Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic eye detection using intensity and edge information

We propose a new algorithm to detect the pupils of both eyes from a human face in an intensity image. First, feature points which are the candidates for the pupils of both eyes are extracted from the face image by using the feature template proposed by Lin and Wu (see IEEE Trans. Image Processing, vol.8, no.6, p.834-45 (1999). Next, the proposed algorithm computes a cost for each pair of feature points satisfying a spatial constraint. The cost is computed by searching for a circular region corresponding to the iris around each feature point. Finally, the algorithm determines a pair of feature points with the smallest cost to be the pupils of both eyes. As a result of the experiment using all faces without spectacles in the face database of the University of Bern, the success rate of the proposed algorithm was 93.0% on the average. Also, if looking-down faces are excluded, the success rate of the proposed algorithm was 97.1% on the average.
Estimating Adjusted Associations between Random Effects from Multilevel Models: The Reffadjust Package

We describe a method to estimate associations between random effects from multilevel models. We provide two new postestimation commands, reffadjustsim and reffadjust4nlcom, which are distributed as the reffadjust package. These commands produce the estimates and their associated confidence intervals. The commands are used after official Stata multilevel model estimation commands mixed, meqrlogit, and meqrpoisson (formerly named xtmixed, xtmelogit, and xtmepoisson, respectively, before Stata 13) and with models fit in the MLwiN statistical software package via the runmlwin command. We demonstrate our commands with several simulated datasets and for a bivariate outcome model investigating the relationship between weight and mean arterial pressure in pregnant women using data from the Avon Longitudinal Study of Parents and Children. Our method and commands help to improve the interpretability of estimated random-effects variance components from multilevel models.
An adaptive digital domain calibration technique for pipelined Analog-To-Digital Converters

An adaptive digital calibration technique for enhancing the accuracy of Pipelined Analog-to-Digital Converters (ADC) is discussed in this paper. In contrast to the traditional approach that uses ideal bit-weights for calculation of digital output, the approach adopted here is based on calculating digital output using actual bit-weights. Actual bit-weights are extracted by measuring discontinuities at points where the bit transition occurs and applying the Least Mean Square (LMS) algorithm in the sense that mean-squared error is minimized to improve the linearity of the ADC. In contrast to the traditional MDAC residue characteristics that introduce errors in both stage-gain and reference terms, the modified MDAC architecture used here has only one error term in residue characteristic. Simulation results demonstrate the extension of inherent accuracy of ADC to 16-Bits and more.
Neural network chaos and computational algorithms of forecast in finance

Dynamic system chaos theory has given us a new view of the world. Systems that are apt to chaotic behavior must possess the main properties: non-linearity, openness as a system, multicomponentness, evolutionism and randomness. In such systems, even a slight change in initial conditions could cause it to evolve into a new macroscopic structure or, in other words, a cooperative mode discovered under fluctuations. The significance of asymmetry and non-linearity, which were increased on introducing a restricted N-shaped synaptic relation in a two-mode dynamic model, is emphasized. Population dynamics, economics, weather and financial market forecasting possess these properties. Long-term forecasting of chaotic systems is almost impossible because a small error can amplify itself over time to cause a new quality of events. Short-term forecasting of chaotic systems can easily realized, moreover chaos is a catalyst in the learning of artificial neural systems. An improved neural network computational algorithm linked with the kernel function approach and the recursive prediction error method has been developed and applied to stock market time series forecasting. The main idea of neural network learning by the kernel function is that this function stimulates changes to weights to achieve convergence of desired and forecast output functions.
MORPHOLOGICAL STRUCTURE AND THE PROCESSING OF INFLECTED WORDS

This paper is concerned with how our mind/brain processes morphologically complex words. We will show that the morphological structure of an inflected word determines the way it is processed. Specifically, we will argue that regularly inflected words have stem + affix representations and are typically computed via their constituent morphemes, whereas irregular inflection is represented in terms of lexical entries which are stored in associative memory
The impact of online teaching videos on Canadian pre‐service teachers

Purpose – One of the major challenges in teacher training programs is the gap between the theory that is presented to pre‐service teachers and actual classroom practice. Many researchers, educators, and pre‐service teachers have emphasized the difficulty of linking theory and practice in teacher education programs. The purpose of this study is to better understand the impact of online teaching videos on the development of self‐efficacy beliefs in pre‐service teachers.Design/methodology/approach – Over 400 student teachers participated in this study. Statistical analyses of questionnaires were conducted to assess the impact of online teaching videos. The results reveal that online videos did affect the self‐efficacy beliefs of pre‐service teachers.Findings – Online videos of teaching practices appear to contribute positively to feelings of self‐efficacy in pre‐service teachers.Practical implications – Given the importance of self‐efficacy for teachers' professional development, online videos could prove hi.
Power System Dynamics and Stability

1. Introduction. 2. Electromagnetic Transients. 3. Synchronous Machine Modeling. 4. Synchronous Machine Control Models. 5. Single-Machine Dynamic Models. 6. Multimachine Dynamic Models. 7. Multimachine Simulation. 8. Small-Signal Stability. 9. Energy Function Methods. Appendix A: Integral Manifolds for Model. Bibliography. Index.
The efficiency of visual transparency

This thesis examines the phenomenon of visual transparency in a novel application of the efficiency approach. Transparency provides a useful stimulus to probe the visual mechanisms that underlie the visual surface representation, introduced in Chapter One. Previous research has found that there is a cost in processing visual transparency defined purely by motion or stereo cues. This has been interpreted in terms of visual mechanisms constraining the recovery of transparency. However, the cost for transparency may reflect the increased complexity of the stimuli. To address this issue I computed the efficiency for motion and stereo defined transparency tasks by comparing human performance with that of the ideal observer. The efficiency approach has two key advantages over traditional psychophysical measures: 1) it provides a performance measure normalised relative to the available information, 2) it is an absolute measure and can be compared directly across diverse tasks. I provide a review of the efficiency approach in Chapter Two. In Chapter Three, I present a study of the efficiency for speed discrimination of transparent random dot stimuli and comparable non-transparent random dot stimuli, as a function of the speed ratio and the dot density of the stimuli. In Chapter Four, I present a study of the efficiency for depth discrimination of transparent and non-transparent random dot stereograms, across a range of disparity ratios and dot densities. In Chapter Five, I present an extension of the efficiency approach to the motor domain, for the smooth pursuit of high-density transparent and non-transparent random-dot stimuli. Finally, in Chapter Six I provide physiologically plausible accounts of the findings.
Improving expertise-sensitive help systems

ii ACKNOWLEDGEMENTS. iii DEDICATION. iv TABLE OF CONTENTS. v LIST OF FIGURES. x LIST OF TABLES. xii LIST OF ABBREVIATIONS. xiii CHAPTER 1: INTRODUCTION. 1 1.1 Research Problem. 2 1.2 Solution. 3 1.3 Steps in the Solution. 4 1.4 Evaluation. 6 1.5 Contributions. 6 1.6 Thesis Outline. 7 CHAPTER 2: RELATED WORK. 9 2.1 Differentiating Experts‟ and Non-experts‟ Behaviour. 10
Aplicación de la TG en el deporte para el estudio de la validez, fiabilidad y estimación de la muestra

Normal 0 21 false false false MicrosoftInternetExplorer4 La Teoria de la Generalizabilidad (TG) es una teoria de los errores multifaceta que asume que cualquier situacion de medida posee infinitas fuentes de variacion. En este sentido esta teoria puede aplicarse en el ambito de la observacion con la intencion de conocer lainfluencia de estas fuentes de error sobre la medida. De todas las posibilidades, en este trabajo se presentan, a modo de ejemplo, tres aplicaciones de la TG en las primeras fases de una investigacion en el ambito observacional: 1) para el estudio de la validez, 2) para la estimacion de la muestra; y, 3) para el estudio de la fiabilidad. Actualmente existen aplicaciones informaticas (GT, EduG o, recientemente, SAGT) que facilitan la implementacion de este tipo de analisis que permiten al investigador contar con recursos procedimentales para que las decisiones que tiene que tomar en el proceso de investigacion puedan estar justificadas antes de ser implementadas como la muestra necesaria para poder generalizar con precision o disponer de una herramienta de observacion valida y fiable para afrontar el arduo proceso de codificacion y registro de las conductas acontecidas en los contextos naturales donde se aplican.
Navigation Support for Learners in Informal Learning Networks

Drachsler, H. (2009). Navigation Support for Learners in Informal Learning Networks. October, 16, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC. SIKS Dissertation Series No. 2009-37. ISBN 9789079447312.
Non-Intrusive Decentralized Attack Analyzer & Network Controller to Monitoring and Prevent Vulnerability in VM Network System

Cloud security is one of most important issues that have attracted a lot of research and development effort in past few years Vulnerable virtual machines are an easy target and existence of such weak nodes in a network its entire security structure. Resource sharing nature of cloud favors the attacker, in that, compromised machines can be used to launch further devastating attacks. We propose a hybrid intrusion detection framework to detect vulnerabilities, attacks, and their carriers, i.e. malicious processes in the virtual network and virtual machines. This framework is built on attack graph based decentralized analytical models, VMM-based malicious process detection, and reconfigurable virtual network-based countermeasures. The proposed framework leverages Software Defined networking to build a monitor and control plane over distributed programmable virtual switches in order to significantly improve the attack detection and mitigate the attack consequences.
Investigation of genetic factors regulating chlorophyll and carotenoid biosynthesis in red pepper fruit

Chlorophylls and carotenoids are synthesized in the chloroplast and chromoplast, respectively. Even though the two pigments are generated from the same precursor, the genetic correlation between chlorophyll and carotenoid biosynthesis has not yet been fully understood. We investigated the genetic correlation of chlorophyll and carotenoid biosynthesis during fruit ripening. Two recombinant inbred lines populations, “Long Sweet” × “AC2212” (“LA”) RILs derived from a cross between Capsicum annuum “Long Sweet” with light-green and light-red fruit and C. annuum “AC2212” with dark-green and brown-fruit and “3501 (F)” × “3509 (C)” (“FC”) RILs from C. annuum “3501” with dark-green and dark-red fruit and C. annuum “3509” with intermediate green and light-red fruit, were used. As the fruit ripened, three accessions produced high levels of xanthophyll. The dark-green immature fruit accumulated more total carotenoids than the light-green fruit. This trend corresponded to the expression pattern of 1-deoxy-d-xylulose 5-phosphate synthase (DXS) and CaGLK2 genes during fruit development. The expression levels of DXS and CaGLK2 in the dark-green accession “3501” were significantly higher than those of “3509” and “Long Sweet” during the early stages of fruit development. Furthermore, the genotype analysis of the transcription factor controlling chloroplast development (CaGLK2) in LA RILs revealed that CaGLK2 expression affected both carotenoid and chlorophyll contents. The single nucleotide polymorphism (SNP) linkage maps were constructed using genotyping-by-sequencing (GBS) for the two populations, and QTL analysis was performed for green fruit color intensity and carotenoid content. The QTL (LA_BG-CST10) for capsanthin content in LA RILs located at 24.4 to 100.4 Mbp on chromosome 10 was overlapped with the QTL (FC15-Cap10) for capsanthin content in FC RILs. Three QTLs for capsanthin content, American spice trade association (ASTA) value, and immature green fruit color intensity were also overlapped from 178.2 to 204 Mbp on chromosome 10. At the location, 151.6 to 165 Mbp on chromosome 8, QTLs (FC15-tcar8, FC17-ASTA8.1, and FC17-ASTA8.2) for total carotenoid content and ASTA value were discovered, and this region contained 2-C-methyl-d-erythritol 4-phosphate cytidylyltransferase (MCT), which is involved in the MEP pathway. This result is the first report to show the correlation between carotenoid and chlorophyll biosynthesis in pepper. This research will expand our understanding of the mechanism of the chloroplast-to-chromoplast transition and the development of high pigment pepper varieties.
Triggering cell death in cancers using self-illuminating nanocomposites

Bioinspired photocatalysis has resulted in efficient solutions for many areas of science and technology spanning from solar cells to medicine. Here we show a new bioinspired semiconductor nanocomposite (nanoTiO2-DOPA-luciferase, TiDoL) capable of converting light energy within cancerous tissues into chemical species that are highly disruptive to cell metabolism and lead to cell death. This localized activity of semiconductor nanocomposites is triggered by cancer-generated activators. Adenosine triphosphate (ATP) is produced in excess in cancer tissues only and activates nearby immobilized TiDoL composites, thereby eliminating its off-target toxicity. The interaction of TiDoL with cancerous cells was probed in situ and in real-time to establish a detailed mechanism of nanoparticle activation, triggering of the apoptotic signaling cascade, and finally, cancer cell death. Activation of TiDoL with non-cancerous cells did not result in cell toxicity. Exploring the activation of antibody-targeted semiconductor conjugates using ATP is a step toward a universal approach to single-cell-targeted medical therapies with more precision, efficacy, and potentially fewer side effects.
Wireless communications : signal processing perspectives

Each chapter concludes with 'References' and 'Acknowledgments. ' List of Contributors. 1. Linear Diversity Techniques for Fading Channels, Gregory W. Wornell. 1.1 System and Fading Channel Models. 1.2 Transmission without Diversity. 1.3 Spectral Diversity. 1.4 Temporal Diversity. 1.5 Diversity Methods for Multiuser Systems. 1.6 Spatial Diversity. 1.7 Concluding Remarks. 2. Adaptive Interference Suppression, Michael L. Honig and H. Vincent Poor. 2.1 Multiple-Access Signal Model. 2.2 Elements of Multiuser Detection. 2.3 Linear Interference Suppression. 2.4 Application to DS-CDMA. 2.5 Adaptive Algorithms. 2.6 Further Issues and Refinements. 2.7 Concluding Remarks. 3. Equalization of Multiuser Channels, Haralabos C. Papadopoulos. 3.1 Characterization of Wireless Channels. 3.2 Equalization of Known Multipath Fading Channels. 3.3 Blind Equalization in Multipath, Slowly Time-Varying Channels. 3.4 Concluding Remarks. 4. Blind Space-Time Signal Processing, Arogyaswami J. Paulraj, Constantinos B. Papadias, Vellenki U. Reddy, and Alle-Jan van der Veen. 4.1 The Wireless Propagation Environment. 4.2 Signal Model and Structure. 4.3 Channel Identification and Equalization. 4.4 Blind Techniques. 4.5 Concluding Remarks. 5. Network Capacity, Power Control, and Effective Bandwidth, David N. C. Tse and Stephen V. Hanly. 5.1 Basic Spread-Spectrum Model and the MMSE Receiver 5.2 Performance under Random Spreading Sequences 5.3 Capacity and Performance under Power Control 5.4 Multiple Classes, Maximum Power Constraints, and Effective Bandwidths. 5.5 The Decorrelator. 5.6 Antenna Diversity. 5.7 Concluding Remarks. 6. Architectural Principles for Multimedia Networks, Paul Haskell, David G. Messerschmitt, and Louis Yun. 6.1 Basic Considerations. 6.2 Modularity of Services and Bitway Layers. 6.3 Edge vs. Link Architecture for Service Layer. 6.4 Design Examples. 6.5 Concluding Remarks. 7. Multiresolution Joint Source-Channel Coding, Kannan Ramchandran and Martin Vetterli. 7.1 Multiresolution Source Coding for Images and Video. 7.2 Multiresolution Channel Coding. 7.3 Multiresolution Joint Source-Channel Coding. 7.4 Concluding Remarks. 8. Underwater Acoustic Communications, David Brady and James C. Preisig. 8.1 The Underwater Acoustic Channel. 8.2 Platform Constraints in Underwater Acoustic Communications. 8.3 A Brief History of Underwater Acoustic Communications. 8.4 Signal Processing in Digital Underwater Communications. 8.5 Concluding Remarks. Epilogue: Four Laws of Nature and Society: The Governing Principles of Digital Wireless Communication Networks, Andrew J. Viterbi. E.1 Overview. E.2 Wireless Propagation and Its Anomalies. E.3 Shannon Theory: Limitations on Signal Processing. E.4 Half a Century of Wireless Spread Spectrum: From Military to Commercial Applications. E.5 Moore's Law: The Socio-Economic Basis for Digital Wireless. E.6 Metcalfe's Law: Implications for Wireless Networks. List of Acronyms. Index.
Fog Computing: Will it be the Future of Cloud Computing?

Cloud computing is the newest computing paradigm that makes computing resources available over the Internet on a utility costing basis. Cloud computing offers many advantages to users in terms of reduced cost, elimination of system administrative functions, increased flexibility, better reliability and location independence. Though these are definite advantages, cloud computing also suffers from certain limitations. These limitations arise from the very same reason that is considered an advantage too. Hosting of cloud data centres in the Internet creates large and unpredictable network latencies and undefined security issues as sensitive data is now entrusted to a third party. Also location independence of processing in cloud computing may also not desirable for certain types of networks such as sensor networks and Internet of Things. These services are known as location aware services and require location dependent fast processing. In order to overcome these limitations, researchers have proposed a new cloud computing model called fog computing where the cloud system is located either at the edge of the private network or very close to it. In this paper, the authors take an in depth look at both these technologies to investigate fog computing can reliably overcome the limitations of cloud computing and effectively replace it and become the de facto cloud computing model of the future.
Minimum resource zero-knowledge proofs (extended abstracts)

What are the resources of a zero-knowledge Proof? Interaction, communication, and envelops. That interaction, that is the number of rounds of a protocol, is a resource is clear. Actually, it is not a very available one: having someone on the line to answer your questions all the time is quite a luxury. Thus, minimizing the number of rounds in zero-knowledge proofs will make these proofs much more attractive from a practical standpoint. That communication, that is the number of bits exchanged in a protocol, is a resource is also immediately clear. Perhaps, what is less clear is why envelopes are a resource. Let us explain why this is the case.Zero-knowledge proofs work by hiding data from a verifier. Only some of this data will be later revealed, at the verifier's request: enough to convince him that the statement at end is true, but not enough to give him any knowledge beyond that. Data can be hidden in two ways: physically - e.g. by putting it into an envelope - or digitally - by encrypting it. But why is it important to minimize the number of envelopes? Physically, because a GOOD envelope is expensive - it actually must be a led box or a safe. Digitally, because minimizing the number of envelopes corresponds to reducing the transmitted bits. In fact, to transmit an encrypted message, one needs to send more bits than in the message itself. For instance, to send an encrypted bit, one needs to send at least 60 bits in some probabilistic encryption scheme. Also, to decrypt each ciphertext, one has to send the decryption key. However, many bits may be encrypted and decrypted with the same overhead of a few bits. Thus if one manages to package the data that should be hidden in as few envelopes as possible, while maintaining zero-knowledge, the protocol will require transmitting much less bits.
A method for identifying geometrically simple surfaces from three-dimensional images

Computational neuroanatomy is an exciting new area where digital tools are used with great advantage in the analysis of structure and function of the brain. One major area of research in this field involves automatically creating computerized representations of the brain which are in a form suitable for neuroanatomic analysis. The principle drawback of contemporary methods of generating these digital models is the incompleteness and ambiguity of the input data, which is typically under-sampled relative to the features of interest. A method is presented here for creating surface-based models of neuroanatomy that address the data incompleteness issue with an integrated combination of two model-based approaches. The first approach involves applying proximity and self-intersection restrictions on surfaces in order to create a plausible neuroanatomical model in the face of data with topologies inconsistent with medical knowledge. The second approach involves identifying multiple surfaces simultaneously, with inter-surface constraints, in order to use general neuroanatomical information to correct areas where the data is incomplete or ambiguous. The overall method is one of deforming a set of polyhedral meshes, with the above constraints and others incorporated into an objective function, which is minimized to find the best fit of a model to the data. Validation of this method on simple phantoms as well as in the task of segmentation of human cortical surfaces is demonstrated. Discussion of limitations and future work is presented.