title: Micro artificial muscle fiber using NiTi spring for soft robotics

abstract: For a new class of soft robotic platforms, development of flexible and robust actuators is quintessential. Remarkable resilience, shape memory effect, high energy density, and scalability are attributed to nickel titanium (NiTi) making it an excellent actuator candidate for meso-scale applications. This paper presents a micro-muscle fiber crafted from shape memory alloy (NiTi) coiled springs. An enhanced spring NiTi model describes the combination of martensite deformation and spring effect due to its geometry. This paper also describes a manufacturing process and characterization for micro-scale NiTi coil actuators in various annealing temperatures. The presented fiber is 400µm in diameter and 0.5m in length exhibiting 50% contraction and 1226J/kg of energy density with 40g of force. By changing the geometry of the spring, force-displacement characteristics can be tuned. An enhanced-performance inverted-spring manufacturing method is also described and characterized. A method of discrete displacement control is presented. Taking advantage of the flexibility of micro-coil spring, we present a novel mesh-worm prototype that utilizes bio-inspired antagonistic actuation for its body deformation and locomotion.


title: A Unified Approach to Threat Assessment and Control for Automotive Active Safety

abstract: This paper presents the design of a novel active safety system preventing unintended roadway departures. The proposed framework unifies threat assessment, stability, and control of passenger vehicles into a single combined optimization problem. A nonlinear model predictive control (MPC) problem is formulated, where nonlinear vehicle dynamics, in closed-loop with a driver model, is used to optimize the steering and braking actions needed to keep the driver safe. A model of the driver's nominal behavior is estimated based on his observed behavior. The driver commands the vehicle, whereas the safety system corrects the driver's steering and braking actions in case there is a risk that the vehicle will unintentionally depart from the road. The resulting predictive controller is always active, and mode switching is not necessary. We show simulation results detailing the behavior of the proposed controller and experimental results obtained by implementing the proposed framework on embedded hardware in a passenger vehicle. The results demonstrate the capability of the proposed controller to detect and avoid roadway departures while avoiding unnecessary interventions.


Application of Machine Learning in Knowledge Discovery for Pharmaceutical Drug-drug Interactions


title: Gastric pepsin and acid secretion in patients with acute and healed duodenal ulcer.

abstract: The relationship between gastric pepsin, acid secretion, and duodenal ulcer activity was studied in 33 patients with an endoscopically proven carter, in 17 patients who were studied within 3 mo of an acute crater but who no longer had a crater endoscopically, and in 10 patients who had no duodenal ulcer symptoms for longer than 3 mo after a crater and who had negative endoscopy. There were an additional 11 patients who had paired studies in the early-healed and late-healed stages of their disease. There were no significant differences between the values obtained in the paired and nonpaired group. Basal and pentagastrin-stimulated outputs of both pepsin and acid were significantly higher (p less than 0.001) when an acute crater was present than in 23 control subjects. At 3 mo or more after healing there was a significant (p less than 0.001) fall in both basal and stimulated pepsin and acid output which became insignificantly different from that of controls. In 104 basal studies the ratio of pepsin to acid secretion was 33,000 +/- 2500 (mean +/- SE) pepsin units/mmol H+ with no difference between any groups including controls. In 102 stimulated studies the ratio was 13,600 +/- 400 PU/mmol H+, also with no differences between any groups. Although data in the literature are conflicting, these results support a direct relationship between the activity of duodenal ulcer disease and acid-pepsin secretion.


title: A Deep Learning Approach to the Prediction of Short-term Traffic Accident Risk

abstract: With the rapid development of urbanization, the boom of vehicle numbers has resulted in serious traffic accidents, which led to casualties and huge economic losses. The ability to predict the risk of traffic accident is important in the prevention of the occurrence of accidents and to reduce the damages caused by accidents in a proactive way. However, traffic accident risk prediction with high spatiotemporal resolution is difficult, mainly due to the complex traffic environment, human behavior, and lack of real-time traffic-related data. In this study, we collected heterogeneous traffic-related data, including traffic accident, traffic flow, weather condition and air pollution from the same city; proposed a deep learning model based on recurrent neural network toward a prediction of traffic accident risk. The predictive accident risk can be potential applied to the traffic accident warning system. We ranked the predictive power of various factors considered in our model through the method of Granger causality analysis, and established the order of predictive power as traffic flow > traffic accident > geographical position >> weather + air quality + holiday + time period, which indicate that traffic flow is the most essential factor for the occurrence of traffic accidents. The proposed method can be integrated into an intelligent traffic control system toward a more reasonable traffic prediction and command organization.


An Embedded Declarative Language for Hierarchical Object Structure Traversal


title: Discovering Diverse and Salient Threads in Document Collections

abstract: We propose a novel probabilistic technique for modeling and extracting salient structure from large document collections. As in clustering and topic modeling, our goal is to provide an organizing perspective into otherwise overwhelming amounts of information. We are particularly interested in revealing and exploiting relationships between documents. To this end, we focus on extracting diverse sets of threads---singly-linked, coherent chains of important documents. To illustrate, we extract research threads from citation graphs and construct timelines from news articles. Our method is highly scalable, running on a corpus of over 30 million words in about four minutes, more than 75 times faster than a dynamic topic model. Finally, the results from our model more closely resemble human news summaries according to several metrics and are also preferred by human judges.


title: Medical messages in the media - barriers and solutions to improving medical journalism

abstract: Context Medical issues are widely reported in the mass media. These reports influence the general public, policy makers and health-care professionals. This information should be valid, but is often criticized for being speculative, inaccurate and misleading. An understanding of the obstacles medical reporters meet in their work can guide strategies for improving the informative value of medical journalism. ::: ::: ::: ::: Objective To investigate constraints on improving the informative value of medical reports in the mass media and elucidate possible strategies for addressing these. ::: ::: ::: ::: Design We reviewed the literature and organized focus groups, a survey of medical journalists in 37 countries, and semi-structured telephone interviews. ::: ::: ::: ::: Results We identified nine barriers to improving the informative value of medical journalism: lack of time, space and knowledge; competition for space and audience; difficulties with terminology; problems finding and using sources; problems with editors and commercialism. Lack of time, space and knowledge were the most common obstacles. The importance of different obstacles varied with the type of media and experience. Many health reporters feel that it is difficult to find independent experts willing to assist journalists, and also think that editors need more education in critical appraisal of medical news. Almost all of the respondents agreed that the informative value of their reporting is important. Nearly everyone wanted access to short, reliable and up-to-date background information on various topics available on the Internet. A majority (79%) was interested in participating in a trial to evaluate strategies to overcome identified constraints. ::: ::: ::: ::: Conclusion Medical journalists agree that the validity of medical reporting in the mass media is important. A majority acknowledge many constraints. Mutual efforts of health-care professionals and journalists employing a variety of strategies will be needed to address these constraints.


title: Energy aware resource allocation of cloud data center: review and open issues

abstract: The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.


title: Random forest methodology for model-based recursive partitioning: the mobForest package for R

abstract: BackgroundRecursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data.ResultsHere we present an R package called “mobForest” that implements bagging and random forests methodology for model-based recursive partitioning. The mobForest package constructs large number of model-based trees and the predictions are aggregated across these trees resulting in more stable predictions. The package also includes functions for computing predictive accuracy estimates and plots, residuals plot, and variable importance plot.ConclusionThe mobForest package implements a random forest type approach for model-based recursive partitioning. The R package along with it source code is available at http://CRAN.R-project.org/package=mobForest.


title: Large-scale cortical synchronization promotes multisensory processing: An EEG study of visual-tactile pattern matching

abstract: The integration of sensory signals from different modalities requires flexible interaction of remote brain areas. One candidate mechanism to establish local and long-range communication in the brain is transient synchronization of neural assemblies. In addition to the analysis of oscillatory power, assessment of the phase dynamics of multiple brain signals is a promising avenue to examine the integration of distributed information in multisensory networks. In the current study, human participants were engaged in a visual-tactile pattern matching task while high-density electroencephalograms (EEG) were recorded. To investigate the neural correlates of multisensory integration and assess effects of crossmodal stimulus congruence, we adapted an approach for purely data-driven analysis of neuronal coupling in source space that has recently been developed within our group. This method allows imaging of large-scale cortical networks in space, time and frequency without defining a priori constraints. We identified three clusters of interacting sources that synchronized in the beta-band (∼ 20 Hz). The spatial and spectro-temporal profile of the first two clusters suggest an involvement in crossmodal sensory processing, whereas the third cluster appears to reflect decision-related processes. By directly relating coupling features to task performance, we demonstrate that the phase of neural coherence within the observed networks predicts behavior. Our results provide further evidence that neural synchronization is crucial for long-range communication in the brain and suggest a possible role of beta-band activity in multisensory integration.


title: Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees

abstract: In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summarization. We propose an innovative sentence compression method by considering every node in the constituent parse tree and deciding its status – remove or retain. Integer liner programming with discriminative training is used to solve the problem. Under this model, we incorporate various constraints to improve the linguistic quality of the compressed sentences. Then we utilize a pipeline summarization framework where sentences are first compressed by our proposed compression model to obtain top-n candidates and then a sentence selection module is used to generate the final summary. Compared with state-ofthe-art algorithms, our model has similar ROUGE-2 scores but better linguistic quality on TAC data.


title: BabyTalk: Understanding and Generating Simple Image Descriptions

abstract: We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.


title: Using ontologies as queries in information retrieval

abstract: Current expectations from nowadays information retrieval systems (IRS) have grown beyond the “document contains these terms” requirement that was considered common sense 10–15 years ago. Nowadays systems are expected to return results that are relevant to the intended meaning of the query. In the general IRS usage scenario, the user is not really interested if the returned documents contain or not the words he entered in the query, but the user expects the returned documents to be relevant to the intended meaning of his query. The subject of analyzing the query (expressed as a sentence in natural language) in order to infer the meaning of the query, has spawned lots of research publications in the last 10 years, but without any large impact in the user search experience. We propose a system (the OntoSense User Interface) that guides the user in providing the query directly as a query ontology, thus providing the meaning of the query directly to the IRS. This approach is especially useful in mobile platform information retrieval scenarios where the query sentence is usually too short (very few words are used) for the IRS to match relevant documents.


title: Human Appearance Transfer

abstract: We propose an automatic person-to-person appearance transfer model based on explicit parametric 3d human representations and learned, constrained deep translation network architectures for photographic image synthesis. Given a single source image and a single target image, each corresponding to different human subjects, wearing different clothing and in different poses, our goal is to photo-realistically transfer the appearance from the source image onto the target image while preserving the target shape and clothing segmentation layout. Our solution to this new problem is formulated in terms of a computational pipeline that combines (1) 3d human pose and body shape estimation from monocular images, (2) identifying 3d surface colors elements (mesh triangles) visible in both images, that can be transferred directly using barycentric procedures, and (3) predicting surface appearance missing in the first image but visible in the second one using deep learning-based image synthesis techniques. Our model achieves promising results as supported by a perceptual user study where the participants rated around 65% of our results as good, very good or perfect, as well in automated tests (Inception scores and a Faster-RCNN human detector responding very similarly to real and model generated images). We further show how the proposed architecture can be profiled to automatically generate images of a person dressed with different clothing transferred from a person in another image, opening paths for applications in entertainment and photo-editing (e.g. embodying and posing as friends or famous actors), the fashion industry, or affordable online shopping of clothing.


title: Face Alignment via Regressing Local Binary Features

abstract: This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.


title: Fabrication of hemispherical fused silica micro-resonator with tailored stiffness and mass distribution

abstract: This paper presents initial progress toward a precision shell integrating (PSI) gyroscope designed for high Coriolis force sensitivity and high shock and vibration tolerance. We demonstrate control over the stiffness and mass distribution of a 3D hemispherical shell resonator formed with blowtorch reflow molding of fused silica (FS). The thickness profile of the resonator is designed to reduce the frequency of the n = 2 wine-glass (WG) modes and to maximize the parasitic mode frequencies, which increases shock and vibration tolerance. We simulate the dynamic reflow process with a non-isothermal finite element model, allowing us to design a substrate pattern that will reflow into the desired geometry. The PSI resonator is simulated to have a larger effective mass (M eff ) than other MEMS gyroscope resonator designs.


title: TextWorld: A Learning Environment for Text-based Games

abstract: We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.


title: Deep Reasoning with Knowledge Graph for Social Relationship Understanding

abstract: Social relationships (e.g., friends, couple etc.) form the basis of the social network in our daily life. Automatically interpreting such relationships bears a great potential for the intelligent systems to understand human behavior in depth and to better interact with people at a social level. Human beings interpret the social relationships within a group not only based on the people alone, and the interplay between such social relationships and the contextual information around the people also plays a significant role. However, these additional cues are largely overlooked by the previous studies. We found that the interplay between these two factors can be effectively modeled by a novel structured knowledge graph with proper message propagation and attention. And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects. Meanwhile, a graph attentional mechanism is introduced to explicitly reason about the discriminative objects to promote recognition. Extensive experiments on the public benchmarks demonstrate the superiority of our method over the existing leading competitors.


application testing : A systematic literature review


title: UCO.MIPSIM: pipelined computer simulator for teaching purposes

abstract: Since pipelining is a very important implementation technique for processors, students in Computer Science need to achieve a good understanding of it. UCO.MIPSIM simulator has been developed to support teaching such concepts. This paper introduces the bas


title: Efficient view-based 3d reflection symmetry detection

abstract: Symmetries exist in many 3D models while efficiently finding their symmetry planes is important and useful for many related applications. This paper presents a simple and efficient view-based reflection symmetry detection method based on the viewpoint entropy features of a set of sample views of a 3D model. Before symmetry detection, we align the 3D model based on the Continuous Principal Component Analysis (CPCA) method. To avoid the high computational load resulting from a directly combinatorial matching among the sample views, we develop a fast symmetry plane detection method by first generating a candidate symmetry plane based on a matching pair of sample views and then verifying whether the number of remaining matching pairs is within a minimum number. Experiments demonstrate better accuracy, efficiency, and flexibility of our algorithm than state-of-the-art approaches.


title: Design and properties of integrated millimeter-wave bandpass filters using nonradiative dielectric waveguide for broadband wireless system

abstract: Conventional band-pass filter design is reviewed with respect to (non-radiative dielectric) NRD-guide technology and a new step-by-step method is proposed for the design of dual-path band-pass filter using small NRD guide resonator. A mode matching formulation, accounting for inherent higher- order modal coupling effects, is used to calculate the generalized S matrix of discontinuities encountered in all kind of filters, as well as to simulate the entire filters. Upon a reduction of the transfer function of the dual-path band-pass filter, a low-pass prototype network is obtained and calculated. Based on our mode-matching software tool, an iterative optimization process is then developed to design a filter with deep attenuation poles. A practical example is given and a short comparison with the conventional band-pass filter is made.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: How Teachers Use Data to Help Students Learn: Contextual Inquiry for the Design of a Dashboard

abstract: Although learning with Intelligent Tutoring Systems (ITS) has been well studied, little research has investigated what role teachers can play, if empowered with data. Many ITSs provide student performance reports, but they may not be designed to serve teachers’ needs well, which is important for a well-designed dashboard. We investigated what student data is most helpful to teachers and how they use data to adjust and individualize instruction. Specifically, we conducted Contextual Inquiry interviews with teachers and used Interpretation Sessions and Affinity Diagramming to analyze the data. We found that teachers generate data on students’ concept mastery, misconceptions and errors, and utilize data provided by ITSs and other software. Teachers use this data to drive instruction and remediate issues on an individual and class level. Our study uncovers how data can support teachers in helping students learn and provides a solid foundation and recommendations for designing a teacher’s dashboard.


title: Cell-free DNA (cfDNA): Clinical Significance and Utility in Cancer Shaped By Emerging Technologies.

abstract: Precision oncology is predicated upon the ability to detect specific actionable genomic alterations and to monitor their adaptive evolution during treatment to counter resistance. Because of spatial and temporal heterogeneity and comorbidities associated with obtaining tumor tissues, especially in the case of metastatic disease, traditional methods for tumor sampling are impractical for this application. Known to be present in the blood of cancer patients for decades, cell-free DNA (cfDNA) is beginning to inform on tumor genetics, tumor burden, and mechanisms of progression and drug resistance. This substrate is amenable for inexpensive noninvasive testing and thus presents a viable approach to serial sampling for screening and monitoring tumor progression. The fragmentation, low yield, and variable admixture of normal DNA present formidable technical challenges for realization of this potential. This review summarizes the history of cfDNA discovery, its biological properties, and explores emerging technologies for clinically relevant sequence-based analysis of cfDNA in cancer patients. Molecular barcoding (or Unique Molecular Identifier, UMI)-based methods currently appear to offer an optimal balance between sensitivity, flexibility, and cost and constitute a promising approach for clinically relevant assays for near real-time monitoring of treatment-induced mutational adaptations to guide evidence-based precision oncology. Mol Cancer Res; 14(10); 898-908. ©2016 AACR.


title: AppCloak: Rapid Migration of Legacy Applications into Cloud

abstract: Although cloud has been adopted by many organizations as their main infrastructure for IT delivery, there are still a large number of legacy applications running in non-cloud hosting environments. Thus, it is crucial to have migration techniques for such legacy applications so that they can benefit from many advantages of cloud such as elasticity, low upfront investment, and fast time-to-market. However, migrating large number of legacy applications into cloud in a timely manner is a daunting task. Common techniques such as redeveloping (i.e., modernizing) them or reinstalling from the scratch entails high costs. To mitigate these problems, we have developed a rapid migration technique, called AppCloak, that allows users to literally copy an already-installed application to cloud and run it without any modifications. The technique is based on intercepting a selected set of system calls and replacing the parameters and return values to hide any differences of environments to the application. We demonstrate that our technique works in Amazon EC2 and quantify the performance overhead.


A Meta-Analysis Of The Effect Of Quercetin Supplementation On Endurance Performance And Maximal Oxygen Consumption: 1783


Comparing Intel ’ s PowerPlay ’ s Estimations to Measured Power on a Cyclone IV FPGA


title: Mono-camera based simultaneous obstacle recognition and distance estimation for obstacle avoidance of power transmission lines inspection robot

abstract: This paper presents a mono-camera based simultaneous obstacle recognition and distance estimation method for power transmission lines(PTLs) inspection robot to avoid obstacles on or around them. The proposed robot inspects the PTLs while moving along them between power transmission towers. For autonomous navigation, our robot recognizes obstacles and avoids a collision with them. In addition, it can stop at the ends of the PTLs by recognizing its structures, such as insulators, installed at the ends of them. In order to recognize obstacles or insulators efficiently, initially, a robust PTLs detection method based on vanishing point estimation is proposed since they are installed on or around the PTLs. Then, obstacle models with various scales are built, and multiple regions-of-interest(ROIs) according to the scales of the model are constructed along the detected PTLs. Finally, obstacles are recognized within the multiple ROIs. Because each ROI represents a corresponding scale of the obstacle model to be matched, the proposed approach can efficiently deal with scale changes and also estimate distance between the robot and the obstacle simultaneously with obstacle recognition. Experimental results not only show that the proposed method correctly recognizes obstacles but also that the distance between the robot and the obstacles is estimated efficiently.


title: Interactions Between the Nucleus Accumbens and Auditory Cortices Predict Music Reward Value

abstract: We used functional magnetic resonance imaging to investigate neural processes when music gains reward value the first time it is heard. The degree of activity in the mesolimbic striatal regions, especially the nucleus accumbens, during music listening was the best predictor of the amount listeners were willing to spend on previously unheard music in an auction paradigm. Importantly, the auditory cortices, amygdala, and ventromedial prefrontal regions showed increased activity during listening conditions requiring valuation, but did not predict reward value, which was instead predicted by increasing functional connectivity of these regions with the nucleus accumbens as the reward value increased. Thus, aesthetic rewards arise from the interaction between mesolimbic reward circuitry and cortical networks involved in perceptual analysis and valuation.


Information-Processing Theory of Human Problem Solving


title: metaphorik.de 05/2003 – Koller, Metaphor Clusters, Metaphor Chains Metaphor Clusters, Metaphor Chains: Analyzing the Multifunctionality of Metaphor in Text

abstract: This paper applies Halliday's (1994) theory of the interpersonal, ideational and textual meta-functions of language to conceptual metaphor. Starting from the observation that metaphoric expressions tend to be organized in chains across texts, the question is raised what functions those expressions serve in different parts of a text as well as in relation to each other. The empirical part of the article consists of the sample analysis of a business magazine text on marketing. This analysis is two-fold, integrating computer-assisted quantitative investigation with qualitative research into the organization and multifunctionality of metaphoric chains as well as the cognitive scenarios evolving from those chains. The paper closes by summarizing the main insights along the lines of the three Hallidayan meta-functions of conceptual metaphor and suggesting functional analysis of metaphor at levels beyond that of text.


title: Fulfilment of preoperative expectations and postoperative patient satisfaction after total knee replacement. A prospective analysis of 200 patients.

abstract: Abstract Background In total knee arthroplasty (TKA), the association between preoperative patient expectations, their postoperative fulfilment, and satisfaction is not well understood. The aim of this study was to quantify these relationships in a cohort of TKA patients with differing socio-economic backgrounds from across Scotland. Methods Expectations of 200 patients undergoing TKA were assessed using the Hospital for Special Surgery Knee Replacement Expectation Score. Postoperatively, expectation fulfilment was assessed at six weeks and one year. Satisfaction was measured using a five-point Likert scale. Preoperative and postoperative scores were compared across satisfaction groups. Relationships between individual and overall expectation fulfilment, satisfaction, and demographics were analysed. Results At six weeks, 30% of expectations were fulfilled, increasing to 48% at one year. Corresponding satisfaction rates were 84% and 89%. Higher fulfilment scores were associated with greater satisfaction. Fulfilment of pain and mobility expectations were most predictive of satisfaction. Expectations of kneeling, squatting, paid work and sexual function went unfulfilled. Preoperative expectations were not related to postoperative satisfaction. Males had higher postoperative fulfilment scores. Conclusions This study showed a clear association between fulfilment of preoperative expectations and patient satisfaction following TKA. However, less than 50% of patient expectations were completely fulfilled. While higher fulfilment scores were predictive of greater satisfaction, low overall fulfilment score did not preclude satisfaction. The high correlation of pain and function expectation fulfilment with satisfaction emphasises the need to relate these as appropriate preoperative expectations, while awareness of unfulfilled expectations may allow engagement with and resolution of ongoing concerns and disappointments.


title: Method and model on site selection of aviation spare distribution center of military aircraft

abstract: Aviation spare support is the main part of materiel support work and has direct influence on the completion of campaigning and training mission. The paper deeply studies the method and model on site selection of aviation spare distribution center of military aircraft and explores its new method and means. This has significant meaning on the scientific site selection of distribution center and improving the effectiveness of aviation spare support work. According to the features of aviation spare support, the paper takes the factors of mission succeed rate, economic and damage degree of roadway in wartime into consideration, puts forward the method on site selection of aviation spare distribution center of military aircraft and builds the model of site selection. The paper verifies the scientificity and validity of the model by a specified example.


title: Threshold compression for 3G scalable monitoring

abstract: We study the problem of scalable monitoring of operational 3G wireless networks. Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. A fine-grained threshold setting (e.g., perlocation hourly) incurs prohibitively high management complexity, while a single static threshold fails to capture the network dynamics, thus resulting in unacceptably poor alarm quality (up to 70% false/miss alarm rates). In this paper, we propose a scalable monitoring solution, called threshold-compression that can characterize the location- and time-specific threshold trend of each individual network element (NE) with minimal threshold setting. The main insight is to identify groups of NEs with similar threshold behaviors across location and time dimensions, forming spatial-temporal clusters to reduce the number of thresholds while maintaining acceptable alarm accuracy in a large-scale 3G network. Our evaluations based on the operational experience on a commercial 3G network have demonstrated the effectiveness of the proposed solution. We are able to reduce the threshold setting up to 90% with less than 10% false/miss alarms.


title: Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images

abstract: In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications.


title: Cognitive caching for the future sensors in fog networking

abstract: Abstract In this paper, we propose a Cognitive Caching approach for the Future Fog (CCFF) that takes into consideration the value of the exchanged data in Information Centric Sensor Networks (ICSNs). Our approach depends on four functional parameters in ICSNs. These four main parameters are: age of the data, popularity of on-demand requests, delay to receive the requested information and data fidelity. These parameters are considered together to assign a value to the cached data while retaining the most valuable one in the cache for prolonged time periods. This CCFF approach provides significant availability for most valuable and difficult to retrieve data in the ICSNs. Extensive simulations and case studies have been examined in this research in order to compare to other dominant cache management frameworks in the literature under varying circumstances such as data popularity, cache size, data publisher load, and node connectivity degree. Formal fidelity and trust analysis has been applied as well to emphasize the effectiveness of CCFF in Fog paradigms, where edge devices can retrieve unsecured data from the authorized nodes in the cloud.


title: From fashion based production to customer-oriented networks for healthy and fashionable footwear

abstract: In order to stay competitive, European manufacturing companies need to enter new markets implementing innovative production methodologies based on networking economy. Comparing the "as-is" fashion and orthopaedic footwear supply networks through multiple case studies, this paper aims at highlighting guidelines for designing an innovative "footwear supply network reference model" that will allow to industrialize fashion footwear production for new niche markets like elderly, obese, disabled and diabetic people can be. Through business processes and requirements analysis, the paper proposes a supply chain topology that maps the most relevant characteristics, procedures and techniques of the different companies of the two sectors.


title: Online Feedback for Structure-from-Motion Image Acquisition

abstract: The quality and completeness of 3D models obtained by Structure-fromMotion (SfM) heavily depend on the image acquisition process. If the user gets feedback about the reconstruction quality already during the acquisition, he can optimize this process. The goal of this paper is to support a user during image acquisition by giving online feedback of the current reconstruction quality. We propose an online SfM method that integrates wide-baseline still-images in an online fashion into a consistent reconstruction and we derive a surface model given the SfM point cloud. To guide the user to scene parts that are captured not very well, we colour the mesh according to redundancy and resolution information. In the experiments, we show that our approach makes the final SfM result predictable already during image acquisition. The method is suited for large-scale reconstructions as obtained by flying micro aerial vehicles as well as on small indoor environments. We propose a method that supports a user in the acquisition process in two ways: (a) sparse online SfM with accuracy close to offline methods and (b) surface extraction and quality visualization. The workflow of our method is shown in Figure 1.


title: Assessing readiness for business process reengineering

abstract: Purpose – The purpose of this paper is to propose how to minimize the risks of implementing business process reengineering (BPR) by measuring readiness. For this purpose, the paper proposes an assessment approach for readiness in BPR efforts based on the critical success and failure factors.Design/methodology/approach – A relevant literature review, which investigates success and failure indicators in BPR efforts is carried out and a new categorized list of indicators are proposed. This is a base for conducting a survey to measure the BPR readiness, which has been run in two companies and compared based on a diamond model.Findings – In this research, readiness indicators are determined based on critical success and failure factors. The readiness indicators include six categories. The first five categories, egalitarian leadership, collaborative working environment, top management commitment, supportive management, and use of information technology are positive indicators. The sixth category, resistance to ...


title: Internet paradox: A social technology that reduces social involvement and psychological well-being

abstract: The Internet could change the lives of average citizens as much as did the telephone in the early part of the 20th century and television in the 1950s and 1960s. Researchers and social critics are debating whether the Internet is improving or harming participation in community life and social relationships. This research examined the social and psychological impact of the Internet on 169 people in 73 households during their first 1 to 2 years on-line. We used longitudinal data to examine the effects of the Internet on social involvement and psychological well-being. In this sample, the Internet was used extensively for communication. Nonetheless, greater use of the Internet was associated with declines in participants' communication with family members in the household, declines in the size of their social circle, and increases in their depression and loneliness. These findings have implications for research, for public policy and for the design of technology.


title: Esophageal Gross Tumor Volume Segmentation Using a 3D Convolutional Neural Network

abstract: Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighboring tissues in CT scans, this problem has been addressed weakly. In this paper, we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverage design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to deep supervision, feature re-usability, and parameter reduction while aiding the network to be more accurate. The proposed architecture was trained and tested on a dataset containing 553 scans from 49 distinct patients. The proposed network achieved a Dice value of \(0.73\pm 0.20\), and a 95\(\%\) mean surface distance of \(3.07 \pm 1.86\) mm for 85 test scans. The experimental results indicate the effectiveness of the proposed method for clinical diagnosis and treatment systems.


title: Massive hybrid antenna array for millimeter-wave cellular communications

abstract: A massive hybrid array consists of multiple analog subarrays, with each subarray having its digital processing chain. It offers the potential advantage of balancing cost and performance for massive arrays and therefore serves as an attractive solution for future millimeter-wave (mm- Wave) cellular communications. On one hand, using beamforming analog subarrays such as phased arrays, the hybrid configuration can effectively collect or distribute signal energy in sparse mm-Wave channels. On the other hand, multiple digital chains in the configuration provide multiplexing capability and more beamforming flexibility to the system. In this article, we discuss several important issues and the state-of-the-art development for mm-Wave hybrid arrays, such as channel modeling, capacity characterization, applications of various smart antenna techniques for single-user and multiuser communications, and practical hardware design. We investigate how the hybrid array architecture and special mm-Wave channel property can be exploited to design suboptimal but practical massive antenna array schemes. We also compare two main types of hybrid arrays, interleaved and localized arrays, and recommend that the localized array is a better option in terms of overall performance and hardware feasibility.


title: Student attendance monitoring at the university using NFC

abstract: There are several complex business processes in the higher education. As the number of university students has been tripled in Hungary the automation of these task become necessary. The Near Field Communication (NFC) technology provides a good opportunity to support the automated execution of several education related processes. Recently a new challenge is identified at the Budapest University of Technology and Economics. As most of the lecture notes had become available in electronic format the students especially the inexperienced freshman ones did not attend to the lectures significantly decreasing the rate of successful exams. This drove to the decision to elaborate an accurate and reliable information system for monitoring the student's attendance at the lectures. Thus we have developed a novel, NFC technology based business use case of student attendance monitoring. In order to meet the requirements of the use case we have implemented a highly autonomous distributed environment assembled by NFC enabled embedded devices, so-called contactless terminals and a scalable backoffice. Beside the opportunity of contactless card based student identification the terminals support biometric identification by fingerprint reading. These features enable the implementation of flexible and secure identification scenarios. The attendance monitoring use case has been tested in a pilot project involving about 30 access terminals and more that 1000 students. In this paper we are introducing the developed attendance monitoring use case, the implemented NFC enabled system, and the experiences gained during the pilot project.


Experimental and Numerical Investigation of Interdie Thermal Resistance in Three-Dimensional Integrated Circuits


title: Ranking in Professional Sports: An Application of Linear Algebra for Computer Science Students

abstract: Linear algebra is one of the required mathematics courses for students majoring in computer science. With the small class sizes at our institution, we have the opportunity to use teaching strategies often reserved for senior level courses at larger universities. In this paper the authors discuss their experience with an innovative project schema that is designed for students in an elementary linear algebra course, and how it fullfills the requirements from the report to ACM [11].


title: Design of Linear Equalizers Optimized for the Structural Similarity Index

abstract: We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm.


title: Big data analytics: hadoop and tools

abstract: Information technology gives utmost importance to processing of data. Some petabytes of data is not sufficient for storing large amount of data. Large volume of unstructured and structured data that gets created from various sources such as Emails, web logs, social media like Twitter, Facebook etc. The major obstacles with processing Big Data include capturing, storing, searching, sharing and analysis. Hadoop enables to explore complex data. It is an open source framework written in Java which supports parallel and distributed data processing and is used for reliable storage of data. With the help of big data analytics, many enterprises are able to improve customer retention, help with product development and gain competitive advantage, speed and reduce complexity. E-commerce companies study traffic on web sites or navigation patterns to determine probable views, interests and dislikes of a person or a group as a whole depending on the previous purchases. In this paper, we compare some typically used data analytic tools.


title: Big Data, Ethics, and the Social Implications of Knowledge Production

abstract: This position paper addresses current debates about data in general, and big data specifically, by examining the ethical issues arising from advances in knowledge production. Typically ethical issues such as privacy and data protection are discussed in the context of regulatory and policy debates. Here we argue that this overlooks a larger picture whereby human autonomy is undermined by the growth of scientific knowledge. To make this argument, we first offer definitions of data and big data, and then examine why the uses of data-driven analyses of human behaviour in particular have recently experienced rapid growth. Next, we distinguish between the contexts in which big data research is used, and argue that this research has quite different implications in the context of scientific as opposed to applied research. We conclude by pointing to the fact that big data analyses are both enabled and constrained by the nature of data sources available. Big data research will nevertheless inevitably become more pervasive, and this will require more awareness on the part of data scientists, policymakers and a wider public about its contexts and often unintended consequences.


title: Comparing Semantic Models for Evaluating Automatic Document Summarization

abstract: The main focus of this paper is the examination of semantic modelling in the context of automatic document summarization and its evaluation. The main area of our research is extractive summarization, more specifically, contrastive opinion summarization. And as it is with all summarization tasks, the evaluation of their performance is a challenging problem on its own. Nowadays, the most commonly used evaluation technique is ROUGE Recall-Oriented Understudy for Gisting Evaluation. It includes measures such as the count of overlapping n-grams or word sequences for automatically determining the quality of summaries by comparing them to ideal human-made summaries. However, these measures do not take into account the semantics of words and thus, for example, synonyms are not treated as equal. We explore this issue by experimenting with various language models, examining their performance in the task of computing document similarity. In particular, we chose four semantic models LSA, LDA, Word2Vec and Doc2Vec and one frequency-based model TfIdf, for extracting document features. The experiments were then performed on our custom dataset and the results of each model are then compared to the similarity values assessed by human annotators. We also compare these values with the ROUGE scores and observe the correlations between them. The aim of our experiments is to find a model, which can best imitate a human estimate of document similarity.


title: FPGA-based ORB feature extraction for real-time visual SLAM

abstract: Simultaneous Localization And Mapping (SLAM) is the problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. How to enable SLAM robustly and durably on mobile, or even IoT grade devices, is the main challenge faced by the industry today. The main problems we need to address are: 1.) how to accelerate the SLAM pipeline to meet real-time requirements; and 2.) how to reduce SLAM energy consumption to extend battery life. After delving into the problem, we found out that feature extraction is indeed the bottleneck of performance and energy consumption. Hence, in this paper, we design, implement, and evaluate a hardware ORB feature extractor and prove that our design is a great balance between performance and energy consumption compared with ARM Krait and Intel Core i5.


Transfer learning for object category detection


title: Multiple Text Document Summarization System using hybrid Summarization technique

abstract: Text Summarization plays an important role in the area of text mining and natural language processing. As the information resources are increasing tremendously, readers are overloaded with loads of information. Finding out the relevant data and manually summarizing it in short time is much more difficult, challenging and tedious task for a human being. Text Summarization aims to compress the source text into a shorter and concise form with preserving its information content and overall meaning. Summarization can be classified into two main categories i.e. extractive summarization and abstractive summarization. This paper presents a novel approach to generate abstractive summary from extractive summary using WordNet ontology. An experimental result shows the generated summary in well-compressed, grammatically correct and human readable format.


title: Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines

abstract: This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.


title: Correlation and Causation in the Study of Personality

abstract: Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences. Copyright © 2012 John Wiley & Sons, Ltd.


title: Regularizing Relation Representations by First-order Implications

abstract: Methods for automated knowledge base construction often rely on trained fixed-length vector representations of relations and entities to predict facts. Recent work showed that such representations can be regularized to inject first-order logic formulae. This enables to incorporate domain-knowledge for improved prediction of facts, especially for uncommon relations. However, current approaches rely on propositionalization of formulae and thus do not scale to large sets of formulae or knowledge bases with many facts. Here we propose a method that imposes first-order constraints directly on relation representations, avoiding costly grounding of formulae. We show that our approach works well for implications between pairs of relations on artificial datasets.


abstract: ABSTRACT Introduction In the Diagnostic and Statistical Manual of Mental Disorders , Fourth Edition, text revision (DSM‐IV‐TR) (and earlier editions), a disturbance in “identity” is one of the defining features of borderline personality disorder (BPD). Gender identity, a person's sense of self as a male or a female, constitutes an important aspect of identity formation, but this construct has rarely been examined in patients with BPD. Aims In the present study, the presence of gender identity disorder or confusion was examined in women diagnosed with BPD. Main Outcome Measures We used a validated dimensional measure of gender dysphoria. Recalled gender identity and gender role behavior from childhood was also assessed with a validated dimensional measure, and current sexual orientation was assessed by two self‐report measures. Methods A consecutive series of 100 clinic‐referred women (mean age, 34 years) with BPD participated in the study. The women were diagnosed with BPD using the International Personality Disorder Exam‐BPD Section. Results None of the women with BPD met the criterion for caseness on the dimensional measure of gender dysphoria. Women who self‐reported either a bisexual or a homosexual sexual orientation had a significantly higher score on the dimensional measure of gender dysphoria than the women who self‐reported a heterosexual sexual orientation, and they also recalled significantly more cross‐gender behavior during childhood. Results were compared with a previous study on a diagnostically heterogeneous group of women with other clinical problems. Conclusion The importance of psychosexual assessment in the clinical evaluation of patients with BPD is discussed. Singh D, McMain S, and Zucker KJ. Gender identity and sexual orientation in women with borderline personality disorder. J Sex Med 2011;8:447–454.


title: New Digital Media Marketing and Micro Business: A UK Perspective

abstract: This paper considers innovative marketing within the context of a micro firm, exploring how such firmâ€™s marketing practices can take advantage of digital media. Factors that influence a micro firmâ€™s innovative activities are examined and the development and implementation of digital media in the firmâ€™s marketing practice is explored. Despite the significance of marketing and innovation to SMEs, a lack of literature and theory on innovation in marketing theory exists. Research suggests that small firmsâ€™ marketing practitioners and entrepreneurs have identified their marketing focus on the 4Is. This paper builds on knowledge in innovation and marketing and examines the process in a micro firm. A qualitative approach is applied using action research and case study approach. The relevant literature is reviewed as the starting point to diagnose problems and issues anticipated by business practitioners. A longitudinal study is used to illustrate the process of actions taken with evaluations and reflections presented. The exploration illustrates that in practice much of the marketing activities within micro firms are driven by incremental innovation. This research emphasises that integrating Information Communication Technologies (ICTs) successfully in marketing requires marketers to take an active managerial role far beyond their traditional areas of competence and authority.


title: A spectral clustering approach to finding communities in graphs

abstract: Clustering nodes in a graph is a useful general technique in data mining of large network data sets. In this context, Newman and Girvan [9] recently proposed an objective function for graph clustering called the Q function which allows automatic selection of the number of clusters. Empirically, higher values of the Q function have been shown to correlate well with good graph clusterings. In this paper we show how optimizing the Q function can be reformulated as a spectral relaxation problem and propose two new spectral clustering algorithms that seek to maximize Q. Experimental results indicate that the new algorithms are efficient and effective at finding both good clusterings and the appropriate number of clusters across a variety of real-world graph data sets. In addition, the spectral algorithms are much faster for large sparse graphs, scaling roughly linearly with the number of nodes n in the graph, compared to O(n) for previous clustering algorithms using the Q function.


title: An Effective Feature Selection Approach for Network Intrusion Detection

abstract: Processing huge amounts of network data is one of the largest challenges for network-based intrusion detection system (IDS). Usually these data contain lots of irrelevant or redundant features. To improve the efficiency of IDS, relevant features are necessary to be extracted from original data via feature selection approaches. In this paper, an effective feature selection approach based on Bayesian Network classifier is proposed. And with the same intrusion detection benchmark dataset (NSL-KDD), the performance of the proposed approach is evaluated and compared with other commonly used feature selection methods. It is shown by empirical results that features selected by our approach have decreased the time to detect attacks and increased the classification accuracy as well as the true positive rates significantly.


Biofilms and antibiotic susceptibility of multidrug-resistant bacteria from wild animals


title: Deep learning based large scale handwritten Devanagari character recognition

abstract: In this paper, we introduce a new public image dataset for Devanagari script: Devanagari Handwritten Character Dataset (DHCD). Our dataset consists of 92 thousand images of 46 different classes of characters of Devanagari script segmented from handwritten documents. We also explore the challenges in recognition of Devanagari characters. Along with the dataset, we also propose a deep learning architecture for recognition of those characters. Deep Convolutional Neural Network (CNN) have shown superior results to traditional shallow networks in many recognition tasks. Keeping distance with the regular approach of character recognition by Deep CNN, we focus the use of Dropout and dataset increment approach to improve test accuracy. By implementing these techniques in Deep CNN, we were able to increase test accuracy by nearly 1 percent. The proposed architecture scored highest test accuracy of 98.47% on our dataset.


title: Inverse kinematics in robotics using neural networks

abstract: The inverse kinematics problem in robotics requires the determination of the jointangles for a desired position of the end-effector. For this underconstrained and ill-conditioned problem we propose a solution based on structured neural networks that can be trained quickly. The proposed method yields multiple and precise solutions and it is suitable for real-time applications.


title: Rhythm, Metrics, and the Link to Phonology

abstract: Since Ramus et al. (1999) a number of statistical metrics have been routinely employed by researchers (Ramus 2003, Grabe & Low 2002 etc.) in an effort to rhythmically classify languages. However, recent studies by Arvaniti (2009), Tilsen & Arvaniti (2013), Arvaniti & Rodriquez (2013) etc., have challenged both the validity of these metrics in reflecting speech rhythm, and the physical measurability of rhythm itself. The present study takes a comparative evaluative approach, and explores the applicability of the proposed metrics to a Papuan language (Urama) with a phonology quite different than traditional Western European (W.E.) languages. It is argued here that the statistical underpinning of the existing rhythm metrics is a direct outcome of an overt effort to capture the temporal durational characteristics of the phonotactics of W.E. languages. As such, these metrics are only capable of providing a crude measure of timing.


title: Design and control for a compliant knee exoskeleton

abstract: In this paper, we design a novel knee exoskeleton. The designed exoskeleton has a 1 DOF with knee joint actuated in the sagittal plane. The exoskeleton is actuated base on Series Elastic Actuator (SEA) instead of traditonal stiff actuator. SEA is a compliant actuator, having the advantages of low output impedance, low friction, high quality force control and backdrivability. Thanks to these nice characteristics, it is easy to realize a safe and friendly human-machine interaction compare with traditonal stiff actuator. In order to achieve the goal of assistance as needed, a robust force control must be guaranteed. Sliding-Mode Control (SMC) is a way to robust force control. Instead of using the standard SMC, we employ Integral Sliding-Mode Control Algorithm (ISM) to avoid reaching phase, where the performance of system is not so good. Meanwhile, we also use continuous approximation of SMC, which can eliminate the effect of chattering. To verify the efficiency of ISM, we excutes the torque tarcking experiments. We also use the classical PID algorithm to compare with ISM. From the results of experiments, ISM outperform much better than PID, tracking the torque trajectory with high accuracy whereas employing PID produce high errors.


title: Reliable algorithm for iris segmentation in eye image

abstract: The paper presents a novel algorithm for iris segmentation in eye images taken under visible and near infrared light. The proposed approach consists of the following stages: reflections localization, reflections filling in, iris boundaries localization and eyelids boundaries localization. Here, each of these stages is detailed. Authors' solution obtained the second rank in the ''Noisy Iris Challenge Evaluation - Part I'' contest, in which all iris segmentation algorithms submitted to the contest were evaluated and compared.


title: Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation.

abstract: In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN). As opposed to most cardiac segmentation methods which focus on the left ventricle (and especially the left cavity), our method segments both the left ventricular cavity, the left ventricular epicardium, and the right ventricular cavity. The novelty of our network lies in its maximum a posteriori loss function, which is specifically designed for the cardiac anatomy. Our loss function incorporates the cross-entropy of the predicted labels, the predicted contours, a cardiac shape prior, and an a priori term. Our model also includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. ::: We trained and tested our model on the ACDC MICCAI'17 challenge dataset of 150 patients whose diastolic and systolic images were manually outlined by 2 medical experts. Results reveal that our method can segment all three regions of a 3D MRI cardiac volume in $0.4$ second with an average Dice index of $0.90$, which is significantly better than state-of-the-art deep learning methods.


title: Unsupervised Hypernym Detection by Distributional Inclusion Vector Embedding

abstract: Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limit the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional inclusion vector embedding (DIVE), a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings learned by modeling diversity of word context with specialized negative sampling. In an experimental evaluation more comprehensive than any previous literature of which we are aware - evaluating on 11 datasets using multiple existing as well as newly proposed scoring metrics - we find that our method can provide up to double or triple the precision of previous unsupervised methods, and also sometimes outperforms previous semi-supervised methods, yielding many new state-of-the-art results.


title: Can you fool AI with adversarial examples on a visual Turing test?

abstract: Deep learning has achieved impressive results in many areas of Computer Vision and Natural Language Pro- cessing. Among others, Visual Question Answering (VQA), also referred to a visual Turing test, is considered one of the most compelling problems, and recent deep learning models have reported significant progress in vision and language modeling. Although Artificial Intelligence (AI) is getting closer to passing the visual Turing test, at the same time the existence of adversarial examples to deep learning systems may hinder the practical application of such systems. In this work, we conduct the first extensive study on adversarial examples for VQA systems. In particular, we focus on generating targeted adversarial examples for a VQA system while the target is considered to be a question-answer pair. Our evaluation shows that the success rate of whether a targeted adversarial example can be generated is mostly dependent on the choice of the target question-answer pair, and less on the choice of images to which the question refers. We also report the language prior phenomenon of a VQA model, which can explain why targeted adversarial examples are hard to generate for some question-answer targets. We also demonstrate that a compositional VQA architecture is slightly more resilient to adversarial attacks than a non-compositional one. Our study sheds new light on how to build deep vision and language resilient models robust against adversarial examples.


title: A SeqGAN for Polyphonic Music Generation

abstract: We propose an application of SeqGAN, generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences. Instead of monophonic melody generation suggested in the original work, we present an efficient representation of polyphony MIDI file that captures chords and melodies with dynamic timings simultaneously. The network can create sequences that are musically coherent. We also report that careful tuning of reinforcement learning signals of the model is crucial for general application.


A highly parallel database system


title: A 50-mA 99.2% Peak Current Efficiency, 250-ns Settling Time Digital Low-Dropout Regulator With Transient Enhanced PI Controller

abstract: A fully integrated digital low-dropout regulator (DLDO) with a fast transient response, providing a regulated supply for system-on-chip (SoC) power management applications is proposed. Wideband operation and fast transient response are achieved through a transient enhanced proportional-integral controller, without compromising the stability of the DLDO at steady-state operation. The transient enhancement stage boosts loop-gain dynamically during load transients. In the gain boosting mode, the DLDO closed-loop bandwidth is increased, resulting in reduced undershoot/overshoot and fast settling. When the output voltage recovers to the desired level, the boost mode operation is disabled. For a load change with a 4-mA/ns slew rate between 10 and 50 mA, utilizing transient enhancement mode reduced the measured undershoot and overshoot by 35% and 17%, respectively. The characterization results show that the transient enhancement mode can reduce the settling time from 500 to 250 ns for a 10–50-mA load current change. The proposed DLDO operates with an input voltage ranging from 0.84 to 1.24 V, and output voltage ranging from 0.6 to 1 V. The maximum output current of the DLDO is 50 mA and the DLDO achieves a peak current efficiency of 99.2%, with DLDO figure of merit (FOM2) of 63.25 ps. The DLDO prototype chip is fabricated on a 0.13- $\mu \text{m}$ CMOS technology and occupies a 0.0631-mm2 die area.


title: Non-Orthogonal Random Access (NORA) for 5G Networks

abstract: The massive amounts of machine-type user equipments (UEs) will be supported in the future fifth generation (5G) networks. However, the potential large random access (RA) delay calls for a new RA scheme and for a detailed assessment of its performance. Motivated by the key idea of non-orthogonal multiple access, the non-orthogonal random access (NORA) scheme based on successive interference cancellation (SIC) is proposed in this paper to alleviate the access congestion problem. Specifically, NORA utilizes the difference of time of arrival to identify multiple UEs with the identical preamble, and enables power domain multiplexing of collided UEs in the following access process, while the base station performs SIC based on the channel conditions obtained through preamble detection. Our analysis show that the performance of NORA is superior to the conventional orthogonal random access (ORA) scheme in terms of the preamble collision probability, access success probability and throughput of random access. Simulation results verify our analysis and further show that our NORA scheme can improve the number of the supported UEs by more than 30%. Moreover, the number of preamble transmissions and the access delay for successfully accessed UEs are also reduced significantly by using the proposed random access scheme.


title: Parallax error in the monocular head-mounted eye trackers

abstract: This paper investigates the parallax error, which is a common problem of many video-based monocular mobile gaze trackers. The parallax error is defined and described using the epipolar geometry in a stereo camera setup. The main parameters that change the error are introduced and it is shown how each parameter affects the error. The optimum distribution of the error (magnitude and direction) in the field of view varies for different applications. However, the results can be used for finding the optimum parameters that are needed for designing a head-mounted gaze tracker. It has been shown that the difference between the visual and optical axes does not have a significant effect on the parallax error, and the epipolar geometry can be used for describing the parallax error in the HMGT.


title: Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review

abstract: Missing data are often a problem in social science data. Imputation methods fill in the missing responses and lead, under certain conditions, to valid inference. This article reviews several imputation methods used in the social sciences and discusses advantages and disadvantages of these methods in practice. Simpler imputation methods as well as more advanced methods, such as fractional and multiple imputation, are considered. The paper introduces the reader new to the imputation literature to key ideas and methods. For those already familiar with imputation methods the paper highlights some new developments and clarifies some recent misconceptions in the use of imputation methods. The emphasis is on efficient hot deck imputation methods, implemented in either multiple or fractional imputation approaches. Software packages for using imputation methods in practice are reviewed highlighting newer developments. The paper discusses an example from the social sciences in detail, applying several imputation methods to a missing earnings variable. The objective is to illustrate how to choose between methods in a real data example. A simulation study evaluates various imputation methods, including predictive mean matching, fractional and multiple imputation. Certain forms of fractional and multiple hot deck methods are found to perform well with regards to bias and efficiency of a point estimator and robustness against model misspecifications. Standard parametric imputation methods are not found adequate for the application considered.[NCRM WP]


title: POMDP-lite for robust robot planning under uncertainty

abstract: The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case. This paper introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables are constant or only change deterministically. We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks. We develop a simple model-based Bayesian reinforcement learning algorithm to solve POMDP-lite models. The algorithm performs well on large-scale POMDP-lite models with up to 1020 states and outperforms the state-of-the-art general-purpose POMDP algorithms. We further show that the algorithm is near-Bayesian-optimal under suitable conditions.


title: Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network

abstract: Word pairs, which are one of the most easily accessible features between two text segments, have been proven to be very useful for detecting the discourse relations held between text segments. However, because of the data sparsity problem, the performance achieved by using word pair features is limited. In this paper, in order to overcome the data sparsity problem, we propose the use of word embeddings to replace the original words. Moreover, we adopt a gated relevance network to capture the semantic interaction between word pairs, and then aggregate those semantic interactions using a pooling layer to select the most informative interactions. Experimental results on Penn Discourse Tree Bank show that the proposed method without using manually designed features can achieve better performance on recognizing the discourse level relations in all of the relations.


title: Document Cards: A Top Trumps Visualization for Documents

abstract: Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.


Initial SRAM state as a fingerprint and source of true random numbers for RFID tags


title: Quantum computation in brain microtubules: Decoherence and biological feasibility

abstract: The Penrose-Hameroff (`Orch OR') model of quantum computation in brain microtubules has been criticized as regards the issue of environmental decoherence. A recent report by Tegmark finds that microtubules can maintain quantum coherence for only $10^{-13}$ s, far too short to be neurophysiologically relevant. Here, we critically examine the assumptions behind Tegmark's calculation and find that: 1) Tegmark's commentary is not aimed at an existing model in the literature but rather at a hybrid that replaces the superposed protein conformations of the `Orch OR' theory with a soliton in superposition along the microtubule, 2) Tegmark predicts decreasing decoherence times at lower temperature, in direct contradiction of the observed behavior of quantum states, 3) recalculation after correcting Tegmark's equation for differences between his model and the `Orch OR' model (superposition separation, charge vs. dipole, dielectric constant) lengthens the decoherence time to $10^{-5} - 10^{-4}$ s and invalidates a critical assumption of Tegmark's derivation, 4) incoherent metabolic energy supplied to the collective dynamics ordering water in the vicinity of microtubules at a rate exceeding that of decoherence can counter decoherence effects (in the same way that lasers avoid decoherence at room temperature), and 5) phases of actin gelation may enhance the ordering of water around microtubule bundles, further increasing the decoherence-free zone by an order of magnitude and the decoherence time to $10^{-2} - 10^{-1}$ s. These revisions bring microtubule decoherence into a regime in which quantum gravity can interact with neurophysiology.


title: Traumatic deaths from dog attacks in the United States.

abstract: A newspaper survey and search of the medical literature identified 74 deaths from dog attacks, 51 of which occurred in a designated five-year study period. Most attacks were by single pet dogs without a preceding history of viciousness and without known provocation by the victim. The highest number of deaths (23) occurred in infants less than 1 year of age. Most of the remaining victims were children aged 1 through 8 years, and elderly women.


title: Serious Security Weakness in RSA Cryptosystem

abstract: Nowadays, RSA is the well-known cryptosystem which supports most of electronic commercial communications. RSA is working on the base of multiplication of two prime numbers. Currently different kinds of attacks have indentified against RSA by cryptanalysis. This paper has shown that regardless to the size of secret key and public key, it is possible to decrypt one cipher text by different secret keys RSA algorithm and in excellent condition, there are two similar key at least available in domain of two prime numbers multiplication.


title: Cancer transcriptome profiling at the juncture of clinical translation

abstract: Although cancer genome sequencing is becoming routine in cancer research, cancer transcriptome profiling through methods such as RNA sequencing (RNA-seq) provides information not only on mutations but also on their functional cellular consequences. This Review discusses how technical and analytical advances in cancer transcriptomics have provided various clinically valuable insights into gene expression signatures, driver gene prioritization, cancer microenvironments, immuno-oncology and prognostic biomarkers.


title: A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance

abstract: This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.


title: Provably Secure Steganography on Generative Media

abstract: In this paper, we propose provably secure steganography on generative media. Firstly, we discuss the essence of the steganographic security, which is identical to behavioral security. The behavioral security implies that the generative media are suitable for information hiding as well. Based on the duality of source coding and generating discrete distribution from fair coins and the explicit probability distribution yielded by generative model, perfectly secure steganography on generative media is proposed. Instead of random sampling from the probability distribution as ordinary generative models do, we combine the source decoding into the process of generation, which can implement the sampling according to the probability distribution as well as embed the encrypted message. Adaptive Arithmetic Coding is selected as the source coding method, and it is proved theoretically that the proposed generative steganography framework using adaptive Arithmetic Coding is asymptotically perfect secure. Taking text-to-speech system based on WaveNet as an instance, we describe the process of embedding and extracting message in detail, and the experimental results show that the proposed method is nearly perfectly secure when resists state-of-the-art steganalysis.


The use of plant models in deep learning: an application to leaf counting in rosette plants


title: VNF chain allocation and management at data center scale

abstract: Recent advances in network function virtualization have prompted the research community to consider data-center-scale deployments. However, existing tools, such as E2 and SOL, limit VNF chain allocation to rack-scale and provide limited support for management of allocated chains. We define a narrow API to let data center tenants and operators allocate and manage arbitrary VNF chain topologies, and we introduce N et P ack , a new stochastic placement algorithm, to implement this API at data-center-scale. We prototyped the resulting system, dubbed Daisy, using the Sonata platform. In data-center-scale simulations on realistic scenarios and topologies that are orders of magnitude larger than prior work, we achieve in all cases an allocation density within 96% of a recently introduced, theoretically complete, constraint-solver-based placement engine, while being 82x faster on average. In detailed emulation with real packet traces, we find that D aisy performs each of our six API calls with at most one second of throughput drop.


title: Studies on a new solid–liquid Cs separation using the ionic liquid extraction system in comparison with the conventional adsorption

abstract: The process characteristics of a new solid–liquid Cs+ separation from the radioactive liquid waste using a crown ether dicyclohexano-18-crown-6 as an extractant and an ionic liquid of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide were studied. The physicochemical properties of the precipitate containing Cs+ which is an solid–liquid extraction product were investigated to identify the solid–liquid phase separability and Cs+ loading. The Cs+ separation process performances were compared experimentally with those of the conventional adsorption system for pretreatment requirement and the amount of Cs+ waste. It was observed that the solid–liquid separation method using ILs has high Cs+ separation performance especially in the low Cs+ concentration.


title: Gaze-touch: combining gaze with multi-touch for interaction on the same surface

abstract: Gaze has the potential to complement multi-touch for interaction on the same surface. We present gaze-touch, a technique that combines the two modalities based on the principle of 'gaze selects, touch manipulates'. Gaze is used to select a target, and coupled with multi-touch gestures that the user can perform anywhere on the surface. Gaze-touch enables users to manipulate any target from the same touch position, for whole-surface reachability and rapid context switching. Conversely, gaze-touch enables manipulation of the same target from any touch position on the surface, for example to avoid occlusion. Gaze-touch is designed to complement direct-touch as the default interaction on multi-touch surfaces. We provide a design space analysis of the properties of gaze-touch versus direct-touch, and present four applications that explore how gaze-touch can be used alongside direct-touch. The applications demonstrate use cases for interchangeable, complementary and alternative use of the two modes of interaction, and introduce novel techniques arising from the combination of gaze-touch and conventional multi-touch.


title: Hierarchical spatio-temporal context modeling for action recognition

abstract: The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases under controlled environments or focus on excessively localized features without sufficiently encapsulating the spatio-temporal context. In this paper, we propose to model the spatio-temporal context information in a hierarchical way, where three levels of context are exploited in ascending order of abstraction: 1) point-level context (SIFT average descriptor), 2) intra-trajectory context (trajectory transition descriptor), and 3) inter-trajectory context (trajectory proximity descriptor). To obtain efficient and compact representations for the latter two levels, we encode the spatiotemporal context information into the transition matrix of a Markov process, and then extract its stationary distribution as the final context descriptor. Building on the multichannel nonlinear SVMs, we validate this proposed hierarchical framework on the realistic action (HOHA) and event (LSCOM) recognition databases, and achieve 27% and 66% relative performance improvements over the state-of-the-art results, respectively. We further propose to employ the Multiple Kernel Learning (MKL) technique to prune the kernels towards speedup in algorithm evaluation.


title: Unethical Research: How to Create a Malevolent Artificial Intelligence

abstract: Cybersecurity research involves publishing papers about malicious exploits as much as publishing information on how to design tools to protect cyber-infrastructure. It is this information exchange between ethical hackers and security experts, which results in a well-balanced cyber-ecosystem. In the blooming domain of AI Safety Engineering, hundreds of papers have been published on different proposals geared at the creation of a safe machine, yet nothing, to our knowledge, has been published on how to design a malevolent machine. Availability of such information would be of great value particularly to computer scientists, mathematicians, and others who have an interest in AI safety, and who are attempting to avoid the spontaneous emergence or the deliberate creation of a dangerous AI, which can negatively affect human activities and in the worst case cause the complete obliteration of the human species. This paper provides some general guidelines for the creation of a Malevolent Artificial Intelligence (MAI).


title: Teredo tunneling‐based secure transmission between UAVs and ground ad hoc networks

abstract: Summary ::: ::: Coordination between distributed ad hoc units requires ambient intelligence to form a guidance system. The network formed by this coordination comprises two variedly operating ad hoc units. One such coordinating network is formed between the unmanned aerial vehicles (UAVs) and the ground nodes. Such network formations require highly sensitive and fault-tolerant data transfer over the secure channels. A secure channel is required that can ensure the secure connectivity between nodes of the two networks. This secure channel can be realized using a teredo tunneling approach. Using this approach, a service-oriented security framework is developed. The proposed framework focuses over security in terms of cognitive, corridor, and localization of the multi-UAV guided system based on the estimation of teredo units using Gauss–Markov-based α − β − γ filter. The effectiveness of the proposed approach is demonstrated, with significant improvements in terms of system fairness, data delivery ratio, network throughput, and average delays in comparison with the baseline and Hubenko architecture. The proposed model provides 16.25% better fairness, 12% better data rate, 15.7% better throughput, and 10.3% lesser delays than the baseline and 12.9% better fairness, 10% better data rate, 8% better throughput, and 7.9% lesser delays than the Hubenko architecture. Copyright © 2016 John Wiley & Sons, Ltd.


title: AI Challenges in Human-Robot Cognitive Teaming

abstract: Among the many anticipated roles for robots in future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome on the hardware and control sides to make robots fit for work with humans, the added complication here is that humans have many conscious and subconscious expectations of their teammates -- indeed, teaming is mostly a cognitive rather than physical coordination activity. This focus on cognitive coordination, however, introduces new challenges for the robotics community that require fundamental changes to the traditional view of autonomous agents. ::: In this paper, we provide an analysis of the differences between traditional autonomous robots and robots that team with humans, identifying the necessary teaming capabilities that are largely missing from current robotic systems. We then focus on the important challenges that are unique and of particular importance to human-robot teaming, especially from the point of view of the deliberative process of the autonomous agent, and sketch potential ways to address them.


Virtual Reality-Based Attention Bias Modification Training for Social Anxiety: A Feasibility and Proof of Concept Study


title: Impact of Community Structure on SAT Solver Performance

abstract: Modern CDCL SAT solvers routinely solve very large industrial SAT instances in relatively short periods of time. It is clear that these solvers somehow exploit the structure of real-world instances. However, to-date there have been few results that precisely characterise this structure. In this paper, we provide evidence that the community structure of real-world SAT instances is correlated with the running time of CDCL SAT solvers. It has been known for some time that real-world SAT instances, viewed as graphs, have natural communities in them. A community is a sub-graph of the graph of a SAT instance, such that this sub-graph has more internal edges than outgoing to the rest of the graph. The community structure of a graph is often characterised by a quality metric called Q. Intuitively, a graph with high-quality community structure (high Q) is easily separable into smaller communities, while the one with low Q is not. We provide three results based on empirical data which show that community structure of real-world industrial instances is a better predictor of the running time of CDCL solvers than other commonly considered factors such as variables and clauses. First, we show that there is a strong correlation between the Q value and Literal Block Distance metric of quality of conflict clauses used in clause-deletion policies in Glucose-like solvers. Second, using regression analysis, we show that the the number of communities and the Q value of the graph of real-world SAT instances is more predictive of the running time of CDCL solvers than traditional metrics like number of variables or clauses. Finally, we show that randomly-generated SAT instances with 0.05 ≤ Q ≤ 0.13 are dramatically harder to solve for CDCL solvers than otherwise.


Measuring Emotional Arousal in Clickbait: Eye-Tracking Approach.