Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and analysis of switching DC-DC converter with coupled-inductor

A coupled-inductor step-down switching DC-DC converter with continuous input and output current is investigated by use of a new time averaging equivalent circuit (TAEC) analysis method. By using the TAEC, one can analyze such a converter in a more simple way as the parameters of the TAEC model can easily be determined by the new approach. As a result, the analytical expression for the DC steady state and AC small signal equivalent circuit model of the converter are obtained. DC and AC small signal analysis can then be performed. <<ETX>>
Surface winds from the SSM/I using neural networks

An improved neural network (NN) wind speed retrieval algorithm which covers the entire range permitted for retrievals both with respect to moisture and wind speed is presented. Improvements in the NN approach have permitted the development of a wind speed retrieval algorithm which can retrieve wind speeds up to /spl sim/25 m/sec for LWP concentrations <0.5 kg/m/sup 2/, with a bias of /spl sim/0.2 m/s and an rms error of /spl sim/1.75 m/s. Also, results are presented which demonstrate significant correlation (correlation coefficient /spl sim/0.75) between wind directions retrieved, using a NN, and buoy wind direction. Although the F10 SSM/I data set which was used in this study was noisy, higher quality data are now being used to develop a prototype for a NN wind direction retrieval algorithm for the SSM/I.
RECOGNIZING NAMED ENTITIES IN TURKISH TWEETS

Named entity recognition (NER) is one of the well-studied sub-branch of natural language processing (NLP). State of the art NER systems give highly accurate results in domain of formal texts. With the expansion of microblog sites and social media, this informal text domain has become a new trend in NLP studies. Recent works has shown, social media texts are hard to process and the performance of the current systems substantially decrease when switched to this domain. We give our experience in improving named entity recognition on informal social media texts for the case of tweets.
Exploiting spectral and spatial information for classifying hyperspectral data in urban areas

This paper is devoted to urban hyperspectral remote sensing. Very high resolution hyperspectral data are used to provide detailed maps of urban land cover, exploiting different classification tools. In particular, multiple classifications and spatial refinement step are used to improve the mapping accuracy. We show results on DAIS data over the town of Pavia, Northern Italy. The four flight lines over the area, kindly provided by DLR in the framework of the HySens project, are partially overlapping. This helps, besides the test of the classification procedure here presented, even to understand the advantages of combining different views of the same area.
A vulnerability evaluation framework for online social network profiles: axioms and propositions

Online social network (OSN) usage has led to personal details being presented on online profiles readily. This can cause profile owners to be vulnerable to social engineering attacks. Our approach to quantifying vulnerability consists of a model with three components: individual, relative and absolute vulnerabilities. The individual vulnerability is calculated by allocating weights to profile attribute values disclosed which may contribute towards the personal vulnerability of the profile owner. The relative vulnerability is the collective vulnerability of the profiles' friends. The absolute vulnerability is the overall vulnerability for the profile which considers the individual and relative vulnerabilities. This paper extends research done on axioms based on the vulnerability model, by stating propositions to explore the effects of different operators on the profiles relative and absolute vulnerabilities. The case studies show that our approach offers a formal background for estimating how attributes and operator changes influence the individual, relative and absolute vulnerability of OSN profiles.
Project estimation using Screenflow Engineering

Software project estimation is a topic that has been widely researched, yielding a multitude of different estimation models, tools and techniques aimed at increasing the accuracy of cost, effort and time estimates of proposed software projects. However, surveys in the United Kingdom, The Netherlands and New Zealand have identified a very low use of such models and tools, despite recognition of their importance. This paper introduces a method of software project estimation used in a New Zealand case. The method used is part of the Screenflow Engineering process. This is based on the premise that computer system applications should share a common pool of data, which is updated on-line and made available simultaneously to any user of any application.
Computer vision -- ECCV 2010 : 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5 - 11, 2010 : proceedings

Visual Learning. - Constrained Spectral Clustering via Exhaustive and Efficient Constraint Propagation. - Object Recognition with Hierarchical Stel Models. - MIForests: Multiple-Instance Learning with Randomized Trees. - Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations. - Stacked Hierarchical Labeling. - Spotlights and Posters R2. - Fully Isotropic Fast Marching Methods on Cartesian Grids. - Clustering Complex Data with Group-Dependent Feature Selection. - On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation. - Exploring the Identity Manifold: Constrained Operations in Face Space. - Multi-label Linear Discriminant Analysis. - Convolutional Learning of Spatio-temporal Features. - Learning Pre-attentive Driving Behaviour from Holistic Visual Features. - Detecting People Using Mutually Consistent Poselet Activations. - Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo. - Multi-stage Sampling with Boosting Cascades for Pedestrian Detection in Images and Videos. - Learning to Detect Roads in High-Resolution Aerial Images. - Thinking Inside the Box: Using Appearance Models and Context Based on Room Geometry. - A Structural Filter Approach to Human Detection. - Geometric Constraints for Human Detection in Aerial Imagery. - Handling Urban Location Recognition as a 2D Homothetic Problem. - Recursive Coarse-to-Fine Localization for Fast Object Detection. - A Local Bag-of-Features Model for Large-Scale Object Retrieval. - Velocity-Dependent Shutter Sequences for Motion Deblurring. - Colorization for Single Image Super Resolution. - Programmable Aperture Camera Using LCoS. - A New Algorithmic Approach for Contrast Enhancement. - Seeing through Obscure Glass. - A Continuous Max-Flow Approach to Potts Model. - Hybrid Compressive Sampling via a New Total Variation TVL1. - Perspective Imaging under Structured Light. - Lighting and Pose Robust Face Sketch Synthesis. - Predicting Facial Beauty without Landmarks. - Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary. - Motion Profiles for Deception Detection Using Visual Cues. - A Robust and Scalable Approach to Face Identification. - Emotion Recognition from Arbitrary View Facial Images. - Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model. - Robust Head Pose Estimation Using Supervised Manifold Learning. - Knowledge Based Activity Recognition with Dynamic Bayesian Network. - View and Style-Independent Action Manifolds for Human Activity Recognition. - Figure-Ground Image Segmentation Helps Weakly-Supervised Learning of Objects. - Enhancing Interactive Image Segmentation with Automatic Label Set Augmentation. - Hough Transform and 3D SURF for Robust Three Dimensional Classification.
Surgical Management of Hepatobiliary and Pancreatic Disorders

Sometimes when picking up a book, perusing the contents list and browsing through the illustrations and subject headings one gets a feeling of promise with regard to the detailed content. I had this experience with Poston and Blumgart's book and was not disappointed. This is a work of high quality in both information and production. The editors aimed at a problem-based approach rather than didactic descriptions and this is nicely done with a format of frequently asked questions followed by answers backed up by detailed review of the published work. Editorial control has been sufficient to give a uniform approach while allowing the authors to retain individual character. A special feature is the way anatomical descriptions and operative techniques are illuminated by computer assisted diagrammatic representations and in some chapters image reconstruction techniques in three dimensions. I would pick out the chapters on hepatic resection, bilobar metastatic colon cancer and non-resectional treatment of colorectal cancer as particularly hard to beat in any competing textbook, in their detailed descriptions and reviews of recent work. Towards the end are two excellent chapters that will appeal to the general surgeon, on management of trauma of the duodenum, pancreas and liver. Expertly handled they offer solutions to management of these rare but critical injuries. Inevitably there are a few gaps where the editors have been selective, and it seems odd to have a chapter on management of carcinoma of the head of pancreas involving the portal vein without another describing the standard pancreatic resectional procedures. Neither of the chapters dealing with neuroendocrine tumours mentions the significant advances in the genetics of these conditions, which can profoundly affect patient management. In general, the editors and authors have done an excellent job. Here is the evidence base for day-to-day management and decision making in hepatobiliary diseases. I recommend the book to any general or specialist surgeon who manages these diseases, and to trainees approaching examinations at completion of surgical training.
Yield analysis and optimization of VLSI interconnects in multichip modules

A CAD approach integrating multidimensional correlated Monte-Carlo analysis and generalized l/sub 1/ optimization with lossy transmission line network simulations is presented. It is implemented in a CAD framework for statistical analysis and yield optimization of high-speed VLSI interconnects. Physical/geometrical based hierarchical Monte-Carlo analysis and yield optimization examples are presented. <<ETX>>
Robot mediated round table: Analysis of the effect of robot's gaze

This paper investigates the relationship between the direction of a robot's face and its gaze as noticed by a person. Non-verbal information such as gaze movement or gestures is crucial to conversation between people. In particular, the effect of a gaze is most important in non-verbal communication because gaze movements convey various information such as timing of taking turns or a target indicated by a person. This paper argues that the important role of a gaze also applies to interaction between a human and a robot. In spite of the importance of the gaze, there has been no study on the relationship between a robot's head orientation and its gaze. We have conducted an experiment to investigate this relationship in an experimental environment named RM-RT, where the robot's head orientation is fixed to a particular person during a conversation. The results indicate that people notice the robot's gaze according to the robot's head orientation.
Blind channel estimation for CDMA based on modulation-induced cyclostationarity

In this paper, a novel approach is presented for the CDMA channel identification. The approach makes use of the modulation-induced cyclostationarity to separate the second order cyclic statistics for every user, and being able to identify individual channels of different users. The approach is rather simple, suitable for the CDMA uplink. The optimum condition for modulating sequences is originally discussed, and a new channel identifiability condition with its proof is included in the paper. And finally the identification algorithm is given with simulation results.
Application of RFID and Neuro-Fuzzy to safety learning

Abstract This study aims to integrate safety learning system, and let the system become intelligent. We adopt the artifical intelligence and active Ultra-High Frequency RFID for safety learning system, and develop friendly human-computer-interface software for users use the personal computers or notebooks. We program the system and software with Extensible Markup Language (XML) and C sharp language. If the users begin to search, the kernel safety learning system automatically communicates with other RFID readers by agents, and the agents can search the closer camera for users. This study’s result has successfully implemented to Chin-Huo educational organization, and it would be helpful for the paterfamilias to hold all situations about their children at Chin-Huo educational organization. Paterfamilias can understand their children’s learning, going to Chin-Huo and leaving Chin-Huo through personal computers, or notebooks simultaneously or asynchronously by the computer-mediated communication. That will be great help in the grip of whole after-school remedial education, teaching and learning situation.
MANET-Evaluation of DSDV and AROD Routing Protocol

Mobile ad-hoc networks (MANETS) are formed by a collection of mobile nodes that have the ability to form a communication network without the help of any fixed infrastructure. MANET is a self organized a nd self configurable network where the mobile nodes move arbitrarily. The mobile nodes can receive and forward packets as a router. Because of the nature of these network s, routing protocols play a prominent role in their scalability and overall performance. Due to limited radio transmiss ion range, multiple hops may be required in order t o exchange data among the communicating nodes. So, a key requirement of any efficient routing protocol is to find a route between two communicating nodes quickly and with low bandwidth overheads. Consequently, many routing algorithms have come into existence to satisfy the needs of communications in such networks. This stud y inspects two MANET protocols DSDV (Destination Sequenced Distance Vector) and AROD (Adaptive Routing in Dynamic Ad-hoc Networks).
Applied Bayesian Inference in R using

Sampling) project is a long-running project to provide a user-friendly language and environment for Bayesian inference. The first article, by Andrew Thomas and colleagues, describes the BRugs package which provides an R interface to the OpenBUGS engine. The second article by Andrew Thomas describes the BUGS language itself and the design philosophy behind it. Somewhat unusually for an article in R News, this article does not describe any R software, but it is included to highlight some of the differences in the way statistical models are represented in R and OpenBUGS. The issue ends with an article by Jouni Kerman and Andrew Gelman, who give a personal perspective on what the next generation of Bayesian software may look like, and preview some of their own work in this area, notably the rv package for representing simulation-based random variables, and the forthcoming “Universal Markov Chain Sampler” package, Umacs.
A Survey on Uyghur Ontology

Ontology has become a hot research topic in the fields of artificial intelligence such as knowledge representation, knowledge engineering and natural language processing (NLP) etc. In this paper, according to the application requirements in the intelligent Uyghur information retrieval system, by giving the brief description about the ontology and its construction rules, methods, tools and descriptive languages, have conducted the contrastive analysis the current research status about the ontology in domestic and abroad, and then sum up some key issues in Uyghur ontology construction procedures and some early achievements. After all, the further research directions are also proposed in this paper.
An improved heuristic algorithm for over-the-cell channel routing

At any iterative step, a net interval is chosen for over-the-cell routing such that: (i) it dilutes the portion of the channel having the maximum congestion and span, and (ii) it subsequently forces the smallest set of other nets to be routed inside the channel. Furthermore, selection of over-the-cell candidates on the top and bottom sides of the channel is carried out simultaneously, rather than the by usual practice of treating them in isolation. A heuristic cost function befitting the authors needs is introduced for guiding the search. Experiments on standard benchmarks establish the superiority of the proposed method over other techniques. <<ETX>>
Output feedback stochastic H∞ stabilization of networked fault-tolerant control systems

Abstract In the current paper, static output feedback stochastic stabilization and disturbance attenuation issues for a class of discrete-time networked control systems subject to random failures and random delays are addressed. The different random processes are modelled as Markovian chains, and the resulting closed-loop system belongs to the class of discrete-time Markovian jump linear systems. Results are formulated as matrix inequalities. A numerical algorithm based on non-convex optimization is provided and its running is illustrated on a classical example from literature.
Can motion increase user interface bandwidth in complex systems?

Current user interfaces to complex systems suffer from data displays which are increasingly cumbersome since the representation techniques exceed the human's perceptual capacity to efficiently interpret them. New display dimensions are required to support the user in integrating and relating information across displays and representations. Advances in technology have made animation a viable alternative. Motion holds promise as a perceptually rich and efficient display dimension but little is known about its properties for information display. We summarize types of movement characterization from diverse disciplines and proposes an initial taxonomy of motion properties and application to serve as a framework for further empirical investigation into motion as a useful display dimension.
Horizontal sea surface temperature gradients: MODIS satellite observations versus Reynolds analysis

This study compares horizontal gradients of sea surface temperature (SST) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard of Terra satellite with those in the Reynolds analysis. Large differences exists in the horizontal gradients between the two SST products, suggesting that existing surface heat balance analyses based on the Reynolds SST analysis is inaccurate. Impact of the inaccuracies on the surface heat balance of the equatorial Pacific region during 2001-02 is estimated. Sensitivity of the SST horizontal gradients with respect to the grid resolution of the data is also studied and the importance of horizontal diffusion in surface heat balance of the coarse-resolution grid is suggested.
Analysis of composite walls and their effects on short-path propagation modeling

For short-propagation paths, correctly representing reflections of electromagnetic energy from surfaces is critical for accurate signal-level predictions. In this paper, the method of homogenization is used to determine the effective material properties of composite materials commonly used in construction. The reflection coefficients for block walls and other types of materials calculated with these homogenized effective material properties are presented. The importance of accurately representing the reflections for signal-level prediction models is also investigated. It is shown that a 5-10-dB error in received signal strength (RSS) can occur if the composite walls are not handled appropriately. Such accurate predictions of signal propagation over a short distance is applicable to microcellular personal communications services deployments in urban canyons as well as indoor wireless private branch exchanges and local-area networks.
Multiple-input signature registers: an improved design

For testable design purposes, a multiple-output digital system needs a multiple-input signature register. This register accelerates the testing task by compressing multiple-input data streams into one signature. Existing designs depend on parallel feeding the input polynomials to the storage elements. These designs, give a good compression but, suffer from some drawbacks. The paper proposes a new design for the multiple-input signature register. This proposed design aggregates all inputs at a certain point and feeds them to the shift register. By adopting this concept, we are able to improve the operation of the multiple-input signature register in a number of ways. Complete VHDL modeling and simulation for the existing and the proposed designs verified the advantages of the new structure.
TaDb: A time-aware diffusion-based recommender algorithm

Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose a time-aware diffusion-based (TaDb) recommender algorithm, which assigns different temporal weights to the leading links existing before the target user's collection and the following links appearing after that in the diffusion process. Experiments on four real datasets, Netflix, MovieLens, FriendFeed and Delicious show that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.
Performance Analysis of Encryption Algorithms' Text Length Size on Web Browsers

Summary Information can easily be acquired over the Internet, as we know it today. Some are meant to be visible, while others are considered to be sensitive and meant to be secretive. This however, brings about the basic problem of susceptibility to abuse if mistreated. Thus, the proprietor would want sensitive data to remain sensitive and confidential. In order to do this, the data has to be secured. In this study, the security measure that has been selected is encryption. We have proposed a Web programming language to be analyzed with four Web browsers in term of their performances to process the encryption of the programming language’s script with the Web browsers. Firstly, we introduce one of the encryption techniques that can be applied, which is the eXclusive OR (XOR) operation, and how the text data are converted into hexadecimals. This is followed by conducting tests simulation in order to obtain the best encryption algorithm versus Web browser. The results of the analysis are presented in the form of graphs. We finally conclude on the findings that different algorithms perform differently to different Web browsers. Hence, we now determine which algorithm works best and most compatible with which Web browser.
Fault tolerant constructive algorithm for feedforward neural networks

In this paper, a constructive algorithm for fault tolerant feedforward neural network, called FTCA, is proposed. The algorithm starts with a network with a single hidden neuron, and a new hidden unit is added to the network whenever it fails to converge. Before inserting the new hidden neuron into the network, only the weights connecting the new hidden neuron to the other neurons are trained (i.e. updated) until there is no significant reduction of the output error. To generate a fault tolerant network, the relevance of synaptic weights is estimated in each cycle. And only the weights which have a relevance less than a specified threshold are updated in that cycle. The loss of connections between neurons (which are equivalent to stuck-at-0 faults) are assumed. The simulation results indicate that the network constructed by FTCA has a significant fault tolerance ability.