CompNet: Neural networks growing via the compact network morphism

It is often the case that the performance of a neural network can be improved by adding layers. In real-world practices, we always train dozens of neural network architectures in parallel which is a wasteful process. We explored CompNet, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact. The work of the paper makes two contributions: a). The modified network can converge fast and keep the same functionality so that we do not need to train from scratch again; b). The layer size of the added layer in the neural network is controlled by removing the redundant parameters with sparse optimization. This differs from previous network morphism approaches which tend to add more neurons or channels beyond the actual requirements and result in redundance of the model. The method is illustrated using several neural network structures on different data sets including MNIST and CIFAR10.


Driver Drowsiness Detection System

Sleepiness or fatigue in drivers driving for long hours is the major cause of accidents on highways worldwide. The International statistics shows that a large number of road accidents are caused by driver fatigue. Therefore, a system that can detect oncoming driver fatigue and issue timely warning could help in preventing many accidents, and consequently save money and reduce personal suffering. The authors have made an attempt to design a system that uses video camera that points directly towards the driver‟s face in order to detect fatigue. If the fatigue is detected a warning signal is issued to alert the driver. The authors have worked on the video files recorded by the camera. Video file is converted into frames.Once the eyes are located from each frame, by determining the energy value of each frame one can determine whether the eyes are open or close. A particular condition is set for the energy values of open and close eyes. If the average of the energy value for 5 consecutive frames falls in a given condition then the driver will be detected as drowsy and issues a warning signal. The algorithm is proposed, implemented, tested, and found working satisfactorily.


Enterprise resource planning: A taxonomy of critical factors


Computable Elastic Distances Between Shapes

We define distances between geometric curves by the square root of the minimal energy required to transform one curve into the other. The energy is formally defined from a left invariant Riemannian distance on an infinite dimensional group acting on the curves, which can be explicitly computed. The obtained distance boils down to a variational problem for which an optimal matching between the curves has to be computed. An analysis of the distance when the curves are polygonal leads to a numerical procedure for the solution of the variational problem, which can efficiently be implemented, as illustrated by experiments.


Supervised Locally Linear Embedding

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common framework and applied to a number of benchmark data sets. Results show that they perform very well on high-dimensional data which exhibits a manifold structure.


Differentially Private Histogram Publication for Dynamic Datasets: an Adaptive Sampling Approach

Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on "one-time" release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods.


LBP based recursive averaging for babble noise reduction applied to automatic speech recognition

Improved automatic speech recognition (ASR) in babble noise conditions continues to pose major challenges. In this paper, we propose a new local binary pattern (LBP) based speech presence indicator (SPI) to distinguish speech and non-speech components. Babble noise is subsequently estimated using recursive averaging. In the speech enhancement system optimally-modified log-spectral amplitude (OMLSA) uses the estimated noise spectrum obtained from the LBP based recursive averaging (LRA). The performance of the LRA speech enhancement system is compared to the conventional improved minima controlled recursive averaging (IMCRA). Segmental SNR improvements and perceptual evaluations of speech quality (PESQ) scores show that LRA offers superior babble noise reduction compared to the IMCRA system. Hidden Markov model (HMM) based word recognition results show a corresponding improvement.


Using flipped classroom approach to teach computer programming

Flipped classroom approach has been increasingly adopted in higher institutions. Although this approach has many advantages, there are also many challenges that should be considered. In this paper, we discuss the suitability of this approach to teach computer programming, and we report on our pilot experience of using this approach at Qatar University to teach one subject of computer programming course. It is found that students has positive attitude to this approach, it improves their learning. However, the main challenge was how to involve some of the students in online learning activities.


Towards a New Structural Model of the Sense of Humor: Preliminary Findings

In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factormodel of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.


Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.


Design of spoof surface plasmon polariton based terahertz delay lines

We present the analysis and design of fixed physical length, spoof Surface Plasmon Polariton based waveguides with adjustable delay at terahertz frequencies. The adjustable delay is obtained using Corrugated Planar Goubau Lines (CPGL) by changing its corrugation depth without changing the total physical length of the waveguide. Our simulation results show that electrical lengths of 237.9°, 220.6°, and 310.6° can be achieved by physical lengths of 250 μm and 200 μm at 0.25, 0.275, and 0.3 THz, respectively, for demonstration purposes. These simulations results are also consistent with our analytical calculations using the physical parameter and material properties. When we combine pairs of same length delay lines as if they are two branches of a terahertz phase shifter, we achieved an error rate of relative phase shift estimation better than 5.8%. To the best of our knowledge, this is the first-time demonstration of adjustable spoof Surface Plasmon Polariton based CPGL delay lines. The idea can be used for obtaining tunable delay lines with fixed lengths and phase shifters for the terahertz band circuitry.


Infrared Colorization Using Deep Convolutional Neural Networks

This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks. A direct and integrated transfer between NIR and RGB pixels is trained. The trained model does not require any user guidance or a reference image database in the recall phase to produce images with a natural appearance. To preserve the rich details of the NIR image, its high frequency features are transferred to the estimated RGB image. The presented approach is trained and evaluated on a real-world dataset containing a large amount of road scene images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which ensures a perfect pixel to pixel registration.


Colorful Image Colorization

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a “colorization Turing test,” asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32% of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.


Wasserstein GAN

The problem this paper is concerned with is that of unsupervised learning. Mainly, what does it mean to learn a probability distribution? The classical answer to this is to learn a probability density. This is often done by defining a parametric family of densities (Pθ)θ∈Rd and finding the one that maximized the likelihood on our data: if we have real data examples {x}i=1, we would solve the problem


Generative Adversarial Text to Image Synthesis

Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. We demonstrate the capability of our model to generate plausible images of birds and flowers from detailed text descriptions.


U-Net: Convolutional Networks for Biomedical Image Segmentation


Active learning for on-road vehicle detection: a comparative study

In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG–SVM), and Haar-like features and Adaboost classification (Haar–Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.


HyFlex nickel-titanium rotary instruments after clinical use: metallurgical properties.

AIM
To analyse the type and location of defects in HyFlex CM instruments after clinical use in a graduate endodontic programme and to examine the impact of clinical use on their metallurgical properties.

METHODOLOGY
A total of 468 HyFlex CM instruments discarded from a graduate endodontic programme were collected after use in three teeth. The incidence and type of instrument defects were analysed. The lateral surfaces of the defect instruments were examined by scanning electron microscopy. New and clinically used instruments were examined by differential scanning calorimetry (DSC) and x-ray diffraction (XRD). Vickers hardness was measured with a 200-g load near the flutes for new and clinically used axially sectioned instruments. Data were analysed using one-way anova or Tukey's multiple comparison test.

RESULTS
Of the 468 HyFlex instruments collected, no fractures were observed and 16 (3.4%) revealed deformation. Of all the unwound instruments, size 20, .04 taper unwound the most often (n = 5) followed by size 25, .08 taper (n = 4). The trend of DSC plots of new instruments and clinically used (with and without defects) instruments groups were very similar. The DSC analyses showed that HyFlex instruments had an austenite transformation completion or austenite-finish (Af ) temperature exceeding 37 °C. The Af temperatures of HyFlex instruments (with or without defects) after multiple clinical use were much lower than in new instruments (P < 0.05). The enthalpy values for the transformation from martensitic to austenitic on deformed instruments were smaller than in the new instruments at the tip region (P < 0.05). XRD results showed that NiTi instruments had austenite and martensite structure on both new and used HyFlex instruments at room temperature. No significant difference in microhardness was detected amongst new and used instruments (with and without defects).

CONCLUSIONS
The risk of HyFlex instruments fracture in the canal is very low when instruments are discarded after three cases of clinical use. New HyFlex instruments were a mixture of martensite and austenite structure at body temperature. Multiple clinical use caused significant changes in the microstructural properties of HyFlex instruments. Smaller instruments should be considered as single-use.


Integrated control of a multi-fingered hand and arm using proximity sensors on the fingertips

In this study, we propose integrated control of a robotic hand and arm using only proximity sensing from the fingertips. An integrated control scheme for the fingers and for the arm enables quick control of the position and posture of the arm by placing the fingertips adjacent to the surface of an object to be grasped. The arm control scheme enables adjustments based on errors in hand position and posture that would be impossible to achieve by finger motions alone, thus allowing the fingers to grasp an object in a laterally symmetric grasp. This can prevent grasp failures such as a finger pushing the object out of the hand or knocking the object over. Proposed control of the arm and hand allowed correction of position errors on the order of several centimeters. For example, an object on a workbench that is in an uncertain positional relation with the robot, with an inexpensive optical sensor such as a Kinect, which only provides coarse image data, would be sufficient for grasping an object.


Do Multi-Sense Embeddings Improve Natural Language Understanding?

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while ‘multi-sense’ methods have been proposed and tested on artificial wordsimilarity tasks, we don’t know if they improve real natural language understanding tasks. In this paper we introduce a multisense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language un-


Design and Simulation of Bridgeless PFC Boost Rectifiers

This work presents new three-level unidirectional single-phase PFC rectifier topologies well-suited for applications targeting high efficiency and/or high power density. The characteristics of a selected novel rectifier topology, including its principles of operation, modulation strategy, PID control scheme, and a power circuit design related analysis are presented. Finally, a 220-V/3-kW laboratory prototype is constructed and used in order to verify the characteristics of the new converter, which include remarkably low switching losses and single ac-side boost inductor, that allow for a 98.6% peak efficiency with a switching frequency of 140 kHz.


Computational models of trust and reputation: agents, evolutionary games, and social networks

Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to. Thesis Supervisor: Peter Szolovits Title: Professor of Electrical Engineering and Computer Science


Surface Management System Field Trial Results

NASA Ames Research Center, in cooperation with the FAA, has completed research and development of a proof-ofconcept Surface Management System (SMS). This paper reports on two recent SMS field tests as well as final performance and benefits analyses. Field tests and analysis support the conclusion that substantial portions of SMS technology are ready for transfer to the FAA and deployment throughout the National Airspace System (NAS). Other SMS capabilities were accepted in concept but require additional refinement for inclusion in subsequent development spirals. SMS is a decision support tool that helps operational specialists at Air Traffic Control (ATC) and NAS user facilities to collaboratively manage the movements of aircraft on the surface of busy airports, thereby improving capacity, efficiency, and flexibility. SMS provides accurate predictions of the future demand and how that demand will affect airport resources – information that is not currently available. The resulting shared awareness enables the Air Traffic Control Tower (ATCT), Terminal Radar Approach Control (TRACON), Air Route Traffic Control Center (ARTCC), and air carriers to coordinate traffic management decisions. Furthermore, SMS uses its ability to predict how future demand will play out on the surface to evaluate the effect of various traffic management decisions in advance of implementing them, to plan and advise surface operations. The SMS concept, displays, and algorithms were evaluated through a series of field tests at Memphis International Airport (MEM). An operational trial in September, 2003 evaluated SMS traffic management components, such as runway configuration change planning; shadow testing in January, 2004 tested tactical components (e.g., Approval Request (APREQ) coordination, sequencing for departure, and Expected Departure Clearance Time (EDCT) compliance). Participants in these evaluations rated the SMS concept and many of the traffic management displays very positively. Local and Ground controller displays will require integration with other automation systems. Feedback from FAA and NAS user participants support the conclusion that SMS algorithms currently provide information that has acceptable and beneficial accuracy for traffic management applications. Performance analysis results document the current accuracy of SMS algorithms. Benefits/cost analysis of delay cost reduction due to SMS provides the business case for SMS deployment.


Recognizing Malicious Intention in an Intrusion Detection Process

Generally, theintrudermustperformseveralactions,organizedin anintrusionscenario, to achieve hisor hermaliciousobjective.Wearguethatintrusionscenarioscan bemodelledasa planningprocessandwesuggestmodellinga maliciousobjectiveas anattemptto violatea givensecurityrequirement. Our proposalis thento extendthe definitionof attackcorrelationpresentedin [CM02] to correlateattackswith intrusion objectivesThis notionis usefulto decideif a sequenceof correlatedactionscanlead to a securityrequirementviolation.This approachprovidesthesecurityadministrator with aglobalview of whathappensin thesystem.In particular, it controlsunobserved actionsthroughhypothesisgeneration,clustersrepeatedactionsin a singlescenario, recognizesintrudersthatarechangingtheir intrusionobjectivesandis efficient to detectvariationsof anintrusionscenario.Thisapproachcanalsobeusedto eliminatea category of falsepositivesthatcorrespondto falseattacks,that is actionsthatarenot furthercorrelatedto anintrusionobjective.


Reconfigurable RF MEMS Phased Array Antenna Integrated Within a Liquid Crystal Polymer (LCP) System-on-Package

For the first time, a fully integrated phased array antenna with radio frequency microelectromechanical systems (RF MEMS) switches on a flexible, organic substrate is demonstrated above 10 GHz. A low noise amplifier (LNA), MEMS phase shifter, and 2 times 2 patch antenna array are integrated into a system-on-package (SOP) on a liquid crystal polymer substrate. Two antenna arrays are compared; one implemented using a single-layer SOP and the second with a multilayer SOP. Both implementations are low-loss and capable of 12deg of beam steering. The design frequency is 14 GHz and the measured return loss is greater than 12 dB for both implementations. The use of an LNA allows for a much higher radiated power level. These antennas can be customized to meet almost any size, frequency, and performance needed. This research furthers the state-of-the-art for organic SOP devices.


iCanTrace : Avatar Personalization through Selfie Sketches

This paper introduces a novel system that allows users to generate customized cartoon avatars through a sketching interface. The rise of social media and personalized gaming has given a need for personalized virtual appearances. Avatars, self-curated and customized images to represent oneself, have become a common means of expressing oneself in these new media. Avatar creation platforms face the challenge of granting user significant control over the avatar creation, and the challenge of encumbering the user with too many choices in their avatar customization. This paper demonstrates a sketch-guided avatar customization system and its potential to simplify the avatar creation process. Author


Teaching Syntax by Adversarial Distraction

Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of natural entailment examples in SNLI or FEVER, to teach aspects of grammar and word order. We show that without retraining, popular entailment models are unaware that these syntactic differences change meaning. With retraining, some but not all popular entailment models can learn to compare the syntax properly.


A large-stroke flexure fast tool servo with new displacement amplifier

As the rapid progress of science and technology, the free-form surface optical component has played an important role in spaceflight, aviation, national defense, and other areas of the technology. While the technology of fast tool servo (FTS) is the most promising method for the machining of free-form surface optical component. However, the shortcomings of short-stroke of fast tool servo device have constrained the development of free-form surface optical component. To address this problem, a new large-stroke flexible FTS device is proposed in this paper. A series of mechanism modeling and optimal designs are carried out via compliance matrix theory, pseudo-rigid body theory, and Particle Swarm Optimization (PSO) algorithm, respectively. The mechanism performance of the large-stroke FTS device is verified by the Finite Element Analysis (FEA) method. For this study, a piezoelectric (PZT) actuator P-840.60 that can travel to 90 µm under open-loop control is employed, the results of experiment indicate that the maximum of output displacement can achieve 258.3µm, and the bandwidth can achieve around 316.84 Hz. Both theoretical analysis and the test results of prototype uniformly verify that the presented FTS device can meet the demand of the actual microstructure processing.


Mindfulness practice leads to increases in regional brain gray matter density

Therapeutic interventions that incorporate training in mindfulness meditation have become increasingly popular, but to date little is known about neural mechanisms associated with these interventions. Mindfulness-Based Stress Reduction (MBSR), one of the most widely used mindfulness training programs, has been reported to produce positive effects on psychological well-being and to ameliorate symptoms of a number of disorders. Here, we report a controlled longitudinal study to investigate pre-post changes in brain gray matter concentration attributable to participation in an MBSR program. Anatomical magnetic resonance (MR) images from 16 healthy, meditation-naïve participants were obtained before and after they underwent the 8-week program. Changes in gray matter concentration were investigated using voxel-based morphometry, and compared with a waiting list control group of 17 individuals. Analyses in a priori regions of interest confirmed increases in gray matter concentration within the left hippocampus. Whole brain analyses identified increases in the posterior cingulate cortex, the temporo-parietal junction, and the cerebellum in the MBSR group compared with the controls. The results suggest that participation in MBSR is associated with changes in gray matter concentration in brain regions involved in learning and memory processes, emotion regulation, self-referential processing, and perspective taking.


Animating pictures with stochastic motion textures

In this paper, we explore the problem of enhancing still pictures with subtly animated motions. We limit our domain to scenes containing passive elements that respond to natural forces in some fashion. We use a semi-automatic approach, in which a human user segments the scene into a series of layers to be individually animated. Then, a "stochastic motion texture" is automatically synthesized using a spectral method, i.e., the inverse Fourier transform of a filtered noise spectrum. The motion texture is a time-varying 2D displacement map, which is applied to each layer. The resulting warped layers are then recomposited to form the animated frames. The result is a looping video texture created from a single still image, which has the advantages of being more controllable and of generally higher image quality and resolution than a video texture created from a video source. We demonstrate the technique on a variety of photographs and paintings.


Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel

Most previously proposed statistical models for the indoor multipath channel include only time of arrival characteristics. However, in order to use statistical models in simulating or analyzing the performance of systems employing spatial diversity combining, information about angle of arrival statistics is also required. Ideally, it would be desirable to characterize the full spare-time nature of the channel. In this paper, a system is described that was used to collect simultaneous time and angle of arrival data at 7 GHz. Data processing methods are outlined, and results obtained from data taken in two different buildings are presented. Based on the results, a model is proposed that employs the clustered "double Poisson" time-of-arrival model proposed by Saleh and Valenzuela (1987). The observed angular distribution is also clustered with uniformly distributed clusters and arrivals within clusters that have a Laplacian distribution.


Analysis and Comparison of a Fast Turn-On Series IGBT Stack and High-Voltage-Rated Commercial IGBTS

High-voltage-rated solid-state switches such as insulated-gate bipolar transistors (IGBTs) are commercially available up to 6.5 kV. Such voltage ratings are attractive for pulsed power and high-voltage switch-mode converter applications. However, as the IGBT voltage ratings increase, the rate of current rise and fall are generally reduced. This tradeoff is difficult to avoid as IGBTs must maintain a low resistance in the epitaxial or drift region layer. For high-voltage-rated IGBTs with thick drift regions to support the reverse voltage, the required high carrier concentrations are injected at turn on and removed at turn off, which slows the switching speed. An option for faster switching is to series multiple, lower voltage-rated IGBTs. An IGBT-stack prototype with six, 1200 V rated IGBTs in series has been experimentally tested. The six-series IGBT stack consists of individual, optically isolated, gate drivers and aluminum cooling plates for forced air cooling which results in a compact package. Each IGBT is overvoltage protected by transient voltage suppressors. The turn-on current rise time of the six-series IGBT stack and a single 6.5 kV rated IGBT has been experimentally measured in a pulsed resistive-load, capacitor discharge circuit. The IGBT stack has also been compared to two IGBT modules in series, each rated at 3.3 kV, in a boost circuit application switching at 9 kHz and producing an output of 5 kV. The six-series IGBT stack results in improved turn-on switching speed, and significantly higher power boost converter efficiency due to a reduced current tail during turn off. The experimental test parameters and the results of the comparison tests are discussed in the following paper


A Nonlinear-Disturbance-Observer-Based DC-Bus Voltage Control for a Hybrid AC/DC Microgrid

DC-bus voltage control is an important task in the operation of a dc or a hybrid ac/dc microgrid system. To improve the dc-bus voltage control dynamics, traditional approaches attempt to measure and feedforward the load or source power in the dc-bus control scheme. However, in a microgrid system with distributed dc sources and loads, the traditional feedforward-based methods need remote measurement with communications. In this paper, a nonlinear disturbance observer (NDO) based dc-bus voltage control is proposed, which does not need the remote measurement and enables the important “plug-and-play” feature. Based on this observer, a novel dc-bus voltage control scheme is developed to suppress the transient fluctuations of dc-bus voltage and improve the power quality in such a microgrid system. Details on the design of the observer, the dc-bus controller and the pulsewidth-modulation (PWM) dead-time compensation are provided in this paper. The effects of possible dc-bus capacitance variation are also considered. The performance of the proposed control strategy has been successfully verified in a 30 kVA hybrid microgrid including ac/dc buses, battery energy storage system, and photovoltaic (PV) power generation system.


Prediction , operations , and condition monitoring in wind energy

Recent developments in wind energy research including wind speed prediction, wind turbine control, operations of hybrid power systems, as well as condition monitoring and fault detection are surveyed. Approaches based on statistics, physics, and data mining for wind speed prediction at different time scales are reviewed. Comparative analysis of prediction results reported in the literature is presented. Studies of classical and intelligent control of wind turbines involving different objectives and strategies are reported. Models for planning operations of different hybrid power systems including wind generation for various objectives are addressed. Methodologies for condition monitoring and fault detection are discussed. Future research directions in wind energy are proposed. 2013 Elsevier Ltd. All rights reserved.


NEUROPSI: a brief neuropsychological test battery in Spanish with norms by age and educational level.

The purpose of this research was to develop, standardize, and test the reliability of a short neuropsychological test battery in the Spanish language. This neuropsychological battery was named "NEUROPSI," and was developed to assess briefly a wide spectrum of cognitive functions, including orientation, attention, memory, language, visuoperceptual abilities, and executive functions. The NEUROPSI includes items that are relevant for Spanish-speaking communities. It can be applied to illiterates and low educational groups. Administration time is 25 to 30 min. Normative data were collected from 800 monolingual Spanish-speaking individuals, ages 16 to 85 years. Four age groups were used: (1) 16 to 30 years, (2) 31 to 50 years, (3) 51 to 65 years, and (4) 66 to 85 years. Data also are analyzed and presented within 4 different educational levels that were represented in this sample; (1) illiterates (zero years of school); (2) 1 to 4 years of school; (2) 5 to 9 years of school; and (3) 10 or more years of formal education. The effects of age and education, as well as the factor structure of the NEUROPSI are analyzed. The NEUROPSI may fulfill the need for brief, reliable, and objective evaluation of a broad range of cognitive functions in Spanish-speaking populations.


Should TED talks be teaching us something?

The nonprofit phenomenon “TED,” the brand name for the concepts of Technology Education and Design, was born in 1984. It launched into pop culture stardom in 2006 when the organization’s curators began offering short, free, unrestricted, and educational video segments. Known as “TEDTalks,” these informational segments are designed to be no longer than 18 minutes in length and provide succinct, targeted enlightenment on various topics or ideas that are deemed “worth spreading.” TED Talks, often delivered in sophisticated studios with trendy backdrops, follow a format that focuses learners on the presenter and limited, extremely purposeful visual aids. Topics range from global warming to running to the developing world. Popular TED Talks, such as Sir Ken Robinson’s “Schools Kill Creatively” or Dan Gilbert’s “Why Are We Happy?” can easily garner well over a million views. TED Talks are a curious phenomenon for educators to observe. They are in many ways the antithesis of traditional lectures, which are typically 60-120 minutes in length and delivered in cavernous halls by faculty members engaged in everyday academic lives. Perhaps the formality of the lecture is the biggest superficial difference in comparison to casual TEDTalks (Table 1). However, TED Talks are not as unstructured as they may appear. Presenters are well coached and instructed to follow a specific presentation formula, whichmaximizes storyboarding and highlights passion for the subject. While learning is not formally assessed, TED Talks do seem to accomplish their goals of spreading ideas while sparking curiosity within the learner. The fact that some presentations have been viewed more than 16 million times points to the effectiveness of the platform in at least reaching learners and stimulating a desire to click, listen, and learn.Moreover, the TEDTalks website is the fourth most popular technology website and the single most popular conference and events website in the world. The TED phenomenon may have both direct and subliminal messages for academia. Perhaps an initial question to ponder is whether the TED phenomenon is a logical grassroots educational evolution or a reaction to the digital generation and their preference for learning that occurs “wherever, whenever.” The diverse cross-section of TED devotees ranging in background and age would seem to provide evidence that the platform does not solely appeal to younger generations of learners. Instead, it suggests that adult learners are either more drawn to digital learning than they think they are or than they are likely to admit. The perceived efficacy of TED once again calls into question the continued reliance of academia on the lecture as the primary currency of learning. TED Talks do not convey large chunks of information but rather present grander ideas. Would TED-like educational modules or blocks of 18-20 minutes be more likely to pique student curiosity across a variety of pharmacy topics, maintain attention span, and improve retention? Many faculty members who are recognized as outstanding teachers or lecturers might confess that they already teach through a TED-like lens. Collaterally, TED Talks or TED-formatted learning experiences might be ideal springboards for incorporation into inverted or flipped classroom environments where information is gathered and learned at home, while ideas are analyzed, debated, and assimilated within the classroom. Unarguably, TED Talks have given scientists and other researchers a real-time, mass media driven opportunity to disseminate their research, ideas, and theories that might otherwise have gone unnoticed. Similar platforms or approaches may be able to provide opportunities for the academy to further transmit research to the general public. The TED approach to idea dissemination is not without its critics. Several authors have criticized TED for flattening or dumbing down ideas so they fit into a preconceived, convenient format that is primarily designed to entertain. Consequently, the oversimplified ideas and conceptsmay provoke little effort from the learner to analyze data, theory, or controversy. Some Corresponding Author: Frank Romanelli, PharmD, MPH, 789 South Limestone Road, University of Kentucky College of Pharmacy, Lexington, KY 40536. E-mail: froma2@email. uky.edu American Journal of Pharmaceutical Education 2014; 78 (6) Article 113.


Geometry and Meaning

Geometry and Meaning is an interesting book about a relationship between geometry and logic defined on certain types of abstract spaces and how that intimate relationship might be exploited when applied in computational linguistics. It is also about an approach to information retrieval, because the analysis of natural language, especially negation, is applied to problems in IR, and indeed illustrated throughout the book by simple examples using search engines. It is refreshing to see IR issues tackled from a different point of view than the standard vector space (Salton, 1968). It is an enjoyable read, as intended by the author, and succeeds as a sort of tourist guide to the subject in hand. The early part of the book concentrates on the introduction of a number of elementary concepts from mathematics: graph theory, linear algebra (especially vector spaces), lattice theory, and logic. These concepts are well motivated and illustrated with good examples, mostly of a classificatory or taxonomic kind. One of the major goals of the book is to argue that non-classical logic, in the form of a quantum logic, is a candidate for analyzing language and its underlying logic, with a promise that such an approach could lead to improved search engines. The argument for this is aided by copious references to early philosophers, scientists, and mathematicians, creating the impression that when Aristotle, Descartes, Boole, and Grassmann were laying the foundations for taxonomy, analytical geometry, logic, and vector spaces, they had a more flexible and broader view of these subjects than is current. This is especially true of logic. Thus the historical approach taken to introducing quantum logic (chapter 7) is to show that this particular kind of logic and its interpretation in vector space were inherent in some of the ideas of these earlier thinkers. Widdows claims that Aristotle was never respected for his mathematics and that Grassmann’s Ausdehnungslehre was largely ignored and left in obscurity. Whether Aristotle was never admired for his mathematics I am unable to judge, but certainly Alfred North Whitehead (1925) was not complimentary when he said:


Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity.

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.


A new exact method for the two-dimensional orthogonal packing problem

The two-dimensional orthogonal packing problem (2OPP ) consists of determining if a set of rectangles (items) can be packed into one rectangle of fixed size (bin). In this paper we propose two exact algorithms for solving this problem. The first algorithm is an improvement on a classical branch&bound method, whereas the second algorithm is based on a two-step enumerative method. We also describe reduction procedures and lower bounds which can be used within the branch&bound method. We report computational experiments for randomly generated benchmarks, which demonstrate the efficiency of both methods.


Sensitivity and diagnosticity of NASA-TLX and simplified SWAT to assess the mental workload associated with operating an agricultural sprayer.

The objectives of the present study were: a) to investigate three continuous variants of the NASA-Task Load Index (TLX) (standard NASA (CNASA), average NASA (C1NASA) and principal component NASA (PCNASA)) and five different variants of the simplified subjective workload assessment technique (SSWAT) (continuous standard SSWAT (CSSWAT), continuous average SSWAT (C1SSWAT), continuous principal component SSWAT (PCSSWAT), discrete event-based SSWAT (D1SSWAT) and discrete standard SSWAT (DSSWAT)) in terms of their sensitivity and diagnosticity to assess the mental workload associated with agricultural spraying; b) to compare and select the best variants of NASA-TLX and SSWAT for future mental workload research in the agricultural domain. A total of 16 male university students (mean 30.4 +/- 12.5 years) participated in this study. All the participants were trained to drive an agricultural spraying simulator. Sensitivity was assessed by the ability of the scales to report the maximum change in workload ratings due to the change in illumination and difficulty levels. In addition, the factor loading method was used to quantify sensitivity. The diagnosticity was assessed by the ability of the scale to diagnose the change in task levels from single to dual. Among all the variants of NASA-TLX and SSWAT, PCNASA and discrete variants of SSWAT showed the highest sensitivity and diagnosticity. Moreover, among all the variants of NASA and SSWAT, the discrete variants of SSWAT showed the highest sensitivity and diagnosticity but also high between-subject variability. The continuous variants of both scales had relatively low sensitivity and diagnosticity and also low between-subject variability. Hence, when selecting a scale for future mental workload research in the agricultural domain, a researcher should decide what to compromise: 1) between-subject variability or 2) sensitivity and diagnosticity. STATEMENT OF RELEVANCE: The use of subjective workload scales is very popular in mental workload research. The present study investigated the different variants of two popular workload rating scales (i.e. NASA-TLX and SSWAT) in terms of their sensitivity and diagnositicity and selected the best variants of each scale for future mental workload research.


Stadium Hashing: Scalable and Flexible Hashing on GPUs

Hashing is one of the most fundamental operations that provides a means for a program to obtain fast access to large amounts of data. Despite the emergence of GPUs as many-threaded general purpose processors, high performance parallel data hashing solutions for GPUs are yet to receive adequate attention. Existing hashing solutions for GPUs not only impose restrictions (e.g., inability to concurrently execute insertion and retrieval operations, limitation on the size of key-value data pairs) that limit their applicability, their performance does not scale to large hash tables that must be kept out-of-core in the host memory. In this paper we present Stadium Hashing (Stash) that is scalable to large hash tables and practical as it does not impose the aforementioned restrictions. To support large out-of-core hash tables, Stash uses a compact data structure named ticket-board that is separate from hash table buckets and is held inside GPU global memory. Ticket-board locally resolves significant portion of insertion and lookup operations and hence, by reducing accesses to the host memory, it accelerates the execution of these operations. Split design of the ticket-board also enables arbitrarily large keys and values. Unlike existing methods, Stash naturally supports concurrent insertions and retrievals due to its use of double hashing as the collision resolution strategy. Furthermore, we propose Stash with collaborative lanes (clStash) that enhances GPU's SIMD resource utilization for batched insertions during hash table creation. For concurrent insertion and retrieval streams, Stadium hashing can be up to 2 and 3 times faster than GPU Cuckoo hashing for in-core and out-of-core tables respectively.


Real time detection of lane markers in urban streets

We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.


Robust Lane Detection and Tracking in Challenging Scenarios

A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.


Lane detection and tracking using B-Snake

In this paper, we proposed a B-Snake based lane detection and tracking algorithm without any cameras’ parameters. Compared with other lane models, the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. Furthermore, a robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake. Also, a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images. It is also applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads. q 2003 Elsevier B.V. All rights reserved.


Using vanishing points for camera calibration

In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.


Realtime lane tracking of curved local road

A lane detection system is an important component of many intelligent transportation systems. We present a robust realtime lane tracking algorithm for a curved local road. First, we present a comparative study to find a good realtime lane marking classifier. Once lane markings are detected, they are grouped into many lane boundary hypotheses represented by constrained cubic spline curves. We present a robust hypothesis generation algorithm using a particle filtering technique and a RANSAC (random sample concensus) algorithm. We introduce a probabilistic approach to group lane boundary hypotheses into left and right lane boundaries. The proposed grouping approach can be applied to general part-based object tracking problems. It incorporates a likelihood-based object recognition technique into a Markov-style process. An experimental result on local streets shows that the suggested algorithm is very reliable


The Ontological Foundation of REA Enterprise Information Systems

Philosophers have studied ontologies for centuries in their search for a systematic explanation of existence: “What kind of things exist?” Recently, ontologies have emerged as a major research topic in the fields of artificial intelligence and knowledge management where they address the content issue: “What kind of things should we represent?” The answer to that question differs with the scope of the ontology. Ontologies that are subject-independent are called upper-level ontologies, and they attempt to define concepts that are shared by all domains, such as time and space. Domain ontologies, on the other hand, attempt to define the things that are relevant to a specific application domain. Both types of ontologies are becoming increasingly important in the era of the Internet where consistent and machine-readable semantic definitions of economic phenomena become the language of e-commerce. In this paper, we propose the conceptual accounting framework of the Resource-Event-Agent (REA) model of McCarthy (1982) as an enterprise domain ontology, and we build upon the initial ontology work of Geerts and McCarthy (2000) which explored REA with respect to the ontological categorizations of John Sowa (1999). Because of its conceptual modeling heritage, REA already resembles an established ontology in many declarative (categories) and procedural (axioms) respects, and we also propose here to extend formally that framework both (1) vertically in terms of entrepreneurial logic (value chains) and workflow detail, and (2) horizontally in terms of type and commitment images of enterprise economic phenomena. A strong emphasis throughout the paper is given to the microeconomic foundations of the category definitions.


Dual- and Multiband U-Slot Patch Antennas

A wide band patch antenna fed by an L-probe can be designed for dual- and multi-band application by cutting U-slots on the patch. Simulation and measurement results are presented to illustrate this design.


Advances in Collaborative Filtering

The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.


VirtualTable: a projection augmented reality game

VirtualTable is a projection augmented reality installation where users are engaged in an interactive tower defense game. The installation runs continuously and is designed to attract people to a table, which the game is projected onto. Any number of players can join the game for an optional period of time. The goal is to prevent the virtual stylized soot balls, spawning on one side of the table, from reaching the cheese. To stop them, the players can place any kind of object on the table, that then will become part of the game. Depending on the object, it will become either a wall, an obstacle for the soot balls, or a tower, that eliminates them within a physical range. The number of enemies is dependent on the number of objects in the field, forcing the players to use strategy and collaboration and not the sheer number of objects to win the game.


A Context-Aware Usability Model for Mobile Health Applications

Mobile healthcare is a fast growing area of research that capitalizes on mobile technologies and wearables to provide realtime and continuous monitoring and analysis of vital signs of users. Yet, most of the current applications are developed for general population without taking into consideration the context and needs of different user groups. Designing and developing mobile health applications and diaries according to the user context can significantly improve the quality of user interaction and encourage the application use. In this paper, we propose a user context model and a set of usability attributes for developing mobile applications in healthcare. The proposed model and the selected attributes are integrated into a mobile application development framework to provide user-centered and context-aware guidelines. To validate our framework, a mobile diary was implemented for patients undergoing Peritoneal Dialysis (PD) and tested with real users.


New approach for clustering of big data: DisK-means

The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.


A probabilistic constrained clustering for transfer learning and image category discovery

Neural network-based clustering has recently gained popularity, and in particular a constrained clustering formulation has been proposed to perform transfer learning and image category discovery using deep learning. The core idea is to formulate a clustering objective with pairwise constraints that can be used to train a deep clustering network; therefore the cluster assignments and their underlying feature representations are jointly optimized end-toend. In this work, we provide a novel clustering formulation to address scalability issues of previous work in terms of optimizing deeper networks and larger amounts of categories. The proposed objective directly minimizes the negative log-likelihood of cluster assignment with respect to the pairwise constraints, has no hyper-parameters, and demonstrates improved scalability and performance on both supervised learning and unsupervised transfer learning.


Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.


EEG Classification of Different Imaginary Movements within the Same Limb

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.


Single-Stage Single-Switch Four-Output Resonant LED Driver With High Power Factor and Passive Current Balancing

A resonant single-stage single-switch four-output LED driver with high power factor and passive current balancing is proposed. By controlling one output current, the other output currents of four-output LED driver can be controlled via passive current balancing, which makes its control simple. When magnetizing inductor current operates in critical conduction mode, unity power factor is achieved. The proposed LED driver uses only one active switch and one magnetic component, thus it benefits from low cost, small volume, and light weight. Moreover, high-efficiency performance is achieved due to single-stage power conversion and soft-switching characteristics. The characteristics of the proposed LED driver are studied in this paper and experimental results of two 110-W four-output isolated LED drivers are provided to verify the studied results.


Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.


Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages

In this paper, we report a study that examines the relationship between image-based computational analyses of web pages and users' aesthetic judgments about the same image material. Web pages were iteratively decomposed into quadrants of minimum entropy (quadtree decomposition) based on low-level image statistics, to permit a characterization of these pages in terms of their respective organizational symmetry, balance and equilibrium. These attributes were then evaluated for their correlation with human participants' subjective ratings of the same web pages on four aesthetic and affective dimensions. Several of these correlations were quite large and revealed interesting patterns in the relationship between low-level (i.e., pixel-level) image statistics and design-relevant dimensions.


American Sign Language Recognition Using Leap Motion Sensor

In this paper, we present an American Sign Language recognition system using a compact and affordable 3D motion sensor. The palm-sized Leap Motion sensor provides a much more portable and economical solution than Cyblerglove or Microsoft kinect used in existing studies. We apply k-nearest neighbor and support vector machine to classify the 26 letters of the English alphabet in American Sign Language using the derived features from the sensory data. The experiment result shows that the highest average classification rate of 72.78% and 79.83% was achieved by k-nearest neighbor and support vector machine respectively. We also provide detailed discussions on the parameter setting in machine learning methods and accuracy of specific alphabet letters in this paper.


Cognitive, emotional, and behavioral considerations for chronic pain management in the Ehlers-Danlos syndrome hypermobility-type: a narrative review.

BACKGROUND
Ehlers-Danlos syndrome (EDS) hypermobility-type is the most common hereditary disorder of the connective tissue. The tissue fragility characteristic of this condition leads to multi-systemic symptoms in which pain, often severe, chronic, and disabling, is the most experienced. Clinical observations suggest that the complex patient with EDS hypermobility-type is refractory toward several biomedical and physical approaches. In this context and in accordance with the contemporary conceptualization of pain (biopsychosocial perspective), the identification of psychological aspects involved in the pain experience can be useful to improve interventions for this under-recognized pathology.

PURPOSE
Review of the literature on joint hypermobility and EDS hypermobility-type concerning psychological factors linked to pain chronicity and disability.

METHODS
A comprehensive search was performed using scientific online databases and references lists, encompassing publications reporting quantitative and qualitative research as well as unpublished literature.

RESULTS
Despite scarce research, psychological factors associated with EDS hypermobility-type that potentially affect pain chronicity and disability were identified. These are cognitive problems and attention to body sensations, negative emotions, and unhealthy patterns of activity (hypo/hyperactivity).

CONCLUSIONS
As in other chronic pain conditions, these aspects should be more explored in EDS hypermobility-type, and integrated into chronic pain prevention and management programs. Implications for Rehabilitation Clinicians should be aware that joint hypermobility may be associated with other health problems, and in its presence suspect a heritable disorder of connective tissue such as the Ehlers-Danlos syndrome (EDS) hypermobility-type, in which chronic pain is one of the most frequent and invalidating symptoms. It is necessary to explore the psychosocial functioning of patients as part of the overall chronic pain management in the EDS hypermobility-type, especially when they do not respond to biomedical approaches as psychological factors may be operating against rehabilitation. Further research on the psychological factors linked to pain chronicity and disability in the EDS hypermobility-type is needed.


A brief review: factors affecting the length of the rest interval between resistance exercise sets.

Research has indicated that multiple sets are superior to single sets for maximal strength development. However, whether maximal strength gains are achieved may depend on the ability to sustain a consistent number of repetitions over consecutive sets. A key factor that determines the ability to sustain repetitions is the length of rest interval between sets. The length of the rest interval is commonly prescribed based on the training goal, but may vary based on several other factors. The purpose of this review was to discuss these factors in the context of different training goals. When training for muscular strength, the magnitude of the load lifted is a key determinant of the rest interval prescribed between sets. For loads less than 90% of 1 repetition maximum, 3-5 minutes rest between sets allows for greater strength increases through the maintenance of training intensity. However, when testing for maximal strength, 1-2 minutes rest between sets might be sufficient between repeated attempts. When training for muscular power, a minimum of 3 minutes rest should be prescribed between sets of repeated maximal effort movements (e.g., plyometric jumps). When training for muscular hypertrophy, consecutive sets should be performed prior to when full recovery has taken place. Shorter rest intervals of 30-60 seconds between sets have been associated with higher acute increases in growth hormone, which may contribute to the hypertrophic effect. When training for muscular endurance, an ideal strategy might be to perform resistance exercises in a circuit, with shorter rest intervals (e.g., 30 seconds) between exercises that involve dissimilar muscle groups, and longer rest intervals (e.g., 3 minutes) between exercises that involve similar muscle groups. In summary, the length of the rest interval between sets is only 1 component of a resistance exercise program directed toward different training goals. Prescribing the appropriate rest interval does not ensure a desired outcome if other components such as intensity and volume are not prescribed appropriately.


Silicon carbide power MOSFETs: Breakthrough performance from 900 V up to 15 kV

Since Cree, Inc.'s 2nd generation 4H-SiC MOSFETs were commercially released with a specific on-resistance (RON, SP) of 5 mΩ·cm2 for a 1200 V-rating in early 2013, we have further optimized the device design and fabrication processes as well as greatly expanded the voltage ratings from 900 V up to 15 kV for a much wider range of high-power, high-frequency, and high-voltage energy-conversion and transmission applications. Using these next-generation SiC MOSFETs, we have now achieved new breakthrough performance for voltage ratings from 900 V up to 15 kV with a RON, SP as low as 2.3 mΩ·cm2 for a breakdown voltage (BV) of 1230 V and 900 V-rating, 2.7 mΩ·cm2 for a BV of 1620 V and 1200 V-rating, 3.38 mΩ·cm2 for a BV of 1830 V and 1700 V-rating, 10.6 mΩ·cm2 for a BV of 4160 V and 3300 V-rating, 123 mΩ·cm2 for a BV of 12 kV and 10 kV-rating, and 208 mΩ·cm2 for a BV of 15.5 kV and 15 kV-rating. In addition, due to the lack of current tailing during the bipolar device switching turn-off, the SiC MOSFETs reported in this work exhibit incredibly high frequency switching performance over their silicon counter parts.


The evolution of human skin coloration.

Skin color is one of the most conspicuous ways in which humans vary and has been widely used to define human races. Here we present new evidence indicating that variations in skin color are adaptive, and are related to the regulation of ultraviolet (UV) radiation penetration in the integument and its direct and indirect effects on fitness. Using remotely sensed data on UV radiation levels, hypotheses concerning the distribution of the skin colors of indigenous peoples relative to UV levels were tested quantitatively in this study for the first time. The major results of this study are: (1) skin reflectance is strongly correlated with absolute latitude and UV radiation levels. The highest correlation between skin reflectance and UV levels was observed at 545 nm, near the absorption maximum for oxyhemoglobin, suggesting that the main role of melanin pigmentation in humans is regulation of the effects of UV radiation on the contents of cutaneous blood vessels located in the dermis. (2) Predicted skin reflectances deviated little from observed values. (3) In all populations for which skin reflectance data were available for males and females, females were found to be lighter skinned than males. (4) The clinal gradation of skin coloration observed among indigenous peoples is correlated with UV radiation levels and represents a compromise solution to the conflicting physiological requirements of photoprotection and vitamin D synthesis. The earliest members of the hominid lineage probably had a mostly unpigmented or lightly pigmented integument covered with dark black hair, similar to that of the modern chimpanzee. The evolution of a naked, darkly pigmented integument occurred early in the evolution of the genus Homo. A dark epidermis protected sweat glands from UV-induced injury, thus insuring the integrity of somatic thermoregulation. Of greater significance to individual reproductive success was that highly melanized skin protected against UV-induced photolysis of folate (Branda & Eaton, 1978, Science201, 625-626; Jablonski, 1992, Proc. Australas. Soc. Hum. Biol.5, 455-462, 1999, Med. Hypotheses52, 581-582), a metabolite essential for normal development of the embryonic neural tube (Bower & Stanley, 1989, The Medical Journal of Australia150, 613-619; Medical Research Council Vitamin Research Group, 1991, The Lancet338, 31-37) and spermatogenesis (Cosentino et al., 1990, Proc. Natn. Acad. Sci. U.S.A.87, 1431-1435; Mathur et al., 1977, Fertility Sterility28, 1356-1360).As hominids migrated outside of the tropics, varying degrees of depigmentation evolved in order to permit UVB-induced synthesis of previtamin D(3). The lighter color of female skin may be required to permit synthesis of the relatively higher amounts of vitamin D(3)necessary during pregnancy and lactation. Skin coloration in humans is adaptive and labile. Skin pigmentation levels have changed more than once in human evolution. Because of this, skin coloration is of no value in determining phylogenetic relationships among modern human groups.


Robust and false positive free watermarking in IWT domain using SVD and ABC

Watermarking is used to protect the copyrighted materials from being misused and help us to know the lawful ownership. The security of any watermarking scheme is always a prime concern for the developer. In this work, the robustness and security issue of IWT (integer wavelet transform) and SVD (singular value decomposition) based watermarking is explored. Generally, SVD based watermarking techniques suffer with an issue of false positive problem. This leads to even authenticating the wrong owner. We are proposing a novel solution to this false positive problem; that arises in SVD based approach. Firstly, IWT is employed on the host image and then SVD is performed on this transformed host. The properties of IWT and SVD help in achieving high value of robustness. Singular values are used for the watermark embedding. In order to further improve the quality of watermarking, the optimization of scaling factor (mixing ratio) is performed with the help of artificial bee colony (ABC) algorithm. A comparison with other schemes is performed to show the superiority of proposed scheme. & 2015 Elsevier Ltd. All rights reserved.


A Literature Survey of Benchmark Functions For Global Optimization Problems

Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.


Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems

In this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.


A low-voltage, low quiescent current, low drop-out regulator

The demand for low voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDOs are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDOs at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 μA. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V respectively.


Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatio-temporal intervals between neighbor checkins, which are essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose a new variant of LSTM, named STLSTM, which implements time gates and distance gates into LSTM to capture the spatio-temporal relation between successive check-ins. Specifically, one time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update. Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.


Absorptive Frequency-Selective Transmission Structure With Square-Loop Hybrid Resonator

A novel design of an absorptive frequency-selective transmission structure (AFST) is proposed. This structure is based on the design of a frequency-dependent lossy layer with square-loop hybrid resonator (SLHR). The parallel resonance provided by the hybrid resonator is utilized to bypass the lossy path and improve the insertion loss. Meanwhile, the series resonance of the hybrid resonator is used for expanding the upper absorption bandwidth. Furthermore, the absorption for out-of-band frequencies is achieved by using four metallic strips with lumped resistors, which are connected with the SLHR. The quantity of lumped elements required in a unit cell can be reduced by at least 50% compared to previous structures. The design guidelines are explained with the aid of an equivalent circuit model. Both simulation and experiment results are presented to demonstrate the performance of our AFST. It is shown that an insertion loss of 0.29 dB at 6.1 GHz and a 112.4% 10 dB reflection reduction bandwidth are obtained under the normal incidence.


Implementation of AES-GCM encryption algorithm for high performance and low power architecture Using FPGA

Evaluation of the Advanced Encryption Standard (AES) algorithm in FPGA is proposed here. This Evaluation is compared with other works to show the efficiency. Here we are concerned about two major purposes. The first is to define some of the terms and concepts behind basic cryptographic methods, and to offer a way to compare the myriad cryptographic schemes in use today. The second is to provide some real examples of cryptography in use today. The design uses an iterative looping approach with block and key size of 128 bits, lookup table implementation of S-box. This gives low complexity architecture and easily achieves low latency as well as high throughput. Simulation results, performance results are presented and compared with previous reported designs. Since its acceptance as the adopted symmetric-key algorithm, the Advanced Encryption Standard (AES) and its recently standardized authentication Galois/Counter Mode (GCM) have been utilized in various security-constrained applications. Many of the AES-GCM applications are power and resource constrained and requires efficient hardware implementations. In this project, AES-GCM algorithms are evaluated and optimized to identify the high-performance and low-power architectures. The Advanced Encryption Standard (AES) is a specification for the encryption of electronic data. The Cipher Block Chaining (CBC) mode is a confidentiality mode whose encryption process features the combining (“chaining”) of the plaintext blocks with the previous Cipher text blocks. The CBC mode requires an IV to combine with the first plaintext block. The IV need not be secret, but it must be unpredictable. Also, the integrity of the IV should be protected. Galois/Counter Mode (GCM) is a block cipher mode of operation that uses universal hashing over a binary Galois field to provide authenticated encryption. Galois Hash is used for authentication, and the Advanced Encryption Standard (AES) block cipher is used for encryption in counter mode of operation. To obtain the least-complexity S-box, the formulations for the Galois Field (GF) sub-field inversions in GF (24) are optimized By conducting exhaustive simulations for the input transitions, we analyze the synthesis of the AES S-boxes considering the switching activities, gate-level net lists, and parasitic information. Finally, by implementation of AES-GCM the high-performance GF (2128) multiplier architectures, gives the detailed information of its performance. An optimized coding for the implementation of Advanced Encryption Standard-Galois Counter Mode has been developed. The speed factor of the algorithm implementation has been targeted and a software code in Verilog HDL has been developed. This implementation is useful in wireless security like military communication and mobile telephony where there is a grayer emphasis on the speed of communication.


Multi-Document Summarization using Sentence-based Topic Models

Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix. However, the knowledge on the document side, i.e. the topics embedded in the documents, can help the context understanding and guide the sentence selection in the summarization procedure. In this paper, we propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. An efficient variational Bayesian algorithm is derived for model parameter estimation. Experimental results on benchmark data sets show the effectiveness of the proposed model for the multi-document summarization task.


Intrusion detection by machine learning: A review

The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided. 2009 Elsevier Ltd. All rights reserved.


Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments

Software Defined Networks (SDNs) based on the OpenFlow (OF) protocol export controlplane programmability of switched substrates. As a result, rich functionality in traffic management, load balancing, routing, firewall configuration, etc. that may pertain to specific flows they control, may be easily developed. In this paper we extend these functionalities with an efficient and scalable mechanism for performing anomaly detection and mitigation in SDN architectures. Flow statistics may reveal anomalies triggered by large scale malicious events (typically massive Distributed Denial of Service attacks) and subsequently assist networked resource owners/operators to raise mitigation policies against these threats. First, we demonstrate that OF statistics collection and processing overloads the centralized control plane, introducing scalability issues. Second, we propose a modular architecture for the separation of the data collection process from the SDN control plane with the employment of sFlow monitoring data. We then report experimental results that compare its performance against native OF approaches that use standard flow table statistics. Both alternatives are evaluated using an entropy-based method on high volume real network traffic data collected from a university campus network. The packet traces were fed to hardware and software OF devices in order to assess flow-based datagathering and related anomaly detection options. We subsequently present experimental results that demonstrate the effectiveness of the proposed sFlow-based mechanism compared to the native OF approach, in terms of overhead imposed on usage of system resources. Finally, we conclude by demonstrating that once a network anomaly is detected and identified, the OF protocol can effectively mitigate it via flow table modifications. 2013 Elsevier B.V. All rights reserved.


Software-Defined Networking: A Comprehensive Survey

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network’s control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms with a focus on aspects such as resiliency, scalability, performance, security, and dependabilityVas well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined


Revisiting Traffic Anomaly Detection Using Software Defined Networking

Despite their exponential growth, home and small office/home office networks continue to be poorly managed. Consequently, security of hosts in most home networks is easily compromised and these hosts are in turn used for largescale malicious activities without the home users’ knowledge. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and contain network security problems in home and home office networks. We show how four prominent traffic anomaly detection algorithms can be implemented in an SDN context using Openflow compliant switches and NOX as a controller. Our experiments indicate that these algorithms are significantly more accurate in identifying malicious activities in the home networks as compared to the ISP. Furthermore, the efficiency analysis of our SDN implementations on a programmable home network router indicates that the anomaly detectors can operate at line rates without introducing any performance penalties for the home network traffic.


A clean slate 4D approach to network control and management

Today's data networks are surprisingly fragile and difficult to manage. We argue that the root of these problems lies in the complexity of the control and management planes--the software and protocols coordinating network elements--and particularly the way the decision logic and the distributed-systems issues are inexorably intertwined. We advocate a complete refactoring of the functionality and propose three key principles--network-level objectives, network-wide views, and direct control--that we believe should underlie a new architecture. Following these principles, we identify an extreme design point that we call "4D," after the architecture's four planes: decision, dissemination, discovery, and data. The 4D architecture completely separates an AS's decision logic from pro-tocols that govern the interaction among network elements. The AS-level objectives are specified in the decision plane, and en-forced through direct configuration of the state that drives how the data plane forwards packets. In the 4D architecture, the routers and switches simply forward packets at the behest of the decision plane, and collect measurement data to aid the decision plane in controlling the network. Although 4D would involve substantial changes to today's control and management planes, the format of data packets does not need to change; this eases the deployment path for the 4D architecture, while still enabling substantial innovation in network control and management. We hope that exploring an extreme design point will help focus the attention of the research and industrial communities on this crucially important and intellectually challenging area.


Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model

Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithmby considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes. ARTICLE HISTORY Received 21 March 2016 Accepted 28 September 2016