title: A Framework for Real-Time Physical Human-Robot Interaction using Hand Gestures

abstract: A physical Human-Robot Interaction (pHRI) framework is proposed using vision and force sensors for a two-way object hand-over task. Kinect v2 is integrated with the state-of-the-art 2D skeleton extraction library namely Openpose to obtain a 3D skeleton of the human operator. A robust and rotation invariant (in the coronal plane) hand gesture recognition system is developed by exploiting a convolutional neural network. This network is trained such that the gestures can be recognized without the need to pre-process the RGB hand images at run time. This work establishes a firm basis for the robot control using hand-gestures. This will be extended for the development of intelligent human intention detection in pHRI scenarios to efficiently recognize a variety of static as well as dynamic gestures.


Chronic suppurative osteomyelitis of mandible complicating molar extraction: A case report.


title: Freenet: A Distributed Anonymous Information Storage and Retrieval System

abstract: We describe Freenet, an adaptive peer-to-peer network application that permits the publication, replication, and retrieval of data while protecting the anonymity of both authors and readers. Freenet operates as a network of identical nodes that collectively pool their storage space to store data files and cooperate to route requests to the most likely physical location of data. No broadcast search or centralized location index is employed. Files are referred to in a location-independent manner, and are dynamically replicated in locations near requestors and deleted from locations where there is no interest. It is infeasible to discover the true origin or destination of a file passing through the network, and difficult for a node operator to determine or be held responsible for the actual physical contents of her own node.


title: Sketch-based 3-D modeling for piecewise planar objects in single images

abstract: 3-D object modeling from single images has many applications in computer graphics and multimedia. Most previous 3-D modeling methods which directly recover 3-D geometry from single images require user interactions during the whole modeling process. In this paper, we propose a semi-automatic 3-D modeling approach to recover accurate 3-D geometry from a single image of a piecewise planar object with less user interaction. Our approach concentrates on these three aspects: (1) requiring rough sketch input only, (2) accurate modeling for a large class of objects, and (3) automatically recovering the invisible part of an object and providing a complete 3-D model. Experimental results on various objects show that the proposed approach provides a good solution to these three problems. Graphical abstractWe propose a sketch-based planar object modeling system. The flow chart of our approach goes as the following steps: (1) The original image. (2) Roughly sketched vertices and edges for the visible part of the desired object. (3) Automatically generated 2-D initial wireframe W 2 di . Note that some vertices in W 2 di have no precise locations, which are highlighted with red stars. (4) Precise reconstruction of 3-D wireframe M v 3 dw from W 2 di . (5) Inference of the hidden 3-D geometries (marked with dotted lines). (6) Complete 3-D objects.Display Omitted HighlightsWe reconstruct a complete object from a user-drawn sketch depicting its visible part.The input sketch consists of two elements: sketch vertices and sketch lines.The 3-D reconstruction process in our system is semi-automatic.The system can produce a precise planar object.


title: Subscriber classification within telecom networks utilizing big data technologies and machine learning

abstract: This paper describes a scalable solution for identifying influential subscribers in for example telecom networks. The solution estimates one weighted value of influence out of several Social Network Analysis(SNA) metrics. The novel method for aggregation of several metrics utilizes machine learning to train models. A prototype solution has been implemented on a Hadoop platform to support scalability and to reduce hard ware cost by enabling the usage of commodity computers. The SNA algorithms have been adapted to efficiently execute on the MapReduce distributed platform. The prototype solution has been tested on a Hadoop cluster. The tests have verified that the solution can scale to support networks with millions of subscribers. Both real data from a telecom network operator with 2.4 million subscribers and synthetic data for networks up to 100 million subscribers have been used to verify the scalability and accuracy of the solution. The correlation between metrics have been analyzed to identify the information gain from each metric.


title: Retinal image enhancement and registration for the evaluation of longitudinal changes

abstract: Retinal images are long-accepted clinical diagnostic method for ocular diseases. Of late, automated assessment of retinal ::: images has proven to be a useful adjunct in clinical decision support systems. In this paper, we propose a retinal image ::: registration method, which combine retinal image enhancement and non-rigid image registration methods, for ::: longitudinal retinal image alignment. A further illumination correction and gray value matching methods are applied for ::: the longitudinal image comparison and subtraction. The solution can enhance the assessment of longitudinal changes of ::: retinal images and image subtraction in a clinical application system. The performance of the proposed solution has been ::: tested on longitudinal retinal images. Preliminary results have demonstrated the accuracy and robustness of the solutions ::: and their potential application in a clinical environment.


NeuralTalk on Embedded System and GPU-accelerated RNN


title: Background Subtraction for Freely Moving Cameras

abstract: Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects by detecting areas in a video that change over time. In this paper, we extend the concept of ‘subtracting’ areas at rest to apply to video captured from a freely moving camera. We do not assume that the background is well-approximated by a plane or that the camera center remains stationary during motion. The method operates entirely using 2D image measurements without requiring an explicit 3D reconstruction of the scene. A sparse model of background is built by robustly estimating a compact trajectory basis from trajectories of salient features across the video, and the background is ‘subtracted’ by removing trajectories that lie within the space spanned by the basis. Foreground and background appearance models are then built, and an optimal pixel-wise foreground/background labeling is obtained by efficiently maximizing a posterior function.


title: Impact of Bilingualism on Cognitive Outcome After Stroke

abstract: BACKGROUND AND PURPOSE ::: Bilingualism has been associated with slower cognitive aging and a later onset of dementia. In this study, we aimed to determine whether bilingualism also influences cognitive outcome after stroke. ::: ::: ::: METHODS ::: We examined 608 patients with ischemic stroke from a large stroke registry and studied the role of bilingualism in predicting poststroke cognitive impairment in the absence of dementia. ::: ::: ::: RESULTS ::: A larger proportion of bilinguals had normal cognition compared with monolinguals (40.5% versus 19.6%; P<0.0001), whereas the reverse was noted in patients with cognitive impairment, including vascular dementia and vascular mild cognitive impairment (monolinguals 77.7% versus bilinguals 49.0%; P<0.0009). There were no differences in the frequency of aphasia (monolinguals 11.8% versus bilinguals 10.5%; P=0.354). Bilingualism was found to be an independent predictor of poststroke cognitive impairment. ::: ::: ::: CONCLUSIONS ::: Our results suggest that bilingualism leads to a better cognitive outcome after stroke, possibly by enhancing cognitive reserve.


title: Facial feature extraction for face recognition: a review

abstract: Over the last decade facial feature extraction has been actively researched for face recognition. This paper provides an up-to-date review of major human facia recognition research. Earlier sections we presented an overview of face recognition and its applications. In later sections, a literature review of the most recent face recognition technique is presented. The most prominent feature extraction and the techniques are also given. Finally, we summarized all research results discussed.


title: Dense Associative Memory for Pattern Recognition

abstract: A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality between this dense associative memory and neural networks commonly used in deep learning. On the associative memory side of this duality, a family of models that smoothly interpolates between two limiting cases can be constructed. One limit is referred to as the feature-matching mode of pattern recognition, and the other one as the prototype regime. On the deep learning side of the duality, this family corresponds to feedforward neural networks with one hidden layer and various activation functions, which transmit the activities of the visible neurons to the hidden layer. This family of activation functions includes logistics, rectified linear units, and rectified polynomials of higher degrees. The proposed duality makes it possible to apply energy-based intuition from associative memory to analyze computational properties of neural networks with unusual activation functions - the higher rectified polynomials which until now have not been used in deep learning. The utility of the dense memories is illustrated for two test cases: the logical gate XOR and the recognition of handwritten digits from the MNIST data set.


Data Flow Program Graphs


A KINEMATIC ANALYSIS AND SIMULATION BASED ON ADAMS FOR EGGPLANT PICKING ROBOT / 基于 ADAMS 的茄子采摘机器人运动学分析与仿真


title: Salt and sugar: direct synthesis of high surface area carbon materials at low temperatures via hydrothermal carbonization of glucose under hypersaline conditions

abstract: Hydrothermal carbonization of carbohydrates, here glucose as a model, in salt–water mixtures results in high surface area carbonaceous materials with surface areas up to 650 m2 g−1 where porosity is created by aggregation of very small primary nanoparticles, similar to aerogels or high surface area soot. These materials can be obtained by simple washing with water and are useful without further activation processes. Furthermore, no special technical equipment for isolation is needed since the materials are exceptionally stable throughout vacuum drying, thus keeping the overall approach sustainable and very simple.


title: Global outer-urban navigation with OpenStreetMap

abstract: Publicly available map services are widely used by humans for navigation and nowadays provide almost complete road network data. When utilizing such maps for autonomous navigation with mobile robots one is faced with the problem of inaccuracies of the map and the uncertainty about the position of the robot relative to the map. In this paper, we present a probabilistic approach to autonomous robot navigation using data from OpenStreetMap that associates tracks from Open-StreeetMap with the trails detected by the robot based on its 3D-LiDAR data. It combines semantic terrain information, derived from the 3D-LiDAR data, with a Markov-Chain Monte-Carlo technique to match the tracks from OpenStreetMap with the sensor data. This enables our robot to utilize OpenStreetMap for navigation planning and to still stay on the trails during the execution of these plans. We present the results of extensive experiments carried out in real world settings that demonstrate the robustness of our system regarding the alignment of the vehicle pose relative to the OpenStreetMap data.


title: Test Automation Architectures: Planning for Test Automation

abstract: Designing a practical test automation architecture provides a solid foundation for a successful automation effort. This paper describes key elements of automated testing that need to be considered, models for testing that can be used for designing a test automation architecture, and considerations for successfully combining the elements to form an automated test environment. The paper first develops a general framework for discussion of software testing and test automation. This includes a definition of test automation, a model for software tests, and a discussion of test oracles. The remainder of the paper focuses on using the framework to plan for a test automation architecture that addresses the requirements for the specific software under test (SUT).


title: Collaborative Filtering for Information Recommendation Systems

abstract: INTRODUCTION In order to draw users’ attention and to increase their satisfaction towards online information search results, search engine developers and vendors try to predict user preference based on the user behavior. Recommendations are provided by the search engines or online vendors to the users. Recommendation systems are implemented in commercial and non-profit web sites to predict the user preferences. For commercial web sites, accurate predictions may result in higher selling rates. The main functions of recommendation systems include analyzing user data and extracting useful information for further predictions. Recommendation systems are designed to allow users to locate the preferable items quickly and to avoid the possible information overloads. Recommendation systems apply data mining techniques to determine the similarity among thousands or even millions of data.


title: Bandwidth Enhancement and Radiation Characteristics Improvement of Rectangular Dielectric Resonator Antenna

abstract: Abstract-A simple, compact, wideband rectangular dielectric resonator antenna (RDRA) is presented. The bandwidth is enhanced using a proper tapered strip excitation from one side of the DR. The radiation characteristics are improved by adding a shorted narrow strip to the opposite side of the excitation. In addition, by using this shorted strip, further improvement of the bandwidth is obtained. A parametric study on the strip dimension is carried out. The proposed DRA with good radiation characteristics offers a measured bandwidth of 96% between 2.13 and 6.08 GHz for VSWR <; 2.


title: CT Image Enhancement for Feature Detection and Localization

abstract: In recent years, many pre-processing filters have been developed in order to enhance anatomical structures on chest CT images. These filters are typically based on the analysis of the multiscale second-order local information of the image, that helps identify structures with even (tubes) or odd (surfaces) symmetries. Therefore, they often require specific parameter tuning to enhance the different structures. Moreover, while the filters seem to be able to isolate the structure of interest, they do not provide information about the sub-voxel location of the feature. In this work, we present a novel method for vessel, airway, and fissure strength computation on chest CT images using convolutional neural networks. A scale-space particle segmentation is used to isolate training points for vessels, airways, and fissures which are then used to train an 8-layer neural network with 3 convolutional layers which define high order local information of the image. The network returns a probability map of each feature and provides information on the feature offset from the voxel sampling center, allowing for sub-voxel location of the different structures. The proposed method has been evaluated on clinical CT images and compared to other methods for feature enhancement available in the literature. Results show that the proposed method outperforms competing algorithms in terms of enhancement and is also unique in providing subvoxel information.


title: Design of a multi-DOF cable-driven mechanism of a miniature serial manipulator for robot-assisted minimally invasive surgery

abstract: While multi-fingered robotic hands have been developed for decades, none has been used for surgical operations. µAngelo is an anthropomorphic master-slave system for teleoperated robot-assisted surgery. As part of this system, this paper focuses on its slave instrument, a miniature three-digit hand. The design of the mechanism of such a manipulator poses a challenge due to the required miniaturization and the many active degrees of freedom. As the instrument has a human-centered design, its relation to the human hand is discussed. Two ways of routing its cable-driven mechanism are investigated and the method of deriving the input-output functions that drive the mechanism is presented.


title: Security as a CoAP resource: An optimized DTLS implementation for the IoT

abstract: The growing number of applications based on Internet of Things (IoT) technologies is pushing towards standardized protocol stacks for machine-to-machine (M2M) communication and the adoption of standard-based security solutions, such as the Datagram Transport Layer Security (DTLS). Despite the huge diffusion of DTLS, there is a lack of optimized implementations tailored to resource constrained devices. High energy consumption and long delays of current implementations limit their effective usage in real-life deployments. The aim of this paper is to explain how to integrate the DTLS protocol inside the Constrained Application Protocol (CoAP), exploiting Elliptic Curve Cryptography (ECC) optimizations and minimizing ROM occupancy. We have implemented our solution on an off-the-shelf mote platform and evaluated its performance. Results show that our ECC optimizations outperform priors scalar multiplication in state of the art for class 1 mote platforms, and improve network lifetime by a factor of up to 6.5 with respect to a standard-based not optimized implementation.


title: Recommending people to people: the nature of reciprocal recommenders with a case study in online dating

abstract: People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must satisfy both parties; we call this type of recommender reciprocal. This article is the first to present a comprehensive view of this important recommender class. We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites. We then present a series of studies and evaluations of a content-based reciprocal recommender in the domain of online dating. It uses a large dataset from a major online dating website. We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected. Our experiments indicate that, by considering reciprocity, the rate of successful connections can be significantly improved. They also show that, despite the existence of rich explicit profiles, the use of implicit profiles provides more effective recommendations. We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders. Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.


title: Sensing vehicle dynamics for determining driver phone use

abstract: This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over $90\%$ with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., $95\%$) with a lower false positive rate.


title: Modeling and managing ETL processes

abstract: Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. The design, development and deployment of ETL processes, which is currently, performed in an ad-hoc, in house fashion, needs modeling, design and methodological foundations. Unfortunately, the research community has a lot of work to do to confront this shortcoming. Our research explores a coherent framework for the conceptual, the logical, and the physical design of ETL processes. We delve into the modeling of ETL activities and provide a conceptual and a logical abstraction for the representation of these processes. Moreover, we focus on the optimization of the ETL processes, in order to minimize the execution time of an ETL process.


title: Automatic generation of natural language summaries for Java classes

abstract: Most software engineering tasks require developers to understand parts of the source code. When faced with unfamiliar code, developers often rely on (internal or external) documentation to gain an overall understanding of the code and determine whether it is relevant for the current task. Unfortunately, the documentation is often absent or outdated. This paper presents a technique to automatically generate human readable summaries for Java classes, assuming no documentation exists. The summaries allow developers to understand the main goal and structure of the class. The focus of the summaries is on the content and responsibilities of the classes, rather than their relationships with other classes. The summarization tool determines the class and method stereotypes and uses them, in conjunction with heuristics, to select the information to be included in the summaries. Then it generates the summaries using existing lexicalization tools. A group of programmers judged a set of generated summaries for Java classes and determined that they are readable and understandable, they do not include extraneous information, and, in most cases, they are not missing essential information.


title: Precision: precomputing environment semantics for contact-rich character animation

abstract: The widespread availability of high-quality motion capture data and the maturity of solutions to animate virtual characters has paved the way for the next generation of interactive virtual worlds exhibiting intricate interactions between characters and the environments they inhabit. However, current motion synthesis techniques have not been designed to scale with complex environments and contact-rich motions, requiring environment designers to manually embed motion semantics in the environment geometry in order to address online motion synthesis. This paper presents an automated approach for analyzing both motions and environments in order to represent the different ways in which an environment can afford a character to move. We extract the salient features that characterize the contact-rich motion repertoire of a character and detect valid transitions in the environment where each of these motions may be possible, along with additional semantics that inform which surfaces of the environment the character may use for support during the motion. The precomputed motion semantics can be easily integrated into standard navigation and animation pipelines in order to greatly enhance the motion capabilities of virtual characters. The computational efficiency of our approach enables two additional applications. Environment designers can interactively design new environments and get instant feedback on how characters may potentially interact, which can be used for iterative modeling and refinement. End users can dynamically edit virtual worlds and characters will automatically accommodate the changes in the environment in their movement strategies.


title: TOXICITY OF GLYPHOSATE-BASED PESTICIDES TO FOUR NORTH AMERICAN FROG SPECIES

abstract: Glyphosate-based herbicides are among the most widely used pesticides in the world. We compared the acute toxicity of the glyphosate end-use formulation Roundup Originalt to four North American amphibian species (Rana clamitans, R. pipiens, R. sylvatica, and Bufo americanus) and the toxicity of glyphosate technical, the polyethoxylated tallowamine surfactant (POEA) commonly used in glyphosate-based herbicides, and five newer glyphosate formulations to R. clamitans. For R. clamitans, acute toxicity values in order of decreasing toxicity were POEA . Roundup Original . Roundup Transorbt . Glyfos AUt; no significant acute toxicity was observed with glyphosate technical material or the glyphosate formulations Roundup Biactivet, Touchdownt, or Glyfos BIOt. Comparisons between the four amphibian species showed that the toxicity of Roundup Original varied with species and developmental stage. Rana pipiens tadpoles chronically exposed to environmentally relevant concentrations of POEA or glyphosate formulations containing POEA showed decreased snout-vent length at metamorphosis and increased time to metamor- phosis, tail damage, and gonadal abnormalities. These effects may be caused, in some part, by disruption of hormone signaling, because thyroid hormone receptor b mRNA transcript levels were elevated by exposure to formulations containing glyphosate and POEA. Taken together, the data suggest that surfactant composition must be considered in the evaluation of toxicity of glyphosate- based herbicides.


title: A Joint Human/Machine Process for Coding Events and Conflict Drivers

abstract: Constructing datasets to analyse the progression of conflicts has been a longstanding objective of peace and conflict studies research. In essence, the problem is to reliably extract relevant text snippets and code (annotate) them using an ontology that is meaningful to social scientists. Such an ontology usually characterizes either types of violent events (killing, bombing, etc.), and/or the underlying drivers of conflict, themselves hierarchically structured, for example security, governance and economics, subdivided into conflict-specific indicators. Numerous coding approaches have been proposed in the social science literature, ranging from fully automated “machine” coding to human coding. Machine coding is highly error prone, especially for labelling complex drivers, and suffers from extraction of duplicated events, but human coding is expensive, and suffers from inconsistency between annotators; thus hybrid approaches are required. In this paper, we analyse experimentally how human input can most effectively be used in a hybrid system to complement machine coding. Using two newly created real-world datasets, we show that machine learning methods improve on rule-based automated coding for filtering large volumes of input, while human verification of relevant/irrelevant text leads to improved performance of machine learning for predicting multiple labels in the ontology.


title: Adaptively generating high quality fixes for atomicity violations

abstract: It is difficult to fix atomicity violations correctly. Existing gate lock algorithm (GLA) simply inserts gate locks to serialize exe-cutions, which may introduce performance bugs and deadlocks. Synthesized context-aware gate locks (by Grail) require complex source code synthesis. We propose Fixer to adaptively fix ato-micity violations. It firstly analyses the lock acquisitions of an atomicity violation. Then it either adjusts the existing lock scope or inserts a gate lock. The former addresses cases where some locks are used but fail to provide atomic accesses. For the latter, it infers the visibility (being global or a field of a class/struct) of the gate lock such that the lock only protects related accesses. For both cases, Fixer further eliminates new lock orders to avoid introducing deadlocks. Of course, Fixer can produce both kinds of fixes on atomicity violations with locks. The experi-mental results on 15 previously used atomicity violations show that: Fixer correctly fixed all 15 atomicity violations without introducing deadlocks. However, GLA and Grail both intro-duced 5 deadlocks. HFix (that only targets on fixing certain types of atomicity violations) only fixed 2 atomicity violations and introduced 4 deadlocks. Fixer also provides an alternative way to insert gate locks (by inserting gate locks with proper visibility) considering fix acceptance.


title: Smart subtitles for vocabulary learning

abstract: Language learners often use subtitled videos to help them learn. However, standard subtitles are geared more towards comprehension than vocabulary learning, as translations are nonliteral and are provided only for phrases, not vocabulary. This paper presents Smart Subtitles, which are interactive subtitles tailored towards vocabulary learning. Smart Subtitles can be automatically generated from common video sources such as subtitled DVDs. They provide features such as vocabulary definitions on hover, and dialog-based video navigation. In our pilot study with intermediate learners studying Chinese, participants correctly defined over twice as many new words in a post-viewing vocabulary test when they used Smart Subtitles, compared to dual Chinese-English subtitles. Learners spent the same amount of time watching clips with each tool, and enjoyed viewing videos with Smart Subtitles as much as with dual subtitles. Learners understood videos equally well using either tool, as indicated by self-assessments and independent evaluations of their summaries.


title: Controlling a multi-joint arm actuated by pneumatic muscles with quasi-DDP optimal control

abstract: Pneumatic actuators have inherent compliance and hence they are very interesting for applications involving interaction with environment or human. But controlling such kind of actuators is not trivial. The paper presents an implementation of iterative Linear Quadratic regulator (iLQR) based optimal control framework to control an anthropomorphic arm with each joint actuated by an agonist-antagonistic pair of Mckibben artificial muscles. The method is applied to positioning tasks and generation of explosive movements by maximizing the link speed. It is then compared to traditional control strategies to justify that optimal control is effective in controlling the position in highly non-linear pneumatic systems. Also the importance of varying compliance is highlighted by repeating the tasks at different compliance level. The algorithm validation is reported here by several simulations and hardware experiments in which the shoulder and elbow flexion are controlled simultaneously.


title: Substrate Integrated Horn Antenna Loaded With Open Parallel Transitions

abstract: The substrate integrated H-plane horn antenna has been struggling with very limited impedance bandwidth since its invention, which is coming from its natural aperture discontinuity. Apart from a variety of series transitions introduced so far, here, parallel transitions are applied to the antenna aperture in addition to narrow slots on the horn walls around the aperture to widen the antenna impedance bandwidth. It is shown that the depth of the transitions can easily determine the center frequency of the matching bandwidth. The new parallel transitions also reduce the antenna back radiation since they act as chokes around the horn aperture.


title: Hedged Deep Tracking

abstract: In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.


title: Clinical Research Informatics and Electronic Health Record Data

abstract: Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.


title: Intact JAK-STAT signaling pathway is a prerequisite for STAT1 to reinforce the expression of RIG-G gene.

abstract: We previously reported that IRF-9/STAT2 functional interaction could drive the expression of retinoic acid-induced gene G (RIG-G), independently of STAT1 and the classical JAK-STAT pathway, providing a novel alternative pathway for interferons (IFN) to mediate their multiple biological properties. In addition, we also found that IRF-1 could regulate RIG-G induction as well as the expression of IRF-9 and STAT2 in some cases. But the mechanisms by which IRF-1 exerted its action remained to be elucidated. Here, we showed that STAT1 could significantly enhance the effects of the IRF-9/STAT2 complex or IRF-1 on RIG-G induction through an activated JAK-STAT pathway, though it was not essential for RIG-G expression. In STAT1-deficient U3A cells, IRF-1 could induce RIG-G expression via the IFN-stimulated response elements in the RIG-G gene promoter, but it failed to upregulate IRF-9 and STAT2 unless the U3A cells were reconstituted by exogenous STAT1. In STAT1-expressing cells, IRF-1 indirectly activated RIG-G expression through an IRF-9/STAT2-dependent manner. Taken together, we concluded that the expression of RIG-G was independent on the classical JAK-STAT pathway, but could be greatly increased by it. This work will be of great benefit to us for a better understanding of the mechanisms on RIG-G gene expression regulation.


Bachelor ’ s Level Flipped Classroom A Literature Review on the Benefits and Drawbacks of the Reversed Classroom


Public versus Private Blockchains Part 1 : Permissioned Blockchains


title: Parenting stress among mothers of children with different physical, mental, and psychological problems

abstract: Background: Parents of children with developmental problems are always bearing a load of stress. The aim of this study is to compare the stress in mothers of children with different disabilities to each other, considering their demographic background. Materials and Methods: This was a cross-sectional study conducted in Isfahan, Iran during 2012 on 285 mothers of 6-12 years old children with chronic physical disease, psychological disorder, and sensory-motor and mental problems. Abedinâ€™s parenting stress questionnaire was used and obtained data were analyzed using multivariate analysis of variance or covariance as appropriate. ::: Results: Mothers of children with sensory-motor mental and chronic physical problems experience more stress than mothers of children with psychological disorders (P < 0.05). The stress score of mothers of children with psychological disorders was lower than the other two groups. Also there was a significant difference between the score of mothers of children with chronic physical problems and mothers of children with psychological disorders regarding parent-child dysfunctional interaction (P < 0.01). A significant difference was observed in terms of stress among mothers of children with sensory-motor mental problems with different number ::: of children (P < 0.05); also mothers of children with chronic physical problems in different levels of education have experienced different levels of parenting stress (P < 0.05) Conclusion: Due to high level of parenting stress among our studied samples, special education and early intervention are needed for parents in our study population in order to deepening their diagnostic knowledge and professional consultation on stress management. ::: ::: Key words: Developmental disability, mental health, parenting stress, physical chronic problem, psychological disorder


title: Engaging Stakeholders through Twitter: How Nonprofit Organizations are Getting More Out of 140 Characters or Less

abstract: One hundred forty characters seems like too small a space for any meaningful information to be exchanged, but Twitter users have found creative ways to get the most out of each Tweet by using different communication tools. This paper looks into how 73 nonprofit organizations use Twitter to engage stakeholders not only through their tweets, but also through other various communication methods. Specifically, it looks into the organizations’ utilization of tweet frequency, following behavior, hyperlinks, hashtags, public messages, retweets, and multimedia files. After analyzing 4,655 tweets, the study found that the nation’s largest nonprofits are not using Twitter to maximize stakeholder involvement. Instead, they continue to use social media as a one-way communication channel, as less than 20% of their total tweets demonstrate conversations and roughly 16% demonstrate indirect connections to specific users.


title: Optimizing Learning in College: Tips From Cognitive Psychology

abstract: Every fall, thousands of college students begin their first college courses, often in large lecture settings. Many students, even those who work hard, flounder. What should students be doing differently? Drawing on research in cognitive psychology and our experience as educators, we provide suggestions about how students should approach taking a course in college. We discuss time management techniques, identify the ineffective study strategies students often use, and suggest more effective strategies based on research in the lab and the classroom. In particular, we advise students to space their study sessions on a topic and to quiz themselves, as well as using other active learning strategies while reading. Our goal was to provide a framework for students to succeed in college classes.


title: Domain Transfer for Deep Natural Language Generation from Abstract Meaning Representations

abstract: Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%.


title: A Mosaicing Scheme for Pose-Invariant Face Recognition

abstract: Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiproflle face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images.


title: Efficient User Selection for Downlink Zero-Forcing Based Multiuser MIMO Systems

abstract: In a downlink multiuser multiple-input and multiple-output (MU-MIMO) system, a base station (BS) communicates with multiple mobile stations (MS) simultaneously in a given spectrum band. The performance of a MU-MIMO system depends on the choice of user selection, power allocation and precoding schemes. In this paper, we study user selection in Zero-Forcing (ZF) precoding based MU-MIMO systems to maximize the sum rate for high data rate applications. We derive analytical results for simultaneous transmission to two MSs with zero forcing (ZF) precoding, based on which we propose a extended low-complexity algorithm that jointly considers the noise power, the channel gain of the candidate MSs and orthogonality with respect to the selected MSs' channels. Analysis shows that the proposed scheme requires much lower complexity than current schemes. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of higher throughput.


title: High-Efficiency Soft-Switching AC–DC Converter With Single-Power-Conversion Method

abstract: This paper presents a high-efficiency isolated ac–dc converter topology. The proposed converter consists of a full-bridge diode rectifier, an isolated resonant dc–dc converter, and only one controller. The proposed converter provides the soft-switching technique for all components operating at high frequency, allowing for an improvement in power density without a cost of power-conversion efficiency. Furthermore, by using a novel control algorithm that controls both power factor and output power, the converter performs ac–dc power conversion in only a single-power-processing step. These characteristics enable the proposed converter to provide high efficiency, high power density, and a high power factor. A 2-kW prototype was implemented, and its performance and validity were evaluated based on experimental results.


title: Compassion in palliative care: a review

abstract: PURPOSE OF REVIEW ::: Compassion has been recognized as a key aspect of high-quality healthcare, particularly in palliative care. This article provides a general review of the current understanding of compassion in palliative care and summarizes emergent compassionate initiatives in palliative care at three interdependent levels: compassion for patients, compassion in healthcare professionals, and compassionate communities at the end of life. ::: ::: ::: RECENT FINDINGS ::: Compassion is a constructive response to suffering that enhances treatment outcomes, fosters the dignity of the recipient, and provides self-care for the giver. Patients and healthcare professionals value compassion and perceive a general lack of compassion in healthcare systems. Compassion for patients and for professionals' self-care can be trained and implemented top-down (institutional policies) and bottom-up (compassion training). 'Compassionate communities' is an important emerging movement that complements regular healthcare and social services with a community-level approach to offer compassionate care for people at the end of life. ::: ::: ::: SUMMARY ::: Compassion can be enhanced through diverse methodologies at the organizational, professional, and community levels. This enhancement of compassion has the potential to improve quality of palliative care treatments, enhance healthcare providers' satisfaction, and reduce healthcare costs.


title: Discovering General-Purpose Active Learning Strategies

abstract: We propose a general-purpose approach to discovering active learning (AL) strategies from data. These strategies are transferable from one domain to another and can be used in conjunction with many machine learning models. To this end, we formalize the annotation process as a Markov decision process, design universal state and action spaces and introduce a new reward function that precisely model the AL objective of minimizing the annotation cost. We seek to find an optimal (non-myopic) AL strategy using reinforcement learning. We evaluate the learned strategies on multiple unrelated domains and show that they consistently outperform state-of-the-art baselines.


title: Experimental electrostatic separator for charged particles of plastic mixture

abstract: Electrostatic separation of triboelectrically charged plastic particles is investigated and verified on an experimental device. After presenting the fundamentals of the process in the introductory part, attention is paid to the stucture of the device and functions of its particular elements. The last part of the paper discusses the results of experiments aimed at separation of a mixture consisting of acrylonitrile butadiene styrene (ABS) and thermoplastic copolyester (TPC). Measured are various parameters of the process such as deflections of individual kinds of particles in electric field, efficiency of separation and purity of particular levels to be separated.


title: Application of 2D MUSIC algorithm to range-azimuth FMCW radar data

abstract: This paper investigates the use of a 2D MUSIC algorithm for the joint estimation of angular and range target locations. Coherency and correlation characteristics of the target signals are taken into account and 2D spatial smoothing technique is preliminarily applied in order to ensure the non-singularity of the received signals covariance matrix. A comparison between the performances offered by conventional digital beam forming methods and the presented 2D MUSIC, in multiple target scenarios, is illustrated. Simulated frequency modulated continuous wave (FMCW) radar data are used for the analysis of the implemented techniques.


title: Source Code Interaction on Touchscreens

abstract: Direct interaction with touchscreens has become a primary way of using a device. This work seeks to devise interaction methods for editing textual source code on touch-enabled devices. With the advent of the “Post-PC Era”, touch-centric interaction has received considerable attention in both research and development. However, various limitations have impeded widespread adoption of programming environments on modern platforms. Previous attempts have mainly been successful by simplifying or constraining conventional programming but have only insufficiently supported source code written in mainstream programming languages. This work includes the design, development, and evaluation of techniques for editing, selecting, and creating source code on touchscreens. The results contribute to text editing and entry methods by taking the syntax and structure of programming languages into account while exploiting the advantages of gesture-driven control. Furthermore, this work presents the design and software architecture of a mobile development environment incorporating touch-enabled modules for typical software development tasks.


title: A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis

abstract: Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.


title: High Power Microstrip GaN-HEMT Switches for Microwave Applications

abstract: In this paper the design, fabrication and test of X-band and 2-18 GHz wideband high power SPDT MMIC switches in microstrip GaN technology are presented. Such switches have demonstrated state-of-the-art performances. In particular the X-band switch exhibits 1 dB insertion loss, better than 37 dB isolation and a power handling capability at 9 GHz of better than 39 dBm at 1 dB insertion loss compression point; the wideband switch has an insertion loss lower than 2.2 dB, better than 25 dB isolation and a power handling capability of better than 38 dBm in the entire bandwidth.


title: Vocal quality factors: Analysis, synthesis, and perception

abstract: The purpose of this study was to examine several factors of vocal quality that might be affected by changes in vocal fold vibratory patterns. Four voice types were examined: modal, vocal fry, falsetto, and breathy. Three categories of analysis techniques were developed to extract source-related features from speech and electroglottographic (EGG) signals. Four factors were found to be important for characterizing the glottal excitations for the four voice types: the glottal pulse width, the glottal pulse skewness, the abruptness of glottal closure, and the turbulent noise component. The significance of these factors for voice synthesis was studied and a new voice source model that accounted for certain physiological aspects of vocal fold motion was developed and tested using speech synthesis. Perceptual listening tests were conducted to evaluate the auditory effects of the source model parameters upon synthesized speech. The effects of the spectral slope of the source excitation, the shape of the glottal excitation pulse, and the characteristics of the turbulent noise source were considered. Applications for these research results include synthesis of natural sounding speech, synthesis and modeling of vocal disorders, and the development of speaker independent (or adaptive) speech recognition systems.


title: Speed profile generation based on quintic Bézier curves for enhanced passenger comfort

abstract: Automated ground vehicles are becoming a reality for future deployment due to their potential improvement of safety, comfort or emission reductions. However, some challenges remain unsolved such as navigation in dynamic urban environments, where safety and comfort are paramount. In this paper, a novel speed profile generator, based on quintic Bezier curves, is presented. This approach permits to improve the comfort in automated vehicles by constraining the global acceleration in the whole ride. Moreover, a comparison with a Jerk limitation algorithm for continuous acceleration profile and a smooth speed profile is performed. The quintic Bezier curves assure continuity of velocity curves and therefore a smooth Jerk and acceleration profiles. The ISO 2631-1 standard, for evaluation of human exposure to whole-body vibration, is considered in both methods for lateral and longitudinal comfort constraints. Simulations show that this approach improves comfort by smoothing the acceleration and jerk profiles, while also constraining the total acceleration perceived by the passengers.


title: Building Relation Extraction Templates via Unsupervised Learning

abstract: The vast amount of text published daily over the internet pose an opportunity to build unsupervised text mining models with a better or a comparable performance than existing models. In this paper, we investigate the problem of relation extraction and generation from text using an unsupervised model learned from news published online. We propose a clustering-based method to build a dataset of relations examples. News articles are clustered and once a cluster of sentences for each event in each piece of news is formed, relations between important entities in each event cluster are extracted and considered as examples of relations. Relations examples are used to build extraction templates in order to extract and generate readable relations summaries from new instances of news. The proposed unsupervised relation extraction and generation method is evaluated against multiple methods for relation extraction over different datasets where the proposed method has shown a comparable performance.


title: An Ontological Approach to Creating an Andean Weaving Knowledge Base

abstract: Andean textiles are products of one of the richest, oldest and continuous weaving traditions in the world. Understanding the knowledge and practice of textile production as a form of cultural heritage is particularly relevant in the Andean context due to erosion of clothing traditions, reuse of traditional textiles on commodities targeted at the tourism market, and loss of knowledge embedded in textile production. “Weaving Communities of Practice” was a pilot project that aimed to create a knowledge base of Andean weaving designed to contribute to curatorial practice and heritage policy. The research team gathered data on the chain of activities, instruments, resources, peoples, places and knowledge involved in the production of textiles, relating to over 700 textile samples. A major part of the project has been the modelling and representation of the knowledge of domain experts and information about the textile objects themselves in the form of an OWL ontology, and the development of a suite of search facilities to be supported by the ontology. This paper describes the research challenges faced in developing the ontology and search facilities, the methodology adopted, the design and implementation of the system, and the design and outcomes of a user evaluation of the system undertaken with a group of domain experts.


title: Predicting Acute Appendicitis? A comparison of the Alvarado Score, the Appendicitis Inflammatory Response Score and Clinical Assessment

abstract: BACKGROUND ::: Patients presenting with suspected appendicitis pose a diagnostic challenge. The appendicitis inflammatory response (AIR) score has outperformed the Alvarado score in two retrospective studies. The aim of this study was to evaluate the AIR Score and compare its performance in predicting risk of appendicitis to both the Alvarado score and the clinical impression of a senior surgeon. ::: ::: ::: METHODS ::: All parameters included in the AIR and Alvarado scores as well as the initial clinical impression of a senior surgeon were prospectively recorded on patients referred to the surgical on call team with acute right iliac fossa pain over a 6-month period. Predictions were correlated with the final diagnosis of appendicitis. ::: ::: ::: RESULTS ::: Appendicitis was the final diagnosis in 67 of 182 patients (37 %). The three methods of assessment stratified similar proportions (~40 %) of patients to a low probability of appendicitis (p = 0.233) with a false negative rate of <8 % that did not differ between the AIR score, Alvarado score or clinical assessment. The AIR score assigned a smaller proportion of patients to the high probability zone than the Alvarado score (14 vs. 45 %) but it did so with a substantially higher specificity (97 %) and positive predictive value (88 %) than the Alvarado score (76 and 65 %, respectively). ::: ::: ::: CONCLUSIONS ::: The AIR score is accurate at excluding appendicitis in those deemed low risk and more accurate at predicting appendicitis than the Alvarado score in those deemed high risk. Its use as the basis for selective CT imaging in those deemed medium risk should be considered.


On-Line Q-Learning Using Connectionist Systems


title: Estimating fetal age: computer-assisted analysis of multiple fetal growth parameters.

abstract: Regression models for predicting menstrual age based on real-time sonographic measurements of four fetal parameters (biparietal diameter, head circumference, abdominal circumference, and femur length), used alone and in combination, were developed in a cross-sectional study of 361 fetuses between 14 and 42 menstrual weeks. The head circumference and femur length were the strongest individual predictors of age. A number of combinations of fetal parameters, including the combination of head circumference and femur length, provided age estimates that were significantly better (p = 0.05) than those using any single parameter alone. It was also demonstrated that simply averaging individual age estimates in a given case could provide results that were not significantly different from those obtained by using the same parameters in a complex regression equation. The advantages and potential pitfalls of this system of fetal dating are discussed.


title: Generating optimal topologies in structural design using a homogenization method

abstract: Optimal shape design of structural elements based on boundary variations results in final designs that are topologically equivalent to the initial choice of design, and general, stable computational schemes for this approach often require some kind of remeshing of the finite element approximation of the analysis problem. This paper presents a methodology for optimal shape design where both these drawbacks can be avoided. The method is related to modern production techniques and consists of computing the optimal distribution in space of an anisotropic material that is constructed by introducing an infimum of periodically distributed small holes in a given homogeneous, i~otropic material, with the requirement that the resulting structure can carry the given loads as well as satisfy other design requirements. The computation of effective material properties for the anisotropic material is carried out using the method of homogenization. Computational results are presented and compared with results obtained by boundary variations.


Alterations in semen parameters in men wıth epilepsy treated with valproate


title: Estimating the Randomness of Domain Names for DGA Bot Callbacks

abstract: Bots are some of the most serious threats. Although the administrators must remove bots that reside in their networks, many bots have domain generation algorithms (DGAs) to avoid detection. A DGA is a technique for hiding the callbacks of bots by frequently changing the domain name of a command-and-control server. In this letter, we propose an approach for detecting the callbacks based on the randomness of domain names. Our evaluation indicates high performance, with a recall of 0.9960 and a precision of 0.9029. By enabling one to swiftly address various bots, our approach contributes to dramatically improving network security.


title: Spelling Error Trends and Patterns in Sindhi

abstract: Statistical error Correction technique is the most accurate and widely used approach today, but for a language like Sindhi which is a low resourced language the trained corpora’s are not available, so the statistical techniques are not possible at all. Instead a useful alternative would be to exploit various spelling error trends in Sindhi by using a Rule based approach. For designing such technique an essential prerequisite would be to study the various error patterns in a language. This paper presents various studies of spelling error trends and their types in Sindhi Language. The research shows that the error trends common to all languages are also encountered in Sindhi but their do exist some error patters that are catered specifically to a Sindhi language.


title: A circular polarization reconfigurable substrate integrated waveguide antenna

abstract: In this paper, a cavity-backed substrate integrated waveguide (SIW) antenna with two reconfigurable circular polarizations is proposed and investigated. The circular polarizations are generated by a combination of complementary square split ring on the top layer and a shorting via between the top layer and the ground plane which introduces current perturbation. The polarization states of the proposed antenna can be dynamically switched between left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) by selectively connecting one of the two symmetrical shorting vias to the bottom ground plane. To validate our design, the proposed antenna is fabricated and measured. The simulated and experimental results are observed, which illustrates good performance of the proposed antenna. This single-port antenna polarization reconfigurable is useful for the design of array antenna for wireless applications.


title: Context-aware Recommendation of Visualization Components

abstract: Although many valuable visualizations have been developed to gain insights from large data sets, selecting an appropriate visualization for a specific data set and goal remains challenging for non-experts. In this paper, we propose a novel approach for knowledge-assisted, context-aware visualization recommendation. Both semantic web data and visualization components are annotated with formalized visualization knowledge from an ontology. We present a recommendation algorithm that leverages those annotations to provide visualization components that support the users’ data and task. We successfully proved the practicability of our approach by integrating it into two research prototypes. Keywords-recommendation, visualization, ontology, mashup


title: CPR: Composable performance regression for scalable multiprocessor models

abstract: Uniprocessor simulators track resource utilization cycle by cycle to estimate performance. Multiprocessor simulators, however, must account for synchronization events that increase the cost of every cycle simulated and shared resource contention that increases the total number of cycles simulated. These effects cause multiprocessor simulation times to scale superlinearly with the number of cores. Composable performance regression (CPR) fundamentally addresses these intractable multiprocessor simulation times, estimating multiprocessor performance with a combination of uniprocessor, contention, and penalty models. The uniprocessor model predicts baseline performance of each core while the contention models predict interfering accesses from other cores. Uniprocessor and contention model outputs are composed by a penalty model to produce the final multiprocessor performance estimate. Trained with a production quality simulator, CPR is accurate with median errors of 6.63, 4.83 percent for dual-, quad-core multiprocessors. Furthermore, composable regression is scalable, requiring 0.33× the simulations required by prior regression strategies.


title: Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas

abstract: Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer’s and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.


title: Comparison of 18F-FACBC and 11C-choline PET/CT in patients with radically treated prostate cancer and biochemical relapse: preliminary results

abstract: Purpose ::: We assessed the rate of detection rate of recurrent prostate cancer by PET/CT using anti-3-18F-FACBC, a new synthetic amino acid, in comparison to that using 11C-choline as part of an ongoing prospective single-centre study.


title: Low-Cost and High-Efficient W-Band Substrate Integrated Waveguide Antenna Array Made of Printed Circuit Board Process

abstract: A novel class of low-cost, small-footprint and high-gain antenna arrays is presented for W-band applications. A 4 × 4 antenna array is proposed and demonstrated using substrate-integrated waveguide (SIW) technology for the design of its feed network and longitudinal slots in the SIW top metallic surface to drive the array antenna elements. Dielectric cubes of low-permittivity material are placed on top of each 1 × 4 antenna array to increase the gain of the circular patch antenna elements. This new design is compared to a second 4 × 4 antenna array which, instead of dielectric cubes, uses vertically stacked Yagi-like parasitic director elements to increase the gain. Measured impedance bandwidths of the two 4 × 4 antenna arrays are about 7.5 GHz (94.2-101.8 GHz) at 18 ± 1 dB gain level, with radiation patterns and gains of the two arrays remaining nearly constant over this bandwidth. While the fabrication effort of the new array involving dielectric cubes is significantly reduced, its measured radiation efficiency of 81 percent is slightly lower compared to 90 percent of the Yagi-like design.


title: Artificial intelligence in medicine.

abstract: Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.


title: Participatory Design of a Pediatric Virtual Patient Creation Tool

abstract: Virtual patients are computer simulations that behave in the same way that an actual patient would in a medical context. Since these characters are simulated, they can provide realistic yet repetitive practice in patient interaction since they can represent a wide range of patients and each scenario can be practiced until the student achieves competency. However, the development costs for virtual patients are high, since creation of a single scenario may take up to nine months. In this work, we present a virtual patient platform that reduces development costs. The SIDNIE (Scaffolded Interviews Developed by Nurses in Education) system can adapt a single scenario to multiple levels of learners and supports the selection of multiple learning goals. We report on the participatory design of a scenario-builder tool that enables nursing faculty to create their own scenarios for SIDNIE, without the aid of a computer scientist.


BREATH ACETONE-BASED NON-INVASIVE DETECTION OF BLOOD GLUCOSE LEVELS


title: Simple miniaturized Wilkinson power divider using a compact stub structure

abstract: The paper presents a study on the miniaturization of microwave power dividers/ combiners. The authors propose a modified two-way Wilkinson power divider with dimensions of 33×24mm, twice reduced compared to the standard structure (65×24mm). This new structure has been designed based on an "artificial transmission line" consisting of a microstrip line and two shunt capacitors at its edges where the capacitors are replaced by two open-ended stubs located inside the circuit loop and do not take any additional space. The power divider has good performance at the designed frequency 900 MHz and its bandwidth of over 1 GHz is sufficient for various applications. The simulation has been performed in both Agilent ADS/ Momentum and CST Microwave Studio and good agreement of the results obtained in these design tools was obtained. Two prototypes have been fabricated and tested, and the measurement results fit well with the simulation. This approach can be applied to design other microwave circuits where quarter-wavelength transmission lines are required, and the modified Wilkinson power divider can be used for various applications such as measurement systems or in antenna array feeding circuits.


title: Skin Mapping With Punch Biopsies for Defining Margins in Melanoma: When You Don’t Know How Far to Go

abstract: BACKGROUND ::: Wide local excision of primary cutaneous melanoma usually provides clear margins and excellent local control. Nonclassical presentations of cutaneous melanoma, however, can challenge this treatment algorithm. Specifically, persistent melanoma-in-situ (MIS) at the margin not suspected clinically makes planning definitive wide local excision more difficult. We hypothesized that the use of punch biopsies as a mapping tool would allow us to obtain clear margins in these challenging cases. ::: ::: ::: METHODS ::: Punch biopsies were performed at sites 1 to 2 cm from prior positive margins. Subsequent wide local excision was planned, including all punch biopsy sites with positive findings: atypical melanocytic hyperplasia, MIS, or invasive melanoma. The management of three patients was documented prospectively. Standard surgical techniques were used independent of an experimental protocol. Medical records were reviewed, and data were summarized under institutional review board protocol HIC 10803. ::: ::: ::: RESULTS ::: The results of punch biopsies identified invasive melanoma, MIS, or atypical melanocytic hyperplasia in all three patients with MIS at the margins. All three mapping procedures were well tolerated and resulted in resection with negative margins in a single definitive resection. ::: ::: ::: CONCLUSION ::: Melanoma mapping with punch biopsy technique allows for definitive excision in cases when disease persists at the margins of the reexcision or in cases with unclear clinical examinations.


title: A low-voltage, low quiescent current, low drop-out regulator

abstract: The demand for low-voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDO's are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery-operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDO's at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 /spl mu/A. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V, respectively.


title: Feeling Love and Doing More for Distant Others: Specific Positive Emotions Differentially Affect Prosocial Consumption

abstract: Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love's effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion— is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other co...


title: Stochastic Dynamics for Video Infilling

abstract: In this paper, we introduce a stochastic generation framework (SDVI) to infill long intervals in video sequences. To enhance the temporal resolution, video interpolation aims to produce transitional frames for a short interval between every two frames. Video Infilling, however, aims to complete long intervals in a video sequence. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation to guarantee the spatial-temporal coherency among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear and varied sequences. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the referenced start frame to the end frame.


title: A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning

abstract: The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.


title: Filtering and Performance Evaluation for Restoration of Grayscale Image Corrupted by Salt & Pepper Noise Using Low Pass Filtering Schemes

abstract: In this paper, different novel low pass filtering schemes are investigated to restore a gray scale image corrupted by Salt & Pepper noise. The filtering methods with different window sizes are applied to corrupted images with varying strengths and different noise probability. We have proposed both subjective and objective methods to evaluate the performance of different filtering techniques for noise removal with an aim to find an efficient filter which will be suitable for real time image restoration applications. Subjective methods have been performed by visually comparing the different restoration characteristics in the output image. The mean square error, signal to noise ratio, peak signal to noise ratio and signal to noise ratio improvement have been used for objective evaluation of the different filtering schemes. Finally, to validate the efficiency of filtering schemes, simulation study has been carried out using MATLAB 5.0.


title: Modeling Computer Attacks: An Ontology for Intrusion Detection

abstract: We state the benefits of transitioning from taxonomies to ontologies and ontology specification languages, which are able to simultaneously serve as recognition, reporting and correlation languages. We have produced an ontology specifying a model of computer attack using the DARPA Agent Markup Language+Ontology Inference Layer, a descriptive logic language. The ontology’s logic is implemented using DAMLJessKB. We compare and contrast the IETF’s IDMEF, an emerging standard that uses XML to define its data model, with a data model constructed using DAML+OIL. In our research we focus on low level kernel attributes at the process, system and network levels, to serve as those taxonomic characteristics. We illustrate the benefits of utilizing an ontology by presenting use case scenarios within a distributed intrusion detection system.


title: Secure and efficient user authentication scheme for multi-gateway wireless sensor networks

abstract: By utilizing Internet of Things (IoT), the collected information from the sensor nodes in wireless sensor networks (WSNs) could be provided to the users who are permitted to get access of sensor nodes. As the information from the sensors are transmitted via public network and sensor nodes have limited battery, shift the focus on security and efficiency in WSNs. User authentication is the security task for limiting the access. It is achieved by equipping authorized users with passwords, tokens or biometrics. However, password and token are easy being stolen and forgotten; even biometrics inherit some limitation. More suitable approach is to combine both password and biometric authenticator to harvest benefits in security. This paper proposes a novel authentication and key agreement scheme for WSNs using biohashing. Biohashing facilitates elimination of false accept rates without increasing occurrence of false rejection rate. Additionally, biohashing has highly clear separation of imposter populations and genuine, and zero equal error rate level. The proposed scheme also supports dynamic node addition and user friendly password change mechanism. Using the BAN-logic, we prove that the proposed scheme provides mutual authentication. In addition, we simulate proposed scheme for the security against man-in-the middle attack and replay attack using the AVISPA tool, and the simulation results show that our scheme is safe. Through the informal security analysis, we show that the proposed scheme is secure against the known attacks for authentication protocols.


title: Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

abstract: Suppose we are given a vector $f$ in $\R^N$. How many linear measurements do we need to make about $f$ to be able to recover $f$ to within precision $\epsilon$ in the Euclidean ($\ell_2$) metric? Or more exactly, suppose we are interested in a class ${\cal F}$ of such objects--discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy $\epsilon$? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal $f \in {\cal F}$ decay like a power-law (or if the coefficient sequence of $f$ in a fixed basis decays like a power-law), then it is possible to reconstruct $f$ to within very high accuracy from a small number of random measurements.


title: Model-based User Interface Testing With Spec Explorer and ConcurTaskTrees

abstract: Analytic usability analysis methods have been proposed as an alternative to user testing in early phases of development due to the cost of the latter approach. By working with models of the systems, analytic models are not capable of identifying implementation related problems that might have an impact on usability. Model-based testing enables the testing of an implemented software artefact against a model of what it should be (the oracle). In the case of model-based user interface testing, the models should be expressed at an adequate level of abstraction, adequately modelling the interaction process. This paper describes an effort to develop tool support enabling the use of task models as oracles for model-based testing of user interfaces.


title: Capacity limits of LoRaWAN technology for smart metering applications

abstract: In this paper we analyze and discuss the capacity limits of LoRaWAN technology for smart metering applications using a real LoRa network simulator including uplink and downlink. First, we present the modeling of the network simulator. Then results for a critical case in the center of Paris show that, with meters all located deep indoor, the capacity of a network covering a dense urban area of 17 km2 with 19 gateways distant from 1 km, considering only UL emissions, could be enough for an average targeted QoS of 98 % as soon as there is no other traffic in the considered band. But we also show that we could very quickly reach the limits when considering that meters will not be the only LoRa devices communicating and above all considering the important impact of DL emissions on capacity.


title: Learning Distributed Representations of Texts and Entities from Knowledge Base

abstract: We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.


title: Domain Adaptation via Maximum Independence of Domain Features

abstract: When the distributions of the source and the target domains are different, domain adaptation techniques are needed. For example, in the field of sensors and measurement, discrete and continuous distributional change often exist in data because of instrumental variation and time-varying sensor drift. In this paper, we propose maximum independence domain adaptation (MIDA) to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time. Then, MIDA learns features which have maximal independence with the domain features, so as to reduce the inter-domain discrepancy in distributions. A feature augmentation strategy is designed so that the learned projection is background-specific. Semi-supervised MIDA (SMIDA) extends MIDA by exploiting the label information. The proposed methods can handle not only discrete domains in traditional domain adaptation problems but also continuous distributional change such as the time-varying drift. In addition, they are naturally applicable in supervised/semi-supervised/unsupervised classification or regression problems with multiple domains. This flexibility brings potential for a wide range of applications. The effectiveness of our approaches is verified by experiments on synthetic datasets and four real-world ones on sensors, measurement, and computer vision.


title: PREDICTION SYSTEM FOR HEART DISEASE USING NAIVE BAYES

abstract: The main objective of this research is to develop a n Intelligent System using data mining modeling tec hnique, namely, Naive Bayes. It is implemented as web based application in this user answers the predefined questions. It retrieves hidden data from stored database and compares the user values with trained data set. It can answer com plex queries for diagnosing heart disease and thus assist healthcare practitioners to make intelligent clinical decisio ns which traditional decision support systems cannot. By providing effec tive treatments, it also helps to reduce treatment costs.


title: Shape and Source from Shading

abstract: Display arrangements useful as art forms and useful in advertising applications can be constructed by coating areas of the surface of a support with differently colored compositions, each of which is capable of appearing as being of one color at one temperature and as being of another color at a different temperature. The compositions should be differently colored at at least one temperature. The color-temperature effects achieved are obtained through the use of what are termed reversible thermochromic compounds or compositions. Elements may be provided on the support for regulating the temperatures of the various areas.


Thinking, Fast and Slow, by D. Kahneman


title: Restful Design and Implementation of Smart Appliances for Smart Home

abstract: The world is changing and we are moving towards the day when everything will be network accessible, named 'Internet of Things (IoT)'. The growing popularity of IoT leads various services such as healthcare, connected car, smart education and smart home services. Among these services, smart home becomes commercialized most rapidly. We developed the Smart Home Protocol (SHP) to enable connectivity between various our smart home devices including smart appliances, LED bulbs, IP cameras, etc. In this paper, we present an overview of the SHP based on Restful architecture, followed by a detailed description of architecture and application profile for smart home devices. We also introduce our smart home services and describe the implementation of smart home protocol in smart appliances. To our understanding, this study is important, since it is a basis for enabling connectivity between our smart home products as well as those from other device and appliance manufacturers. We believe this work plays an important role for designing and implementing smart home devices such as smart appliances.


title: Adaptation and Re-identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-identification

abstract: Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.


title: Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

abstract: Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them. In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space. We describe an effective learning scheme, capable of tackling large-scale problems, and validate it on the Recipe1M dataset containing nearly 1 million picture-recipe pairs. We show the effectiveness of our approach regarding previous state-of-the-art models and present qualitative results over computational cooking use cases.


Sub-character Neural Language Modelling in Japanese Viet


title: Distraction osteogenesis in the surgical treatment of craniostenosis: a comparison of internal and external craniofacial distractor devices

abstract: Purpose ::: To compare the practical difficulties of external and internal craniofacial distraction devices used in craniofacial advancement in terms of technical surgical limitations and patients’ discomfort.


title: Designing A Framework for the Development of Domain-Specific Process Modelling Languages

abstract: Domain-specific process modelling has gained increased attention, since traditional modelling languages struggle to meet the demands of highly specialized businesses. However, methodological support on the development of such domain-specific languages is still scarce, which hampers the specification of adequate modelling support. To this end, the paper applies a design-oriented research approach to create an integrated framework that facilitates the development of domain-specific process modeling languages. The framework is a result of 23 consolidated requirements from relevant literature and contains essential building blocks that need to be considered during the development process. It is demonstrated that the framework satisfies the identified requirements by structuring and systematizing the development of domain-specific languages, which increases language adequacy and quality.


title: Birds, primates, and spoken language origins: behavioral phenotypes and neurobiological substrates

abstract: Vocal learners such as humans and songbirds can learn to produce elaborate patterns of structurally organized vocalizations, whereas many other vertebrates such as non-human primates and most other bird groups either cannot or do so to a very limited degree. To explain the similarities among humans and vocal-learning birds and the differences with other species, various theories have been proposed. One set of theories are motor theories, which underscore the role of the motor system as an evolutionary substrate for vocal production learning. For instance, the motor theory of speech and song perception proposes enhanced auditory perceptual learning of speech in humans and song in birds, which suggests a considerable level of neurobiological specialization. Another, a motor theory of vocal learning origin, proposes that the brain pathways that control the learning and production of song and speech were derived from adjacent motor brain pathways. Another set of theories are cognitive theories, which address the interface between cognition and the auditory-vocal domains to support language learning in humans. Here we critically review the behavioral and neurobiological evidence for parallels and differences between the so-called vocal learners and vocal non-learners in the context of motor and cognitive theories. In doing so, we note that behaviorally vocal-production learning abilities are more distributed than categorical, as are the auditory-learning abilities of animals. We propose testable hypotheses on the extent of the specializations and cross-species correspondences suggested by motor and cognitive theories. We believe that determining how spoken language evolved is likely to become clearer with concerted efforts in testing comparative data from many non-human animal species.


title: Product type and consumers' perception of online consumer reviews

abstract: Consumers hesitate to buy experience products online because it is hard to get enough information about experience products via the Internet. Online consumer reviews may change that, as they offer consumers indirect experiences about dominant attributes of experience products, transforming them into search products. When consumers are exposed to an online consumer review, it should be noted that there are different kinds of review sources. This study investigates the effects of review source and product type on consumers’ perception of a review. The result of the online experiment suggests that product type can moderate consumers’ perceived credibility of a review from different review sources, and the major findings are: (1) consumers are more influenced by a review for an experience product than for a search product when the review comes from consumer-developed review sites, and (2) a review from an online community is perceived to be the most credible for consumers seeking information about an experience product. The findings provide managerial implications for marketers as to how they can better manage online consumer reviews.


Enhancing Intrinsic Motivation Through The Use of a Token Economy