Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability of slotted ALOHA access with capture provided by an adaptive array

We study slotted ALOHA multiple access when a satellite contains an on-board antenna array to attenuate interference. This leads to a capture phenomenon in slotted ALOHA that is provided by the adaptive array. As expected, and as earlier reported in the literature, the adaptive array can improve system throughput. We find that it also improves system robustness. That is, the probabilities of new packet generation and retransmission can vary over a wider range than for slotted ALOHA and still maintain nearly maximum throughput. The same is true for the average packet delay and average system backlog. As in the earlier literature the adaptive beamforming capability on-board the satellite is taken to be ideal. For a system with two uplink and two downlink beams the throughput increase relative to slotted ALOHA is 128% for a wide range of system parameters.
The Determinants and Information Content of the Forward-looking Statements in Corporate Filings - A Naive Bayesian Machine Learning Approach

This paper examines the tone and content of the forward-looking statements (FLS) in corporate 10-K and 10-Q filings using a Naive Bayesian machine learning algorithm. I first manually categorize 30,000 sentences of randomly selected FLS extracted from the MDA and (2) content (i.e., profitability, operations, and liquidity etc.). These manually coded sentences are then used as training data in a Naive Bayesian machine learning algorithm to classify the tone and content of about 13 million forward-looking statements from more than 140,000 corporate 10-K and 10-Q MD&As between 1994 and 2007. I find that firms with better current performance, lower accruals, smaller size, lower market-to-book ratio, and less return volatility tend to have more positive forward-looking statements in MD&As. The average tone of the forward-looking statements in a firm's MD&A is positively associated with future earnings and liquidity, even after controlling for other determinants of future performance and there is no systematic change in the information content of MD&As over time. Finally, the evidence indicates that financial analysts do not fully understand the information content of the MD&As in making their forecasts.
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization

From the Publisher: Topographic map formation is an important component of both artificial and biological networks, and is emerging as a key topic in the theory of Neural Networks, a popular area of artificial intelligence research. This book discusses in detail, the science behind a computer's ability to self-organize, an area which has important practical applications in data mining and pattern recognition. Written by a well-known researcher in the field and providing an introductory chapter to bring newcomers to the topic up-to-speed, this title provides complete coverage of self-organizing maps.
Recent Developments in Experimental AIPS

An experimental version of AIPS is being developed by the author with the intent of trying new ideas in several areas. Copies of this version are made available periodically. For interferometric data, there have been significant developments in spectral-line calibration, data editing, wide-field imaging, self-calibration, and analysis of spectral cubes. For single-dish data, there have been improvements in the imaging of “on-the-fly” spectral-line data and new programs to analyze and image continuum, beam-switched data. Image display techniques which retain their state information to any other computer running this version have been developed. Results from several of these areas are presented.
Signal processing in mobile communication systems

Digital signal processing is a key technology for the expansion of mobile communications and more and more functions in mobile units and base stations are performed digitally. The evolution concerning two major functions is reviewed, namely A/D conversion using an integrated band-pass high performance sigma-delta modulator and adaptive channel equalisation/estimation for optimum detection. Then, antenna array processing techniques are discussed, in view of the potential they have in the context of space division multiple access (SDMA), to improve the efficiency of cellular networks.
Use of neural networks for the recognition of place of articulation

The Boltzmann machine algorithm and the error back propagation algorithm were used to learn to recognize the place of articulation of vowels (front, center or back), represented by a static description of spectral lines. The error rate is shown to depend on the coding. Results are comparable or better than those obtained by us on the same data using hidden Markov models. The authors also show a fault tolerant property of the neural nets, i.e. that the error on the test set increases slowly and gradually when an increasing number of nodes fail. <<ETX>>
C20. Identifying Unique Flatbed Scanner Characteristics for Matching a Scanned Image to its source

Scanner identification is the ability to discern the devices by which an image was scanned. In this paper, a new and robustness individual source scanner identification scheme is proposed. This scheme formulates a unique fingerprint for each scanner using traces of dust, dirt, and scratches over scanner platen on scanned images. A single Support Vector machine (SVM) classifier is implemented and trained using correlation features of scanned images to classify different scanners brands and different models for the same scanner brand, and a 99.68% detection accuracy is obtained. In addition, the robustness of the used individual source scanner identification scheme on resized and different resolutions scanned images is experimentally tested. The experimental results using the proposed classifier for different scanner brands and different models for the same scanner brand approved the validity, efficiency, and robustness of the proposed scheme to match the scanned image to its unique source.
Classificação automática de gêneros musicais utilizando métodos de bagging e boosting

This paper presents a study that uses meta-learning techniques to the task of automatic music genre classification. The meta-learning techniques we used are Bagging and Boosting. In both cases the component classifiers used in both approaches are Decision Trees, k-NN (k nearest neighbors) and Naive Bayes. The experiments were performed on a dataset containing 1,000 songs with 10 different genres. The achieved results show that the Bagging approach is promising while the Boosting approach seems to be inadequate to the problem.
The Virtual Lab for teleoperated control of real experiments

The Virtual Lab is an experimental setup for students in control systems engineering, which is accessible via Internet. The experimental system is an omnidirectional vehicle; several different tasks can be carried out with this vehicle ranging from kinematics and dynamics analysis to controller design and system identification. Teleoperation control options for these experiments have been chosen in addition to conventional practice because of the following two major reasons: to share this experimental setup with other universities, and to simplify experiments for home-learners and professionals in a distance teaching environment.
Improving user satisfaction: the questionnaire for user interaction satisfaction version 5

The Questionnaire for User Interaction Satisfaction(QUIS) is a usability testing tool designed to gauge computer user's subjective satisfaction with the computer interface. The QUIS contains a demographic questionnaire, an overall measure of satisfaction, and measures of user satisfaction in four specific interface aspects (screen factors, terminology and system feedback, learning factors, and system capabilities). The current study establishes some normal values for QUIS evaluations. Undergraduate students attending one of six classes in the AT&T Teaching Theater evaluated their experiences using a variety of software packages in the course of one semester. A new version of the on-line QUIS 5.5 ran in the Teaching Theater's Windows™ 3.1 environment and collected the students satisfaction data. The on-line version of the QUIS and the results it yielded are discussed.
Come As You Are

RELEASED: June 28, 2013 CHOREO: Doug & Cheryel Byrd 1443 Britt Lauren Way, Soddy Daisy, TN 37379 (423) 842-7626 dbyrdhouse@comcast.net MUSIC: Come As You Are DOWNLOAD: www.casa-musica.de ARTIST: Aaliyah Jones & Jackson Burke ALBUM: Latin Unlimited Latino Contigo ALTERNATE ARTIST: Jaci Velasquez (speed to suit) ALTERNATE ALBUM: Crystal Clear (www.amazon.com) FOOTWORK: Opposite except where indicated TIME: 3:09 RHYTHM: Rumba RAL PHASE: III + 2 [Aida, Switch Cross] + 1 [Shadow New Yorker] SEQUENCE: INTRO ABC ABC INTLD C ENDING DEGREE OF DIFFICULTY: Avg
Fast Background Extracting Algorithms of Video Sequence Based on Invariance

In order to reduce the complexity and computation of the background extraction algorithm, this paper presents a new background extraction algorithm corresponding to the intelligent traffic environment based on the invariance of background. In the algorithm, we partition the background into sub-blocks, and compare the characteristics of the several continues frames in the video stream, then update the background block by fuzzy similarity processing in real time. The evaluation proves that the new algorithm can accomplish the higher efficient extraction with less computation complexity.
Editorial: Effective construction of linear state-variable models from input/output functions

The algorithm can also be used to prove various abstract results in realization theory; we give a proof of the well known fact that any two minima! realizations are isomorphic. Es wird ein neuer weitreichender Algorithmus abgeleitet, der zu der minimalen Realisierung von linearen dynamischen Systemen endlicher Ordnung führt, wenn man von den Beziehungen aus-geht, wie sie bei der Beschreibung von Systemen durch die Ein- und Ausgangsgröße Verwendung finden; er macht von den sogenannten Markovschen
Transmission Control Protocol Specification.

Abstract : This is the specification of the Transmission Control Protocol (TCP) which is the host-to-host protocol for the AUTODIN II network. The AUTODIN II system provides the capability for geographically distributed computers, called hosts, to communicate with each other. The hosts are a diverse set of computers of differing manufacture, speed, word size, and operating system. The AUTODIN II system provides a mechanism for communication between hosts; the TCP specifies how the hosts use this mechanism to provide communication services to processes and ultimately to human users. The reader of this document is assumed to be familiar with the concepts of the AUTODIN II system, and with operating system concepts. In particular, the reader is expected to have read the AUTODIN II Specification. It is intended that the information presented here be sufficient and complete enough to allow a competent systems programmer to implement an operating system program module to carry out this protocol. In the following sections a brief background of the AUTODIN II network, and the protocol environment within it, is presented. This introduces the more detailed specification in the sections on TCP function, environment, and implementation. The document includes appendices on several details of TCP implementation. (Author)
Structural equation modelling : tips for getting started with your research

At a time when numerical expression of data is becoming more important in the business of obtaining grant money research tools such as structural equation modelling (SEM) are becoming more popular. Structural equation modelling enables researchers to show relationships between variables numerically. It is of particular use to educational researchers who are interested in developing or using standard questionnaires in their research. This session will introduce SEM and explore its uses in educational research to enable the reader to decide whether it is a tool to use to add an extra dimension to their research findings.
Using neural networks and LPCC to improve speech recognition

Linear Predictive Coding (LPC), powerful speech analysis technique, is very useful for encoding speech at a low bit rate and provides extremely accurate estimates of speech parameters - based on the assumption that speech signal is produced by a buzzer at the end of the tube (the glottis produces the buzz, characterized by its intensity and frequency, and the vocal tract forms the tube, characterized by resonance frequencies (formants) according to Calliope(1989), is very efficient for the vocalic areas. The model is less efficient for transient, unvowel or not stationary regions according to R. Lawrence and B. Hwang Juang (1993). A Radial Basis Function network is able to recognize in a satisfying percent a set of phonemes pronounced by different speakers, using LPC sets as input.
Who Wrote the 15th Book of Oz? An Application of Multivariate Analysis to Authorship Attribution

Lyman Frank Baum (I H56-1(19), according to Martin Gardner, was "America's greatest writer of children's fantasy. " "His Wonderful Wizard of OZ has long been the nation's best known, best loved native fairy tale" (Gardner and Nye 1957, p. 19). Although initially rejected by publishers in Chicago, it became a children's best seller by Christmas of 1900. Such an instant success led biographer Bussell MacFall to write a chapter about 1900, calling it Haum's annus mirahilis (Baurn and Mac Fall 19(1). After writing the first Oz book, Baurn could not separate himself from the Fantasv land he created. In less than two decades, he produced a series consisting of 14 books. He consequently earned the title, 'The Royal Historian of Oz. " Nye describes the scenario in the early 1900s:
Évaluation individuelle et collective dans les jeux sérieux collaboratifs : application à la gestion de crise

Serious Games (SG) are increasingly used in the field of training, SG aims to teach one or more skills(s) related to a business or to more general qualities, to one or more learners (players), this with a playful dimension. Usually, SG aims a specific training objective related to a specific context training which can be linked to a trade (eg fire ), a body of knowledge (school or university), or the acquisition of social skills (conflict management, cooperation .). To Measure the success or failure of this learning is ultimately an assessment process of the learner(s) and in recent years became a major issue in SG in addition to issues classic design and screenwriting.In the context of multi-player SG, with a collaborative tasks learning performed by different learners (or virtual actor) sharing a common goal, our research focuses on the assessment of learning processes, leading to both individual and collective assessment. We propose first a methodological framework for learners assessment. This methodological framework is based on the "Evaluation space" concept associated with a specific view corresponding to a particular assessment objective, leading to the calculation of indicators, based on specific knowledge models representation and assessment. In order to operationalize this methodological assessment framework, we propose a agents based architecture, on the one hand allowing human learners and virtual players (simulation of behavior) interaction, and other hand supporting individual and collective learners' assessment. This architecture is implemented and integrated to the SIMFOR SG, and illustrated in a crisis management scenario exercise.
Quadtree Structured Approximation Algorithms

The success of many image restoration algorithms is often due to their ability to sparsely describe the original signal. Many sparse promoting transforms exist, including wavelets, the so called ‘lets’ family of transforms and more recent non-local learned transforms. The first part of this thesis reviews sparse approximation theory, particularly in relation to 2-D piecewise polynomial signals. We also show the connection between this theory and current state of the art algorithms that cover the following image restoration and enhancement applications: denoising, deconvolution, interpolation and multi-view super resolution. In [63], Shukla et al. proposed a compression algorithm, based on a sparse quadtree decomposition model, which could optimally represent piecewise polynomial images. In the second part of this thesis we adapt this model to image restoration by changing the rate-distortion penalty to a description-length penalty. Moreover, one of the major drawbacks of this type of approximation is the computational complexity required to find a suitable subspace for each node of the quadtree. We address this issue by searching for a suitable subspace much more e ciently using the mathematics of updating matrix factorisations. Novel algorithms are developed to tackle the four problems previously mentioned. Simulation results indicate that we beat state of the art results when the original signal is in the model (e.g. depth images) and are competitive for natural images when the degradation is high.
Partial Evaluation of Standard ML

This thesis describes offline partial evaluation of the core of Standard ML, a large typed functional language. Unlike previous partial evaluators for larger languages (like for instance Similix for a subset of Scheme or C-Mix for a subset of C) we have chosen not do to the partial evaluation directly, but to use an untraditional method which we call the cogen approach to transform a program into its generating extension. We show in this thesis that this approach is in many aspects superior to the traditional approach and that it eliminates the need for self-applying the specializer. We develop a binding-time analysis based on non-standard type inference and produce a very efficient implementation of it using constraints. While this has been done before, we have for the first time succeeded in using the typedness of the source language to make the analysis simple and therefore more trustworthy. To our best knowledge this thesis also describes the first successful strategy for partially evaluating complicated patterns with variable bindings. Earlier attempts have either been for a much simpler class of patterns or have stranded on the need/wish for self-application of the specializer. Note that partially evaluating complicated patterns is different from partially evaluating programs dealing with complicated patterns; that has been done successfully. A complete system for partial evaluation of Standard ML with parsing, type checking, binding-time analysis, compiler generation, and pretty printing has been implemented and we report on some experiments with this system. We have not implemented everything described in the thesis, though. Details can be found in Chapter 6.
A Rapide-1.0 Definition of the ADAGE Avionics System

We have used the Rapide prototyping-languages, developed by Stanford and TRW under the ARPA ProtoTech Program, in a series of exercises to model an early version of IBM''s ADAGE software architecture for helicopter avionics systems. These exercises, conducted under the ARPA Domain Specific Software Architectures (DSSA) Program, also assisted the evolution of the Rapide languages. The resulting Rapide-1.0 model of the ADAGE architecture in this paper is substantially more succinct and illuminating than the original models, developed in Rapide-0.2 and Preliminary Rapide-1.0. All Rapide versions include these key features: interfaces, by which types of components and their possible interactions with other components are defined; actions, by which the events that can be observed or generated by such components are defined; and pattern-based constraints, which define properties of the computation of interacting components in terms of partially ordered sets of events. Key features of Rapide-1.0 include services, which abstract whole communication patterns between components; behavior rules, which provide a state-transition oriented specification of component behavior and from which computation component instances can be synthesized; and architectures, which describe implementations of components with a particular interface, by showing a composition of subordinate components and their interconnections. The Rapide-1.0 model is illustrated with corresponding diagrammatic representations.
Semantic labeling of indoor scenes from RGB-D images with discriminative learning

Recently emerged RGB-D sensors provide great promise for indoor scene understanding, which is a fundamental and challenging problem in computer vision. We present a discriminative model in this paper to semantically label indoor scenes from RGB-D images. Unlike previous work which only labels pre-determined superpixels, we characterize the scenes with a set of planes and compose them into objects. The optimal way to composition and corresponding labels are inferred simultaneously using a greedy algorithm. Our model considers unary features and pairwise and co-occurrence context, as well as latent variables that account for multi-mode distributions of each object category. We train the model with latent structural SVM learning framework. Our approach achieves state-of-the-art performance on the Cornell RGB-D indoor scene dataset [1].
Medical Image Registration with Fourier basis Functions

Registration is one of the most interesting, yet challenging computer-aided tasks in medical image processing, aimed at bringing two or more data sets into spatial and/or temporal correlation. If the represented data are medical images, there are countless situations where it is of interest to attain such correlation, as it has become routine practice in many diagnostic and image-guided therapeutic procedures. For example, one of the most frequent clinical applications is to align two scans of a given patient, e.g. for easy identification of equivalent structures on both registered images, follow-up of disease, etc. Another use of image registration is commonplace during brain activation studies, where several functional Magnetic Resonance (fMRI) or Positron Emission Tomography (PET) scans are repeated on the same or different subjects while receiving sensorial or cognitive stimulations. In order to perform a statistical analysis on the brain images, it is often necessary to register all data sets with respect to a brain atlas. In this way, both statistical power and signal-to-noise ratio are enhanced in what is known as intramodality registration (Friston et al, 2006). One of the most exciting applications is inter-modality registration, e.g. correlating PET and MR (Magnetic Resonance) scans of a same subject, or PET vs. CT (Computed Tomography). PET imaging provides distinctive functional and metabolic information, but lacks the high anatomical resolution which is in turn provided by conventional MR or CT. Thus PET/CT registration is synergic, since it facilitates the location of malignancies in their anatomical context. Moreover, this modality combination is often useful for Radiotherapy Planning (Townsend, 2008). An automatic image registration algorithm normally includes a floating image to be aligned to the coordinate system of a reference image. To do this, a spatial transformation function – containing a number of parameters-, must be proposed and applied to the former data set. The parameters are chosen in such a way that a proposed similarity measure between both data sets is optimized. This measure may be –for example-, the Normalized Mutual Information (NMI) or the Cross Correlation Coefficient. Methods which apply rigid-body transformations perform reasonably well for registering images of the head (brain) and extremity portions. In a 3D space, a rigid-body transformation involves only six parameters, e.g. three translations (Δx, Δy, Δz) and three