title: Database description with SDM: a semantic database model

abstract: SDM is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it. SDM is designed to enhance the effectiveness and usability of database systems. An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system.


title: Multitouch tablet applications and activities to enhance the social skills of children with autism spectrum disorders

abstract: In spite of great improvements in early diagnosis and interventions, most children diagnosed with autism spectrum disorders (ASD) are unlikely to live independently when they reach adulthood. We have been conducting research on novel computer-based interventions with the goal of promoting social skills. Working with 26 children with ASD, their teachers, and other stakeholders, we have iteratively developed a set of activities based on applications that run on multitouch tablets. Our observations suggest these activities increased pro-social behaviors such as collaboration and coordination, augmented appreciation for social activities, and provided children with novel forms of expression.


title: Predictive, personalized, preventive, participatory (P4) cancer medicine

abstract: Medicine will move from a reactive to a proactive discipline over the next decade--a discipline that is predictive, personalized, preventive and participatory (P4). P4 medicine will be fueled by systems approaches to disease, emerging technologies and analytical tools. There will be two major challenges to achieving P4 medicine--technical and societal barriers--and the societal barriers will prove the most challenging. How do we bring patients, physicians and members of the health-care community into alignment with the enormous opportunities of P4 medicine? In part, this will be done by the creation of new types of strategic partnerships--between patients, large clinical centers, consortia of clinical centers and patient-advocate groups. For some clinical trials it will necessary to recruit very large numbers of patients--and one powerful approach to this challenge is the crowd-sourced recruitment of patients by bringing large clinical centers together with patient-advocate groups.


title: How Augmented Reality Enables Conceptual Understanding of Challenging Science Content

abstract: Introduction Research on learning about science has revealed that students often hold robust misconceptions or naive conceptions about a number of scientific ideas (Chi, 2005; Bransford, Brown, & Cocking, 1999). For example, studies on student understanding of the Bernoulli's principle, which is the subject of our exploration, have shown that students find learning the content challenging due to, among other things, the counterintuitive experiences of pressure-related events observed in the real world (Stepans, 2003). Digital simulation and dynamic visualization tools have helped to ameliorate these learning challenges by providing scaffolding (Honey & Hilton, 2011; Kim & Hannafin, 2011) to understand various aspects of phenomenon that may contribute to misconceptions. Related to this, a recent focus in the learning sciences has investigated how augmented reality (AR) tools can support science learning (Dunleavy, Dede, & Mitchell, 2009; Dunleavy & Dede, 2014; Klopfer & Squire, 2008). At its simplest, augmented reality describes systems that integrate computer-generated virtual elements or information (known as "digital augmentations") with the real world environment (Zhou et al., 2008). By superimposing virtual elements onto the real world environments, AR allows users to experience and perceive the newly incorporated information as part of their present world, thereby enhancing their perception of the real world (Kirkley & Kirkley, 2004; New Media Consortium, 2012). Everyday examples of AR include Google Effects in Hangouts, AR games for Nintendo 3DS, and Webcam Greeting cards from Hallmark. Over the last 4 years, our project, Augmented Reality for Interpretive and Experiential Learning (ARIEL), has investigated optimal uses of AR in science museums (e.g., Yoon, Elinich, Wang, Steinmeier, & Tucker, 2012a; Yoon, Elinich, Wang, Van Schooneveld, & Anderson, 2013; Yoon & Wang, 2014), where misconceptions about science are rarely addressed. In this study, we hypothesized that the use of AR, because it provides a visualization of the underlying causal mechanisms, can assist students in developing a more accurate conception of Bernoulli's principle. We found that after participating in brief, informal investigations of the principle at a science museum, students who interacted with an exhibit using AR were better able to understand the science than students in a non-AR condition. Findings from our interviews and surveys suggest that the tool supported students' learning by revealing typically invisible features of the phenomenon. Theoretical considerations Common misconceptions and challenges associated with understanding Bernoulli's principle Bernoulli's principle states that "when an incompressible, smoothly flowing fluid gains speed, internal pressure in the fluid decreases, and vice versa" (Hewitt, 2004). In other words, there is an inversely proportional relationship between fluid speed and pressure. When the fluid's speed increases, the pressure drops. As it turns out, this is a conceptually challenging and counterintuitive idea to understand for students, who typically believe that when speed increases, so does the pressure (Faulkner & Ytreberg, 2011). Stepans (2003) explains, Children learn from experience that when they blow on something--like a bubble or dandelion plume--it goes away. These experiences make it difficult to make sense of the fact that when you blow on a surface, it comes toward you, or that when you blow between things, they come together. These experiences make it difficult to accept the concept of Bernoulli's Principle. (p. 46) In a test given to private and public 6th, 7th, and 8th grade Turkish students on the outcomes of discrepant events related to Bernoulli's principle, Bulunuz, Jarrett, and Bulunuz (2009) found that the majority of students held incorrect conceptions of the phenomenon. …


title: Immunoglobulin G Antithyroid Peroxidase Antibodies in Hashimoto's Thyroiditis: Epitope-Mapping Analysis

abstract: Patients with autoimmune thyroid disease frequently have high affinity antibodies to thyroid peroxidase (TPO), although the role they play in disease pathogenesis is not known. We have previously prepared 37 monoclonal anti-TPO IgG kappa Fab fragments from two patients with Hashimoto's thyroiditis and demonstrated the similarity of these Fab sequences to those published previously, mainly derived from patients with Graves' disease. In this paper, we described epitope mapping of these Fabs using a previously characterized panel of murine monoclonal antibody (mAb) and show that the Fabs bind to two neighboring epitopes on native TPO. Although the epitope-mapping method differs from that used to characterize previously published TPO-reactive Fab sequences, it indicates a similarly restricted response to neighboring epitopes in both Graves' disease and Hashimoto's thyroiditis. The epitope mapping included mAb 47, which binds to a linear TPO peptide of known sequence in addition to native TPO. Although TPO-reactive Fab did not inhibit the binding of mAb 47, mAb 47 did inhibit the binding of Fab, indicating the likely site of the immunodominant region on native TPO. These results confirm the restricted nature of TPO antibody and further delineate the immunodominant region of native TPO as defined by the mAb.


A Review of Inference Algorithms for Hybrid Bayesian Networks


title: Social norms and self-presentation on social network sites: Profile work in action

abstract: “Profile work,” that is strategic self-presentation in social network sites, is configured by both the technical affordances and related social norms. In this article, we address technical and social psychological aspects that underlie acts of sharing by analyzing the social in relation to the technical. Our analysis is based on two complementary sets of qualitative data gleaned from in situ experiences of Finnish youth and young adults within the sharing mechanisms of Facebook and Last.fm. In our analysis, we identified social norms that were formed around the prevailing sharing practices in the two sites and compared them in relation to the sharing mechanisms. The analysis revealed that automated and manual sharing were sanctioned differently. We conclude that although the social norms that guide content sharing differed between the two contexts, there was an identical sociocultural goal in profile work: presentation of authenticity.


Social Network Services: Competition and Privacy


title: SECURITY CHALLENGES , ISSUES AND THEIR SOLUTIONS FOR VANET

abstract: Vehicular Ad hoc Networks (VANETs) are the promising approach to provide safety and other applications to the drivers as well as passengers. It becomes a key component of the intelligent transport system. A lot of works have been done towards it but security in VANET got less attention. In this article, we have discussed about the VANET and its technical and security challenges. We have also discussed some major attacks and solutions that can be implemented against these attacks. We have compared the solution using different parameters. Lastly we have discussed the mechanisms that are used in the solutions.


title: CHARACTERISATIONOF ACOUSTICSCENES USINGATEMPORALLY-CONSTRAINED SHIFT-INVARIANTMODEL

abstract: In this paper, we propose a method for modeling and classifying acoustic scenes using temporally-constrained shift-invariant probabilistic latent component analysis (SIPLCA). SIPLCA can be used for extracting time-frequency patches from spectrograms in an unsupervised manner. Component-wise hidden Markov models are incorporated to the SIPLCA formulation for enforcing temporal constraints on the activation of each acoustic component. The time-frequency patches are converted to cepstral coefficients in order to provide a compact representation of acoustic events within a scene. Experiments are made using a corpus of train station recordings, classified into 6 scene classes. Results show that the proposed model is able to model salient events within a scene and outperforms the non-negative matrix factorization algorithm for the same task. In addition, it is demonstrated that the use of temporal constraints can lead to improved performance.


title: Advances in construction techniques of AC induction motors: preparation for super-premium efficiency levels

abstract: Design, material, and production techniques are evolving on AC induction motors leading to improved efficiencies over older designs. IEEE 841 and even NEMA Premium efficiency levels are now quite easy to meet and exceed. New research and production techniques will allow construction of ac motors with die-cast copper rotors, allowing even higher efficiency levels and greater longevity.


Bauback Yeganeh and David Kolb Mindfulness and Experiential Learning


title: Location Privacy via Differential Private Perturbation of Cloaking Area

abstract: The increasing use of mobile devices has triggered the development of location based services (LBS). By providing location information to LBS, mobile users can enjoy variety of useful applications utilizing location information, but might suffer the troubles of private information leakage. Location information of mobile users needs to be kept secret while maintaining utility to achieve desirable service quality. Existing location privacy enhancing techniques based on K-anonymity and Hilbertcurve cloaking area generation showed advantages in privacy protection and service quality but disadvantages due to the generation of large cloaking areas that makes query processing and communication less effective. In this paper we propose a novel location privacy preserving scheme that leverages some differential privacy based notions and mechanisms to publish the optimal size cloaking areas from multiple rotated and shifted versions of Hilbert curve. With experimental results, we show that our scheme significantly reduces the average size of cloaking areas compared to previous Hilbert curve method. We also show how to quantify adversary's ability to perform an inference attack on user location data and how to limit adversary's success rate under a designed threshold.


title: Dynamic modeling, design, and simulation of a combined PEM fuel cell and ultracapacitor system for stand-alone residential applications

abstract: The available power generated from a fuel cell (FC) power plant may not be sufficient to meet sustained load demands, especially during peak demand or transient events encountered in stationary power plant applications. An ultracapacitor (UC) bank can supply a large burst of power, but it cannot store a significant amount of energy. The combined use of FC and UC has the potential for better energy efficiency, reducing the cost of FC technology, and improved fuel usage. In this paper, we present an FC that operates in parallel with a UC bank. A new dynamic model and design methodology for an FCand UC-based energy source for stand-alone residential applications has been developed. Simulation results are presented using MATLAB, Simulink, and SimPowerSystems environments based on the mathematical and dynamic electrical models developed for the proposed system


A GENETIC ALGORITHM FOR STRUCTURAL OPTIMIZATION OF STEEL TRUSS ROOFS


title: Statistical Testing of Random Number Generators

abstract: Abstract: Random Number Generators (RNGs) are an important building block for algorithms and protocols in cryptography. They are paramount in the construction of encryption keys and other cryptographic algorithm parameters. In practice, statistical testing is employed to gather evidence that a generator indeed produces numbers that appear to be random. Few resources are readily available to researchers in academia and industry who wish to analyze their newly developed RNG. To address this problem, NIST has developed new metrics that may be employed to investigate the randomness of cryptographic RNGs. In this paper, issues such as statistical test suites, evaluation frameworks, and the interpretation of results are addressed.


title: i2tag: RFID Mobility and Activity Identification Through Intelligent Profiling

abstract: Many radio frequency identification (RFID) applications, such as virtual shopping cart and tag-assisted gaming, involve sensing and recognizing tag mobility. However, existing RFID localization methods are mostly designed for static or slowly moving targets (less than 0.3m/sec). More importantly, we observe that prior methods suffer from serious performance degradation for detecting real-world moving tags in typical indoor environments with multipath interference. In this article, we present i2tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments (e.g., indoors). i2tag employs a supervised learning framework based on our novel fine-grain mobility provile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i2tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative phase--based fingerprint. Additionally, we design a multidimensional dynamic time warping--based algorithm to robustly detect mobility and the associated activities. We show that i2tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a ThingMagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.


title: A Modified Firefly Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading

abstract: Photovoltaic modules subjected to partial shading conditions (PSC) can drastically decrease their power output. Hence, there have been various maximum power point tracking (MPPT) control algorithms developed to reduce or counteract the shading effects. Recently, a new metaheuristic algorithm known as firefly algorithm (FA) was developed, which, under PSC, has been shown to successfully track the global maximum point (GMP). Nevertheless, the FA still has some inherent problems that may hinder the performance of the MPPT. This paper modifies the existing FA to counteract these problems. As will be demonstrated in this paper, the proposed modified FA method can reduce the number of computation operations and the time for converging to the GMP that the existing FA requires. Experimental results show that the proposed method can track the global point under various PSC, has a faster convergence time compared with the FA, and can effectively suppress power and voltage fluctuations.


title: Reversible Recursive Instance-Level Object Segmentation

abstract: In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes the two subnetworks during joint training, in which the refined object proposals and improved segmentation predictions are alternately fed into each other to progressively increase the network capabilities. By being reversible, the proposal refinement sub-network adaptively determines an optimal number of refinement iterations required for each proposal during both training and testing. Furthermore, to handle multiple overlapped instances within a proposal, an instance-aware denoising autoencoder is introduced into the segmentation sub-network to distinguish the dominant object from other distracting instances. Extensive experiments on the challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of R2-IOS over other state-of-the-art methods. In particular, the APr over 20 classes at 0:5 IoU achieves 66:7%, which significantly outperforms the results of 58:7% by PFN [17] and 46:3% by [22].


title: Research on SLA information model based on SID

abstract: Referring to the international specifications on NGOSS released by TMF (eTOM which specifies the Business Process Framework and SID which describes the Shared Information/Data and SLA which describes the service level agreement in NGOSS knowledge base), and domestic business specifications and status of related to BSS/OSS, the author studied the materials related to the SLA Information Model in OSS, explored the information modeling methods. It includes the correlation content of SLA information model from resource, service to customer.


title: Aesthetic Primitives of Images for Visualization

abstract: Images play an important role in visualization. As users are more willing to adopt a product if it evokes pleasurable feelings the aesthetic appeal of interfaces becomes more important. Thus, there is a growing need to generate also images which appear aesthetically to the user. Starting with the modularities of the human visual system, we derive six dimensions of visual aesthetics. For each dimension we explore, inspired by principles of the visual arts and insights of cognitive neuroscience, which pecularities of the dimensions are particularly adequate for an aesthetic impression. Accompanied by a fair number of image examples, these considerations result in an easy to understand guideline for computer scientists and interface designers how to deal with images in terms of aesthetics.


title: Indonesian Social Media Sentiment Analysis With Sarcasm Detection

abstract: Sarcasm is considered one of the most difficult problem in sentiment analysis. In our ob-servation on Indonesian social media, for cer-tain topics, people tend to criticize something using sarcasm. Here, we proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. The features are the negativity information and the number of interjection words. We also employed translated SentiWordNet in the sentiment classification. All the classifications were conducted with machine learning algorithms. The experimental results showed that the additional features are quite effective in the sarcasm detection.


title: Radio frequency energy for non-invasive and minimally invasive skin tightening.

abstract: This article reviews the non-invasive and minimally invasive options for skin tightening, focusing on peer-reviewed articles and presentations and those technologies with the most proven or promising RF non-excisional skin-tightening results for excisional surgeons. RF has been the mainstay of non-invasive skin tightening and has emerged as the "cutting edge" technology in the minimally invasive skin-tightening field. Because these RF skin-tightening technologies are capital equipment purchases with a significant cost associated, this article also discusses some business issues and models that have proven to work in the plastic surgeon's office for non-invasive and minimally invasive skin-tightening technologies.


title: Polyps auto-detection in Wireless Capsule Endoscopy images using improved method based on image segmentation

abstract: Wireless Capsule Endoscopy (WCE) is a noninvasive instrument that widely used in screening the whole intestine and it has been utilized as a model especially for the examination of gastrointestinal (GI) diseases. However, it is numerous images of the detecting result produced by WCE that always burdens the physicians. To solve this problem, it is necessary to combine the manual diagnosis with the image segmentation technology. In this paper we proposed a feasible method by using K-means clustering and localizing region-based active contour segmentation for polyps auto-detection in WCE images. Experimental results shows the method is promising and efficient.


title: Imaging cerebral atrophy: normal ageing to Alzheimer's disease

abstract: CONTEXT ::: With ageing populations, the prevalence of dementia, especially Alzheimer's disease, is set to soar. Alzheimer's disease is associated with progressive cerebral atrophy, which can be seen on MRI with high resolution. Longitudinal MRI could track disease progression and detect neurodegenerative diseases earlier to allow prompt and specific treatment. Such use of MRI requires accurate understanding of how brain changes in normal ageing differ from those in dementia. ::: ::: ::: STARTING POINT ::: Recently, Henry Rusinek and colleagues, in a 6-year longitudinal MRI study of initially healthy elderly subjects, showed that an increased rate of atrophy in the medial temporal lobe predicted future cognitive decline with a specificity of 91% and sensitivity of 89% (Radiology 2003; 229: 691-96). WHERE NEXT? As understanding of neurodegenerative diseases increases, specific disease-modifying treatments might become available. Serial MRI could help to determine the efficacy of such treatments, which would be expected to slow the rate of atrophy towards that of normal ageing, and might also detect the onset of neurodegeneration. The amount and pattern of excess atrophy might help to predict the underlying pathological process, allowing specific therapies to be started. As the precision of imaging improves, the ability to distinguish healthy ageing from degenerative dementia should improve.


title: Homicide due to mental disorder in England and Wales over 50 years

abstract: Background ::: ::: It has been stated that rates of homicide due to mental disorder are constant over time. ::: ::: Aims ::: ::: To examine whether there were changes in the rates of homicide due to mental disorder over time, and whether changes in these rates were associated with changes in the rates of other homicides in England and Wales. ::: ::: Method ::: ::: Examination of four sets of official homicide statistics from England and Wales from 1946 to 2004. ::: ::: Results ::: ::: The rate of total homicide and the rate of homicide due to mental disorder rose steadily until the mid-1970s. From then there was a reversal in the rate of homicides attributed to mental disorder, which declined to historically low levels, while other homicides continued to rise. ::: ::: Conclusions ::: ::: The reasons for the rise and fall in homicides attributed to mental disorder are not clear. The earlier increase in such homicides may have been due to the same sociological factors that caused the increase in other homicides over that time. The subsequent decline may have been due to improvements in psychiatric treatments and service organisation. Another possibility is that there has been an informal change to the legal tests for the finding of homicide due to mental disorder.


title: Deep visual words: Improved fisher vector for image classification

abstract: Image classification has been revolutionized by deep convolutiosnal neural networks. Using previous state-of-the-art classification methods like Fisher vector encoding in combination with deep CNNs has been shown to be promising. Motivated by the recent work on dense CNN features to extract Fisher encoding(FV-CNN), we present a scheme to discover better visual words with CNNs, to obtain improved Fisher vector features. Our method (Deep Visual Words-DVW) learns semantic visual clusters per each category, by iteratively learning and refining groups of visual patches. DVW represents an efficient feature space embedding to capture the discriminative potential between meaningful visual clusters. We evaluate our approach on popular datasets in object, scene and action classification and outperformed the state-of-the-art: scene classification MIT indoor, object categorization PASCAL VOC 2007 and Stanford40 human actions.


title: Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles

abstract: Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, we present our semi-automatic approach to building an ontology for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related Wikipedia articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. We also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.


title: Structure, development and function of the branchial sieve of the common bream, Abramis brama, white bream, Blicca bjoerkna and roach, Rutilus rutilus

abstract: The filter feeding organ of cyprinid fishes is the branchial sieve, which consists of a mesh formed by gill rakers and tiny channels on the gill arches. In order to establish its possible role during growth we measured the following morphological gill raker parameters over a range of sizes in three cyprinid fishes, bream, white bream and roach: inter raker distance, bony raker length, raker width, cushion length and channel width. At any given standard length common bream has the largest inter raker distance, roach the lowest and white bream is intermediate. In the ‘comb model’ of filter feeding the inter raker distance is considered to be a direct measure of the mesh size and retention ability (= minimal size of prey that can be retained) of a filter. For the three species under study there is a conflict between the comb model and experimental data on particle retention. Lammens et al. (1987) found that common bream has a large retention ability whereas roach and white bream have a much smaller one. A new model, the ‘channel model’ (Hoogenboezem et al. 1991) has been developed for common bream; in this model the lateral gill rakers can regulate the mesh size of the medial channels on the other side of the gill slit. The present data indicate that this model is not appropriate for white bream and roach. At any given standard length white bream and roach only reach 70% of the raker length of common bream, which means that in this model the gill slits should to be very narrow during filter feeding. The gill rakers consist of a bony raker and a fleshy cushion. The bony rakers have a rather long needle-like part outside the cushion in bream, but not in white bream and roach which have blunt gill rakers. Blunt gill rakers are not suited to reduce the diameter of the medial channels. The comb model seems more appropriate for white bream and roach, but doubts about the validity of this simple model remain. The sum of the areas of the medial channels is an approximation of the area through which water flows in the filter. This channel area therefore gives an impression of the capacity or flow rate of the filter. With this capacity estimation and an estimation of energy consumption we calculated an energy ratio of filter feeding. The energy ratio decreases with increasing standard length with an exponent close to the expected exponent of -0.40. The energy ratio is highest in bream, intermediate in white bream and lowest in roach.


title: Functional Performance Testing for Power and Return to Sports

abstract: Context: Functional performance testing of athletes can determine physical limitations that may affect sporting activities. Optimal functional performance testing simulates the athlete’s activity. Evidence Acquisition: A Medline search from 1960 to 2012 was implemented with the keywords functional testing, functional impairment testing, and functional performance testing in the English language. Each author also undertook independent searches of article references. Conclusion: Functional performance tests can bridge the gap between general physical tests and full, unrestricted athletic activity.


title: Neural network potentials for dynamics and thermodynamics of gold nanoparticles

abstract: For understanding the dynamical and thermodynamical properties of metal nanoparticles, one has to go beyond static and structural predictions of a nanoparticle. Accurate description of dynamical properties may be computationally intensive depending on the size of nanoparticle. Herein, we demonstrate the use of atomistic neural network potentials, obtained by fitting quantum mechanical data, for extensive molecular dynamics simulations of gold nanoparticles. The fitted potential was tested by performing global optimizations of size selected gold nanoparticles (Aun, 17 ≤ n ≤ 58). We performed molecular dynamics simulations in canonical (NVT) and microcanonical (NVE) ensembles on Au17, Au34, Au58 for a total simulation time of around 3 ns for each nanoparticle. Our study based on both NVT and NVE ensembles indicate that there is a dynamical coexistence of solid-like and liquid-like phases near melting transition. We estimate the probability at finite temperatures for set of isomers lying below 0.5 eV from th...


title: Mechanomyography Assisted Myoeletric Sensing for Upper-Extremity Prostheses: A Hybrid Approach

abstract: The myoelectric upper-limb prosthetic manipulation is inherently limited by the unreliable sensor-skin interface. This paper presents a hybrid approach to overcome the limitation of electromyography (EMG) through mechanomyography (MMG) assisted myoelectric sensing. An integrated hybrid sensor system was developed for simultaneous EMG and MMG measurement. The hybrid system formed a platform to capture muscular activations in different frequencies. To evaluate the effectiveness of hybrid EMG-MMG sensing, hand motion experiments have been carried out on seven able-bodied and two transradial amputee subjects. It convincingly demonstrated, a significantly ( ${p} ) improved classification accuracy (CA). Furthermore, the CA was compensated by 8.7% ~ 33.7% in the presence of 2 ~ 3 fault EMG channels. These results suggest that MMG assisted myoelectric sensing can improve the control performance and robustness. It has great potential to promote the clinical application of multi-functional prosthetic hand with hybrid EMG-MMG sensor system.


title: Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction

abstract: Bitcoin and Cryptocurrency Technologies provides a comprehensive introduction to the revolutionary yet often misunderstood new technologies of digital currency. Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age. How do Bitcoin and its block chain actually work? How secure are your bitcoins? How anonymous are their users? Can cryptocurrencies be regulated? These are some of the many questions this book answers. It begins by tracing the history and development of Bitcoin and cryptocurrencies, and then gives the conceptual and practical foundations you need to engineer secure software that interacts with the Bitcoin network as well as to integrate ideas from Bitcoin into your own projects. Topics include decentralization, mining, the politics of Bitcoin, altcoins and the cryptocurrency ecosystem, the future of Bitcoin, and more. An essential introduction to the new technologies of digital currency Covers the history and mechanics of Bitcoin and the block chain, security, decentralization, anonymity, politics and regulation, altcoins, and much more Features an accompanying website that includes instructional videos for each chapter, homework problems, programming assignments, and lecture slides Also suitable for use with the authors' Coursera online course Electronic solutions manual (available only to professors)


title: Tracing the Changjiang River's flowing route entering the sea during the last ice age maximum

abstract: Based on the stratigraphic and buried geomorphic analysis of three seismic profiles located in the inner and outer shelves of the East China Sea out of the present Changjiang River's mouth, and contrasting with some relevant geologic cores and Quaternary researches on the modern Changjiang River Delta, it is found that there are no paleosediments and paleochannels of the Changjiang River formed in the last ice age maximum in the study area. The paleochannels of the Changjiang River as which former researchers were considered are actually present tidal channels. Therefore, probably the Changjiang River did not flow by the East China Sea shelf and enter the Okinawa Trough during that period.


title: The Role of Trunk Muscle Strength for Physical Fitness and Athletic Performance in Trained Individuals: A Systematic Review and Meta-Analysis

abstract: Background ::: The importance of trunk muscle strength (TMS) for physical fitness and athletic performance has been demonstrated by studies reporting significant correlations between those capacities. However, evidence-based knowledge regarding the magnitude of correlations between TMS and proxies of physical fitness and athletic performance as well as potential effects of core strength training (CST) on TMS, physical fitness and athletic performance variables is currently lacking for trained individuals.


title: Adaptive unsupervised multi-view feature selection for visual concept recognition

abstract: To reveal and leverage the correlated and complemental information between different views, a great amount of multi-view learning algorithms have been proposed in recent years. However, unsupervised feature selection in multi-view learning is still a challenge due to lack of data labels that could be utilized to select the discriminative features. Moreover, most of the traditional feature selection methods are developed for the single-view data, and are not directly applicable to the multi-view data. Therefore, we propose an unsupervised learning method called Adaptive Unsupervised Multi-view Feature Selection (AUMFS) in this paper. AUMFS attempts to jointly utilize three kinds of vital information, i.e., data cluster structure, data similarity and the correlations between different views, contained in the original data together for feature selection. To achieve this goal, a robust sparse regression model with the l2,1-norm penalty is introduced to predict data cluster labels, and at the same time, multiple view-dependent visual similar graphs are constructed to flexibly model the visual similarity in each view. Then, AUMFS integrates data cluster labels prediction and adaptive multi-view visual similar graph learning into a unified framework. To solve the objective function of AUMFS, a simple yet efficient iterative method is proposed. We apply AUMFS to three visual concept recognition applications (i.e., social image concept recognition, object recognition and video-based human action recognition) on four benchmark datasets. Experimental results show the proposed method significantly outperforms several state-of-the-art feature selection methods. More importantly, our method is not very sensitive to the parameters and the optimization method converges very fast.


title: Unsupervised transfer classification: application to text categorization

abstract: We study the problem of building the classification model for a target class in the absence of any labeled training example for that class. To address this difficult learning problem, we extend the idea of transfer learning by assuming that the following side information is available: (i) a collection of labeled examples belonging to other classes in the problem domain, called the auxiliary classes; (ii) the class information including the prior of the target class and the correlation between the target class and the auxiliary classes. Our goal is to construct the classification model for the target class by leveraging the above data and information. We refer to this learning problem as unsupervised transfer classification. Our framework is based on the generalized maximum entropy model that is effective in transferring the label information of the auxiliary classes to the target class. A theoretical analysis shows that under certain assumption, the classification model obtained by the proposed approach converges to the optimal model when it is learned from the labeled examples for the target class. Empirical study on text categorization over four different data sets verifies the effectiveness of the proposed approach.


title: Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology

abstract: Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels. This 4-neighbourhood topological simplicity makes the application of convolutional masks straightforward for time series data, such as video applications, but many high-dimensional time series data are not organised in regular lattices, and instead values may have adjacency relationships with non-trivial topologies, such as small-world networks or trees. In our application case, human kinematics, it is currently unclear how to generalise convolutional kernels in a principled manner. Therefore we define and implement here a framework for general graph-structured CNNs for time series analysis. Our algorithm automatically builds convolutional layers using the specified adjacency matrix of the data dimensions and convolutional masks that scale with the hop distance. In the limit of a lattice-topology our method produces the well-known image convolutional masks. We test our method first on synthetic data of arbitrarily-connected graphs and human hand motion capture data, where the hand is represented by a tree capturing the mechanical dependencies of the joints. We are able to demonstrate, amongst other things, that inclusion of the graph structure of the data dimensions improves model prediction significantly, when compared against a benchmark CNN model with only time convolution layers.


title: Social CLWPR: A socially enhanced position based routing protocol for handling misbehaviour inVANETs

abstract: In this paper, we enhance CLWPR, a cross-layer optimized position-based routing protocol for urban VANET environments, introducing social aspects to its design so as to efficiently address misbehaving (selfish or malicious) nodes that intentionally drop packets and ultimately promote cooperation in the network. The proposed Social CLWPR protocol favors nodes with close favorable social relationship (family members, friends, users with trust connections) as next forwarder nodes, while nodes with explicit distrust connections as indicated from online social media are not preferred in the network. Social CLWPR performance is comparatively evaluated against CLWPR and it demonstrates improved performance in terms of packet delivery ratio.


title: Text document categorization by term association

abstract: A good text classifier is a classifier that efficiently categorizes large sets of text documents in a reasonable time frame and with an acceptable accuracy, and that provides classification rules that are human readable for possible fine-tuning. If the training of the classifier is also quick, this could become in some application domains a good asset for the classifier. Many techniques and algorithms for automatic text categorization have been devised. According to published literature, some are more accurate than others, and some provide more interpretable classification models than others. However, none can combine all the beneficial properties enumerated above. In this paper we present a novel approach for automatic text categorization that borrows from market basket analysis techniques using association rule mining in the data-mining field. We focus on two major problems: (1) finding the best term association rules in a textual database by generating and pruning; and (2) using the rules to build a text classifier. Our text categorization method proves to be efficient and effective, and experiments on well-known collections show that the classifier performs well. In addition, training as well as classification are both fast and the generated rules are human readable.


title: Research of Car Inverted Pendulum Model Based on Lagrange Equation

abstract: This paper analysed the movement of single, double and triple car inverted pendulum. It gives out the expression of general kinetic energy and general potential energy of N level car inverted pendulum in detail. In the end, it reaches the general model of car N level inverted pendulum system. Lagrange Equation based on analytical mechanics focus on derivation with mathematics tools and has more superiority then vector mechanics.


title: A New Approach for Scalable Analysis of Microbial Communities

abstract: Microbial communities play important roles in the function and maintenance of various biosystems, ranging from human body to the environment. Current methods for analysis of microbial communities are typically based on taxonomic phylogenetic alignment using 16S rRNA metagenomic or Whole Genome Sequencing data. In typical characterizations of microbial communities, studies deal with billions of micobial sequences, aligning them to a phylogenetic tree. We introduce a new approach for the efficient analysis of microbial communities. Our new reference-free analysis tech- nique is based on n-gram sequence analysis of 16S rRNA data and reduces the processing data size dramatically (by 105 fold), without requiring taxonomic alignment. The proposed approach is applied to characterize phenotypic microbial community differ- ences in different settings. Specifically, we applied this approach in classification of microbial com- munities across different body sites, characterization of oral microbiomes associated with healthy and diseased individuals, and classification of microbial communities longitudinally during the develop- ment of infants. Different dimensionality reduction methods are introduced that offer a more scalable analysis framework, while minimizing the loss in classification accuracies. Among dimensionality re- duction techniques, we propose a continuous vector representation for microbial communities, which can widely be used for deep learning applications in microbial informatics.


title: The hardware design and implementation of four-channel UHF RFID reader based on Impinj R2000

abstract: It is well known that UHF RFID, one of the key technologies of Internet of Things, is becoming the mainstream research of RFID applications. In a RFID system, reader plays an important role, however, RFID reader with only one channel often cannot satisfy the application requirements due to the complicated working environment. This study introduces a novel design solution of Four-channel RFID reader based on Impinj R2000, Atmel's AT91SAM7S256-MU is chosen as the microcontroller. Moreover, concepts and methods of the hardware design, such as components selection, design of schematic circuit diagram and PCB, etc. are expatiated in the paper. According to the testing results, when the transmit power is set to be 25dBm, the reader can achieve reading in a round-robin manner excellently and the readable range for passive tags can reach 2.5m.


title: Analysing Qualitative Data: More Than 'Identifying Themes'

abstract: Too often, qualitative researchers rely on the presentation of key themes supported by quotes from participants’ text as the primary form of analysis and reporting of their data. In this paper I argue that qualitative data require and support much deeper analysis. Strategies that might assist researchers to enrich their analysis of qualitative data are described. These strategies include improving interpretation and naming of categories; using comparison and pattern analysis to refine and relate categories or themes; using divergent views and negative cases to challenge generalisations; returning to substantive, theoretical or methodological literature; creating displays using matrices, graphs, flow charts and models; and using writing itself to prompt deeper thinking. Each strategy is illustrated by examples.


title: Bitsquatting: exploiting bit-flips for fun, or profit?

abstract: Over the last fifteen years, several types of attacks against domain names and the companies relying on them have been observed. The well-known cybersquatting of domain names gave way to typosquatting, the abuse of a user's mistakes when typing a URL in her browser's address bar. Recently, a new attack against domain names surfaced, namely bitsquatting. In bitsquatting, an attacker leverages random bit-errors occurring in the memory of commodity computers and smartphones, to redirect Internet traffic to attacker-controlled domains. In this paper, we report on a large-scale experiment, measuring the adoption of bitsquatting by the domain-squatting community through the tracking of registrations of bitsquatting domains targeting popular web sites over a 9-month period. We show how new bitsquatting domains are registered daily and how attackers are trying to monetize their domains through the use of ads, abuse of affiliate programs and even malware installations. Lastly, given the discovered prevalence of bitsquatting, we review possible defense measures that companies, software developers and Internet Service Providers can use to protect against it.


title: Triangular Bernstein-Bkzier patches

abstract: 4. Hermite Interpolants . . , . . . . . . . . . . . . . 104 4.1. The Co nine parameter interpolant ...... 104 4.2. C’ quintic interpolants 104 4.3. The general case 106 5. Split Triangle Interpolants . . . . . . . . . 107 5.1. The C’ Clough-Tocher interpolant . . _ . . 108 5.2. Limitations of the Clough-Tocher split . 110 5.3. The C’ Powell-Sabin interpolants . . . . 111 5.4. C’ Split square interpolants . . . . . . . . . 112


title: The automatic creation of literature abstracts

abstract: Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the "auto-abstract."


title: ReticularSpaces: activity-based computing support for physically distributed and collaborative smart spaces

abstract: Smart spaces research focuses on technology for multiple displays and devices for collocated participants. In most approaches, however, users have to cope with heterogeneous interfaces and information organization, as well as a lack of support for collaboration with mobile and remote users outside the smart space. In this paper, we present ReticularSpaces; a multi-display smart space system built on the principles of activity-based computing. The focus of ReticularSpaces is to support: (i) unified interaction with applications and documents through ReticularUI, a novel distributed user interfaces design; (ii) management of the complexity of tasks between users and displays; (iii) mobile users in a local, remote or 'nomadic' settings; and (iv) collaboration among local and remote users. We describe the motivation, design, and architecture of ReticularSpaces, and report from a preliminary feasibility study. The study shows that participants found ReticularSpaces useful and effective, but at the same time reveals new areas for research on smart environments.


title: Packet duplication for URLLC in 5G dual connectivity architecture

abstract: This paper addresses the problem of satisfying the extreme requirements related to Ultra-Reliable Low Latency Communications (URLLC) in 5G Radio Access Network (RAN). Complementary to the existing Physical (PHY) layer techniques, this paper focuses primarily on higher layer solutions, particularly, on Packet Duplication (PD) as a practical and low complexity technique for URLLC. The theoretic framework behind PD is investigated and the recent enhancements made in the 5G Dual Connectivity (DC) architecture for supporting PD are discussed. For improving the radio resource utilization and to dynamically control the activation of PD, an optimization problem subject to URLLC constraints is formulated and solved heuristically to give the resource configuration in terms of MCS and PRB allocation over multiple links. Following this, it is shown numerically that performing PD in various deployment scenarios results in better utilization of radio resources compared to using a single highly reliable link while effectively satisfying the URLLC requirements.


title: A kind of bicycle robot dynamic modeling and nonlinear control

abstract: The bicycle robot is a natural unstable nonlinear system. Its inherent nonlinear and deeply coupling characters of bicycle robot make the problem of control it much more difficult. Taking the steering torque and the driving torque of rear wheel as system inputs, the goal of bicycle robot self balancing was achieved. The rolling angle and the steering angle were taken as system generalized coordinates. A kind of two-input and two-output nonlinear dynamic model of the bicycle robot was presented based on Lagrange method. A multi-input and multi-output affine nonlinear system was presented to describe the dynamic model. And the input-output exactitude linearization theory was applied to analyze the affine nonlinear system. Then a control algorithm was proposed to achieve the goal of decoupling and accurate linearization. Computer simulation verified the validity of the dynamic model. The effect of the controller is testified by simulation experiments.


title: Analysis of positioning uncertainty in simultaneous localization and mapping (SLAM)

abstract: This paper studies the time evolution of the covariance of the position estimates in single-robot simultaneous localization and mapping (SLAM). A closed-form expression is derived, that establishes a functional relation between the noise parameters of the robot's proprioceptive and exteroceptive sensors, the number of features being mapped, and the attainable accuracy of SLAM. Furthermore, it is demonstrated how prior information about the spatial density of landmarks can be utilized in order to compute a tight upper bound on the expected covariance of the positioning errors. The derived closed-form expressions enable the prediction of SLAM positioning performance, without resorting to extensive simulations, and thus offer an analytical tool for determining the sensor characteristics required to achieve a desired degree of accuracy. Simulation experiments are conducted, that corroborate the presented theoretical analysis.


title: The hydrodynamics of water-walking arthropods

abstract: We present the results of a combined experimental and theoretical investigation of the dynamics of water-walking insects and spiders. Using high-speed videography, we describe their numerous gaits, some analogous to those of their terrestrial counterparts, others specialized for life at the interface. The critical role of the rough surface of these water walkers in both floatation and propulsion is demonstrated. Their waxy, hairy surface ensures that their legs remain in a water-repellent state, that the bulk of their leg is not wetted, but rather contact with the water arises exclusively through individual hairs. Maintaining this water-repellent state requires that the speed of their driving legs does not exceed a critical wetting speed. Flow visualization reveals that the wakes of most water walkers are characterized by a series of coherent subsurface vortices shed by the driving stroke. A theoretical framework is developed in order to describe the propulsion in terms of the transfer of forces and momentum between the creature and its environment. The application of the conservation of momentum to biolocomotion at the interface confirms that the propulsion of water walkers may be rationalized in terms of the subsurface flows generated by their driving stroke. The two principal modes of propulsion available to small water walkers are elucidated. At driving leg speeds in excess of the capillary wave speed, macroscopic curvature forces are generated by deforming the meniscus, and the surface behaves effectively as a trampoline. For slower speeds, the driving legs need not substantially deform the surface but may instead simply brush it: the resulting contact or viscous forces acting on the leg hairs crossing the interface serve to propel the creature forward.


title: An empirical study of algorithms for point-feature label placement

abstract: A major factor affecting the clarity of graphical displays that include text labels is the degree to which labels obscure display features (including other labels) as a result of spatial overlap. Point-feature label placement (PFLP) is the problem of placing text labels adjacent to point features on a map or diagram so as to maximize legibility. This problem occurs frequently in the production of many types of informational graphics, though it arises most often in automated cartography. In this paper we present a comprehensive treatment of the PFLP problem, viewed as a type of combinatorial optimization problem. Complexity analysis reveals that the basic PFLP problem and most interesting variants of it are NP-hard. These negative results help inform a survey of previously reported algorithms for PFLP; not surprisingly, all such algorithms either have exponential time complexity or are incomplete. To solve the PFLP problem in practice, then, we must rely on good heuristic methods. We propose two new methods, one based on a discrete form of gradient descent, the other on simulated annealing, and report on a series of empirical tests comparing these and the other known algorithms for the problem. Based on this study, the first to be conducted, we identify the best approaches as a function of available computation time.


title: Millimeter-Wave TE20-Mode SIW Dual-Slot-Fed Patch Antenna Array With a Compact Differential Feeding Network

abstract: A millimeter-wave series–parallel patch antenna array is presented, in which the dual-slot feeding structure is handily implemented using the intrinsic field distribution of TE20 mode in substrate-integrated waveguide (SIW). One 28 GHz patch antenna element fed by the TE20-mode SIW is first designed, achieving a 10 dB impedance bandwidth of 10.2% and a simulated peak gain of 6.48 dBi. Based on the antenna element, a $4 \times 4$ array with a compact series–parallel differential feeding network is developed accordingly. Due to the novel compact SIW-based series–parallel feeding network, the antenna array can achieve superior radiation performances, which is the highlight of this communication. The simulation and measurement results of the proposed antenna array are in good agreement, demonstrating a performance of 8.5% impedance bandwidth, 19.1 dBi peak gain, symmetrical radiation patterns, and low cross-polarization levels (−30 dB in E-plane and −25 dB in H-plane) in the operating frequency band of 26.65–29.14 GHz.


title: Document Summarization for Answering Non-Factoid Queries

abstract: We formulate a document summarization method to extract passage-level answers for non-factoid queries, referred to as answer-biased summaries . We propose to use external information from related Community Question Answering (CQA) content to better identify answer bearing sentences. Three optimization-based methods are proposed: (i) query-biased, (ii) CQA-answer-biased, and (iii) expanded-query-biased, where expansion terms were derived from related CQA content. A learning-to-rank-based method is also proposed that incorporates a feature extracted from related CQA content. Our results show that even if a CQA answer does not contain a perfect answer to a query, their content can be exploited to improve the extraction of answer-biased summaries from other corpora. The quality of CQA content is found to impact on the accuracy of optimization-based summaries, though medium quality answers enable the system to achieve a comparable (and in some cases superior) accuracy to state-of-the-art techniques. The learning-to-rank-based summaries, on the other hand, are not significantly influenced by CQA quality. We provide a recommendation of the best use of our proposed approaches in regard to the availability of different quality levels of related CQA content. As a further investigation, the reliability of our approaches was tested on another publicly available dataset.


title: Recursive Neural Network Rule Extraction for Data With Mixed Attributes

abstract: In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods.


title: NUS-WIDE: a real-world web image database from National University of Singapore

abstract: This paper introduces a web image dataset created by NUS's Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,018 unique tags; (2) six types of low-level features extracted from these images, including 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments extracted over 5x5 fixed grid partitions, and 500-D bag of words based on SIFT descriptions; and (3) ground-truth for 81 concepts that can be used for evaluation. Based on this dataset, we highlight characteristics of Web image collections and identify four research issues on web image annotation and retrieval. We also provide the baseline results for web image annotation by learning from the tags using the traditional k-NN algorithm. The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval.


title: Learning DTW Global Constraint for Time Series Classification

abstract: Neighbor with the Dynamic Time Warping (DTW) distance is one of the most effective classifiers on time series domain. Since the global constraint has been introduced in speech community, many global constraint models have been proposed including Sakoe-Chiba (S-C) band, Itakura Paral- lelogram, and Ratanamahatana-Keogh (R-K) band. The R-K band is a general global constraint model that can rep- resent any global constraints with arbitrary shape and size effectively. However, we need a good learning algorithm to discover the most suitable set of R-K bands, and the current R-K band learning algorithm still suffers from an 'overfit- ting' phenomenon. In this paper, we propose two new learn- ing algorithms, i.e., band boundary extraction algorithm and iterative learning algorithm. The band boundary extraction is calculated from the bound of all possible warping paths in each class, and the iterative learning is adjusted from the original R-K band learning. We also use a Silhouette index, a well-known clustering validation technique, as a heuris- tic function, and the lower bound function, LB Keogh, to enhance the prediction speed. Twenty datasets, from the Workshop and Challenge on Time Series Classification, held in conjunction of the SIGKDD 2007, are used to evaluate our approach.


title: Catch you if you lie to me: Efficient verifiable conjunctive keyword search over large dynamic encrypted cloud data

abstract: Encrypted data search allows cloud to offer fundamental information retrieval service to its users in a privacy-preserving way. In most existing schemes, search result is returned by a semi-trusted server and usually considered authentic. However, in practice, the server may malfunction or even be malicious itself. Therefore, users need a result verification mechanism to detect the potential misbehavior in this computation outsourcing model and rebuild their confidence in the whole search process. On the other hand, cloud typically hosts large outsourced data of users in its storage. The verification cost should be efficient enough for practical use, i.e., it only depends on the corresponding search operation, regardless of the file collection size. In this paper, we are among the first to investigate the efficient search result verification problem and propose an encrypted data search scheme that enables users to conduct secure conjunctive keyword search, update the outsourced file collection and verify the authenticity of the search result efficiently. The proposed verification mechanism is efficient and flexible, which can be either delegated to a public trusted authority (TA) or be executed privately by data users. We formally prove the universally composable (UC) security of our scheme. Experimental result shows its practical efficiency even with a large dataset.


title: Mortality and Cause of Death after Hospital Discharge in 10,981 Patients with Ischemic Stroke and Transient Ischemic Attack

abstract: BACKGROUND ::: The aim of this study was to examine the 1-year cumulative mortality rate and cause of death, and to identify the predictive factors for death after hospital discharge following ischemic stroke and transient ischemic attack (TIA) using data from the Japan Multicenter Stroke Investigators' Collaboration study. ::: ::: ::: METHODS ::: We prospectively registered 16,922 consecutive patients with acute ischemic stroke or TIA from May 1999 to April 2000 in 156 Japanese hospitals. We mailed a questionnaire to the 15,322 patients who were alive at hospital discharge. ::: ::: ::: RESULTS ::: 10,981 patients (6,945 men, 4,036 women, age 70 +/- 11 years, median 71, range 19-100 years) were enrolled in the follow-up study. The mean follow-up period was 271 +/- 110 days (median 272 days; range 1-487 days). The 1-year cumulative mortality was 6.8% (7.0% for 10,234 stroke patients and 3.5% for 747 TIA patients). The causes of death were: cerebrovascular disease, 24.1%; pneumonia, 22.6%; heart disease, 18.1%; cancer, 11.0%, and miscellaneous causes, 24.1%. Multivariate analysis suggested that male gender, age, diabetes mellitus, atrial fibrillation, history of stroke, nonlacunar stroke, functional disability and transfer to another hospital or nursing home on discharge were significant independent predictors of death during the follow-up period. ::: ::: ::: CONCLUSIONS ::: The major causes of death after hospital discharge were found to be cerebrovascular diseases, pneumonia and heart diseases. Thus, in order to improve survival after hospital discharge, in addition to appropriate management of vascular risk factors following stroke, it appears to be important to take measures to prevent pneumonia and to discharge patients to their own home, if possible.


title: Learning a Concept-Based Document Similarity Measure

abstract: Document similarity measures are crucial components of many text-analysis tasks, including information retrieval, document classification, and document clustering. Conventional measures are brittle: They estimate the surface overlap between documents based on the words they mention and ignore deeper semantic connections. We propose a new measure that assesses similarity at both the lexical and semantic levels, and learns from human judgments how to combine them by using machine-learning techniques. Experiments show that the new measure produces values for documents that are more consistent with people's judgments than people are with each other. We also use it to classify and cluster large document sets covering different genres and topics, and find that it improves both classification and clustering performance. © 2012 Wiley Periodicals, Inc.


title: A numerical evaluation of sparse direct solvers for the solution of large sparse symmetric linear systems of equations

abstract: In recent years a number of solvers for the direct solution of large sparse symmetric linear systems of equations have been developed. These include solvers that are designed for the solution of positive definite systems as well as those that are principally intended for solving indefinite problems. In this study, we use performance profiles as a tool for evaluating and comparing the performance of serial sparse direct solvers on an extensive set of symmetric test problems taken from a range of practical applications.


title: Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy

abstract: Near-infrared spectroscopy (NIRS) and diffuse optical imaging (DOI) are finding widespread application in the study of human brain activation, motivating further application-specific development of the technology. NIRS and DOI offer the potential to quantify changes in deoxyhemoglobin (HbR) and total hemoglobin (HbT) concentration, thus enabling distinction of oxygen consumption and blood flow changes during brain activation. While the techniques implemented presently provide important results for cognition and the neurosciences through their relative measures of HbR and HbT concentrations, there is much to be done to improve sensitivity, accuracy, and resolution. In this paper, we review the advances currently being made and issues to consider for improving optical image quality. These include the optimal selection of wavelengths to minimize random and systematic error propagation in the calculation of the hemoglobin concentrations, the filtering of systemic physiological signal clutter to improve sensitivity to the hemodynamic response to brain activation, the implementation of overlapping measurements to improve image spatial resolution and uniformity, and the utilization of spatial prior information from structural and functional MRI to reduce DOI partial volume error and improve image quantitative accuracy.


title: How to gamify software engineering

abstract: Software development, like any prolonged and intellectually demanding activity, can negatively affect the motivation of developers. This is especially true in specific areas of software engineering, such as requirements engineering, test-driven development, bug reporting and fixing, where the creative aspects of programming fall short. The developers' engagement might progressively degrade, potentially impacting their work's quality.


title: Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation

abstract: Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12-15 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks.


title: STL Tutorial and Reference Guide: C++ Programming with the Standard Template Library

abstract: "The second edition is clearer and adds more examples on how to use STL in a practical environment. Moreover, it is more concerned with performance and tools for its measurement. Both changes are very welcome."--Lawrence Rauchwerger, Texas A&M University "So many algorithms, so little time! The generic algorithms chapter with so many more examples than in the previous edition is delightful! The examples work cumulatively to give a sense of comfortable competence with the algorithms, containers, and iterators used."--Max A. Lebow, Software Engineer, Unisys Corporation The STL Tutorial and Reference Guide is highly acclaimed as the most accessible, comprehensive, and practical introduction to the Standard Template Library (STL). Encompassing a set of C++ generic data structures and algorithms, STL provides reusable, interchangeable components adaptable to many different uses without sacrificing efficiency. Written by authors who have been instrumental in the creation and practical application of STL, STL Tutorial and Reference Guide, Second Edition includes a tutorial, a thorough description of each element of the library, numerous sample applications, and a comprehensive reference. You will find in-depth explanations of iterators, generic algorithms, containers, function objects, and much more. Several larger, non-trivial applications demonstrate how to put STL's power and flexibility to work. This book will also show you how to integrate STL with object-oriented programming techniques. In addition, the comprehensive and detailed STL reference guide will be a constant and convenient companion as you learn to work with the library. This second edition is fully updated to reflect all of the changes made to STL for the final ANSI/ISO C++ language standard. It has been expanded with new chapters and appendices. Many new code examples throughout the book illustrate individual concepts and techniques, while larger sample programs demonstrate the use of the STL in real-world C++ software development. An accompanying Web site, including source code and examples referenced in the text, can be found at http://www.cs.rpi.edu/~musser/stl-book/index.html.


title: Android malware detection method based on naive Bayes and permission correlation algorithm

abstract: In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ ::: and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.


title: Evaluation of the Efficacy of Saccharomyces cerevisiae Cell Wall to Ameliorate the Toxic Effects of Aflatoxin in Broilers

abstract: A study was carried out to evaluate the efficacy of Saccharomyces cerevisiae (SC) as an inhibitor of aflatoxicosis in broilers. Clinic, pathologic, immune and productive aspects of broilers fed with aflatoxin in the diets were studied. The results showed that the feed intake and weight gain were negatively affected (P < 0.01) for aflatoxin in diet (1000 ppb). The SC (0, 20%) added to diet did not ameliorated the negatively aflatoxin effect at broiler weight gain, however the SC improve the feed conversion in this birds. The humoral immune response of broilers vaccinated against Newcastle virus disease (NDV) was reduced for aflatoxin in diet, as also the liver, kidneys and bursa of Fabricius were negatively affected for this toxin at diet. The SC did not improve the hemagglutination inhibiting antibody titers in birds fed with aflatoxin in diet, however improve the immune response of broilers at challenge with strain velogenic of NDV.


title: Neutral-point-clamped circuits of single-phase PV inverters: Generalized principle and implementation

abstract: The common-mode leakage current should be carefully considered when designing a transformer-less photovoltaic (PV) inverter since the leakage current can cause the output current distortion and increase the operational risk. The unipolar SPWM of traditional H-bridge inverter can produce superior output performance but will cause a high-frequency common-mode voltage and consequently the non-negligible leakage current. To attenuate the fluctuation of common-mode voltage, few neutral-point-clamped circuits have been designed to clamp the neutral point voltage and maintain the common mode voltage constant. This paper reviews the already published single-phase inverters with constant common mode voltage in theory and proposes a generalized design principle of multi-terminal neutral-point-clamped circuits, whose unidirectional and bidirectional variations are fully analyzed respectively. Subsequently, two families of new single-phase PV inverters with neutral-point-clamped circuits are proposed. Also, the efficiency, cost and control complexity are compared between the proposed topologies and the traditional NPC inverters to prove the superiority of the proposed topologies. The simulation and experimental results verified the theoretical findings.


title: Multi-microphone approach for reliable acoustic data transmission

abstract: Acoustic data transmission (ADT) is a branch of the audio data hiding techniques enabling data communication in short-range aerial space between a loudspeaker and a microphone. In this paper, we extend ADT method to a multichannel approach utilizing multiple microphones in order to enhance transmission reliability while ensuring compatibility with the previous single-microphone receiver. In the proposed multichannel method, the data coefficients obtained from each channel are linearly combined with weighting factors determined based on the Wiener-type channel estimation. From the experimental results, the proposed multichannel method has been shown to be effective in reducing bit errors in reverberant environments.


title: Characterizations of Noise in Kinect Depth Images: A Review

abstract: In this paper, we characterize the noise in Kinect depth images based on multiple factors and introduce a uniform nomenclature for the types of noise. In the process, we briefly survey the noise models of Kinect and relate these to the factors of characterization. We also deal with the noise in multi-Kinect set-ups and summarize the techniques for the minimization of interference noise. Studies on noise in Kinect depth images are distributed over several publications and there is no comprehensive treatise on it. This paper, to the best of our knowledge, is the maiden attempt to characterize the noise behavior of Kinect depth images in a structured manner. The characterization would help to selectively eliminate noise from depth images either by filtering or by adopting appropriate methodologies for image capture. In addition to the characterization based on the results reported by others, we also conduct independent experiments in a number of cases to fill up the gaps in characterization and to validate the reported results.


title: A versatile wavelet domain noise filtration technique for medical imaging

abstract: We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.


title: Quality questions need quality code: classifying code fragments on stack overflow

abstract: Stack Overflow (SO) is a question and answers (Q&A) web platform on software development that is gaining in popularity. With increasing popularity often comes a very unwelcome side effect: A decrease in the average quality of a post. To keep Q&A websites like SO useful it is vital that this side effect is countered. Previous research proved to be reasonably successful in using properties of questions to help identify low quality questions to be later reviewed and improved. We present an approach to improve the classification of high and low quality questions based on a novel source of information: the analysis of the code fragments in SO questions. We show that we get similar performance to classification based on a wider set of metrics thus potentially reaching a better overall classification.


title: Cross-platform machine learning characterization for task allocation in IoT ecosystems

abstract: With the emergence of the Internet of Things (IoT) and Big Data era, many applications are expected to assimilate a large amount of data collected from environment to extract useful information. However, how heterogeneous computing devices of IoT ecosystems can execute the data processing procedures has not been clearly explored. In this paper, we propose a framework which characterizes energy and performance requirements of the data processing applications across heterogeneous devices, from a server in the cloud and a resource-constrained gateway at edge. We focus on diverse machine learning algorithms which are key procedures for handling the large amount of IoT data. We build analytic models which automatically identify the relationship between requirements and data in a statistical way. The proposed framework also considers network communication cost and increasing processing demand. We evaluate the proposed framework on two heterogenous devices, a Raspberry Pi and a commercial Intel server. We show that the identified models can accurately estimate performance and energy requirements with less than error of 4.8% for both platforms. Based on the models, we also evaluate whether the resource-constrained gateway can process the data more efficiently than the server in the cloud. The results present that the less-powerful device can achieve better energy and performance efficiency for more than 50% of machine learning algorithms.


title: Bearingless 300-W PMSM for Bioreactor Mixing

abstract: This paper presents a novel exterior rotor topology of a bearingless brushless synchronous motor with rated power of 300 W. Owing to the large possible magnetic gap and the absence of mechanical bearings, this motor is especially qualified for high-purity and low-shear applications (e.g., bioreactor mixing). Both torque and magnetic bearing forces are created inside this disk-shaped motor using a sophisticated control (proportional-integral-differential vector control) with superimposed drive and bearing currents fed to the concentrated combined stator coils. Optimal design is derived based on an electromagnetic analysis using the three-dimensional finite element method (3D-FEM), and the simulation results are verified with a prototype mixer setup.


title: Automated multimode system design for high performance DSP applications

abstract: In a mobile society, more and more devices need to continuously adapt to changing environments. Such mode switches can be smoothly done in software using a general purpose or digital signal processor. However hardware components are required to cope with throughput and power constraints. Reconfigurable hardware technologies like FPGA offer partial reconfiguration at run-time but require too long reconfiguration times to rapidly changing applications. In this paper we propose an automated design flow to implement multiple configuration and multi-constraint systems into a single circuit using conventional hardware technologies.


title: MANET simulation studies: the incredibles

abstract: Simulation is the research tool of choice for a majority of the mobile ad hoc network (MANET) community. However, while the use of simulation has increased, the credibility of the simulation results has decreased. To determine the state of MANET simulation studies, we surveyed the 2000-2005 proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). From our survey, we found significant shortfalls. We present the results of our survey in this paper. We then summarize common simulation study pitfalls found in our survey. Finally, we discuss the tools available that aid the development of rigorous simulation studies. We offer these results to the community with the hope of improving the credibility of MANET simulation-based studies.


title: A gene‐phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach

abstract: Motivation The fundamental challenge of modern genetic analysis is to establish gene‐phenotype correlations that are often found in the large‐scale publications. Because lexical features of gene are relatively regular in text, the main challenge of these relation extraction is phenotype recognition. Due to phenotypic descriptions are often study‐ or author‐specific, few lexicon can be used to effectively identify the entire phenotypic expressions in text, especially for plants. Results We have proposed a pipeline for extracting phenotype, gene and their relations from biomedical literature. Combined with abbreviation revision and sentence template extraction, we improved the unsupervised word‐embedding‐to‐sentence‐embedding cascaded approach as representation learning to recognize the various broad phenotypic information in literature. In addition, the dictionary‐ and rule‐based method was applied for gene recognition. Finally, we integrated one of famous information extraction system OLLIE to identify gene‐phenotype relations. To demonstrate the applicability of the pipeline, we established two types of comparison experiment using model organism Arabidopsis thaliana. In the comparison of state‐of‐the‐art baselines, our approach obtained the best performance (F1‐Measure of 66.83%). We also applied the pipeline to 481 full‐articles from TAIR gene‐phenotype manual relationship dataset to prove the validity. The results showed that our proposed pipeline can cover 70.94% of the original dataset and add 373 new relations to expand it. Availability and implementation The source code is available at http://www.wutbiolab.cn:82/Gene‐Phenotype‐Relation‐Extraction‐Pipeline.zip.


title: Synthetic magnetic soils for landmine detector testing

abstract: This study aims to move towards a reliable method for synthesizing artificial soils that emulate the effect of real soils on pulse-induction metal detectors. The signals resulting from some mineralized soils can greatly impair the effectiveness of mine detectors. We analysed a sample of one such soil from Cambodia, using resistivity measurement, X-ray diffraction, magnetic susceptibility measurement and electron microscopy. The physical origin of the soil signal was found to be high proportions of small grained magnetite. By mixing a U.K. topsoil with finely crushed magnetite, a synthetic soil with similar magnetic susceptibility to the real Cambodian soil was created. Other problematic soils from around the world were also synthesized. Comparative tests with a pulse induction metal detector showed decay characteristics for the artificial soils to be within 13% of those for the real soil. We achieved close susceptibility matching between artificial and real soils, either for a 2kHz measurement or a pulse-induction measurement. In our experiments it was not possible to match with both measurements at once. We propose that this discrepancy is due to differences in the average properties of magnetic grains between those in real soil and the magnetite concentrate used for the synthesis tests.


title: Multilevel intelligent universal transformer for medium voltage applications

abstract: The solid-state transformer allows add-on intelligence to enhance power quality compatibility between source and load. It is desired to demonstrate the benefits gained by the use of such a device. Recent advancement in semiconductor devices and converter topologies facilitated a newly proposed intelligent universal transformer (IUT), which can isolate a disturbance from either source or load. This paper describes the basic circuit and the operating principle for the multilevel converter based IUT and its applications for medium voltages. Various power quality enhancement features are demonstrated with computer simulation for a complete IUT circuit.


title: A circularly polarized short backfire antenna excited by an unbalance-fed cross aperture

abstract: An unbalance-fed cross aperture is developed to excite a short backfire antenna (SBA) for circular polarization. The cross aperture consists of two orthogonal H-shaped slots with a pair of capacitive stubs and is fed by a single probe that forms an unbalanced feed with a shorting pin. It is demonstrated that the cross-aperture-excited SBA can achieve an axial ratio (les 3 dB) bandwidth of 4.2% with a voltage standing wave ratio (VSWR) bandwidth of 6.5% (VSWR<1.2) and a gain of 14 dBi. The antenna structure is described and the simulation and experimental results are presented. The mechanisms for impedance matching and circular-polarization production are analyzed


title: A 10-MHz GaNFET-Based-Isolated High Step-Down DC–DC Converter: Design and Magnetics Investigation

abstract: This paper presents design of an isolated high-step-down dc–dc converter based on a class-DE power stage, operating at a 10-MHz switching frequency using enhancement-mode gallium nitride transistors. The converter operating principles are discussed, and the power stage design rated for 20 W is presented for a step down from 200–300 V input to 0–28 V output. Commercially available magnetic materials were explored and the high-frequency resonant inductor and transformer designs using a low-loss Fair-Rite type 67 material are presented. Finite-element simulations have been performed to estimate the parameters of magnetics at 10-MHz. Experimental results are presented at 12 W, 254 V input to 22 V output and 5 W, 254 V input to 14 V output on a laboratory prototype operating at 10-MHz. At 20 W, the experimental prototype achieved an efficiency of 85.2%.


title: Is the glass half empty or half full? A prospective study of optimism and coronary heart disease in the normative aging study.

abstract: ObjectiveA sense of optimism, which derives from the ways individuals explain causes of daily events, has been shown to protect health, whereas pessimism has been linked to poor physical health. We examined prospectively the relationship of an optimistic or pessimistic explanatory style with coronar


Serum nitric oxide levels in "red" patients: separating corticosteroid-addicted patients from those with chronic eczema.


title: Weight-based victimization toward overweight adolescents: observations and reactions of peers.

abstract: BACKGROUND: Weight-based victimization has become increasingly reported among overweight youth, but little is known about adolescents’ perceptions and observations of weight-based teasing and bullying. This study examined adolescents’ observations of and reactions to weight-based victimization toward overweight students at school. METHODS: Adolescents (N = 1555) at 2 high schools in central Connecticut completed a questionnaire that examined their perceptions of how common weight-based victimization is compared to other forms of teasing at school, what types of weight-based teasing are frequently observed, who typical perpetrators of weight-based victimization are, and their own reactions to observed teasing incidents. Participants also completed the Fat Phobia Scale. RESULTS: Participants perceived being overweight as a primary reason that peers are victimized at school. At least 84% of participants observed overweight students being teased in a mean way and teased during physical activities, and 65% to 77% of students observed overweight and obese peers being ignored, avoided, excluded from social activities, having negative rumors spread about them, and being teased in the cafeteria. Most students also observed verbal threats and physical harassment toward overweight and obese students. Although the majority of participants felt comfortable stepping in to help an overweight peer who has been teased, many remain passive bystanders following these incidents. CONCLUSION: Youth perceive frequent and multiple forms of weight-based victimization. Schools’ efforts to address weight bias and assist overweight and obese students are important.


title: Hierarchical Memory Networks

abstract: Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is often addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which require the network to read from extremely large memories. On the other hand, it is well known that hard attention mechanisms based on reinforcement learning are challenging to train successfully. In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks. The memory is organized in a hierarchical structure such that reading from it is done with less computation than soft attention over a flat memory, while also being easier to train than hard attention over a flat memory. Specifically, we propose to incorporate Maximum Inner Product Search (MIPS) in the training and inference procedures for our hierarchical memory network. We explore the use of various state-of-the art approximate MIPS techniques and report results on SimpleQuestions, a challenging large scale factoid question answering task.


Recurrent Neural Networks


title: An electromyography-driven central pattern generator model for robotic control application

abstract: Central pattern generator (CPG) models have been designed at abstract levels of the rhythm phenomena and widely applied in robot control. The robot controlled with a CPG model is hard to perceive and respond to the motion intention coming from human beings. A new CPG model driven by surface electromyography (sEMG) was presented in this paper to manipulate robots more favorably without loss of their autonomous capability. The CPG model was designed based on an echo state network (ESN) which was a large, random, recurrent neural network. The frequency modulating from inputs to outputs was researched in this study. It was illustrated that ESNs could learn and generalize the frequency transition pattern. The flexion and extension motion of forearms and the sEMG at biceps and triceps muscles were sampled as the teacher signals to train the CPG model. The prediction error of the trained model was analyzed carefully and the model output was applied to control a rehabilitation exoskeleton. Finally, the future work was discussed on the model structure optimizing.


title: Review of Virtual Reality Treatment in Psychiatry: Evidence Versus Current Diffusion and Use

abstract: Purpose of Review ::: This review provides an overview of the current evidence base for and clinical applications of the use of virtual reality (VR) in psychiatric practice, in context of recent technological developments.


title: Password-Based Authentication: Preventing Dictionary Attacks

abstract: Password-based authentication is susceptible to attack if used on insecure communication channels like the Internet. Researchers have engineered several protocols to prevent attacks, but we still need formal models to analyze and aid in the effective design of acceptable password protocols geared to prevent dictionary attacks.


title: Modeling of split-phase machines in Park's coordinates. Part II: Equivalent circuit representation

abstract: A split-phase machine is a special electric machine whose stator winding is split into multiple (N) three-phase sets. The possibility to supply it through N independent inverters makes it attractive especially when high power and reliability are required. So far, the dq0 refeence frame representation, originally introduced for three-phase machines, has been applied in detail to split-phase configurations in the N=2 case only. In this paper, the extension to an arbitrary number of stator sets is investigated from an equivalent circuit representation viewpoint. The equivalent circuit topologies proposed in this paper to represent a split-phase machine with N three-phase sets is directly derived from the theory presented in Part I. In particular, it will be stressed that as long as N is equal to 1 or 2, dq equivalent circuits remain decoupled as known from the literature, otherwise for N higher than or equal to 3, dq cross couplings must be taken into account in the equivalent circuit representation.


SignSpeak – Understanding, Recognition, and Translation of Sign Languages