title: Perception and Usage Pattern of e-books among Library and Information Science Students in Selected Universities in Nigeria

abstract: This study examined the perception and usage pattern of e-book among LIS undergraduate students from selected five universities. Questionnaire was used to collect data from the respondents. Five research questions were developed and answered. The findings demonstrate that LIS undergraduate students still use e-books more compare to print books. The results also reveal that LIS undergraduate students have positive perception of e-book as it enhance learning effectiveness, assist to finish course content/outline; makes reading faster; easy to use and with clear graphical illustrations. The usage pattern of e-books among LIS undergraduate students is majorly for research purpose and the majority is highly satisfied with reading e-books. The major problem encountered by the students with e-books is the difficulties reading on the computers or internet, followed by the cost of e-book reader. The study recommended based on these findings that university authority and library of the participating institutions should make e-books readers available in large quantity. Doing so is assumed will increase the use of e-books and further arouse the interest of the students .


title: Inspection of Complex Objects Using Multiple-X-Ray Views

abstract: This paper presents a new methodology for identifying parts of interest inside of a complex object using multiple X-ray views. The proposed method consists of ve steps: A)


title: Deep Learning the Physics of Transport Phenomena

abstract: We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning. Using conditional generative adversarial networks (cGAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow purely on observation without knowledge of the underlying governing equations. Rather than using iterative numerical methods to approximate the solution of the constitutive equations, cGANs learn to directly generate the solutions to these phenomena, given arbitrary boundary conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art computational performance. The cGAN framework can be used to learn causal models directly from experimental observations where the underlying physical model is complex or unknown.


title: Motives for Social Isolation Following a Negative Emotional Episode

abstract: Abstract. After experiencing an emotional event, people either seek out others’ presence (social affiliation) or avoid others’ presence (social isolation). The determinants and effects of social affiliation are now well-known, but social psychologists have not yet thoroughly studied social isolation. This study aims to ascertain which motives and corresponding regulation strategies participants report for social isolation following negative emotional events. A group of 96 participants retrieved from memory an actual negative event that led them to temporarily socially isolate themselves and freely listed up to 10 motives for social isolation. Through semantic categorization of the 423 motives reported by the participants, we found that “cognitive clarification” and “keeping one’s distance” – that is, the need for cognitive regulation and the refusal of socioaffective regulation, respectively – were the most commonly and quickly reported motives for social isolation. We discuss the findings in terms of ide...


Comparing TensorFlow Deep Learning Performance Using CPUs, GPUs, Local PCs and Cloud


title: Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences

abstract: We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.


title: IoT-based monitoring system using tri-level context making model for smart home services

abstract: In future smart home based on Internet of Things (IoT), data acquisition and information analysis through various sensors will play a crucial role, and it will be a core technology to develop IoT-based smart home services. This paper proposes an IoT-based monitoring system using a tri-level context making model for context-aware services in smart homes.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Making Choices Impairs Subsequent Self-Control: A Limited-Resource Account of Decision Making, Self-Regulation, and Active Initiative

abstract: The current research tested the hypothesis that making many choices impairs subsequent self-control. Drawing from a limited-resource model of self-regulation and executive function, the authors hypothesized that decision making depletes the same resource used for self-control and active responding. In 4 laboratory studies, some participants made choices among consumer goods or college course options, whereas others thought about the same options without making choices. Making choices led to reduced self-control (i.e., less physical stamina, reduced persistence in the face of failure, more procrastination, and less quality and quantity of arithmetic calculations). A field study then found that reduced self-control was predicted by shoppers’ self-reported degree of previous active decision making. Further studies suggested that choosing is more depleting than merely deliberating and forming preferences about options and more depleting than implementing choices made by someone else and that anticipating the choice task as enjoyable can reduce the depleting effect for the first choices but not for many choices.


title: Multi-Column Deep Neural Networks for offline handwritten Chinese character classification

abstract: Multi-Column Deep Neural Networks achieve state of the art recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human accuracy. This performance is the result of averaging 11-layers deep networks with hundreds of maps per layer, trained on raw, distorted images to prevent them from overfitting. The entire framework runs on a normal desktop computer with a CUDA capable graphics card.


title: Future memory technology including emerging new memories

abstract: There have been concerns about how far we can extend the so far so successful conventional semiconductor memories Such as DRAM, SRAM and Flash memory and what will be the future directions of memory development. In this article, we will review the key technical limits of conventional memory scaling and the directions to overcome the problem. In addition, we will review the technical challenges and opportunities of emerging. new memories such as ferroelectric RAM (FRAM), magnetic RAM (MRAM) and phase change RAM (PRAM) which has been recently focused as candidates for ideal memory which can solve the problems of conventional memories.


title: An advanced data driven model for residential electric vehicle charging demand

abstract: As the electric vehicle (EV) is becoming a significant component of the loads, an accurate and valid model for the EV charging demand is the key to enable accurate load forecasting, demand respond, system planning, and several other important applications. We propose a data driven queuing model for residential EV charging demand by performing big data analytics on smart meter measurements. The data driven model captures the non-homogeneity and periodicity of the residential EV charging behavior through a self-service queue with a periodic and non-homogeneous Poisson arrival rate, an empirical distribution for charging duration and a finite calling population. Upon parameter estimation, we further validate the model by comparing the simulated data series with real measurements. The hypothesis test shows the proposed model accurately captures the charging behavior. We further acquire the long-run average steady state probabilities and simultaneous rate of the EV charging demand through simulation output analysis.


title: Solving the Emotion Paradox: Categorization and the Experience of Emotion

abstract: In this article, I introduce an emotion paradox: People believe that they know an emotion when they see it, and as a consequence assume that emotions are discrete events that can be recognized with some degree of accuracy, but scientists have yet to produce a set of clear and consistent criteria for indicating when an emotion is present and when it is not. I propose one solution to this paradox: People experience an emotion when they conceptualize an instance of affective feeling. In this view, the experience of emotion is an act of categorization, guided by embodied knowledge about emotion. The result is a model of emotion experience that has much in common with the social psychological literature on person perception and with literature on embodied conceptual knowledge as it has recently been applied to social psychology.


title: Using machine learning for black-box autoscaling

abstract: Autoscaling is the practice of automatically adding or removing resources for an application deployment to meet performance targets in response to changing workload conditions. However, existing autoscaling approaches typically require expert application and system knowledge to minimize resource costs and performance target violations, thus limiting their applicability. We present MLscale, an application-agnostic, machine learning based autoscaler that is composed of: (i) a neural network based online (black-box) performance modeler, and (ii) a regression based metrics predictor to estimate post-scaling application and system metrics. Implementation results for diverse applications across several traces highlight MLscale's application-agnostic behavior and show that MLscale (i) reduces resource costs by about 50% compared to the optimal static policy, (ii) is within 15% of the cost of the optimal dynamic policy, and (iii) provides similar cost-performance tradeoffs, without requiring any tuning, when compared to carefully tuned threshold-based policies.


title: Sliding mode control of a photovoltaic-wind hybrid system

abstract: This paper proposes a hybrid energy system combing solar photovoltaic and wind turbine as a small-scale for stand-alone applications. The proposed system consists of a variable-speed wind generator, wind-side converter, solar array, DC-DC converter and isolating DC load. The power control strategy is to extract the maximum energy from the photovoltaic array and wind turbine under varying climatic conditions. The SMC proposed in this paper, to controlling the output voltage of the DC-DC converter and the out voltage of the AC-DC converter, in order to maintain and stabilize the constant DC-link voltage in climatic and load variations. Furthermore, classical controls PI compared with the SMC. The results show that the SMC technique is more suitable than those classical controls. It improves the transient response and reduces the steady state error of the DC-link voltage because offers a very good way to implement a control action which exploits the inherent variable structure nature of converters. This paper too proposes a control strategy of a maximum power point of a stand-alone PV system using the SMC technique. The efficiency of the proposed control approach is compared with the traditional P&O technique. The MPPT proposed has good performances of the PV system. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink.


Power comparisons of Shapiro-Wilk , Kolmogorov-Smirnov , Lilliefors and Anderson-Darling tests


title: A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset

abstract: Imbalanced data sets are considered a special case for the classification problems.Map reducing with prototype reduction can easily handle large scale data set with good speedup and less time consuming.For high quality, four reduction types: data cleaning, rule aggregation, rule synthesis and rule update were compared.A real DNA dataset consists of 90 million pair has been used with reduction types.The proposed MapReduce based K-NN classifier reduced the imbalance data set and achieved accurate results for the DNA data. BackgroundIn the age of information superhighway, big data play a significant role in information processing, extractions, retrieving and management. In computational biology, the continuous challenge is to manage the biological data. Data mining techniques are sometimes imperfect for new space and time requirements. Thus, it is critical to process massive amounts of data to retrieve knowledge. The existing software and automated tools to handle big data sets are not sufficient. As a result, an expandable mining technique that enfolds the large storage and processing capability of distributed or parallel processing platforms is essential. MethodIn this analysis, a contemporary distributed clustering methodology for imbalance data reduction using k-nearest neighbor (K-NN) classification approach has been introduced. The pivotal objective of this work is to illustrate real training data sets with reduced amount of elements or instances. These reduced amounts of data sets will ensure faster data classification and standard storage management with less sensitivity. However, general data reduction methods cannot manage very big data sets. To minimize these difficulties, a MapReduce-oriented framework is designed using various clusters of automated contents, comprising multiple algorithmic approaches. ResultsTo test the proposed approach, a real DNA (deoxyribonucleic acid) dataset that consists of 90 million pairs has been used. The proposed model reduces the imbalance data sets from large-scale data sets without loss of its accuracy. ConclusionsThe obtained results depict that MapReduce based K-NN classifier provided accurate results for big data of DNA.


title: Joint Channel Assignment and Routing for Throughput Optimization in Multiradio Wireless Mesh Networks

abstract: Multihop infrastructure wireless mesh networks offer increased reliability, coverage, and reduced equipment costs over their single-hop counterpart, wireless local area networks. Equipping wireless routers with multiple radios further improves the capacity by transmitting over multiple radios simultaneously using orthogonal channels. Efficient channel assignment and routing is essential for throughput optimization of mesh clients. Efficient channel assignment schemes can greatly relieve the interference effect of close-by transmissions; effective routing schemes can alleviate potential congestion on any gateways to the Internet, thereby improving per-client throughput. Unlike previous heuristic approaches, we mathematically formulate the joint channel assignment and routing problem, taking into account the interference constraints, the number of channels in the network, and the number of radios available at each mesh router. We then use this formulation to develop a solution for our problem that optimizes the overall network throughput subject to fairness constraints on allocation of scarce wireless capacity among mobile clients. We show that the performance of our algorithms is within a constant factor of that of any optimal algorithm for the joint channel assignment and routing problem. Our evaluation demonstrates that our algorithm can effectively exploit the increased number of channels and radios, and it performs much better than the theoretical worst case bounds


title: "Understanding a fury in your words": The effects of posting and viewing electronic negative word-of-mouth on purchase behaviors

abstract: Marketing scholars and practitioners have long recognized that the power of electronic negative word-of-mouth (e-NWOM) can influence brand revenues and firm performance, but most previous studies have only examined the effect of viewing. This study is one of the initial attempts to test the effects of e-NWOM on both posters and viewers. We also test the moderating effects of company usefulness and company apology in a separate study. Using an observational dataset that contains NWOM viewing and posting records and customers' purchase transactions from a real company, Study 1 finds that viewing e-NWOM has a negative effect on subsequent purchases, whereas posting e-NWOM has a positive interaction effect with company usefulness. Study 2 shows that a company's public apology has a positive effect on viewers, but not posters. We conclude with the theoretical, methodological, and managerial implications of e-NWOM and webcare research. We distinguish the effects of posting and viewing e-NWOM on purchase behavior.We identify two moderators of e-NWOM and test their effects in two studies.Viewing e-NWOM decreases subsequent purchases.Posting e-NWOM has a positive interaction effect with company usefulness.Company apology only works for e-NWOM viewers, not posters.


title: SIGACT News Complexity Theory Column 46

abstract: Can NP-complete problems be solved efficiently in the physical universe? I survey proposals including soap bubbles, protein folding, quantum computing, quantum advice, quantum adiabatic algorithms, quantum-mechanical nonlinearities, hidden variables, relativistic time dilation, analog computing, Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and "anthropic computing." The section on soap bubbles even includes some "experimental" results. While I do not believe that any of the proposals will let us solve NP-complete problems efficiently, I argue that by studying them, we can learn something not only about computation but also about physics.


title: Phase Retrieval from Coded Diffraction Patterns

abstract: Abstract This paper considers the question of recovering the phase of an object from intensity-only measurements, a problem which naturally appears in X-ray crystallography and related disciplines. We study a physically realistic setup where one can modulate the signal of interest and then collect the intensity of its diffraction pattern, each modulation thereby producing a sort of coded diffraction pattern. We show that PhaseLift, a recent convex programming technique, recovers the phase information exactly from a number of random modulations, which is polylogarithmic in the number of unknowns. Numerical experiments with noiseless and noisy data complement our theoretical analysis and illustrate our approach.


title: The Effect of Stereoscopic Immersive Environments on Projection-Based Multi-dimensional Data Visualization

abstract: Multidimensional data impose a challenge for visual analyses. Commonly, dimensionality reduction techniques are used to project the multidimensional data into a 2D visual space. Poco et al. [9] showed that projection into a 3D visual space can increase the performance of common visual analysis tasks due to a higher projection precision. They also backed up their findings with a user study. However, when conducting the user study they displayed the 3D visual space on a 2D screen, which may impede the correct perception of the third dimension. In this paper, we present a study that investigates the effect of stereoscopic environments when used for the visual analysis of multidimensional data after projection into a 3D visual space. We conducted a controlled user study to compare correctness, timing, and confidence in segregation and precision tasks when performed in stereoscopic immersive environments and on a nonstereoscopic 2D screen. In terms of the stereoscopic immersive environments, we operated on and compared results obtained with two setup: a single screen and a six-sided highly immersive system, in both of which interaction was performed with a 3D input device. We investigated whether the stereoscopic immersive environments have an effect on user performance depending on the visual encodings. We used both 3D scatter plots and cluster visualizations in the form of enclosing surfaces or hulls for the visual analysis tasks.


title: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE How Can Evolutionary Psychology Successfully Explain Personality and Individual Differences?

abstract: Although evolutionary psychology has been successful in explaining some species-typical and sex- differentiated adaptations, a large question that has largely eluded the field is this: How can the field success- fully explain personality and individual differences? This article highlightssomepromising theoretical directions for tackling this question. These include life-history theory, costlysignalingtheory,environmentalvariabilityinfitness optima, frequency-dependent selection, mutation load, and flexibly contingent shifts in strategy according to envi- ronmental conditions. Tackling the explanatory question also requires progress on three fronts: (a) reframing some personality traits as forms of strategic individual differ- ences; (b) providing a nonarbitrary, evolutionary-based formulation of environments as distributions and salience profiles of adaptive problems; and (c) identifying which strategies thrive and which falter in these differing prob- lem-defined environments.


Driving Automotive Middleware Towards a Secure IP-based Future


title: Long-Legged Hexapod Giacometti Robot Using Thin Soft McKibben Actuator

abstract: This letter introduces a lightweight hexapod robot, Giacometti robot, made with long and narrow legs following the Alberto Giacometti's sculpture conception. The goal is achieved by, first, using multiple links with thin and soft McKibben actuators, and second, choosing a leg design which is narrow in comparison to its body's length and height, unlike conventional robot design. By such design characteristic, the leg will exhibit elastic deformations due to the low stiffness property of the thin link structure. Then, we model the leg structure and conduct the deflection analysis to confirm the capability of the leg to perform walking motion. The high force to weight ratio characteristics of the actuator provided the ability to drive the system, as shown by a static model and further validated experimentally. To compensate for the high elastic structural flexibility of the legs, two walking gaits namely customized Wave gait and Giacometti gait were introduced. The robot could walk successfully with both gaits at maximum speed of 0.005 and 0.05 m/s, respectively. It is envisaged that the lightweight Giacometti robot design can be very useful in legged robotic exploration.


title: A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields

abstract: Graph cuts method such as α-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision. Higher-order Markov Random Fields (MRF's), which are important for numerous applications, have proven to be very difficult, especially for multilabel MRF's (i.e. more than 2 labels). In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF's. Primal-dual methods provide guaranteed approximation bounds, and can exploit information in the dual variables to improve their efficiency. Our algorithm generalizes the PD3 [19] technique for first-order MRFs, and relies on a variant of max-flow that can exactly optimize certain higher-order binary MRF's [14]. We provide approximation bounds similar to PD3 [19], and the method is fast in practice. It can optimize non-submodular MRF's, and additionally can in- corporate problem-specific knowledge in the form of fusion proposals. We compare experimentally against the existing approaches that can efficiently handle these difficult energy functions [6, 10, 11]. For higher-order denoising and stereo MRF's, we produce lower energy while running significantly faster.


title: Similarity testing for role-based access control systems

abstract: Access control systems demand rigorous verification and validation approaches, otherwise, they can end up with security breaches. Finite state machines based testing has been successfully applied to RBAC systems and enabled to obtain effective test cases, but very expensive. To deal with the cost of these test suites, test prioritization techniques can be applied to improve fault detection along test execution. Recent studies have shown that similarity functions can be very efficient at prioritizing test cases. This technique is named similarity testing and assumes the hypothesis that resembling test cases tend to have similar fault detection capabilities. Thus, there is no gain from similar test cases, and fault detection ratio can be improved if test diversity increases. In this paper, we propose a similarity testing approach for RBAC systems named RBAC similarity and compare to simple dissimilarity and random prioritization. RBAC similarity combines the dissimilarity degree of pairs of test cases with their relevance to the RBAC policy under test to maximize test diversity and the coverage of its constraints. Five RBAC policies and fifteen test suites were prioritized using each of the three test prioritization techniques and compared using the Average Percentage Faults Detected metric. Our results showed that the combination of the dissimilarity degree to the relevance of a test case to RBAC policies in the RBAC similarity can be more effective than random prioritization and simple dissimilarity, by itself, in most of the cases. The RBAC similarity criterion is suitable as a test prioritization criteria for test suites generated from finite state machine models specifying RBAC systems.


title: A unifying reference framework for multi-target user interfaces

abstract: This paper describes a framework that serves as a reference for classifying user interfaces supporting multiple targets, or multiple contexts of use in the field of context-aware computing. In this framework, a context of use is decomposed in three facets: the end users of the interactive system, the hardware and software computing platform with which the user have to carry out their interactive tasks and the physical environment where they are working. Therefore, a context-sensitive user interface is a user interface that exhibits some capability to be aware of the context (context awareness) and to react to changes of this context. This paper attempts to provide a unified understanding of context-sensitive user interfaces rather than a prescription of various ways or methods of tackling different steps of development. Rather, the framework structures the development life cycle into four levels of abstraction: task and concepts, abstract user interface, concrete user interface and final user interface. These levels are structured with a relationship of reification going from an abstract level to a concrete one and a relationship of abstraction going from a concrete level to an abstract one. Most methods and tools can be more clearly understood and compared relative to each other against the levels of this framework. In addition, the framework expresses when, where and how a change of context is considered and supported in the context-sensitive user interface thanks to a relationship of translation. In the field of multi-target user interfaces is also introduced, defined, and exemplified the notion of plastic user interfaces. These user interfaces support some adaptation to changes of the context of use while preserving a predefined set of usability properties


title: Sudarshan Kriya Yogic Breathing in the Treatment of Stress, Anxiety, and Depression: Part I—Neurophysiologic Model

abstract: Mind–body interventions are beneficial in stress-related mental and physical disorders. Current research is finding associations between emotional disorders and vagal tone as indicated by heart rate variability. A neurophysiologic model of yogic breathing proposes to integrate research on yoga with polyvagal theory, vagal stimulation, hyperventilation, and clinical observations. Yogic breathing is a unique method for balancing the autonomic nervous system and influencing psychologic and stress-related disorders. Many studies demonstrate effects of yogic breathing on brain function and physiologic parameters, but the mechanisms have not been clarified. Sudarshan Kriya yoga (SKY), a sequence of specific breathing techniques (ujjayi, bhastrika, and Sudarshan Kriya) can alleviate anxiety, depression, everyday stress, post-traumatic stress, and stress-related medical illnesses. Mechanisms contributing to a state of calm alertness include increased parasympathetic drive, calming of stress response systems, neur...


title: Ultra-Wideband Impedance Matching Technique for Resistively Loaded Vee Dipole Antenna

abstract: The resistively loaded vee dipole (RVD) antenna has many advantages as an ultra-wideband (UWB) radar antenna, especially ground-penetrating radar. However, it has the drawback of having considerable reflections at the feed point due to an impedance mismatch between the antenna and the feeding transmission line. In this communication, a matching network is proposed to reduce the reflection and the design procedure is presented. The network is composed of two L-sections in cascade: an inductive L-section for high frequency matching and a high-pass type L-section for low frequency matching. It is demonstrated that the high-pass type L-section hardly destroys the high frequency matching. The RVD with the proposed matching network is implemented and the performance is measured. The results show that the RVD is matched over the desired band of 500 MHz-3 GHz, and more energy is radiated in the main pulse.


title: An agri-food supply chain traceability system for China based on RFID & blockchain technology

abstract: For the past few years, food safety has become an outstanding problem in China. Since traditional agri-food logistics pattern can not match the demands of the market anymore, building an agri-food supply chain traceability system is becoming more and more urgent. In this paper, we study the utilization and development situation of RFID (Radio-Frequency IDentification) and blockchain technology first, and then we analyze the advantages and disadvantages of using RFID and blockchain technology in building the agri-food supply chain traceability system; finally, we demonstrate the building process of this system. It can realize the traceability with trusted information in the entire agri-food supply chain, which would effectively guarantee the food safety, by gathering, transferring and sharing the authentic data of agri-food in production, processing, warehousing, distribution and selling links.


title: Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study

abstract: Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in academic research and industrial initiatives. Cloud deployments are paramount for institution and organizations of all scales. The availability of a flexible, free open source cloud platform designed with no propriety software and the ability of its integration with legacy systems and third-party applications are fundamental. Open stack is a free and opensource software released under the terms of Apache license with a fragmented and distributed architecture making it highly flexible. This project was initiated and aimed at designing a secured cloud infrastructure called BradStack, which is built on OpenStack in the Computing Laboratory at the University of Bradford. In this report, we present and discuss the steps required in deploying a secured BradStack Multi-node cloud infrastructure and conducting Penetration testing on OpenStack Services to validate the effectiveness of the security controls on the BradStack platform. This report serves as a practical guideline, focusing on security and practical infrastructure related issues. It also serves as a reference for institutions looking at the possibilities of implementing a secured cloud solution.


title: SMAS Debulking for Management of the Thick-Skinned Nose

abstract: Introduction Oneof the greatest challenges in cosmetic rhinoplasty is the overly thicknasal skin envelope. In addition toexacerbatingunwantednasalwidth, thicknasal skin is amajor impediment to aesthetic refinement of the nose. Owing to its bulk, noncompliance, and tendency to scar, overly thick skin frequently obscures topographic definition of the nasal framework, thereby limiting or negating cosmetic improvements. Maskingof the skeletal contour is usuallymostevident followingaggressive reduction rhinoplasty where overly thick and noncompliantnasal skin fails toshrinkandconformtothesmallerskeletal framework. The result is excessive subcutaneous dead space leading to further fibrotic thickening of the already bulky nasal covering. Despite the decrease in nasal size, the resulting nasal contour is typically amorphous, ill-defined and devoid of beauty and elegance. Tooptimizecosmetic results in thick-skinnednoses, contourenhancement is best achieved by elongating and projecting the skeletal framework whenever possible (Figure 1). Skeletal augmentation not only reduces dead space tominimize fibrotic thickening, it also stretches and thins the outer soft-tissue covering for improved surface definition. However, in noses in which the nasal framework is already too large, skeletal augmentation is not a viable option, and the overly thick skin envelope must be surgically thinned to achieve better skin contractility and improved cosmetic outcomes. Histologicexaminationofoverly thicknasal tip skin reveals comparatively little dermal thickening or increased adipose content but ratherasubstantial increaseinthicknessofthesubcutaneousfibromuscular tissues.1Dubbed the “nasal SMAS” layer,2 the fibromuscular tissue layer lies just beneath the subdermal fat andmay account for an additional2 to3mmofskin flapthickness.OwingtoadiscretedissectionplaneseparatingthenasalSMAS layer fromtheoverlyingsubdermal fat, surgical excisionof thehypertrophicnasal SMAS layer canbe performedsafely inhealthycandidatesusingtheexternal rhinoplasty approach.3However,theoverlyingsubdermalplexus(containedwithin the subdermal fat)must be carefully protected.3-5 Similarly, inadvertentdisruptionof thepaired lateralnasal arteries—major feedingvessels to the subdermal plexus—must alsobeavoided, and special care should be exercisedwhenworking near the alar crease.3-5 SMASdebulking is also contraindicated in skin less than 3-mm thick because overlyaggressivesurgicaldebulkingmayleadtounsightlyprominence oftheskeletal topography.However, intheappropriatepatient,SMAS debulkingcanreduceskinenvelopethicknessbyasmuchas3.0mm, with greater reductions common in revision rhinoplasty caseswhen vascularity permits.6


title: Effective Implementation of MatrixVector Multiplication on Intel's AVX multicore Processor

abstract: Implement dense parallel matrixvector multiplication using Intel's AVX instructions.The results are compared inline assembly versus intrinsic functions for programming.A comparative study Intel C++ compiler and MVSC++ compiler has been investigated.Intrinsic functions have better performance than inline assembly.Implement matrixvector multiplication using Intel's AVX and optimization techniques.The obtained results are compared against Intel MKL 2017 SGEMV subroutines.The obtained results are compared using single-thread against multithreads.y = A.x and y = AT.x achieved a performance of 18.2% and 14.1% on multi-core platform. Matrixvector multiplication kernel is one of the most important and common computational operations which form the core of varied important application areas such as scientific and engineering applications. Therefore, it is substantial to optimize and accelerate its implementation. This paper proposes an optimized algorithm for single-precision matrixvector multiplication (SGEMV) on the Intel core i7 processor. An overview of the Intel's advanced vector extension instructions in implementing dense matrixvector multiplication kernels in parallel has been comprehensively addressed. Also, a variety of performance optimization techniques using Intel's advanced vector extension (AVX) instruction sets, memory access optimization, and OpenMP parallelization has been designed. Additionally, the performance of the proposed algorithms is evaluated in compared to the latest version of Intel Math Kernel Library SGEMV 2017 subroutines because Intel Math Kernel Library subroutines also consider the same optimization methods that are used in this paper. In this paper, we have introduced an overview of the optimization techniques, have explained the specific details of handling them in the proposed algorithm, and also have showed the advantages and the challenges of combining them together in contrast to the previous works which usually have concentrated on a single technique and the performance achieved by it. The guidelines of parallel implementation of the proposed algorithm and the characteristics of the target architecture that should be considered when implementing this algorithm have been investigated. An overview of the Intel's advanced vector extension instructions in implementing dense matrixvector multiplication kernels in parallel has been comprehensively addressed. A comparative study of the two most popularly used C++ compilers: Intel C++ compiler 17.0 in Intel Parallel Studio XE 2017 against Microsoft Visual Studio C++ compiler 2015 has been investigated. Finally, the comparison between two primary ways of utilizing AVX instructions: inline assembly and intrinsic functions, and the comparison between single-core and multi-core platforms have introduced. The results are evaluated in Intel Core i7-5600U processor of 2.6GHz with 128KB L1 cache, 512KB L2 cache, and 4MB L3 cache running on windows 10 operating system and on a Broadwell system. The obtained results of the proposed optimized algorithm are implemented on square matrices of different large sizes range from 1024 to 19456. The results indicate a performance improvement of 18.2% and 14.1% for (y=A. x) and (y=AT. x) respectively in compared with the results which are obtained using the latest version of Intel Math Kernel Library 2017(SGEMV) subroutines on multi-core platform.


title: An optimal packet switch architecture for ATM

abstract: The architecture of a high speed packet switch for an ATM system is presented. The switch is based on input electronic modules, where the incoming packets are processed and buffered, and on an optical interconnection network, connecting the inputs to the outputs. During each slot each output can receive a single packet, and each input can transmit up to L packets. A reservation mechanism avoids collisions on the optical network, implementing a series of distributed queues. The switch performance was studied by simulation. The effect of the input switching capacity L on the performance is discussed. >


title: Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions

abstract: This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.


title: A lorentz force magnetometer based on a piezoelectric-on-silicon radial-contour mode disk

abstract: We report a unique MEMS magnetometer based on a disk shaped radial contour mode thin-film piezoelectric on silicon (TPoS) CMOS-compatible resonator. This is the first device of its kind that targets operation under atmospheric pressure conditions as opposed that existing Lorentz force MEMS magnetometers that depend on vacuum. We exploit the chosen vibration mode to enhance coupling to deliver a field sensitivity of 10.92 mV/T while operating at a resonant frequency of 6.27 MHz, despite of a sub-optimal mechanical quality (Q) factor of 697 under ambient conditions in air.


title: Amplified spontaneous emission from opal photonic crystals engineered with structural defects

abstract: In this work, we report on the optical properties and amplified spontaneous emissions (ASE) of polystyrene opals engineered with planar structural defects containing a conjugated polymer emitter. Defects in opals give rise to allowed states inside the photonic stop band, which are probed by transmittance and reflectance spectroscopy. The emission spectrum of the polymer embedded in the defect layer is strongly modified and fingerprints of defect states located inside the stop band are recognized. Amplified spontaneous emission for these engineered photonic crystals is clearly observed.


title: Rao-Blackwellized Particle Filter for Multiple Target Tracking

abstract: In this article we propose a new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle filtering, and the efficiency of the Monte Carlo sampling is improved by using Rao-Blackwellization.


title: Using Simulation to Model Customer Behavior in the Context of Customer Lifetime Value Estimation

abstract: This article illustrates how simulation can be used in the classroom for modeling customer behavior in the context of customer lifetime value estimation. Operations research instructors could use this exercise to introduce multiperiod spreadsheet simulation models in a business setting that is of great importance in practice, and the simulation approach to teaching this subject could be of interest also to marketing and accounting instructors. At Babson College, the spreadsheet simulation exercise is part of an integrated one-case teaching day of the marketing, accounting, and operations research disciplines in the full-time MBA program, but the exercise is directly transferable to stand-alone courses as well. In our experience, students have felt empowered by the ability to incorporate their ideas about customer behavior directly into customer lifetime value models, and have appreciated the ease with which simulation enables them to obtain intuition about the sensitivity of their estimates to different assumptions.


title: Automating vertical profiling

abstract: Last year at OOPSLA we presented a methodology, vertical profiling, for understanding the performance of object-oriented programs. The key insight behind this methodology is that modern programs run on top of many layers (virtual machine, middleware, etc) and thus we need to collect and combine information from all layers in order to understand system performance. Although our methodology was able to explain previously unexplained performance phenomena, it was extremely labor intensive. In this paper we describe and evaluate techniques for automating two significant activities of vertical profiling: trace alignment and correlation. Trace alignment aligns traces obtained from separate runs so that one can reason across the traces. We are not aware of any prior approach that effectively and automatically aligns traces. Correlation sifts through hundreds of metrics to find ones that have a bearing on a performance anomaly of interest. In prior work we found that statistical correlation was only sometimes effective. We have identified highly-effective approaches for both activities.For aligning traces we explore dynamic time warping, and for correlation we explore eight correlators based on statistical correlation, distance measures, and piecewise linear segmentation. Although we explore these activities in the context of vertical profiling, both activities are widely applicable in the performance analysis area.


Automatic Text Summarization Using Reinforcement Learning with Embedding Features


title: Logistic regression analysis on the determinants of stillbirth in Ethiopia

abstract: Background ::: Stillbirth is often defined as fetal death after 24 weeks of gestation, but a fetus greater than any combination of 16, 20, 22, 24, or 28 weeks gestational age and 350 g, 400 g, 500 g, or 1000 g birth weight may be considered stillborn depending on local law. Once the fetus has died, the mother may or may not have contractions and undergo childbirth or in some cases, a Caesarean section. Most stillbirths occur in full-term pregnancies.


title: Composite Lay-up Optimization for Horizontal Axis Wind Turbine Blades

abstract: Research has shown that design concepts based on advanced lay-ups can improve wind turbine blades. Adding carbon fiber reinforcement at outboard sections can make blades lighter reducing edge-wise bending loads. Adding biased fibers can introduce bend-twist coupling reducing the fatigue damage. Optimizations is a powerful tool that can be used to solve the best configuration. To successfully explore these concepts through optimization this paper introduces a parameterization scheme that reflects the layered nature of these designs. The scheme incorporates span-wise variation of material to accurately model the affect of span-wise transition. This parameterization scheme will be compared with other schemes typically seen in literature. Application of this scheme will be demonstrated by developing an optimal glass/carbon blade and an optimal blade with bend-twist coupling.


title: Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia

abstract: In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia's content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment-a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors' results are intriguing: They suggest that i semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia's plain link structure alone and ii that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors' work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.


title: 1-W 1:16 DEMUX and one-chip CDR with 1:4 DEMUX for 10 Gbit/s optical communication systems

abstract: Using InP/InGaAs heterojunction bipolar transistor (HBT) technology, we have successfully designed and fabricated a low power 1:16 demultiplexer (DEMUX) IC and one-chip clock and data recovery (CDR) with a 1:4 DEMUX IC for 10 Gbit/s optical communication systems. The HBTs were fabricated by a non-self-aligned process to achieve high productivity and uniformity of device characteristics. The 1:16 DEMUX IC and the one-chip CDR with the 1:4 DEMUX IC consist of approximately 1200 and 460 transistors, respectively. We have confirmed error-free operation at 10 Gbit/s for all data outputs of both ICs. The 1:16 DEMUX IC and the one-chip CDR with the 1:4 DEMUX IC consume only 1 W and 950 mW, respectively. These results demonstrate the feasibility of InP/InGaAs HBTs for low-power, high-integration optical communication ICs.


title: Faerie: efficient filtering algorithms for approximate dictionary-based entity extraction

abstract: Dictionary-based entity extraction identifies predefined entities (e.g., person names or locations) from a document. A recent trend for improving extraction recall is to support approximate entity extraction, which finds all substrings in the document that approximately match entities in a given dictionary. Existing methods to address this problem support either token-based similarity (e.g., Jaccard Similarity) or character-based dissimilarity (e.g., Edit Distance). It calls for a unified method to support various similarity/dissimilarity functions, since a unified method can reduce the programming efforts, hardware requirements, and the manpower. In addition, many substrings in the document have overlaps, and we have an opportunity to utilize the shared computation across the overlaps to avoid unnecessary redundant computation. In this paper, we propose a unified framework to support many similarity/dissimilarity functions, such as jaccard similarity, cosine similarity, dice similarity, edit similarity, and edit distance. We devise efficient filtering algorithms to utilize the shared computation and develop effective pruning techniques to improve the performance. The experimental results show that our method achieves high performance and outperforms state-of-the-art studies.


title: Assisted and unassisted suicide in men and women: longitudinal study of the Swiss population

abstract: BACKGROUND ::: In Switzerland assisted suicide is legal if no self-interest is involved. ::: ::: ::: AIMS ::: To compare the strength and direction of associations with sociodemographic factors between assisted and unassisted suicides. ::: ::: ::: METHOD ::: We calculated rates and used Cox and logistic regression models in a longitudinal study of the Swiss population. ::: ::: ::: RESULTS ::: Analyses were based on 5 004 403 people, 1301 assisted and 5708 unassisted suicides from 2003 to 2008. The rate of unassisted suicides was higher in men than in women, rates of assisted suicides were similar in men and women. Higher education was positively associated with assisted suicide, but negatively with unassisted. Living alone, having no children and no religious affiliation were associated with higher rates of both. ::: ::: ::: CONCLUSIONS ::: Some situations that indicate greater vulnerability such as living alone were associated with both assisted and unassisted suicide. Among the terminally ill, women were more likely to choose assisted suicide, whereas men died more often by unassisted suicide.


title: End-to-end service assurance in IoT MQTT-SN

abstract: MQTT-SN offers energy efficient data transportation under unreliable wireless environment with limited energy/ bandwidth. However, MQTT-SN faces a new set of challenges for service guarantees when operated for a reliable or time-critical applications. The impact of various network parameters on the MQTT-SN service assurance requires an end-to-end system study, which are unexplored yet. In this paper we analyze in detail about the end-to-end service assurance parameters such as content delivery delay and probability of content delivery for a MQTT-SN to be used in health care Internet of Things (IoT). To model the end-to-end delay, various entities of the MQTT-SN including handshake messages in MQTT-SN Pub/sub architecture over TCP, over UDP and then the queuing delay involved in server have been considered. We model the MQTT content and request server as a round-robin queue scheduler and also model a TOPIC ID based matching station that delivers the content on requested TOPIC ID. With this model we derive the end-to-end content delivery probability and content delivery delay estimate as functions of MQTT-SN system parameters such as content arrival rate, request arrival rate, possession time of content in server, possession time of request in server, number of content arrivals and number of requests arrivals. Both these service assurance parameters will give more insight into the number of content/request arrivals that can be supported for a given service assurance requirement. In addition these service assurance parameters will be of help to design the system more effectively. We carry out a detailed ns2 based simulation to show impacts of various system parameters on the network performances for various QoS services designed for MQTT-SN system.


title: State-of-the-Art in Force and Tactile Sensing for Minimally Invasive Surgery

abstract: Haptic perception plays a very important role in surgery. It enables the surgeon to feel organic tissue hardness, measure tissue properties, evaluate anatomical structures, and allows him/her to commit appropriate force control actions for safe tissue manipulation. However, in minimally invasive surgery, the surgeon's ability of perceiving valuable haptic information through surgical instruments is severely impaired. Performing the surgery without such sensory information could lead to increase of tissue trauma and vital organic tissue damage. In order to restore the surgeon's perceptual capability, methods of force and tactile sensing have been applied with attempts to develop instruments that can be used to detect tissue contact forces and generate haptic feedback to the surgeon. This paper reviews the state-of-the-art in force and tactile sensing technologies applied in minimally invasive surgery. Several sensing strategies including displacement-based, current-based, pressure-based, resistive-based, capacitive-based, piezoelectric-based, vibration-based, and optical-based sensing are discussed.


title: Inhibitor of Apoptosis (IAP) Proteins-Modulators of Cell Death and Inflammation

abstract: Misregulated innate immune signaling and cell death form the basis of much human disease pathogenesis. Inhibitor of apoptosis (IAP) protein family members are frequently overexpressed in cancer and contribute to tumor cell survival, chemo-resistance, disease progression, and poor prognosis. Although best known for their ability to regulate caspases, IAPs also influence ubiquitin (Ub)-dependent pathways that modulate innate immune signaling via activation of nuclear factor kappa B (NF-kappa B). Recent research into IAP biology has unearthed unexpected roles for this group of proteins. In addition, the advances in our understanding of the molecular mechanisms that IAPs use to regulate cell death and innate immune responses have provided new insights into disease states and suggested novel intervention strategies. Here we review the functions assigned to those IAP proteins that act at the intersection of cell death regulation and inflammatory signaling.


title: Efficient Character Skew Rectification in Scene Text Images

abstract: We present an efficient method for character skew rectification in scene text images. The method is based on a novel skew estimators, which exploit intuitive glyph properties and which can be efficiently computed in a linear time. The estimators are evaluated on a synthetically generated data (including Latin, Cyrillic, Greek, Runic scripts) and real scene text images, where the skew rectification by the proposed method improves the accuracy of a state-of-the-art scene text recognition pipeline.


title: Anomalous Lattice Vibrations of Single- and Few-Layer MoS2

abstract: Molybdenum disulfide (MoS2) of single- and few-layer thickness was exfoliated on SiO2/Si substrate and characterized by Raman spectroscopy. The number of S−Mo−S layers of the samples was independently determined by contact-mode atomic force microscopy. Two Raman modes, E12g and A1g, exhibited sensitive thickness dependence, with the frequency of the former decreasing and that of the latter increasing with thickness. The results provide a convenient and reliable means for determining layer thickness with atomic-level precision. The opposite direction of the frequency shifts, which cannot be explained solely by van der Waals interlayer coupling, is attributed to Coulombic interactions and possible stacking-induced changes of the intralayer bonding. This work exemplifies the evolution of structural parameters in layered materials in changing from the three-dimensional to the two-dimensional regime.


title: Few-qubit quantum-classical simulation of strongly correlated lattice fermions

abstract: We study a proof-of-principle example of the recently proposed hybrid quantum-classical simulation of strongly correlated fermion models in the thermodynamic limit. In a ‘two-site’ dynamical mean-field theory (DMFT) approach we reduce the Hubbard model to an effective impurity model subject to self-consistency conditions. The resulting minimal two-site representation of the non-linear hybrid setup involves four qubits implementing the impurity problem, plus an ancilla qubit on which all measurements are performed. We outline a possible implementation with superconducting circuits feasible with near-future technology.


title: High power, high repetition rate, fast capacitor charging circuit

abstract: High power, high repetition rate capacitor charging circuit is presented. The circuit uses four SiC MOSFET switches in parallel in a modified boost converter configuration. Switching losses, already reduced due to the fast switching characteristics of the SiC devices are further mitigated by an auxiliary switch enforcing zero voltage turn-on of the main switches. The charging circuit is expected to enable a nanosecond range high-voltage pulse generator to operate at 100kHz repetition rate. The optimum number of main switches is determined by the available liquid cooling area of the SiC device packages and resulting power density for an acceptable junction temperature rise of approximately 85°C.


title: First-time mothers: social support, maternal parental self-efficacy and postnatal depression.

abstract: Aims and objectives. To examine the relationships between social support, maternal parental self-efficacy and postnatal depression in first-time mothers at 6 weeks post delivery. Background. Social support conceptualised and measured in different ways has been found to positively influence the mothering experience as has maternal parental self-efficacy. No research exists which has measured the relationships between social support, underpinned by social exchange theory and maternal parental self-efficacy using a domain-specific instrument, underpinned by self-efficacy theory and postnatal depression, with first-time mothers at 6 weeks post delivery. Design. A quantitative correlational descriptive design was used. Method. Data were collected using a five-part questionnaire package containing a researcher developed social support questionnaire, the Perceived Maternal Parental Self-Efficacy Scale and the Edinburgh Postnatal Depression Scale. Four hundred and ten mothers completed questionnaires at 6 weeks post delivery. Results. Significant relationships were found between functional social support and postnatal depression; informal social support and postnatal depression; maternal parental self-efficacy and postnatal depression and informal social support and maternal parental self-efficacy at 6 weeks post delivery. Conclusion. Nurses and midwives need to be aware of and acknowledge the significant contribution of social support, particularly from family and friends in positively influencing first-time mothers’ mental health and well-being in the postpartum period. The development of health care policy and clinical guidelines needs to define and operationalise social support to enhance maternal parental self-efficacy. Relevance to clinical practice. These findings suggest that nurses and midwives need to be cognisant of the importance of social support for first-time mothers in both enhancing maternal parental self-efficacy and reducing postnatal depressive symptomatology in the early postpartum period.


ROVER CHASSIS EVALUATION TOOLS


title: Home food preparation practices, experiences and perceptions: A qualitative interview study with photo-elicitation

abstract: Food-related choices have an important impact on health. Food preparation methods may be linked to diet and health benefits. However, the factors influencing people’s food choices, and how they are shaped by food preparation experiences, are still not fully understood. We aimed to study home food preparation practices, experiences and perceptions amongst adults in North East England. A matrix was used to purposively sample participants with diverse socio-demographic characteristics. Participants developed photographic food diaries that were used as prompts during semi-structured interviews. Data were analysed using the Framework Method. Interviews were conducted with 18 adults (five men and 13 women), aged approximately 20 to 80 years, to reach data saturation. Participants’ practices varied widely, from reliance on pre-prepared foods, to preparing complex meals entirely from basic ingredients. Key themes emerged regarding the cook (identity), the task (process of cooking), and the context (situational drivers). Resources, in terms of time, money and facilities, were also underpinning influences on food preparation. Participants’ practices were determined by both personal motivations to cook, and the influence of others, and generally reflected compromises between varied competing demands and challenges in life. Most people appeared to be overall content with their food preparation behaviour, though ideally aspired to cook more frequently, using basic ingredients. This often seemed to be driven by social desirability. Home food preparation is complex, with heterogeneous practices, experiences and perceptions both between individuals and within the same individual over time, according to shifting priorities and circumstances. Generalisability of these findings may be limited by the regional participant sample; however the results support and build upon previous research. Focussing interventions on life transition points at which priorities and circumstances change, with careful targeting to stimulate personal motivation and social norms, may prove effective in encouraging home food preparation.


title: Graphene Hybrid Materials in Gas Sensing Applications †

abstract: Graphene, a two dimensional structure of carbon atoms, has been widely used as a material for gas sensing applications because of its large surface area, excellent conductivity, and ease of functionalization. This article reviews the most recent advances in graphene hybrid materials developed for gas sensing applications. In this review, synthetic approaches to fabricate graphene sensors, the nano structures of hybrid materials, and their sensing mechanism are presented. Future perspectives of this rapidly growing field are also discussed.


title: A Corpus of Images and Text in Online News

abstract: In recent years, several datasets have been released that include images and text, giving impulse ::: to new methods that combine natural language processing and computer vision. However, there is a need for datasets of images in their natural textual context. The ION corpus contains 300K news articles published between August 2014 - 2015 in five online newspapers from two countries. The 1-year coverage over multiple publishers ensures a broad scope in terms of topics, image quality and editorial viewpoints. The corpus consists of JSON-LD files with the following data about each article: the original URL of the article on the news publisher’s website, the date of publication, the headline of the article, the URL of the image displayed with the article (if any), and the caption of that image. Neither the article text nor the images themselves are included in the corpus. Instead, the images are distributed as high-dimensional feature vectors extracted from a Convolutional Neural Network, anticipating their use in computer vision tasks. The article text is represented as a list of automatically generated entity and topic annotations in the form of Wikipedia/DBpedia pages. This facilitates the selection of subsets of the corpus for separate analysis or evaluation.


title: Development of the asthma control test: a survey for assessing asthma control.

abstract: Abstract Background Asthma guidelines indicate that the goal of treatment should be optimum asthma control. In a busy clinic practice with limited time and resources, there is need for a simple method for assessing asthma control with or without lung function testing. Objectives The objective of this article was to describe the development of the Asthma Control Test (ACT), a patient-based tool for identifying patients with poorly controlled asthma. Methods A 22-item survey was administered to 471 patients with asthma in the offices of asthma specialists. The specialist's rating of asthma control after spirometry was also collected. Stepwise regression methods were used to select a subset of items that showed the greatest discriminant validity in relation to the specialist's rating of asthma control. Internal consistency reliability was computed, and discriminant validity tests were conducted for ACT scale scores. The performance of ACT was investigated by using logistic regression methods and receiver operating characteristic analyses. Results Five items were selected from regression analyses. The internal consistency reliability of the 5-item ACT scale was 0.84. ACT scale scores discriminated between groups of patients differing in the specialist's rating of asthma control (F = 34.5, P P 1 (F = 4.3, P = .0052). As a screening tool, the overall agreement between ACT and the specialist's rating ranged from 71% to 78% depending on the cut points used, and the area under the receiver operating characteristic curve was 0.77. Conclusion Results reinforce the usefulness of a brief, easy to administer, patient-based index of asthma control.


title: Pixel- and region-based image fusion with complex wavelets

abstract: A number of pixel-based image fusion algorithms (using averaging, contrast pyramids, the discrete wavelet transform and the dual-tree complex wavelet transform (DT-CWT) to perform fusion) are reviewed and compared with a novel region-based image fusion method which facilitates increased flexibility with the definition of a variety of fusion rules. A DT-CWT is used to segment the features of the input images, either jointly or separately, to produce a region map. Characteristics of each region are calculated and a region-based approach is used to fuse the images, region-by-region, in the wavelet domain. This method gives results comparable to the pixel-based fusion methods as shown using a number of metrics. Despite an increase in complexity, region-based methods have a number of advantages over pixel-based methods. These include: the ability to use more intelligent semantic fusion rules; and for regions with certain properties to be attenuated or accentuated.


title: A effective algorithm for detecting fuzzy community structure based on vertex similarity analysis

abstract: The problem of fuzzy community structure detection in complex network has received a considerable amount of attention in recent years. In this study, we present a useful genetic algorithm which aims to obtain fuzzy network partition by a new modular function. For improving the efficiency of algorithm, both initialization process and genetic operation are specifically optimized based on the local topology information of network. Experiments on various scale computer-generated networks and real world networks show the capability of the method to find the fuzzy community structure of network.


title: Multi-Agent Coordination by Decentralized Estimation and Control

abstract: We describe a framework for the design of collective behaviors for groups of identical mobile agents. The approach is based on decentralized simultaneous estimation and control, where each agent communicates with neighbors and estimates the global performance properties of the swarm needed to make a local control decision. Challenges of the approach include designing a control law with desired convergence properties, assuming each agent has perfect global knowledge; designing an estimator that allows each agent to make correct estimates of the global properties needed to implement the controller; and possibly modifying the controller to recover desired convergence properties when using the estimates of global performance. We apply this framework to the problem of controlling the moment statistics describing the location and shape of a swarm. We derive conditions which guarantee that the formation statistics are driven to desired values, even in the presence of a changing network topology.


title: Effects of Curing and Chemical Aging on Warpage—Characterization and Simulation

abstract: In this paper, the effect of the curing process on the warpage of an encapsulated electronic package is considered by using a coupled chemical-thermomechanical modeling methodology. A cure-dependent constitutive model that consists of a cure-kinetic model, a curing- and chemical-aging-induced shrinkage model, and a degree of cure-dependent viscoelastic relaxation model was developed and implemented in a numerical finite-element model for warpage simulation. Effects of polymerization conversion and chemical aging on the warpage evolution of a bimaterial dummy package after molding and during the postmold curing (PMC) process were investigated by using the proposed modeling methodology. Shadow Moire warpage analyses were also performed to validate the numerical results. It was found from the warpage analyses that the chemical aging, while contributing little to the overall cross linking during the PMC, has a significant effect on the package warpage. The coupled chemical-thermomechanical model can be applied for performing numerical optimization for the packaging process and the assembly reliability.


title: Visualizing mobile design pattern relationships

abstract: This paper describes the process of creating a design pattern management interface for a collection of mobile design patterns. The need to communicate how patterns are interrelated and work together to create solutions motivated the creation of this interface. Currently, most design pattern collections are presented in alphabetical lists. The Oracle Mobile User Experience team approach is to communicate relationships visually by highlighting and connecting related patterns. Before the team designed the interface, we first analyzed common relationships between patterns and created a pattern language map. Next, we organized the patterns into conceptual design categories. Last, we designed a pattern management interface that enables users to browse patterns and visualize their relationships.


A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices


title: Open Source Cloud Computing Platforms

abstract: With the popularization of cloud computing, several enterprises and open-source communities have developed their own cloud solutions. A number of factors weigh on user selection, as each one has peculiar characteristics and may target different usage scenarios. Considering such challenge, this paper focuses on giving the reader an understanding of some major existing open cloud computing solutions – XCP, Eucalyptus and Open Nebula. Hopefully, a deep comparison of such solutions can leverage the cloud computing research area providing a good starting point to research groups and interested readers.


Structured Prediction via Learning to Search under Bandit Feedback.


title: Non-Bayesian Social Learning with Imperfect Private Signal Structure

abstract: As one of the classic models that describe the belief dynamics over social networks, a non-Bayesian social learning model assumes that members in the network possess accurate signal knowledge through the process of Bayesian inference. In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming the existence of private signal structure bias. Each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief and then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report. First, we investigated the intrinsic learning ability of an isolated agent and deduced the conditions that the signal structure needs to satisfy for this isolated agent to make an eventually correct decision. According to these conditions, agents' signal structures were further divided into three different types, "conservative," "radical," and "negative." Then, we switched the context from isolated agents to a connected network; our propositions and simulations show that the conservative agents are the dominant force for the social network to learn the real state, while the other two types might prevent the network from successful learning. Although fragilities do exist in non-Bayesian social learning mechanism, "be more conservative" and "avoid overconfidence" could be effective strategies for each agent in the real social networks to collectively improve social learning processes and results.


title: Person re-identification by descriptive and discriminative classification

abstract: Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representational strategies capture a large extent of complementary information we propose to combine both approaches. First, given a specific query, we rank all samples according to a feature-based similarity, where appearance is modeled by a set of region covariance descriptors. Next, a discriminative model is learned using boosting for feature selection, which provides a more specific classifier. The proposed approach is demonstrated on two datasets, where we show that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone. In addition, we give a comparison to the state-of-the-art on a publicly available benchmark dataset.


title: Low-Rank Total Variation for Image Super-Resolution

abstract: Most natural images can be approximated using their low-rank components. This fact has been successfully exploited in recent advancements of matrix completion algorithms for image recovery. However, a major limitation of low-rank matrix completion algorithms is that they cannot recover the case where a whole row or column is missing. The missing row or column will be simply filled as an arbitrary combination of other rows or columns with known values. This precludes the application of matrix completion to problems such as super-resolution (SR) where missing values in many rows and columns need to be recovered in the process of up-sampling a low-resolution image. Moreover, low-rank regularization considers information globally from the whole image and does not take proper consideration of local spatial consistency. Accordingly, we propose in this paper a solution to the SR problem via simultaneous (global) low-rank and (local) total variation (TV) regularization. We solve the respective cost function using the alternating direction method of multipliers (ADMM). Experiments on MR images of adults and pediatric subjects demonstrate that the proposed method enhances the details of the recovered high-resolution images, and outperforms the nearest-neighbor interpolation, cubic interpolation, non-local means, and TV-based up-sampling.


title: Fake BTS Attacks of GSM System on Software Radio Platform

abstract: The 2G GSM communication system only provides one-way authentication mechanism which just authenticate the identities of mobile users. As we know, this is not resistant to fake BTS attack. But for the huge cost for building a fake BTS before, this kind of attack were not really implemented before. This paper presents an implement of fake BTS based on software radio technologies. Furthermore, this paper discusses two types of fake BTS attacks on our software radio platform. The first attack is IMSI/IMEI catch attack, which can get the mobile phone’s IMSI and IMEI. With this information, attacker can got the track of the man with this phone. The second attack is selective jamming attack. After get the IMSI and IMEI of the mobile phone, the attackers can decide whether the mobile phone is blocked or not. We will analyze the GSM protocol which is relevant to the interception system and later present the performance of such a system by real tests and demonstrate its feasibility.


title: A boost type control strategy in dual active bridge bidirectional DC-DC converter

abstract: In this paper, a Boost type control strategy is proposed to solve the problems of large power backflow and low conversion efficiency in dual active bridge bidirectional DC-DC converter which occur in the traditional phase-shifted control strategies. In the condition that the main topology circuit is not changed, the control strategy proposed in this paper can make the circuit work in Boost state every switching cycle by changing the control timing sequence and logic of the switches, so as to transfer the energy from the input side to the output side through the energy storaging and releasing of the inductor, and the control principle of the backward mode is the same as that of the forward mode, then bidirectional flow of energy can be realized. In the Boost type control strategy, the current in the inductor is discontinuous, and then the energy stored in the inductor in each switching cycle is all released to the output side, which can decrease the power backflow of the input power supply to zero and improve the efficiency of energy transmission. Finally, the boost type control strategy proposed in this paper is simulated and verified through simulink, simulation results show that the power backflow of the converter can be reduced effectively under the proposed control strategy and the conversion efficiency is improved.


Micrographia with cognitive dysfunction: “Minimal” sequelae of a putaminal infarct


title: Artificial Intelligence and Collective Intelligence

abstract: The vision of artificial intelligence (AI) is often manifested through an autonomous software module (agent) in a complex and uncertain environment. The agent is capable of thinking ahead and acting for long periods of time in accordance with its goals/objectives. It is also capable of learning and refining its understanding of the world. The agent may accomplish this based on its own experience, or from the feedback provided by humans. Famous recent examples include self-driving cars (Thrun 2006) and the IBM Jeopardy player Watson (Ferrucci et al. 2010). This chapter explores the immense value of AI techniques for collective intelligence, including ways to make interactions between large numbers of humans more efficient. By defining collective intelligence as “groups of individuals acting collectively in an intelligent manner,” one soon wishes to nail down the meaning of individual. In this chapter, individuals may be software agents and/or people and the collective may consist of a mixture of both. The rise of collective intelligence allows novel possibilities of seamlessly integrating machine and human intelligence at a large scale – one of the holy grails of AI (known in the literature as mixed-initiative systems (Horvitz 2007)). Our chapter focuses on one such integration – the use of machine intelligence for the management of crowdsourcing platforms (Weld, Mausam, and Dai 2011). Crowdsourcing is a special case of collective intelligence, where a third party (called the requestor) with some internal objective solicits a group of individuals (called workers) to perform a set of inter-related tasks in service of that objective. The requestor’s objective may be expressed in the form of a utility function to be maximized. For example, a requestor might wish to obtain labels for a large set of images; in this case, her utility function might be the average quality of labels subject to a constraint that no more than $ X dollars be spent paying workers. We assume that the workers act independently, interacting only through the shared tasks. Each worker has an individual utility function, which is often different from the collective’s utility function. Furthermore, we assume that their utility functions are independent of each other. The AI subfield of multi-agent systems considers even richer models, in which individual agents may reason about the objectives of other agents, negotiate, and bargain with each other (Weiss 2013). We won’t discuss these techniques


title: g-tries: an efficient data structure for discovering network motifs

abstract: In this paper we propose a novel specialized data structure that we call g-trie, designed to deal with collections of subgraphs. The main conceptual idea is akin to a prefix tree in the sense that we take advantage of common topology by constructing a multiway tree where the descendants of a node share a common substructure. We give algorithms to construct a g-trie, to list all stored subgraphs, and to find occurrences on another graph of the subgraphs stored in the g-trie. We evaluate the implementation of this structure and its associated algorithms on a set of representative benchmark biological networks in order to find network motifs. To assess the efficiency of our algorithms we compare their performance with other known network motif algorithms also implemented in the same common platform. Our results show that indeed, g-tries are a feasible, adequate and very efficient data structure for network motifs discovery, clearly outperforming previous algorithms and data structures.


title: Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching

abstract: In a way similar to the string-to-string correction problem, we address discrete time series similarity in light of a time-series-to-time-series-correction problem for which the similarity between two time series is measured as the minimum cost sequence of edit operations needed to transform one time series into another. To define the edit operations, we use the paradigm of a graphical editing process and end up with a dynamic programming algorithm that we call time warp edit distance (TWED). TWED is slightly different in form from dynamic time warping (DTW), longest common subsequence (LCSS), or edit distance with real penalty (ERP) algorithms. In particular, it highlights a parameter that controls a kind of stiffness of the elastic measure along the time axis. We show that the similarity provided by TWED is a potentially useful metric in time series retrieval applications since it could benefit from the triangular inequality property to speed up the retrieval process while tuning the parameters of the elastic measure. In that context, a lower bound is derived to link the matching of time series into down sampled representation spaces to the matching into the original space. The empiric quality of the TWED distance is evaluated on a simple classification task. Compared to edit distance, DTW, LCSS, and ERP, TWED has proved to be quite effective on the considered experimental task.


title: Upper limb motion estimation from inertial measurements in stroke rehabilitation", unpublished

abstract: In this paper we introduce a real-time human arm motion detector that has been developed to aid the home-based rehabilitation of stroke patients. Two tri-axial inertial sensors are adopted to measure the orientation of the arm. Kinematics models then allow us to recover the coordinates of the wrist and elbow joints, given a still shoulder joint. One of the significant contributions of this paper is the use of a total variation based optimization in smoothing the erroneous measurements due to rapid or unstable movements. Comprehensive experiments demonstrate favorable performance of the proposed inertial tracking system in different sensor positions and motion speeds, compared to the outcomes of a marker-based optical motion tracker that is commercially available. Keyword: Rehabilitation, motion tracking, inertial sensor, total variation.


title: A Novel Circularly Polarized Microstrip Antenna With Two Connected Quasi Monopoles for Wideband Applications

abstract: A novel microstrip antenna with an interesting radiation structure and L-shaped ground plane is presented to generate a wideband circular polarization (CP) characteristic. For this purpose, the radiation structure comprises of two truncated monopole resonators, connected to each other by a microstrip link. By adjusting the position of the link between the resonators, CP quality and bandwidth can be controlled. A single microstrip line is employed to feed one of the monopoles, and the other monopole is only matched by a proper chip resistor. Two triangle sections are also sliced from the other radiation edges to improve the impedance and CP characteristics. Moreover, because of the symmetric design of the antenna, a high purity between the right- and left-handed CP patterns and wide CP beamwidth are achieved. The antenna is fully studied and manufactured that its results are 53% (1.69~2.92 GHz) for 3-dB axial-ratio bandwidth, 62% (1.68~3.2 GHz) for 10-dB return-loss bandwidth, and gain range between 2 and 4.2 dBi across the CP band.


title: Polymorphisms of the SLCO1B1 gene predict methotrexate-related toxicity in childhood acute lymphoblastic leukemia.

abstract: BACKGROUND ::: Methotrexate (MTX) is an important component of the therapy for childhood acute lymphoblastic leukemia. Treatment with high-dose MTX often causes toxicity, recommending a dose reduction and/or cessation of treatment. Polymorphisms in genes involved in the MTX metabolism have been associated with toxicity with controversial results. The discrepancies could be due to differences in treatment protocols among studies, small, or non-homogeneous populations or the use of different toxicity criteria. The aim of the present study was to analyze the possible correlation of polymorphisms of genes involved in the MTX metabolism with the toxicity during therapy with the well-established LAL/SHOP protocol. ::: ::: ::: PROCEDURE ::: We analyzed 10 polymorphisms in seven genes (MTHFR, TS, SHMT1, RFC1, ABCB1, ABCG2, and SLCO1B1) from the MTX metabolism in 115 Spanish pediatric B-ALL patients, using MTX plasma concentration as an objective and quantifiable marker of toxicity. ::: ::: ::: RESULTS ::: We confirmed the suitability of MTX plasma levels as a toxicity marker. We found a statistically significant association between MTX plasma concentration and the SLCO1B1 rs11045879 CC genotype (P = 0.030). The rs4149081 AA genotype, in the same gene, could also be an indicator for high-MTX plasma concentrations. We did not find any significant association in the other genetic polymorphisms analyzed. ::: ::: ::: CONCLUSIONS ::: Identification of the rs4149081 and rs11045879 SLCO1B1 polymorphisms in children with ALL could be a useful tool for monitoring patients at risk of low-MTX clearance in order to avoid MTX-related toxicity.


title: Angelic Semantics for High-Level Actions

abstract: High-level actions (HLAs) lie at the heart of hierarchical planning. Typically, an HLA admits multiple refinements into primitive action sequences. Correct descriptions of the effects of HLAs may be essential to their effective use, yet the literature is mostly silent. We propose an angelic semantics for HLAs, the key concept of which is the set of states reachable by some refinement of a high-level plan, representing uncertainty that will ultimately be resolved in the planning agent's own best interest. We describe upper and lower approximations to these reachable sets, and show that the resulting definition of a high-level solution automatically satisfies the upward and downward refinement properties. We define a STRIPS-like notation for such descriptions. A sound and complete hierarchical planning algorithm is given and its computational benefits are demonstrated.


title: Iterative detection and decoding for SCMA systems with LDPC codes

abstract: Sparse code multiple access (SCMA) is a promising multiplexing approach to achieve high system capacity. In this paper, we develop a novel iterative detection and decoding scheme for SCMA systems combined with Low-density Parity-check (LDPC) decoding. In particular, we decompose the output of the message passing algorithm (MPA) based SCMA multiuser detection into intrinsic part and prior part. Then we design a joint detection and decoding scheme which iteratively exchanges the intrinsic information between the detector and the decoder, yielding a satisfied performance gain. Moreover, the proposed scheme has almost the same complexity compared to the traditional receiver for LDPC-coded SCMA systems. As numerical results demonstrate, the proposed scheme has a substantial gain over the traditional SCMA receiver on AWGN channels and Rayleigh fading channels.


Evaluating & improving fault localization techniques


title: Boosted multi-task learning

abstract: In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between l 1-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.


title: REALTIME COMPOSITING OF PROCEDURAL FACADE TEXTURES ON THE GPU

abstract: The real time rendering of complex virtual city models has become more important in the last few years for many practical applications like realistic navigation or urban planning. For maximum rendering performance, the complexity of the geometry or textures can be reduced by decreasing the resolution until the data set can fully reside on the memory of the graphics card. This typically results in a low quality of the virtual city model. Alternatively, a streaming algorithm can load the high quality data set from the hard drive. However, this approach requires a large amount of persistent storage providing several gigabytes of static data. We present a system that uses a texture atlas containing atomic tiles like windows, doors or wall patterns, and that combines those elements on-the-ﬂy directly on the graphics card. The presented approach beneﬁts from a sophisticated randomization approach that produces lots of different facades while the grammar description itself remains small. By using a ray casting apporach, we are able to trace through transparent windows revealing procedurally generated rooms which further contributes to the realism of the rendering. The presented method enables real time rendering of city models with a high level of detail for facades while still relying on a small memory footprint.


title: Human Factors Considerations in the Evaluation of Processor-Based Signal and Train Control Systems: Human Factors in Railroad Operations

abstract: In August 2001, the Federal Railroad Administration issued the notice of proposed rulemaking: Standards for Development and Use of Processor-Based Signal and Train Control Systems (49 Code of Federal Regulations Part 236). This proposed rule addresses the design and implementation of processor-based train control systems. Under the proposed rule, a railroad wishing to implement a positive train control (PTC) system in revenue service must develop and submit a product safety plan (PSP) and assess the risk associated with the new system. This report attempts to fill the gap provided by the lack of knowledge about the kinds of human performance challenges and safety risks that will occur with these proposed systems. To fill this gap, the authors identified human factors issues that arose in other industries where similar kinds of technology and human-machine interfaces were used. This literature review, along with an analysis of PTC-preventable accidents, served as the basis for structuring interviews with employees at several railroads that had experience with train control technology containing elements of PTC. The answers to these questions provide the reader with a roadmap of human performance issues to consider in preparing or evaluating a PSP, along with the implications for risk.


title: Visual field map clusters in human cortex

abstract: We describe the location and general properties of nine human visual field maps. The cortical location of each map, as well as many examples of the eccentricity and angular representations within these maps, are shown in a series of images that summarize a large set of functional MRI data. The organization and properties of these maps are compared and contrasted with descriptions by other investigators. We hypothesize that the human visual field maps are arranged in several clusters, each comprising a group of maps that share a common foveal representation and semicircular eccentricity map. The spatial organization of these clusters suggests that the perceptual processing within each cluster serves related functions.


title: Controlling calcium and phosphate ion release of 3D printed bioactive ceramic scaffolds: An in vitro study

abstract: This paper characterizes in an in vitro setting the release of calcium (Ca) and phosphate (PO4) of 3D printed bioactive ceramic scaffold prepared from extrudable paste containing hydroxyapatite and β-tricalcium phosphate (β-TCP). Hydroxyapatite and β-TCP were calcined at 800 °C for 11 h, fabricated into four experimental groups (100% HA, 100% β-TCP, 15%/85% HA/β-TCP, and 15%/85% HA/β-TCP (design)), sintered to 1100 °C for 4 h. Calcium and phosphorus concentrations were evaluated using ICP spectroscopy, and the release of Ca and PO4 ions during dissolution of the CaP-based scaffolds was measured by submerging in 0.05 mol/L Tris(hydroxymethyl)aminomethane-HCl and maintaining a temperature of 37 °C. The Ca and PO4 concentrations of the solutions were measured with the utilization of a calcium assay kit and a phosphate assay kit and read in a UV–visible spectrophotometer. The 100% HA scaffold group showed the greatest concentration of Ca ions (~1.9 mg/dL), but ultimately released at a lower amount as time increased; the 100% HA scaffold also showed the lowest total amount of calcium ions released over the course of evaluation. The results for the 100% β-TCP were on the opposite of the HA with the highest amount of calcium ion release over the study. While the PO4 ion release showed a similar trend as those observed with Ca ions with an apparent difference in the 100% HA scaffold group. There was nearly 0 mg/dL of the phosphate ions released in the first 24 h, in comparison to the amount of Ca ions released during the same time frame. Since various formulations can lead to different properties of these bioactive ceramic scaffolds, it is important to understand how the tailoring of this important biphasic material can impact the long-term outcome of an ever-important in vivo clinical trial in the future.


title: Estimation of ECG features using LabVIEW

abstract: ons instead of lines of text to create programs. Unlike text based programming language, LabVIEW uses the data flow programming, where the flow of data determines execution. The flexibility, modular nature and ease to use programming possible with labview, makes it less complex. The proposed algorithm is executed in two stages. Firstly, it preprocesses (denoises) the signal to remove the noise from the ECG signal. Then itdetects P-wave, T- wave, QRS complex and their onsets and offsets. LabVIEW and the related toolkits, advanced signal processi ng toolkit and math script are used to build the graphical progr amme for both the stages. The algorithm is evaluated with ECG data files, with different ECG morphologies and noise content taken from CSE multilead data base which contains 5000 samples per ECG, recorded at a sampling frequency of 500Hz.