title: Adams method for solving uncertain differential equations

abstract: For uncertain differential equations, we cannot always obtain their analytic solutions. Early researchers have described the Euler method and Runge-Kutta method for solving uncertain differential equations. This paper proposes a new numerical method-Adams method to solve uncertain differential equations. Some numerical experiments are given to illustrate the efficiency of our numerical method. Moreover, this paper also gives two numerical methods for calculating the extreme value and the time integral of solutions of uncertain differential equations.


Effect of Oxygen on the Wettability of Solid Oxide with Molten Iron


title: Robust Learning of Tactile Force Estimation through Robot Interaction

abstract: Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust model that maps tactile sensor signals to force. We specifically explore learning a mapping for the SynTouch BioTac sensor via neural networks. We propose a voxelized input feature layer for spatial signals and leverage information about the sensor surface to regularize the loss function. To learn a robust tactile force model that transfers across tasks, we generate ground truth data from three different sources: (1) the BioTac rigidly mounted to a force torque (FT) sensor, (2) a robot interacting with a ball rigidly attached to the same FT sensor, and (3) through force inference on a planar pushing task by formalizing the mechanics as a system of particles and optimizing over the object motion. A total of 140k samples were collected from the three sources. We achieve a median angular accuracy of 3.5 degrees in predicting force direction (66% improvement over the current state of the art) and a median magnitude accuracy of 0.06 N (93% improvement) on a test dataset. Additionally, we evaluate the learned force model in a force feedback grasp controller performing object lifting and gentle placement. Our results can be found on https: //sites.google.com/view/tactile-force.


title: Content-based audio classification and segmentation by using support vector machines

abstract: Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.


title: Selecting Criteria to Evaluate Qualitative Research

abstract: Abstract While the evaluation of quantitative research frequently depends on judgements based on the “holy trinity” of objectivity, reliability and validity (Spencer, Ritchie, Lewis, & Dillon, 2003, p. 59), applying these traditional criteria to qualitative research is not always a “good fit” (Schofield, 2002). Instead, educational researchers who engage in qualitative research have suggested various sets of alternative criteria including: transferability, generalisability, ontological authenticity, reciprocity, dependability, confirmability, reflexivity, fittingness, vitality and, even, sacredness and goodness (Creswell, 2002; Garman, 1996; Guba & Lincoln, 1989; Patton, 2002; Spencer et al., 2003; Stige, Malterud, & Midtgarden, 2009). While over one hundred sets of qualitative research criteria have been identified (Stige et al., 2009), some researchers warn against the absolute application of any criteria to qualitative research which is, by its nature, wide‐ranging and varied, and does not necessarily lend itself to the straightforward application of any evaluation criteria. Nevertheless, whether or not criteria are applied at all in the research evaluation process, postgraduate students face a number of decisions associated with the process of evaluating qualitative research: 1) whether or not to adopt a set of appraisal criteria; 2) which criteria to select, if criteria are used; and 3) how to apply alternative approaches to criteria‐focused evaluation. These decisions often require a paradigm shift (Khun, 1962) in the way postgraduate students perceive and approach their research. The messiness and complexity associated with such decisions can be confronting. This paper examines a number of approaches used by researchers to evaluate qualitative investigations in educational research.


title: Statistics in Brief: The Importance of Sample Size in the Planning and Interpretation of Medical Research

abstract: The increasing volume of research by the medical community often leads to increasing numbers of contradictory findings and conclusions. Although the differences observed may represent true differences, the results also may differ because of sampling variability as all studies are performed on a limited number of specimens or patients. When planning a study reporting differences among groups of patients or describing some variable in a single group, sample size should be considered because it allows the researcher to control for the risk of reporting a false-negative finding (Type II error) or to estimate the precision his or her experiment will yield. Equally important, readers of medical journals should understand sample size because such understanding is essential to interpret the relevance of a finding with regard to their own patients. At the time of planning, the investigator must establish (1) a justifiable level of statistical significance, (2) the chances of detecting a difference of given magnitude between the groups compared, ie, the power, (3) this targeted difference (ie, effect size), and (4) the variability of the data (for quantitative data). We believe correct planning of experiments is an ethical issue of concern to the entire community.


title: Efficient Curve Fitting

abstract: We propose a method for fitting a piecewise parametric curve to a sequence of digitized points such as those acquired from a computer mouse or digitizing pen. The fitting is done on-the-fly rather than after a complete sequence of points has been acquired, thereby providing high-quality parameterized curves for immediate display during drawing. The method is fast, accurate, and robust, handles complex input paths, maintains corners, and ensures at least G 1 continuity at non-corner points. The method uses vector distance fields, which we describe here, to represent the intended input path of the digitized points in order to achieve its quality and performance. Detailed pseudocode is available online.


title: A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification

abstract: Abstract The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. Most of them, however, are greedy algorithms that have the drawback of obtaining only local optimums. Besides, conventional split criteria they used, e.g. Shannon entropy, Gain Ratio and Gini index, are based on one-term that lack adaptability to different datasets. To address the above issues, we propose a less-greedy two-term Tsallis Entropy Information Metric (TEIM) algorithm with a new split criterion and a new construction method of decision trees. Firstly, the new split criterion is based on two-term Tsallis conditional entropy, which is better than conventional one-term split criteria. Secondly, the new tree construction is based on a two-stage approach that reduces the greediness and avoids local optimum to a certain extent. The TEIM algorithm takes advantages of the generalization ability of two-term Tsallis entropy and the low greediness property of two-stage approach. Experimental results on UCI datasets indicate that, compared with the state-of-the-art decision trees algorithms, the TEIM algorithm yields statistically significantly better decision trees and is more robust to noise.


title: Inverse-Category-Frequency based supervised term weighting scheme for text categorization

abstract: Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs. The widely used term weighting scheme in text categorization, i.e., tf.idf, is originated from information retrieval (IR) field. The intuition behind idf for text categorization seems less reasonable than IR. In this paper, we introduce inverse category frequency (icf) into term weighting scheme and propose two novel approaches, i.e., tf.icf and icf-based supervised term weighting schemes. The tf.icf adopts icf to substitute idf factor and favors terms occurring in fewer categories, rather than fewer documents. And the icf-based approach combines icf and relevance frequency (rf) to weight terms in a supervised way. Our cross-classifier and cross-corpus experiments have shown that our proposed approaches are superior or comparable to six supervised term weighting schemes and three traditional schemes in terms of macro-F1 and micro-F1.


title: Mapping the column density and dust temperature structure of IRDCs with Herschel

abstract: Infrared dark clouds (IRDCs) are cold and dense reservoirs of gas potentially available to form stars. Many of these clouds are likely to be pristine structures representing the initial conditions for star formation. The study presented here aims to construct and analyze accurate column density and dust temperature maps of IRDCs by using the first Herschel data from the Hi-GAL galactic plane survey. These fundamental quantities, are essential for understanding processes such as fragmentation in the early stages of the formation of stars in molecular clouds. We have developed a simple pixel-by-pixel SED fitting method, which accounts for the background emission. By fitting a grey-body function at each position, we recover the spatial variations in both the dust column density and temperature within the IRDCs. This method is applied to a sample of 22 IRDCs exhibiting a range of angular sizes and peak column densities. Our analysis shows that the dust temperature decreases significantly within IRDCs, from background temperatures of 20-30 K to minimum temperatures of 8-15 K within the clouds, showing that dense molecular clouds are not isothermal. Temperature gradients have most likely an important impact on the fragmentation of IRDCs. Local temperature minima are strongly correlated with column density peaks, which in a few cases reach NH2 = 1 x 10^{23} cm^{-2}, identifying these clouds as candidate massive prestellar cores. Applying this technique to the full Hi-GAL data set will provide important constraints on the fragmentation and thermal properties of IRDCs, and help identify hundreds of massive prestellar core candidates.


title: Hunting Malicious TLS Certificates with Deep Neural Networks

abstract: Encryption is widely used across the internet to secure communications and ensure that information cannot be intercepted and read by a third party. However, encryption also allows cybercriminals to hide their messages and carry out successful malware attacks while avoiding detection. Further aiding criminals is the fact that web browsers display a green lock symbol in the URL bar when a connection to a website is encrypted. This symbol gives a false sense of security to users, who are in turn more likely to fall victim to phishing attacks. The risk of encrypted traffic means that information security researchers must explore new techniques to detect, classify, and take countermeasures against malicious traffic. So far there exists no approach for TLS detection in the wild. In this paper, we propose a method for identifying malicious use of web certificates using deep neural networks. Our system uses the content of TLS certificates to successfully identify legitimate certificates as well as malicious patterns used by attackers. The results show that our system is capable of identifying malware certificates with an accuracy of 94.87% and phishing certificates with an accuracy of 88.64%.


title: Real time responsive animation with personality

abstract: Building on principles from prior work on procedural texture synthesis (K. Perlin, 1985), we are able to create remarkably lifelike, responsively animated characters in real time. Rhythmic and stochastic noise functions are used to define time varying parameters that drive computer generated puppets. Because we are conveying just the "texture" of motion, we are able to avoid computation of dynamics and constraint solvers. The subjective impression of dynamics and other subtle influences on motion can be conveyed with great visual realism by properly tuned expressions containing pseudo random noise functions. For example, we can make a character appear to be dynamically balancing herself, to appear nervous, or to be gesturing in a particular way. Each move has an internal rhythm, and transitions between moves are temporally constrained so that "impossible" transitions are precluded. For example, if while the character is walking we specify a dance turn, the character will always step into the turn onto the correct weight bearing foot. An operator can make a character perform a properly connected sequence of actions, while conveying particular moods and attitudes, merely by pushing buttons at a high level. Potential uses of such high level "textural" approaches to computer graphic simulation include role playing games, simulated conferences, "clip animation", graphical front ends for MUDs, and synthetic performances. >


title: Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning

abstract: Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. Inspired by the fact that an unseen class is not exactly `unseen' if it belongs to the same superclass as a seen class, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap. Specifically, we first build a class hierarchy of multiple superclass layers and a single class layer, where the superclasses are automatically generated by data-driven clustering over the semantic representations of all seen and unseen class names. We then exploit the superclasses from the class hierarchy to tackle the domain gap challenge in two aspects: deep feature learning and projection function learning. First, to narrow the domain gap in the feature space, we integrate a recurrent neural network (RNN) defined with the superclasses into a convolutional neural network (CNN), in order to enforce the superclass hierarchy. Second, to further learn a transferrable projection function for ZSL, a novel projection function learning method is proposed by exploiting the superclasses to align the two domains. Importantly, our transferrable feature and projection learning methods can be easily extended to a closely related task -- few-shot learning (FSL). Extensive experiments show that the proposed model significantly outperforms the state-of-the-art alternatives in both ZSL and FSL tasks.


title: Endophytic bacterial communities of field-grown potato plants and their plant-growth-promoting and antagonistic abilities.

abstract: To study the effect of plant growth on potato-associated bacteria, the composition and properties of bacteria colonizing the endosphere of field-grown potato were analyzed by a multiphasic approach. The occurrence and diversity of potato-associated bacteria were monitored by a cultivation-independent approach, using terminal restriction fragment length polymorphism analysis of 16S rDNA. The patterns obtained revealed a high heterogeneity of community composition and suggested the existence of plant-specific communities. However, endophytic populations correlated to a certain extent with plant growth performance. Endophytes were also isolated from plants that grew well or grew poorly and were identified by partial sequencing of the 16S rRNA genes. A broad phylogenetic spectrum was found among isolates and differently growing plants hosted different bacterial populations. In an approach to investigate the plant-growth-promoting potential of potato-associated bacteria, a total of 35 bacteria were screened by dual testing for in vitro antagonism towards (i) the fungal pathogens Verticillium dahliae, Rhizoctonia solani, Sclerotinia sclerotiorum, and Phytophthora cactorum and (ii) the bacterial pathogens Erwinia carotovora, Streptomyces scabies, and Xanthomonas campestris. The proportion of isolates with antagonistic activity was highest against Streptomyces sp. (43%) followed by those against Xanthomonas sp. (29%). As all plants showed more or less severe disease symptoms of scab disease caused by Streptomyces scabies, we assume that the presence of the pathogen induced the colonization of antagonists. The antifungal activity of the isolates was generally low. The biotechnological potential of endophytic isolates assessed by their antagonistic activity and by in vitro production of enzymes, antibiotics, siderophores, and the plant growth hormone indole-1,3-acetic acid was generally high. Overall, seven endophytes were found to antagonize fungal as well as bacterial pathogens and showed a high production of active compounds and were therefore considered promising biological control agents.


title: Semantic Zooming for Ontology Graph Visualizations

abstract: Visualizations of ontologies, in particular graph visualizations in the form of node-link diagrams, are often used to support ontology development, exploration, verification, and sensemaking. With growing size and complexity of ontology graph visualizations, their represented information tend to become hard to comprehend due to visual clutter and information overload. We present a new approach of semantic zooming for ontology graph visualizations that abstracts and simplifies the underlying graph structure. It separates the comprised information into three layers with discrete levels of detail. The approach is applied to a force-directed graph layout using the VOWL notation. The mental map is preserved by using smart expanding and ordering of elements in the layout. Navigation and sensemaking are supported by local and global exploration methods, halo visualization, and smooth zooming. The results of a user study confirm an increase in readability, visual clarity, and information clarity of ontology graph visualizations enhanced with our semantic zooming approach.


title: Nasal tip-suspending suture technique: cat's nape suture.

abstract: Variation in nasal-tip deformity is the most common complaint of the patients requesting rhinoplastic surgery, and the goal of nearly every rhinoplasty is to achieve ideal nasal-tip rotation and projection. We describe a procedure that allows the surgeon to secure nasal-tip rotation and projection through a suspension suture technique. Preserving the interalar soft tissues over the dorsal caudal septum and interdomal ligaments during the surgery is the major basis of this technique. We use the soft tissues as a suspending material for the tip complex, which acts like one whole part.


title: Can students really multitask? An experimental study of instant messaging while reading

abstract: Students often ''multitask'' with electronic media while doing schoolwork. We examined the effects of one form of media often used in such multitasking, instant messaging (IM). We predicted that students who engaged in IMing while reading a typical academic psychology passage online would take longer to read the passage and would perform more poorly on a test of comprehension of the passage. Participants were randomly assigned to one of three conditions (IM before reading, IM during reading, or no IM). We found that students took significantly longer to read the passage when they IMed during reading (not including time taken to IM) than in other conditions. However, test performance did not differ by condition. Students who are managing busy lives may think they are accomplishing more by multitasking, but our findings suggest they will actually need more time to achieve the same level of performance on an academic task.


title: Taxonomy Model for Cyber Threat Intelligence Information Exchange Technologies

abstract: The cyber threat intelligence information exchange ecosystem is a holistic approach to the automated sharing of threat intelligence. For automation to succeed, it must handle tomorrow's attacks, not just yesterday's. There are numerous ontologies that attempt to enable the sharing of cyber threats, such as OpenIOC, STIX, and IODEF. To date, most ontologies are based on various use cases. Ontology developers collect threat indicators that through experi-ence seem to be useful for exchange. This approach is pragmatic and offers a collection of useful threat indicators in real-world scenarios. However, such a selection method is episodic. What is useful today may not be useful tomorrow. What we consider to be chaff or too hard to share today might become a critically im-portant piece of information. Therefore, in addition to use case-based ontology, ontologies need to be based on first principles. In this document we propose taxonomy for classifying threat-sharing technologies. The purpose of this taxonomy is to classify existing technologies using an agnostic framework, identify gaps in existing technologies, and explain their differences from a sci-entific perspective. We are currently working on a thesaurus that will describe, compare, and classify detailed cyber security terms. This paper focuses on the classification of the ontologies them-selves.


title: Automated Process Initialization of Laser Surface Structuring Processes by Inline Process Metrology

abstract: Laser micro machining as well as laser surface structuring are innovative manufacturing technologies with a wide range of machinable materials and a high level of flexibility. These techniques are characterized by different machine, workpiece and environmental parameters. The large amount of process dependencies lead however to a time consuming process initialization and a complex process control. Currently no automated solution exists to achieve material specific process parameters, nor does a sufficient inline process control exist to adapt processing parameters or strategies inline. Therefore a novel scanner based inline metrology solution and an automated process initialization strategy has been developed.


title: A Deep Survey on Human Activity Recognition in Video Surveillance

abstract: Recognition of the activities of human from videos has garnered numerous demands in Computer Vision This paper focuses on the literature reviews on different ways of Human Activity Recognition (HAR) in different types of videos by recognizing human activities in videos using different techniques such as, feature extraction, object segmentation, bag of word, Hidden markov model etc. Human Activity recognition is used in various applications such as Human-Computer Interaction, Human fall detection, Ambient Intelligence, Video Indexing, Content-based Video Analytics, Robotics, Visual Surveillance. The intention for this deep survey is to identify activities of humans in different environments using divergent techniques


Learning Word Representations with Cross-Sentence Dependency for End-to-End Co-reference Resolution


title: Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor

abstract: Deployment of convolutional neural networks (ConvNets) in always-on Internet of Everything (IoE) edge devices is severely constrained by the high memory energy consumption of hardware ConvNet implementations. Leveraging the error resilience of ConvNets by accepting bit errors at reduced voltages presents a viable option for energy savings, but few implementations utilize this due to the limited quantitative understanding of how bit errors affect performance. This paper demonstrates the efficacy of SRAM voltage scaling in a 9-layer CIFAR-10 binarized ConvNet processor, achieving memory energy savings of 3.12× with minimal accuracy degradation (∼99% of nominal). Additionally, we quantify the effect of bit error accumulation in a multi-layer network and show that further energy savings are possible by splitting weight and activation voltages. Finally, we compare the measured error rates for the CIFAR-10 binarized ConvNet against MNIST networks to demonstrate the difference in bit error requirements across varying complexity in network topologies and classification tasks.


title: Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

abstract: Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and storefronts. Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions. Much information available to solve this problem is frequently ignored or used sequentially. Similarity between character images is often overlooked as useful information. Because of language priors, a recognizer may assign different labels to identical characters. Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label. Lexicons improve recognition accuracy but are used post hoc. We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision. Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses. By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy. In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19 percent, the lexicon reduces word recognition error by 35 percent, and sparse belief propagation reduces the lexicon words considered by 99.9 percent with a 12X speedup and no loss in accuracy.


title: Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

abstract: Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.


title: A Novel ZVZCS Full-Bridge DC/DC Converter Used for Electric Vehicles

abstract: This paper presents a novel ZVZCS full-bridge DC/DC converter, which is able to process and deliver power efficiently over very wide load variations. The proposed DC/DC converter is part of a plug-in AC/DC converter used to charge the traction battery (high voltage battery) in an electric vehicle. The key challenge in this application is operation of the full-bridge converter from absolutely no-load to full-load conditions. In order to confirm reliable operation of the full-bridge converter under such wide load variations, the converter should not only operate with soft-switching from full load to no-load condition with satisfactory efficiency for the full range of operation, but also the voltage across the output diode bridge needs to be clamped to avoid any adverse voltage overshoots arising during turn-OFF of the output diodes as commonly found in regular full bridge converters. In order to achieve such stringent requirements and high reliability, the converter employs a symmetric passive near lossless auxiliary circuit to provide the reactive current for the full-bridge semiconductor switches, which guarantees zero voltage switching at turn-ON times for all load conditions. Moreover the proposed topology is based on a current driven rectifier in order to clamp the voltage of the output diode bridge and also satisfy ZVZCS operation of the converter resulting in superior efficiency for all load conditions. In this paper operation of the converter is presented in detail followed by analytical design procedure. Experimental results provided from a 3KW prototype validate the feasibility and superior performance of the proposed converter.


title: Emergency department management of vaginal bleeding in the nonpregnant patient.

abstract: Abnormal uterine bleeding is the most common reason women seek gynecologic care, and many of these women present to an emergency department for evaluation. It is essential that emergency clinicians have a thorough understanding of the underlying physiology of the menstrual cycle to appropriately manage a nonpregnant woman with abnormal bleeding. Evidence to guide the management of nonpregnant patients with abnormal bleeding is limited, and recommendations are based mostly on expert opinion. This issue reviews common causes of abnormal bleeding, including anovulatory, ovulatory, and structural causes in both stable and unstable patients. The approach to abnormal bleeding in the prepubertal girl is also discussed. Emergency clinicians are encouraged to initiate treatment to temporize an acute bleeding episode until timely follow-up with a gynecologist can be obtained.


title: The Rosenberg Self-Esteem Scale: Translation and Validation in University Students

abstract: The aim of this study was to translate into Spanish and to validate the Rosenberg Self-Esteem Scale (RSES), completed by 420 university students. Confirmatory factor analysis revealed that the model that best fit the data, both in the total sample and in the male and female subsamples, was the one-factor structure with method effects associated with positively worded items. The results indicated high, positive correlations between self-esteem and the five dimensions of selfconcept. The scale showed satisfactory levels of internal consistency and temporal stability over a four-week period. Lastly, gender differences were obtained. These findings support the use of the RSES for the assessment of self-esteem in higher education.


title: The Influence of Cognitive Decline on Rural Identity

abstract: PURPOSE ::: The purpose of this study was to examine the perspectives of community-dwelling rural, older women concerning the meaning of cognitive decline and to ascertain how cognitive decline affects their lives and the lives of those around them. ::: ::: ::: DESIGN ::: An ethnographic design guided 1 year of cultural immersion in a rural, farming county in Nebraska. ::: ::: ::: METHOD ::: Four life history interviews, participant observations, field notes, and cultural artifacts were collected for case-focused analysis. ::: ::: ::: FINDINGS ::: Cognitive decline was believed to threaten one's social identity as a "good woman" because of three strongly held beliefs that (1) the rural lifestyle protected health, (2) demands of the farm were more important than personal health needs, and (3) mainstream health care services were unnatural and insensitive, and therefore best avoided. Using mainstream health care also resulted in the loss of informal social support, which existed as a protective social silence and helped sustain older women's rural identity. The older women feared developing cognitive decline and believed the loss of one's life purpose would be the outcome of the condition. ::: ::: ::: CONCLUSIONS ::: Holistic nursing actions that preserve older women's rural identity and social support may increase the likelihood that women accept rural health care aimed at treating cognitive decline.


title: Learning the Behavior of Users in a Public Space through Video Tracking

abstract: The paper describes a video tracking system that tracks and analyzes the behavioral pattern of users in a public space. We have obtained important statistical measurements about users' behavior, which can be used to evaluate architectural design in terms of human spatial behavior and model the behavior of users in public spaces. Previously, such measurements could only be obtained through costly manual processes, e.g. behavioral mapping and time-lapse filming with human examiners. Our system has automated the process of analyzing the behavior of users. The system consists of a head detector for detecting people in each single frame of the video and data association for tracking people through frames. We compared the results obtained using our system with those obtained by manual counting, for a small data set, and found the results to be fairly accurate. We then applied the system to a large-scale data set and obtained substantial statistical measurements of parameters such as the total number of users who entered the space, the total number of users who sat by a fountain, the time that each spent by the fountain, etc. These statistics allow fundamental rethinking of the way people use a public space. This research is a novel application of computer vision in evaluating architectural design in terms of human behavior


title: Using deep convolutional neural network to classify urban sounds

abstract: The environmental sounds are usually classified by a convolutional neural network. However, there are few studies investigates the network input construction issue. In this paper, we investigate the impact of time resolution index of input spectrogram on classification performance. We want to verify whether the impact exists and quantify the impact. To this end, we adopt an efficient convolutional network architecture for urban sound classification and conducted a series of experiments with different network inputs. Experiment results confirm our expectation, and suggest that the best classification performance on urban sounds is usually obtained when the input spectrograms have moderate time resolution, especially for those sounds with relatively short temporal structures.


title: What Makes for Effective Visualisation in Cyber Situational Awareness for Non-Expert Users?

abstract: As cyber threats continue to become more prevalent, there is a need to consider how best we can understand the cyber landscape when acting online, especially so for non-expert users. Satellite navigation systems provide the de facto standard for many modern day navigation tasks in the physical domain, so we consider the question of how one could navigate the online domain using similar concepts. In this paper, we study the design of a cyber sat nav for improving situational awareness of nonexpert users. We focus on three core tasks: understanding where we are in cyber space, understanding how we got there, and understanding future states that we may traverse to. To support understanding, we explore the use of visualisation techniques to portray complex online activities in clear and engaging formats for non-expert users.


title: Toward Interconnected Virtual Reality: Opportunities, Challenges, and Enablers

abstract: Just recently, the concept of augmented and virtual reality (AR/VR) over wireless has taken the entire 5G ecosystem by storm, spurring an unprecedented interest from academia, industry, and others. However, the success of an immersive VR experience hinges on solving a plethora of grand challenges cutting across multiple disciplines. This article underscores the importance of VR technology as a disruptive use case of 5G (and beyond) harnessing the latest development of storage/ memory, fog/edge computing, computer vision, artificial intelligence, and others. In particular, the main requirements of wireless interconnected VR are described followed by a selection of key enablers; then research avenues and their underlying grand challenges are presented. Furthermore, we examine three VR case studies and provide numerical results under various storage, computing, and network configurations. Finally, this article exposes the limitations of current networks and makes the case for more theory, and innovations to spearhead VR for the masses.


title: Design and Implementation of a Sun Tracker with a Dual-Axis Single Motor for an Optical Sensor-Based Photovoltaic System

abstract: The dual threats of energy depletion and global warming place the development of methods for harnessing renewable energy resources at the center of public interest. Solar energy is one of the most promising renewable energy resources. Sun trackers can substantially improve the electricity production of a photovoltaic (PV) system. This paper proposes a novel design of a dual-axis solar tracking PV system which utilizes the feedback control theory along with a four-quadrant light dependent resistor (LDR) sensor and simple electronic circuits to provide robust system performance. The proposed system uses a unique dual-axis AC motor and a stand-alone PV inverter to accomplish solar tracking. The control implementation is a technical innovation that is a simple and effective design. In addition, a scaled-down laboratory prototype is constructed to verify the feasibility of the scheme. The effectiveness of the Sun tracker is confirmed experimentally. To conclude, the results of this study may serve as valuable references for future solar energy applications.


title: CITlab ARGUS for historical handwritten documents Description of CITlab ’ s System for the HTRtS 2015 Task : Handwritten Text Recognition on the tranScriptorium Dataset

abstract: We describe CITlab's recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET's ARGUS framework for intelligent text recognition and image processing.


title: Stress reduction correlates with structural changes in the amygdala

abstract: Stress has significant adverse effects on health and is a risk factor for many illnesses. Neurobiological studies have implicated the amygdala as a brain structure crucial in stress responses. Whereas hyperactive amygdala function is often observed during stress conditions, cross-sectional reports of differences in gray matter structure have been less consistent. We conducted a longitudinal MRI study to investigate the relationship between changes in perceived stress with changes in amygdala gray matter density following a stress-reduction intervention. Stressed but otherwise healthy individuals (N = 26) participated in an 8-week mindfulness-based stress reduction intervention. Perceived stress was rated on the perceived stress scale (PSS) and anatomical MR images were acquired pre- and post-intervention. PSS change was used as the predictive regressor for changes in gray matter density within the bilateral amygdalae. Following the intervention, participants reported significantly reduced perceived stress. Reductions in perceived stress correlated positively with decreases in right basolateral amygdala gray matter density. Whereas prior studies found gray matter modifications resulting from acquisition of abstract information, motor and language skills, this study demonstrates that neuroplastic changes are associated with improvements in a psychological state variable.


title: Microcontroller based intelligent DC/DC converter to track Maximum Power Point for solar photovoltaic module

abstract: Maximum Power Point Tracking (MPPT) is widely used control technique to extract maximum power available from the solar cell of photovoltaic (PV) module. Since the solar cells have non-linear i–v characteristics. The efficiency of PV module is very low and power output depends on solar insolation level and ambient temperature, so maximization of power output with greater efficiency is of special interest. Moreover there is great loss of power due to mismatch of source and load. So, to extract maximum power from solar panel a MPPT needs to be designed. The objective of the paper is to present a novel cost effective and efficient microcontroller based MPPT system for solar photovoltaic system to ensure fast maximum power point operation at all fast changing environmental conditions. The proposed controller scheme utilizes PWM techniques to regulate the output power of boost DC/DC converter at its maximum possible value and simultaneously controls the charging process of battery. Incremental Conductance algorithm is implemented to track maximum power point. For the feasibility study, parameter extraction, model evaluation and analysis of converter system design a MATLAB/Simulink model is demonstrated and simulated for a typical 40W solar panel from Kyocera KC-40 for hardware implementation and verification. Finally, a hardware model is designed and tested in lab at different operating conditions. Further, MPPT system has been tested with Solar Panel at different solar insolation level and temperature. The resulting system has high-efficiency, lower-cost, very fast tracking speed and can be easily modified for additional control function for future use.


title: A Statistical Model to Detect DRG Upcoding

abstract: The Medicare program, private insurers, and managed care organizations reimburse hospitals for inpatient admissions using the Diagnosis Related Group (DRG). The DRG is determined from a complicated algorithm based on patient medical records. Previous studies generated concerns about ‘DRG upcoding’, in which incorrect DRG codes may be selected by the hospital to obtain higher reimbursement. Insurers rely on expensive manual audits of claims to verify the appropriateness of the DRG coding.A statistical system that can adaptively detect claims with incorrect DRG codes would provide a powerful improvement to current practice. This paper describes two aspects of the statistical system that provides proof that the concept is viable. The first aspect of the paper is the design of a hierarchical Bayesian model to be applied to claims data (without audit) to estimate the probability that a claim is coded incorrectly. The second aspect of the paper is the use of the Bayesian model to aid in the selection process of claims to audit by proposing that a claim should be investigated if the predicted recovery is more than the cost of auditing that claim. This approach improves upon that used currently by auditing 88% of the claims and recovering 98% of the overpayments. While these results improve upon the current approach for determining which claims to investigate, they are based on data that have been systematically selected for audit based on one insurer's past experience. Future work will create an adaptive system to determine the selection of claims to audit from the entire paid claims database, and that can be generalized for use by other insurers.


title: A software radio-based UHF RFID reader for PHY/MAC experimentation

abstract: We present the design and evaluation of a flexible UHF RFID reader that enables new PHY/MAC designs to be prototyped and evaluated. Our reader is built using the USRP software radio platform in conjunction with software we developed in the open-source GNU Radio framework. We believe it is the first inexpensive tool that readily enables changes to the physical and MAC layer of RFID systems. We evaluate our reader and show that it can inventory commercial tags out to 6 meters, which approximates the range of a commercial reader with comparable transmit power. We then show two applications of our reader. The first evaluates the real-world performance of the EPC frame selection algorithm and finds that it performs better than expected. Second, using the Intel WISP programmable RFID tag, we implement and evaluate an extension to the Gen 2 standard that results in up to a five-fold increase in sample rate for streamed sensor data.


title: Human Indignity: From Legal AI Personhood to Selfish Memes

abstract: It is possible to rely on current corporate law to grant legal personhood to Artificially Intelligent (AI) agents. In this paper, after introducing pathways to AI personhood, we analyze consequences of such AI empowerment on human dignity, human safety and AI rights. We emphasize possibility of creating selfish memes and legal system hacking in the context of artificial entities. Finally, we consider some potential solutions for addressing described problems.


Test data augmentation: Generating new test data from existing test data


title: Emotional processing in anterior cingulate and medial prefrontal cortex

abstract: Negative emotional stimuli activate a broad network of brain regions, including the medial prefrontal (mPFC) and anterior cingulate (ACC) cortices. An early influential view dichotomized these regions into dorsal–caudal cognitive and ventral–rostral affective subdivisions. In this review, we examine a wealth of recent research on negative emotions in animals and humans, using the example of fear or anxiety, and conclude that, contrary to the traditional dichotomy, both subdivisions make key contributions to emotional processing. Specifically, dorsal–caudal regions of the ACC and mPFC are involved in appraisal and expression of negative emotion, whereas ventral–rostral portions of the ACC and mPFC have a regulatory role with respect to limbic regions involved in generating emotional responses. Moreover, this new framework is broadly consistent with emerging data on other negative and positive emotions.


title: Implementation of wireless remote monitoring and control of solar photovoltaic (PV) system

abstract: This paper describes the implementation of a wireless remote monitoring and control system of a solar photovoltaic distributed generator (PV-DG) for microgrids applications. To this aim, a small-scale PV-DG system is implemented using a 1.28 kWp assembly of PV polycrystalline modules connected to a single-phase utility grid through a commercial inverter. Then, a flexible, robust and reliable measurement and control system based on wireless sensor network (WSN) architecture is deployed. The wireless communication technology utilizes a full duplex digital system using the ZigBee protocol, based on the IEEE 802.15.4 standard for Wireless Personal Area Network (WPAN). The supervisory control system is implemented on a digital signal processor (DSP) and a human-machine interface (HMI) software is developed for interacting with and managing remote sensor systems (RSSs).


title: HERCULE: attack story reconstruction via community discovery on correlated log graph

abstract: Advanced cyber attacks consist of multiple stages aimed at being stealthy and elusive. Such attack patterns leave their footprints spatio-temporally dispersed across many different logs in victim machines. However, existing log-mining intrusion analysis systems typically target only a single type of log to discover evidence of an attack and therefore fail to exploit fundamental inter-log connections. The output of such single-log analysis can hardly reveal the complete attack story for complex, multi-stage attacks. Additionally, some existing approaches require heavyweight system instrumentation, which makes them impractical to deploy in real production environments. To address these problems, we present HERCULE, an automated multi-stage log-based intrusion analysis system. Inspired by graph analytics research in social network analysis, we model multi-stage intrusion analysis as a community discovery problem. HERCULE builds multi-dimensional weighted graphs by correlating log entries across multiple lightweight logs that are readily available on commodity systems. From these, HERCULE discovers any "attack communities" embedded within the graphs. Our evaluation with 15 well known APT attack families demonstrates that HERCULE can reconstruct attack behaviors from a spectrum of cyber attacks that involve multiple stages with high accuracy and low false positive rates.


title: 3-D electrothermal simulation of active cycling on smart power MOSFETs during short-circuit and UIS conditions

abstract: Abstract Active cycling of power devices operated in harsh conditions causes high power dissipation, resulting in critical electrothermal and thermo-mechanical effects that may lead to catastrophic failures. This paper analyzes the ageing-induced degradation of the chip metallization of a power MOSFET and its impact on the device robustness during short-circuit and unclamped inductive switching tests. A 3-D electrothermal simulator relying on a full circuit representation of the whole device is used to predict the influence of various ageing levels. It is found that ageing can jeopardize the robustness of the transistor when subject to short-circuit conditions due to the exacerbated de-biasing effect on the gate-source voltage distribution; conversely, this mechanism does not arise under unclamped inductive switching conditions. This allows explaining the difference in time-to-failure experimentally observed for the transistors subject to these tests and dissipating the same energy.


title: Hyaluronic acid gel fillers in the management of facial aging

abstract: Time affects facial aging by producing cellular and anatomical changes resulting in the consequential loss of soft tissue volume. With the advent of new technologies, the physician has the opportunity of addressing these changes with the utilization of dermal fillers. Hyaluronic acid (HA) dermal fillers are the most popular, non-permanent injectable materials available to physicians today for the correction of soft tissue defects of the face. This material provides an effective, non invasive, non surgical alternative for correction of the contour defects of the face due to its enormous ability to bind water and easiness of implantation. HA dermal fillers are safe and effective. The baby-boomer generation, and their desire of turning back the clock while enjoying an active lifestyle, has expanded the popularity of these fillers. In the US, there are currently eight HA dermal fillers approved for commercialization by the Food and Drug Administration (FDA). This article reviews the innate properties of FDA-approved HA fillers and provides an insight on future HA products and their utilization for the management of the aging face.


title: First Impressions Making Up Your Mind After a 100-Ms Exposure to a Face

abstract: People often draw trait inferences from the facial appearance of other people. We investigated the minimal conditions under which people make such infer- ences. In five experiments, each focusing on a specific trait judgment,wemanipulatedtheexposuretimeofunfamiliar faces.Judgmentsmadeaftera100-msexposurecorrelated highly with judgments made in the absence of time con- straints, suggesting that this exposure time was sufficient for participants to form an impression. In fact, for all judgments—attractiveness, likeability, trustworthiness, competence,andaggressiveness—increasedexposuretime did not significantly increase the correlations. When ex- posure time increased from 100 to 500 ms, participants' judgments became more negative, response times for judgments decreased, and confidence in judgments in- creased. When exposure time increased from 500 to 1,000 ms, trait judgments and response times did not change significantly (with one exception), but confidence in- creased for some of the judgments; this result suggests that additional time may simply boost confidence in judgments. However, increased exposure time led to more differenti- ated person impressions.


title: Reimagining Gamification through the Lens of Activity Theory

abstract: Gamification is maturing as an academic research object, but still suffers from growing pains. Challenges identified in a practical gamification activity are used to anchor a discussion of theoretical challenges currently facing gamification. We argue that some of the pain can be attributed to the exceptionalist and formalist definitions of game, and believe that these interpretations assume a dualistic ontological perspective of the world, resulting in problematic dichotomies. An alternative dialectical perspective from which to reimagine gamification and address some of the challenges is offered. This perspective is concretized through the lens of activity theory. The value of applying activity theory is illustrated by reflecting back on the practical and theoretical challenges identified. Reimagining gamification has the potential to afford participants with greater opportunities for action, and also to drive transformation of existing practices and thus enable agency. It is captured by considering it as an expansive cycle of activity.


title: Learning Reviews to Improve the Classification Accuracy

abstract: The development of communication technology has led to easy access of information through the internet. Nowadays, the use of mobile devices is increasing rapidly which in turn has popularized the pedagogical methods such as learning through mobile devices. Several mobile learning systems are available and also the user opinions about these systems are aired in the social blogs or review websites. Neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. In Knowledge Discovery in Databases (KDD), Neural Networks are employed in classification process. This research paper develops an opinion mining system for M-Learning reviews, the goal of this system is to extract the opinions and reviews, and determine whether these reviews and opinions are positive or negative. This research works proposes to score the words in the opinion using Singular Value Decomposition; select information gain based attributes, additionally feed forward from input layer to the output layer and presents a novel neural network classification algorithm to improve the classification accuracy.


title: Test scenario generation from a structured requirements specification

abstract: A brief overview of the requirements engineering, its history and state of practice are given. A semi-formal method to structure the behavioral requirements for real-time embedded systems is presented. This method is based on a set of forms that contain both informal text based descriptions and formally defined language constructs. After documentation of requirements into these forms, an algorithm to automatically generate event scenarios is presented. This algorithm extracts the needed information from the requirements forms and produces a set of scenarios that can be used to test transaction oriented systems. A design example is presented that is used to illustrate the process of converting text based requirements onto the structured requirements form and to illustrate the operation of the scenario generation algorithm.


Forgetting Classification and Measurement for Decomposition-based Reinforcement Learning


Metrics of games-with-a-purpose for NLP applications


title: Supplementing evolutionary developmental systems with abstract models of neurogenesis

abstract: This work investigates an intermediate abstraction level, that of neural groups, for modelling the development of complex artificial neural networks. Based on Neural Darwinism \cite{edelman:2000}, Displacement Theory \cite{deacon:1998} and The Neuromeric Model \cite{striedter:2005}, our DEACANN system avoids the complexities of axonal and dendritic growth while maintaining key aspects of cell signalling, competition and cooperation that appear to govern the formation of neural topologies in nature. DEACANN also includes a genetic-algorithm for evolving developmental recipes, and the mature networks employ several forms of learning.


title: Creating knowledge maps by exploiting dependent relationships

abstract: Knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. In more recent times, researchers have investigated knowledge in a more applied way with the chief aim of bringing knowledge to life in machines. Artificial Intelligence has provided some degree of rigour to the study of knowledge and Expert Systems are able to use knowledge to solve problems and answer questions. ::: ::: Current business, social, political and technological pressures have forced organisations to take greater control of the knowledge asset. Software suppliers and others offering valuable solutions in this area have unfortunately clouded the issue of knowledge. Information and data control are seen as implicit knowledge management tools and many have abandoned the search for explicit knowledge management methods. ::: ::: Knowledge representation schemes help to identify knowledge. They allow for human understanding and machine application and they can support the automated use of knowledge in problem solving. Some of these representation methods also employ spatial techniques that add an extra dimension to human understanding. ::: ::: Knowledge mapping defined in this work uses learning dependency to organise the map and draws on the ideas of what knowledge is and on spatial representation structures. Knowledge maps can support metrics that provide information about the knowledge asset. Knowledge maps create a visible knowledge framework that supports the explicit management of knowledge by organisation managers and directors. Knowledge maps also offer other advantages to the organisation, the individual and to educational institutions.


title: Heuristic evaluation of user interfaces

abstract: Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four experiments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found between 20 and 51% of the usability problems in the interfaces they evaluated. On the other hand, we could aggregate the evaluations from several evaluators to a single evaluation and such aggregates do rather well, even when they consist of only three to five people.


title: Reliable Fusion of ToF and Stereo Depth Driven by Confidence Measures

abstract: In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi-Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms.


Graph Based Convolutional Neural Network


title: Attitudes of oncologists, family doctors, medical students and lawyers to euthanasia

abstract: The purpose of this survey was to define attitudes and opinions of two types of physicians, medical students and lawyers in the area of euthanasia and related issues and problems. A questionnaire was used as the source of data. There were four groups of test persons: oncologists, home care physicians (family doctors), third-year medical students and lawyers. The questionnaire included 22 questions, 4 of which concerned general characteristics of tested persons (including religious belief), while 18 referred to the problems of euthanasia. The total number of tested persons was 123, 55 men and 68 women with a median age of 38 +/- 11 years ( +/- SD). There were 30 test persons in the group of oncologists, 31 in the group of family doctors, 31 in the group of third-year students, and 31 in the group of lawyers. Between 97% and 100% of individuals gave scored responses to most items. More than half of the individuals (57%) were against euthanasia, and 61% are against the legalization of euthanasia. The views of doctors and medical students were similar (2/3 against) and significantly different from the view of lawyers (2/3 for, P < 0.01). The legalization of euthanasia is favored by 61% of lawyers, in contrast to 43%, 30% and 23% of oncologists, family doctors and medical students, respectively. Overall, 31% sais they would apply euthanasia if they were asked for it, and 36% that would if it had been legalized. Lawyers are twice as willing to perform euthanasia as students or physicians. The least ready to apply euthanasia are physicians working as oncologists (only 1 in 5). Compared with oncologists, one-third of home-care physicians would perform euthanasia anyway, whether legalized or not. Most of the test persons were of the opinion that euthanasia should be performed in the case of children born with a severe anomaly. None of the tested groups considered invalidity or being a burden to the family important reasons for the termination of somebody's life. Approximately 40% of responders believed that the decision for euthanasia should be made by the patient alone. Only lawyers were of the opinion that the misuse of euthanasia could be controlled. Our study shows that it is probably more important to determine factors associated with behavior pertaining to euthanasia in physicians working closely with suffering patients. Reducing suffering and launching a hospice movement and palliative care services might be the most appropriate way to deal with the problem of euthanasia.


title: Modeling and simulation of incremental conductance MPPT using self lift SEPIC converter

abstract: This paper presents the modeling and simulation of incremental conductance (IncCond) Maximum Power Point Tracking (MPPT) using self lift Single Ended Primary Inductance Converter (SEPIC). The solar panel model is developed using the basic circuit equations of photovoltaic cell. The MPP of a solar panel varies with irradiation and temperature. The IncCond algorithm is used to track the maximum power from the solar panel. The unregulated voltage from the panel will be regulated by using the self lift SEPIC converter. It will reduce the voltage and current ripple. As for component stress the self lift SEPIC converter has a smaller voltage and current stresses than the original SEPIC converter. Matlab/Simulink was used for simulation studies.


title: An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment

abstract: This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structured posture space using the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented as gesture trajectories through posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.


FEBEI-Face Expression Based Emoticon Identification CSB 657 Computer Vision


title: The Forensic Analysis of WeChat Message

abstract: WeChat is a popular instant messaging application on Android, iPhone and BlackBerry smart phones, whose chat messages are all stored in local installation folder. This paper studied the data forensic techniques of WeChat messages including the identification of storage location, storage structure and information extraction methods. Since the text message is stored in encrypted SQLite database, we detailedly analyze its cryptographic algorithm, key derivation principle and present the corresponding database decryption process in different practical forensic circumstances. In addition, we exploit the data recovery of voice and deleted messages which would also be helpful in data forensic for criminal investigation.


title: Youth Unemployment in Korea: From a German and Transitional Labour Market Point of View

abstract: By conventional statistics, youth unemployment seems to be quite moderate in Korea: ‘only’ 9.6 percent of the ‘active’ youth labour force was unemployed compared to 21.4 percent in EU-27 in 2011. Germany, with a youth unemployment rate of 8.5 percent, is one of the very few European countries outperforming Korea. But the Korean case is in one respect unusual. From the perspective of intergenerational risk sharing Korea’s youth unemployment rate is 4.6 times higher than the unemployment rate of adults aged 45 to 54; in Germany, this figure is only 1.7. Further peculiarities come up if unemployment is measured by the number of youth not in employment, education or training (NEET) in percent of the total youth population. Korea’s NEET figures are at the top in OECD countries, especially for youth with tertiary education. This paper throws some light to explain this conundrum: It sketches, first, the main causes of youth unemployment and the general policy interventions; because a large part of the problem is structural, possible immediate measures to avoid long-term scar effects for the unemployed youth are briefly reviewed; differences between Europe and the United States show in particular the importance of automatic stabilizers like unemployment insurance in order to reduce the pressure on unfavourable risk sharing for youth in times of recession. The main part is devoted to possible lessons for Korea from Europe, in particular from Germany. Dual education and vocational training systems that emphasise middle level and market oriented skills are identified as institutional device both for fairer intergenerational risk sharing as well as for a smoother transition from school to work. In its outlook, the paper comes back to the puzzle of highly and academically inflated youth unemployment by referring to a possible hidden cause in Korea: A strong insurance motive might explain the overall striving for an academic degree inducing not only wasteful congestion at labour market entries but also unfair job allocation through credentialism.


title: A Sufficient Condition for Convergences of Adam and RMSProp

abstract: Adam and RMSProp are two of the most influential adaptive stochastic algorithms for training deep neural networks, which have been pointed out to be divergent even in the convex setting via a few simple counterexamples. Many attempts, such as decreasing an adaptive learning rate, adopting a big batch size, incorporating a temporal decorrelation technique, seeking an analogous surrogate, etc., have been tried to promote Adam/RMSProp-type algorithms to converge. In contrast with existing approaches, we introduce an alternative easy-to-check sufficient condition, which merely depends on the parameters of the base learning rate and combinations of historical second-order moments, to guarantee the global convergence of generic Adam/RMSProp for solving large-scale non-convex stochastic optimization. Moreover, we show that the convergences of several variants of Adam, such as AdamNC, AdaEMA, etc., can be directly implied via the proposed sufficient condition in the non-convex setting. In addition, we illustrate that Adam is essentially a specifically weighted AdaGrad with exponential moving average momentum, which provides a novel perspective for understanding Adam and RMSProp. This observation coupled with this sufficient condition gives much deeper interpretations on their divergences. At last, we validate the sufficient condition by applying Adam and RMSProp to tackle a certain counterexample and train deep neural networks. Numerical results are exactly in accord with our theoretical analysis.


title: Early fire and smoke detection based on colour features and motion analysis

abstract: This work addresses the issue of fire and smoke detection in a scene within a video surveillance framework. Detection of fire and smoke pixels is at first achieved by means of a motion detection algorithm. In addition, separation of smoke and fire pixels using colour information (within appropriate spaces, specifically chosen in order to enhance specific chromatic features) is performed. In parallel, a pixel selection based on the dynamics of the area is carried out in order to reduce false detection. The output of the three parallel algorithms are eventually fused by means of a MLP.


title: Combining Models and Exemplars for Face Recognition: An Illuminating Example

abstract: We propose a model- and exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.


title: Identification and Prediction of Internet Traffic Using Artificial Neural Networks

abstract: This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.


title: Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood

abstract: We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority of the existing methods for learning Gaussian graphical models are either restricted to decomposable graphs or require specification of a tuning parameter that may have a substantial impact on learned structures. By combining a simple sparsity inducing prior for the graph structures with a default reference prior for the model parameters, we obtain a fast and easily applicable scoring function that works well for even high-dimensional data. We demonstrate the favourable performance of our approach by large-scale comparisons against the leading methods for learning non-decomposable Gaussian graphical models. A theoretical justification for our method is provided by showing that it yields a consistent estimator of the graph structure. A Bayesian approximate method for structure learning of Gaussian graphical models.The method does not require any user defined tuning parameters.The method is not restricted to learning decomposable graphs.Consistency of the method is proven.The method can be easily parallelized.


title: A lightweight component caching scheme for satisfiability solvers

abstract: We introduce in this paper a lightweight technique for reducing work repetition caused by non-chronological backtracking commonly practiced by DPLL-based SAT solvers. The presented technique can be viewed as a partial component caching scheme. Empirical evaluation of the technique reveals significant improvements on a broad range of industrial instances.


title: Numerical investigation of the aerodynamic characteristics of a hovering Coleopteran insect.

abstract: Abstract The aerodynamic characteristics of the Coleopteran beetle species Epilachna quadricollis , a species with flexible hind wings and stiff elytra (fore wings), are investigated in terms of hovering flight. The flapping wing kinematics of the Coleopteran insect are modeled through experimental observations with a digital high-speed camera and curve fitting from an ideal harmonic kinematics model. This model numerically simulates flight by estimating a cross section of the wing as a two-dimensional elliptical plane. There is currently no detailed study on the role of the elytron or how the elytron–hind wing interaction affects aerodynamic performance. In the case of hovering flight, the relatively small vertical or horizontal forces generated by the elytron suggest that the elytron makes no significant contribution to aerodynamic force.


title: Wearable Smart System for Visually Impaired People

abstract: In this paper, we present a wearable smart system to help visually impaired persons (VIPs) walk by themselves through the streets, navigate in public places, and seek assistance. The main components of the system are a microcontroller board, various sensors, cellular communication and GPS modules, and a solar panel. The system employs a set of sensors to track the path and alert the user of obstacles in front of them. The user is alerted by a sound emitted through a buzzer and by vibrations on the wrist, which is helpful when the user has hearing loss or is in a noisy environment. In addition, the system alerts people in the surroundings when the user stumbles over or requires assistance, and the alert, along with the system location, is sent as a phone message to registered mobile phones of family members and caregivers. In addition, the registered phones can be used to retrieve the system location whenever required and activate real-time tracking of the VIP. We tested the system prototype and verified its functionality and effectiveness. The proposed system has more features than other similar systems. We expect it to be a useful tool to improve the quality of life of VIPs.


title: DSRD-Based High-Power Repetitive Short-Pulse Generator Containing GDT: Theory and Experiment

abstract: A high-power short-pulse generator based on the diode step recovery phenomenon and high repetition rate discharges in a two-electrode gas discharge tube is presented. The proposed circuit is simple and low cost and driven by a low-power source. A full analysis of this generator is presented which, considering the nonlinear behavior of the gas tube, predicts the waveform of the output pulse. The proposed method has been shown to work properly by implementation of a kW-range prototype. Experimental measurements of the output pulse characteristics showed a rise time of 3.5 ns, with pulse repetition rate of 2.3 kHz for a 47- $\Omega $ load. The input peak power was 2.4 W, which translated to about 0.65-kW output, showing more than 270 times increase in the pulse peak power. The efficiency of the prototype was 57%. The overall price of the employed components in the prototype was less than U.S. $2.0. An excellent agreement between the analytical and experimental test results was established. The analysis predicts that the proposed circuit can generate nanosecond pulses with more than 100-kW peak powers by using a subkW power supply.


title: Design aspects for 5G V2X physical layer

abstract: Next generation 5G communication systems will need to employ a range of diverse technologies in order to support vehicle-to-everything (V2X) use cases, ultimately leading to accident-free, cooperative autonomous vehicles that use the available roadway efficiently. V2X is considered as one of the most challenging applications of 5G, as it requires ultra-reliable and low-latency communication for safety-critical use cases and has to provide high data rates in many scenarios. This paper discusses design aspects for the radio access in 5G V2X. Selected key technologies and their integration towards future 5G V2X physical layer are addressed. We discuss channel modeling in the context of 5G V2X use cases and we present first results for frame structure and numerology design, coexistence with earlier systems, multi-link synchronization, and multi-antenna transmission techniques.


title: The Process of Cultural Competence in the Delivery of Healthcare Services: A Model of Care

abstract: Several models of service care delivery have emerged to meet the challenges of providing health care to our growing multi-ethnic world. This article will present Campinha-Bacote's model of cultural competence in health care delivery: The Process of Cultural Competence in the Delivery of Healthcare Services. This model views cultural competence as the ongoing process in which the health care provider continuously strives to achieve the ability to effectively work within the cultural context of the client (individual, family, community). This ongoing process involves the integration of cultural awareness, cultural knowledge, cultural skill, cultural encounters, and cultural desire.


title: Agreement of Self-Reported and Genital Measures of Sexual Arousal in Men and Women: A Meta-Analysis

abstract: The assessment of sexual arousal in men and women informs theoretical studies of human sexuality and provides a method to assess and evaluate the treatment of sexual dysfunctions and paraphilias. Understanding measures of arousal is, therefore, paramount to further theoretical and practical advances in the study of human sexuality. In this meta-analysis, we review research to quantify the extent of agreement between self-reported and genital measures of sexual arousal, to determine if there is a gender difference in this agreement, and to identify theoretical and methodological moderators of subjective-genital agreement. We identified 132 peer- or academically-reviewed laboratory studies published between 1969 and 2007 reporting a correlation between self-reported and genital measures of sexual arousal, with total sample sizes of 2,505 women and 1,918 men. There was a statistically significant gender difference in the agreement between self-reported and genital measures, with men (r = .66) showing a greater degree of agreement than women (r = .26). Two methodological moderators of the gender difference in subjective-genital agreement were identified: stimulus variability and timing of the assessment of self-reported sexual arousal. The results have implications for assessment of sexual arousal, the nature of gender differences in sexual arousal, and models of sexual response.


title: Breast segmentation in MRI using Poisson surface reconstruction initialized with random forest edge detection

abstract: Segmentation of breast tissue in MRI images is an important pre-processing step for many applications. We present a new method that uses a random forest classifier to identify candidate edges in the image and then applies a Poisson reconstruction step to define a 3D surface based on the detected edge points. Using a leave one patient out cross validation we achieve a Dice overlap score of 0.96 ± 0.02 for T1 weighted non-fat suppressed images in 8 patients. In a second dataset of 332 images acquired using a Dixon sequence, which was not used in training the random classifier, the mean Dice score was 0.90 ± 0.03. Using this approach we have achieved accurate, robust segmentation results using a very small training set.


title: An AHP (Analytic Hierarchy Process)/ANP (Analytic Network Process)-based multi-criteria decision approach for the selection of solar-thermal power plant investment projects

abstract: In this paper the AHP (Analytic Hierarchy Process) and the ANP (Analytic Network Process) are applied to help the managing board of an important Spanish solar power investment company to decide whether to invest in a particular solar-thermal power plant project and, if so, to determine the order of priority of the projects in the company's portfolio.


title: NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical Usage.

abstract: Much of the focus in the design of deep neural networks has been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios, particularly on edge devices such as mobile and other consumer devices, given their high computational and memory requirements. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical usage. In particular, we propose a new balanced metric called NetScore, which is designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 50 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field. The proposed NetScore metric, along with the other tested metrics, are by no means perfect, but the hope is to push the conversation towards better universal metrics for evaluating deep neural networks for use in practical scenarios to help guide practitioners in model design.


title: Analysis of decision making factors for equity investment by DEMATEL and Analytic Network Process

abstract: Existing methodologies of equity investment, such as fundamental analysis, technical analysis, and institutional investor analysis, explore important factors of stock price behaviors. However, the interdependent relationships of the key factors have not yet been fully studied. This paper provides the first analysis on the interactive relationships among the factors in incorporating the methods of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). The empirical results show that factors from the existing analytical methodologies have significant interactive and self-feedback dynamics. Among the key factors, profitability is the most important one affecting investment decision, followed by growth and trading volume. In addition, due to the complexity of the ANP, this study proposes a new methodology to simplify the process, and empirical evidences indicate that the approach is effective and efficient.


title: Measuring Semantic Similarity in the Taxonomy of WordNet

abstract: This paper presents a new model to measure semantic similarity in the taxonomy of WordNet, using edge-counting techniques. We weigh up our model against a benchmark set by human similarity judgment, and achieve a much improved result compared with other methods: the correlation with average human judgment on a standard 28 word pair dataset is 0.921, which is better than anything reported in the literature and also significantly better than average individual human judgments. As this set has been effectively used for algorithm selection and tuning, we also cross-validate an independent 37 word pair test set (0.876) and present results for the full 65 word pair superset (0.897).


title: Recurrent Neural Networks with External Memory for Spoken Language Understanding

abstract: Recurrent Neural Networks RNNs have become increasingly popular for the task of language understanding. In this task, a semantic tagger is deployed to associate a semantic label to each word in an input sequence. The success of RNN may be attributed to its ability to memorise long-term dependence that relates the current-time semantic label prediction to the observations many time instances away. However, the memory capacity of simple RNNs is limited because of the gradient vanishing and exploding problem. We propose to use an external memory to improve memorisation capability of RNNs. Experiments on the ATIS dataset demonstrated that the proposed model was able to achieve the state-of-the-art results. Detailed analysis may provide insights for future research.


title: Google Book Search: Document Understanding on a Massive Scale

abstract: Unveiled in late 2004, Google Book Search is an ambitious program to make all the world's books discoverable online. The sheer scale of the problem brings a number of unique document analysis and understanding challenges that are outlined in this paper. We also go over some of the ways that Google is working with the document analysis research community to help push the state of the art.


title: A 280-KBytes Twin-Bit-Cell Embedded NOR Flash Memory With a Novel Sensing Current Protection Enhanced Technique and High-Voltage Generating Systems

abstract: A novel sensing current protection enhanced (SCPE) technique and high-voltage (HV) generating systems are adopted to greatly enhance the advantages of the twin-bit cell. The SCPE technique provides the tradeoff of sensing margin loss between “1” and “0” bits sensing case to realize fast read access. The read speed is improved by 7.7%. HV generating systems with parallel-series-transform and capacitance-shared techniques are proposed to satisfy the complicated requirements of output capability and current drivability with lower area penalty. The HV periphery area decreases by 71%. A 1.5-V 280-KBytes embedded NOR flash memory IP has been fabricated in HHGrace (Shanghai Huahong Grace Semiconductor Manufacturing Corporation) 90-nm 4 poly 4 metal CMOS process. The complicated operation voltages and access time of 23.5 ns have been achieved at 1.5 V. The die size of the proposed IP is 0.437 mm $^{2}$ and the area size of charge pump has been reduced to 0.006 mm $^{2}$ on average.


title: Differentially Private Random Forest with High Utility

abstract: Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.


title: Ringing out fault tolerance. A new ring network for superior low-cost dependability

abstract: Dependability properties of bi-directional and braided rings are well recognized in improving communication availability. However, current ring-based topologies have no mechanisms for extreme integrity and have not been considered for emerging high-dependability markets where cost is a significant driver, such as the automotive "by-wire" applications. This paper introduces a braided-ring architecture with superior guardian functionality and complete Byzantine fault tolerance while simultaneously reducing cost. This paper reviews anticipated requirements for high-dependability low-cost applications and emphasizes the need for regular safe testing of core coverage functions. The paper describes the ring's main mechanisms for achieving integrity and availability levels similar to SAFEbus/spl reg/ but at low automotive costs. The paper also presents a mechanism to achieve self-stabilizing TDMA-based communication and design methods for fault-tolerant protocols on a network of simplex nodes. The paper also introduces a new self-checking pair concept that leverages braided-ring properties. This novel message-based self-checking-pair concept allows high-integrity source data at extremely low cost.


title: Friendly home automation system using cell phone and J2ME with feedback instant voice messages

abstract: This paper introduces a friendly system to control the home appliances remotely by the use of mobile cell phones; this system is well known as “Home Automation System” (HAS).


title: Image Restoration using Group Sparse Representation via Weighted Nuclear Norm Minimization.

abstract: As the matrix formed by nonlocal similar patches in a natural image is of a low rank, the nuclear norm minimization (NNM) has been widely studied for image processing. Since the singular values have clear meanings and should be treated differently, NNM regularizes each of them equally, which often restricts its capability and flexibility. Recent advances have suggested that the weighted nuclear norm minimization (WNNM) has shown great potential in different image restoration studies, where singular values are assigned different value. However, it still lacks a mathematical derivation why the weighted nuclear norm is more appropriate than the nuclear norm. In this paper, we proposed a new scheme for image restoration using group sparse representation via weighted nuclear norm minimization (GSR-WNNM). We show mathematically the advantage of WNNM, from a group sparse representation perspective, where GSR offers a powerful mechanism of combining local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. Then, an effective dictionary for each group is learned from the reconstructed image itself rather a large number of natural image dataset, ensuring a low computational complexity. Moreover, to further improve the computational efficiency of the proposed method, we have developed an implementation of fast convergence via the alternating direction method of multipliers (ADMM). Experimental results have shown that the proposed GSR-WNNM method significantly outperforms the state-of-the-art methods both quantitatively and qualitatively.


title: Model-based dynamic gait generation for a leg-wheel transformable robot

abstract: We report on the model-based approach to dynamic trotting and pronking gait generation for a leg-wheel transformable robot. The rolling spring-loaded inverted pendulum (R-SLIP) model served as the template for robot locomotion by programming the robot's motion according to the stable fixed-point trajectories of the model. Two strategies are developed to match the robot leg motions to the virtual model leg: First, using the two active degrees of freedom on each leg to simulate the passive spring effect of the R-SLIP model leg. Second, installing a torsion spring on the leg-wheel module to render the leg-wheel morphology identical to that of the model leg. This model-based approach to dynamic behavior generation for the robot is experimentally evaluated. The robot can successfully generate an R-SLIP-like stable pronking gait with a flight phase.


An Approach to Face Detection and Feature Extraction using Canny Method


title: V.: Rt to win! predicting message propagation in twitter

abstract: Twitter is a very popular way for people to share information on a bewildering multitude of topics. Tweets are propagated using a variety of channels: by following users or lists, by searching or by retweeting. Of these vectors, retweeting is arguably the most effective, as it can potentially reach the most people, given its viral nature. A key task is predicting if a tweet will be retweeted, and solving this problem furthers our understanding of message propagation within large user communities. We carry out a human experiment on the task of deciding whether a tweet will be retweeted which shows that the task is possible, as human performance levels are much above chance. Using a machine learning approach based on the passive-aggressive algorithm, we are able to automatically predict retweets as well as humans. Analyzing the learned model, we find that performance is dominated by social features, but that tweet features add a substantial boost.


title: Evaluation of Graphene for Effective Cleanup of Fruit and Vegetable Extracts in Pesticide Residue Analysis

abstract: Graphene is a novel class of carbon nanostructures which possess an ultra-high specific surface area. It has already been used for pre-concentration, extraction, and electrochemical selective determination of pesticides but not much work has been focused on the cleanup efficiency of graphene. In this work, graphene was evaluated as an alternative cleanup sorbent for gas chromatography-mass spectrometry (GC-MS) analysis of 17 pesticides analyzed in vegetables and fruit with the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method. QuEChERS is a standard method published by CEN (European Committee for Standardization) Code EN 15662: 2008. The recoveries of the 17 pesticides in carrot, grape, tomato and rape were 92–120 % with relative standard deviations below 10 %. Comparative studies showed that graphene not only has good removal efficiencies of pigments in vegetables and fruit, but also has better cleanup ability than commonly used graphitized carbon black (GCB).


title: Two energy-efficient cluster head selection techniques based on distance for wireless sensor networks

abstract: Energy efficiency in data collection and transmission is always a crucial factor in wireless sensor networks (WSN). Thus, minimizing energy dissipation and maximizing network life-time are key factors in the design of routing protocols for WSNs. In this paper, based on Low Energy Adaptive Clustering Hierarchy (LEACH) protocols, we propose two new distance-based clustering routing protocols, which we call DB-LEACH and DBEA-LEACH. The first approach (distance-based) selects a cluster head node by considering geometric distance between the candidate nodes to the base station. To further improve DB-LEACH, DBEA-LEACH (distance-based energy-aware) additionally selects a cluster head not only based on distance, but also by examining residual energy of the node greater than the average residual energy level of nodes in the network. The simulation results show that our two proposed algorithm outperformed a traditional LEACH as well as its derivatives in terms of conserving energy such that prolonging network lifetime.