title: BUILDING EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES USING HOUGH TRANSFORM D. Koc San a, * and M. Turker b

abstract: An approach was developed for the automatic extraction of the rectangular and circular shaped buildings from high resolution satellite imagery using Hough transform. First, the candidate building patches are detected from the imagery using the binary Support Vector Machines (SVM) classification technique. In addition to original image bands, the bands NDVI (Normalized Difference Vegetation Index), and nDSM (normalized Digital Surface Model) are also used in the classification. After detecting the building patches, their edges are detected using the Canny edge detection algorithm. The edge image is then converted into vector form using the Hough transform, which is a widely used technique for extracting the lines or curves of the objects. The vector lines and curves that represent the building edges are grouped based on perceptual groupings, and the building boundaries are constructed. The proposed approach was implemented using a program written in MATLAB® v. 7.1 programming environment. The experimental tests were carried out in the residential and industrial urban blocks selected in the Batikent district of Ankara, the capital city of Turkey using the pan-sharpened and panchromatic IKONOS images. The results obtained indicate that the proposed building extraction procedure based on SVM and Hough transform can be effectively used to extract the boundaries of the rectangular and circular shaped buildings. For the industrial buildings, we obtained quite satisfactory results with the average Building Detection Percentage (BDP) and the Quality Percentage (QP) values of 93.45% and 79.51%, respectively. For the residential rectangular buildings, the average BDP and QP values were computed to be 95.34% and 79.05%, respectively. For the residential circular buildings, the average BDP and QP values were found to be 78.74% and 66.81%, respectively.


title: Consumer design priorities for upper limb prosthetics.

abstract: PURPOSE ::: To measure consumer satisfaction with upper limb prosthetics and provide an enumerated list of design priorities for future developments. ::: ::: ::: METHODS ::: A self-administered, anonymous survey collected information on participant demographics, history of and goals for prosthesis use, satisfaction, and design priorities. The questionnaire was available online and in paper format and was distributed through healthcare providers, community support groups, and one prosthesis manufacturer; 242 participants of all ages and levels of upper limb absence completed the survey. ::: ::: ::: RESULTS ::: Rates of rejection for myoelectric hands, passive hands, and body-powered hooks were 39%, 53%, and 50%, respectively. Prosthesis wearers were generally satisfied with their devices while prosthesis rejecters were dissatisfied. Reduced prosthesis weight emerged as the highest priority design concern of consumers. Lower cost ranked within the top five design priorities for adult wearers of all device types. Life-like appearance is a priority for passive/cosmetic prostheses, while improved harness comfort, wrist movement, grip control and strength are required for body-powered devices. Glove durability, lack of sensory feedback, and poor dexterity were also identified as design priorities for electric devices. ::: ::: ::: CONCLUSIONS ::: Design priorities reflect consumer goals for prosthesis use and vary depending on the type of prosthesis used and age. Future design efforts should focus on the development of more light-weight, comfortable prostheses.


title: Arguing About Firewall Policy.

abstract: In this paper, we present a new framework to analyze firewall policy by using argumentation. At the core of this new idea is extending firewall rules with the concept of “reasons” and arguing about the reasons, not the rules. Depending on how the reasons are designed, the resulting framework can be useful in a number of ways: new anomalies in a firewall policy can be identified while, at the same time, stronger recommendations can be given to resolve those anomalies that are detected.


title: Opening Pandora’s Box: Effective Techniques for Reverse Engineering IoT Devices

abstract: With the growth of the Internet of Things, many insecure embedded devices are entering into our homes and businesses. Some of these web-connected devices lack even basic security protections such as secure password authentication. As a result, thousands of IoT devices have already been infected with malware and enlisted into malicious botnets and many more are left vulnerable to exploitation.


title: Applying machine learning to software fault-proneness prediction

abstract: The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module's fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA's Metrics Data Program data repository.


title: Joint Depth Estimation and Camera Shake Removal from Single Blurry Image

abstract: Camera shake during exposure time often results in spatially variant blur effect of the image. The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene. The objects close to the camera sensors are likely to appear more blurry than those at a distance in such cases. However, recent non-uniform deblurring methods do not explicitly consider the depth factor or assume fronto-parallel scenes with constant depth for simplicity. While single image non-uniform deblurring is a challenging problem, the blurry results in fact contain depth information which can be exploited. We propose to jointly estimate scene depth and remove non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with only single blurry image as input. To this end, we present a unified layer-based model for depth-involved deblurring. We provide a novel layer-based solution using matting to partition the layers and an expectation-maximization scheme to solve this problem. This approach largely reduces the number of unknowns and makes the problem tractable. Experiments on challenging examples demonstrate that both depth and camera shake removal can be well addressed within the unified framework.


title: Identification residential appliance using NIALM

abstract: Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances' load signatures of active and reactive powers, harmonic components and their magnitudes. We have introduced a new approach of ‘multi point sensing’ and ‘group control’ rather than the ‘single point sensing’ and ‘individual control’, used so far in NIALM techniques. The disaggregation system based on Central Public Works Department of India (CPWD). One feature i.e. amplitude of first 8 odd harmonics of current signature of home appliances were selected for the classification. Further principle component analysis (PCA) is used. A comparison between these classification algorithms has been done and it is revealed that artificial neural network classifier and Bayes classifier provides 99.18% and 98.08% accuracy respectively for the experimental data.


title: Behavioral Biometrics for Smartphone User Authentication

abstract: Pervasive in nature and extensively used for a wide range of features, smartphone provides functionality such as social networking, online shopping, mobile gaming, private/group communication, etc. While using these services, a user has to provide private information such as account credentials, credit card details, etc., which are then stored on the device. This information, if lost, can result in a user's privacy leakage and monetary loss. Therefore, significance of securing a smartphone from adversarial access becomes paramount. Despite being security and privacy critical, smartphones are still protected by traditional authentication mechanisms such as PINs and passwords, whose limitations and drawbacks are well known and well documented in the security community. The recent introduction of physical biometrics like facial, fingerprint and iris recognition, in smartphone authentication, has mitigated the problems with user input, however, they still suffer from other usability and security issues. Hence, new, accurate, and user-friendly authentication mechanisms are required. In this direction, behavior-based authentication solutions have recently attracted a significant amount of interest in both commercial and academic contexts. Most of the smartphone users prefer convenience over security and consider authentication mechanism more annoying as compared to other technological problems, such as lack of coverage, power consumption, etc. In this dissertation, we discuss limitations of existing authentication methods in terms of security and usability, and propose their replacements with behavioral biometric based authentication mechanisms. The underlying principle of our approach is to design solutions that authenticate users with either minimal or no cooperation from the users. We design, prototype and test the proposed authentication mechanisms based on our identified human behavior, such as how a person holds the phone, lifts the phone, types free-text PIN on the phone, signs her name on the touchscreen, etc. Moreover, we provide a comparative evaluation, based on accuracy, performance and usability, of our proposed mechanisms with the available state-of-the-art solutions. All of our solutions exploit the existing hardware (avoiding additional hardware requirement), and hence can be implemented on most of the smartphones available in the market today.


title: Educational data mining using jmp

abstract: Educational Data Mining is a growing trend in case of higher education. The quality of the Educational Institute may be enhanced through discovering hidden knowledge from the student databases/ data warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree) Course students of different colleges affiliated to Dibrugarh University. The study is conducted with major subject wise, gender wise and category/caste wise. The experimental results may be visualized with Scatterplot3D, Bubble Plot, Fit Y by X, Run Chart, Control Chart etc. of the SAS JMP Software.


title: Oral Vaccination of Mice with Adenoviral Vectors Is Not Impaired by Preexisting Immunity to the Vaccine Carrier

abstract: Adenovirus vectors with E1 deleted of the human serotype 5 (AdHu5) and the chimpanzee serotype 68 (AdC68) expressing the glycoprotein of the Evelyn Rokiniki Abelseth strain of rabies virus were tested upon oral application for induction of systemic and mucosal transgene product-specific antibody responses in mice. Both vectors induced systemic and mucosal antibodies to rabies virus, including virus-neutralizing antibodies and protection against a severe intracerebral challenge with a mouse-adapted strain of rabies virus. Pre-existing immunity of AdHu5 virus, which dampens induction of transgene product-specific immunity elicited by AdHu5 vectors given systemically did not impair the response induced by oral vaccination. Oral priming-boosting regimens with either heterologous or homologous adenoviral vectors used sequentially increased both mucosal and systemic antibody titers to rabies virus [corrected]


PROCEDURAL GENERATION OF IMAGINATIVE TREES USING A SPACE COLONIZATION ALGORITHM


title: Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition

abstract: In recent years, the performance of face verification and recognition systems based on deep convolutional neural networks (DCNNs) has significantly improved. A typical pipeline for face verification includes training a deep network for subject classification with softmax loss, using the penultimate layer output as the feature descriptor, and generating a cosine similarity score given a pair of face images or videos. The softmax loss function does not optimize the features to have higher similarity score for positive pairs and lower similarity score for negative pairs, which leads to a performance gap. In this paper, we propose a new loss function, called Crystal Loss, that restricts the features to lie on a hypersphere of a fixed radius. The loss can be easily implemented using existing deep learning frameworks. We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems. We achieve state-of-the-art performance for face verification and recognition on challenging LFW, IJB-A, IJB-B and IJB-C datasets over a large range of false alarm rates (10-1 to 10-7).


title: Adjective Intensity and Sentiment Analysis

abstract: For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives that share a common semantic property. In this paper, we present a semi-supervised approach to assign intensity levels to adjectives, viz. high, medium and low, where adjectives are compared when they belong to the same semantic category. For example, in the semantic category of EXPERTISE, expert, experienced and familiar are respectively of level high, medium and low. We obtain an overall accuracy of 77% for intensity assignment. We show the significance of considering intensity information of adjectives in predicting star-rating of reviews. Our intensity based prediction system results in an accuracy of 59% for a 5-star rated movie review corpus.


title: To be or not to be a pathogen: that is the mucosally relevant question

abstract: The human interface with the microbial world has so far largely been considered through the somewhat restrictive angle of host–pathogen interactions resulting in disease. It has consequently largely ignored the daily symbiosis with the microbiota, an ensemble of symbiotic microorganisms engaged in a commensal, and for some of them mutualistic, interaction. This microbiota heavily populates essential surfaces such as the oral and intestinal cavity, the upper respiratory tract, the vagina, and the skin. Host response to the pathogens is characterized by quick recognition combined with strong innate (i.e., inflammatory) and adaptive immune responses, causing microbial eradication often at the cost of significant tissue damage. Response to the symbiotic microbiota is characterized by a process called tolerance that encompasses a complex integration of microbial recognition and tightly controlled innate (i.e., physiological inflammation) and adaptive immune responses. This dichotomy in host response is critical at the gut mucosal surface that is massively colonized by a diverse population of bacteria. The host is therefore permanently facing the challenge of discriminating among symbiotic and pathogenic bacteria in order to offer an adapted response. This asks the fundamental existential question: “to be or not to be… a pathogen.” This review has attempted to consider this question from the host angle. What do host mucosal sensing systems see in the bacteria to which they become exposed to establish proper discrimination? A new facet of medicine resides in the dysfunction of this complex balance that has likely forged the complexity of the immune system.


title: High-Isolation XX-Polar Antenna

abstract: A new configuration of the dual-band and dual-polarized antenna is presented, which is fed by aperture coupling for the existing mobile communication systems working over 870-960 MHz (GSM) and 1710-2180 MHz (DCS/UMTS) frequency band. XX-Polar antenna stands for an antenna with dual-band and dual linear slant polarization. In this paper DU band stands for DCS/UMTS band. Measurement results show that the proposed antenna yields good broadside radiation characteristics including symmetric radiation patterns, low cross-polarization level ( ; 20 dB) and high isolation (>; 30 dB) at both bands. The designed antenna has an impedance bandwidth of 20.4% (790-970 MHz) for VSWR <; 1.5 in the lower band and 29.3% for VSWR <; 1.6 (1630-2190 MHz) in the upper band. The measured average gains are about 9.3-10.2 and 8.6-10 dBi in the lower and upper band, respectively. It is also promising for array antenna in various wireless systems.


FreeCam: A Hybrid Camera System for Interactive Free-Viewpoint Video


title: Using Design Structure Matrix for improving Electronic Medical Record usability

abstract: Low adoption rate of Electronic Medical Record (EMR) systems is hindering achieving a national health care cost reduction estimated at $12 billion over the next 10 years. Physicians' EMR adoption rates are as a low as 10% nation-wide, due in part to poor design of the system user interface. The poor design is impacting EMR usability where physicians are faced with information overload. This is causing physicians to accept and validate erroneous data that may inadvertently produce unintended clinical consequences. The subject research describes the shortcomings of the EMR system leading to low adoption, and then proposes a new approach of using the Design Structure Matrix (DSM) method for improving EMR system usability through the analysis of system functionality. DSM is a modeling tool for developing complex systems and enables users to analyze and model dependencies among system elements in order to improve system functionality. A clustering algorithm is used to evaluate the interactions between system elements, and results are analyzed to decide if further design improvements can be made. The preliminary research results suggest that the application of DSM may allow improvements to the EMR functional design and enable improved physician acceptance of such systems.


title: Modularized prototype of 5G mmWave base station system at 38 GHz

abstract: This paper presents a 4-way modularized prototype millimeter-wave base station system operating at 38 GHz. The base station contains several separated modules operating for different functions in IF frequency and RF frequency. A 4×4 phased array antenna is utilized in the system for beam-steering or beamforming applications in the future 5G technologies. The whole system is fabricated by standard industrial printed circuit board (PCB) process with Rogers RO4350B material and manufactured circuit components. Measurements of the phased array antenna and the prototype system were done to verify the design concept. The measurement result of the 1-way system has a −28 dB EVM with 600 MHz bandwidth in a 6-meter wireless link scenario.


title: On monitoring development indicators using high resolution satellite images

abstract: We develop a machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery. The diverse set of indicators are often not intuitively related to observable features in satellite images, and are not even always well correlated with each other. Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies. Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India.


A Survey on Security Threats and Vulnerability attacks on Bluetooth Communication


title: Combining motion and appearance cues for anomaly detection

abstract: In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of "cut-bin" in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel scheme based on Support Vector Data Description (SVDD), which obtains a spherically shaped boundary around the normal objects to exclude abnormal objects. The two complementary cues are finally combined to achieve more comprehensive detection results. Experimental results show that the proposed approach can effectively locate abnormal objects in multiple public video scenarios, achieving comparable performance to other state-of-the-art anomaly detection techniques. HighlightsAn algorithm integrating motion and appearance cues for video anomaly detection.Motion model uses the "cut-bin" to detect abnormal motions.Appearance model uses a spherical boundary to exclude unusual objects.Integration of the two cues achieves higher detection rate and fewer false alarms.


title: Anatomy of a Vulnerable Fitness Tracking System: Dissecting the Fitbit Cloud, App, and Firmware

abstract: Fitbit fitness trackers record sensitive personal information, including daily step counts, heart rate profiles, and locations visited. By design, these devices gather and upload activity data to a cloud service, which provides aggregate statistics to mobile app users. The same principles govern numerous other Internet-of-Things (IoT) services that target different applications. As a market leader, Fitbit has developed perhaps the most secure wearables architecture that guards communication with end-to-end encryption. In this article, we analyze the complete Fitbit ecosystem and, despite the brand's continuous efforts to harden its products, we demonstrate a series of vulnerabilities with potentially severe implications to user privacy and device security. We employ a range of techniques, such as protocol analysis, software decompiling, and both static and dynamic embedded code analysis, to reverse engineer previously undocumented communication semantics, the official smartphone app, and the tracker firmware. Through this interplay and in-depth analysis, we reveal how attackers can exploit the Fitbit protocol to extract private information from victims without leaving a trace, and wirelessly flash malware without user consent. We demonstrate that users can tamper with both the app and firmware to selfishly manipulate records or circumvent Fitbit's walled garden business model, making the case for an independent, user-controlled, and more secure ecosystem. Finally, based on the insights gained, we make specific design recommendations that can not only mitigate the identified vulnerabilities, but are also broadly applicable to securing future wearable system architectures.


title: PDNet: Prior-Model Guided Depth-Enhanced Network for Salient Object Detection

abstract: Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, there still remains two issues needed to be addressed in deep learning based saliency detection. One is the lack of tremendous amount of annotated data to train a network. The other is the lack of robustness for extracting salient objects in images containing complex scenes. In this paper, we present a new architecture—PDNet, a robust prior-model guided depth-enhanced network for RGB-D salient object detection. In contrast to existing works, in which RGB-D values of image pixels are fed directly to a network, the proposed architecture is composed of a master network for processing RGB values, and a sub-network making full use of depth cues and incorporate depth-based features into the master network. To overcome the limited size of the labeled RGB-D dataset for training, we employ a large conventional RGB dataset to pre-train the master network, which proves to contribute largely to the final accuracy. Extensive evaluations over five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches.


title: 3-Dimensional Localization via RFID Tag Array

abstract: In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.


title: Air-filled SIW transmission line and phase shifter for high-performance and low-cost U-Band integrated circuits and systems

abstract: In this paper, air-filled Substrate Integrated Waveguide (SIW) is proposed and demonstrated for the first time at U-band. This low-loss transmission line is developed on a low-cost multilayer Printed Circuit Board (PCB) process. The top and bottom layers may make use of an extremely low-cost standard substrate such as FR-4 on which base-band or digital circuits can be designed so to obtain a very compact, high performance, low-cost and self-packaged integrated system. For measurement purposes, an optimized-length dielectric- to air-filled SIW transition operating at U-band with 0.21 ±0.055 dB insertion loss is developed. The measured insertion loss of an air-filled SIW of interest at U-band is 0.122 ±0.122 dB/cm compared to 0.4 ±0.13 dB/cm for its dielectric-filled counterpart. Furthermore, an air-filled SIW phase shifter is reported for the first time. It achieves a measured 0.15 ±0.14 dB transmission loss at U-band. The proposed air-filled SIW transmission line and phase shifter are of particular interest for high performance and low-cost millimeter-wave circuits and systems.


title: Motor effects of locally administered pinaverium bromide in the sigmoid tract of patients with irritable bowel syndrome.

abstract: The effects of pinaverium bromide, a non-absorbable antispasmodic agent, administered locally, on sigmoid-rectal motility was investigated in 20 patients with irritable bowel syndrome. The influence of either pinaverium (10 subjects) or placebo (10 subjects) on a neostigmine-induced increase of sigmoid pressure was assessed and compared by means of computerized electromanometry. The drug was found to counteract significantly the motor effects of neostigmine, thus appearing to be an effective compound for the treatment of functional disorders of the colon.


title: RLC Matched GaN HEMT Power Amplifier with 2 GHz Bandwidth

abstract: We have demonstrated a RLC matched GaN HEMT power amplifier with 12 dB gain, 0.05-2.0 GHz bandwidth, 8 W CW output power and 36.7-65.4% drain efficiency over the band. The amplifier is packaged in a ceramic S08 package and contains a GaN on SiC device operating at 28 V drain voltage, alongside GaAs integrated passive matching circuitry. A second circuit designed for 48 V operation and 15 W CW power over the same band, obtains over 20 W under pulsed condition with 10% duty cycle and 100 mus pulse width. CW measurements are pending after assembly in an alternate high power package. These amplifiers are suitable for use in wideband digital cellular infrastructure, handheld radios, and jamming applications.


title: Context-Based Speech Act Classification in Intelligent Tutoring Systems

abstract: In intelligent tutoring systems with natural language dialogue, speech act classification, the task of detecting learners’ intentions, informs the system’s response mechanism. In this paper, we propose supervised machine learning models for speech act classification in the context of an online collaborative learning game environment. We explore the role of context (i.e. speech acts of previous utterances) for speech act classification. We compare speech act classification models trained and tested with contextual and non-contextual features (contents of the current utterance). The accuracy of the proposed models is high. A surprising finding is the modest role of context in automatically predicting the speech acts.


title: Learning Anisotropic RBF Kernels

abstract: We present an approach for learning an anisotropic RBF kernel in a game theoretical setting where the value of the game is the degree of separation between positive and negative training examples. The method extends a previously proposed method (KOMD) to perform feature re-weighting and distance metric learning in a kernel-based classification setting. Experiments on several benchmark datasets demonstrate that our method generally outperforms state-of-the-art distance metric learning methods, including the Large Margin Nearest Neighbor Classification family of methods.


title: Deep Learning of Graphs with Ngram Convolutional Neural Networks

abstract: Convolutional Neural Network (CNN) has gained attractions in image analytics and speech recognition in recent years. However, employing CNN for classification of graphs remains to be challenging. This paper presents the Ngram graph-block based convolutional neural network model for classification of graphs. Our Ngram deep learning framework consists of three novel components. First, we introduce the concept of $n$ -gram block to transform each raw graph object into a sequence of $n$ -gram blocks connected through overlapping regions. Second, we introduce a diagonal convolution step to extract local patterns and connectivity features hidden in these $n$ -gram blocks by performing $n$ -gram normalization. Finally, we develop deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a $n$ -gram deep learning model using convolutional neural network. We evaluate the effectiveness of our approach by comparing it with the existing state of art methods using five real graph repositories from bioinformatics and social networks domains. Our results show that the Ngram approach outperforms existing methods with high accuracy and comparable performance.


title: Stable n-pointed trees of projective lines

abstract: Stable n-pointed trees arise in a natural way if one tries to find moduli for totally degenerate curves: Let C be a totally degenerate stable curve of genus g ≥ 2 over a field k. This means that C is a connected projective curve of arithmetic genus g satisfying ::: o ::: ::: (a) every irreducible component of C is a rational curve over κ. ::: ::: ::: (b) every singular point of C is a κ-rational ordinary double point. ::: ::: ::: (c) every nonsingular component L of C meets C−L in at least three points. It is always possible to find g singular points P1,..., Pg on C such that the blow up C of C at P1,..., Pg is a connected projective curve with the following properties: ::: o ::: ::: (i) every irreducible component of C is isomorphic to Pk1 ::: ::: ::: (ii) the components of C intersect in ordinary κ-rational double points ::: ::: ::: (iii) the intersection graph of C is a tree. ::: ::: ::: ::: ::: ::: ::: ::: ::: ::: The morphism φ : C → C is an isomorphism outside 2g regular points Q1, Q1′, Qg, Qg′ and identifies Qi with Qj′. is uniquely determined by the g pairs of regular κ-rational points (Qi, Qi′). A curve C satisfying (i)-(iii) together with n κ-rational regular points on it is called a n-pointed tree of projective lines. C is stable if on every component there are at least three points which are either singular or marked. The object of this paper is the classification of stable n-pointed trees. We prove in particular the existence of a fine moduli space Bn of stable n-pointed trees. The discussion above shows that there is a surjective map πB2g → Dg of B2g onto the closed subscheme Dg of the coarse moduli scheme Mg of stable curves of genus g corresponding to the totally degenerate curves. By the universal property of Mg, π is a (finite) morphism. π factors through B2g = B2g mod the action of the group of pair preserving permutations of 2g elements (a group of order 2gg, isomorphic to a wreath product of Sg and ℤ/2ℤ ::: ::: The induced morphism π: B2g → Dg is an isomorphism on the open subscheme of irreducible curves in Dg, but in general there may be nonequivalent choices of g singular points on a totally degenerated curve for the above construction, so π has nontrivial fibres. In particular, π is not the quotient map for a group action on B2g. This leads to the idea of constructing a Teichmuller space for totally degenerate curves whose irreducible components are isomorphic to B2g and on which a discontinuous group acts such that the quotient is precisely Dg; π will then be the restriction of this quotient map to a single irreducible component. This theory will be developped in a subsequent paper. ::: ::: In this paper we only consider stable n-pointed trees and their moduli theory. In § 1 we introduce the abstract cross ratio of four points (not necessarily on the same projective line) and show that for a field κ the κ-valued points in the projective variety Bn of cross ratios are in 1 − 1 correspondence with the isomorphy classes of stable n-pointed trees of projective lines over κ. We also describe the structure of the subvarieties B(T, ψ) of stable n-pointed trees with fixed combinatorial type. ::: ::: We generalize our notion in § 2 to stable n-pointed trees of projective lines over an arbitrary noetherian base scheme S and show how the cross ratios for the fibres fit together to morphisms on S. This section is closely related to [Kn], but it is more elementary since we deal with a special case. ::: ::: § 3 contains the main result of the paper: the canonical projection Bn + 1 → Bn is the universal family of stable n-pointed trees. As a by-product of the proof we find that Bn is a smooth projective scheme of relative dimension 2n - 3 over ℤ. We also compare Bn to the fibre product Bn−1 × Bn-2 Bn − 1 and investigate the singularities of the latter. ::: ::: In § 4 we prove that the Picard group of Bn is free of rank 2n−1−(n+1)−n(n−3)/2. ::: ::: We also give a method to compute the Betti numbers of the complex manifold Bn(ℂ). ::: ::: In § 5 we compare Bn to the quotient Qn: = ℙssn/PGL2 of semi-stable points in ℙ1n for the action of fractional linear transformations in every component. This orbit space has been studied in greater detail by several authors, see [GIT], [MS], [G]. It turns out that Bn is a blow-up of Qn, and we describe the blow-up in several steps where at each stage the obtained space is interpreted as a solution to a certain moduli problem.


title: Glomerular filtration rate, proteinuria, and the incidence and consequences of acute kidney injury: a cohort study

abstract: Summary Background Low values of estimated glomerular filtration rate (eGFR) predispose to acute kidney injury, and proteinuria is a marker of kidney disease. We aimed to investigate how eGFR and proteinuria jointly modified the risks of acute kidney injury and subsequent adverse clinical outcomes. Methods We did a cohort study of 920 985 adults residing in Alberta, Canada, between 2002 and 2007. Participants not needing chronic dialysis at baseline and with at least one outpatient measurement of both serum creatinine concentration and proteinuria (urine dipstick or albumin-creatinine ratio) were included. We assessed hospital admission with acute kidney injury with validated administrative codes; other outcomes were all-cause mortality and a composite renal outcome of end-stage renal disease or doubling of serum creatinine concentration. Findings During median follow-up of 35 months (range 0–59 months), 6520 (0·7%) participants were admitted with acute kidney injury. In those with eGFR 60 mL/min per 1·73 m 2 or greater, the adjusted risk of admission with this disorder was about 4 times higher in those with heavy proteinuria measured by dipstick (rate ratio 4·4 vs no proteinuria, 95% CI 3·7–5·2). The adjusted rates of admission with acute kidney injury and kidney injury needing dialysis remained high in participants with heavy dipstick proteinuria for all values of eGFR. The adjusted rates of death and the composite renal outcome were also high in participants admitted with acute kidney injury, although the rise associated with this injury was attenuated in those with low baseline eGFR and heavy proteinuria. Interpretation These findings suggest that information on proteinuria and eGFR should be used together when identifying people at risk of acute kidney injury, and that an episode of acute kidney injury provides further long-term prognostic information in addition to eGFR and proteinuria. Funding The study was funded by an interdisciplinary team grant from Alberta Heritage Foundation for Medical Research.


title: Behavioral resource-aware model inference

abstract: Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use. Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume separates otherwise identical requests that hit a cache from those that miss it, which can aid understanding how the cache affects system behavior and removing cache-related bugs. A small user study demonstrates that using Perfume is more effective than using logs and another model inference tool for system comprehension. A case study on the TCP protocol demonstrates that Perfume models can help understand non-trivial protocol behavior. Perfume models capture key system properties and improve system comprehension, while being reasonably robust to noise likely to occur in real-world executions.


title: Continuous space language models

abstract: This paper describes the use of a neural network language model for large vocabulary continuous speech recognition. The underlying idea of this approach is to attack the data sparseness problem by performing the language model probability estimation in a continuous space. Highly efficient learning algorithms are described that enable the use of training corpora of several hundred million words. It is also shown that this approach can be incorporated into a large vocabulary continuous speech recognizer using a lattice rescoring framework at a very low additional processing time. The neural network language model was thoroughly evaluated in a state-of-the-art large vocabulary continuous speech recognizer for several international benchmark tasks, in particular the Nist evaluations on broadcast news and conversational speech recognition. The new approach is compared to four-gram back-off language models trained with modified Kneser-Ney smoothing which has often been reported to be the best known smoothing method. Usually the neural network language model is interpolated with the back-off language model. In that way, consistent word error rate reductions for all considered tasks and languages were achieved, ranging from 0.4% to almost 1% absolute.


Monitoring and Analysis of Epilepsy Patient using Android Platform


title: What Happens When Recommendation System Meets Reputation System? The Impact of Recommendation Information on Purchase Intention

abstract: Recommendation systems are intended to increase consumer purchasing intentions and may enhance consumers' experience value via personalized service. This study investigates how recommendation system and reputation system simultaneously affect customer purchase intention from an information communication perspective. A laboratory experiment was conducted to test the research framework which was proposed based on the social influence theory and the theory of reasoned action. The results demonstrate that consumer purchase intention is primarily affected by consumer attitude toward the purchase of the recommended product, and this attitude is driven mainly by the relevance to personal interests while also being affected by the external subjective norm regarding the purchase of the recommended product. This study also finds that the online feedbacks of reputation system exert a significant influence on the formation of external subjective norms. The results of this study provide useful insights into how recommendation information affects consumers' attitude and purchase intention.


title: Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks

abstract: A very efficient energy-management system for hybrid electric vehicles (HEVs), using neural networks (NNs), was developed and tested. The system minimizes the energy requirement of the vehicle and can work with different primary power sources like fuel cells, microturbines, zinc-air batteries, or other power supplies with a poor ability to recover energy from a regenerative braking, or with a scarce power capacity for a fast acceleration. The experimental HEV uses lead-acid batteries, an ultracapacitor (UCAP) bank, and a brushless dc motor with nominal power of 32 kW, and a peak power of 53 kW. The digital signal processor (DSP) control system measures and stores the following parameters: primary-source voltage, car speed, instantaneous currents in both terminals (primary source and UCAP), and actual voltage of the UCAP. When UCAPs were installed on the vehicle, the increase in range was around 5.3% in city tests. However, when optimal control with NN was used, this figure increased to 8.9%. The car used for this experiment is a Chevrolet light utility vehicle (LUV) truck, similar in shape and size to Chevrolet S-10, which was converted to an electric vehicle (EV) at the Universidad Catolica de Chile. Numerous experimental tests under different conditions are compared and discussed.


title: Intelligent Anti-Money Laundering System

abstract: Criminal elements in today's technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many anti-money laundering solutions have been in place for some time within the financial community, they cannot adapt to the ever-changing risk and methods in relation to money laundering. In order for a more adaptive, intelligent and flexible solution for anti-money laundering, the intelligent agent technology is applied in this research. Intelligent agents with their properties of autonomy, reactivity and proactivity are well suited for money laundering prevention controls. Several types of agents are proposed and a novel and open multi-agent architecture is presented for anti-money laundering. A prototype system for money laundering detection is also developed to demonstrate the advances of the proposed system architecture and business value.


title: Attribute-based signatures

abstract: We introduce Attribute-Based Signatures (ABS), a versatile primitive that allows a party to sign a message with fine-grained control over identifying information. In ABS, a signer, who possesses a set of attributes from the authority, can sign a message with a predicate that is satisfied by his attributes. The signature reveals no more than the fact that a single user with some set of attributes satisfying the predicate has attested to the message. In particular, the signature hides the attributes used to satisfy the predicate and any identifying information about the signer (that could link multiple signatures as being from the same signer). Furthermore, users cannot collude to pool their attributes together. ::: ::: We give a general framework for constructing ABS schemes, and then show several practical instantiations based on groups with bilinear pairing operations, under standard assumptions. Further, we give a construction which is secure even against a malicious attribute authority, but the security for this scheme is proven in the generic group model. We describe several practical problems that motivated this work, and how ABS can be used to solve them. Also, we show how our techniques allow us to extend Groth-Sahai NIZK proofs to be simulationextractable and identity-based with low overhead.


title: LUDOCORE: A logical game engine for modeling videogames

abstract: LUDOCORE is a logical “game engine”, linking game rules as reasoned about by game designers to the formal logic used by automated reasoning tools in AI. A key challenge in designing this bridge is engineering a concise, safe, and flexible representation that is compatible with the semantics of the games that logical models created with our engine intend to represent. Building on the event calculus, a formalism for reasoning about state and events over time, and a set of common structures and idioms used in modeling games, we present a tool that is capable of generating gameplay traces that illustrate the game's dynamic behavior. It supports incremental modeling of player and non-player entities in the game world, modification of game rules without extensive non-local changes, and exploratory temporal and structural queries. In addition, its logical models can support play as real-time, graphical games with minimal user-interface description.


title: App Inventor: Create Your Own Android Apps

abstract: Create mobile services and applications regardless of your computer programming knowledge. This extraordinary book introduces you to App Inventor for Android, a powerful tool that exposes you to the world of computer programming, so you can create technology rather than merely consume it. You don't need years of training to build your own Android apps. This book teaches you how to quickly design and code apps for anything from texting to location awareness to data storage on the Web, using App Inventor's unique visual interface. Ideal for beginning and intermediate Android developers, hobbyists and makers, and students of any age, App Inventor will help you turn your great idea into a full-functioning app in no time. * Take advantage of App Inventor's GPS-location sensor: Build an app shows the location of friends or colleagues at a concert or conference, or one that gives you a custom tour of your school, workplace, or a museum. * Use an Android device's phone features: Write an app that periodically texts "missing you" to loved ones, an app that responds to texts automatically when you're driving, and an app that reads incoming texts aloud. * Communicate with the Web: Create Android apps that talk to your favorite web sites, such as Amazon and Twitter.


title: Telepressure and College Student Employment: The Costs of Staying Connected Across Social Contexts

abstract: Telepressure is a psychological state consisting of the preoccupation and urge to respond quickly to message-based communications from others. Telepressure has been linked with negative stress and health outcomes, but the existing measure focuses on experiences specific to the workplace. The current study explores whether an adapted version of the workplace telepressure measure is relevant to general social interactions that rely on information and communication technologies. We validated a general telepressure measure in a sample of college students and found psychometric properties similar to the original workplace measure. Also, general telepressure was related to, but distinct from, the fear of missing out, self-control and technology use. Using a predictive validity design, we also found that telepressure at the beginning of the semester was related to student reports of burnout, perceived stress and poor sleep hygiene 1 month later (but not work-life balance or general life satisfaction). Moreover, telepressure was more strongly related to more negative outcomes (burnout, stress and poor sleep hygiene) and less positive outcomes (work-life balance and life satisfaction) among employed compared with non-employed students. Thus, the costs of staying connected to one's social network may be more detrimental to college students with additional employment obligations. Copyright © 2016 John Wiley & Sons, Ltd.


title: 1-MHz self-driven ZVS full-bridge converter for 48-V power pod and DC/DC brick

abstract: In this paper, a self-driven zero-voltage-switching (ZVS) full-bridge converter is proposed. With the proposed self-driven scheme, the combination of the ZVS technique and Self-driven technique recycles the gate driving energy by making use of the input capacitor of the secondary side synchronous rectifier (SR) as the snubber capacitor of the primary side switches. Compared with the external driver, the proposed converter can save driving loss and synchronous rectifier body diode conduction loss. Additionally, compared with the existed level-shifted self-driven scheme for bridge-type symmetrical topologies, its gate signal is very clean and suitable for high-frequency applications. A 1-MHz, 1.2-V/70-A prototype is built to verify the analysis. Experimental results show that it can achieve 81.7% efficiency. And there is an efficiency improvement of 4.7% over conventional phase-shifted full-bridge converter with an external driver.


title: Pain Management for the Terminally Ill: The Role of Race and Religion

abstract: Pain management is a primary concern of both patients facing terminal illness and their loved ones. Pain itself is known to be both a universal and a very personal experience. Given the private nature of pain, the individual must express it in order to make it known. The expression of pain and its companion, suffering, are subjective and heavily influenced by one’s culture. The clinical and social manifestations of pain and suffering are varied as numerous anthropological studies have shown.1


title: A Li-ion battery management system based on CAN-bus for electric vehicle

abstract: A battery management system (BMS) based on the CAN-bus was designed for the Li-ion battery pack which consisted of many series-connected battery cells and was distributed dispersedly on the electric vehicle (EV). The BMS consisted of one master module and several sampling modules. The hardware design of the sampling circuit and the CAN expanding circuit was introduced. The strategies of the battery SOC (state of charge) estimation and the battery safety management were also presented.


title: Semantic Annotation of Scholarly Documents and Citations

abstract: Scholarly publishing is in the middle of a revolution based on the use of Web-related technologies as medium of communication. In this paper we describe our ongoing study of semantic publishing and automatic annotation of scholarly documents, presenting several models and tools for the automatic annotation of structural and semantic components of documents. In particular, we focus on citations and their automatic classification obtained by CiTalO, a framework that combines ontology learning techniques with NLP techniques.


title: Single- and multi-objective particle swarm optimization of reservoir structure in Echo State Network

abstract: Echo State Networks ESNs are specific kind of recurrent networks providing a black box modeling of dynamic non-linear problems. Their architecture is distinguished by a randomly recurrent hidden infra-structure called dynamic reservoir. Coming up with an efficient reservoir structure depends mainly on selecting the right parameters including the number of neurons and connectivity rate within it. Despite expertise and repeatedly tests, the optimal reservoir topology is hard to be determined in advance. Topology evolving can provide a potential way to define a suitable reservoir according to the problem to be modeled. This last can be mono- or multi-constrained. Throughout this paper, a mono-objective as well as a multi-objective particle swarm optimizations are applied to ESN to provide a set of optimal reservoir architectures. Both accuracy and complexity of the network are considered as objectives to be optimized during the evolution process. These approaches are tested on various benchmarks such as NARMA and Lorenz time series.


title: Music training for the development of auditory skills

abstract: In this Perspective, Kraus and Chandrasekaran show that changes in the nervous system that are induced by music training enhance auditory processing, not only of music but also of speech and language. They argue that music training should be provided in schools to benefit children throughout society.


title: No double-dissociation between optic ataxia and visual agnosia: Multiple sub-streams for multiple visuo-manual integrations

abstract: The current dominant view of the visual system is marked by the functional and anatomical dissociation between a ventral stream specialised for perception and a dorsal stream specialised for action. The "double-dissociation" between visual agnosia (VA), a deficit of visual recognition, and optic ataxia (OA), a deficit of visuo-manual guidance, considered as consecutive to ventral and dorsal damage, respectively, has provided the main argument for this dichotomic view. In the first part of this paper, we show that the currently available empirical data do not suffice to support a double-dissociation between OA and VA. In the second part, we review evidence coming from human neuropsychology and monkey data, which cast further doubts on the validity of a simple double-dissociation between perception and action because they argue for a far more complex organisation with multiple parallel visual-to-motor connections: 1. A dorso-dorsal pathway (involving the most dorsal part of the parietal and pre-motor cortices): for immediate visuo-motor control-with OA as typical disturbance. The latest research about OA is reviewed, showing how these patients exhibit deficits restricted to the most direct and fast visuo-motor transformations. We also propose that mild mirror ataxia, consisting of misreaching errors when the controlesional hand is guided to a visual goal though a mirror, could correspond to OA with an isolated "hand effect". 2. A ventral stream-prefrontal pathway (connections from the ventral visual stream to pre-frontal areas, by-passing the parietal areas): for "mediate" control (involving spatial or temporal transpositions [Rossetti, Y., & Pisella, L. (2003). Mediate responses as direct evidence for intention: Neuropsychology of Not to-, Not now- and Not there-tasks. In S. Johnson (Ed.), Cognitive Neuroscience perspectives on the problem of intentional action (pp. 67-105). MIT Press.])-with VA as typical disturbance. Preserved visuo-manual guidance in patients with VA is restricted to immediate goal-directed guidance, they exhibit deficits for delayed or pantomimed actions. 3. A ventro-dorsal pathway (involving the more ventral part of the parietal lobe and the pre-motor and pre-frontal areas): for complex planning and programming relying on high representational levels with a more bilateral organisation or an hemispheric lateralisation-with mirror apraxia, limb apraxia and spatial neglect as representatives. Mirror apraxia is a deficit that affects both hands after unilateral inferior parietal lesion with the patients reaching systematically and repeatedly toward the virtual image in the mirror. Limb apraxia is localized on a more advanced conceptual level of object-related actions and results from deficient integrative, computational and "working memory" capacities of the left inferior parietal lobule. A component of spatial working memory has recently been revealed also in spatial neglect consecutive to lesion involving the network of the right inferior parietal lobule and the right frontal areas. We conclude by pointing to the differential temporal constraints and integrative capabilities of these parallel visuo-motor pathways as keys to interpret the neuropsychological deficits.


A topic-based sentiment analysis model to predict stock market price movement using Weibo mood


title: Transosseous Physeal-Sparing Rotator Cuff Repair in an Adolescent Football Player.

abstract: Rotator cuff injuries in the pediatric and adolescent population are rare. These injuries are normally caused by an acute event or by repeated microtrauma as a result of poor mechanics during overhead sporting activities. Overall, these injuries account for fewer than 1% of all rotator cuff tears. Physeal injuries, tuberosity avulsions ("rotator cuff equivalents"), and proximal humeral fractures commonly occur in the pediatric population. Traumatic full-thickness rotator cuff ruptures that occur in this age group during contact sports are an extremely uncommon cause of shoulder pain. The rarity of this type of injury in the pediatric and adolescent population may be the result of the excellent tensile properties of rotator tendons in childhood that often resist mechanical forces better than the growth plate or the apophyses. This report describes a full-thickness rotator cuff tear and a posterior labral tear in a 12-year-old boy who had a direct injury to the shoulder while playing football. The authors' goal in reporting this case is to increase orthopedic surgeons' awareness of this type of injury. Early use of magnetic resonance imaging for persistent shoulder pain after direct injury during contact sports may avoid undue delay in diagnosis. This report also describes the mini-open transosseous extraphyseal technique of cuff repair and reviews the literature on this infrequent injury. Overall, excellent clinical outcomes and return to sports can be expected after surgical repair of torn and often retracted tendons.


title: Integrable sl(N, ℂ) tops as Calogero-Moser systems

abstract: We show a relation between systems of integrable tops on the algebras sl(N, ℂ) and Calogero-Moser systems of N particles. We construct classical Lax operators corresponding to these systems. We show that these operators are related to certain new trigonometric and rational solutions of the Yang-Baxter equations for the algebras sl(N, ℂ) and give explicit formulas for N = 2, 3.


title: Best paper -- Follow the money: understanding economics of online aggregation and advertising

abstract: The large-scale collection and exploitation of personal information to drive targeted online advertisements has raised privacy concerns. As a step towards understanding these concerns, we study the relationship between how much information is collected and how valuable it is for advertising. We use HTTP traces consisting of millions of users to aid our study and also present the first comparative study between aggregators. We develop a simple model that captures the various parameters of today's advertising revenues, whose values are estimated via the traces. Our results show that per aggregator revenue is skewed (5% accounting for 90% of revenues), while the contribution of users to advertising revenue is much less skewed (20% accounting for 80% of revenue). Google is dominant in terms of revenue and reach (presence on 80% of publishers). We also show that if all 5% of the top users in terms of revenue were to install privacy protection, with no corresponding reaction from the publishers, then the revenue can drop by 30%.


title: Pendulum Therapy of Molar Distalization in Mixed Dentition

abstract: Early and timely pedo-orthodontic treatment is aimed ateliminating the disturbances of skeletal or dentoalveolardevelopment, to harmonize the stomatognathic system beforethe full eruption of all permanent teeth. The advantages ofpendulum appliance are its minimal dependence on patient’scompliance (child cooperation), ease of fabrication, onetimeactivation and adjustment of the springs if necessaryto correct minor transverse and vertical molar positions.This article reports a successful treatment method of class IImalocclusion with pendulum appliance in mixed dentitionphase. Distalization of maxillary molar was done, followed byguidance of canine impaction orthodontically and other dentalcorrection using 0.022 MBT appliances. Posttreatment resultswere stable and remarkable.


title: Effects of training on postural control and agility when wearing socks of different compression levels

abstract: Study aim: ::: The aim of this study was to evaluate the effects of training while wearing socks differing in compression level (clinical, sub-clinical, regular) on performance of static and dynamic balancing and agility tasks in healthy, physically active people. We sought to understand whether socks with different compression properties supported postural regulation and agility task performance by enhancing somatosensory perception, unskewed by specific age range effects. Material and methods: ::: Participants comprised 61 adults aged 18–75 years, divided into three groups (two experimental groups wearing clinical or sub-clinical level compression socks, and one control group wearing regular non-compression socks during training). An 8-week (2 × 1h per week) intervention programme was administered to train static and dynamic balance and postural control, leg strength and agility. Results: ::: A mixed model ANOVA revealed no differences in static and dynamic balance and postural control and agility performance between clinical, sub-clinical, and control groups before and after training. All groups significantly improved their test performance, suggesting that training had some benefit on motor performance. Conclusions: ::: These results raised interesting questions requiring further investigation to examine the effects of wearing socks (with and without different levels of compression) on motor behaviours in specific groups of elderly vs. young participants, in physically active vs. less physically active people, and in performance settings outside standardized laboratory tests to study applications in natural performance environments.


title: Efficient visualization of high-throughput targeted proteomics experiments: TAPIR

abstract: MOTIVATION ::: Targeted mass spectrometry comprises a set of powerful methods to obtain accurate and consistent protein quantification in complex samples. To fully exploit these techniques, a cross-platform and open-source software stack based on standardized data exchange formats is required. ::: ::: ::: RESULTS ::: We present TAPIR, a fast and efficient Python visualization software for chromatograms and peaks identified in targeted proteomics experiments. The input formats are open, community-driven standardized data formats (mzML for raw data storage and TraML encoding the hierarchical relationships between transitions, peptides and proteins). TAPIR is scalable to proteome-wide targeted proteomics studies (as enabled by SWATH-MS), allowing researchers to visualize high-throughput datasets. The framework integrates well with existing automated analysis pipelines and can be extended beyond targeted proteomics to other types of analyses. ::: ::: ::: AVAILABILITY AND IMPLEMENTATION ::: TAPIR is available for all computing platforms under the 3-clause BSD license at https://github.com/msproteomicstools/msproteomicstools.


title: Individual differences in social comparison: development of a scale of social comparison orientation.

abstract: Development and validation of a measure of individual differences in social comparison orientation (the Iowa-Netherlands Comparison Orientation Measure [INCOM]) are described. Assuming that the tendency toward social comparison is universal, the scale was constructed so as to be appropriate to and comparable in 2 cultures: American and Dutch. It was then administered to several thousand people in each country. Analyses of these data are presented indicating that the scale has good psychometric properties. In addition, a laboratory study and several field studies are described that demonstrated the INCOM's ability to predict comparison behavior effectively. Possible uses of the scale in basic and applied settings are discussed.


title: Botryosphaeriales associated with stem blight and dieback of blueberry (Vaccinium spp.) in New South Wales and Western Australia

abstract: Stem blight and dieback caused by species of the Botryosphaeriales are important diseases of blueberry worldwide. In recent years, stem blight and dieback symptoms have been increasingly observed affecting blueberry production in Australia. Thirty samples were collected from symptomatic plants in an orchard at Corindi NSW, a major blueberry growing region. In addition, samples from symptomatic blueberry plants were submitted by growers to the Plant Health Diagnostic Service, NSW Department of Primary Industries from eight orchards in New South Wales (NSW), and a single orchard in Western Australia (WA). Culture isolations, DNA sequencing and pathogenicity testing were undertaken to determine the species causing the disease. Fifty-two isolates were recovered in total, forty-eight from NSW, and four from WA. A multi-locus sequencing approach was used to assist species identification including the internal transcribed spacer region of rDNA including 5.8S (ITS), partial translation elongation factor 1-alpha (tef1-α), and DNA-directed RNA polymerase II second largest subunit (rpb2). Eight species from three genera were identified; the most common was Neofusicoccum parvum (n = 34), followed by N. kwambonambiense (n = 7), N. occulatum (n = 5), L. theobromae (n = 2), Botryosphaeria dothidea (n = 1), N. australe (n = 1), N. macroclavatum (n = 1) and Lasiodiplodia pseudotheobromae (n = 1). The pathogenicity testing showed all isolates produced lesions on blueberry stems. This study provides the first survey of Botryosphaeriales causing blueberry stem blight and dieback in Australia, and is a valuable resource for plant pathologists and growers trying to manage the disease.


title: Improving online learning: Student perceptions of useful and challenging characteristics

abstract: Abstract Online courses and programs continue to grow in higher education settings. Students are increasingly demanding online access, and universities and colleges are working to meet the demands. Yet many questions remain re: the viability and veracity of online learning, particularly from the learner perspective. The purpose of this study was to gain insights into learners' perceptions of online learning. Seventy-six (76) graduate students were surveyed to identify helpful components and perceived challenges based on their online learning experiences. Results of the study indicated that most learners agreed that course design, learner motivation, time management, and comfortableness with online technologies impact the success of an online learning experience. Participants indicated that technical problems, a perceived lack of sense of community, time constraints, and the difficulty in understanding the objectives of the online courses as challenges. Suggestions for addressing the challenges are provided.


title: Adiabatic Quantum Computation Applied to Deep Learning Networks

abstract: Training deep learning networks is a difficult task due to computational complexity, and this is traditionally handled by simplifying network topology to enable parallel computation on graphical processing units (GPUs). However, the emergence of quantum devices allows reconsideration of complex topologies. We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum computation known as quantum annealing performed by a D-Wave processor. We provide a brief description of the hardware and how it solves Ising models, how we translate our data into the corresponding Ising models, and how we use available expanded topology options to explore potential performance improvements. Although we focus on the application of quantum annealing in this article, the work discussed here is just one of three approaches we explored as part of a larger project that considers alternative means for training deep learning networks. The other approaches involve using a high performance computing (HPC) environment to automatically find network topologies with good performance and using neuromorphic computing to find a low-power solution for training deep learning networks. Our results show that our quantum approach can find good network parameters in a reasonable time despite increased network topology complexity; that HPC can find good parameters for traditional, simplified network topologies; and that neuromorphic computers can use low power memristive hardware to represent complex topologies and parameters derived from other architecture choices.


title: Ultrasonic Communication Using Consumer Hardware

abstract: We have implemented a near-ultrasonic communication protocol in the 18.5–20 kHz band, which is inaudible to most humans, using commodity smartphone speakers and microphones to transmit and receive signals. The protocol described in this paper is a component of Google's Nearby platform, where near-ultrasound signals are used to establish copresence between nearby devices by transmitting a short token. High-frequency sound does not pass through walls (most energy is reflected), so identified devices are constrained to approximately the same room, “within earshot” of one another. Our protocol has a raw data rate of 94.5 b/s, and we find in real indoor environments that transmission between mobile devices is reliable at 2 m distance and often works at 10 m. We use direct-sequence spread spectrum modulation, which makes it highly robust to multipath, motion, and narrowband noise. We use a 127-chip pseudorandom code, repeating once per data symbol, and modulate its amplitude with orthogonal sine waveforms encoding 4-bit symbol values. We add the orthogonal sines to a constant “pedestal,” which is inefficient in an information-theoretic sense, but makes synchronization easier. We describe a robust and computationally efficient transmitter and receiver implementations and show experiments on real and simulated data.


title: A Theory of Reading: From Eye Fixations to Comprehension

abstract: This article presents a model of reading comprehension that accounts for the allocation of eye fixations of college students reading scientific passages. The model deals with processing at the level of words, clauses, and text units. Readers make longer pauses at points where processing loads are greater. Greater loads occur while readers are accessing infrequent words, integrating information from important clauses, and making inferences at the ends of sentences. The model accounts forthe gaze duration on each word of text as a function of the involvement of the various levels of processing. The model is embedded in a theoretical framework capable of accommodating the flexibility of reading.


title: Shape-aware Instance Segmentation

abstract: We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation process, such as too small or shifted boxes. In this paper, we introduce a novel object segment representation based on the distance transform of the object masks. We then design an object mask network (OMN) with a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. This allows us to predict masks that go beyond the scope of the bounding boxes and are thus robust to inaccurate object candidates. We integrate our OMN into a Multitask Network Cascade framework, and learn the resulting shape-aware instance segmentation (SAIS) network in an end-to-end manner. Our experiments on the PASCAL VOC 2012 and the CityScapes datasets demonstrate the benefits of our approach, which outperforms the state-of-the-art in both object proposal generation and instance segmentation.


title: High-Speed tracking-by-detection without using image information

abstract: Tracking-by-detection is a common approach to multi-object tracking. With ever increasing performances of object detectors, the basis for a tracker becomes much more reliable. In combination with commonly higher frame rates, this poses a shift in the challenges for a successful tracker. That shift enables the deployment of much simpler tracking algorithms which can compete with more sophisticated approaches at a fraction of the computational cost. We present such an algorithm and show with thorough experiments its potential using a wide range of object detectors. The proposed method can easily run at 100K fps while outperforming the state-of-the-art on the DETRAC vehicle tracking dataset.


title: Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective

abstract: There is great interest in building intrinsic motivation into artificial systems using the reinforcement learning framework. Yet, what intrinsic motivation may mean computationally, and how it may differ from extrinsic motivation, remains a murky and controversial subject. In this paper, we adopt an evolutionary perspective and define a new optimal reward framework that captures the pressure to design good primary reward functions that lead to evolutionary success across environments. The results of two computational experiments show that optimal primary reward signals may yield both emergent intrinsic and extrinsic motivation. The evolutionary perspective and the associated optimal reward framework thus lead to the conclusion that there are no hard and fast features distinguishing intrinsic and extrinsic reward computationally. Rather, the directness of the relationship between rewarding behavior and evolutionary success varies along a continuum.


title: AppSachet: Distributed App Delivery from the Edge Cloud

abstract: With total app installs touching 100 Billion in 2015, the increasing number of active devices that support apps are posed to result in 200 billion downloads by 2017. Data center based App stores offering users convenient app access, however, cause congestion in the last mile of the Internet, despite use of content delivery networks (CDNs) or ISP-based caching. This paper explores the new paradigm of eBoxes, situated in the ‘edge cloud’ tier beyond the last mile, which can be used to alleviate this congestion. With redesigned app caches – termed AppSachet – such edge cloud based distributed caching can achieve a hit ratio of up to 83 %, demonstrated on real-world Internet traffic. The redesign leverages proposed new caching policies, termed p-LRU and c-LRU, specifically targeted at eBoxes’ limited storage and for the traffic caused by app installs and updates. A cost benefit analysis shows that the additional cost required to deploy AppSachet on eBoxes can be recovered within the first three months of operation.


title: Design of Reversible Counter

abstract: This article presents a research work on the design and synthesis of sequential circuits and flip-flops that are available in digital arena; and describes a new synthesis design of reversible counter that is optimized in terms of quantum cost, delay and garbage outputs compared to the existing designs. We proposed a new model of reversible T flip-flop in designing reversible counter


title: Learning utterance-level representations for speech emotion and age/gender recognition using deep neural networks

abstract: Accurately recognizing speaker emotion and age/gender from speech can provide better user experience for many spoken dialogue systems. In this study, we propose to use deep neural networks (DNNs) to encode each utterance into a fixed-length vector by pooling the activations of the last hidden layer over time. The feature encoding process is designed to be jointly trained with the utterance-level classifier for better classification. A kernel extreme learning machine (ELM) is further trained on the encoded vectors for better utterance-level classification. Experiments on a Mandarin dataset demonstrate the effectiveness of our proposed methods on speech emotion and age/gender recognition tasks.


title: Closed-Form Mathematical Solutions to Some Network Analyzer Calibration Equations

abstract: A general evaluation procedure is described for calibration of a linear complex reflectometer (network analyzer) and for two-port measurements. New closed-form formulas and procedures are given for two practical cases: 1) calibration or measurement with two known standards and a sliding termination; 2) calibration or measurement by one standard and two different sliding terminations. The reflection coefficient magnitudes and phases of these sliding terminations need not be known. The closed-form formulas make it possible to determine calibration constants by direct calculation without approximations or complicated multivariable iterations.


title: Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection

abstract: The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated (sandbox) environment will be automatically analyzed and will generate behavior reports. These reports will be preprocessed into sparse vector models for further machine learning (classification). The classifiers used in this research are k-Nearest Neighbors (kNN), Naive Bayes, J48 Decision Tree, Support Vector Machine (SVM), and Multilayer Perceptron Neural Network (MlP). Based on the analysis of the tests and experimental results of all the 5 classifiers, the overall best performance was achieved by J48 decision tree with a recall of 95.9%, a false positive rate of 2.4%, a precision of 97.3%, and an accuracy of 96.8%. In summary, it can be concluded that a proof-of-concept based on automatic behavior-based malware analysis and the use of machine learning techniques could detect malware quite effectively and efficiently.


title: Analyzing language development from a network approach

abstract: Abstract Children acquire their language in different ways. In this paper we propose some new measures from a network approach in quantifying these individual differences. Children's and care-takers' speech data are represented as a series of networks, word forms being taken as nodes and collocation of words as links. First, we compare two independent indices on network growth, including the size and the connectivity. Children with a small vocabulary (i.e. a small network size) may have more flexibility in word combination (i.e. a large connectivity), and vice versa. Second, we examine the changes of hub and authority nodes in the networks. The change in the roles of the two articles “the” and “a” reflects the progress of syntactic development. While they constantly appear as authorities in adults' networks, the two articles often start in children's networks as hubs and shift to authorities later. Children shift by different routes and at different rates.


title: Aworldwide tourism recommendation system based on geotaggedweb photos

abstract: This work aims to build a system to suggest tourist destinations based on visual matching and minimal user input. A user can provide either a photo of the desired scenary or a keyword describing the place of interest, and the system will look into its database for places that share the visual characteristics. To that end, we first cluster a large-scale geotagged web photo collection into groups by location and then find the representative images for each group. Tourist destination recommendations are produced by comparing the query against the representative tags or representative images under the premise of ”if you like that place, you may also like these places“.


title: Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition

abstract: We present an unconstrained ear recognition framework that outperforms state-of-the-art systems in different publicly available image databases. To this end, we developed CNN-based solutions for ear normalization and description, we used well-known handcrafted descriptors, and we fused learned and handcrafted features to improve recognition. We designed a two-stage landmark detector that successfully worked under untrained scenarios. We used the results generated to perform a geometric image normalization that boosted the performance of all evaluated descriptors. Our CNN descriptor outperformed other CNN-based works in the literature, specially in more difficult scenarios. The fusion of learned and handcrafted matchers appears to be complementary as it achieved the best performance in all experiments. The obtained results outperformed all other reported results for the UERC challenge, which contains the most difficult database nowadays.


title: WE-Safe: A wearable IoT sensor node for safety applications via LoRa

abstract: This paper presents a wearable Internet of Things (IoT) sensor node aimed at monitoring harmful environmental conditions for safety applications via LoRa wireless technology. The proposed sensor node is low-power and supports multiple environmental sensors. A LoRa based gateway is used to connect sensors to the Internet. We mainly focus on monitoring carbon monoxide, carbon dioxide, ultraviolet, and some general environmental parameters. Poor environment quality could cause severe health problems to individuals. Therefore, surrounding environmental data is gathered by the wearable node in a real-time manner and then transmitted to a remote cloud server. The data can then be displayed to authorized users through a web-based application located in the cloud server and the device will give alert to the user via mobile application when an emergency condition occurs. The experimental results indicate that our safety monitoring network can work reliably with low power consumption.


title: The identity of Cintractia disciformis: reclassification and synonymy of a southern Asian smut parasitic on Carex sect. Aulocystis

abstract: The identity of a neglected smut fungus, Cintractia disciformis, described from Carex hirtella in the Western Himalaya, India is reassessed. The species is excluded from Cintractia and is confirmed as a distinct species of Anthracoidea. Two smuts, A. nepalensis on Carexnakaoana in Nepal, and A. haematostomae on Carex haematostoma in China, are similar morphologically and considered to be later heterotypic synonyms of Cintractia disciformis. The appropriate nomenclatural combination for this species, Anthracoidea disciformis comb. nov., is validated.


title: Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach

abstract: Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes.


title: Effect of Gender and Physical Activity on Internet Addiction in Medical Students

abstract: OBJECTIVE ::: To determine the effect of gender and physical activity on internet addiction in medical students. ::: ::: ::: METHODS ::: In this cross sectional, analytical study Young's internet addiction test questionnaire was distributed to 350 MBBS students of Army Medical College, Rawalpindi. The study was conducted from January to May 2015. A dichotomous response from students regarding physical activity was obtained which was verified from the sports department of the institution. Based upon total score, internet addiction was categorized as no addiction if the score was less than or equal to 49, moderate addiction when the score was 50 to 79 and severe when the score was 80 to 100. ::: ::: ::: RESULTS ::: Out of 322 respondents 175 (54.3%) were males and 147 (42.7%) females with a mean age of 19.27±1.01 years. Total internet addiction score and frequency of internet addiction were similar between males and females (37.71±11.9 vs 38.63±14.00, p=0.18 and 25 vs 29, p=0.20). However, total score and frequency of internet addiction were higher in students lacking physical activity as compared to those with regular physical activity (40.37±15.05 vs 36.38±11.76, p=0.01 and 30 vs 24, p=0.01). ::: ::: ::: CONCLUSION ::: Internet addiction is unrelated to gender however it is inversely related to physical activity.


title: Mowgli: A Bipedal Jumping and Landing Robot with an Artificial Musculoskeletal System

abstract: Jumping and landing movements are characterized by large instantaneous forces, short duration, and a high uncertainty concerning take off and landing points. Such characteristics make conventional types of control and robot design inadequate. Here we present an approach to realize motor control of jumping and landing which exploits the synergy between control and mechanical structure. Our experimental system is a pneumatically actuated bipedal robot called "Mowgli". Mowgli's artificial musculoskeletal system consists of six McKibben pneumatic muscle actuators including bi-articular muscle and two legs with hip, knee, and ankle joints. Mowgli can reach jump heights of more than 50% of its body height and can land softly. Our results show a proximo-distal sequence of joint extensions during jumping despite simultaneous motor activity. Extensions in the whole body motion are caused by the compliance and the natural dynamics of the legs. In addition to the experiments with the real robot, we also simulated two types of open loop controllers for vertical jumping with disturbance. We found that the model controlled by open loop motor command through a muscle-tendon mechanism could jump robustly. The simulation results demonstrate the contribution of the artificial musculoskeletal system as a physical feedback loop in explosive movements.


title: Model-Based Lane Detection and Lane Following for Intelligent Vehicles

abstract: A robust model-based lane detection and tracking algorithm has been studied. Initially, Dynamic programming (DP) and Hough transform are employed to detect lane boundaries. The Dynamic programming is known to be a powerful algorithm for optimal path finding on a cost field. Utilizing the intensities of edge image as costs of the DP, most likely road boundaries are obtained in this paper. An improved Hough Transform is applied to detect practically useful road boundaries with straight line segments. In the following stage, based on the prior information of lane boundaries in the detection procedure, the road boundary region has been obtained. A linear lane model has been employed with an energy function in tracking algorithm. Experiment results in inter-frame processing describe the proposed method can fit for real condition on expressway.


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