title: Commodity or competitive advantage? Analysis of the ERP value paradox

abstract: A true e-business enabled firm needs the support from a well-tuned enterprise resource planning (ERP) system for providing real time data. However, many companies complain that after their huge investments in ERP systems, they found the ERP systems do not bring them new orders, new profits, or competitive advantage as ERP vendors claim. Academic studies also found mixed results regarding ERP's payoff. In line with resource based view (RBV), the study proposes an integrated model to shed light on the ERP value paradox. We try to answer ''With what organizational resources and by building what firm specific capabilities, the investment in ERP systems may bring firms competitive advantage''. Using a sample of 150 ERP and e-business adopters in the US, we found that (1) organizational resources such as managerial skills and organizational change management play a more important role than IT resources (ERP, e-Business technologies) in generating business integration capability. (2) However, neither IT resources nor organizational resources directly provide firms with competitive advantage. Instead, business integration capability built from the two resources plays a mediating role through which business achieves competitive advantage.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Knowing Me, Knowing You: The Accuracy and Unique Predictive Validity of Self-Ratings and Other-Ratings of Daily Behavior

abstract: Many people assume that they know themselves better than anyone else knows them. Recent research on inaccuracies in self-perception, however, suggests that self-knowledge may be more limited than people typically assume. In this article, the authors examine the possibility that people may know a person as well as (or better than) that person knows himself or herself. In Study 1, the authors document the strength of laypeople's beliefs that the self is the best expert. In Study 2, the authors provide a direct test of self- and other-accuracy using an objective and representative behavioral criterion. To do this, the authors compared self- and other-ratings of daily behavior to real-life measures of act frequencies assessed unobtrusively over 4 days. Our results show that close others are as accurate as the self in predicting daily behavior. Furthermore, accuracy varies across behaviors for both the self and for others, and the two perspectives often independently predict behavior. These findings suggest that there is no single perspective from which a person is known best and that both the self and others possess unique insight into how a person typically behaves.


title: Discovering influential authors in heterogeneous academic networks by a co-ranking method

abstract: Research in ranking networked entities is widely applicable to many problems such as optimizing search engines, building recommendation systems and discovering influential nodes in social networks. However, many famous ranking approaches like PageRank are limited to solving this problem in homogeneous networks and are not applicable to heterogeneous networks. Faced with this problem, we propose a co--ranking method to evaluate scientific publications and authors. This novel approach is a flexible framework based on a set of customized rules taking into account both topological features of networks and the included citations. The approach ranks authors and publications iteratively and uses the results of each round to reinforce the ranks of authors and publications. Unlike traditional approaches to assessing publication, which require a great number of citations, our method lowers this requirement. This co--ranking approach has been validated using data collected from DBLP and CiteSeer, and the results suggest that it is effective and efficient in ranking authors and publications based on limited numbers of citations in heterogeneous networks and that it has fast convergence.


title: An Evaluation of Inclusive Dialogue-Based Interfaces for the Takeover of Control in Autonomous Cars

abstract: This paper presents formative research to inform the design of intelligent automotive user interfaces. It describes an evaluation of dialogue-based interfaces, mediating the driver to take back control from the autonomous mode of a car. Four concepts designed to increase driver Situation Awareness were evaluated in a driving simulator. They used dialogue-based interaction, where driving-related information was either asked from or repeated by the driver, with the alternative of a countdown-based interface with no additional information. An inclusive set of participants, with a wide age spectrum, tested the interfaces. The shorter and simpler interaction of the countdown timer was most accepted. The interface seeking answers to driving-related questions came next, and the interface requiring repetition of driving-related information, even when augmented by visual and tactile cues, was least accepted. Design guidelines on utilizing dialogue as a means of keeping the driver in the loop during a takeover were thus derived.


title: GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

abstract: The accuracy of the positions of a pedestrian is very important and useful information for the statistics, advertisement, and safety of different applications. Although the GPS chip in a smartphone is currently the most convenient device to obtain the positions, it still suffers from the effect of multipath and nonline-of-sight propagation in urban canyons. These reflections could greatly degrade the performance of a GPS receiver. This paper describes an approach to estimate a pedestrian position by the aid of a 3-D map and a ray-tracing method. The proposed approach first distributes the numbers of position candidates around a reference position. The weighting of the position candidates is evaluated based on the similarity between the simulated pseudorange and the observed pseudorange. Simulated pseudoranges are calculated using a ray-tracing simulation and a 3-D map. Finally, the proposed method was verified through field experiments in an urban canyon in Tokyo. According to the results, the proposed approach successfully estimates the reflection and direct paths so that the estimate appears very close to the ground truth, whereas the result of a commercial GPS receiver is far from the ground truth. The results show that the proposed method has a smaller error distance than the conventional method.


title: Nonlinear control design for open-loop unstable quadratic discrete-time systems

abstract: This paper deals with the problem of local stabilization, with a guaranteed stability domain and performance, of open-loop unstable quadratic discrete-time systems. For some polyhedral region of the state-space containing the origin, a method is derived for designing a nonlinear static state feedback controller that ensures an enlarged stability domain for the closed-loop system. The stabilization method is then extended to ensure a quadratic performance for a set of admissible initial states. The controller designs are tailored via finite sets of state-dependent linear matrix inequalities. Numerical examples are presented to illustrate the proposed control designs.


title: Clinical Nurse Leader Impact on Clinical Microsystems Outcomes

abstract: The clinical nurse leader (CNL) role is being rapidly implemented in healthcare settings. A major component of the CNL role is to provide a leader at the center of the microsystem to promote quality outcomes. To examine the impact of CNLs at the Department of Veterans Affairs Tennessee Valley Healthcare System, outcomes from 5 diverse microsystems were evaluated before and after CNL implementation using electronic scheduling system reports, patient medical records, and quality improvement reports. Statistically significant improvements were found in all 5 of the indicators evaluated. The authors discuss the implications of improvement for clinical, satisfaction, and financial performance.


title: Dynamic Facial Expression Analysis and Synthesis With MPEG-4 Facial Animation Parameters

abstract: This paper describes a probabilistic framework for faithful reproduction of dynamic facial expressions on a synthetic face model with MPEG-4 facial animation parameters (FAPs) while achieving very low bitrate in data transmission. The framework consists of a coupled Bayesian network (BN) to unify the facial expression analysis and synthesis into one coherent structure. At the analysis end, we cast the FAPs and facial action coding system (FACS) into a dynamic Bayesian network (DBN) to account for uncertainties in FAP extraction and to model the dynamic evolution of facial expressions. At the synthesizer, a static BN reconstructs the FAPs and their intensity. The two BNs are connected statically through a data stream link. Using the coupled BN to analyze and synthesize the dynamic facial expressions is the major novelty of this work. The novelty brings about several benefits. First, very low bitrate (9 bytes per frame) in data transmission can be achieved. Second, a facial expression is inferred through both spatial and temporal inference so that the perceptual quality of animation is less affected by the misdetected FAPs. Third, more realistic looking facial expressions can be reproduced by modelling the dynamics of human expressions.


title: Aircraft Ground-Taxiing Model for Congested Airport Using Cellular Automata

abstract: Efficient airport surface operation is considered key to successful implementation of 4-D trajectories. Here, an airport surface aircraft model is developed to improve simulation accuracy. The new simulation method is developed based on the Nagel-Schreckenberg (NS) model, which is a car congestion model, and it considers the taxiing speed and the time histories of taxiing, particularly for a heavy traffic environment. To validate the model, airport surface surveillance data at Tokyo International Airport are used, and it is proven that the congestion phenomenon is modeled well with an average accuracy of about 30 s.


title: Affine and Regional Dynamic Time Warping

abstract: Pointwise matches between two time series are of great importance in time series analysis, and dynamic time warping (DTW) is known to provide generally reasonable matches. There are situations where time series alignment should be invariant to scaling and offset in amplitude or where local regions of the considered time series should be strongly reflected in pointwise matches. Two different variants of DTW, affine DTW (ADTW) and regional DTW (RDTW), are proposed to handle scaling and offset in amplitude and provide regional emphasis respectively. Furthermore, ADTW and RDTW can be combined in two different ways to generate alignments that incorporate advantages from both methods, where the affine model can be applied either globally to the entire time series or locally to each region. The proposed alignment methods outperform DTW on specific simulated datasets, and one-nearest-neighbor classifiers using their associated difference measures are competitive with the difference measures associated with state-of-the-art alignment methods on real datasets.


title: Design of a lane change driver assistance system, with implementation and testing on motorbike

abstract: This paper addresses the problem of the development, implementation and testing of a Lane Change Decision Aid System on a motorcycle, by using a short range radar sensor and a set of LEDs to interface with the driver. First, a feasibility analysis for such application is performed, then the algorithm is described, and finally the results of on-road tests are presented to illustrate and validate the method. The algorithm is composed by a first block for stabilizing the detected objects through a finite state machine and filtering the data coming from the sensor by implementing a Kalman filter that reduces the sensor's inaccuracies. A decision block sets the state of the system by checking the position and speed of the detected objects, and finally an HMI block computes the Hazard Level considering position and Time-to-Collision of the detected objects and delivers a warning to the driver.


title: An Indoor Location-Aware System for an IoT-Based Smart Museum

abstract: The new technologies characterizing the Internet of Things (IoT) allow realizing real smart environments able to provide advanced services to the users. Recently, these smart environments are also being exploited to renovate the users’ interest on the cultural heritage, by guaranteeing real interactive cultural experiences. In this paper, we design and validate an indoor location-aware architecture able to enhance the user experience in a museum. In particular, the proposed system relies on a wearable device that combines image recognition and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. The localization information is obtained by a Bluetooth low energy (BLE) infrastructure installed in the museum. Moreover, the system interacts with the Cloud to store multimedia contents produced by the user and to share environment-generated events on his/her social networks. Finally, several location-aware services, running in the system, control the environment status also according to users’ movements. These services interact with physical devices through a multiprotocol middleware. The system has been designed to be easily extensible to other IoT technologies and its effectiveness has been evaluated in the MUST museum, Lecce, Italy.


title: Microfabricated adhesive mimicking gecko foot-hair.

abstract: The amazing climbing ability of geckos has attracted the interest of philosophers and scientists alike for centuries1,2,3. However, only in the past few years2,3 has progress been made in understanding the mechanism behind this ability, which relies on submicrometre keratin hairs covering the soles of geckos. Each hair produces a miniscule force ≈10−7 N (due to van der Waals and/or capillary interactions) but millions of hairs acting together create a formidable adhesion of ≈10 N cm−2: sufficient to keep geckos firmly on their feet, even when upside down on a glass ceiling. It is very tempting3 to create a new type of adhesive by mimicking the gecko mechanism. Here we report on a prototype of such 'gecko tape' made by microfabrication of dense arrays of flexible plastic pillars, the geometry of which is optimized to ensure their collective adhesion. Our approach shows a way to manufacture self-cleaning, re-attachable dry adhesives, although problems related to their durability and mass production are yet to be resolved.


title: Does parenting affect children's eating and weight status?

abstract: BackgroundWorldwide, the prevalence of obesity among children has increased dramatically. Although the etiology of childhood obesity is multifactorial, to date, most preventive interventions have focused on school-aged children in school settings and have met with limited success. In this review, we focus on another set of influences that impact the development of children's eating and weight status: parenting and feeding styles and practices. Our review has two aims: (1) to assess the extent to which current evidence supports the hypothesis that parenting, via its effects on children's eating, is causally implicated in childhood obesity; and (2) to identify a set of promising strategies that target aspects of parenting, which can be further evaluated as possible components in childhood obesity prevention.MethodsA literature review was conducted between October 2006 and January 2007. Studies published before January 2007 that assessed the association between some combination of parenting, child eating and child weight variables were included.ResultsA total of 66 articles met the inclusion criteria. The preponderance of these studies focused on the association between parenting and child eating. Although there was substantial experimental evidence for the influence of parenting practices, such as pressure, restriction, modeling and availability, on child eating, the majority of the evidence for the association between parenting and child weight, or the mediation of this association by child eating, was cross-sectional.ConclusionTo date, there is substantial causal evidence that parenting affects child eating and there is much correlational evidence that child eating and weight influence parenting. There are few studies, however, that have used appropriate meditational designs to provide causal evidence for the indirect effect of parenting on weight status via effects on child eating. A new approach is suggested for evaluating the effectiveness of intervention components and creating optimized intervention programs using a multiphase research design. Adoption of approaches such as the Multiphase Optimization Strategy (MOST) is necessary to provide the mechanistic evidence-base needed for the design and implementation of effective childhood obesity prevention programs.


title: Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques

abstract: Data mining is the process of identifying the hidden patterns from large and complex data. It may provide crucial role in decision making for complex agricultural problems. Data visualisation is also equally important to understand the general trends of the effect of various factors influencing the crop yield. The present study examines the application of data visualisation techniques to find correlations between the climatic factors and rice crop yield. The study also applies data mining techniques to extract the knowledge from the historical agriculture data set to predict rice crop yield for Kharif season of Tropical Wet and Dry climatic zone of India. The data set has been visualised in Microsoft Office Excel using scatter plots. The classification algorithms have been executed in the free and open source data mining tool WEKA. The experimental results provided include sensitivity, specificity, accuracy, F1 score, Mathews correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error. General trends in the data visualisation show that decrease in precipitation in the selected climatic zone increases the rice crop yield and increase in minimum, average or maximum temperature for the season increases the rice crop yield. For the current data set experimental results show that J48 and LADTree achieved the highest accuracy, sensitivity and specificity. Classification performed by LWL classifier displayed the lowest accuracy, sensitivity and specificity results.


title: Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

abstract: In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autonoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.


title: Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions

abstract: Distance or similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance/similarity measures that are applicable to compare two probability density functions, pdf in short, are reviewed and categorized in both syntactic and semantic relationships. A correlation coefficient and a hierarchical clustering technique are adopted to reveal similarities among numerous distance/similarity measures.


Performance of VLCC ship with podded propulsion system and rudder


title: Magnetic Field Sensors Based on Giant Magnetoresistance (GMR) Technology: Applications in Electrical Current Sensing

abstract: The 2007 Nobel Prize in Physics can be understood as a global recognition to the rapid development of the Giant Magnetoresistance (GMR), from both the physics and engineering points of view. Behind the utilization of GMR structures as read heads for massive storage magnetic hard disks, important applications as solid state magnetic sensors have emerged. Low cost, compatibility with standard CMOS technologies and high sensitivity are common advantages of these sensors. This way, they have been successfully applied in a lot different environments. In this work, we are trying to collect the Spanish contributions to the progress of the research related to the GMR based sensors covering, among other subjects, the applications, the sensor design, the modelling and the electronic interfaces, focusing on electrical current sensing applications.


title: An agent-based model for emotion contagion and competition in online social media

abstract: Recent studies suggest that human emotions diffuse in not only real-world communities but also online social media. However, a comprehensive model that considers up-to-date findings and multiple online social media mechanisms is still missing. To bridge this vital gap, an agent-based model, which concurrently considers emotion influence and tie strength preferences, is presented to simulate the emotion contagion and competition. Our model well reproduces patterns observed in the empirical data, like anger’s preference on weak ties, anger-dominated users’ high vitalities and angry tweets’ short retweet intervals, and anger’s competitiveness in negative events. The comparison with a previously presented baseline model further demonstrates its effectiveness in modeling online emotion contagion. It is also surprisingly revealed by our model that as the ratio of anger approaches joy with a gap less than 12%, anger will eventually dominate the online social media and arrives the collective outrage in the cyber space. The critical gap disclosed here can be indeed warning signals at early stages for outrage control. Our model would shed lights on the study of multiple issues regarding emotion contagion and competition in terms of computer simulations.


title: Noninvasive and Nonocclusive Blood Pressure Estimation Via a Chest Sensor

abstract: The clinical demand for a device to monitor blood pressure (BP) in ambulatory scenarios with minimal use of inflation cuffs is increasing. Based on the so-called pulse wave velocity (PWV) principle, this paper introduces and evaluates a novel concept of BP monitor that can be fully integrated within a chest sensor. After a preliminary calibration, the sensor provides nonocclusive beat-by-beat estimations of mean arterial pressure (MAP) by measuring the pulse transit time (PTT) of arterial pressure pulses travelling from the ascending aorta toward the subcutaneous vasculature of the chest. In a cohort of 15 healthy male subjects, a total of 462 simultaneous readings consisting of reference MAP and chest PTT were acquired. Each subject was recorded at three different days: D, D+3, and D+14. Overall, the implemented protocol induced MAP values to range from 80 ± 6 mmHg in baseline, to 107 ± 9 mmHg during isometric handgrip maneuvers. Agreement between reference and chest-sensor MAP values was tested by using intraclass correlation coefficient (ICC = 0.78) and Bland-Altman analysis (mean error = 0.7 mmHg, standard deviation = 5.1 mmHg). The cumulative percentage of MAP values provided by the chest sensor falling within a range of ±5 mmHg compared to reference MAP readings was of 70%, within ±10 mmHg was of 91%, and within ±15 mmHg was of 98%. These results point at the fact that the chest sensor complies with the British Hypertension Society requirements of Grade A BP monitors, when applied to MAP readings. Grade A performance was maintained even two weeks after having performed the initial subject-dependent calibration. In conclusion, this paper introduces a sensor and a calibration strategy to perform MAP measurements at the chest. The encouraging performance of the presented technique paves the way toward an ambulatory compliant, continuous, and nonocclusive BP monitoring system.


title: Stroke in women.

abstract: Morbidity and mortality rates for stroke are higher in men than women with the exception of subarachnoid hemorrhage. Temporal trends are similar in the two sexes with a slowdown in the decline in mortality rates and a leveling or increase in morbidity rates. Women are more likely than men to have cervical bruits but less likely to have carotid stenosis on ultrasonography. Some factors associated with stroke are unique to women: pregnancy, use of oral contraceptives, postmenopausal hormonal replacement, and choriocarcinoma. Postmenopausal hormonal use is associated with a decreased risk of stroke in observational studies but clinical trials are needed. Control of hypertension and cessation of smoking to reduce the risk of stroke is equally effective in both sexes. Aspirin and ticlopidine are effective in secondary prevention of stroke in women, whereas women may be more sensitive to warfarin than men.


title: Scalable proximity estimation and link prediction in online social networks

abstract: Proximity measures quantify the closeness or similarity between nodes in a social network and form the basis of a range of applications in social sciences, business, information technology, computer networks, and cyber security. It is challenging to estimate proximity measures in online social networks due to their massive scale (with millions of users) and dynamic nature (with hundreds of thousands of new nodes and millions of edges added daily). To address this challenge, we develop two novel methods to efficiently and accurately approximate a large family of proximity measures. We also propose a novel incremental update algorithm to enable near real-time proximity estimation in highly dynamic social networks. Evaluation based on a large amount of real data collected in five popular online social networks shows that our methods are accurate and can easily scale to networks with millions of nodes. To demonstrate the practical values of our techniques, we consider a significant application of proximity estimation: link prediction, i.e., predicting which new edges will be added in the near future based on past snapshots of a social network. Our results reveal that (i) the effectiveness of different proximity measures for link prediction varies significantly across different online social networks and depends heavily on the fraction of edges contributed by the highest degree nodes, and (ii) combining multiple proximity measures consistently yields the best link prediction accuracy.


A Testicular Torsion and Contralateral Hydrocele in a Neonate


title: The Average Mixing Matrix Signature

abstract: Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.


title: Short-term Electric Load Prediction Using Multiple Linear Regression Method

abstract: This paper provides new techniques to predict electric loads using a multiple linear regression (MLR) model, which adopts a statistical approach that assumes that past load and weather data can provide information for forecasting the target load. However, there are some application problems when the observed data is insufficient or the reference load deviates from the training data set. To solve these problems, we introduce new methods such as approximately adaptive searching and compensation. The results of case study show whether our new methods work well with real data.


title: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

abstract: Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering. Several applications are considered.


title: Security threats in cloud computing

abstract: Cloud computing is set of resources and services offered through the Internet. Cloud services are delivered from data centers located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. General example of cloud services is Google apps, provided by Google and Microsoft SharePoint. The rapid growth in field of “cloud computing” also increases severe security concerns. Security has remained a constant issue for Open Systems and internet, when we are talking about security cloud really suffers. Lack of security is the only hurdle in wide adoption of cloud computing. Cloud computing is surrounded by many security issues like securing data, and examining the utilization of cloud by the cloud computing vendors. The wide acceptance www has raised security risks along with the uncountable benefits, so is the case with cloud computing. The boom in cloud computing has brought lots of security challenges for the consumers and service providers. How the end users of cloud computing know that their information is not having any availability and security issues? Every one poses, Is their information secure? This study aims to identify the most vulnerable security threats in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing. Our work will enable researchers and security professionals to know about users and vendors concerns and critical analysis about the different security models and tools proposed.


title: Mean shift-based clustering

abstract: In this paper, a mean shift-based clustering algorithm is proposed. The mean shift is a kernel-type weighted mean procedure. Herein, we first discuss three classes of Gaussian, Cauchy and generalized Epanechnikov kernels with their shadows. The robust properties of the mean shift based on these three kernels are then investigated. According to the mountain function concepts, we propose a graphical method of correlation comparisons as an estimation of defined stabilization parameters. The proposed method can solve these bandwidth selection problems from a different point of view. Some numerical examples and comparisons demonstrate the superiority of the proposed method including those of computational complexity, cluster validity and improvements of mean shift in large continuous, discrete data sets. We finally apply the mean shift-based clustering algorithm to image segmentation.


title: Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification

abstract: We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis—invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.


title: HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT

abstract: Recent developments in memory-augmented neural networks allowed sequential problems requiring long-term memory to be solved, which were intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to an external memory component can be encoded geometrically through a novel HyperNEAT-based Neural Turing Machine (HyperNTM). The indirect HyperNEAT encoding allows for training on small memory vectors in a bit vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy nine bit vectors can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, the HyperNTM approach could now allow memory-augmented neural networks to scale to problems requiring large memory vectors and sequence lengths.


title: Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

abstract: Data intensive analysis is the major challenge in smart cities because of the ubiquitous deployment of various kinds of sensors. The natural characteristic of geodistribution requires a new computing paradigm to offer location-awareness and latency-sensitive monitoring and intelligent control. Fog Computing that extends the computing to the edge of network, fits this need. In this paper, we introduce a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to integrate intelligence in our Fog Computing architecture, e.g., to perform data representation and feature extraction, to identify anomalous and hazardous events, and to offer optimal responses and controls. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.


title: Cancer Chemoprevention by Caroteno

abstract: Carotenoids are natural fat-soluble pigments that provide bright coloration to plants and animals. Dietary intake of carotenoids is inversely associated with the risk of a variety of cancers in different tissues. Preclinical studies have shown that some carotenoids have potent antitumor effects both in vitro and in vivo, suggesting potential preventive and/or therapeutic roles for the compounds. Since chemoprevention is one of the most important strategies in the control of cancer development, molecular mechanism-based cancer chemoprevention using carotenoids seems to be an attractive approach. Various carotenoids, such as β-carotene, -carotene, lycopene, lutein, zeaxanthin, β-cryptoxanthin, fucoxanthin, canthaxanthin and astaxanthin, have been proven to have anti-carcinogenic activity in several tissues, although high doses of β-carotene failed to exhibit chemopreventive activity in clinical trials. In this review, cancer prevention using carotenoids are reviewed and the possible mechanisms of action are described.


title: Social Software in Higher Education: The Diversity of Applications and Their Contributions to Students’ Learning Experiences

abstract: Higher education institutions are increasingly using social software tools to support teaching and learning. Despite the fact that social software is often used in a social context, these applications can significantly contribute to the educational experience of a student. However, as the social software domain comprises a considerable diversity of tools, the respective tools can be expected to differ in the way they can contribute to teaching and learning. In this review on the educational use of social software, we systematically analyze and compare the diverse social software tools and identify their contributions to teaching and learning. By integrating established learning theory and the extant literature on the individual social software applications we seek to contribute to a theoretical foundation for social software use and the choice of tools. Case vignettes from several UK higher education institutions are used to illustrate the different applications of social software tools in teaching and learning.


Clustering Based On Association Rule Hypergraphs


title: Segmentation Based Approach to Detect Parasites and RBCs in Blood Cell Images

abstract: paper presents an approach to detect the parasites and RBCs in the blood cell images in view of malaria detection. We have used S and V component images of HSV color model. These images are segmented by using Zack's thresholding technique, sequential edge linking algorithm, Euclidian distance based clustering. Proposed approach is implemented with Pathology and CDC DPDx blood cell images.


title: A study on power system's transient stability in determination of the appropriate generator type for wind turbines: Comparison between fixed-speed induction generator equipped with energy storage system and doubly-fed induction generator

abstract: With an ever-increasing importance of wind power generation in power systems, the necessity of choosing the appropriate type of technology for the wind turbine generators becomes more significant. In the current paper, fixed and variable speed wind turbines and the application of energy storage system (ESS) to smoothen the wind farm output power are studied. A detailed analysis of the influence of equipping the fixed speed induction generators (FSIG) with an energy storage system on the power system's transient stability is presented. Further, a comparison is made between the wind farm with variable speed induction generators (DFIG) and wind farm with ESS-equipped FSIG to suggest the better choice of generator for the wind farm from power system transient stability point of view. The simulations are performed using PSCAD software, with a 9 bus system. It is suggested that ESS-equipped FSIG wind turbine has a better transient performance than DFIG wind turbine and would be the better choice in wind farms.


Neural Network Optimization


title: Design of a Novel 4-DOF Wrist-Type Surgical Instrument With Enhanced Rigidity and Dexterity

abstract: This paper presents a new type of 4-degree-of-freedom (DOF) robotic surgical instrument for a minimally invasive surgical robot system. The forceps wrist mechanism was designed here on the basis of the 3-DOF parallel structure with three prismatic-spherical-revolute kinematic chains. The pitch and yaw motions of the moving platform generated the wrist rotational motions of the forceps. The axial translation of the parallel mechanism was converted into the forceps grasp motion by an inversion of the slider-crank mechanism. Furthermore, for a more dexterous movement of the forceps, a full revolution of the forceps for the axial rotation is also possible with the instrument. While the proposed instrument realized all the required DOFs of a forceps, the parallel structure of the wrist and the driving mechanism that was designed using only rod elements made the proposed instrument more reliable and rigid than other wire-driven instruments. The kinematic constraints and inverse kinematics of the proposed instrument were derived. Furthermore, the screw-based Jacobian was formulated geometrically, and the static force relation and the linear constraints on a twist were derived. Finally, a prototype of the proposed instrument with a diameter of 8 mm was introduced, and the performance of the prototype was verified through several experiments.


title: Economic denial of sustainability (EDoS) mitigation approaches in cloud: Analysis and open challenges

abstract: Economic denial of sustainability (EDoS) appeared to be a new menace of cloud computing. This pristine attack is a breed of DoS or DDoS attack that targets the vulnerabilities of cloud consumers utility pricing model. EDoS attackers steadily send illegitimate traffic to gradually consume cloud resources such as virtual machines, virtual network devices, virtual security devices and databases so that it can trigger auto scaling features of cloud. Consequently, targeted cloud consumer ends up with an exorbitant amount of bill that may lead to bankruptcy. The other side effect of this attack is the persistent degradation of services faced by benign cloud users. This paper gives an overview of mitigation approaches proposed by researchers over the years exclusively for EDoS attack defense. We present a taxonomy of EDoS mitigation strategies with circumspect. The taxonomy focus evaluation metric used to mitigate EDoS along with its applicability in cloud environment. We further explore some open challenges that are yet to come into the light. Finally, a probable future direction is given at the end of this paper.


title: Essays in the Political Economy of Natural Resource Booms

abstract: I study the political effects of resource booms on the behavior of politicians, citizens and government and discuss why the existing literature provide unsatisfactory answers to understand the complex nature of resource booms. In the first chapter, I explore how resource booms affect the incentives of politicians and how this is related to the contested issue of the impact of natural resources on living standards. Then, in the second chapter, I analyze how resource booms can affect the citizens’ beliefs about the performance of democracy and its implications in terms of quality of the democratic regime. Finally, in the third chapter, I study how resource booms affect the performance of local government in terms of delivering public goods in an efficient manner. Together, these chapters offer a complete understanding of some of the most relevant issues highlighted by the theoretical and empirical literatures of the so-called resource curse. Are resource booms beneficial or detrimental for citizens? What is the role of politicians’ incentives in this regard? In the first chapter “The Political Effects of Resource Booms: Political Outcomes, Clientelism and Public Goods Provision in Peru” I exploit variation in natural resource rents and mineral production among Peruvian municipalities to analyze the impact of resource booms on local politicians’ behavior and citizens’ well-being. Although this topic has recently attracted the attention of several scholars, the existing empirical evidence remains inconclusive regarding whether resource booms are beneficial or detrimental to citizens via their effects on public good provision and welfare outcomes. I argue that, despite the fact that many of the existing theoretical models allow for the possibility of non-monotonic responses, the empirical literature has mostly approached this phenomena using linear models, failing to correctly understand the nature of resource booms. Exploiting the recent extraordinary increase in mineral prices along with a set of rules for the distribution of natural resource rents in Peru, I show that the effects of resource booms in reelection outcomes, political competition, and public goods provision are function of the size of the rents in a non-monotonic fashion. For municipalities that experienced a modest increase in rents, the evidence suggests that the boom is associated with increases in public good provision and living standards, whereas the opposite occurs for the case of extremely rich municipalities in terms of mining rents. These results are robust to endogenous production responses and are consistent with a simple model of electoral competition in a resource rich economy. In the second chapter “Resource Booms and Political Support: Evidence from Peru”, I use the same identification strategy as in Chapter 1 in order to study how resource booms can affect the performance of democracy in resource-rich areas. I motivate this chapter by noticing that resource abundance has been usually associated with poor democratic performance. Particularly, some scholars suggest that in resource-rich countries democracy faces constraints to consolidate and survive. Interestingly, current theoretical explanations emphasize the role of politicians and elites in this regard implicitly assuming that citizens are always pro-democracy. However, historical and empirical evidence suggest that, in countries where democracy is new or unconsolidated, citizens are critical about its performance and willing to replace it with an authoritarian regime if they perceive this regime is better for delivering the policy outcomes they care about. In this chapter, I study this issue exploiting sub-national exogenous variation across mineral-rich local governments in Peru related to the allocation of mineral resource rents during a recent boom in mineral prices. Using a difference in difference approach, I estimate a non-monotonic effect of natural resource rents on the perception about the effectiveness of democracy. For modest increases in rents a positive impact on citizens’ support for democracy is observed whereas the opposite occurs in districts that experienced large transfers. These results are consistent with a model on citizen’s learning about the effectiveness of democracy during a resource boom. Finally, in the third chapter “Natural Resource Windfalls and Efficiency of Local Government Expenditures: Evidence from Peru” I analyze the role of natural resource windfalls in explaining the efficiency of public expenditures. Using a rich dataset of expenditures and public good provision for 1,836 municipalities in Peru for period 2001-2010, I estimate a non-monotonic relationship between the efficiency of public good provision and the level of natural resource transfers. Local governments that were extremely favored by the boom of mineral prices were more efficient in using fiscal windfalls whereas those benefited with modest transfers were more inefficient. These results can be explained by the increase in political competition associated with the boom, as it was found in Chapter 1. However, the fact that increases in efficiency were related to reductions in public good provision casts doubts about the beneficial effects of political competition in promoting efficiency. These chapters provide different and more complex views than the existing literature. They emphasize the existence of non-monotonic patterns in the relationship between resource booms and political and economic outcomes that were not previously addressed empirically exploiting subnational variation to uncover causality. This approach allows us to understand the previously inconsistent findings in the literature with respect to the lack of impacts on living standards despite the large increase in rents experienced by resource-rich areas.


Hexacarbon Neuropathy


title: The development of reaching in blind children

abstract: The development of object awareness was studied longitudinally in five blind infants by presenting them with tasks designed to investigate the sequential development of reaching and search. The tasks tapped the relative importance of sound and touch in eliciting a reach and the development of object permanence from Stage 3 to Stage 5. The children's pattern of mastery of the tasks suggests that it is touch rather than sound which initially cues the children to the existence and/or location of an object and that the sequential development of object permanence in blind children parallels that of sighted children.


Smart Farm: Extending Automation To The Farm Level


title: Kinematic and kinetic comparisons of transfemoral amputee gait using C-Leg and Mauch SNS prosthetic knees.

abstract: The C-Leg (Otto Bock, Duderstadt, Germany) is a microprocessor-controlled prosthetic knee that may enhance amputee gait. This intrasubject randomized study compared the gait biomechanics of transfemoral amputees wearing the C-Leg with those wearing a common noncomputerized prosthesis, the Mauch SNS (Ossur, Reykjavik, Iceland). After subjects had a 3-month acclimation period with each prosthetic knee, typical gait biomechanical data were collected in a gait laboratory. At a controlled walking speed (CWS), peak swing phase knee-flexion angle decreased for the C-Leg group compared with the Mauch SNS group (55.2 degrees +/- 6.5 degrees vs 64.41 degrees +/- 5.8 degrees , respectively; p = 0.005); the C-Leg group was similar to control subjects' peak swing knee-flexion angle (56.0 degrees +/- 3.4 degrees ). Stance knee-flexion moment increased for the C-Leg group compared with the Mauch SNS group (0.142 +/- 0.05 vs 0.067 +/- 0.07 N"m, respectively; p = 0.01), but remained significantly reduced compared with control subjects (0.477 +/- 0.1 N"m). Prosthetic limb step length at CWS was less for the C-Leg group compared with the Mauch SNS group (0.66 +/- 0.04 vs 0.70 +/- 0.06 m, respectively; p = 0.005), which resulted in increased symmetry between limbs for the C-Leg group. Subjects also walked faster with the C-Leg versus the Mauch SNS (1.30 +/- 0.1 vs 1.21 +/- 0.1 m/s, respectively; p = 0.004). The C-Leg prosthetic limb vertical ground reaction force decreased compared with the Mauch SNS (96.3 +/- 4.7 vs 100.3 +/- 7.5 % body weight, respectively; p = 0.0092).


title: Recursive estimator for blind MIMO equalization via BSS and fractional sampling

abstract: This paper addresses the problem of blind equalization based on a multi-input multi-output (MIMO) model. Using a more general structure for MIMO channels, we present a method that combines fractional sampling and a blind source separation (BSS) algorithm to recover transmitted symbols in the presence of ISI (intersymbol interference). Oversampling allows for converting the FIR MIMO model to an instantaneous mixing model. The equalization may then be performed via BSS. We propose a recursive estimator stemming from the Kalman filter for this task. The method achieves equalization even in the case of a slowly time-varying channel and in the presence of additive noise. Simulation results are presented illustrating the good performance of the method.


title: Introducing Neuroberry, a platform for pervasive EEG signaling in the IoT domain

abstract: The emergence of inexpensive off-the-shelf wireless EEG devices led researchers to explore novel paradigms in the field of Human Computer Interaction. In fact, the compliance of these devices with the IoT principles towards pervasive EEG signaling in smart home environments enables new models of interaction and a different perspective from traditional affective computing. In this paper, the implementation of wireless EEG (Emotiv EPOC and Mindawave) IoT connectivity of real time raw signals, through IoT hardware devices and through the Raspberry Pi 2, is presented.


title: Learning Disentangled Representations with Semi-Supervised Deep Generative Models

abstract: Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework's ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.


title: Image biomarker standardisation initiative

abstract: The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics). Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field. Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers. The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.


title: Electronic mail forensic algorithm for crime investigation and dispute settlement

abstract: Over the past decades, the Electronic Mail (email) has replaced the traditional postal system. Email service is ubiquitously used to transmit text and multimedia message globally. The high reliance on the email service has motivated criminals to exploit email applications for spreading malicious messages. Moreover, policing the cyberspace has been a complicated issue considering factors such as difficult to pinpoint culprits and jurisdictional complexity. Existing research publications address this problem by considering email header analysis techniques. Nevertheless, current email applications are susceptible to spoofed emails that often contain fake email header. This study presents the email investigation algorithm for criminal investigation and dispute settlement. We present the three-tiered algorithm that can be used by law enforcement and other investigation units in order to identify the culprits spreading malicious and disputed emails. Unlike existing publications, our study considers email header, email server logs, and local devices analysis in addressing email related disputes. Finally, we present a case study that shows the applicability of our algorithm.


title: Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance

abstract: Subordinate-level categorization typically rests on establishing salient distinctions between part-level characteristics of objects, in contrast to basic-level categorization, where the presence or absence of parts is determinative. We develop an approach for subordinate categorization in vision, focusing on an avian domain due to the fine-grained structure of the category taxonomy for this domain. We explore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Training pose detectors requires a relatively large amount of training data per category when done from scratch; using a subordinate-level approach, we exploit a pose classifier trained at the basic-level, and extract part appearance and shape information to build subordinate-level models. Our model associates the underlying image pattern parameters used for detection with corresponding volumetric part location, scale and orientation parameters. These parameters implicitly define a mapping from the image pixels into a pose-normalized appearance space, removing view and pose dependencies, facilitating fine-grained categorization from relatively few training examples.


Byzantine Consensus from Moderately-Hard Puzzles : A Model for Bitcoin


title: Data clustering using particle swarm optimization

abstract: This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.


title: Symmetry in locomotor central pattern generators and animal gaits

abstract: Animal locomotion is controlled, in part, by a central pattern generator (CPG), which is an intraspinal network of neurons capable of generating a rhythmic output1,2,3,4. The spatio-temporal symmetries of the quadrupedal gaits walk, trot and pace5,6,7,8 lead to plausible assumptions about the symmetries of locomotor CPGs9,10,11. These assumptions imply that the CPG of a quadruped should consist of eight nominally identical subcircuits, arranged in an essentially unique matter. Here we apply analogous arguments to myriapod CPGs. Analyses based on symmetry applied to these networks lead to testable predictions, including a distinction between primary and secondary gaits, the existence of a new primary gait called ‘jump’, and the occurrence of half-integer wave numbers in myriapod gaits. For bipeds, our analysis also predicts two gaits with the out-of-phase symmetry of the walk and two gaits with the in-phase symmetry of the hop. We present data that support each of these predictions. This work suggests that symmetry can be used to infer a plausible class of CPG network architectures from observed patterns of animal gaits.


title: The design space of shape-changing interfaces: a repertory grid study

abstract: Technologies for shape-changing user interfaces are rapidly evolving, but our understanding of the design space of such interfaces is still limited. We report a repertory grid study that aims to describe the design space from the users' point of view by eliciting personal constructs about shape-change. The study is based on six similar-sized, shape-changing artifacts that combine simple sensing of users with actuation that change volume, texture, and orientation. Our results show that the 18 respondents distinguish artifacts on dimensions that differ from those of most models of shape change. For instance, they characterize shape-change in terms of personality, territoriality, and state of mind, in addition to more common categories such as appearance and product properties. We discuss how the dimensions derived from users might be used to design shape-changing interfaces.


title: Cooperative gestures: multi-user gestural interactions for co-located groupware

abstract: Multi-user, touch-sensing input devices create opportunities for the use of cooperative gestures -- multi-user gestural interactions for single display groupware. Cooperative gestures are interactions where the system interprets the gestures of more than one user as contributing to a single, combined command. Cooperative gestures can be used to enhance users' sense of teamwork, increase awareness of important system events, facilitate reachability and access control on large, shared displays, or add a unique touch to an entertainment-oriented activity. This paper discusses motivating scenarios for the use of cooperative gesturing and describes some initial experiences with CollabDraw, a system for collaborative art and photo manipulation. We identify design issues relevant to cooperative gesturing interfaces, and present a preliminary design framework. We conclude by identifying directions for future research on cooperative gesturing interaction techniques.


title: Power loss analysis of multi-phase and modular interleaved boost DC-DC converters with coupled inductor for electric vehicles

abstract: Efficiency is one of the most important aspects to consider in the design of electric systems for mobility applications. In this study, the interface between the storage system and the inverter is considered. This interface is a step-up DC-DC converter aimed to boost the energy storage voltage to the inverter voltage. This paper introduces the analysis, design, and comparison of four topologies of the interleaved boost DC-DC converter evaluating the effect of magnetic four-phase coupled inductorcoupling in multi-phase and modular circuits. Additionally, a novel idea of a four-phase coupled inductor is presented. These power DC-DC converters are designed in order to find the suitable arrangement with the best efficiency.


title: A signal conditioner for high-frequency inductive position sensors

abstract: The use of integrated circuit techniques in position sensing applications can offer significant advantages over discrete component solutions, such as miniaturization, reduced cost, increased reliability and enhanced sensitivity. We realized a signal conditioner that includes an application specific integrated circuit (ASIC) and an external microcontroller for readout and control of non-contact high-frequency inductive position sensors. Both the system architecture and details on the key blocks are described. The ASIC was designed in a 0.6-?m CMOS process technology and preliminary measured results are presented. The signal conditioner architecture is universal and can be used for other sensor types such as LVDTs.


title: A Review Paper on Oculus Rift-A Virtual Reality Headset

abstract: Oculus rift: Virtual reality (VR) is a burgeoning field that has the inherent potential of manipulating peoples mind with a superlative 3D experience. Oculus rift is one such application that assists in achieving the same. With the fleeting enhancements in VR it now seems very feasible to provide the user with experiences that were earlier thought to be merely a dream or a nightmare.


title: Dual-polarized omnidirectional planar slot antenna for WLAN applications

abstract: A novel planar slot antenna is presented in this paper. The antenna provides dual-polarization and omnidirectional radiation patterns. Hence, it is suitable for hand-held equipments of wireless systems. The proposed antenna is optimized for operation at 5.2 GHz. The antenna, without ground plane extension, occupies an area of 2.17/spl times/2.17 cm/sup 2/. An impedance bandwidth of 10.6% is obtained, with a very good isolation between the two excitation ports. Appealing omnidirectional radiation patterns are maintained over the entire impedance bandwidth. The gains of the two polarizations are 2.59 and 3.52 dB, while the radiation efficiencies are 93% and 99.8%.


title: SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets

abstract: Time series analysis, as an application for high dimensional data mining, is a common task in biochemistry, meteorology, climate research, bio-medicine or marketing. Similarity search in data with increasing dimensionality results in an exponential growth of the search space, referred to as Curse of Dimensionality. A common approach to postpone this effect is to apply approximation to reduce the dimensionality of the original data prior to indexing. However, approximation involves loss of information, which also leads to an exponential growth of the search space. Therefore, indexing an approximation with a high dimensionality, i. e. high quality, is desirable. We introduce Symbolic Fourier Approximation (SFA) and the SFA trie which allows for indexing of not only large datasets but also high dimensional approximations. This is done by exploiting the trade-off between the quality of the approximation and the degeneration of the index by using a variable number of dimensions to represent each approximation. Our experiments show that SFA combined with the SFA trie can scale up to a factor of 5--10 more indexed dimensions than previous approaches. Thus, it provides lower page accesses and CPU costs by a factor of 2--25 respectively 2--11 for exact similarity search using real world and synthetic data.


title: Game-based learning: The use of Kahoot in teacher education

abstract: Recently, technologies have been increasingly used in the educational context, not only because they facilitate communication and access to information, but also because of their motivational potential. Game-based learning through digital tools such as Kahoot! combines game dynamics with the potential of monitoring student learning. In this sense, this article reports a study carried out in a teacher program, in order to analyze future teachers and teacher educators' perspectives on the use of the game-based learning platform Kahoot!. The participants point out students' greater involvement and participation and the promotion of learning as main advantages of this digital tool.


title: A Commercial Conducting Polymer as Both Binder and Conductive Additive for Silicon Nanoparticle-Based Lithium-Ion Battery Negative Electrodes

abstract: This work describes silicon nanoparticle-based lithium-ion battery negative electrodes where multiple nonactive electrode additives (usually carbon black and an inert polymer binder) are replaced with a single conductive binder, in this case, the conducting polymer PEDOT:PSS. While enabling the production of well-mixed slurry-cast electrodes with high silicon content (up to 95 wt %), this combination eliminates the well-known occurrence of capacity losses due to physical separation of the silicon and traditional inorganic conductive additives during repeated lithiation/delithiation processes. Using an in situ secondary doping treatment of the PEDOT:PSS with small quantities of formic acid, electrodes containing 80 wt % SiNPs can be prepared with electrical conductivity as high as 4.2 S/cm. Even at the relatively high areal loading of 1 mg/cm2, this system demonstrated a first cycle lithiation capacity of 3685 mA·h/g (based on the SiNP mass) and a first cycle efficiency of ∼78%. After 100 repeated cycles a...


title: Gland Instance Segmentation by Deep Multichannel Side Supervision

abstract: In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional and boundary patterns, with side supervision (deep supervision on side responses) in gland histology images. Our proposed system, deep multichannel side supervision (DMCS), alleviates heavy feature design due to the use of convolutional neural networks guided by side supervision. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge, we observe state-of-the-art results based on a number of evaluation metrics.


title: A fuzzy optimization method for multicriteria decision making: An application to reservoir flood control operation

abstract: This paper presents a fuzzy optimization method based on the concept of ideal and anti-ideal points to solve multi-criteria decision making problems under fuzzy environments. The quantitative criteria values of each alternative are represented by triangular fuzzy numbers, and its qualitative counterparts and the weight of each criterion are described by linguistic terms, which can also be expressed as triangular fuzzy numbers in the proposed method. With the definition of fuzzy ideal and anti-ideal weight distances, an objective function is constructed to derive the optimal evaluation for each alternative denoted by a fuzzy membership degree. The ranking of alternatives and the best one can be determined directly on the basis of the fuzzy membership degrees without a need to compare fuzzy numbers. The evaluation process is simple and easy to use in practice. A case study of reservoir flood control operation is given to demonstrate the proposed method's effectiveness.


title: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

abstract: Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.


title: PiOS: Detecting Privacy Leaks in iOS Applications

abstract: With the introduction of Apple’s iOS and Google’s Android operating systems, the sales of smartphones have exploded. These smartphones have become powerful devices that are basically miniature versions of personal computers. However, the growing popularity and sophistication of smartphones have also increased concerns about the privacy of users who operate these devices. These concerns have been exacerbated by the fact that it has become increasingly easy for users to install and execute third-party applications. To protect its users from malicious applications, Apple has introduced a vetting process. This vetting process should ensure that all applications conform to Apple’s (privacy) rules before they can be offered via the App Store. Unfortunately, this vetting process is not welldocumented, and there have been cases where malicious applications had to be removed from the App Store after


title: Cognitive and affective development in adolescence

abstract: Questions about the nature of normative and atypical development in adolescence have taken on special significance in the last few years, as scientists have begun to recast old portraits of adolescent behavior in the light of new knowledge about brain development. Adolescence is often a period of especially heightened vulnerability as a consequence of potential disjunctions between developing brain, behavioral and cognitive systems that mature along different timetables and under the control of both common and independent biological processes. Taken together, these developments reinforce the emerging understanding of adolescence as a critical or sensitive period for a reorganization of regulatory systems, a reorganization that is fraught with both risks and opportunities.


title: Neural Machine Translation of Text from Non-Native Speakers

abstract: Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that augmenting training data with sentences containing artificially-introduced grammatical errors can make the system more robust to such errors. In combination with an automatic grammar error correction system, we can recover 1.5 BLEU out of 2.4 BLEU lost due to grammatical errors. We also present a set of Spanish translations of the JFLEG grammar error correction corpus, which allows for testing NMT robustness to real grammatical errors.


title: Efficient Antifungal and Flame-Retardant Properties of ZnO-TiO2-Layered Double-Nanostructures Coated on Bamboo Substrate

abstract: A facile method to synthesize ZnO-TiO2-layered double-nanostructures with the average thickness of 20 μm on a bamboo substrate was proposed to improve the antifungal and flame-retardant properties. The cross-linked wurtzite ZnO nanostructures with an average thickness of approximately 0.14 μm were uniformly distributed on the anatase TiO2 surface. The energy-dispersive X-ray spectroscopy (EDS) confirmed that the ZnO-TiO2 coating on bamboo was a layered double nanostructure. During a two-month antifungal test conducted in an outdoor environment, the fungi began to grow after one week on pristine bamboo and three weeks on ZnO-bamboo and TiO2-bamboo. Furthermore, there was an infected area of 100% after four weeks for pristine bamboo and six weeks for ZnO-bamboo, while there was an infected area of 43% after eight weeks for TiO2-bamboo. By comparison, there was no visible fungal growth on ZnO-TiO2-bamboo until the end of the test. The electron spin resonance (ESR) technique has demonstrated that the reactive oxygen species (ROS) of •O2− and •OH were produced from the ZnO-TiO2 surface under visible light irradiation (λ > 420 nm). This large quantity of •O2− compared to •OH is considered to be mainly responsible for the inactivation of fungi. Additionally, the limiting oxygen index has increased from 25.6% to 30.2% after being covered with a ZnO-TiO2 coating, which revealed a significant enhancement of its flame-retardant property.


title: A 3D object detection and pose estimation pipeline using RGB-D images

abstract: 3D object detection and pose estimation has been studied extensively in recent decades for its potential applications in robotics. However, there still remains challenges when we aim at detecting multiple objects while retaining low false positive rate in cluttered environments. This paper proposes a robust 3D object detection and pose estimation pipeline based on RGB-D images, which can detect multiple objects simultaneously while reducing false positives. Detection begins with template matching and yields a set of template matches. A clustering algorithm then groups templates of similar spatial location and produces multiple-object hypotheses. A scoring function evaluates the hypotheses using their associated templates and non-maximum suppression is adopted to remove duplicate results based on the scores. Finally, a combination of point cloud processing algorithms are used to compute objects' 3D poses. Existing object hypotheses are verified by computing the overlap between model and scene points. Experiments demonstrate that our approach provides competitive results comparable to the state-of-the-arts and can be applied to robot random bin-picking.


title: Smart mm-Wave Beam Steering Algorithm for Fast Link Re-Establishment under Node Mobility in 60 GHz Indoor WLANs

abstract: Millimeter-wave (mm-wave) wireless local area networks (WLANs) are expected to provide multi-Gbps connectivity by exploiting the large amount of unoccupied spectrum in e.g. the unlicensed 60 GHz band. However, to overcome the high path loss inherent at these high frequencies, mm-wave networks must employ highly directional beamforming antennas, which makes link establishment and maintenance much more challenging than in traditional omnidirectional networks. In particular, maintaining connectivity under node mobility necessitates frequent re-steering of the transmit and receive antenna beams to re-establish a directional mm-wave link. A simple exhaustive sequential scanning to search for new feasible antenna sector pairs may introduce excessive delay, potentially disrupting communication and lowering the QoS. In this paper, we propose a smart beam steering algorithm for fast 60 GHz link re-establishment under node mobility, which uses knowledge of previous feasible sector pairs to narrow the sector search space, thereby reducing the associated latency overhead. We evaluate the performance of our algorithm in several representative indoor scenarios, based on detailed simulations of signal propagation in a 60 GHz WLAN in WinProp with realistic building materials. We study the effect of indoor layout, antenna sector beamwidth, node mobility pattern, and device orientation awareness. Our results show that the smart beam steering algorithm achieves a 7-fold reduction of the sector search space on average, which directly translates into lower 60 GHz link re-establishment latency. Our results also show that our fast search algorithm selects the near-optimal antenna sector pair for link re-establishment.


title: High-Curie-temperature relaxor piezoelectric single crystals for 1.5D ultrasound phased-array applications

abstract: Abstract In this letter, a 2.5 MHz 320-element 1.5D active matrix phased-array ultrasound transducer was designed and fabricated using high-temperature relaxor-based piezoelectric ternary Pb(In 1/2 Nb 1/2 )O 3 –Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 (PIN–PMN–PT) single crystals. The experiment and finite element analysis with the PZFlex package software were developed to design and optimize the 1.5D transducer. The FEM results show that the −6 dB fractional bandwidth is up to 83% using two λ /3.75 matching layers. A 64×5 active element PIN–PMN–PT single-crystal phased array was fabricated and tested. The measured center frequency and −6 dB fractional bandwidth were 2.5 MHz and 71%, respectively. Elevation beam profiles were obtained using a hydrophone, which showed that the measured −6 dB slice thickness was approximately 3 mm throughout the entire range of field of interest. The PIN–PMN–PT single crystal with over 30 degree usage temperature and higher E c field should satisfy the demands of advanced high-density ultrasound transducer applications.


title: Goal-oriented Trajectories for Efficient Exploration

abstract: Exploration is a difficult challenge in reinforcement learning and even recent state-of-the art curiosity-based methods rely on the simple epsilon-greedy strategy to generate novelty. We argue that pure random walks do not succeed to properly expand the exploration area in most environments and propose to replace single random action choices by random goals selection followed by several steps in their direction. This approach is compatible with any curiosity-based exploration and off-policy reinforcement learning agents and generates longer and safer trajectories than individual random actions. To illustrate this, we present a task-independent agent that learns to reach coordinates in screen frames and demonstrate its ability to explore with the game Super Mario Bros. improving significantly the score of a baseline DQN agent.


title: Beyond war and PTSD: The crucial role of transition stress in the lives of military veterans

abstract: Abstract Although only a relatively small minority of military veterans develop Posttraumatic Stress Disorder (PTSD), mental health theory and research with military veterans has focused primarily on PTSD and its treatment. By contrast, many and by some accounts most veterans experience high levels of stress during the transition to civilian life, however transition stress has received scant attention. In this paper we attempt to address this deficit by reviewing the wider range of challenges, rewards, successes, and failures that transitioning veterans might experience, as well as the factors that might moderate these experiences. To illuminate this argument, we briefly consider what it means to become a soldier (i.e., what is required to transition into military service) and more crucially what kind of stressors veterans might experience when they attempt to shed that identity (i.e., what is required to transition out of military service). We end by suggesting how an expanded research program on veteran transition stress might move forward.


title: Dense CNN Learning with Equivalent Mappings

abstract: Large receptive field and dense prediction are both important for achieving high accuracy in pixel labeling tasks such as semantic segmentation. These two properties, however, contradict with each other. A pooling layer (with stride 2) quadruples the receptive field size but reduces the number of predictions to 25\%. Some existing methods lead to dense predictions using computations that are not equivalent to the original model. In this paper, we propose the equivalent convolution (eConv) and equivalent pooling (ePool) layers, leading to predictions that are both dense and equivalent to the baseline CNN model. Dense prediction models learned using eConv and ePool can transfer the baseline CNN's parameters as a starting point, and can inverse transfer the learned parameters in a dense model back to the original one, which has both fast testing speed and high accuracy. The proposed eConv and ePool layers have achieved higher accuracy than baseline CNN in various tasks, including semantic segmentation, object localization, image categorization and apparent age estimation, not only in those tasks requiring dense pixel labeling.


LuxR- and Acyl-Homoserine-Lactone-Controlled Non-lux Genes Define a Quorum-Sensing Regulon in Vibrio fischeri


title: A Motion Planning Strategy for the Active Vision-Based Mapping of Ground-Level Structures

abstract: This paper presents a strategy to guide a mobile ground robot equipped with a camera or depth sensor, in order to autonomously map the visible part of a bounded 3-D structure. We describe motion planning algorithms that determine appropriate successive viewpoints and attempt to fill holes automatically in a point cloud produced by the sensing and perception layer. The emphasis is on accurately reconstructing a 3-D model of a structure of moderate size rather than mapping large open environments, with applications for example in architecture, construction, and inspection. The proposed algorithms do not require any initialization in the form of a mesh model or a bounding box, and the paths generated are well adapted to situations where the vision sensor is used simultaneously for mapping and for localizing the robot, in the absence of additional absolute positioning system. We analyze the coverage properties of our policy, and compare its performance with the classic frontier-based exploration algorithm. We illustrate its efficacy for different structure sizes, levels of localization accuracy, and range of the depth sensor, and validate our design on a real-world experiment. Note to Practitioners —The objective of this paper is to automate the process of building a 3-D model of a structure of interest that is as complete as possible, using a mobile camera or depth sensor, in the absence of any prior information about this structure. Given that increasingly robust solutions for the visual simultaneous localization and mapping problem are now readily available, the key challenge that we address here is to develop motion planning policies to control the trajectory of the sensor in a way that improves the mapping performance. We target in particular scenarios where no external absolute positioning system is available, such as mapping certain indoor environments where GPS signals are blocked. In this case, it is often important to revisit previously seen locations relatively quickly, in order to avoid excessive drift in the dead-reckoning localization system. Our system works by first determining the boundaries of the structure, before attempting to fill the holes in the constructed model. Its performance is illustrated through simulations, and a real-world experiment performed with a depth sensor carried by a mobile manipulator.


title: Differential Dual-Band Dual-Polarized Dielectric Resonator Antenna

abstract: This communication presents a differential dual-band dual-polarized dielectric resonator antenna (DRA) with high isolation and low cross polarization using a cross-shaped DR. In the differential DRA, some of the higher order modes are eliminated and electromagnetic fields of each excited mode have no interaction with its corresponding orthogonal mode. Two groups of modes are utilized to realize dual-band characteristic and each group with two orthogonal modes is used to supply dual-polarized function. Differential feeding provides the DRA with suppression of unwanted modes, much higher isolation and lower cross-polarization in both frequency bands, compared with the traditional single-end feeding. The experimental prototypes of the proposed differential DRA and its single-ended counterpart are designed and implemented. The comparison between them is also made in reflection coefficient, isolation, and radiation properties. Both simulated and measured results show significantly improved performance of the proposed differential DRA.


title: Efficient FPGA Implementation of Sigmoid and Bipolar Sigmoid Activation Functions for Multilayer Perceptrons

abstract: Artificial Neural Networks base their processing capabilities in a parallel architecture. This makes them extremely useful in pattern recognition, system identification and control problems. Multilayer Perceptron is an artificial neural network with one or more hidden layers. The Activation function determines the performance of a Multilayer Perceptron. In Multi Layer Perceptron, the most commonly used activation functions are sigmoid and bipolar sigmoid activation functions. In this paper we present a FPGA based digital hardware implementation of Sigmoid and Bipolar Sigmoid Activation function. The digital hardware was designed for 32 bit fixed point arithmetic and was modeled using Verilog HDL. The synthesis tool used was Xilinx 10.1 ISE and the design was implemented in Spartan 3 FPGA.


title: Large-Scale Automatic Classification of Phishing Pages

abstract: Phishing websites, fraudulent sites that impersonate a trusted third party to gain access to private data, continue to cost Internet users over a billion dollars each year. In this paper, we describe the design and performance characteristics of a scalable machine learning classifier we developed to detect phishing websites. We use this classifier to maintain Google’s phishing blacklist automatically. Our classifier analyzes millions of pages a day, examining the URL and the contents of a page to determine whether or not a page is phishing. Unlike previous work in this field, we train the classifier on a noisy dataset consisting of millions of samples from previously collected live classification data. Despite the noise in the training data, our classifier learns a robust model for identifying phishing pages which correctly classifies more than 90% of phishing pages several weeks after training concludes.


title: I'm Sorry, Dave, I'm Afraid I Can't Do That: Chatbot Perception and Expectations

abstract: Artificial intelligence continues to grow in popularity on mobile platforms, increasing exposure to chatbot apps. Chatbot technology has evolved over time, yet the purpose and added value that chatbots offer has not been clearly defined. In order to design a chatbot that provides a meaningful experience, we must first understand what expectations people have for this technology, and what opportunities are there for chatbots based on user needs. This study includes qualitative data from 54 participants in the US and India, sharing their expectations and experiences with a chatbot. The research objectives include:1) understand user perception and expectations of chatbots 2) surface preferences for input modality and 3) identify domains where chatbots can add meaningful purpose.


VHDL Implementation Of Reconfigurable Crossbar Switch For Binoc Router


title: On the PMEPR of Binary Golay Sequences of Length $2^{n}$

abstract: In this paper, some questions on the distribution of the peak-to-mean envelope power ratio (PMEPR) of standard binary Golay sequences are solved. For n odd, we prove that the PMEPR of each standard binary Golay sequence of length 2n is exactly 2, and determine the location(s), where peaks occur for each sequence. For n even, we prove that the envelope power of such sequences can never reach 2n+1 at time points t ∈ {(v/2u)|0 ≤ v ≤ 2u, v,u ∈ N}. We further identify eight sequences of length 24 and eight sequences of length 26 that have PMEPR exactly 2, and raise the question whether, asymptotically, it is possible for standard binary Golay sequences to have PMEPR less than 2 - ϵ, where, ϵ > 0.


title: A latent factor model with a mixture of sparse and dense factors to model gene expression data with confounding effects

abstract: One important problem in genome science is to determine sets of co-regulated genes based on measurements of gene expression levels across samples, where the quantification of expression levels includes substantial technical and biological noise. To address this problem, we developed a Bayesian sparse latent factor model that uses a three parameter beta prior to flexibly model shrinkage in the loading matrix. By applying three layers of shrinkage to the loading matrix (global, factor-specific, and element-wise), this model has non-parametric properties in that it estimates the appropriate number of factors from the data. We added a two-component mixture to model each factor loading as being generated from either a sparse or a dense mixture component; this allows dense factors that capture confounding noise, and sparse factors that capture local gene interactions. We developed two statistics to quantify the stability of the recovered matrices for both sparse and dense matrices. We tested our model on simulated data and found that we successfully recovered the true latent structure as compared to related models. We applied our model to a large gene expression study and found that we recovered known covariates and small groups of co-regulated genes. We validated these gene subsets by testing for associations between genotype data and these latent factors, and we found a substantial number of biologically important genetic regulators for the recovered gene subsets.


title: CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

abstract: Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.


title: Stability analysis of negative impedance converter

abstract: Negative Impedance Converter (NIC) have received much attention to improve the restriction of antenna gain-bandwidth tradeoff relationship. However, a significant problem with NIC is the potential instability of unwanted oscillation due to the presence of positive feedback. For solving this problem, we propose a NIC circuit technique which is stable and has wideband characteristics of negative impedance.


Flare stars in the Pleiades


title: MEMS Reconfigurable Optimized E-Shaped Patch Antenna Design for Cognitive Radio

abstract: Reconfigurable antennas offer attractive potential solutions to solve the challenging antenna problems related to cognitive radio systems using the ability to switch patterns, frequency, and polarization. In this paper, a novel frequency reconfigurable E-shaped patch design is proposed for possible applications in cognitive radio systems. This paper provides a methodology to design reconfigurable antennas with radio frequency microelectromechanical system (RF-MEMS) switches using particle swarm optimization, a nature-inspired optimization technique. By adding RF-MEMS switches to dynamically change the slot dimensions, one can achieve wide bandwidth which is nearly double the original E-shaped patch bandwidth. Utilizing an appropriate fitness function, an optimized design which works in the frequency range from 2 GHz to 3.2 GHz (50% impedance bandwidth at 2.4 GHz ) is obtained. RF-MEMS switch circuit models are incorporated into the optimization as they more effectively represent the actual switch effects. A prototype of the final optimized design is developed and measurements demonstrate good agreement with simulations.


title: Reactive path planning for emergency steering maneuvers on highway roads

abstract: A path planning algorithm is proposed for emergency steering maneuvers on highway roads. A geometric approach is used to generate a graph which is searched to find an optimal path based on a customized cost function. We ensure that the resulted path will be within road boundaries, collision-free and feasible with respect to all kinematic and dynamic constraints of the host vehicle and the surrounding environment. The proposed algorithm is computationally fast and is reactive to all changes on the highway roads. Real-time simulation results in the Hardware-In-the-Loop platform shows the effectiveness of the algorithm in avoiding collision with stationary and moving obstacles on the road.


title: Modified Artificial Killer Whale Optimization Algorithm for Maximum Power Point Tracking under Partial Shading Condition

abstract: Maximum solar power harnessing from photovoltaic (PV) modules under different conditions is extremely important. It becomes prominent for photovoltaic modules to operate at maximum power point during all conditions. This paper proposes a modified artificial whale optimization algorithm for tracking maximum power point of photovoltaic module under partial shaded condition. Simulation and programming involving this MAKWO algorithm is performed in MATLAB/SIMULINK to show the suitability and reliability under one shading pattern only. The obtained results have been juxtaposed with modified artificial wolf pack algorithm (MAWP), artificial bee colony (ABC) and particle swarm optimization (PSO) method which strongly show the excellency and superiority of MAKWO algorithm over all the existing MPPT techniques.


title: Guaranteed Rank Minimization via Singular Value Projection

abstract: Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization under affine constraints (ARMP) and show that SVP recovers the minimum rank solution for affine constraints that satisfy a restricted isometry property (RIP). Our method guarantees geometric convergence rate even in the presence of noise and requires strictly weaker assumptions on the RIP constants than the existing methods. We also introduce a Newton-step for our SVP framework to speed-up the convergence with substantial empirical gains. Next, we address a practically important application of ARMP - the problem of low-rank matrix completion, for which the defining affine constraints do not directly obey RIP, hence the guarantees of SVP do not hold. However, we provide partial progress towards a proof of exact recovery for our algorithm by showing a more restricted isometry property and observe empirically that our algorithm recovers low-rank incoherent matrices from an almost optimal number of uniformly sampled entries. We also demonstrate empirically that our algorithms outperform existing methods, such as those of [5, 18, 14], for ARMP and the matrix completion problem by an order of magnitude and are also more robust to noise and sampling schemes. In particular, results show that our SVP-Newton method is significantly robust to noise and performs impressively on a more realistic power-law sampling scheme for the matrix completion problem.


title: Making existing production systems Industry 4.0-ready

abstract: AbstractThis paper presents an approach to how existing production systems that are not Industry 4.0-ready can be expanded to participate in an Industry 4.0 factory. Within this paper, a concept is presented how production systems can be discovered and included into an Industry 4.0 (I4.0) environment, even though they did not have I4.0-interfaces when they have been manufactured. ::: The concept is based on a communication gateway and an information server. Besides the concept itself, this paper presents a validation that demonstrates applicability of the developed concept.