title: A Novel Phase-Locked Loop Based on Frequency Detector and Initial Phase Angle Detector

abstract: This paper proposes a new three-phase software phase-locked loop (PLL) that operates fast and accurately in unbalanced, polluted, and frequency deviating circumstances. The proposed PLL consists of a frequency detector and an initial phase angle detector. In the synchronous reference frame, the initial phase angle detector tracks a ramp phase angle, which is generated from frequency deviation of the inputs, with steady error. This detection error is utilized to estimate the actual grid frequency. Frequency adaptive moving average filters (MAFs) are applied in this new PLL to eliminate noises, harmonics, and negative sequence components. In this paper, the effect of discrete sampling on the MAFs is analyzed, and a linear interpolation is employed to enhance the performances of the MAFs. The stability of the proposed PLL is also analyzed, a sufficient stability condition is identified, and the design procedures of the control parameters are also presented. Simulations and experiments verify the performances of the novel PLL.


title: Switching terahertz waves with gate-controlled active graphene metamaterials

abstract: The properties of graphene have been widely studied for applications in electronics. Expanding its use in photonics as well, it is now demonstrated that the propagation of terahertz waves can be electronically switched by such a single atomic layer of carbon.


title: PyNetLogo: Linking NetLogo with Python

abstract: Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Pythonâ€™s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogoâ€™s features are demonstrated by controlling one of NetLogoâ€™s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.


Plot Units and Narrative Summarization*


Investigating Image Inpainting via the Alternating DirectionMethod of Multipliers


title: Automatic fruit quality inspection system

abstract: This paper presents the recent development in automatic vision based technology. Use of this technology is increasing in agriculture and fruit industry. An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here. The main aim of this system is to replace the manual inspection system. This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt. Then image processing is done to get required features of fruits such as texture, color and size. Defected fruit is detected based on blob detection, color detection is done based on thresholding and size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.


title: Landmark Detection in Cardiac MRI Using Learned Local Image Statistics

abstract: We propose a supervised learning approach for detecting landmarks in cardiac images from different views. A set of candidate landmark points are obtained using morphological operations and graph cut segmentation. The final landmarks are determined using random forests (RF) classifiers which were trained on low level features derived from the neighborhood of annotated landmarks on training images. We use features like intensity, texture, shape asymmetry and context information for landmark detection. Experimental results on the STACOM LV landmark detection challenge dataset show that our approaching is promising with room for further improvement.


title: Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients

abstract: Current evidence supports a clear association between clinical and pathologic factors and recurrence-free survival (RFS) in breast cancer patients. The Cox regression model is the most common tool for investigating simultaneously the influence of several factors on the survival time of patients. But it gives no estimate of the degree of separation of the different subgroups. We propose to analyze different decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) and use them additionally to the well-known Kaplan-Meier estimates to investigate the predictive power of these methods. Five hundred patients were included to the study. Two hundred and seventy-nine of them had complete data for prognostic factors and median follow-up is about 40.5 months. First, decision tree methods were analyzed for prognostic factors. Then, according to multidimensional scaling method C4.5 (error rate 0.2258 for training set and 0.3259 for cross-validation) performed slightly better than other methods in predicting risk factors for recurrence. Tumor size, age of menarche, hormonal therapy, histological grade and axillary nodal status are found that an important risk factors for the recurrence. Eight terminal nodes were found and stratified by Kaplan-Meier survival curves. Larger tumor size (>=4.4cm) and receiving no hormonal therapy in a small subgroup of patients were associated with worse prognosis. The five-year RFS is 71.3% in the whole patient population. The sensitivity, specificity and predictive rates calculated by C4.5 method were found 43.8%, 91% and 77.4%, respectively. In this study, C4.5 showed a better degree of separation. As a result, we recommend to use decision tree methods together with Kaplan-Meier analysis to determine risk factors and effect of this factors on survival.


title: Vision based real-time fish detection using convolutional neural network

abstract: Underwater vision has specific characteristics such as high attenuation of lights, severe noise and haze in the images. For real-time fish detection using underwater vision, this paper proposes convolutional neural network based techniques based on You Only Look Once algorithm. Actual fish video images were used to evaluate the reliability and accuracy of the proposed method. As a result, the network recorded 93% classification accuracy, 0.634 intersection over union between predicted bounding box and ground truth, and 16.7 frames per second of fish detection. It also outperforms another fish detector using sliding window algorithm and classifier trained with histogram of oriented gradient features and support vector machine.


title: Self Heated Thermo-Resistive Element Hot Wire Anemometer

abstract: A microelectromechanical systems (MEMS) hot wire anemometer consisting of thermoresistive elements arranged in a differential bridge configuration is presented. The excitation of the elements to the point of self heating allows for dedicated heating elements to be omitted from the device without compromising operation or accuracy. Overall power consumption gives air velocity, and the temperature differential of each element pair is used for wind direction calculation and has demonstrated a sensing resolution better than 1% and a repeatability better than 2%.


title: Performance of Feedforward Current Regulators for Field-Oriented Induction Machine Controllers

abstract: To achieve instantaneous control of induction motor torque using field-orientation techniques, it is necessary that the phase currents be controlled to maintain precise instantaneous relationships. Failure to do so results in a noticeable degradation in torque response. Most of the currently used approaches to achieve this control employ classical control strategies which are only correct for steady-state conditions. A modern control theory approach which circumvents these limitations is developed. The approach uses a state-variable feedback control model of the field-oriented induction machine. This state-variable controller is shown to be intrinsically more robust than PI regulators. Experimental verification of the performance of this state-variable control strategy in achieving current-loop performance and torque control at high operating speeds is included.


title: Accurately detecting trolls in Slashdot Zoo via decluttering

abstract: Online social networks like Slashdot bring valuable information to millions of users - but their accuracy is based on the integrity of their user base. Unfortunately, there are many "trolls" on Slashdot who post misinformation and compromise system integrity. In this paper, we develop a general algorithm called TIA (short for Troll Identification Algorithm) to classify users of an online "signed" social network as malicious (e.g. trolls on Slashdot) or benign (i.e. normal honest users). Though applicable to many signed social networks, TIA has been tested on troll detection on Slashdot Zoo under a wide variety of parameter settings. Its running time is faster than many past algorithms and it is significantly more accurate than existing methods.


title: DESIGNING NEXT GENERATION SMART CITY INITIATIVES - HARNESSING FINDINGS AND LESSONS FROM A STUDY OF TEN SMART CITY PROGRAMS

abstract: The proliferation of “Smart Cities” initiatives around the world is part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. As a framework for urban transformation, Smart City initiatives aim to harness Information and Communication Technologies and Knowledge Infrastructures for economic regeneration, social cohesion, better city administration and infrastructure management. However, experiences from earlier Smart City initiatives have revealed several technical, management and governance challenges arising from the inherent nature of a Smart City as a complex “Sociotechnical System of Systems”. While these early lessons are informing modest objectives for planned Smart Cities programs, no rigorous developed framework based on careful analysis of existing initiatives is available to guide policymakers, practitioners, and other Smart City stakeholders. In response to this need, this paper presents a “Smart City Initiative Design (SCID) Framework” grounded in the findings from the analysis of ten major Smart Cities programs from Netherlands, Sweden, Malta, United Arab Emirates, Portugal, Singapore, Brazil, South Korea, China and Japan. The findings provide a design space for the objectives, implementation options, strategies, and the enabling institutional and governance mechanisms for Smart City initiatives.


title: Getting a grip on tangible interaction: a framework on physical space and social interaction

abstract: Our current understanding of human interaction with hybrid or augmented environments is very limited. Here we focus on 'tangible interaction', denoting systems that rely on embodied interaction, tangible manipulation, physical representation of data, and embeddedness in real space. This synthesis of prior 'tangible' definitions enables us to address a larger design space and to integrate approaches from different disciplines. We introduce a framework that focuses on the interweaving of the material/physical and the social, contributes to understanding the (social) user experience of tangible interaction, and provides concepts and perspectives for considering the social aspects of tangible interaction. This understanding lays the ground for evolving knowledge on collaboration-sensitive tangible interaction design. Lastly, we analyze three case studies, using the framework, thereby illustrating the concepts and demonstrating their utility as analytical tools.


title: A survey on green 5G cellular networks

abstract: In this article, we present a brief survey on green 5G cellular networks, and explore some research issues and challenges of the enabling techniques for green broadband wireless networks. The trend of achieving green power in cellular networks is driving network operators and standardization authorities to work together to reduce carbon footprint of their products in order to bring eco friendly 5G green radio cellular base stations. We will first provide an overview on 5G wireless system architecture and the corresponding new protocol stacks. Next, hybrid multiple access schemes, such as combining orthogonal (OFDMA) and non-orthogonal (CDMA) approaches, aiming at improving the both capacity and coverage performances simultaneously are discussed. Since base stations consume the maximum portion of the total energy in current cellular systems, we explore important renewable energy techniques for green base stations, We will provide a comprehensive survey on how to generate renewable energy, such as solar, wind and fuel cells, for green base stations in 5G green cellular networks.


title: Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics

abstract: Evolutionary techniques driven by behavioural diversity, such as novelty search, have shown significant potential in evolutionary robotics. These techniques rely on priorly specified behaviour characterisations to estimate the similarity between individuals. Characterisations are typically defined in an ad hoc manner based on the experimenter's intuition and knowledge about the task. Alternatively, generic characterisations based on the sensor-effector values of the agents are used. In this paper, we propose a novel approach that allows for systematic derivation of behaviour characterisations for evolutionary robotics, based on a formal description of the agents and their environment. Systematically derived behaviour characterisations (SDBCs) go beyond generic characterisations in that they can contain task-specific features related to the internal state of the agents, environmental features, and relations between them. We evaluate SDBCs with novelty search in three simulated collective robotics tasks. Our results show that SDBCs yield a performance comparable to the task-specific characterisations, in terms of both solution quality and behaviour space exploration.


title: Image Processing Based Real Time Vehicle Theft Detection and Prevention System

abstract: A major problem today for car owners is that they are in constant fear of having their vehicles stolen from a common parking lot or from outside their home. Image processing based real time vehicle theft detection and prevention system provide an ultimate solution for this problem. The system described in this paper automatically take photos of driver and compares his or her face with database to check whether he is an authenticated driver or not. He can have access to the vehicle only if he is an authenticated driver. If he is not an authenticated driver an alarm rings and electrical connections are not activated. The technology used here is face recognition and face detection in real time. As the photos are taken in real time, several problems like unequal illumination and changes in the background may affect the system. To overcome this problem DCT normalization and background cancellation algorithms are incorporated along with basic face detection and face recognition algorithm. In this paper software and hardware details of the system are discussed. Technologically system is simple, accurate and maintainable.


title: Extracting Narrative Timelines as Temporal Dependency Structures

abstract: We propose a new approach to characterizing the timeline of a text: temporal dependency structures, where all the events of a narrative are linked via partial ordering relations like BEFORE, AFTER, OVERLAP and IDENTITY. We annotate a corpus of children's stories with temporal dependency trees, achieving agreement (Krippendorff's Alpha) of 0.856 on the event words, 0.822 on the links between events, and of 0.700 on the ordering relation labels. We compare two parsing models for temporal dependency structures, and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser, achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596. Our analysis of the dependency parser errors gives some insights into future research directions.


title: Impact of active power curtailment on overvoltage prevention and energy production of PV inverters connected to low voltage residential feeders

abstract: As non-controllable power sources, photovoltaics (PV) can create overvoltage in low voltage (LV) distribution feeders during periods of high generation and low load. This is usually prevented passively by limiting the penetration level of PV to very conservative values, even if the critical periods rarely occur. Alternatively, one can use active power curtailment (APC) techniques, reducing the amount of active power injected by the PV inverters, as the voltage at their buses increase above a certain value. In this way, it is possible to increase the installed PV capacity and energy yield while preventing overvoltage. This paper investigates a number of approaches for sizing and controlling the PV power generated by 12 net-zero energy houses equipped with large rooftop PV systems in a typical 240 V/75 kVA Canadian suburban radial distribution feeder. Simulations of a one year period with typical solar irradiance and load profiles are conducted with PSCAD to assess the performance of the different approaches in terms of overvoltage occurrence, sharing of the burden for overvoltage prevention per house and total energy yield of the residential PV feeder.


title: Activity Recognition from Accelerometer Data

abstract: Activity recognition fits within the bigger framework of context awareness. In this paper, we report on our efforts to recognize user activity from accelerometer data. Activity recognition is formulated as a classification problem. Performance of base-level classifiers and meta-level classifiers is compared. Plurality Voting is found to perform consistently well across different settings.


title: The neural basis of unconditional love

abstract: Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.


Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index


title: Contrast masking in human vision

abstract: Contrast masking was studied psychophysically. A two-alternative forced-choice procedure was used to measure contrast thresholds for 2.0 cpd sine-wave gratings in the presence of masking sine-wave gratings. Thresholds were measured for 11 masker contrasts spanning three log units, and seven masker frequencies ranging +/- one octave from the signal frequency. Corresponding measurements were made for gratings with horizontal widths of 0.75 degrees (narrow fields) and 6.0 degrees (wide fields). For high contrast maskers at all frequencies, signal thresholds were related to masking contrast by power functions with exponents near 0.6. For a range of low masking contrasts, signal thresholds were reduced by the masker. For the wide fields, high contrast masking tuning functions peaked at the signal frequency, were slightly asymmetric, and had approximately invariant half-maximum frequencies that lie 3/4 octave below and 1 octave above the signal frequency. The corresponding low contrast tuning functions exhibited peak threshold reduction at the signal frequency, with half-minimum frequencies at roughly +/- 0.25 octaves. For the narrow fields, the masking tuning functions were much broader at both low and high masking contrasts. A masking model is presented that encompasses contrast detection, discrimination, and masking phenomena. Central constructs of the model include a linear spatial frequency filter, a nonlinear transducer, and a process of spatial pooling that acts at low contrasts only.


title: Differences in Perceived Parenting Style Between Mothers and Fathers: Implications for Child Outcomes and Marital Conflict

abstract: The current study focused on relations between maternal and paternal perceived parenting style, marital conflict, and child behavior outcomes. Child participants (N = 152) ranged in age from 3 to 9 years old. Reports from both parents on perceived parenting style, marital conflict, and child behavior problems were collected. Results indicated that (a) parenting styles of mothers and fathers were related, (b) mothers’ and fathers’ authoritarianism and permissiveness were associated with increased child internalizing and externalizing behavior problems, (c) marital conflict was significantly related to child behavior problems, (d) when mother and father reported parenting styles differed, increased marital conflict was reported, (e) increased differences between mothers and fathers in self- and spouse-perceived permissiveness were related to increased child externalizing behavior problems, and (f) the direction of the differences between parents (i.e. whether a particular parent reported being more permissive than the other) was linked with marital conflict and child behavior problems. Namely, marital and child outcomes were poorer when mothers saw themselves as more authoritative than fathers and when fathers were more authoritarian than mothers, and outcomes were better when fathers saw themselves as more permissive than mothers and when mothers were more authoritarian than fathers. Implications for marital and family therapy are discussed.


title: A novel SVM modeling approach for highly imbalanced and overlapping classification

abstract: Traditional classification algorithms can be limited in their performance on highly imbalanced and overlapping data sets, In this paper, we focus on modifying support vector machines (SVMs) to make it suitable for highly imbalanced and overlapping (HIO) classification. Based on the analysis of most SVM learning algorithms for imbalanced classification, we argue that in SVM-based algorithms, due to the linearity property of SVM, the key problem is that the increase of the number of correctly predicted minority samples will lead to even more majority samples be misclassified. Then a novel algorithm HIO-SVM is developed, it can recognize all minority samples while minimizing the error rate of majority ones. The proposed approach can identify the non-overlapping samples in one feature space, furthermore, by iteratively shifting kernel spaces, all non-overlapping samples in different kernel spaces are recognized. Because of the highly imbalanced distribution, the remaining overlapping samples can be regarded as minority. Then all minority samples can be predicted correctly and the error rate of majority samples can be guaranteed minimized simultaneously. Finally, numerous case studies show the properties and effectiveness of the proposed HIO-SVM algorithm.


title: Analysing Errors of Open Information Extraction Systems

abstract: We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation of OIE systems.


title: European Union regulations on algorithmic decision-making and a"right to explanation"

abstract: We summarize the potential impact that the European Union’s new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which “significantly affect” users. The law will also effectively create a “right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks which avoid discrimination and enable explanation.


title: Power assistive and rehabilitation wearable robot based on pneumatic soft actuators

abstract: The aim of this paper is to describe the design of a soft, wearable glove for power assisted and rehabilitation, based on pneumatic soft actuators. The extensor bending type of pneumatic soft actuators was used in this study, which proved these actuators are appropriate by examining their characteristics. A proposed solution for a release movement is presented. Experimental results show this solution works efficiently. Electromyography (EMG) signals are monitored to examine the proposed prototype. The proposed glove provides assistive power for multi-griping and multi-pinching movements depending on the human intention. Elderly, partially disabled and strenuous workers can use this glove. An efficient control algorithm used, depending on signals from sensors located within the glove, that capture the movement type and bending angle to provide appropriate assistance. A wide range of rehabilitation exercises can be carried out using this soft wearable glove.


title: A Context-Aware E-Bike System to Reduce Pollution Inhalation While Cycling

abstract: The effect of transport-related pollution on human health is fast becoming recognized as a major issue in cities worldwide. Cyclists, in particular, face great health risks, as they typically are most exposed to tail-pipe emissions. Four avenues are being explored to combat urban air pollution: 1) policy interventions such as outright bans on polluting vehicles, 2) embracing zero tailpipe emission vehicles, 3) measuring air-quality as a means to better informing citizens of zones of higher pollution, and 4) developing smart mobility devices that seek to minimize the effect of polluting devices on citizens as they transport goods and individuals in our cities. Following this latter direction, in this paper, we present a new way to protect cyclists from the effect of urban pollution. Namely, by exploiting the actuation possibilities afforded by pedelecs or e-bikes (electric bikes), we design a cyber-physical system that mitigates the effect of urban pollution by indirectly controlling the ventilation rates (volume of air inhaled per minute) of cyclists in polluted areas. Results from a real device are presented to illustrate the efficacy of our system.


title: Facilitating Learning and Knowledge Transfer through Mentoring

abstract: Mentoring is a human resources development process supporting learning and knowledge transfer. Social media and Web services can be used for learning, communication with mentors and monitoring bringing also other advantages. In this paper formal and informal mentoring aspects and the use of IT in mentoring particularly social media and Web support will be shortly presented. Examples of projects where besides knowledge transfer, formal, informal learning also learning in a Web-based community is used are outlined.


title: Bionics-Inspired Cellular Automaton Model for Pedestrian Dynamics

abstract: We present a 2-dimensional cellular automaton model for the simulation of pedestrian dynamics. Inspired by the principles of chemotaxis the interactions between the pedestrians are mediated by a so-called floor field. This field has a similar effect as the chemical trace created e.g. by ants to guide other individuals to food places. In our case the floor field modifies the transition rates to neighbouring cells. It has its own dynamics (diffusion and decay) and can be changed by the motion of the pedestrians. This means that in our model pedestrians follow a virtual rather than a chemical trace as in the case of chemotaxis. The approach is extremely efficient and makes faster-than-real-time simulations of large crowds possible. Already the inclusion of only nearest-neighbour interactions allows to reproduce many of the collective effects and self-organization phenomena (lane formation, flow oscillations at doors etc.) encountered in pedestrian dynamics.


title: Evolving Better Stoplists for Document Clustering and Web Intelligence

abstract: Text classification, document clustering and similar document analysis tasks are currently the subject of significant global research, since such areas underpin web intelligence, web mining, search engine design, and so forth. A fundamental tool in such document analysis tasks is a list of so-called 'stop' words, called a 'stoplist'. A stoplist is a specific collection of so-called 'noise' words, which tend to appear frequently in documents, but are believed to carry no usable information which would aid learning tasks, and so the idea is that the words in the stoplist are removed from the documents concerned before processing begins. It is well-known that the results of document classification experiments (for example) are invariably considerably improved when a stoplist is employed. Current stoplists in regular use are, however, rather outdated. We have explored this claim in recent work which produced new stoplists based on word-entropy over modem collections of documents. In this work we introduce the notion of optimising a stoplist, and use stochastic search in conjunction with k-means clustering to converge on stoplists which lead to better performance on certain tasks than any previously published.


title: A survey on control of electric power distributed generation systems for microgrid applications

abstract: The introduction of microgrids in distribution networks based on power electronics facilitates the use of renewable energy resources, distributed generation (DG) and storage systems while improving the quality of electric power and reducing losses thus increasing the performance and reliability of the electrical system. The hierarchical control structure, which consists of primary, secondary, and tertiary levels for microgrids that mimic the behavior of the mains grid, is reviewed. The main objective of this article is to give a description of state of the art for distributed power generation systems (DPGS) based on renewable energy and explore the power converters connected in parallel to the grid which are distinguished by their contribution to the formation of the grid voltage and frequency and are accordingly classified in three classes. This analysis is extended focusing mainly on the three classes of configurations: grid-forming, grid-feeding, and grid-supporting. The article ends up with an overview and a discussion of the control structures and strategies to control distribution power generation system (DPGS) units connected to the network.


title: Deep Neural Networks for YouTube Recommendations

abstract: YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.


title: Fail-operational in safety-related automotive multi-core systems

abstract: Dynamically expendable real-time systems are an essential improvement over current future automotive E/E architectures. New functions and applications like automated driving or the subsequent activation of features require a different approach. This also applies to existing architectures which lack enhanced safety concepts beyond common fail-safe systems. Especially, electronic components without mechanical fallback require a fail-operational implementation to guarantee a correct safety-behavior. Although common hardware architectures already provide a couple of safety features, hardware-supported features to realize such systems are under research. In our work-in-progress paper we provide an overview on existing approaches and present future concepts for the implementation of fail-operational systems on a multi-core processor.


title: Spatio-Temporal Markov Random Field for Video Denoising

abstract: This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in the denoising process. Unlike previous algorithms which estimate the level of noise, our method learns the full noise distribution nonparametrically which serves as the likelihood model in the MRF. Instead of using deterministic motion estimation to align pixels, we set up a temporal likelihood by combining a probabilistic motion field with the learned noise model. The prior of this MRF is modeled by piece-wise smoothness. The main advantage of the proposed spatio-temporal MRF is that it integrates spatial and temporal information adaptively into a statistical inference framework, where the posteriori is optimized using graph cuts with alpha expansion. We demonstrate the performance of the proposed approach on benchmark data sets and real videos to show the advantages of our algorithm compared with previous single frame and multi-frame algorithms.


title: An efficient retraction-based RRT planner

abstract: We present a novel optimization-based retraction algorithm to improve the performance of sample-based planners in narrow passages for 3D rigid robots. The retraction step is formulated as an optimization problem using an appropriate distance metric in the configuration space. Our algorithm computes samples near the boundary of C-obstacle using local contact analysis and uses those samples to improve the performance of RRT planners in narrow passages. We analyze the performance of our planner using Voronoi diagrams and show that the tree can grow closely towards any randomly generated sample. Our algorithm is general and applicable to all polygonal models. In practice, we observe significant speedups over prior RRT planners on challenging scenarios with narrow passages.


title: Mobile peer-to-peer video streaming over information-centric networks

abstract: Information Centric Networking (ICN) is a network paradigm alternative to the classic host-centric communication model: it provides users with content exposed as names, instead of providing communication channels between hosts. In this paper, we present a peer-to-peer application for live streaming of video content encoded at multiple bit rates. The application enables a small set of neighbouring cellular/Wi-Fi devices to increase the quality of video playback by using the Wi-Fi network to share the portion of the live stream downloaded by each peer via the cellular network. The application exploits the main functionalities of ICN: routing by name, in network caching and multicast delivery. Our work includes the implementation of a Java prototype of the application on a test-bed composed by Linux machines running the CCNx tool and streaming MPEG-DASH videos. We measured the performance of our solution and verified on the field that ICN simplifies the development of applications, as it provides built-in functions, which would be much more difficult to implement by relying on classical TCP/IP tools only.


title: Dielectric TE dual-mode resonator filters

abstract: In this paper dielectric TE dual-mode resonator filters are discussed. The dielectric core element of the resonators has a cross-shape to provide dual-mode operation. Two through-holes are formed in the center to improve the spurious performance. Since the holes are placed between the two orthogonal resonators, the choice of different diameters cause coupling between the modes. Concepts for realizing cross-coupling and coupling to coaxial air cavity resonators are discussed. Measurement results for a single resonator and two coupled dual-mode resonators are presented. The resulting 4 pole bandpass filter has a center frequency of of 2022.5MHz and a bandwidth of 28 MHz.


title: Mechatronic Design and Characterization of the Index Finger Module of a Hand Exoskeleton for Post-Stroke Rehabilitation

abstract: This paper presents HANDEXOS, a novel wearable multiphalanges device for post-stroke rehabilitation. It was designed in order to allow for a functional and safe interaction with the user's hand by means of an anthropomorphic kinematics and the minimization of the human/exoskeleton rotational axes misalignment. This paper describes the mechatronic design of the exoskeleton's index finger module, simulation, modeling, and development of the actuation unit and sensory system. Experimental results on the validation of the dynamic model and experimental characterization of the index finger module with healthy subjects are reported, showing promising results that encourage further clinical trials.


title: Immunity-Based Intrusion Detection System: A General Framework

abstract: This paper focuses on investigating immunological principles in designing a multi-agent system for intrusion/anomaly detection and response in networked computers. In this approach, the immunity-based agents roam around the machines (nodes or routers), and monitor the situation in the network (i.e. look for changes such as malfunctions, faults, abnormalities, misuse, deviations, intrusions, etc.). These agents can mutually recognize each other's activities and can take appropriate actions according to the underlying security policies. Specifically, their activities are coordinated in a hierarchical fashion while sensing, communicating and generating responses. Such an agent can learn and adapt to its environment dynamically and can detect both known and unknown intrusions. This research is the part of an effort to develop a multi-agent detection system that can simultaneously monitor networked computer's activities at different levels (such as user level, system level, process level and packet level) in order to determine intrusions and anomalies. The proposed intrusion detection system is designed to be flexible, extendible, and adaptable that can perform real-time monitoring in accordance with the needs and preferences of network administrators. This paper provides the conceptual view and a general framework of the proposed system. 1. Inspiration from the nature: Every organism in nature is constantly threatened by other organisms, and each species has evolved elaborate set of protective measures called, collectively, the immune system. The natural immune system is an adaptive learning system that is highly distributive in nature. It employs multi-level defense mechanisms to make rapid, highly specific and often very protective responses against wide variety of pathogenic microorganisms. The immune system is a subject of great research interest because of its powerful information processing capabilities [5,6]. Specifically, its' mechanisms to extract unique signatures from antigens and ability to recognize and classify dangerous antigenic peptides are very important. It also uses memory to remember signature patterns that have been seen previously, and use combinatorics to construct antibody for efficient detection. It is observed that the overall behavior of the system is an emergent property of several local interactions. Moreover, the immune response can be either local or systemic, depending on the route and property of the antigenic challenge [19]. The immune system is consists of different populations of immune cells (mainly B or T cells) which circulate at various primary and secondary lymphoid organs of the body. They are carefully controlled to ensure that appropriate populations of B and T cells (naive, effector, and memory) are recruited into different location [19]. This differential migration of lymphocyte subpopulations at different locations (organs) of the body is called trafficking or homing. The lymph nodes and organs provide specialized local environment (called germinal center) during pathogenic attack in any part of the body. This dynamic mechanism support to create a large number of antigen-specific lymphocytes (as effector and memory cells) for stronger defense through the process of the clonal expansion and differentiation. Interestingly, memory cells exhibit selective homing to the type of tissue in which they first encountered an antigen. Presumably this ensures that a particular memory cell will return to the location where it is most likely to re-encounter a subsequent antigenic challenge. The mechanisms of immune responses are self-regulatory in nature. There is no central organ that controls the functions of the immune system. The regulation of the clonal expansion and proliferation of B cells are closely regulated (with a co-stimulation) in order to prevent uncontrolled immune response. This second signal helps to ensure tolerance and judge between dangerous and harmless invaders. So the purpose of this accompanying signal in identifying a non-self is to minimize false alarm and to generate decisive response in case of a real danger[19]. 2. Existing works in Intrusion Detection: The study of security in computer networks is a rapidly growing area of interest because of the proliferation of networks (LANs, WANs etc.), greater deployment of shared computer databases (packages) and the increasing reliance of companies, institutions and individuals on such data. Though there are many levels of access protection to computing and network resources, yet the intruders are finding ways to entry into many sites and systems, and causing major damages. So the task of providing and maintaining proper security in a network system becomes a challenging issue. Intrusion/Anomaly detection is an important part of computer security. It provides an additional layer of defense against computer misuse (abuse) after physical, authentication and access control. There exist different methods for intrusion detection [7,23,25,29] and the early models include IDES (later versions NIDES and MIDAS), W & S, AudES, NADIR, DIDS, etc. These approaches monitor audit trails generated by systems and user applications and perform various statistical analyses in order to derive regularities in behavior pattern. These works based on the hypothesis that an intruder's behavior will be noticeably different from that of a legitimate user, and security violations can be detected by monitoring these audit trails. Most of these methods, however, used to monitor a single host [13,14], though NADIR and DIDS can collect and aggregate audit data from a number of hosts to detect intrusions. However, in all cases, there is no real analysis of patterns of network activities and they only perform centralized analysis. Recent works include GrIDS[27] which used hierarchical graphs to detect attacks on networked systems. Other approaches used autonomous agent architectures [1,2,26] for distributed intrusion detection. 3. Computer Immune Systems: The security in the field of computing may be considered as analogous to the immunity in natural systems. In computing, threats and dangers (of compromising privacy, integrity, and availability) may arise because of malfunction of components or intrusive activities (both internal and external). The idea of using immunological principles in computer security [9-11,15,16,18] started since 1994. Stephanie Forrest and her group at the University of New Mexico have been working on a research project with a long-term goal to build an artificial immune system for computers [911,15,16]. This immunity-based system has much more sophisticated notions of identity and protection than those afforded by current operating systems, and it is suppose to provide a general-purpose protection system to augment current computer security systems. The security of computer systems depends on such activities as detecting unauthorized use of computer facilities, maintaining the integrity of data files, and preventing the spread of computer viruses. The problem of protecting computer systems from harmful viruses is viewed as an instance of the more general problem of distinguishing self (legitimate users, uncorrupted data, etc.) from dangerous other (unauthorized users, viruses, and other malicious agents). This method (called the negative-selection algorithm) is intended to be complementary to the more traditional cryptographic and deterministic approaches to computer security. As an initial step, the negativeselection algorithm has been used as a file-authentication method on the problem of computer virus detection [9].


title: Project management in a software product line organization

abstract: In traditional software engineering project management, managers provide focused guidance to a team responsible for producing a specific result in a specified amount of time. Today, however, organizations are increasingly taking a product line approach to software to exploit product commonalities. Software product line organizations have unique practices and project definitions. These unconventional features offer new challenges and directions for traditional project management. How does the traditional concept of a project - a temporary endeavor aimed at creating a unique product or service - hold up under this new paradigm? In this article, we discuss this question, along with how the idea of a "project" and project management techniques must expand to fit a product line context. In particular, we show how the "overall guidelines, policies, and procedures" that Thayer and Pyster spoke of some years ago remain crucially important in product line organizations today.


title: Simultaneous stabilization of MIMO systems via robustly stabilizing a central plant

abstract: In this note, a new formulation and solution to the simultaneous stabilization problem (SSP) is given. The new method is based on finding a central plant from a set of plants to be simultaneously stabilized. The theory of robust stabilization can then be applied to the central plant with a bounded perturbation, which encapsulates the plants to be stabilized, in order to solve the SSP.


title: Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification

abstract: Class imbalance learning tackles supervised learning problems where some classes have significantly more examples than others. Most of the existing research focused only on binary-class cases. In this paper, we study multiclass imbalance problems and propose a dynamic sampling method (DyS) for multilayer perceptrons (MLP). In DyS, for each epoch of the training process, every example is fed to the current MLP and then the probability of it being selected for training the MLP is estimated. DyS dynamically selects informative data to train the MLP. In order to evaluate DyS and understand its strength and weakness, comprehensive experimental studies have been carried out. Results on 20 multiclass imbalanced data sets show that DyS can outperform the compared methods, including pre-sample methods, active learning methods, cost-sensitive methods, and boosting-type methods.


title: Fast and High Quality Fusion of Depth Maps

abstract: Reconstructing the 3D surface from a set of provided range images ‐ acquired by active or passive sensors ‐ is an important step to generate faithful virtual models of real objects or environments. Since several approaches for high quality fusion of range images are already known, the runtime efficiency of the respective methods are of increased interest. In this paper we propose a highly efficient method for range image fusion resulting in very accurate 3D models. We employ a variational formulation for the surface reconstruction task. The global optimal solution can be found by gradient descent due to the convexity of the underlying energy functional. Further, the gradient descent procedure can be parallelized, and consequently accelerated by graphics processing units. The quality and runtime performance of the proposed method is demonstrated on wellknown multi-view stereo benchmark datasets.


Elementary Gates for Quantum Computation


title: A friction model with velocity, temperature and load torque effects for collaborative industrial robot joints

abstract: In this paper, a comprehensive friction model for collaborative industrial robot joints is proposed which takes into account the velocity, temperature and load torque effects. The model indicates that the velocity and temperature have a strong influence on viscous friction nonlinearly, whereas load torque significantly influences the Coulomb friction linearly and causes a slight Stribeck effect. The friction characteristics were investigated on the collaborative robot joints which are equipped with temperature sensor and harmonic reducer. The proposed model has been verified successfully under wide velocity, temperature and load range. In addition, the permillage of the rated current of the motor is used to scale the value of joint torque, so that the model can be applied in common industrial robot.


title: Emotional response as a function of symptoms in schizophrenia

abstract: A group of 30 individuals with schizophrenia and a comparison group of 10 individuals who had never experienced a psychotic episode rated the pleasantness and arousal of 54 slides varying in normative pleasantness. Individuals with schizophrenia rated the slides as less pleasant than the comparison group. Positive symptom, negative symptom and disorganization symptom ratings were made for all patients with schizophrenia. Higher levels of disorganization symptoms were associated with lower ratings of pleasant slides and with more negative mood at the conclusion of the study. The conclusion from previous research that the self-reported emotional response of schizophrenia patients is not diminished compared to other groups may be premature, especially for patients with higher levels of disorganization.


title: Ridogrel in the setting of percutaneous transluminal coronary angioplasty.

abstract: The safety of the combination of heparin and ridogrel therapy and its antiplatelet efficacy was examined in the setting of percutaneous transluminal coronary angioplasty (PTCA). In 32 patients without known aspirin intake for 10 days before PTCA, therapy with ridogrel (300-mg intravenous bolus) was begun just before PTCA and continued orally at a dose of 300 mg twice daily until discharge. Heparin was administered as a 10,000 IU bolus dose before PTCA and followed by an intravenous infusion at a rate of 1,000 IU/hour for 24 hours. Bleeding problems at the arterial entry site occurred in 13 patients, which required a blood transfusion in only 2 patients. One patient underwent emergency bypass surgery without specific problems of hemostasis. Ridogrel virtually eliminated thromboxane B2 from the serum (29,990 +/- 6,555 pg/0.1 ml before vs 63 +/- 7 pg/0.1 ml at 2 hours after ridogrel), with a concomitant increase in serum 6-keto-prostaglandin F1 alpha (511 +/- 34 pg/0.1 ml before vs 1,190 +/- 146 pg/0.1 ml at 24 hours after ridogrel). There were no acute reocclusions in the ridogrel-treated patients, whereas acute reocclusions occurred in 5.6% of the patients taking the standard aspirin + heparin regimen during the same period. Furthermore, at 6-month clinical follow-up patients treated with ridogrel compared favorably with those receiving standard treatment.


title: Design of $LCL$ Filters of Active-Front-End Two-Level Voltage-Source Converters

abstract: This paper introduces a new iterative design procedure of L and LCL filters for low-voltage active-front-end PWM two-level voltage source converters. The analytical expression of the converter harmonic voltages by Bessel functions is applied to design the filter parameters for defined maximum grid current harmonics. Different filter designs are derived for various resonance frequencies and inductance split factors of LCL filter. The minimum amount of stored energy of passive components is used to select a final filter design. A voltage-oriented control scheme, including active damping, is applied in the (400 V and 50 kVA) experimental setup. Both simulation and experimental investigations are presented to verify the accuracy of the filter design procedure. Finally, the steady state and transient performance of the active front end with different LCL filters are depicted.


title: Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

abstract: To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.


title: Analysis of Pressure Rise in a Closed Container Due to Internal Arcing

abstract: When an arc fault occurs in a medium-voltage (MV) metal enclosed switchgear, the arc heats the filling gas, resulting in a pressure rise, which may seriously damage the switchgear, the building it is contained in, or even endanger maintenance personnel. A pressure rise calculation method based on computational fluid dynamics (CFD) has been put forward in this paper. The pressure rise was calculated and the arc tests between the copper electrodes were performed in the container under different gap lengths by the current source. The results show that the calculated pressure rise agrees well with the measurement, and the relative error of the average pressure rise is about 2%. Arc volume has less effect on the pressure distribution in the container. Arc voltage Root-Mean-Square (RMS) has significant randomness with the change of arc current, and increases with the increase of gap length. The average arc voltage gradients measure at about 26, 20 and 16 V/cm when the gap lengths are 5, 10 and 15 cm, respectively. The proportion (thermal transfer coefficient kp) of the arc energy leading to the pressure rise in the container is about 44.9%. The pressure is symmetrically distributed in the container before the pressure wave reaches the walls and the process of the energy release is similar to an explosion. The maximum overpressure in the corner is increased under the reflection and superimposition effects of the pressure wave, but the pressure waves will be of no importance any longer than a few milliseconds in the closed container.


title: Systems modeling and simulation applications for critical care medicine

abstract: Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area.


title: RayCore: A Ray-Tracing Hardware Architecture for Mobile Devices

abstract: We present RayCore, a mobile ray-tracing hardware architecture. RayCore facilitates high-quality rendering effects, such as reflection, refraction, and shadows, on mobile devices by performing real-time Whitted ray tracing. RayCore consists of two major components: ray-tracing units (RTUs) based on a unified traversal and intersection pipeline and a tree-building unit (TBU) for dynamic scenes. The overall RayCore architecture offers considerable benefits in terms of die area, memory access, and power consumption. We have evaluated our architecture based on FPGA and ASIC evaluations and demonstrate its performance on different benchmarks. According to the results, our architecture demonstrates high performance per unit area and unit energy, making it highly suitable for use in mobile devices.


title: Discovering Relations between Noun Categories

abstract: Traditional approaches to Relation Extraction from text require manually defining the relations to be extracted. We propose here an approach to automatically discovering relevant relations, given a large text corpus plus an initial ontology defining hundreds of noun categories (e.g., Athlete, Musician, Instrument). Our approach discovers frequently stated relations between pairs of these categories, using a two step process. For each pair of categories (e.g., Musician and Instrument) it first co-clusters the text contexts that connect known instances of the two categories, generating a candidate relation for each resulting cluster. It then applies a trained classifier to determine which of these candidate relations is semantically valid. Our experiments apply this to a text corpus containing approximately 200 million web pages and an ontology containing 122 categories from the NELL system [Carlson et al., 2010b], producing a set of 781 proposed candidate relations, approximately half of which are semantically valid. We conclude this is a useful approach to semi-automatic extension of the ontology for large-scale information extraction systems such as NELL.


title: A parameter-free algorithm for an optimized tag recommendation list size

abstract: Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend suitable tags to a user for tagging an item. One of its main challenges is the effectiveness of its recommendations. Existing works focus on techniques for retrieving the most relevant tags to give beforehand, with a fixed number of tags in each recommended list. In this paper, we try to optimize the number of recommended tags in order to improve the efficiency of the recommendations. We propose a parameter-free algorithm for determining the optimal size of the recommended list. Thus we introduced some relevance measures to find the most relevant sublist from a given list of recommended tags. More precisely, we improve the quality of our recommendations by discarding some unsuitable tags and thus adjusting the list size. Our solution is an add-on one, which can be implemented on top of many kinds of tag recommenders. The experiments we did on five datasets, using four categories of tag recommenders, demonstrate the efficiency of our technique. For instance, the algorithm we propose outperforms the results of the task 2 of the ECML PKDD Discovery Challenge 20091. By using the same tag recommender than the winners of the contest, we reach a F1 measure of 0.366 while the latter got 0.356. Thus, our solution yields significant improvements on the lists obtained from the tag recommenders.


title: The Triangular Quadrotor: A More Efficient Quadrotor Configuration

abstract: We describe a new configuration of fixed-pitch miniature robot rotorcraft that combines the energetic efficiency of a helicopter and the mechanical simplicity of a quadrotor. The large power required to hover is proportional to the inverse of the rotor radius; thus, for a given diameter footprint, a single large rotor will energetically outperform several smaller rotors within the same boundary. However, smaller rotors are able to respond more quickly than large rotors, which require complex actuation to provide control. Our “triangular quadrotor” configuration uses a single large rotor for lift and three small rotors for control, gaining the benefits of both. The small rotors are canted slightly to also provide the same service as a conventional helicopter's tail rotor. Momentum theory analysis shows that a triangular quadrotor may provide a 20% reduction in required hover power, compared with a quadrotor of the same mass and footprint. This is particularly valuable for flying robots working indoors where maximum rotor size is constrained. Using conventional quadrotor and a triangular quadrotors constructed to be a similar as possible, we demonstrate that the triangular quadrotor uses 15% less power, without optimization. A power efficiency budget is provided, and the influence of drive system efficiency is explored. We present a dynamic model and demonstrate experimentally that the aircraft can be stabilized in flight with simple PID control.


title: 4D Light Field Superpixel and Segmentation

abstract: Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate the ambiguity. The LFSP is first defined mathematically and then a refocus-invariant metric named LFSP self-similarity is proposed to evaluate the segmentation performance. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the state-of-the-arts in terms of traditional evaluation metrics. Additionally the LFSP self-similarity evaluation under different light field refocus levels shows the refocus-invariance of the proposed algorithm.


title: CoC: Secure Supply Chain Management System Based on Public Ledger

abstract: Modern supply chain is a complex system and plays an important role for different sectors under the globalization economic integration background. Supply chain management system is proposed to handle the increasing complexity and improve the efficiency of flows of goods. It is also useful to prevent potential frauds and guarantee trade compliance. Currently, most companies maintain their own IT system for supply chain management. However, this approach has some limitations that prevent one to get most of the supply chain information. Using emerging decentralized ledger technology to build supply chain management system is a promising direction. However, decentralized ledger usually suffers from low performance and lack of capability to protect information stored on the ledger. To overcome these challenges, we propose CoC, a novel supply chain management system based on hybrid decentralized ledger. We develop an efficient block construction method with the model and security mechanism to prevent unauthorized access to data stored on the ledger.


A Corpus of Proverbs Annotated with Metaphors


title: On RGB-D face recognition using Kinect

abstract: Face recognition algorithms generally use 2D images for feature extraction and matching. In order to achieve better performance, 3D faces captured via specialized acquisition methods have been used to develop improved algorithms. While such 3D images remain difficult to obtain due to several issues such as cost and accessibility, RGB-D images captured by low cost sensors (e.g. Kinect) are comparatively easier to acquire. This research introduces a novel face recognition algorithm for RGB-D images. The proposed algorithm computes a descriptor based on the entropy of RGB-D faces along with the saliency feature obtained from a 2D face. The probe RGB-D descriptor is used as input to a random decision forest classifier to establish the identity. This research also presents a novel RGB-D face database pertaining to 106 individuals. The experimental results indicate that the RGB-D information obtained by Kinect can be used to achieve improved face recognition performance compared to existing 2D and 3D approaches.


title: Motion area based exposure fusion algorithm for ghost removal in high dynamic range video generation

abstract: High dynamic range (HDR) image is becoming prevalent for its superiority in recording details of the real-world scene. A conventional exposure fusion method can be utilized to generate a HDR image from a set of differently exposed low dynamic range (LDR) images according to their weight maps. However, this method causes ghosts in the HDR image if there are moving objects or the background changes in the LDR image sequence. Thus, conventional exposure fusion method is only suitable for HDR image generation, not for HDR video generation. A motion area based exposure fusion algorithm is therefore proposed to remove ghosts in HDR video generation. First, the motion areas are detected using block matching based motion estimation method. Then those areas are combined as an additional measurement to change values of original weight maps. The subjective and objective experimental results show ghost-free and highly detailed exposure fused image sequences from HDR videos.


title: Efficient handover authentication with user anonymity and untraceability for Mobile Cloud Computing

abstract: Various wireless communication technologies have been generated and deployed on account of mass requirements. These enable cloud computing with integration with mobility and Mobile Cloud Computing (MCC) becomes the trend of future generation computing paradigm. In this paper, we address a challenging issue of MCC technology-security and privacy of the handover process. We propose a new design of handoff authentication for heterogeneous mobile cloud networks, which provides user anonymity and untraceability. Compared with previous protocols, our proposed mechanism achieves comprehensive features of universality, robust security and efficiency. We address a challenging issue of Mobile Cloud Computing technology.We propose a new handoff authentication scheme with security and privacy.Our proposed mechanism achieves universality, robust security and efficiency.Security and performance analysis shows the excellent performance of our scheme.


title: DCA for Bot Detection

abstract: Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.


title: Micromachined microwave cavity resonator filters for 5G: A feasibility study

abstract: Micromachined microwave cavity filters offer a light-weight, high-Q and highly integrated alternative in the frequency range of 20 GHz–100 GHz as compared to conventional filter types. The filter technology shows potential for use in 5G portable devices and as such, the design of a duplexer operating in the 28 GHz band is demonstrated.


title: An experimental evaluation of test driven development vs. test-last development with industry professionals

abstract: Test-Driven Development (TDD) is a software development approach where test cases are written before actual development of the code in iterative cycles. Context: TDD has gained attention of many software practitioners during the last decade since it has contributed several benefits to the software development process. However, empirical evidence of its dominance in terms of internal code quality, external code quality and productivity is fairly limited. Objective: The aim behind conducting this controlled experiment with professional Java developers is to see the impact of Test-Driven Development (TDD) on internal code quality, external code quality and productivity compared to Test-Last Development (TLD). Results: Experiment results indicate that values found related to number of acceptance test cases passed, McCabe's Cyclomatic complexity, branch coverage, number of lines of code per person hours, number of user stories implemented per person hours are statistically insignificant. However, static code analysis results were found statistically significant in the favor of TDD. Moreover, the results of the survey revealed that the majority of developers in the experiment prefer TLD over TDD, given the lesser required level of learning curve as well as the minimum effort needed to understand and employ TLD compared to TDD.


title: FPGA implementation of Radix-2 2 pipelined FFT processor

abstract: The Fast Fourier Transform (FFT) is very important algorithm in signal processing, software-defined radio, and wireless communication. This paper explains the realization of radix-22 single-path delay feedback pipelined FFT processor. This architecture has the same multiplicative complexity as radix-4 algorithm, but retains the simple butterfly structure of radix-2 algorithm. The implementation was made on a Field Programmable Gate Array (FPGA) because it can achieve higher computing speed than digital signal processors (DSPs), and also can achieve cost effectively ASIC-like performance with lower development time, and risks. The processor has been developed using hardware description language VHDL and simulated up to 465 MHz on an Xilinx xc5vsx35t for transformation length 256-point.


title: Author cocitation: A literature measure of intellectual structure

abstract: It is shown that the mapping of a particular area of science, in this case information science, can be done using authors as units of analysis and the cocitations of pairs of authors as the variable that indicates their “distances” from each other. The analysis assumes that the more two authors are cited together, the closer the relationship between them. The raw data are cocitation counts drawn online from Social Scisearch (Social Sciences Citation Index) over the period 1972–1979. The resulting map shows (1) identifiable author groups (akin to “schools”) of information science, (2) locations of these groups with respect to each other, (3) the degree of centrality and peripherality of authors within groups, (4) proximities of authors within group and across group boundaries (“border authors” who seem to connect various areas of research), and (5) positions of authors with respect to the map's axes, which were arbitrarily set spanning the most divergent groups in order to aid interpretation. Cocitation analysis of authors offers a new technique that might contribute to the understanding of intellectual structure in the sciences and possibly in other areas to the extent that those areas rely on serial publications. The technique establishes authors, as well as documents, as an effective unit in analyzing subject specialties.


title: Multi-Label Learning from Medical Plain Text with Convolutional Residual Models

abstract: Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed-length sentence embedding vector. Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector. A real EHR dataset is considered, and we compare the proposed model with several well-known baselines, to predict diagnoses based on doctor notes. Experimental results demonstrate the superiority of the proposed convolutional residual model.


title: Building detection from aerial images using invariant color features and shadow information

abstract: Robust detection of buildings is an important part of the automated aerial image interpretation problem. Automatic detection of buildings enables creation of maps, detecting changes, and monitoring urbanization. Due to the complexity and uncontrolled appearance of the scene, an intelligent fusion of different methods gives better results. In this study, we present a novel approach for building detection using multiple cues. We benefit from segmentation of aerial images using invariant color features. Besides, we use the edge and shadow information for building detection. We also determine the shape of the building by a novel method.


title: Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods

abstract: Differential methods belong to the most widely used techniques for optic flow computation in image sequences. They can be classified into local methods such as the Lucas–Kanade technique or Bigün's structure tensor method, and into global methods such as the Horn/Schunck approach and its extensions. Often local methods are more robust under noise, while global techniques yield dense flow fields. The goal of this paper is to contribute to a better understanding and the design of novel differential methods in four ways; (i) We juxtapose the role of smoothing/regularisation processes that are required in local and global differential methods for optic flow computation. (ii) This discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches: It yields dense flow fields that are robust against noise. (iii) Spatiotemporal and nonlinear extensions as well as multiresolution frameworks are presented for this hybrid method. (iv) We propose a simple confidence measure for optic flow methods that minimise energy functionals. It allows to sparsify a dense flow field gradually, depending on the reliability required for the resulting flow. Comparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure.


Computational design of metallophone contact sounds Citation


title: Mortality Rates in Appalachian Coal Mining Counties: 24 Years Behind the Nation

abstract: ABSTRACT Appalachia has higher morbidity and mortality compared to the nation, and suffers greater socioeconomic disadvantages. This article investigates the relationship of coal mining to elevated mortality rates in Appalachia. Total mortality rates for the years 1999–2004 were investigated in a national county-level analysis that included coal mining as the primary independent variable. Counties in Appalachia where coal mining is heaviest had significantly higher age-adjusted mortality compared to other Appalachian counties and to other areas of the country. Elevated mortality rates persisted in Appalachian coal mining areas after further statistical adjustment for smoking, poverty, education, rural-urban setting, race/ethnicity, and other variables. After adjustment for all covariates, Appalachian coal mining areas were characterized by 1,607 excess annual deaths over the period 1999–2004. Adjusted mortality rates increase with increasing coal production from 1 to 7 million tons. These findings highlig...


title: Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks

abstract: The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDAR-video inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.


title: Maximal and minimal motor nerve conduction velocities in patients with motor neuron diseases: correlation with age of onset and duration of illness.

abstract: We measured the maximal and minimal motor nerve conduction velocities of the ulnar nerve in 17 patients with spinal muscular atrophy (SMA), 27 patients with amyotrophic lateral sclerosis (ALS), and 40 age-matched control subjects. Crude values of the maximal and minimal motor nerve conduction velocities were reduced in both patient groups, but their difference in each patient was not statistically different from that of a control subject. The conduction velocities adjusted according to each patient's age were faster in SMA patients than in ALS patients. Among patients with either ALS or SMA, the age-adjusted conduction velocities were larger in those who had developed the disease at younger ages and suffered from it for shorter periods of time. These findings suggest that the motor nerve fibers regenerate more in younger patients in the early stages of the disease, particularly in SMA.


title: Amphipathic, alpha-helical antimicrobial peptides.

abstract: Gene-encoded antimicrobial peptides are an important component of host defense in animals ranging from insects to mammals. They do not target specific molecular receptors on the microbial surface, but rather assume amphipathic structures that allow them to interact directly with microbial membranes, which they can rapidly permeabilize. They are thus perceived to be one promising solution to the growing problem of microbial resistance to conventional antibiotics. A particularly abundant and widespread class of antimicrobial peptides are those with amphipathic, alpha-helical domains. Due to their relatively small size and synthetic accessibility, these peptides have been extensively studied and have generated a substantial amount of structure-activity relationship (SAR) data. In this review, alpha-helical antimicrobial peptides are considered from the point of view of six interrelated structural and physicochemical parameters that modulate their activity and specificity: sequence, size, structuring, charge, amphipathicity, and hydrophobicity. It begins by providing an overview of how these vary in peptides from different natural sources. It then analyzes how they relate to the currently accepted model for the mode of action of alpha-helical peptides, and discusses what the numerous SAR studies that have been carried out on these compounds and their analogues can tell us. A comparative analysis of the many alpha-helical, antimicrobial peptide sequences that are now available then provides further information on how these parameters are distributed and interrelated. Finally, the systematic variation of parameters in short model peptides is used to throw light on their role in antimicrobial potency and specificity. The review concludes with some considerations on the potentials and limitations for the development of alpha-helical, antimicrobial peptides as antiinfective agents.


title: In vitro propagation of a high value medicinal plant: Asparagus racemosus Willd.

abstract: Asparagus racemosus Willd. is an important medicinal plant of tropical and subtropical India. Its medicinal usage has been reported in the Indian and British Pharmacopoeias and in traditional systems of medicine such as Ayurveda, Unani, and Siddha. The multiple uses of this species have increased its commercial demand, resulting in over-exploitation. Because of destructive harvesting, the natural population of A. racemosus is rapidly disappearing, and it is recognized as ‘vulnerable’ (Warner et al., Some important medicinal plants of the Western Ghats, India: a profile. International Development Research Centre, Artstock, New Delhi, India, 15 pp, 2001). The development of an efficient micropropagation protocol will play a significant role in meeting the requirements for commercial cultivation, thereby conserving the species in its natural habitat. In the present study, in vitro shoot proliferation was obtained by culturing single node segments in Murashige and Skoog’s (MS) medium supplemented with 3.69 µM 2-isopentyl adenine and 3% sucrose with a multiplication rate of 3.5. For proper root formation, the in vitro-formed shoot clusters were cultured on half strength (major salts reduced to half) MS medium with 1.61 µM 1-naphthalene acetic acid, 0.46 µM kinetin, 98.91 µM adenine sulfate, 500 mg/l malt extract, 198.25 µM phloroglucinol, and 3% sucrose. On this medium, 85% rooting was observed within 20 d. Following a simple hardening procedure involving sequential transfer of plants to a greenhouse, polyhouse, and shade net, the tissue-cultured plants were transferred to the field where the survival rate was 100%.


title: THE ROLE OF CONSTRUCTION, INTUITION, AND JUSTIFICATION IN RESPONDING TO ETHICAL ISSUES AT WORK: THE SENSEMAKING-INTUITION MODEL.

abstract: Proponents of a popular view of how individuals respond to ethical issues at work claim that individuals use deliberate and extensive moral reasoning under conditions that ignore equivocality and uncertainty. I discuss the limitations of these “rationalist approaches” and reconsider their empirical support using an alternative explanation from social psychological and sensemaking perspectives. I then introduce a new theoretical model composed of issue construction, intuitive judgment, and post hoc explanation and justification. I discuss the implications for management theory, methods, and practice.


title: Differential neuronal plasticity in mouse hippocampus associated with various periods of enriched environment during postnatal development

abstract: Enriched environment (EE) is characterized by improved conditions for enhanced exploration, cognitive activity, social interaction and physical exercise. It has been shown that EE positively regulates the remodeling of neural circuits, memory consolidation, long-term changes in synaptic strength and neurogenesis. However, the fine mechanisms by which environment shapes the brain at different postnatal developmental stages and the duration required to induce such changes are still a matter of debate. In EE, large groups of mice were housed in bigger cages and were given toys, nesting materials and other equipment that promote physical activity to provide a stimulating environment. Weaned mice were housed in EE for 4, 6 or 8 weeks and compared with matched control mice that were raised in a standard environment. To investigate the differential effects of EE on immature and mature brains, we also housed young adult mice (8 weeks old) for 4 weeks in EE. We studied the influence of onset and duration of EE housing on the structure and function of hippocampal neurons. We found that: (1) EE enhances neurogenesis in juvenile, but not young adult mice; (2) EE increases the number of synaptic contacts at every stage; (3) long-term potentiation (LTP) and spontaneous and miniature activity at the glutamatergic synapses are affected differently by EE depending on its onset and duration. Our study provides an integrative view of the role of EE during postnatal development in various mechanisms of plasticity in the hippocampus including neurogenesis, synaptic morphology and electrophysiological parameters of synaptic connectivity. This work provides an explanation for discrepancies found in the literature about the effects of EE on LTP and emphasizes the importance of environment on hippocampal plasticity.


title: Design, implementation and security analysis of Bluetooth pairing protocol in NS2

abstract: Secure Simple Pairing (SSP), a characteristic of the Bluetooth Core Version 2.1 specification was build to address two foremost concerns amongst the Bluetooth user community: security and simplicity of the pairing process. It utilizes Elliptic Curve Diffie-Hellmen (ECDH) protocol for generating keys for the first time in Bluetooth pairing. It provides the security properties known session key security, forward security, resistance to key-compromise impersonation attack and to unknown key-share attack, key control. This paper presents the simulation and security analysis of Bluetooth pairing protocol for numeric comparison using ECDH in NS2. The protocol also employs SAGEMATH for cryptographic functions.


title: Networks formed from interdependent networks,” Nat

abstract: Aspects concerning the structure and behaviours of individual networks have been studied intensely in the past decade, but the exploration of interdependent systems in the context of complex networks has started only recently. This article reviews a general framework for modelling the percolation properties of interacting networks and the first results drawn from its study.


Evaluation of the Symptoms of Fibromyalgia in Premenopausal Women with Urinary Incontinence


title: Web user profiling using data redundancy

abstract: The study of Web user profiling can be traced back to 30 years ago, with the goal of extracting "semantic"-based user profile attributes from the unstructured Web. Despite slight differences, the general method is to first identify relevant pages of a specific user and then use machine learning models (e.g., CRFs) to extract the profile attributes from the page. However, with the rapid growth of the Web volume, such a method suffers from data redundancy and error propagation between the two steps. In this paper, we revisit the problem of Web user profiling in the big data era, trying to deal with the new challenges. We propose a simple but very effective approach for extracting user profile attributes from the Web using big data. To avoid error propagation, the approach processes all the extraction subtasks in one unified model. To further incorporate human knowledge to improve the extraction performance, we propose a markov logic factor graph (MagicFG) model. The MagicFG model describes human knowledge as first-order logics and combines the logics into the extraction model. Our experiments on a real data set show that the proposed method significantly improves (+4--6%; p L 0.01, t-test) the extraction performance in comparison with several baseline methods.


title: Pupillary Stroop effects

abstract: We recorded the pupil diameters of participants performing the words’ color-naming Stroop task (i.e., naming the color of a word that names a color). Non-color words were used as baseline to firmly establish the effects of semantic relatedness induced by color word distractors. We replicated the classic Stroop effects of color congruency and color incongruency with pupillary diameter recordings: relative to non-color words, pupil diameters increased for color distractors that differed from color responses, while they reduced for color distractors that were identical to color responses. Analyses of the time courses of pupil responses revealed further differences between color-congruent and color-incongruent distractors, with the latter inducing a steep increase of pupil size and the former a relatively lower increase. Consistent with previous findings that have demonstrated that pupil size increases as task demands rise, the present results indicate that pupillometry is a robust measure of Stroop interference, and it represents a valuable addition to the cognitive scientist’s toolbox.


title: "Psychological Safety, Learning, and Performance: A Comparison of Direct and Contingent Effects"

abstract: In working to understand the predictors of experiential learning in teams, researchers have focused on one variable more than any other – psychological safety, or a shared belief that team members ...


title: Model Selection Management Systems: The Next Frontier of Advanced Analytics

abstract: John Boyd recognized in the 1960's the importance of situation awareness for military operations and introduced the notion of the OODA loop (Observe, Orient, Decide, and Act). Today we realize that many applications have to deal with situation awareness: Customer Relationship Management, Human Capital Management, Supply Chain Management, patient care, power grid management, and cloud services management, as well as any IoT (Internet of Things) related application; the list seems to be endless. Situation awareness requires applications to support the management of data, knowledge, processes, and other services such as social networking in an integrated way. These applications additionally require high personalization as well as rapid and continuous evolution. They must provide a wide variety of operational and functional requirements, including real time processing. Handcrafting these applications is an almost impossible task requiring exhaustive resources for development and maintenance. Due to the resources and time involved in their development, these applications typically fall way short of the desired functionality, operational characteristics, and ease and speed of evolution. We - the authors - have developed a model enabling the development and maintenance of situation-aware applications in a declarative and therefore economical manner; we call this model KIDS - Knowledge Intensive Data-processing System.


title: Digital Enterprise Architecture - Transformation for the Internet of Things

abstract: Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences today's digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services a cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.


title: Scalable Influence Maximization in Social Networks under the Linear Threshold Model

abstract: Influence maximization is the problem of finding a small set of most influential nodes in a social network so that their aggregated influence in the network is maximized. In this paper, we study influence maximization in the linear threshold model, one of the important models formalizing the behavior of influence propagation in social networks. We first show that computing exact influence in general networks in the linear threshold model is #P-hard, which closes an open problem left in the seminal work on influence maximization by Kempe, Kleinberg, and Tardos, 2003. As a contrast, we show that computing influence in directed a cyclic graphs (DAGs) can be done in time linear to the size of the graphs. Based on the fast computation in DAGs, we propose the first scalable influence maximization algorithm tailored for the linear threshold model. We conduct extensive simulations to show that our algorithm is scalable to networks with millions of nodes and edges, is orders of magnitude faster than the greedy approximation algorithm proposed by Kempe et al. and its optimized versions, and performs consistently among the best algorithms while other heuristic algorithms not design specifically for the linear threshold model have unstable performances on different real-world networks.


title: CodingFlow: Enable Video Coding for Video Stabilization

abstract: Video coding focuses on reducing the data size of videos. Video stabilization targets at removing shaky camera motions. In this paper, we enable video coding for video stabilization by constructing the camera motions based on the motion vectors employed in the video coding. The existing stabilization methods rely heavily on image features for the recovery of camera motions. However, feature tracking is time-consuming and prone to errors. On the other hand, nearly all captured videos have been compressed before any further processing and such a compression has produced a rich set of block-based motion vectors that can be utilized for estimating the camera motion. More specifically, video stabilization requires camera motions between two adjacent frames. However, motion vectors extracted from video coding may refer to non-adjacent frames. We first show that these non-adjacent motions can be transformed into adjacent motions such that each coding block within a frame contains a motion vector referring to its adjacent previous frame. Then, we regularize these motion vectors to yield a spatially-smoothed motion field at each frame, named as CodingFlow , which is optimized for a spatially-variant motion compensation. Based on CodingFlow, we finally design a grid-based 2D method to accomplish the video stabilization. Our method is evaluated in terms of efficiency and stabilization quality, both quantitatively and qualitatively, which shows that our method can achieve high-quality results compared with the state-of-the-art methods (feature-based).