title: Petrology and age of the A-type Mulock granite batholith, northern Grenville Province, Ontario

abstract: The Mulock granite batholith is exposed over 530 km2 in the northern part of the Grenville Province, 25 km north of North Bay, Ontario. The batholith has a UPb zircon emplacement age of 1244+4/-3 Ma. Two regional tectonic events affected the batholith: (1) moderate-grade regional metamorphism; and (2) shearing and mylonitization related to the Grenville Front Tectonic Zone which slices across the northern third of the batholith. ::: ::: A suite of fine-to coarse-grained, pink, gneissic granites containing accessory fluorite and with colour indices between 1 and 27, dominate the batholith and are cut by abundant aplite dikes. Anorthosite dikes cut the granites and aplite in the central part of the batholith. The granites were originally alkali feldspar granite and syenogranite, and the coarser-grained varieties were originally hypersolvus. The suite is mainly metaluminous to marginally peraluminous; peralkaline varieties are rare. Most of the suite is subalkalic, but subordinate alkalic granites enriched in biotite and ferrohastingsite are early phases. The suite shows all the major and trace element compositions and elemental ratios diagnostic of A-type granite suites. Metamorphism altered the primary distribution of some of the trace elements, particulary Rb and F. The granites crystallized under relatively low pressures (<2 kb) from a high temperature, fluorine-enriched magma. Alkali feldspar fractionation accompanied by minor amphibole fractionation can account for chemical variations observed within the suite; the aplite dikes are products of residual magma. Magma generation occurred at lower crustal levels, probably by high-temperature, anhydrous partial melting of tonalitic to granodioritic, or felsic granulite source rocks, which had not previously undergone melt depletion. Mixed source rocks may have been involved, and the anorthosite dikes may be an expression of mantle-derived mafic magmatism that caused widespread crustal underplating and partial melting of the source rocks. The batholith appears to be part of an extensive magmatic episode in the northeastern North American craton, during which A-type granites and anorthosite were emplaced between 1.25 and 1.24 Ga.


title: 25.2 A 1.2V 8Gb 8-channel 128GB/s high-bandwidth memory (HBM) stacked DRAM with effective microbump I/O test methods using 29nm process and TSV

abstract: Increasing demand for higher-bandwidth DRAM drive TSV technology development. With the capacity of fine-pitch wide I/O [1], DRAM can be directly integrated on the interposer or host chip and communicate with the memory controller. However, there are many limitations, such as reliability and testability, in developing the technology. It is advantageous to adopt a logic-interface chip between the interposer and stacked-DRAM with thousands of TSV. The logic interface chip in the base level of high-bandwidth memory (HBM) decreases the CIO, repairs the chip-to-chip connection failure, and supports better testability and improves reliability.


title: Reliable Alert Fusion of Multiple Intrusion Detection Systems

abstract: Alert Fusion is a process of combining alerts from multiple Intrusion Detection Systems to make a decision about the presence of attack or intrusion. A reliable decision from an alert fusion requires that Intrusion detectors involved in the fusion process generates fully reliable alerts. The unreliable alerts from intrusion detectors may completely misleads the decision making process. The existing alert fusion operators doesn't incorporate reliability of Intrusion detectors. In this work, we have proposed a novel alert fusion method which overcomes the limitations of existing fusion methods and fulfils the requirements for alert fusion domain. We have demostrated the results for two different approaches of deriving reliability value of intrusion system detector which are based on conflict and true positive rate of intrusion detectors. The results shows the robustness of proposed rule in fusing alerts from multiple intrusion detection system. Our proposed approach shows a drastic reduction in false positive rate without affecting the true positive rate.


title: Improving Fine-Grained Object Classification Using Adversarial Generated Unlabelled Samples

abstract: Recently Convolutional Neural Networks(CNNs) models have achieved remarkable results for fine-grained image classification. However, CNNs require a large amount of training data during supervised learning and labeling so much data is expensive in many cases. To address this issue, this paper innovatively presents a semi-supervised pipeline to improve fine-grained classification tasks without any extra data. We carefully combine CNNs with Generative Adversarial Nets(GANs) for classification, which shows that the result is affirmative. In addition, we propose a multi dimension label regularization(MDLR) method to train labeled images and unlabeled images simultaneously. First we use a pre-trained Yolo v2 object detection model to detect coarse-grained object on the original dataset. Second we feed cropped images to the generator of GAN to produce more generated data and assign a uniform label distribution to the generated images. Third we mix these origin real images and generated images. Then these mixed images are fed to a baseline CNN classifier and a feature-fused CNN classifier. We obtain competitive or state-of-the-art results: using feature-fused CNN model on Stanford Dogs dataset we set a new state-of-the-art result of 90.7%; on Oxford 102 Flowers dataset, we show consistent improvements over baseline.


Intent Detection through Text Mining and Analysis


title: Comprehensible reasoning and automated reporting of medical examinations based on deep learning analysis

abstract: In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.


title: Understanding the Determinants of Volunteer Retention Through Capture-Recapture Analysis: Answering Social Science Questions Using a Wildlife Ecology Toolkit

abstract: Scientists and conservationists increasingly rely on contributions by volunteers recruited from the wider public to work over large and ecologically meaningful spatial scales. Optimizing working in partnership with unpaid, volunteer citizen scientists and conservationists requires an understanding of the determinants of volunteer retention rates and how they are affected by management practices. To this effect, we present the novel use of the mark-recapture framework widely used in wildlife demography in order to quantify volunteer retention probabilities. We illustrate the versatility and power of the approach using a project that removed invasive American mink from 10,000 km2 in Scotland in partnership with volunteer citizen conservationists recruited from local communities. Wide scale adoption of the mark-recapture framework to analyze volunteer management will give novel insights into how volunteers interact with the conservation projects they are involved in and provide evidence for their optimal management.


title: Reconstrucción digital de mezclas asfálticas basada en imágenes de tomografía computarizada.

abstract: More than 80% of pavements in Colombia and worldwide are made of asphalt mixtures. Those mixtures have usually been studied as a single material, but they are actually a three phase material, since they are composed of rocks, mastics and air voids. In addition, the behavior of the asphalt mixtures depends on the characteristics of each phase. The aim of this project is to make an asphalt mixture real sample reconstruction from X-Ray Computerized Axial Tomography (CAT). The reconstruction process has three stages: Scanning, Segmentation, and Data Scaling. All these stages were developed in Python under Object Oriented Programming (OOP) and were implemented through the use of several tools, such as Numpy, Scipy, Pydicom, Scikit-learn, Matplotlib and Mayavi. As a result, a tridimensional digital model called ToyModel was developed. This model is a 3D digital solid represented by a set of 1 mm3 voxels. The reconstructed ToyModel accurately represented the real sample, since the ToyModel air void volume was 3.98% and the real sample air void content volume was 4%. This Python implementation is a good tool to model any asphalt mixtures, not only to extract sample composition, but also to simulate different processes, e.g., Finite Element Method (FEM) analysis.


title: State of the Art and Future Trends on Unmanned Aerial Vehicle

abstract: In the following document we will discuss what UAVs are, what their development history is, where technological applications are implemented, how they are classified, and how the system of operation of these is structured, some practical cases will be presented. developed by researchers, we will know how is the legal framework that regulate them and we will see what are the trends that they can present in the future.


title: Faculty at Saudi Electronic University attitudes toward using augmented reality in education

abstract: This study aims to examine the possibility of implementing the Augmented Reality (AR) application in higher education by answering three questions: Which extended faculty at the Saudi Electronic University are familiar with such applications, what the perceptions do they hold toward using it in education, and what barriers do they believe may hinder implementing this technology. An online survey was designed and distributed to faculty members at two colleges selected randomly to collect data from participants. Even though the faculty were at an accepted level of familiarity with this technology, they did not use it in their classes. Results showed that faculty have a positive attitude toward the use of AR and have confidence in its potential to enrich the learning environment. This result is connected to the AR advantages the faculty were in agreement with. Results also showed that faculty have concerns about some barriers that might impact implementation of AR in the education environment, such as the lack of technical support.


title: The demise and resurrection of Compton Community College: how loss of accreditation can lead to a new beginning

abstract: This article is a personal faculty perspective regarding academic accreditation. It describes the psychological process and consequences of losing accreditation. Compton Community College was one of the oldest community colleges in California and has served a diverse population of students for over 50 years. As a result of severe administrative and financial problems, the college's accreditation became jeopardized and was actually terminated in August 2006 by the Fiscal Crisis and Management Assistance Team (FCMAT). The college is now considered a district and has developed a partnership with El Camino Community College. In this article we discuss how the faculty responded to the process of closure and how it experienced the psychological stages of death and dying. Finally addressed is the need for hope to regain autonomy and independence in the future.


title: Data Driven Strategies for Active Monocular SLAM using Inverse Reinforcement Learning

abstract: Learning a complex task such as low-level robot manoeuvres while preventing failure of monocular SLAM is a challenging problem for both robots and humans. The data-driven identification of basic motion strategies in preventing monocular SLAM failure is a largely unexplored problem. We devise a computational model for representing and inferring strategies, formulated as Markov decision processes, where the reward function models the goal of the task as well as information about the strategy. We show how this reward function can be learnt from expert demonstrations using Inverse Reinforcement Learning. The resulting framework allows one to identify the way in which a few chosen parameters affect the quality of monocular SLAM estimates. The estimated reward function was able to capture expert demonstration information and the inherent expert strategy and it was possible to give an intuitive explanation to the obtained reward structure. A significant improvement in performance as compared to an intuitive hand-crafted reward function is also shown.


title: Megaproject Risk Analysis and Simulation

abstract: Providing new knowledge on risk analysis and simulation for megaprojects, this book is essential reading for both academics and practitioners. Its focus is on technical descriptions of a newly developed dynamic systems approach to megaproject risk analysis and simulation.


title: D-miner: A framework for mining, searching, visualizing, and alerting on darknet events

abstract: Darknet resources are mined for their data in order to provide possible cyber threat intelligence to network operators. Network operators, however, often have limited resources with which to search the darknet for threats. Previous work in this area has failed to address this use case instead focusing on sales volumes, vendor characteristics, and identifying the sale of zero day exploits. In this paper we present D-miner: a modular framework designed to mine data from websites, specializing in darknet sites, and parse the data into JSON objects for searching, visualizations, and alerts. This open source solution to darknet mining is intended to make it easier for network shareholders to monitor the darknet for potential threats. We show how D-miner is customizable for multiple use cases and how it can be used to visualize data to aid analysis.


title: Agent-based simulation of innovation diffusion: a review

abstract: Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.


title: Improved deep metric learning with multi-class N-pair loss objective

abstract: Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N-pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples - more specifically, N-1 negative examples - and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N+1) x N. We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.


Editorial: Cyber-physical systems: Milestones and research challenges


title: Illustrating computer hardware concepts using PIC-based projects

abstract: We are developing a series of interesting projects that give students a hands-on introduction to computer hardware concepts. Our projects are designed around the PIC16F84, a powerful 8-bit microcontroller chip that sells for less than $10. The projects are suitable for classroom use in introductory level courses about computer hardware. The effectiveness of these projects has been evaluated both formally by students (student evaluation forms) and informally - through discussion within the teaching team. The feedback from students indicates that the development and implementation of the projects were successful. This paper describes the details of the projects completed to date, their overall effectiveness and our plans for further projects.


title: Dense 3D Regression for Hand Pose Estimation

abstract: We present a simple and effective method for 3D hand pose estimation from a single depth frame. As opposed to previous state-of-the-art methods based on holistic 3D regression, our method works on dense pixel-wise estimation. This is achieved by careful design choices in pose parameterization, which leverages both 2D and 3D properties of depth map. Specifically, we decompose the pose parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat maps and unit 3D directional vector fields. The 2D/3D joint heat maps and 3D joint offsets are estimated via multi-task network cascades, which is trained end-to-end. The pixel-wise estimations can be directly translated into a vote casting scheme. A variant of mean shift is then used to aggregate local votes while enforcing consensus between the the estimated 3D pose and the pixel-wise 2D and 3D estimations by design. Our method is efficient and highly accurate. On MSRA and NYU hand dataset, our method outperforms all previous state-of-the-art approaches by a large margin. On the ICVL hand dataset, our method achieves similar accuracy compared to the currently proposed nearly saturated result and outperforms various other proposed methods. Code is available $\href{"this https URL"}{\text{online}}$.


title: R-IAC: Robust Intrinsically Motivated Exploration and Active Learning

abstract: Intelligent adaptive curiosity (IAC) was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without preprogramming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in unprepared sensorimotor spaces with large unlearnable subspaces. Then, we introduce a novel formulation of IAC, called robust intelligent adaptive curiosity (R-IAC), and show that its performances as an intrinsically motivated active learning algorithm are far superior to IAC in a complex sensorimotor space where only a small subspace is neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme. Finally, an open source accompanying software containing these algorithms as well as tools to reproduce all the experiments presented in this paper is made publicly available.


title: Fast Approximate Subgraph Counting and Enumeration

abstract: We present a new shared-memory parallel algorithm and implementation called FASCIA for the problems of approximate sub graph counting and sub graph enumeration. The problem of sub graph counting refers to determining the frequency of occurrence of a given sub graph (or template) within a large network. This is a key graph analytic with applications in various domains. In bioinformatics, sub graph counting is used to detect and characterize local structure (motifs) in protein interaction networks. Exhaustive enumeration and exact counting is extremely compute-intensive, with running time growing exponentially with the number of vertices in the template. In this work, we apply the color coding technique to determine approximate counts of non-induced occurrences of the sub graph in the original network. Color coding gives a fixed-parameter algorithm for this problem, using a dynamic programming-based counting approach. Our new contributions are a multilevel shared-memory parallelization of the counting scheme and several optimizations to reduce the memory footprint. We show that approximate counts can be obtained for templates with up to 12 vertices, on networks with up to millions of vertices and edges. Prior work on this problem has only considered out-of-core parallelization on distributed platforms. With our new counting scheme, data layout optimizations, and multicore parallelism, we demonstrate a significant speedup over the current state-of-the-art for sub graph counting.


abstract: ABSTRACT : Academic achievement is a cumulative outcome of different factors. School type has a paramount effect on students’ performance in regional examination. This study was conducted to critically examine the effect of school type on students result in regional examination. An e x-post facto research design was employed to look into the impacts of school type on academic achievement in regional examination. Data analyses were undertaken using chi-square test, t-test, and ANOVA (Analysis of Variance). Independent sample t-test was used to test mean differences between private and government schools. ANOVA test was used to analyze mean differences based on level of satisfaction in school services. Chi-square test was used to see associations between school types with facilities and activities. Key informant interview and observation were also undertaken to substantiate the quantitative results. The t-test results revealed that private schools have scored statistically better than government ones. Students from private schools were highly satisfied with their school services. Private schools were found better equipped in library and laboratory facilities than government schools. Student centered method of teaching and tutorial classes have been more applied in private schools. Parents’ involvement in school related affairs were better in private schools. Based on the findings, the recommendations that experience sharing between private and government schools as well as among schools should be organized by Dessie town administration educational office. More effort has to been done by concerned bodies so as to narrow the performance gab between private and government schools. In addition, government schools should be equipped with facilities as of the private schools. KEY WORDS : Academic achievement, regional examination, private/government schools, school services, library and laboratory facilities, and method of teaching and tutorial classes. About the Author: Amogne Asfaw Eshetu is a Lecturer at the Department of Geography and Environmental Studies WU (Wollo University), Dessie, P.O. Box 1145, North Central Ethiopia. For academic interests, the author can be contacted via phone at: +251913423595 or via e-mail at: amuvenu@yahoo.com How to cite this article? Eshetu, Amogne Asfaw. (2015). “Does School Type Matter in Academic Achievement in Regional Examination? Comparative Study of Private and Government Schools in Dessie Administrative Town, North Central Ethiopia” in EDUCARE: International Journal for Educational Studies , Vol.7(2) February, pp.161-170. Bandung, Indonesia: Minda Masagi Press and UMP Purwokerto, ISSN 1979-7877. Chronicle of the article: Accepted (December 5, 2014); Revised (January 15, 2015); and Published (February 28, 2015).


title: Neural basis of global resting-state fMRI activity.

abstract: Functional MRI (fMRI) has uncovered widespread hemodynamic fluctuations in the brain during rest. Recent electroencephalographic work in humans and microelectrode recordings in anesthetized monkeys have shown this activity to be correlated with slow changes in neural activity. Here we report that the spontaneous fluctuations in the local field potential (LFP) measured from a single cortical site in monkeys at rest exhibit widespread, positive correlations with fMRI signals over nearly the entire cerebral cortex. This correlation was especially consistent in a band of upper gamma-range frequencies (40-80 Hz), for which the hemodynamic signal lagged the neural signal by 6-8 s. A strong, positive correlation was also observed in a band of lower frequencies (2-15 Hz), albeit with a lag closer to zero. The global pattern of correlation with spontaneous fMRI fluctuations was similar whether the LFP signal was measured in occipital, parietal, or frontal electrodes. This coupling was, however, dependent on the monkey's behavioral state, being stronger and anticipatory when the animals' eyes were closed. These results indicate that the often discarded global component of fMRI fluctuations measured during the resting state is tightly coupled with underlying neural activity.


Sovrin : digital identities in the blockchain era


American Educational Research Association Motivating the Academically Unmotivated : A Critical Issue for the 21 st Century Author ( s ) :


title: Using social networks to harvest email addresses

abstract: Social networking is one of the most popular Internet activities with millions of members from around the world. However, users are unaware of the privacy risks involved. Even if they protect their private information, their name is enough to be used for malicious purposes. In this paper we demonstrate and evaluate how names extracted from social networks can be used to harvest email addresses as a first step for personalized phishing campaigns. Our blind harvesting technique uses names collected from the Facebook and Twitter networks as query terms for the Google search engine, and was able to harvest almost 9 million unique email addresses. We compare our technique with other harvesting methodologies, such as crawling the World Wide Web and dictionary attacks, and show that our approach is more scalable and efficient than the other techniques. We also present three targeted harvesting, techniques that aim to collect email addresses coupled with personal information for the creation of personalized phishing emails. By using information available in Twitter to narrow down the search space and, by utilizing the Facebook email search functionality, we are able to successfully map 43.4% of the user profiles to their actual email address. Furthermore, we harvest profiles from Google Buzz, 40% of whom provide a direct mapping to valid Gmail addresses.


title: Infrastructure Cost Comparison of Running Web Applications in the Cloud Using AWS Lambda and Monolithic and Microservice Architectures

abstract: Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be developed, tested, deployed, scaled, operated and upgraded independently. However, aside from gaining agility, independent development, and scalability, infrastructure costs are a major concern for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison to standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices reduces infrastructure costs by 70% or more. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications.


title: A Wideband CP Crossed Slot Antenna Using 1- $\lambda $ Resonant Mode With Single Feeding

abstract: This communication proposes to use the 1- $\lambda $ resonant mode of slot radiator with single feeding for bandwidth enhancement. The impedance of a simple slot radiator is studied using computer simulation. Since operating the slot radiator in the 1- $\lambda $ resonant mode requires a longer slot length, a large square slot is used at both ends of the slot to reduce the required slot length. The results of the study are subsequently used to design a wideband circularly polarized (CP) crossed slot antenna (CSA) with single feeding. The CSA consists of two slot radiators having the same length and placed in a crossed shape, i.e., orthogonal positions. A single microstrip line with a via at the end is used to feed the CSA. By loading reactive elements with appropriate values on each slot near to the feed line, the two slots generate two linearly polarized electric fields with 90°-phase difference for CP operation. The CSA is studied and designed using simulation and measurement. Measured results show that the CSA has a wide impedance bandwidth of 1.52–3.44 GHz (1.92 GHz, 77.4%) and axial-ratio bandwidth of 1.73–3.01 GHz (1.28 GHz, 54%), much wider than the previous directional and bidirectional CSAs using single feeding.


title: Word forms - not just their lengths- are optimized for efficient communication

abstract: The inverse relationship between the length of a word and the frequency of its use, first identified by G.K. Zipf in 1935, is a classic empirical law that holds across a wide range of human languages. We demonstrate that length is one aspect of a much more general property of words: how distinctive they are with respect to other words in a language. Distinctiveness plays a critical role in recognizing words in fluent speech, in that it reflects the strength of potential competitors when selecting the best candidate for an ambiguous signal. Phonological information content, a measure of a word's string probability under a statistical model of a language's sound or character sequences, concisely captures distinctiveness. Examining large-scale corpora from 13 languages, we find that distinctiveness significantly outperforms word length as a predictor of frequency. This finding provides evidence that listeners' processing constraints shape fine-grained aspects of word forms across languages.


title: High performance analytics of bigdata with dynamic and optimized hadoop cluster

abstract: With enterprises collecting feedback down to every possible detail, data repositories are being over flooded with information. In-order to access valuable information, these data should be processed using sophisticated statistical analysis. Traditional analytical tools, existing statistical software and data management systems find it challenging to perform deep analysis upon large data libraries. Users demand a service platform that can store and handle large quantities of data with some features such as easy accessibility, fast performance, durable and secure. These features can be availed without having to spend too much on hardware, upgrading, configuring etc to perform analysis of big data. This project intends to overcome all these obstacles and built a user friendly SAAS platform. It is cloud based web application which stores data in Amazon S3. As this system supports dynamic and optimized cluster nodes size as per the desired time, user doesn't need to calculate and estimate the number of nodes. The system uses Amazon EMR and MapReduce paradigm using opensource R scripting language to perform analysis of bigdata analysis in desire time.


title: Practical issues with the timing analysis of the Controller Area Network

abstract: The Controller Area Network (CAN) bus is widely used and has been studied in several research works to determine the worst-case response time of messages. More results are being added to study systems that are not constructed according to the ideal behavior of the message queuing and CAN controller assumed in the past. In this paper, we provide an assessment on the practical relevance of several of those results. We also present theory and empirical studies on the relative importance of several implementation issues that are quite common in real systems and further deviate from the ideal behavior. In addition, we propose a heuristic for the design of multiple software queues when using TxObjects without preemption, and derive an upper bound on the worst case response time when message output at the CAN driver is polling based.


title: Exploring mobile news reading interactions for news app personalisation

abstract: As news is increasingly accessed on smartphones and tablets, the need for personalising news app interactions is apparent. We report a series of three studies addressing key issues in the development of adaptive news app interfaces. We first surveyed users' news reading preferences and behaviours; analysis revealed three primary types of reader. We then implemented and deployed an Android news app that logs users' interactions with the app. We used the logs to train a classifier and showed that it is able to reliably recognise a user according to their reader type. Finally we evaluated alternative, adaptive user interfaces for each reader type. The evaluation demonstrates the differential benefit of the adaptation for different users of the news app and the feasibility of adaptive interfaces for news apps.


title: Wideband Multi-Mode CMOS VCO Design Using Coupled Inductors

abstract: Coupled inductors can create multiple resonant frequencies in a compact high-order resonator. Together with proper nonlinear active circuitry, such a high-order resonator realizes a multi-mode oscillator covering a wide frequency range. Compared with conventional switched-resonator-based approaches that consume the same chip area, the proposed coupled-inductor-based resonator results in larger quality-factor, and hence, lower oscillator phase noise. In the proposed multi-mode oscillator that uses the multi-port coupled inductors, mode switching is achieved using independent active cores without using lossy switches in the resonator path. The behavior of the multi-mode resonator as a multi-port network in an oscillator, design trade-offs, and switching transient response of the multi-mode oscillator have been studied analytically. As a proof of concept, an integrated voltage-controlled oscillator (VCO) with a 1.28-6.06 GHz tuning range is designed and fabricated in a 0.13 mum CMOS technology. The triple-mode VCO uses a sixth-order resonator based on three coupled inductors with a compact common-centric layout. Depending on the oscillation frequency, the VCO current consumption is automatically adjusted from 2.9 to 6.1 mA to achieve a low phase noise throughout the frequency range. The measured phase noises at 1 MHz offset from carrier frequencies of 1.76, 2.26, 3.3, 4.5, and 5.6 GHz are -119.3 , -120.15 , -118.1 , -117 , and -113.5 dBc/Hz , respectively. The chip area, including the pads, is 1 mm times 1 mm and the supply voltage is 1.5 V.


title: A visual analytics agenda

abstract: Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Center's goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.


title: The Anatomy of the Medial Part of the Knee

abstract: BACKGROUND ::: While the anatomy of the medial part of the knee has been described qualitatively, quantitative descriptions of the attachment sites of the main medial knee structures have not been reported. The purpose of the present study was to verify the qualitative anatomy of medial knee structures and to perform a quantitative evaluation of their anatomic attachment sites as well as their relationships to pertinent osseous landmarks. ::: ::: ::: METHODS ::: Dissections were performed and measurements were made for eight nonpaired fresh-frozen cadaveric knees with use of an electromagnetic three-dimensional tracking sensor system. ::: ::: ::: RESULTS ::: In addition to the medial epicondyle and the adductor tubercle, a third osseous prominence, the gastrocnemius tubercle, which corresponded to the attachment site of the medial gastrocnemius tendon, was identified. The average length of the superficial medial (tibial) collateral ligament was 94.8 mm. The superficial medial collateral ligament femoral attachment was 3.2 mm proximal and 4.8 mm posterior to the medial epicondyle. The superficial medial collateral ligament had two separate attachments on the tibia. The distal attachment of the superficial medial collateral ligament on the tibia was 61.2 mm distal to the knee joint. The deep medial collateral ligament consisted of meniscofemoral and meniscotibial portions. The posterior oblique ligament femoral attachment was 7.7 mm distal and 6.4 mm posterior to the adductor tubercle and 1.4 mm distal and 2.9 mm anterior to the gastrocnemius tubercle. The medial patellofemoral ligament attachment on the femur was 1.9 mm anterior and 3.8 mm distal to the adductor tubercle. ::: ::: ::: CONCLUSIONS ::: The medial knee ligament structures have a consistent attachment pattern.


Metastatic Carcinomas Involving the Testis: A Review


title: Orthodontic treatment of anterior open bite.

abstract: OBJECTIVE ::: To review the currently available treatment options of anterior open bite. ::: ::: ::: METHODS ::: Search all major dental journals and literature on treatment and management of anterior open bite. Medline search (1960-2006). Literature and data on treatment and management of anterior open bite with keywords 'open bite', 'anterior open bite', 'orthodontic treatment', 'long face', 'vertical dentoalveolar problem' and 'vertical skeletal problem'. ::: ::: ::: RESULTS ::: Over 50 articles were found and relevant information and data were reviewed by the authors. It was found that the multifactorial nature of anterior open bite makes its management difficult and various treatment modalities are being used. Clinicians must be able to diagnose the problem and choose the best treatment. ::: ::: ::: CONCLUSION ::: Successful treatment of anterior open bite greatly relies on both diagnosis and therapeutics. Although there are many different treatment modalities available, stability after treatment is still a critical issue as evidence on long term stability of various treatment options is lacking. Thus, clinicians should pay more attention during retention phase and long-term studies on post-treatment changes and stability should be encouraged.


title: A Path Signature Approach to Online Arabic Handwriting Recognition

abstract: The Arabic script is one that has many properties that come together and result in what is commonly cited as one of the most beautiful scripts. Used by over 400 million people worldwide and with a history spanning over 1800 years, the Arabic script remains one of the most important languages in the world. Using tools from the theory of rough paths, combined with state of the art techniques from deep learning, we develop a recognition methodology for Arabic handwriting. Preliminary results using online Arabic handwritten characters show that the methodology developed can result in a significant decrease in error rate.


title: Forensic Image Analysis - A Frame work

abstract: Forensic image analysis is the application of image science & domain expertise to interpret the content of an image and/or image itself in legal matters. Regardless, of the type of image analysis performed, forensic experts need to perform mainly three tasks such as image interpretation, image examination and technical preparation in day today activities [2-7]. First, Interpretation is the application of specific subject matter expertise to draw conclusions about subjects or objects depicted in images. Examples include a podiatrist drawing conclusions about foot shape from an image, a shoeprint expert drawing conclusions Abstract The large volume and variety of digital images currently acquired and used in different application domain has given rise to the requirement of image analysis. Images can be found in many application domains such as journalism, advertizing, medicine, weather forecasting, video surveillance, map production, remote sensing, computer aided designs etc. In particular, there is an increasing need for the development of image analysis in the forensic science, to place the perpetrator at the scene of a crime. While each application has requirements unique from the others, all are concerned with faster, cheaper, and more accurate results. Ease with which the images can be created, they may be manipulated to sway opinions or for monetary gains; one needs to be aware that seeing does not always imply believing, especially in courtrooms. And also the human visual system is, at times remarkably inept at detecting simple geometric inconsistencies that might result from photo tempering. Even sometimes, simple differences may not be identified by the visual system. Analysis of images for information extraction has become very prominent area of interest in recent past. This article provides an overview of various issues used for forensic image analysis (FIA) using the three main components: Pre-processing, Feature extraction and the Image analysis. Embedding such capabilities of image analysis components in visual surveillance systems may automatically detect hostile intent in advance.


title: Fast and accurate influence maximization on large networks with pruned Monte-Carlo simulations

abstract: Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been well-studied, it is still highly challenging to find solutions of high quality in large-scale networks of the day. While Monte-Carlo-simulation-based methods produce near-optimal solutions with a theoretical guarantee, they are prohibitively slow for large graphs. As a result, many heuristic methods without any theoretical guarantee have been developed, but all of them substantially compromise solution quality. To address this issue, we propose a new method for the influence maximization problem. Unlike other recent heuristic methods, the proposed method is a Monte-Carlo-simulation-based method, and thus it consistently produces solutions of high quality with the theoretical guarantee. On the other hand, unlike other previous Monte-Carlo-simulation-based methods, it runs as fast as other state-of-the-art methods, and can be applied to large networks of the day. Through our extensive experiments, we demonstrate the scalability and the solution quality of the proposed method.


title: Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

abstract: Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.


title: A Highly Linear Broadband CMOS LNA Employing Noise and Distortion Cancellation

abstract: A broadband inductorless low-noise amplifier (LNA) design that utilizes simultaneous noise and distortion cancellation is presented. Concurrent cancellation of the intrinsic third-order distortion from individual stages is exhibited with the common-gate and common-source cascade. The LNA is then limited by the second-order interaction between the common source and common gate stages, which is common in all cascade amplifiers. Further removal of this third-order distortion is achieved by incorporating a second-order-distortion-free circuit technique in the common gate stage. Implemented in 0.13 m CMOS technology, this LNA achieved 16 dBm in both the 900 MHz and 2 GHz bands. Measurements demonstrate that the LNA has a minimum internal gain of 14.5 dB, noise figure of 2.6 dB from 800 MHz to 2.1GHz while drawing 11.6 mA from 1.5 V supply voltage.


title: 3D with Kinect

abstract: We analyze Kinect as a 3D measuring device, experimentally investigate depth measurement resolution and error properties and make a quantitative comparison of Kinect accuracy with stereo reconstruction from SLR cameras and a 3D-TOF camera. We propose Kinect geometrical model and its calibration procedure providing an accurate calibration of Kinect 3D measurement and Kinect cameras. We demonstrate the functionality of Kinect calibration by integrating it into an SfM pipeline where 3D measurements from a moving Kinect are transformed into a common coordinate system by computing relative poses from matches in color camera.


title: Multi-order Attentive Ranking Model for Sequential Recommendation

abstract: In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank.


title: Research Commentary: Desperately Seeking the "IT" in IT Research - A Call to Theorizing the IT Artifact

abstract: The field of information systems is premised on the centrality of information technology in everyday socio-economic life. Yet, drawing on a review of the full set of articles published inInformation Systems Research ( ISR) over the past ten years, we argue that the field has not deeply engaged its core subject matter--the information technology (IT) artifact. Instead, we find that IS researchers tend to give central theoretical significance to the context (within which some usually unspecified technology is seen to operate), the discrete processing capabilities of the artifact (as separable from its context or use), or the dependent variable (that which is posited to be affected or changed as technology is developed, implemented, and used). The IT artifact itself tends to disappear from view, be taken for granted, or is presumed to be unproblematic once it is built and installed. After discussing the implications of our findings, we propose a research direction for the IS field that begins to take technology as seriously as its effects, context, and capabilities. In particular, we propose that IS researchers begin to theorize specifically about IT artifacts, and then incorporate these theories explicitly into their studies. We believe that such a research direction is critical if IS research is to make a significant contribution to the understanding of a world increasingly suffused with ubiquitous, interdependent, and emergent information technologies.


title: Ibex - An Intelligent Storage Engine with Support for Advanced SQL Off-loading

abstract: Modern data appliances face severe bandwidth bottlenecks when moving vast amounts of data from storage to the query processing nodes. A possible solution to mitigate these bottlenecks is query off-loading to an intelligent storage engine, where partial or whole queries are pushed down to the storage engine. In this paper, we present Ibex, a prototype of an intelligent storage engine that supports off-loading of complex query operators. Besides increasing performance, Ibex also reduces energy consumption, as it uses an FPGA rather than conventional CPUs to implement the off-load engine. Ibex is a hybrid engine, with dedicated hardware that evaluates SQL expressions at line-rate and a software fallback for tasks that the hardware engine cannot handle. Ibex supports GROUP BY aggregation, as well as projection- and selection- based filtering. GROUP BY aggregation has a higher impact on performance but is also a more challenging operator to implement on an FPGA.


title: The neurological basis of developmental dyslexia: an overview and working hypothesis.

abstract: Five to ten per cent of school-age children fail to learn to read in spite of normal intelligence, adequate environment and educational opportunities. Thus defined, developmental dyslexia (hereafter referred to as dyslexia) is usually considered of constitutional origin, but its actual mechanisms are still mysterious and currently remain the subject of intense research endeavour in various neuroscientific areas and along several theoretical frameworks. This article reviews evidence accumulated to date that favours a dysfunction of neural systems known to participate in the normal acquisition and achievement of reading and other related cognitive functions. Historically, the first arguments for a neurological basis of dyslexia came from neuropathological studies of brains from dyslexic individuals. These early studies, although open to criticism, for the first time drew attention towards a possible abnormality in specific stages of prenatal maturation of the cerebral cortex and suggested a role of atypical development of brain asymmetries. This has prompted a large amount of subsequent work using in vivo imaging methods in the same vein. These latter studies, however, have yielded less clear-cut results than expected, but have globally confirmed some subtle differences in brain anatomy whose exact significance is still under investigation. Neuropsychological studies have provided considerable evidence that the main mechanism leading to these children's learning difficulties is phonological in nature, namely a basic defect in segmenting and manipulating the phoneme constituents of speech. A case has also been made for impairment in brain visual mechanisms of reading as a possible contributing factor. This approach has led to an important conceptual advance with the suggestion of a specific involvement of one subsystem of vision pathways (the so-called magnosystem hypothesis). Both phonological and visual hypotheses have received valuable contribution from modern functional imaging techniques. Results of recent PET and functional MRI studies are reported here in some detail. Finally, one attractive interpretation of available evidence points to dyslexia as a multi-system deficit possibly based on a fundamental incapacity of the brain in performing tasks requiring processing of brief stimuli in rapid temporal succession. It is proposed that this so-called `temporal processing impairment' theory of dyslexia could also account for at least some of the perceptual, motor and cognitive symptoms very often associated with the learning disorder, a coincidence that has remained unexplained so far.


title: Measuring depth accuracy in RGBD cameras

abstract: This paper presents the comparison between the Microsoft Kinect depth sensor and the Asus Xtion for computer vision applications. Depth sensors, known as RGBD cameras, project an infrared pattern and calculate the depth from the reflected light using an infrared sensitive camera. In this research, we compare the depth sensing capabilities of the two sensors under various conditions. The purpose is to give the reader a background to whether use the Microsoft Kinect or Asus Xtion sensor to solve a specific computer vision problem. The properties of the two depth sensors were investigated by conducting a series of experiments evaluating the accuracy of the sensors under various conditions, which shows the advantages and disadvantages of both Microsoft Kinect and Asus Xtion sensors.


title: Crohn's Disease-Associated Adherent-Invasive Escherichia coli Adhesion Is Enhanced by Exposure to the Ubiquitous Dietary Polysaccharide Maltodextrin

abstract: Crohn's disease (CD) is associated with intestinal dysbiosis evidenced by an altered microbiome forming thick biofilms on the epithelium. Additionally, adherent-invasive E. coli (AIEC) strains are frequently isolated from ileal lesions of CD patients indicating a potential role for these strains in disease pathogenesis. The composition and characteristics of the host microbiome are influenced by environmental factors, particularly diet. Polysaccharides added to food as emulsifiers, stabilizers or bulking agents have been linked to bacteria-associated intestinal disorders. The escalating consumption of polysaccharides in Western diets parallels an increased incidence of CD during the latter 20th century. In this study, the effect of a polysaccharide panel on adhesiveness of the CD-associated AIEC strain LF82 was analyzed to determine if these food additives promote disease-associated bacterial phenotypes. Maltodextrin (MDX), a polysaccharide derived from starch hydrolysis, markedly enhanced LF82 specific biofilm formation. Biofilm formation of multiple other E. coli strains was also promoted by MDX. MDX-induced E. coli biofilm formation was independent of polysaccharide chain length indicating a requirement for MDX metabolism. MDX exposure induced type I pili expression, which was required for MDX-enhanced biofilm formation. MDX also increased bacterial adhesion to human intestinal epithelial cell monolayers in a mechanism dependent on type 1 pili and independent of the cellular receptor CEACAM6, suggesting a novel mechanism of epithelial cell adhesion. Analysis of mucosa-associated bacteria from individuals with and without CD showed increased prevalence of malX, a gene essential for MDX metabolism, uniquely in the ileum of CD patients. These findings demonstrate that the ubiquitous dietary component MDX enhances E. coli adhesion and suggests a mechanism by which Western diets rich in specific polysaccharides may promote dysbiosis of gut microbes and contribute to disease susceptibility.


of the Association of Management RETAINING TALENT : ASSESSING JOB SATISFACTION FACETS MOST SIGNIFICANTLY RELATED TO SOFTWARE DEVELOPER TURNOVER INTENTIONS


title: Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark

abstract: In recent years, a wide variety of different texture descriptors has been proposed, including many LBP variants. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected.


title: On pressure and velocity flow boundary conditions and bounceback for the lattice Boltzmann BGK model, Arxiv preprint comp-gas/9611001

abstract: Pressure (density) and velocity boundary conditions inside a flow domain are studied for 2-D and 3-D lattice Boltzmann BGK models (LBGK) and new method to specify these conditions are proposed. These conditions are consistent with the boundary condition we proposed in a previous paper using an idea of bounce-back of non-equilibrium distribution. These conditions give excellent results for the regular LBGK models, and were shown to be second-order accurate by numerical examples. When they are used together with the improved incompressible LBGK model proposed by zou et al. the simulation results recover the analytical solution of the plane Poiseuille flow driven by pressure (density) difference with machine accuracy.


title: Motives for adult participation in physical activity: type of activity, age, and gender

abstract: In recent years, there has been a decline in physical activity among adults. Motivation has been shown to be a crucial factor in maintaining physical activity. The purpose of this study was to examine whether motives for participation could accurately discriminate gender, age, and type of physical activity. A quantitative, cross-sectional descriptive research design was employed. The Physical Activity and Leisure Motivation Scale (PALMS) was used to assess motives for physical activity in 1,360 adults (703 males, 657 females) who had been exercising regularly for at least six months. The PALMS consists of 40 items that constitute eight sub-scales (mastery, enjoyment, psychological condition, physical condition, appearance, others’ expectations, affiliation, competition/ego). Respondents were divided into two age groups (young adults aged 20 to 40 years and middle-aged adults 41 to 64 years) and five types of activity (individual racing sports plus bowls, team sports, racquet sports, martial arts, and exercise). The group discriminant function analyses revealed significant canonical functions correctly classifying the cases into gender (82%), age group (83%), team sport players 76%, individual racing sport plus bowls players 91%, racquet sport players 90%, exercisers 84%, and martial art players 91%. The competition/ego, appearance, physical condition, and mastery sub-scales contributed most to gender differences. Five sub-scales (mastery, psychological condition, others’ expectations, affiliation, and enjoyment) contributed most to the discriminant function for age. For type of activity, different sub-scales were the strongest contributors to the discriminant function for each type of PA. The findings in this study suggest that strong and important motives for participation in physical activity are different across type of activity, age, and gender in adults. Understanding the motives that influence physical activity participation is critical for developing interventions to promote higher levels of involvement.


title: A survey on facial expression recognition in 3D video sequences

abstract: Facial expression recognition constitutes an active research area due to its various applications. This survey addresses methodologies for 3D mesh video facial expression recognition. Recognition is, actually, a special case of intra-class retrieval. The approaches are analyzed and compared in detail. They are primarily categorized according to the 3D dynamic face analysis technique used. In addition, currently available datasets, used for 3D video facial expression analysis, are presented. Finally, future challenges that can be addressed in order for 3D video facial expression recognition field to be further improved, are extensively discussed.


title: Baseband-pulse-antenna techniques

abstract: Frequency-domain concepts and terminology are commonly used to describe antennas. These are very satisfactory for a CW or narrowband application. However, their validity is questionable for an instantaneous wideband excitation. Time-domain and/or wideband analyses can provide more insight and more effective terminology. Two approaches for this time-domain analysis have been described. The more complete one uses the transfer function, a function which describes the amplitude and phase of the response over the entire frequency spectrum. While this is useful for evaluating the overall response of a system, it may not be practical when trying to characterize an antenna's performance, and trying to compare it with that of other antennas. A more convenient and descriptive approach uses time-domain parameters, such as efficiency, energy pattern, receiving area, etc., with the constraint that the reference or excitation signal is known. The utility of both approaches, for describing the time-domain performance, was demonstrated for antennas which are both small and large, in comparison to the length of the reference signal. The approaches have also been used for other antennas, such as arrays, where they also could be applied to measure the effects of mutual impedance, for a wide-bandwidth signal. The time-domain ground-plane antenna range, on which these measurements were made, is suitable for symmetric antennas. However, the approach can be readily adapted to asymmetric antennas, without a ground plane, by using suitable reference antennas. >


title: ParkGauge: Gauging the Occupancy of Parking Garages with Crowdsensed Parking Characteristics

abstract: Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.


title: Jumpstarting BGP Security with Path-End Validation

abstract: Extensive standardization and R&D efforts are dedicated to establishing secure interdomain routing. These efforts focus on two mechanisms: origin authentication with RPKI, and path validation with BGPsec. However, while RPKI is finally gaining traction, the adoption of BGPsec seems not even on the horizon due to inherent, possibly insurmountable, obstacles, including the need to replace today's routing infrastructure, the overhead of online cryptography, and meagre benefits in partial deployment. Consequently, secure interdomain routing remains a distant dream. We propose an easily deployable, modest extension to RPKI, called ``path-end validation'', which does not entail replacing/upgrading today's BGP routers nor online cryptographic operations. We show, through rigorous security analyses and extensive simulations on empirically-derived datasets, that path-end validation yields significant security benefits even in very limited partial adoption. We present an open-source, readily deployable prototype implementation of path-end validation.


title: A file system for continuous media

abstract: The Continuous Media File System, CMFS, supports real-time storage and retrieval of continuous media data (digital audio and video) on disk. CMFS clients read or write files in “sessions,” each with a guaranteed minimum data rate. Multiple sessions, perhaps with different rates, and non-real-time access can proceed concurrently. CMFS addresses several interrelated design issues; real-time semantics fo sessions, disk layout, an acceptance test for new sessions, and disk scheduling policy. We use simulation to compare different design choices.


title: Comprehensive many-to-many phoneme-to-viseme mapping and its application for concatenative visual speech synthesis

abstract: The use of visemes as atomic speech units in visual speech analysis and synthesis systems is well-established. Viseme labels are determined using a many-to-one phoneme-to-viseme mapping. However, due to visual coarticulation effects, an accurate mapping from phonemes to visemes should define a many-to-many mapping scheme instead. In this research it was found that neither the use of standardized nor speaker-dependent many-to-one viseme labels could satisfy the quality requirements of concatenative visual speech synthesis. Therefore, a novel technique to define a many-to-many phoneme-to-viseme mapping scheme is introduced, which makes use of both tree-based and k-means clustering approaches. We show that these many-to-many viseme labels more accurately describe the visual speech information as compared to both phoneme-based and many-to-one viseme-based speech labels. In addition, we found that the use of these many-to-many visemes improves the precision of the segment selection phase in concatenative visual speech synthesis using limited speech databases. Furthermore, the resulting synthetic visual speech was both objectively and subjectively found to be of higher quality when the many-to-many visemes are used to describe the speech database and the synthesis targets.


title: 3D Whole Hand Targets: Evaluating Slap and Contactless Fingerprint Readers

abstract: We design and fabricate wearable whole hand 3D targets complete with four fingerprints and one thumb print for evaluating multi-finger capture devices, e.g., slap and contactless readers. We project 2D calibration patterns onto 3D finger surfaces pertaining to each of the four fingers and the thumb to synthetically generate electronic 3D whole hand targets. A state-of-the-art 3D printer is then used to fabricate physical 3D hand targets with printing materials that are similar in hardness and elasticity to the human skin and are optically compatible for imaging with a variety of fingerprint readers. We demonstrate that the physical 3D whole hand targets can be imaged using three commercial (500/1000 ppi) Appendix F certified slap fingerprint readers and a contactless reader. We further show that the features present in the 2D calibration patterns (e.g. ridge structure) are replicated with high fidelity on both the electronically generated and physically fabricated 3D hand targets. Results of evaluation experiments for the three slap readers and the contactless reader using the generated whole hand targets are also presented.


title: The development, content validity and inter-rater reliability of the SMART-Goal Evaluation Method: A standardised method for evaluating clinical goals

abstract: Introduction ::: Goal setting is a complex skill. The use of formal goal writing procedures (including the use of the SMART goal model) has been advocated. However, a standardised method of writing and evaluating SMART goals is currently lacking. This study comprised of two phases. The aims of phase one was to (i) develop the SMART Goal Evaluation Method (SMART-GEM) based on a SMART goal model; and (ii) investigate the content validity of the SMART-GEM. The aim of phase two of the study was to test the inter-rater reliability of the SMART-GEM. ::: ::: Methods ::: Development of the SMART- GEM involved defining and constructing evaluation criteria suitable for auditing goal statements. A content validity assessment was conducted using an expert panel of Occupational Therapists (n = 10). Inter-rater reliability of the SMART-GEM was examined using a purposive sample of multiple raters (n = 24). ::: ::: Results ::: The SMART- GEM was rated as having good content validity (individual items CVI ranged from 0.90 to 1.00; total SMART- GEM CVI = 0.99, ρ = 0.05). Agreement between raters on individual items ranged from poor (κ = 0.254) to excellent (κ = 0.965) and agreement of overall grades was fair to good (κ = 0.582). Inter-rater agreement on total scores was found to be very good (ICC = 0.895, 95% CI = 0.743 to 0.986, ρ = 0.001) with excellent internal consistency (α = 0.995). ::: ::: Conclusion ::: The SMART-GEM demonstrated good construct validity and very good inter-rater reliability on total score and shows promise as a standardised method to writing and evaluating clinical goals.


title: An SRAM Design-for-Diagnosis Solution Based on Write Driver Voltage Sensing

abstract: Diagnosis is becoming a major concern with the rapid development of semiconductor memories. It provides information about the location of manufacturing defects in the memory, and its effectiveness allows a fast yield ramp up. Most of existing diagnosis methods uses a fault dictionary to provide detailed information on fault localization. However, these solutions are most of the time unable to distinguish between all faults, and more importantly often fail to identify the actual faulty block of the memory. Identifying which block of a memory (core- cell array, write drivers, address decoders, pre-charge circuits, etc ...) is defective allows to saving considerable amount of time during the ramp up phase. In this paper, we propose a very low cost design-for- diagnosis (DfD) solution for identifying faulty write drivers. It consists in verifying logic and analog conditions that guarantee the fault-free behavior of the write driver. The proposed solution allows a fast diagnosis (only three consecutive write operations are needed to fully diagnose the write driver) and induces a low area overhead (about 0.5% for a 512 times 512 SRAM). Beside diagnosis, an additional interest of such a solution is its usefulness during a post-silicon characterization process, where it can be used to extract the main features of the write drivers (logic and analog levels on bit lines).


title: Edge Detection Using Modified Firefly Algorithm

abstract: In order to get an enhanced image and for extracting useful information, an image is first converted into digital form and after that various operations are performed on it. Image edge detection is one of the most important preprocessing step in image processing. In this paper, we compare various traditional edge detection methods such as Canny, Prewitt, Robert and Sobel. Also we propose edge detection algorithm which is a combination of particle swarm and firefly optimization algorithms, known as "Modified Firefly Algorithm. At last we have implemented our method and compare it with the existing edge detection methods.


title: Robust real-time upper body limb detection and tracking

abstract: We describe an efficient and robust system to detect and track the limbs of a human. Of special consideration in the design of this system are real-time and robustness issues. We thus utilize a detection/tracking scheme in which we detect the face and limbs of a user and then track the forearms of the found limbs. Detection occurs by first finding the face of a user. The location and color information from the face can then be used to find limbs. As skin color is a key visual feature in this system, we continuously search for faces and use them to update skin color information. Along with edge information, this is used in the subsequent forearm tracking. Robustness is implicit in this design, as the system automatically re-detects a limbs when its corresponding forearms is lost. This design is also conducive to real-time processing because while detection of the limbs can take up to seconds, tracking is on the order of milliseconds. Thus reasonable frame rates can be achieved with a short latency. Also, in this system we make use of multiple 2D limb tracking models to enhance tracking of the underlying 3D structure. This includes models for lateral forearm views (waving) as well as for pointing gestures. Experiments on test sequences demonstrate the efficacy of this approach.


Survey on Text Classification ( Spam ) Using Machine Learning


title: Analysis of Cyber-bullying in a virtual social networking

abstract: This paper focuses on the social and cultural implications of cyber technologies. Identity, bullying and inappropriate use of communication are major issues that need to be addressed in relation to communication technologies for the security in the Web use. The contribution of this paper is to present a novel approach to explain the performance of a novel Cyber-bullying model applied on a Social Network using Multiagents to improve the understanding of this social behavior.


title: A VEHICLE LICENSE PLATE DETECTION METHOD FOR INTELLIGENT TRANSPORTATION SYSTEM APPLICATIONS

abstract: Detecting license plates is crucial and inevitable in the vehicle license plate recognition system. In this article, a Hue-Saturation-Intensity (HSI) color model is adopted to select automatically statistical threshold value for detecting candidate regions. The focus of this article is on the implementation of a new method to detect candidate regions when vehicle bodies and license plates (LP) have similar color. The proposed method is able to deal with candidate regions under independent orientation and scale of the plate. For the decomposing candidate regions, predetermined LP alphanumeric characters are used by position histogram to verify and detect vehicle LP regions. Various LP images were used with a variety of conditions to test the proposed method and results proved its effectiveness.


A Comparison Paper on Skew Detection of Scanned Document Images Based on Horizontal and Vertical Projection Profile Analysis


The Future of Time Series


title: Interactive Plan Explicability in Human-Robot Teaming

abstract: Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware that its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot cooperation with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where human and robot behaviors can influence each other. We term this new measure as Interactive Plan Explicability. We compare the joint plan generated with the consideration of this measure using the fast forward planner (FF) with the plan created by FF without such consideration, as well as the plan created with actual human subjects. Results indicate that the explicability score of plans generated by our algorithm is comparable to the human plan, and better than the plan created by FF without considering the measure, implying that the plans created by our algorithms align better with expected joint plans of the human during execution. This can lead to more efficient collaboration in practice.


Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval


title: Towards Research Collaboration - a Taxonomy of Social Research Network Sites

abstract: The increase of scientific collaboration coincides with the technological and social advancement of social software applications which can change the way we research. Among social software, social network sites have recently gained immense popularity in a hedonic context. This paper focuses on social network sites as an emerging application designed for the specific needs of researchers. To give an overview about these sites we use a data set of 24 case studies and in-depth interviews with the founders of ten social research network sites. The gathered data leads to a first tentative taxonomy and to a definition of SRNS identifying four basic functionalities identity and network management, communication, information management, and collaboration. The sites in the sample correspond to one of the following four types: research directory sites, research awareness sites, research management sites and research collaboration sites. These results conclude with implications for providers of social research network sites.


title: ROC Curve, Lift Chart and Calibration Plot

abstract: This paper presents ROC curve, lift chart and calibration plot, three well known graphical techniques that are useful for evaluating the quality of classification models used in data mining and machine learning. Each technique, normally used and studied separately, defines its own measure of classification quality and its visualization. Here, we give a brief survey of the methods and establish a common mathematical framework which adds some new aspects, explanations and interrelations between these techniques. We conclude with an empirical evaluation and a few examples on how to use the presented techniques to boost classification accuracy.


title: DLR’s Advanced Telerobotic Concepts and Experiments for On-Orbit Servicing

abstract: Space robotics will become a key technology for the exploration of outer space and the operation and maintenance of space stations, satellites and other platforms, saving costs and relieving man from dangerous tasks. But we do not have to wait until robots are really autonomous or intelligent, since by modern teleoperation and telepresence we are able to remotely control robot systems from the ground in the sense of “prolonging man’s arm into space”. Humans, with their several hundred thousand years of evolution, will not adapt themselves to the hostile space environment, whilst robots, which have only been developed for just over 40 years, can be much more easily adapted to such an environment. As presented within this work, few pioneering telerobotic experiments like ROTEX, the first remotely controlled space robot system, ETS-VII, the first free-floating space robot experiment, or ROKVISS, Germany’s recent advanced space robot experiment on the International Space Station, have been proposed and conducted on the way towards a space robot assistant system for the usage as an artificial astronaut to perform On-Orbit Servicing (OOS) tasks.


Intelligent Predictive Maintenance ( IPdM ) System – Industry 4.0 Scenario


title: Board level reliability of automotive eWLB (embedded wafer level BGA) FOWLP

abstract: With shrinking chip sizes, Wafer Level Packaging (WLP) is becoming an attractive packaging technology with many advantages in comparison to standard Ball Grid Array (BGA) packages. With the advancement of various fan-out Wafer Level Packaging (FOWLP) designs, this advanced technology has proven to be a more optimal and promising solution compared to fan-in WLP because of the greater design flexibility in having more input/output (I/O) and improved thermal performance. In addition, FOWLP shows superior high-frequency performance with its shorter and simpler interconnection compared to flip chip packaging. eWLB (embedded wafer level BGA) is a type of FOWLP that enables applications requiring smaller form factor, excellent heat dissipation and thin package profiles. It also has the potential to evolve into various configurations with proven yields and manufacturing experience based on over 8 years of high volume production. This paper discusses the recent advancements in robust board level reliability performance of eWLB for automotive applications. A Design of Experiment (DOE) study will be reviewed which demonstrates improved Temperature Cycle on Board (TCoB) performance with experimental results. Several DOE studies were planned and test vehicles were prepared with the variables of solder materials, solder mask opening/Cu pad size of redistribution layer (RDL) design, copper (Cu) RDL thickness and under bump metallurgy (UBM), and Cu pad design (NSMD, SMD) on a printed circuit board (PCB). With these parametric studies and TCoB reliability tests, the test vehicle passed 1000x temperature cycles (TC). Daisy chain test vehicles were used for TCoB reliability performance in industry standard test conditions.


title: Child trafficking and the European migration crisis: The role of forensic practitioners.

abstract: Trafficking in children is one of the worst forms of human rights violation and is categorised as a serious crime. Children at high risk of becoming victims of trafficking are runaways, children with a history of abuse, and migrant children. Internationally, cases of child trafficking are increasing the most in Europe, which is likely the result of the current migration crisis. In crises, preventing and combating human trafficking needs to be prioritized, considering that the aims of humanitarian action include saving lives, easing suffering and preserving human dignity. The involvement of forensic practitioners in investigations of cases of child trafficking mainly concerning the identification of victims may save lives and certainly alleviate suffering of the child victims and their families searching for them. Moreover, by aiding the prosecution process through thorough documentation and expert reporting forensic practitioners may contribute to the protection, rehabilitation and possibly compensation of the child victims, and thus to the restoration of their rights and dignity. So far, forensic practitioners were rarely specifically mentioned as actors in the counter-trafficking efforts in the multitude of policies, regulations, guidelines and recommendations concerning different aspects of child trafficking. This seems surprising considering that the expertise and experience of practitioners from forensic sciences including cyber forensics, document analysis, forensic biology, anthropology, and medicine can be utilised for gathering intelligence in cases of suspected human trafficking, for identifying the victims as well as perpetrators, and for securing evidence for legal proceedings as this paper shows. While this article mainly discusses the role of forensic pathologists and anthropologists, with a specific focus on the identification of child victims of trafficking in the context of the European migration crisis, the notions regarding the contribution of forensic sciences to the counter-trafficking efforts can be adapted to other geographical and sociopolitical contexts.


title: Control and Performance Analysis of a Single-Stage Utility-Scale Grid-Connected PV System

abstract: For utility-scale photovoltaic (PV) systems, the control objectives, such as maximum power point tracking, synchronization with grid, current control, and harmonic reduction in output current, are realized in single stage for high efficiency and simple power converter topology. This paper considers a high-power three-phase single-stage PV system, which is connected to a distribution network, with a modified control strategy, which includes compensation for grid voltage dip and reactive power injection capability. To regulate the dc-link voltage, a modified voltage controller using feedback linearization scheme with feedforward PV current signal is presented. The real and reactive powers are controlled by using $dq$ components of the grid current. A small-signal stability/eigenvalue analysis of a grid-connected PV system with the complete linearized model is performed to assess the robustness of the controller and the decoupling character of the grid-connected PV system. The dynamic performance is evaluated on a real-time digital simulator.


title: Capacitive pressure sensing with suspended graphene–polymer heterostructure membranes

abstract: We describe the fabrication and characterisation of a capacitive pressure sensor formed by an ultra-thin graphene–polymer heterostructure membrane spanning a large array of micro-cavities each up to 30 μm in diameter with 100% yield. Sensors covering an area of just 1 mm2 show reproducible pressure transduction under static and dynamic loading up to pressures of 250 kPa. The measured capacitance change in response to pressure is in good agreement with calculations. Further, we demonstrate high-sensitivity pressure sensors by applying a novel strained membrane transfer and optimising the sensor architecture. This method enables suspended structures with less than 50 nm of air dielectric gap, giving a pressure sensitivity of 123 aF Pa−1 mm−2 over a pressure range of 0 to 100 kPa.


title: Formal Linked Data Visualization Model

abstract: Recently, the amount of semantic data available in the Web has increased dramatically. The potential of this vast amount of data is enormous but in most cases it is difficult for users to explore and use this data, especially for those without experience with Semantic Web technologies. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. In this article we devise a formal Linked Data Visualization Model (LDVM), which allows to dynamically connect data with visualizations. We report about our implementation of the LDVM comprising a library of generic visualizations that enable both users and data analysts to get an overview on, visualize and explore the Data Web and perform detailed analyzes on Linked Data.


title: Mobil robot navigation using 2D LIDAR

abstract: This paper describes the development of navigation method of a mobile robot with multiple distance measuring sensor. The Differential Drive Mobile Robot is controlled by our own control panel. For perception of the environment, a SICK LMS 151 type LiDAR (Light Detection and Ranging) and two optical (infrared) and two ultrasonic distance meters were placed on the robot. During the development of the navigation method communication with LIDAR has been developed. The data of the laser and ultrasonic distance meter sensors are processed and transmitted to the computer by the unique developed embedded system. Controlling of motors are done by the PCB (Printed Circuit Board) as well. Using advantages of LIDAR sensor defined navigational direction is rapidly calculated after measurement. Own software has been developed for calculating the route and for communication with control electronics. Several high pass filters were implemented for steady progress of the robot. In this way, the system is able to avoid obstacles rapidly.


title: Conceptual change in science and in science education

abstract: There is substantial evidence that traditional instructional methods have not been successful in helping students to ‘restructure’ their commonsense conceptions and learn the conceptual structures of scientific theories. This paper argues that the nature of the changes and the kinds of reasoning required in a major conceptual restructuring of a representation of a domain are fundamentally the same in the discovery and in the learning processes. Understanding conceptual change as it occurs in science and in learning science will require the development of a common cognitive model of conceptual change. The historical construction of an inertial representation of motion is examined and the potential instructional implications of the case are explored.


title: A shape and image merging technique to solve jigsaw puzzles

abstract: This paper proposes an algorithm for solving subsets of typical (canonical) jigsaw puzzles. This algorithm combines shape and image matching with a cyclic "growth" process that tries to place pieces in their correct positions. First, the jigsaw pieces are extracted from the input image. Then, the corner points of the jigsaw pieces are detected. Next, piece classification and recognition are performed based on jigsaw piece models. Connection relationships between pieces are calculated and finally recovered by using boundary shape matching and image merging. We tested this algorithm by employing real-world images containing dozens of jigsaw pieces. The experiment's results show this algorithm is efficient and effective.


title: Character-Stroke Detection for Text-Localization and Extraction

abstract: In this paper, we present a new approach for analysis of images for text-localization and extraction. Our approach puts very few constraints on the font, size and color of text and is capable of handling both scene text and artificial text well. In this paper, we exploit two well-known features of text: approximately constant stroke width and local contrast, and develop a fast, simple, and effective algorithm to detect character strokes. We also show how these can be used for accurate extraction and motivate some advantages of using this approach for text localization over other color-space segmentation based approaches. We analyze the performance of our stroke detection algorithm on images collected for the robust-reading competitions at ICDAR 2003.


title: Analyzing terror campaigns on the internet : Technical sophistication , content richness , and Web interactivity

abstract: Terrorists and extremists are increasingly utilizing Internet technology to enhance their ability to influence the outside world. Due to the lack of multi-lingual and multimedia terrorist/extremist collections and advanced analytical methodologies, our empirical understanding of their Internet usage is still very limited. To address this research gap, we explore an integrated approach for identifying and collecting terrorist/extremist Web contents. We also propose a Dark Web Attribute System (DWAS) to enable quantitative Dark Web content analysis from three perspectives: technical sophistication, content richness, and Web interactivity. Using the proposed methodology, we identified and examined the Internet usage of major Middle Eastern terrorist/extremist groups. More than 200,000 multimedia Web documents were collected from 86 Middle Eastern multi-lingual terrorist/extremist Web sites. In our comparison of terrorist/extremist Web sites to US government Web sites, we found that terrorists/extremist groups exhibited similar levels of Web knowledge as US government agencies. Moreover, terrorists/extremists had a strong emphasis on multimedia usage and their Web sites employed significantly more sophisticated multimedia technologies than government Web sites. We also found that the terrorists/extremist groups are as effective as the US government agencies in terms of supporting communications and interaction using Web technologies. Advanced Internet-based communication tools such as online forums and chat rooms are used much more frequently in terrorist/extremist Web sites than government Web sites. Based on our case study results, we believe that the DWAS is an effective tool to analyse the technical sophistication of terrorist/extremist groups' Internet usage and could contribute to an evidence-based understanding of the applications of Web technologies in the global terrorism phenomena.


title: Dynamic Information Retrieval: Theoretical Framework and Application

abstract: Theoretical frameworks like the Probability Ranking Principle and its more recent Interactive Information Retrieval variant have guided the development of ranking and retrieval algorithms for decades, yet they are not capable of helping us model problems in Dynamic Information Retrieval which exhibit the following three properties; an observable user signal, retrieval over multiple stages and an overall search intent. In this paper a new theoretical framework for retrieval in these scenarios is proposed. We derive a general dynamic utility function for optimizing over these types of tasks, that takes into account the utility of each stage and the probability of observing user feedback. We apply our framework to experiments over TREC data in the dynamic multi page search scenario as a practical demonstration of its effectiveness and to frame the discussion of its use, its limitations and to compare it against the existing frameworks.


title: A theoretical framework for ontology evaluation and validation

abstract: The need for evaluation-methodologies emerged very early in the field of ontology development and reuse and it has grown steadily. Yet, no comprehensive and global approach to this problem has been proposed to date. This situation may become a serious obstacle for the success of ontology-based Knowledge Technology, especially in the industrial and commercial sectors. In this paper we look at existing ontology-evaluation methods from the perspective of their integration in one single framework. Based on a catalogue of qualitative and quantitative measures for ontologies, we set up a formal model for ontology. The proposed formal model consists of a meta-ontology Othat characterizes ontologies as semiotic objects. The meta-ontology is complemented with an ontology of ontology evaluation and validation oQual. Based on O and oQual, we identify three main types of measures for ontology evaluation: structural measures, that are typical of ontologies represented as graphs; functional measures, that are related to the intended use of an ontology and of its components, i.e. their function; usability-related measures, that depend on the level of annotation of the considered ontology.


title: Statistical Modeling of Nanotechnology Knowledge Diffusion Networks

abstract: Nanotechnology is crucial for industrial and scientific advancement, with millions of dollars being invested each year in nanotechnology-related research. Recent developments in information-technology enables modeling the knowledge diffusion process via online depositories of nanotechnology-related scientific publication records. Understanding the mechanism may help funding agencies use their funding effectively. This study uses Exponential Random Graph Models (ERGMs), a family of theorygrounded statistical models, to explore the knowledge diffusion patterns among nanotechnology researchers. We systematically evaluate how various attributes of researchers and public funding affect the knowledge diffusion processes. Results show that the impact of public funding on nanotechnology knowledge transfer has been increasing in recent years. Funding all kinds of researchers can stimulate knowledge transfer. Also, funding senior researchers help stimulate knowledge sharing. Our analysis framework of knowledge diffusion networks is effective in studying the knowledge diffusion patterns in nanotechnology, and can be easily applied to other fields.