title: Creature features: online motion puppetry for non-human characters

abstract: We present a novel real-time motion puppetry system that drives the motion of non-human characters using human motion input. We aim to control a variety of creatures whose body structures and motion patterns can differ greatly from a human's. A combination of direct feature mapping and motion coupling enables the generation of natural creature motion, along with intuitive and expressive control for puppetry. First, in the design phase, direct feature mappings and motion classification can be efficiently and intuitively computed given crude motion mimicking as input. Later, during the puppetry phase, the user's body motions are used to control the target character in real-time, using the combination of feature mappings generated from the design phase. We demonstrate the effectiveness of our approach with several examples of natural puppetry, where a variety of non-human creatures are controlled in real-time using human motion input from a commodity motion sensing device.


title: Transitivity Analysis of “Heroic Mother” by Hoa Pham

abstract: The paper investigates the application of Halliday’s theory of transitivity in the construction of personality. The essay aims to identify and explain how the main character’s personality is portrayed and represented through language used in Hoa Pham’s “Heroic Mother”. The findings hope to prove that linguistic choices in transitivity play an important role in building up the main character of the story. The essay is divided into six parts. The first part explains the roles of language and language studies in social life. The second part notes the functions of Halliday’s transitivity system in literary studies by reviewing previous studies on transitivity. The next part deals with Halliday’s theoretical framework of transitivity as a guideline for this analysis. The fourth part introduces Hoa Pham – an Australian Vietnamese writer and playwright, the author of “Heroic Mother”. The analysis of transitivity in “Heroic Mother” is provided in the fifth part. The last section of the essay offers concluding remarks about the interpretation of “Heroic Mother”. The discussion of results will show how linguistics analysis together with observations about the text enables a better understanding of the main character, known as a “heroic mother”.


title: Successfully completing case study research: combining rigour, relevance and pragmatism

abstract: The organizational and social issues associated with the development, implementation and use of computer-based information systems have increasingly attracted the attention of information systems researchers. Interest in qualitative research methods such as action research, case study research and ethnography, which focus on understanding social phenomena in their natural setting, has consequently grown. Case study research is the most widely used qualitative research method in information systems research, and is well suited to understanding the interactions between information technology-related innovations and organizational contexts. Although case study research is useful as a means of studying information systems development and use in the field, there can be practical difficulties associated with attempting to undertake case studies as a rigorous and effective method of research. This paper addresses a number of these difficulties and offers some practical guidelines for successfully completing case study research. The paper focuses on the pragmatics of conducting case study research, and draws from the discussion at a panel session conducted by the authors at the 8th Australasian Conference on Information Systems, September 1997 (ACIS 97), from the authors' practical experiences, and from the case study research literature.


title: Minimization of SQL injection in scheduling application development

abstract: SQL (structure query language) injection is one of threat to the application connected to the database. By implementing SQL injection attacker can gain full access to the application or database so that it can remove even significant data irresponsibly. Applications that do not validate the user's input appropriately make them vulnerable against SQL injection. Various methods have been developed to prevent SQL injection each with advantages and disadvantages. Implementation of PDO (php data object) Parameterized Query properly can prevent SQL injection. PDO not only provides a method to facilitate the implementation of parameterized queries but also makes the code is portable because the PDO can be used on multiple databases. This paper describes the results of research on the use of PDO Parameterized Query to minimize SQL Injection on the new scheduling application. The uniqueness of this study lies in the incorporation of PDO Parameterized Query with client-side web technology (indexedDB). By using PDO Parameterized Query on this application, making it is not vulnerable to attack that caused by SQL injection.


title: An overview of automated reasoning and related fields

abstract: This article provides an overview of automated reasoning and of the various fields for which it is relevant. It takes the form of a collection of articles, each covering some field and each written by an expert in that field. A field is introduced, its elements reviewed, the current state of the art given, the basic problems discussed, and the various goals listed. Although individually the goals of each field present a wide spectrum, collectively the fields share the interest of automating the process known as reasoning.


title: Rethinking Automatic Chord Recognition with Convolutional Neural Networks

abstract: Despite early success in automatic chord recognition, recent efforts are yielding diminishing returns while basically iterating over the same fundamental approach. Here, we abandon typical conventions and adopt a different perspective of the problem, where several seconds of pitch spectra are classified directly by a convolutional neural network. Using labeled data to train the system in a supervised manner, we achieve state of the art performance through this initial effort in an otherwise unexplored area. Subsequent error analysis provides insight into potential areas of improvement, and this approach to chord recognition shows promise for future harmonic analysis systems.


title: ASyMTRe: Automated Synthesis of Multi-Robot Task Solutions through Software Reconfiguration

abstract: This paper describes a methodology for automat ically synthesizing task solutions for heterogeneous multi-robot teams. In contrast to prior approaches that require a manual pre definition of how the robot team will accomplish its task (while perhaps automating who performs which task), our approach automates both the how and the who to generate task solution approaches that were not explicitly defined by the designer a priori. The advantages of this new approach are that it: (1) enables the robot team to synthesize new task solutions that use fundamentally different combinations of robot behaviors for different team compositions, and (2) provides a general mechanism for sharing sensory information across networked robots, so that more capable robots can assist less capable robots in accomplishing their objectives. Our approach, which we call ASyMTRe (Automated Synthesis of Multi-robot Task solutions through software Reconfiguration, pronounced “Asymmetry”), is based on mapping environmental, perceptual, and motor control schemas to the required flow of information through the multi-robot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multi-robot behaviors for accomplishing the team objectives. We validate this approach by presenting the results of applying our methodology to two different teaming scenarios: altruistic cooperation involving multi-robot transportation, and coalescent cooperation involving multi-robot box pushing.


title: Adversarial Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

abstract: We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.


title: Relationships between Metacognition, Self-efficacy and Self-regulation in Learning

abstract: The ability to manage study activities by themselves is one of the educational goals that learners should achieve at the end of secondary school. Self-regulation, however, includes a variety of metacognitive issues. Firstly, self-regulated students should be aware of the mental processes they rely on when performing cognitive tasks, of the degree of autonomy they are allowed in managing study activities and of how effective they are in facing school demands. Secondly, students should be able to plan and monitor study activities strategically. Thirdly, students should identify the kind of learning which is expected to be reached. A sample of 130 students were administered 5 different of questionnaires designed to investigate the metacognitive awareness (MAI: Schraw & Dennison, 1994), the perception of autonomy in school learning (AILI: Elishout-Mohr, Van Daalen-Kapteijns, & Meijer, 2004), the sense of self-efficacy (Adaptive Self-Efficacy Scale: Sibilia, Schwarzer, & Jerusalem, 1995), the attitudes of study strategies (LASSI: Weinstein & Palmer, 2002), and the questionnaire on the conceptions of learning (QAPCOL: Perez-Tello, Antonietti, Liverta-Sempio, & Marchetti, 2002). Results revealed that self-regulated learning is linked to metacognitive skills such as planning, monitoring, evaluation and concentration. In addition, the knowledge and the proper use of learning strategies – such as the selection of the main ideas in a text – constitutes a related set of metacognitive skills. Finally, it was found that the acquisition of metacognitive knowledge, skills and attitudes is linked to autonomy in the study and to self-efficacy.


title: Miniature 3-Axis Distal Force Sensor for Minimally Invasive Surgical Palpation

abstract: Minimally invasive surgery (MIS) is a surgical technique that offers distinct advantages in reducing pain and patients' recovery time. However, the drawback due to the lack of force and tactile feedback presents a great deal of limitations in MIS procedures. Tissue palpation, which is easily conducted during traditional open surgery to examine tissue properties and abnormalities, is not possible when performing surgery in a minimally invasive manner. This paper proposes a specially designed miniature 3-axis distal force sensor that can be used to perform tissue palpation, measuring tissue interaction forces at the tip of a surgical instrument. Relying on an optical sensing scheme, the sensor can measure forces within measurement ranges of ±3 N in axial direction and ±1.5 N in radial direction. The resolution is 0.02 N. It is compatible with laparoscopic operations and can be used to localize tissue lesions or relatively hard nodules buried under an organ's surface, which are not detectable by visual means.


title: Stories about life narrated by people with Alzheimer's disease

abstract: Aim ::: ::: To explore how people with Alzheimer's disease present their life story. ::: ::: ::: ::: Background ::: ::: Life story work is a key concept in a person-centred care. An important aspect in understanding the subjective experience and supporting the identity of people with dementia is to listen to their life stories. ::: ::: ::: ::: Design ::: ::: A narrative design with interviews was used. ::: ::: ::: ::: Method ::: ::: Nine participants with Alzheimer's disease were encouraged to tell about their lives from childhood, adult life, to present life and about their thoughts on the future. The interviews were conducted between September 2010–March 2011 in the participants' homes, with their spouses present and were analysed with a method for analysis of narratives. ::: ::: ::: ::: Findings ::: ::: Contentment, Connectedness, Self-reliance and Personal growth were identified as core dimensions in the participants' life stories and shown like threads throughout life, from childhood, adult life to present life. All participants expressed an overall contentment with life, and connectedness was related to their relation with significant persons and to be included in the local community. Self-reliance was expressed as a strong confidence in the own ability and an overall curiosity throughout life as a sustained quest for personal growth. ::: ::: ::: ::: Conclusions ::: ::: It is important for healthcare professionals, who work with people with dementia, to understand that people with Alzheimer's disease can maintain an overall trusting and hopeful approach to life. It is also important to use life story work to enhance feelings of being connected to the world and thereby support their identity and sense of self.


title: Estimating Time Models for News Article Excerpts

abstract: It is often difficult to ground text to precise time intervals due to the inherent uncertainty arising from either missing or multiple expressions at year, month, and day time granularities. We address the problem of estimating an excerpt-time model capturing the temporal scope of a given news article excerpt as a probability distribution over chronons. For this, we propose a semi-supervised distribution propagation framework that leverages redundancy in the data to improve the quality of estimated time models. Our method generates an event graph with excerpts as nodes and models various inter-excerpt relations as edges. It then propagates empirical excerpt-time models estimated for temporally annotated excerpts, to those that are strongly related but miss annotations. In our experiments, we first generate a test query set by randomly sampling 100 Wikipedia events as queries. For each query, making use of a standard text retrieval model, we then obtain top-10 documents with an average of 150 excerpts. From these, each temporally annotated excerpt is considered as gold standard. The evaluation measures are first computed for each gold standard excerpt for a single query, by comparing the estimated model with our method to the empirical model from the original expressions. Final scores are reported by averaging over all the test queries. Experiments on the English Gigaword corpus show that our method estimates significantly better time models than several baselines taken from the literature.


title: On the optimality of classifier chain for multi-label classification

abstract: To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the CC model over a random label order. Then, we present a theoretical analysis of the generalization error for the proposed generalized model. Based on our results, we propose a dynamic programming based classifier chain (CC-DP) algorithm to search the globally optimal label order for CC and a greedy classifier chain (CC-Greedy) algorithm to find a locally optimal CC. Comprehensive experiments on a number of real-world multi-label data sets from various domains demonstrate that our proposed CC-DP algorithm outperforms state-of-the-art approaches and the CC-Greedy algorithm achieves comparable prediction performance with CC-DP.


Semi-Autonomous Vehicle Control for Road Departure and Obstacle Avoidance


title: Estimating the rate and luminosity function of all classes of GRBs

abstract: The aim of the present work is to estimate the rate and luminosity functions of short, intermediate and long gamma-ray bursts (GRBs) by fitting their intensity distributions with parameterized explosion rates and luminosity functions. The results show that the parameters of the rate and luminosity function for long GRBs can be calculated with an accuracy of 10–30%. However, some parameters of intermediate and short GRBs have large uncertainties. An important conclusion is that there was initially a large outburst in the frequency of long GRBs, and consequently a large outburst in the star-formation rate, if they come from collapsars. Finally, a simulated intensity distribution has been constructed to test the ability of the method to recover the simulated parameters.


title: Compressed sensing of audio signals using multiple sensors

abstract: Compressed sensing is an attractive compression scheme due to its universality and lack of complexity on the sensor side. In this paper we present a study on compressed sensing of real, non-sparse, audio signals. We investigate the performance of different bases and reconstruction algorithms. We then explore the performance of multi-sensor compressed sensing of audio signals and present a novel scheme to provide improved performance over standard reconstruction algorithms. We then present simulations and measured results of a new algorithm to perform efficient detection and estimation in a sensor network that is used to track the location of a subject wearing a tracking device, which periodically transmits a very sparse audio signal. We show that our algorithm can dramatically reduce the number of transmissions in such a sensor network.


title: A Novel Quality Assessment Method for Flat Panel Display Defects

abstract: This paper presents a novel quality assessment method for flat panel display (FPD) defects, often called Muras, that employs the characteristics of the human visual system (HVS). Given a Mura image, the brightness difference between the Mura and its surrounding region is first adjusted to reflect the HVS’s property of background-adaptive perception. Then, the resulting adjusted Mura image is further processed using multiscale defect saliency acquisition (MDSA) to obtain a final Mura image with human perception characteristics. In the experiments, the quality scores of Mura test images are measured using the conventional and proposed methods. The results demonstrate that the quality of Mura evaluated by the proposed method correlates with the subjective quality to a much higher degree compared with the conventional methods.


title: Information and Communication Technology: Design, Delivery, and Outcomes from a Nursing Informatics Boot Camp

abstract: In today's information-intensive healthcare industry, information and communication technology (ICT) and informatics are integral for any system to meet the needs of patients and providers and improve the quality and safety of the clinical environment (Canadian Nurses Association. 2006a: Canadian Nurses Association, 2006b: Poe, Abbott, & Pronovost, 2011). ICT and informatics applications, such as electronic health records, order-entry, electronic medication administration, and computerized documentation systems, facilitate timely access to accurate and reliable patient information. These types of evidence-informed resources are essential for clinical decision making at the point of care (Canadian Nurses Association, 2001: Canadian Nurses Association, 2006b). Standardized structures and terminologies available through electronic health records data sets are enabling the capture of patient outcome data such as length of stay and adverse events, both important quality indicators for evaluating the impact of care (Kleib, Sales, Doran, Mallette, & White, 2011).As the largest group of healthcare providers, nurses contribute to the health and well-being of communities, and populations. Nurse interactions with ICT can facilitate many opportunities for improvement in nursing practice, and can also impact the quality and safety of patient care (Canadian Nurses Association, 2006b: College and Association of Registered Nurses of Alberta TCARNAI, 2009). For example, identifying patient outcomes relevant to nursing practice and measuring the effectiveness of nursing interventions in relation to improvements in these outcomes provides decision makers with an evidence-informed evaluation of the value of nurse contributions to healthcare. Evidence-based outcomes shape policy and practice directions to support nursing practice and knowledge development (Doran, 2011). Nurses, therefore, should "advocate for and lead efforts toward the collection, storage, retrieval and use of nursing data to generate information on nursing outcomes," - a goal which can be realized by embracing and utilizing ICT effectively, as opposed to considering it as an add-on to current workload (Canadian Nurses Association. 2006b, p1).While ICTs promise a number of benefits to healthcare providers and users, they are not without risk (Coiera, Aarts, & Kulikowski, 2012: Poe et al., 2011). Usability issues related to human-computer interactions could pose significant risks to patient safety such as "juxtaposition errors," which occur when the desired result is in close proximate to another option on the screen and the wrong option is selected (Ash, Berg, & Coiera, 2003). When clinical decision supports within ICT are made available to clinicians, the types and level of alerts may lead clinicians to override alerts that may be critical to providing safe patient care (Nanji et al., 2013). Another recognized area of potential error that impacts almost all patients is medication management using ICT applications. One issue pertains to inaccuracy of electronically transmitted medication history information used for medication reconciliation. Data may be missing special characters such as decimals; a drug dosage of 2.5mg might read 25mg. Healthcare providers that do not double-check the automated reports or assume that the system is accurate could potentially make serious drug errors (Institute for Safe Medication Practice, 2014). Thus the benefits of ICTs as tools to improve quality, safety, and effectiveness of clinical care must balance with its potential for patient harm, either directly or by inaccurate measurement of targeted outcomes. This paradox requires diligence to mitigate harm via user education and informatics competency development (Canadian Nurses Association, 2006b: Coiera et al., 2012: Darvish, Bahramnezhad, Kevhanian, & Navidhamidi, 2014).The Institute of Medicine (2001) has identified five core competencies required by all healthcare professionals. …


title: Gate Oxide Reliability Issues of SiC MOSFETs Under Short-Circuit Operation

abstract: Silicon-Carbide (SiC) MOSFETs, due to material properties, are designed with smaller thickness in the gate oxide and a higher electric field compared to Si MOSFETs. Consequently, the SiC MOSFETs have a worse reliability which causes higher leakage currents during instantaneous abnormal operating conditions. This paper investigates the reliability issues of the SiC MOSFET gate oxide under standard short-circuit test conditions. In this paper, 1200-V SiC MOSFETs are newly modeled, and also their short-circuit sustainability (tolerance) have been studied at different drain-source and gate-source voltages. A hardware tester circuit was designed and developed to test the devices under such extreme circuit conditions. Then, the gate reliability of SiC MOSFET devices have been compared to that of Si power devices of similar ratings. The results reveal a higher reduction in the instantaneous gate-source voltage of SiC MOSFETs compared to that of Si devices under the same operating conditions. The gate-voltage reduction phenomenon results from the higher leakage currents through the gate. Furthermore, it was found that the gate-source voltage reduction during the test depends on the gate structures. The gate voltage reduction of SiC MOSFETs with planar gate is higher than that of MOSFETs with shield planar gate. As the pulse duration increases in short-circuit tests, the leakage current in the gate-source of SiC devices increases. The results show that even though the SiC MOSFETs are very capable of processing long pulses and high power in the drain-source, the gate-source side is highly degraded by these pulses in the test. Moreover, whenever a small number of the short-circuit tests are applied, the gate structure of SiC MOSFETs becomes broken while the drain-source is still able to block the dc-link voltage. The paper concludes that the short-circuit reliability of the gate was found to be worse compared with commercial Si devices with similar rating.


title: Applications of dynamic monocular machine vision

abstract: The 4-D approach to real-time machine vision presented in the companion paper (Dickmanns and Graefe 1988, this volume) is applied here to two problem areas of widespread interest in robotics. Following a discussion of the vision hardware sed, first, the precise position control for planar docking between 3-D vehicles is discussed; second, the application to high speed road vehicle guidance is demonstrated. With the 5 ton test vehicle VaMoRs, speeds up to 96 km/h (limited by the speed capability of the basic vehicle) have been reached. The test run available, of more than 20 km length, has been driven autonomously several times under various weather conditions.


title: A C.elegans inspired robotic model for pothole detection

abstract: Animals navigate complex and variable environments, but often use only limited sensory information. Here we present a simulated robot system using a C. elegans inspired sensory model and navigation strategy and demonstrate its ability to successfully identify specific, discretely located cues. We show a range of conditions under which this approach has performance benefits over other search strategies.


title: Information retrieval in web crawling: A survey

abstract: In today's scenario, World Wide Web (WWW) is flooded with huge amount of information. Due to growing popularity of the internet, finding the meaningful information among billions of information resources on the WWW is a challenging task. The information retrieval (IR) provides documents to the end users which satisfy their need of information. Search engine is used to extract valuable information from the internet. Web crawler is the principal part of search engine; it is an automatic script or program which can browse the WWW in automatic manner. This process is known as web crawling. In this paper, review on strategies of information retrieval in web crawling has been presented that are classifying into four categories viz: focused, distributed, incremental and hidden web crawlers. Finally, on the basis of user customized parameters the comparative analysis of various IR strategies has been performed.


FleXam: flexible sampling extension for monitoring and security applications in openflow


title: Electric Guitar Playing Technique Detection in Real-World Recording Based on F0 Sequence Pattern Recognition

abstract: For a complete transcription of a guitar performance, the detection of playing techniques such as bend and vibrato is important, because playing techniques suggest how the melody is interpreted through the manipulation of the guitar strings. While existing work mostly focused on playing technique detection for individual single notes, this paper attempts to expand this endeavor to recordings of guitar solo tracks. Specifically, we treat the task as a time sequence pattern recognition problem, and develop a twostage framework for detecting five fundamental playing techniques used by the electric guitar. Given an audio track, the first stage identifies prominent candidates by analyzing the extracted melody contour, and the second stage applies a pre-trained classifier to the candidates for playing technique detection using a set of timbre and pitch features. The effectiveness of the proposed framework is validated on a new dataset comprising of 42 electric guitar solo tracks without accompaniment, each of which covers 10 to 25 notes. The best average F-score achieves 74% in two-fold cross validation. Furthermore, we also evaluate the performance of the proposed framework for bend detection infive studio mixtures, to discuss how it can be applied in transcribing real-world electric guitar solos with accompaniment.


title: Automatic segmentation for cell images based on bottleneck detection and ellipse fitting

abstract: To segment the overlapping cells in microscopic images, an automatic method for cell image segmentation based on bottleneck detection and ellipse fitting is proposed. Firstly, cell image is segmented by threshold method, followed by a polygonal approximation to extract the feature points of cell edge. Secondly, candidate splitting point pairs are obtained by calculating the bottleneck rate between each feature point pair, and further judged by ellipse fitting to find the correct splitting point pair. Then, a cell is separated from the overlapping cells according to the splitting point pair, and the remaining edge is patched up to form a new closed contour by an improved ellipse fitting method. Finally, repeat the above steps on the new closed contour until all cells are separated. The performance of this method is evaluated on the blood and fluorescent cell databases. Experimental results show that the proposed method can effectively segment overlapping cells with high accuracy and less time, which is superior to many existing methods. Splitting point pairs are obtained by bottleneck detection and ellipse fitting.The application of polygonal approximation greatly reduces the computing time.A novel edge modification method is proposed to connect the splitting point pair.Cells can be separated from the overlapping cell contour integrally and completely.Our method outperforms most of the existing algorithms with higher accuracy.


title: Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation

abstract: Recent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner.


Comparison of Different Speech Feature Extraction Techniques with and without Wavelet Transform to Kannada Speech Recognition M.A.Anusuya


title: Synchronous Non-Volatile Logic Gate Design Based on Resistive Switching Memories

abstract: Emerging non-volatile memories (NVM) based on resistive switching mechanism (RS) such as STT-MRAM, OxRRAM and CBRAM etc., are under intense R&D investigation by both academics and industries. They provide high write/read speed, low power and good endurance (e.g., > 1012) beyond mainstream NVMs, which allow them to be embedded directly with logic units for computing purpose. This integration could increase significantly the power/die area efficiency, and then overcome definitively the power/speed bottlenecks of modern VLSIs. This paper presents firstly a theoretical investigation of synchronous NV logic gates based on RS memories (RS-NVL). Special design techniques and strategies are proposed to optimize the structure according to different resistive characteristics of NVMs. To validate this study, we simulated a non-volatile full-adder (NVFA) with two types of NVMs: STT-MRAM and OxRRAM by using CMOS 40 nm design kit and compact models, which includes related physics and experimental parameters. They show interesting power, speed and area gain compared with synchronized CMOS FA while keeping good reliability.


title: All-Metal Beam Steering Lens Antenna for High Power Microwave Applications

abstract: All-metal beam steering antenna based on the concept of Risley prism is presented in this communication. The antenna is composed of a feed antenna and two metal lenses. Mechanically rotate two lenses along the same axis independently can steer the radiation beam in both azimuth and elevation planes. Simulations and measurements were carried out in X-band to verify its performance, and the results agree each other well, which are also in agreement with the theoretical results. Furthermore, the power handling capability of the lens antenna with a dimension of $1~\text {m} \,\, \times \,\, 1 \,\, \text {m}$ is estimated about 4 GW, which indicates the all-metal beam steering antenna has the application potentials for high-power microwave applications.


title: Perioperative administration of vedaprofen, tramadol or their combination does not interfere with platelet aggregation, bleeding time and biochemical variables in cats⋆

abstract: A randomized double blind and placebo controlled design was used to investigate the hemostatic, biochemical, gastrointestinal and behavioral effects of pre- and postoperative administration of vedaprofen 0.5 mg/kg PO (V), tramadol 2 mg/kg SC (T), their association (VT) or placebo (P) in 40 adult female cats (3.0 ± 0.32 kg; 1.8 ± 0.7 years) distributed in groups of 10. Platelet aggregation and bleeding time were measured before and 52 h after ovariohysterectomy. Serum urea, creatinine, alanine aminotransferase, alkaline phosphatase and gamma-glutamyl transferase concentrations were measured before and 7 days postoperatively. The occurrence of vomiting, frequency and consistency of feces, and behavior were observed for 7 days postoperatively. Morphine (0.5 mg/kg, IM) was used as rescue analgesic. Laboratory variables did not change. Vomiting was observed only after morphine administration. Mild euphoria was observed in T and VT. The perioperative use of vedaprofen and/or tramadol did not modify the hemostatic, biochemical and gastrointestinal function in cats.


title: Efficient and Rotation Invariant Fingerprint Matching Algorithm Using Adjustment Factor

abstract: This paper presents a new efficient and rotation invariant algorithm that makes use of local features forfingerprint matching. Minutiae points are first extracted from afingerprint image. Minutiae code mc, defined in this paper, is then generated for each extracted minutiae point. The proposed minutiae code is invariant to rotation of the fingerprint image. Adjustment factor (AF) is introduced to address the problem due to differences in a claimant fingerprint and a template fingerprint of the same person that may be present due to variations in inking or variations in pressure applied between a finger and the scanner. Adjustment factor is calculated from the minutiae code (mc) of the two fingerprints being matched. A two stage fingerprint matching process is proposed. During first stage only a few minutiae codes are checked to decide if the second stage of matching process is required. This makes the matching process faster. The proposed strategy is tested on a number of publicly available images (DB1 of FVC2004 database) and the results are promising.


title: Saliency filters: Contrast based filtering for salient region detection

abstract: Saliency estimation has become a valuable tool in image processing. Yet, existing approaches exhibit considerable variation in methodology, and it is often difficult to attribute improvements in result quality to specific algorithm properties. In this paper we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation. Our algorithm consists of four basic steps. First, our method decomposes a given image into compact, perceptually homogeneous elements that abstract unnecessary detail. Based on this abstraction we compute two measures of contrast that rate the uniqueness and the spatial distribution of these elements. From the element contrast we then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore- and background. We show that the complete contrast and saliency estimation can be formulated in a unified way using high-dimensional Gaussian filters. This contributes to the conceptual simplicity of our method and lends itself to a highly efficient implementation with linear complexity. In a detailed experimental evaluation we analyze the contribution of each individual feature and show that our method outperforms all state-of-the-art approaches.


title: New enhancement algorithm for fingerprint images

abstract: In this paper, an improved algorithm for enhancement of fingerprint image is proposed on the basis of the image normalization and Gabor filter Firstly, the adaptive normalization based on block processing is suggested for improvement of fingerprint images. An input image is partitioned into sub-blocks with the size of K /spl times/ L at first and the region of interest (ROl) of the fingerprint image is acquired The parameters for the image normalization are adaptively determined according to the statistics of each block. By utilizing these parameters, the block image is normalized for the next process. Secondly, a new technique for selection of two important parameters of Gabor filter is devised. These parameters are the ridge direction and the ridge frequency. In this study, the ridge direction of a block image is determined by the probabilistic approach unlike other works. With this ridge direction, the ridge frequency is selected by utilizing the directional projection. The proposed algorithms are tested with NIST fingerprint images and show significant improvement in the experiments.


title: Brief Report: Female-To-Male Transsexual People and Autistic Traits

abstract: The ‘extreme male brain’ theory suggests females with Autism Spectrum Conditions are hyper-masculinized in certain aspects of behavior. We predicted that females with Gender Identity Disorder (who are masculinized) would have elevated Autism Spectrum Quotient (AQ) scores. AQ scores from five groups were compared: (1) n = 61 transmen (female-to-male transsexual people); (2) n = 198 transwomen (male-to-female transsexual people); (3) n = 76 typical males; (4) n = 98 typical females; and (5) n = 125 individuals with Asperger Syndrome (AS). Transmen had a higher mean AQ than typical females, typical males and transwomen, but lower than individuals with AS. Transmen have more autistic traits and may have had difficulty socializing with female peers and thus found it easier to identify with male peer groups.


title: A Complex Adaptive Notch Filter

abstract: A complex adaptive notch filter is developed, for tracking single-sided (a.k.a. analytic or complex) tones immersed in background noise. A complex all-pass based realization is pursued which inherits useful properties from its real counterpart: independent tuning of the notch frequency and attenuation bandwidth, easy realization of the complementary band-pass filter, unbiased frequency estimation, and faster convergence and tracking than a gradient descent algorithm.


title: Surface electromyography: Detection and recording

abstract: A furnace for melting or heating metals comprising a furnace compartment enclosed by a refractory lining and a crucible for the melt arranged in the furnace compartment. Contacts are provided at the lower region of the furnace compartment, these contacts being arranged in the current circuit of a monitoring device and such contacts are electrically connected with one another by metal which may possibly flow out of the crucible into the furnace compartment. A baffle or guide plate, preferably a sheet-metal deflector or guide, engages beneath the contacts and extends at least over the region of the furnace compartment spanned by the crucible. The baffle plate is exposed to the thermal radiation emanating from heating elements.


title: Graphical passwords: a survey

abstract: The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, we conduct a comprehensive survey of the existing graphical password techniques. We classify these techniques into two categories: recognition-based and recall-based approaches. We discuss the strengths and limitations of each method and point out the future research directions in this area. We also try to answer two important questions: "Are graphical passwords as secure as text-based passwords?"; "What are the major design and implementation issues for graphical passwords?" This survey will be useful for information security researchers and practitioners who are interested in finding an alternative to text-based authentication methods


title: Unsupervised document zone identification using probabilistic graphical models

abstract: Document zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine learning methods, which require a large amount of annotated data, which is often difficult and expensive to obtain. In order to overcome this bottleneck, we propose graphical models based on the popular Latent Dirichlet Allocation (LDA) model. The first model, which we call zoneLDA aims to cluster the sentences into zone classes using only unlabelled data. We also study an extension of zoneLDA called zoneLDAb, which makes distinction between common words and non-common words within the different zone types. We present results on two different domains: the scientific domain and the technical domain. For the latter one we propose a new document zone classification schema, which has been annotated over a collection of 689 documents, achieving a Kappa score of 85%. Overall our experiments show promising results for both of the domains, outperforming the baseline model. Furthermore, on the technical domain the performance of the models are comparable to the supervised approach using the same feature sets. We thus believe that graphical models are a promising avenue of research for automatic document zoning.


title: Fat cell adrenergic receptors and the control of white and brown fat cell function.

abstract: Five adrenoceptor subtypes are involved in the adrenergic regulation of white and brown fat cell function. The effects on cAMP production and cAMP-related cellular responses are mediated through the control of adenylyl cyclase activity by the stimulatory beta 1-, beta 2-, and beta 3-adrenergic receptors and the inhibitory alpha 2-adrenoceptors. Activation of alpha 1-adrenoceptors stimulates phosphoinositidase C activity leading to inositol 1,4,5-triphosphate and diacylglycerol formation with a consequent mobilization of intracellular Ca2+ stores and protein kinase C activation which trigger cell responsiveness. The balance between the various adrenoceptor subtypes is the point of regulation that determines the final effect of physiological amines on adipocytes in vitro and in vivo. Large species-specific differences exist in brown and white fat cell adrenoceptor distribution and in their relative importance in the control of the fat cell. Functional beta 3-adrenoceptors coexist with beta 1- and beta 2-adrenoceptors in a number of fat cells; they are weakly active in guinea pig, primate, and human fat cells. Physiological hormones and transmitters operate, in fact, through differential recruitment of all these multiple alpha- and beta-adrenoceptors on the basis of their relative affinity for the different subtypes. The affinity of the beta 3-adrenoceptor for catecholamines is less than that of the classical beta 1- and beta 2-adrenoceptors. Conversely, epinephrine and norepinephrine have a higher affinity for the alpha 2-adrenoceptors than for beta 1-, 2-, or 3-adrenoceptors. Antagonistic actions exist between alpha 2- and beta-adrenoceptor-mediated effects in white fat cells while positive cooperation has been revealed between alpha 1- and beta-adrenoceptors in brown fat cells. Homologous down-regulation of beta 1- and beta 2-adrenoceptors is observed after administration of physiological amines and beta-agonists. Conversely, beta 3- and alpha 2-adrenoceptors are much more resistant to agonist-induced desensitization and down-regulation. Heterologous regulation of beta-adrenoceptors was reported with glucocorticoids while sex-steroid hormones were shown to regulate alpha 2-adrenoceptor expression (androgens) and to alter adenylyl cyclase activity (estrogens).


title: Knowledge Guided Short-Text Classification for Healthcare Applications

abstract: The need for short-text classification arises in many text mining applications particularly health care applications. In such applications shorter texts mean linguistic ambiguity limits the semantic expression, which in turns would make typical methods fail to capture the exact semantics of the scarce words. This is particularly true in health care domains when the text contains domain-specific or infrequently appearing words, whose embedding can not be easily learned due to the lack of training data. Deep neural network has shown great potentials in boost the performance of such problems according to its strength on representation capacity. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) recurrent network to address the short-text classification problem that can be used in two settings. Firstly when a knowledge dictionary is available we adopt the well-known attention mechanism to guide the training of network using the domain knowledge in the dictionary. Secondly, to address the cases when domain knowledge dictionary is not available, we present a multi-task model to jointly learn the domain knowledge dictionary and do the text classification task simultaneously. We apply our method to a real-world interactive healthcare system and an extensively public available ATIS dataset. The results show that our model can positively grasp the key point of the text and significantly outperforms many state-of-the-art baselines.


title: Compact Ultra Wide Band Microstrip Bandpass Filter Based on Multiple-Mode Resonator and Modified Complementary Split Ring Resonator

abstract: A new class of broadband microstrip filters for Ultra Wide Band (UWB) applications is proposed. In the design, different stages of parallel-coupled microstrip line and other stages with a Modified Complementary Split Ring Resonator (MCSRR)—a concept proposed here for the first time—are adjusted to obtain the desired response with broadband, sharp rejection, low insertion loss, and low return loss. Full wave simulation results as well as measurement results from fabricated prototypes are presented, showing good agreement. The proposed technique offers a new alternative to implement low-cost high-performance filter devices, applicable to a wide range of communication systems.


title: Recognizing Products: A Per-exemplar Multi-label Image Classification Approach

abstract: Large-scale instance-level image retrieval aims at retrieving specific instances of objects or scenes. Simultaneously retrieving multiple objects in a test image adds to the difficulty of the problem, especially if the objects are visually similar. This paper presents an efficient approach for per-exemplar multi-label image classification, which targets the recognition and localization of products in retail store images. We achieve runtime efficiency through the use of discriminative random forests, deformable dense pixel matching and genetic algorithm optimization. Cross-dataset recognition is performed, where our training images are taken in ideal conditions with only one single training image per product label, while the evaluation set is taken using a mobile phone in real-life scenarios in completely different conditions. In addition, we provide a large novel dataset and labeling tools for products image search, to motivate further research efforts on multi-label retail products image classification. The proposed approach achieves promising results in terms of both accuracy and runtime efficiency on 680 annotated images of our dataset, and 885 test images of GroZi-120 dataset. We make our dataset of 8350 different product images and the 680 test images from retail stores with complete annotations available to the wider community.


title: Clock Multiplication Techniques Using Digital Multiplying Delay-Locked Loops

abstract: A highly-digital clock multiplication architecture that achieves excellent jitter and mitigates supply noise is presented. The proposed architecture utilizes a calibration-free digital multiplying delay-locked loop (MDLL) to decouple the tradeoff between time-to-digital converter (TDC) resolution and oscillator phase noise in digital phase-locked loops (PLLs). Both reduction in jitter accumulation down to sub-picosecond levels and improved supply noise rejection over conventional PLL architectures is demonstrated with low power consumption. A digital PLL that employs a 1-bit TDC and a low power regulator that seeks to improve supply noise immunity without increasing loop delay is presented and used to compare with the proposed MDLL. The prototype MDLL and DPLL chips are fabricated in a 0.13 μm CMOS technology and operate from a nominal 1.1 V supply. The proposed MDLL achieves an integrated jitter of 400 fs rms at 1.5 GHz output frequency from a 375 MHz reference clock, while consuming 890 μ W. The worst-case supply noise sensitivity of the MDLL is 20 fspp/mVpp which translates to a jitter degradation of 3.8 ps in the presence of 200 mV supply noise. The proposed clock multipliers occupy active die areas of 0.25 mm2 and 0.2 mm2 for the MDLL and DPLL, respectively.


title: Search Ranking And Personalization at Airbnb

abstract: Search ranking is a fundamental problem of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked and the parties affected by ranking, each search ranking problem is somewhat specific. Correspondingly, search ranking at Airbnb is quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this talk, I will discuss challenges we have encountered and Machine Learning solutions we have developed for listing ranking at Airbnb. Specifically, the listing ranking problem boils down to prioritizing listings that are appealing to the guest but at the same time demoting listings that would likely reject the guest, which is not easily solvable using basic matrix completion or a straightforward linear model. I will shed the light on how we jointly optimize the two objectives by leveraging listing quality, location relevance, reviews, host response time as well as guest and host preferences and past booking history. Finally, we will talk about our recent work on using neural network models to train listing and query embeddings for purposes of enhancing search personalization, broad search and type-ahead suggestions, which are core concepts in any modern search.


title: DEFINING SUPPLY CHAIN MANAGEMENT

abstract: A management construct cannot be used effectively by practitioners and researchers if a common agreement on its definition is lacking. Such is the case with the term “supply chain management”—so many definitions are used that there is little consensus on what it means. Thus, the purpose of this paper is to examine the existing research in an effort to understand the concept of “supply chain management.” Various definitions of SCM and “supply chain” are reviewed, categorized, and synthesized. Definitions of supporting constructs of SCM and a framework are then offered to establish a consistent means to conceptualize SCM. Antecedents and consequences of SCM are identified, and the boundaries of SCM in terms of business functions and organizations are proposed. A conceptual model and unified definition of SCM are then presented that indicate the nature, antecedents, and consequences of the phenomena.


title: The real-life dangers of augmented reality

abstract: You know your cellphone can distract you and that you shouldn’t be texting or surfing the Web while walking down a crowded street or driving a car. Augmented reality— in the form of Google Glass, Sony’s SmartEyeglass, or Microsoft HoloLens— may appear to solve that problem. These devices present contextual information transparently or in a way that obscures little, seemingly letting you navigate the world safely, in the same way head-up displays enable fighter pilots to maintain situational awareness. • But can augmented reality really deliver on that promise? We ask this question because, as researchers at Kaiser Permanente concerned with diseases that impair mobility (Sabelman) and with using technology to improve patient care (Lam), we see dangers looming. • With augmented-reality gear barely on the market, rigorous studies of its effects on vision and mobility have yet to be done. But in reviewing the existing research on the way people perceive and interact with the world around them, we found a number of reasons to be concerned. Augmented reality can cause you to misjudge the speed of oncoming cars, underestimate your reaction time, and unintentionally ignore the hazards of navigating in the real world. And the worst thing about it: Until something bad happens, you won’t know you’re at greater risk of harm. The Real-Life Dangers of Augmented Reality SPECTRUM.IEEE.


title: Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

abstract: Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.


title: 4D frequency analysis of computational cameras for depth of field extension

abstract: Depth of field (DOF), the range of scene depths that appear sharp in a photograph, poses a fundamental tradeoff in photography---wide apertures are important to reduce imaging noise, but they also increase defocus blur. Recent advances in computational imaging modify the acquisition process to extend the DOF through deconvolution. Because deconvolution quality is a tight function of the frequency power spectrum of the defocus kernel, designs with high spectra are desirable. In this paper we study how to design effective extended-DOF systems, and show an upper bound on the maximal power spectrum that can be achieved. We analyze defocus kernels in the 4D light field space and show that in the frequency domain, only a low-dimensional 3D manifold contributes to focus. Thus, to maximize the defocus spectrum, imaging systems should concentrate their limited energy on this manifold. We review several computational imaging systems and show either that they spend energy outside the focal manifold or do not achieve a high spectrum over the DOF. Guided by this analysis we introduce the lattice-focal lens, which concentrates energy at the low-dimensional focal manifold and achieves a higher power spectrum than previous designs. We have built a prototype lattice-focal lens and present extended depth of field results.


C LOSE-TO-CLEAN REGULARIZATION RELATES VIRTUAL ADVERSARIAL TRAINING , LADDER NETWORKS AND OTHERS


title: Contour Optimization of Suspension Insulators Using Dynamically Adjustable Genetic Algorithms

abstract: Electrical-field distribution along the insulator surface strongly depends upon the contour design, besides the effect of pollution. The insulator contour should be designed to reach a desired uniform and minimal tangential field to increase the onset voltage of surface flashover. In this paper, with the charge simulation method (CSM) integrated, the dynamically adjustable genetic algorithm (DAGA) approach is proposed for contour optimization of a suspension insulator. The aim of the contour optimization is to minimize and make the tangential electric field uniform and to minimize the size of the insulator, subject to design constraints. In the proposed approach, the cubic spline function based on control (or contour) points on the insulator surface is optimized to derive the desired contour. The results show that a rather uniform and minimal tangential field distribution with a smaller suspension insulator can be obtained through the proposed approach in comparison with the commercial insulator practically deployed in transmission systems.


title: Scale-free fully informed particle swarm optimization algorithm

abstract: This paper proposes a novel PSO algorithm, referred to as SFIPSO (Scale-free fully informed particle swarm optimization). In the proposed algorithm a modified Barabasi-Albert (BA) model [4] is used as a self-organizing construction mechanism, in order to adaptively generate the population topology exhibiting scale-free property. The swarm population is divided into two subpopulations: the active particles and the inactive particles. The former fly around the solution space to find the global optima; whereas the latter are iteratively activated by the active particles via attaching to them, according to their own degrees, fitness values, and spatial positions. Therefore, the topology will be gradually generated as the construction process and the optimization process progress synchronously. Moreover, the cognitive effect and the social effect on the variance of a particle's velocity vector are distributed by its ''contextual fitness'' value, and the social effect is further distributed via a time-varying weighted fully informed mechanism that originated from [27]. It is proved by the results of comparative experiments carried out on eight benchmark test functions that the scale-free population topology construction mechanism and the weighted fully informed learning strategy can provide the swarm population with stronger diversity during the convergent process. As a result, SFIPSO obtained success rate of 100% on all of the eight test functions. Furthermore, SFIPSO also yielded good-quality solutions, especially on multimodal test functions. We further test the network properties of the generated population topology. The results prove that (1) the degree distribution of the topology follows power-law, therefore exhibits scale-free property, and (2) the topology exhibits ''disassortative mixing'' property, which can be interpreted as an important condition for the reinforcement of population diversity.


title: EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition

abstract: Gesture is a natural interface in human–computer interaction, especially interacting with wearable devices, such as VR/AR helmet and glasses. However, in the gesture recognition community, it lacks of suitable datasets for developing egocentric (first-person view) gesture recognition methods, in particular in the deep learning era. In this paper, we introduce a new benchmark dataset named EgoGesture with sufficient size, variation, and reality to be able to train deep neural networks. This dataset contains more than 24 000 gesture samples and 3 000 000 frames for both color and depth modalities from 50 distinct subjects. We design 83 different static and dynamic gestures focused on interaction with wearable devices and collect them from six diverse indoor and outdoor scenes, respectively, with variation in background and illumination. We also consider the scenario when people perform gestures while they are walking. The performances of several representative approaches are systematically evaluated on two tasks: gesture classification in segmented data and gesture spotting and recognition in continuous data. Our empirical study also provides an in-depth analysis on input modality selection and domain adaptation between different scenes.


title: Personal knowledge graph population from user utterances in conversational understanding

abstract: Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from spoken language utterances presents the novelty and numerous challenges. In this paper, we introduce a statistical language understanding approach to automatically construct personal (user-centric) knowledge graphs in conversational dialogs. Such information has the potential to better understand the users' requests, fulfilling them, and enabling other technologies such as developing better inferences or proactive interactions. Knowledge encoded in semantic graphs such as Freebase has been shown to benefit semantic parsing and interpretation of natural language utterances. Hence, as a first step, we exploit the personal factual relation triples from Freebase to mine natural language snippets with a search engine, and the resulting snippets containing pairs of related entities to create the training data. This data is then used to build three key language understanding components: (1) Personal Assertion Classification identifies the user utterances that are relevant with personal facts, e.g., “my mother's name is Rosa”; (2) Relation Detection classifies the personal assertion utterance into one of the predefined relation classes, e.g., “parents”; and (3) Slot Filling labels the attributes or arguments of relations, e.g., “name(parents): Rosa”. Our experiments using the Microsoft conversational understanding system demonstrate the performance of this proposed approach on the population of personal knowledge graphs.


title: Adolescents' moral evaluations and ratings of cyberbullying: The effect of veracity and intentionality behind the event

abstract: We examined adolescents' ratings of cyberbullying events depicting false and true stories.We studied the impact of participants' age and their experience with cyberbullying in the evaluation of such events.Adolescents evaluate as more negative online situations depicting false stories.Younger adolescents have difficulties recognizing intentions underlying online situations.Adolescents who have cyberbullied others are less severe than those who have not in their ratings. The constant exposure to electronic media has increased the likelihood of adolescents experiencing hurtful events such as cyberbullying. The current study examined how adolescents' moral evaluations of cyberbullying are affected by different aspects of the event including falsity of posts, power imbalance and intention to harm. Adolescents between 12-13years of age (n=77) and 15-16years of age (n=77) read moral vignettes and were asked to evaluate the actions of the protagonist. They were also asked if the behaviour in the vignette was an incident of cyberbullying. Participants also filled out a questionnaire about their own experiences with cyberbullying. It was found that adolescents evaluated as more negative those situations depicting false stories, imbalance of power, and intention to harm; Younger children had difficulties recognizing the intentions behind on-line posts. Adolescents who have cyber-intimidated others were less severe in their evaluations and less likely to rate vignettes as cyberbullying compared to other youth.


title: Messy genetic algorithms: motivation, analysis, and first results

abstract: This paper defines and explores a somewhat different type of genetic algorithm (GA)-a messy genetic algorithm (mGA). Messy GAs process variable-length strings that may be either under- or overspecified with respect to the problem being solved. As nature has formed its genotypes by progressing from simple to more complex life forms, messy GAs solve problems by combining relatively short, well-tested building blocks to form longer, more complex strings that increasingly cover all features of a problem. This approach stands in contrast to the usual fixed-length, fixed-coding genetic algorithm, where the existence of the requisite tight linkage is taken for granted or ignored altogether. To compare the two approaches, a 30-bit, order-three-deceptive problem is searched using a simple GA and a messy GA. Using a random but fixed ordering of the bits, the simple GA makes errors at roughly three-quarters of its positions; under a worst-case ordering, the simple GA errs at all positions. In contrast to the simple GA results, the messy GA repeatedly solves the same problem to optimality. Prior to this time, no GA had ever solved a provably difficult problem to optimality without prior knowledge of good string arrangements. The mGA presented herein repeatedly achieves globally optimal results without such knowledge, and it does so at the very first generation in which strings are long enough to cover the problem. The solution of a difficult nonlinear problem to optimality suggests that messy GAs can solve more difficult problems than has been possible to date with other genetic algorithms. The ramifications of these techniques in search and machine learning are explored, including the possibility of messy floating-point codes, messy permutations, and messy classifiers.


title: Inventory and transport of plastic debris in the Laurentian Great Lakes

abstract: Abstract Plastic pollution in the world's oceans has received much attention, but there has been increasing concern about the high concentrations of plastic debris in the Laurentian Great Lakes. Using census data and methodologies used to study ocean debris we derive a first estimate of 9887 metric tonnes per year of plastic debris entering the Great Lakes. These estimates are translated into population-dependent particle inputs which are advected using currents from a hydrodynamic model to map the spatial distribution of plastic debris in the Great Lakes. Model results compare favorably with previously published sampling data. The samples are used to calibrate the model to derive surface microplastic mass estimates of 0.0211 metric tonnes in Lake Superior, 1.44 metric tonnes in Huron, and 4.41 metric tonnes in Erie. These results have many applications, including informing cleanup efforts, helping target pollution prevention, and understanding the inter-state or international flows of plastic pollution.


title: ControlChain: Blockchain as a Central Enabler for Access Control Authorizations in the IoT

abstract: The IoT is pervading our daily activities and lives with devices scattered all over our cities, transport systems, buildings, homes and bodies. This invasion of devices with sensors and communication capabilities brings big concerns, mainly about the privacy and confidentiality of the collected information. These concerns hinder the wide adoption of the IoT. To overcome them, in this work, we present an Blockchain-based architecture for IoT access authorizations. Following the IoT tendency requirements, our architecture is user transparent, user friendly, fully decentralized, scalable, fault tolerant and compatible with a wide range of today's access control models used in the IoT. Finally, our architecture also has a secure way to establish relationships between users, devices and group of both, allowing the assignment of attributes for these relationships and their use in the access control authorization.


title: Real-time predictive maintenance for wind turbines using Big Data frameworks

abstract: This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs.


title: VIRTUAL PRODUCT EXPERIENCE 111 Virtual Product Experience: Effects of Visual and Functional Control of Products on Perceived Diagnosticity and Flow in Electronic Shopping

abstract: The development of electronic commerce has been constrained by the inability of online consumers to feel, touch, and sample products through Web interfaces, as they are able to do in conventional in-store shopping. Previous academic studies have argued that this limitation could be partly alleviated by providing consumers with virtual product experience (VPE), to enable potential customers to experience products virtually. This paper discusses virtual control, a specific type of VPE implementation, and identifies its two dimensions: visual control and functional control. Visual control enables consumers to manipulate Web product images, to view products from various angles and distances; functional control enables consumers to explore and experience different features and functions of products. The individual and joint effects of visual and functional control were investigated in a laboratory experiment, the results of which indicated that visual and functional control increased the perceived diagnosticity (i.e., the extent to which a consumer believes the shopping experience is helpful to evaluate a product) of their corresponding attribute factors, and that both visual and functional control increased consumer overall perceived diagnosticity and flow.


title: ARCHER: using symbolic, path-sensitive analysis to detect memory access errors

abstract: Memory corruption errors lead to non-deterministic, elusive crashes. This paper describes ARCHER (ARray CHeckER) a static, effective memory access checker. ARCHER uses path-sensitive, interprocedural symbolic analysis to bound the values of both variables and memory sizes. It evaluates known values using a constraint solver at every array access, pointer dereference, or call to a function that expects a size parameter. Accesses that violate constraints are flagged as errors. Those that are exploitable by malicious attackers are marked as security holes.Memory corruption errors lead to non-deterministic, elusive crashes. This paper describes ARCHER (ARray CHeckER) a static, effective memory access checker. ARCHER uses path-sensitive, interprocedural symbolic analysis to bound the values of both variables and memory sizes. It evaluates known values using a constraint solver at every array access, pointer dereference, or call to a function that expects a size parameter. Accesses that violate constraints are flagged as errors. Those that are exploitable by malicious attackers are marked as security holes.We carefully designed ARCHER to work well on large bodies of source code. It requires no annotations to use (though it can use them). Its solver has been built to be powerful in the ways that real code requires, while backing off on the places that were irrelevant. Selective power allows it to gain efficiency while avoiding classes of false positives that arise when a complex analysis interacts badly with statically undecidable program properties. ARCHER uses statistical code analysis to automatically infer the set of functions that it should track --- this inference serves as a robust guard against omissions, especially in large systems which can have hundreds of such functions.In practice ARCHER is effective: it finds many errors; its analysis scales to systems of millions of lines of code and the average false positive rate of our results is below 35%. We have run ARCHER over several large open source software projects --- such as Linux, OpenBSD, Sendmail, and PostgreSQL --- and have found errors in all of them (118 in the case of Linux, including 21 security holes).


title: Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle

abstract: We study the watersheds in edge-weighted graphs. We define the watershed cuts following the intuitive idea of drops of water flowing on a topographic surface. We first establish the consistency of these watersheds: they can be equivalently defined by their "catchment basinsrdquo (through a steepest descent property) or by the "dividing linesrdquo separating these catchment basins (through the drop of water principle). Then, we prove, through an equivalence theorem, their optimality in terms of minimum spanning forests. Afterward, we introduce a linear-time algorithm to compute them. To the best of our knowledge, similar properties are not verified in other frameworks and the proposed algorithm is the most efficient existing algorithm, both in theory and in practice. Finally, the defined concepts are illustrated in image segmentation, leading to the conclusion that the proposed approach improves, on the tested images, the quality of watershed-based segmentations.


title: Real-Time Perception Meets Reactive Motion Generation

abstract: We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with reactive motion generation methods in dynamic manipulation scenarios. We compare three different systems that are instantiations of the most common architectures in the field: (i) a traditional sense-plan-act approach that is still widely used, (ii) a myopic controller that only reacts to local environment dynamics and (iii) a reactive planner that integrates feedback control and motion optimization. All architectures rely on the same components for real-time perception and reactive motion generation to allow a quantitative evaluation. We extensively evaluate the systems on a real robotic platform in four scenarios that exhibit either a challenging workspace geometry or a dynamic environment. In 333 experiments, we quantify the robustness and accuracy that is due to integrating real-time feedback at different time scales in a reactive motion generation system. We also report on the lessons learned for system building.


title: Functional behaviors of electric field grading composite materials

abstract: Non-linear resistive field grading materials (FGM) are used in different high voltage applications e.g. cable terminations, joints and rotating machines, where the electric stress is critical. It is already established that the use of FGM is an improved stress grading technique compared to conventional geometric methods. There is a constant endeavor within the electric power industries to extend these concepts to other power equipment. In order to achieve a wide spread use of FGM, they need to satisfy several functional requirements e.g. fire safety, tracking resistance, hydrophobicity, high thermal conductivity, besides certain dielectric properties e.g. field dependent conductivity, permittivity and low dielectric losses. In the present study ZnO filled silicon rubber (SiR) composite with different functional fillers, in total six different material compositions, are evaluated with respect to several important functional behaviors. Further, stress grading behavior of these six materials are demonstrated by finite element simulations on a generic electrode geometry and by performing inclined plane tracking test. Suitability of these material compositions to industrial applications are discussed.


title: What Drives Housing Price Dynamics: Cross-Country Evidence

abstract: House prices generally depend on inflation, the yield curve and bank credit, but national differences in the mortgage markets also matter. House prices are more sensitive to short-term rates where floating rate mortgages are more widely used and more aggressive lending practices are associated with stronger feedback from prices to bank credit.


title: Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

abstract: The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.


title: Weighted extreme learning machine for imbalance learning

abstract: Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ''generalized'' single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able to generalize to balanced data. The proposed method maintains the advantages from original ELM: (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users' needs, the weighted ELM can be generalized to cost sensitive learning.


title: Class prediction by nearest shrunken centroids, with applicaitons to dna microarrays

abstract: We propose a new method for class prediction in DNA microarray studies based on an enhancement of the nearest prototype classifier. Our technique uses "shrunken" centroids as prototypes for each class to identify the subsets of the genes that best characterize each class. The method is general and can be applied to other high-dimensional classification problems. The method is illustrated on data from two gene expression studies: lymphoma and cancer cell lines.


title: Comparison of the Coding Efficiency of Video Coding Standards—Including High Efficiency Video Coding (HEVC)

abstract: The compression capability of several generations of video coding standards is compared by means of peak signal-to-noise ratio (PSNR) and subjective testing results. A unified approach is applied to the analysis of designs, including H.262/MPEG-2 Video, H.263, MPEG-4 Visual, H.264/MPEG-4 Advanced Video Coding (AVC), and High Efficiency Video Coding (HEVC). The results of subjective tests for WVGA and HD sequences indicate that HEVC encoders can achieve equivalent subjective reproduction quality as encoders that conform to H.264/MPEG-4 AVC when using approximately 50% less bit rate on average. The HEVC design is shown to be especially effective for low bit rates, high-resolution video content, and low-delay communication applications. The measured subjective improvement somewhat exceeds the improvement measured by the PSNR metric.


Modern state building and the problem of intermediate institutions : religion, family and military in East Asia


title: Multimodal Sparsity-Eager Support Vector Machines for Music Classification

abstract: As the demand for multimedia grows, the development of information retrieval systems utilizing all available data modalities becomes of paramount importance. The provision of multiple modalities is motivated by usability, presence of noise in one modality and non-universality of a single modality. Radio stations and music TV channels hold archives of millions of music tapes and lyrics. Gigabytes of music files are also spread over the web along with the lyrics and metadata for each file. Searching and organizing large scale multimodal datasets is a challenging task. Supervised methods such as support vector machine (SVM) achieve state of the art performance for music classification on single modality, but suffer from over-fitting on training examples and limitations of single modality approaches. In this paper, we introduce a classifier fusion of multimodal audio and lyrics data to address these single modality classification limitations. We introduce the multimodal l1-SVM classifier, that utilizes sparse methods to deal with over-fitting for music classification. We compare the classification accuracy of the fusion classifier for a genre classification task in a large public dataset with single modality l1-SVM.


title: Effects of feedback and peer pressure on contributions to enterprise social media

abstract: Increasingly, large organizations are experimenting with internal social media (e.g., blogs, forums) as a platform for widespread distributed collaboration. Contributions to their counterparts outside the organization's firewall are driven by attention from strangers, in addition to sharing among friends. However, employees in a workplace under time pressures may be reluctant to participate and the audience for their contributions is comparatively smaller. Participation rates also vary widely from group to group. So what influences people to contribute in this environment? In this paper, we present the results of a year-long empirical study of internal social media participation at a large technology company, and analyze the impact attention, feedback, and managers' and coworkers' participation have on employees' behavior. We find feedback in the form of posted comments is highly correlated with a user's subsequent participation. Recent manager and coworker activity relate to users initiating or resuming participation in social media. These findings extend, to an aggregate level, the results from prior interviews about blogging at the company and offer design and policy implications for organizations seeking to encourage social media adoption.


title: Moving Least-Squares Reconstruction of Large Models with GPUs

abstract: Modern laser range scanning campaigns produce extremely large point clouds, and reconstructing a triangulated surface thus requires both out-of-core techniques and significant computational power. We present a GPU-accelerated implementation of the moving least-squares (MLS) surface reconstruction technique. We believe this to be the first GPU-accelerated, out-of-core implementation of surface reconstruction that is suitable for laser range-scanned data. While several previous out-of-core approaches use a sweep-plane approach, we subdivide the space into cubic regions that are processed independently. This independence allows the algorithm to be parallelized using multiple GPUs, either in a single machine or a cluster. It also allows data sets with billions of point samples to be processed on a standard desktop PC. We show that our implementation is an order of magnitude faster than a CPU-based implementation when using a single GPU, and scales well to 8 GPUs.


title: DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model

abstract: Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. However, the existing convolution approaches focus only on regular data forms and require the transfer of the graph or key node neighborhoods of the graph into the same fixed form. During this transfer process, structural information of the graph can be lost, and some redundant information can be incorporated. To overcome this problem, we propose the disordered graph convolutional neural network (DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a preprocessing layer called the disordered graph convolutional layer (DGCL). The DGCL uses a mixed Gaussian function to realize the mapping between the convolution kernel and the nodes in the neighborhood of the graph. The output of the DGCL is the input of the CNN. We further implement a backward-propagation optimization process of the convolutional layer by which we incorporate the feature-learning model of the irregular node neighborhood structure into the network. Thereafter, the optimization of the convolution kernel becomes part of the neural network learning process. The DGCNN can accept arbitrary scaled and disordered neighborhood graph structures as the receptive fields of CNNs, which reduces information loss during graph transformation. Finally, we perform experiments on multiple standard graph datasets. The results show that the proposed method outperforms the state-of-the-art methods in graph classification and retrieval.


title: Comparison of PM brushless motors, having either all teeth or alternate teeth wound

abstract: The electromagnetic performance of three-phase permanent magnet brushless motors in which the slot and pole numbers are similar and either all the teeth or only alternate teeth carry a concentrated coil is compared. Analytical and finite element techniques are employed to predict the air-gap flux density distribution, the cogging torque, electromotive force (EMF) and static torque waveforms, and the winding inductances, predicted results being validated by measurements. It is shown that there is a significant difference in both the phase and line EMF waveforms, the static torque-rotor position waveforms, and the self and mutual inductances.


title: Design, implementation and first experimental results of an X-band ubiquitous radar system

abstract: This paper presents the design and first experimental results of a ubiquitous frequency modulated continuous wave (FMCW) radar system demonstrator, working at 8.75 GHz (X band). This system, which main application is the surface surveillance, is currently under its field-testing phase. This article presents the demonstrator and describes its main blocks, with particular attention to the receiving hardware. Subsequently, it focuses on signal acquisition and digitization, to later explain the offline coherent signal processing carried out with the captured data. After this, the document introduces the chosen scenario for the first field tests, and shows the first results of the offline processing, illustrated with Range-Doppler matrices and detection figures. Finally, the paper introduces future work lines, including the development of a radar data processor taking advantage of the strong range-speed association achieved.


title: Discovering Rules from Disk Events for Predicting Hard Drive Failures

abstract: Detecting impending failure of hard disks is an important prediction task which might help computer systems to prevent loss of data and performance degradation. Currently most of the hard drive vendors support self-monitoring, analysis and reporting technology (SMART) which are often considered unreliable for such tasks. The problem of finding alternatives to SMART for predicting disk failure is an area of active research. In this paper, we consider events recorded from live disks and show that it is possible to construct decision support systems which can detect such failures. It is desired that any such prediction methodology should have high accuracy and ease of interpretability. Black box models can deliver highly accurate solutions but do not provide an understanding of events which explains the decision given by it. To this end we explore rule based classifiers for predicting hard disk failures from various disk events. We show that it is possible to learn easy to understand rules, from disk events, which have extremely low false alarm rates on real world data.


title: Image search in patents: a review

abstract: To verify the originality of an invention in a patent, the graphical description available in the form of patent drawings often plays a critical role. This paper introduces the importance, requirements, and challenges of a patent image retrieval system. We present a brief account of the work done in the specific and related areas of the patent image domain. We begin with a review of work done dealing specifically with retrieval and analysis of images in the patent domain. Although the literature found dealing with patent images is small, there is a significant amount of work that has been done in related areas that is useful and applicable to the patent image area. From a methodological point of view, we present an overview of the algorithms developed for the retrieval and analysis of CAD and technical drawings, diagrams, data flow diagrams, circuit diagrams, data charts, flowcharts, plots, and symbol recognition.


title: Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test

abstract: Recent progress in neural learning demonstrated that machines can do well in regularized tasks, e.g., the game of Go. However, artistic activities such as poem generation are still widely regarded as human's special capability. In this paper, we demonstrate that a simple neural model can imitate human in some tasks of art generation. We particularly focus on traditional Chinese poetry, and show that machines can do as well as many contemporary poets and weakly pass the Feigenbaum Test, a variant of Turing test in professional domains. Our method is based on an attention-based recurrent neural network, which accepts a set of keywords as the theme and generates poems by looking at each keyword during the generation. A number of techniques are proposed to improve the model, including character vector initialization, attention to input and hybrid-style training. Compared to existing poetry generation methods, our model can generate much more theme-consistent and semantic-rich poems.


title: Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes

abstract: This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.


title: Dynamic Popularity-Based Caching Permission Strategy for Named Data Networking

abstract: Named Data Networking (NDN) is one of the future Internet architectures and has recently attracted a great deal of attention. NDN adopts named routing and caches all contents passing by. So it helps to reduce network traffic, accelerate content distribution and reduces content retrieval latency. However, the default caching strategy named Leave Copies Everywhere (LCE) also brings a large number of redundant data and the higher number of content replacements in NDN. In this paper, we present a Dynamic Popularity-Based Caching Permission strategy (DPCP). DPCP takes advantage of interest packet and data packet to carry content popularity, so routers in the path can obtain the information about the content popularity and uses dynamic popularity threshold to make cache permission policy. It also introduces cache control flag to avoid caching the same redundant copies in adjacent routers. The simulation results show that the DPCP strategy can effectively reduce the amount of redundant data in the network as well as reduce the number of the content replacement while acquiring the similar cache hit ratio compared to LCE.


title: Endothelial dysfunction in pulmonary arterial hypertension: an evolving landscape (2017 Grover Conference Series)

abstract: Endothelial dysfunction is a major player in the development and progression of vascular pathology in pulmonary arterial hypertension (PAH), a disease associated with small vessel loss and obstructive vasculopathy that leads to increased pulmonary vascular resistance, subsequent right heart failure, and premature death. Over the past ten years, there has been tremendous progress in our understanding of pulmonary endothelial biology as it pertains to the genetic and molecular mechanisms that orchestrate the endothelial response to direct or indirect injury, and how their dysregulation can contribute to the pathogenesis of PAH. As one of the major topics included in the 2017 Grover Conference Series, discussion centered on recent developments in four areas of pulmonary endothelial biology: (1) angiogenesis; (2) endothelial-mesenchymal transition (EndMT); (3) epigenetics; and (4) biology of voltage-gated ion channels. The present review will summarize the content of these discussions and provide a perspective on the most promising aspects of endothelial dysfunction that may be amenable for therapeutic development.