networks and mining the relationship between input and output variables in data. This paper describes a new rule extraction algorithm that extracts rules that contain both continuous (real-valued) and discrete literals. This algorithm decomposes a neural network using decision trees and obtains production rules by merging the rules extracted from each tree. Results tested on the databases in UCI repository are presented.


title: Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation

abstract: While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input. We tested the feasibility of three-dimensional robotic assistance for reach-to-grasp movements with a multi-joint exoskeleton during motor imagery-related desynchronization of sensorimotor oscillations in the β-band only. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function. Healthy subjects and stroke survivors showed similar patterns – but different aptitudes – of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e. when the orthosis was actually attached to the subject’s arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task. Brain-robot interfaces may successfully link three-dimensional robotic training to the participants’ efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration.


title: A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

abstract: Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.


title: Knowledge harvesting in the big-data era

abstract: The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources have enabled the automatic construction of very large knowledge bases. Endeavors of this kind include projects such as DBpedia, Freebase, KnowItAll, ReadTheWeb, and YAGO. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and hundreds of millions of facts about them. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, semantic search for entities and relations in Web and enterprise data, and entity-oriented analytics over unstructured contents. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis will be on the twofold role of knowledge bases for big-data analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and better intelligence with Big Data.


Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics


title: From a Set of Shapes to Object Discovery

abstract: This paper presents an approach to object discovery in a given unlabeled image set, based on mining repetitive spatial configurations of image contours. Contours that similarly deform from one image to another are viewed as collaborating, or, otherwise, conflicting. This is captured by a graph over all pairs of matching contours, whose maximum a posteriori multicoloring assignment is taken to represent the shapes of discovered objects. Multicoloring is conducted by our new Coordinate Ascent Swendsen-Wang cut (CASW). CASW uses the Metropolis-Hastings (MH) reversible jumps to probabilistically sample graph edges, and color nodes. CASW extends SW cut by introducing a regularization in the posterior of multicoloring assignments that prevents the MH jumps to arrive at trivial solutions. Also, CASW seeks to learn parameters of the posterior via maximizing a lower bound of the MH acceptance rate. This speeds up multicoloring iterations, and facilitates MH jumps from local minima. On benchmark datasets, we outperform all existing approaches to unsupervised object discovery.


title: LBP-Based Segmentation of Defocus Blur

abstract: Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions. In this paper, we propose a sharpness metric based on local binary patterns and a robust segmentation algorithm to separate in- and out-of-focus image regions. The proposed sharpness metric exploits the observation that most local image patches in blurry regions have significantly fewer of certain local binary patterns compared with those in sharp regions. Using this metric together with image matting and multi-scale inference, we obtained high-quality sharpness maps. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and six comparator methods. The results show that our algorithm achieves comparative segmentation results with the state of the art and have big speed advantage over the others.


title: Solving the dynamic Vehicle Routing Problem using genetic algorithms

abstract: The Vehicle Routing Problem (VRP) is an important problem occurring in many logistics systems. The objective of VRP is to serve a set of customers at minimum cost, such that every node is visited by exactly one vehicle only once. In this paper, we consider the Dynamic Vehicle Routing Problem (DVRP) which new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time minimizing the total travel distance. The main goal of this research is to find a solution of DVRP using genetic algorithm. However we used some heuristics in addition during generation of the initial population and crossover for tuning the system to obtain better result. The computational experiments were applied to 22 benchmarks instances with up to 385 customers and the effectiveness of the proposed approach is validated by comparing the computational results with those previously presented in the literature.


title: Differential privacy with δ-neighbourhood for spatial and dynamic datasets

abstract: Differential privacy provides a strong guarantee in protecting privacy of individuals who contributed to a published dataset. In this paper, we focus on spatial datasets and dynamic datasets, and attempt to exploit the intuition that farther-apart entities should have lesser influences to each other, and thus more privacy budget should be invested to protect close-by entities. To capture such intuition, we propose embedding the underlying spatial or temporal distance function into the notion of dataset neighbourhood. We called the proposed neighbourhood δ-neighbourhood, and discuss its implications in both spatial and dynamic datasets. For dynamic datasets, while there are known negative results on the standard differential privacy, it is possible to continuously and indefinitely publish under δ-neighbourhood by reusing the privacy budgets. Although known mechanisms, by definition, are also differentially private under δ-neighbourhood, they are not designed to exploit the relaxed notion for better utility. For spatial datasets, we propose an approach on 2D spatial points that re-allocates more budgets to nearby entities and thus obtains significantly higher utility. In addition, we give mechanisms that achieve "sustainable privacy" on dynamic datasets under both online and offline setting.


title: A capabilities-based approach to obtaining a deeper understanding of information technology governance effectiveness: Evidence from IT steering committees

abstract: Given the substantial investment in information technology (IT), and the significant impact IT has on organizational success, organizations consume considerable resources to manage acquisition and use of their IT resources. While various arguments proposed suggest which IT governance arrangements may work best, our understanding of the effectiveness of such initiatives is limited. We examine the relationship between the effectiveness of IT steering committee driven IT governance initiatives and firm's IT management and IT infrastructure related capabilities. We further propose that firm's IT-related capabilities generated through IT governance initiatives should improve its business processes and firm-level performance. We test these relationships empirically by a field survey. Results suggest that firms' effectiveness of IT steering committee driven IT governance initiatives positively relates to the level of their IT-related capabilities. We also found positive relationships between IT-related capabilities and internal process-level performance. Our results also support that improvement in internal process-level performance positively relates to improvement in customer service and firm-level performance.


title: Control of Hand Prostheses Using Peripheral Information

abstract: Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.


title: Hybrid CNN-HMM Model for Street View House Number Recognition

abstract: We present an integrated model for using deep neural networks to solve street view number recognition problem. We didn’t follow the traditional way of first doing segmentation then perform recognition on isolated digits, but formulate the problem as a sequence recognition problem under probabilistic treatment. Our model leverage a deep Convolutional Neural Network(CNN) to represent the highly variable appearance of digits in natural images. Meanwhile, hidden Markov model(HMM) is used to deal with the dynamics of the sequence. They are combined in a hybrid fashion to form the hybrid CNN-HMM architecture. By using this model we can perform the training and recognition procedure both at word level. There is no explicit segmentation operation at all which save lots of labour of sophisticated segmentation algorithm design or finegrained character labeling. To the best of our knowledge, this is the first time using hybrid CNN-HMM model directly on the whole scene text images. Experiments show that deep CNN can dramaticly boost the performance compared with shallow Gausian Mixture Model(GMM)-HMM model. We obtaied competitive results on the street view house number(SVHN) dataset.