Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic detection of instruments in laparoscopic images: a first step towards high level command of robotized endoscopic holders

The tracking of surgical instruments offers interesting possibilities for the development of high level commands for robotized camera holders in laparoscopic surgery. We present a new method to detect instruments in laparoscopic images which uses information on the 3D position of the insertion point of an instrument in the abdominal cavity. This information strongly constrains the search for the instrument in each endoscopic image. Hence, the instrument can be detected thanks to shape considerations, which would otherwise not be feasible in near real time. Early results show that the method is rapid and robust to partial occlusion and smoke
Exploring Unknown Environments with Mobile Robots using Coverage Maps

In this paper we introduce coverage maps as a new way of representing the environment of a mobile robot. Coverage maps store for each cell of a given grid a posterior about the amount the corresponding cell is covered by an obstacle. Using this representation a mobile robot can more accurately reason about its uncertainty in the map of the environment than with standard occupancy grids. We present a model for proximity sensors designed to update coverage maps upon sensory input. We also describe how coverage maps can be used to formulate a decision-theoretic approach for mobile robot exploration. We present experiments carried out with real robots in which accurate maps are build from noisy ultrasound data. Finally, we present a comparison of different view-point selection strategies for mobile robot exploration.
Contrast Enhancement by Multiscale and Nonlinear Operators

Image enhancement techniques have been widely used in elds such as radiology, where the subjective quality of images is important for human interpretation (diagnosis). Contrast is an important factor in any subjective evaluation of image quality. Many algorithms for accomplishing contrast enhancement have been developed and applied to problems in medical imaging. A comprehensive survey of existing methods can be found in [1]. Among them, histogram modi cation and edge enhancement techniques have been most commonly used along with traditional methods of image processing. Histogram modi cation techniques [2, 3] are attractive due to their simplicity and speed, and have achieved acceptable results for some applications. In general, a transformation function is derived from a desired histogram and the histogram of an input image. In general, the transformation function is almost always nonlinear. For continuous functions, a
The Awareness and Perception of Spyware amongst Home PC Computer Users

Spyware is a major threat to personal computer based data confidentiality, with criminal elements utilising it as a positive moneymaking device by theft of personal data from security unconscious home internet users. This paper examines the level of understanding and awareness of home computer users to Spyware. An anonymous survey was distributed via email invitation with 205 completed surveys. From an analysis of the survey it was found that the majority of respondents do understand what Spyware is, however, there was found to be a lack of understanding of computer security in defending against Spyware, with 20% of survey respondents not using any Anti­Spyware. In addition, the subjective nature of survey respondent’s ideas of Spyware infected websites was established and compared to past web­crawl research where a high proportion of survey respondent’s opinions were found to be incorrect. It was also found respondents see Spyware as a ‘High/Some Threat’, and due to past infections and news/media articles 72% have changed their browsing habits.
A Neural Network Approach for Wind Retrieval from the ERS-1 Scatterometer Data

Abstract — The objective of the present work is to compute anew Geophysical Model Function (GMF hereinafter) for theERS-1 scatterometer by the use of neural networks (NNhereinafter). This NN-GMF is calibrated with ERS-1scatterometer sigma0 collocated with ECMWF analysedwind vectors. In order to check the validity of the NN-GMFsystematic comparisons with the ESA's CMOD4-GMF(version 2 of 3-25-93) and the IFREMER's CMOD2-I3-GMFare done. The GMF is used in many algorithms to retrievethe scatterometer wind. Resume — Nous proposons dans ce papier une methodepour calculer la fonction du modele geophysique (GMF) dudiffusiometre du satellite ERS-1. Cette methode est fondeesur des techniques de reseaux de neurones (NN). Lafonction anisi obtenue, NN-GMF, est etalonnee grâce a lacollocation des sigma0 mesures par ERS-1 avec les vecteursde vent donnes par le modele ECMWF. La validite de lamethode NN-GMF est verifiee en la comparant avec lamethode CMOD4-GMF (version 2 du 25-3-93) de l'ESA etavec la methode CMOD2-I3-GMF de l'IFREMER.1. INTRODUCTIONThe transfer function allowing to compute the wind fromthe scatterometer signal is very difficult to be determined. Itis a non linear function which may have ambiguities on thedirection. Several algorithms have been proposed to modelthe wind retrieval transfer function. Most of them are basedon the inversion of the Geophysical Model Function (GMF)which gives the sigma0 with respect to the wind vector. Thestudy of the GMF is then of a fundamental interest.Furthermore the GMF can give useful information on thebehaviour of the scatterometer. The present study isdevoted on the modelling of the GMF by the use of NeuralNetworks.Neural Networks have been used with success by thepresent team to retrieve the wind vector from the ERS-1scatterometer data. The methodology is described in [2]. Inthe present study we propose to determine a new GMF theNeural Networks is calibrated onto ECMWF analysed windvectors collocated with scatterometer sigma0.2. THE NEURAL NETWORKS GEOPHYSICALMODEL FUNCTION (NN-GMF) ALGORITHM
Teacher’s Informal Learning Trajectory and Student’s Actual Learning Trajectory On Learning Cube And Cuboid Nets

This article discussed the teachers' informal learning trajectory and students' actual learning trajectories in learning cube and cuboid nets. Informal learning trajectory that is believed to have experienced teachers have taught so as to anticipate all possibilities in interaction. While the actual learning trajectory is the trajectory of student learning derived from real traces of student learning. The writer try to make the observation of teaching and learning in the classroom and try to formalize teachers' informal learning trajectories and students' actual learning trajectory. From the observation of the learning process in fourth class SDI Surya Buana, there is a gap between students' learning actual trajectory and teachers' informal learning trajectory. This seemed to caused props that teachers prepared were inadequate. Keyword: informal learning trajectory, actual learning trajectory
A speaker verification system for access-control

This paper reports on the development of an automatic speaker verification system (ASV-system) integrated into an access-control environment. The development was characterized by a strong feedback loop from extensive out-of-the-lab tests into the R&D program, leading to an ASV-system with a type-I/type-II error rate crossover of < 1%, measured in the field. The ASV system is based on a word recognition approach originally devised by Thomas B. Martin [1]. It is word-dependent and requires a variable number of discrete utterances to be spoken during an access control transaction. In addition to speech algorithm quality, the overall system performance was found to be strongly determined by user interface issues, in particular the microphone type and the enrollment strategy.
Modelling Distributed Multimedia Applications

It is often believed that with the mere knowledge of the kind of media, e.g. "speech, the system and specifically the communication requirements of a distributed multimedia application are automatically fixed. In general this assumption is wrong. As a matter of fact, certain technology aspects including the kind of media, coding, and compression techniques are of p k a r y relevance. However the specific application's functionality imposes fixther requirements. In this paper we present a new model for distributed multimedia applications which is used for the description of such applications. It allows for the retrieval of some communication requirements, and it can be used to structure the described application. This model was created within and used for the development of an audio/video distribution service as application of the HeiTS (IBM ENC Heidelberg High-speed Transport System) project in Heidelberg.
The Fastest Fourier Transform in the West

Abstract : This paper describes FFTW, a portable C package for computing the one- and multidimensional complex discrete Fourier transform "DFT". FFTW is typically faster than all other publicly available DFT software, including the well-known FFTPACK and the code from Numerical Recipes. More interestingly, FFTW is competitive with or better than proprietary, highly-tuned codes such as Sun's PerformanceLibrary and IBM'sESSL library. FFTW implements the Cooley-Tukey fast Fourier transform, and is freely available on the Web at http://theory.lcs. mit.edu/ fftw. Three main ideas are the keys to FFTW's performance. First, the computation of the transform is performed by an executor consisting of highly-optimized, composable blocks of C code called codelets. Second, at runtime, a planner finds an efficient way "called a `plan'" to compose the codelets. Through the planner, FFTWadapts itself to the architecture of the machine it is running on. Third, the codelets are automatically generated by a codelet generator written in the Caml Light dialect of ML. The codelet generator produces long, optimized, unreadable code, which is nevertheless easy to modify via simple changes to the generator.
Towards enterprise transition to object technologies: a CMM-based methodology

This paper presents an SEI Capability Maturity Model-consistent methodology for enterprise transition to object technologies. Transition is accomplished through a process of technology uptake and change management. While earlier models assume that the organisation is capable of incorporating new software processes and focus on the initial project, the current work helps to reduce uncertainty in determining the right time to introduce object technologies. The methodology model is presented with four sub-models that describe the activities and steps involved in the management of change from the technology uptake decision to the achievement of full CMM Level 5 integration of object processes and technologies throughout the IS portfolio.
Personal computer symbolic analysis program for undergraduate engineering courses

The use of symbolic analysis programs in undergraduate engineering education is discussed. Examples of a wide range of applications, such as illustrations of network theorems, determination of sensitivity, parameter variation studies, effect of gain bandwidth, etc., are given. Symbolic analysis programs currently available for the personal computer are discussed. <<ETX>>
A switched-opamp with fast common mode feedback

In this paper a switched-opamp with a fast common mode feedback (CMFB) circuit is presented. The fully differential two stage amplifier needs CMFB only for the second stage, and thus a fast and simple passive CMFB-circuit may be used. The amplifier is capable of 1 volt operation and has no limitation on the maximum supply voltage.
Statistical Analysis in Microbiology: StatNotes

This book is aimed primarily at microbiologists who are undertaking research and who require a basic knowledge of statistics to analyse their experimental data. Computer software employing a wide range of data analysis methods is widely available to experimental scientists. The availability of this software, however, makes it essential that investigators understand the basic principles of statistics. Statistical analysis of data can be complex with many different methods of approach, each of which applies in a particular experimental circumstance. Hence, it is possible to apply an incorrect statistical method to data and to draw the wrong conclusions from an experiment. The purpose of this book, which has its origin in a series of articles published in the Society for Applied Microbiology journal ‘The Microbiologist’, is an attempt to present the basic logic of statistics as clearly as possible and therefore, to dispel some of the myths that often surround the subject. The 28 ‘Statnotes’ deal with various topics that are likely to be encountered, including the nature of variables, the comparison of means of two or more groups, non-parametric statistics, analysis of variance, correlating variables, and more complex methods such as multiple linear regression and principal components analysis. In each case, the relevant statistical method is illustrated with examples drawn from experiments in microbiological research. The text incorporates a glossary of the most commonly used statistical terms and there are two appendices designed to aid the investigator in the selection of the most appropriate test.
SIMULATION OF NONVERBAL SOCIAL INTERACTION AND SMALL GROUPS DYNAMICS IN VIRTUAL ENVIRONMENTS

How can the behaviour of humans who interact with other humans be simulated in virtual environments? This thesis investigates the issue by proposing a number of dedicated models, computer languages, software architectures, and specifications of computational components. It relies on a large knowledge base from the social sciences, which offers concepts, descriptions, and classifications that guided the research process. The simulation of nonverbal social interaction and group dynamics in virtual environments can be divided in two main research problems: (1) an action selection problem, where autonomous agents must be made capable of deciding when, with whom, and how they interact according to individual characteristics of themselves and others; and (2) a behavioural animation problem, where, on the basis of the selected interaction, 3D characters must realistically behave in their virtual environment and communicate nonverbally with others by automatically triggering appropriate actions such as facial expressions, gestures, and postural shifts. In order to introduce the problem of action selection in social environments, a high-level architecture for social agents, based on the sociological concepts of role, norm, and value, is first discussed. A model of action selection for members of small groups, based on proactive and reactive motivational components, is then presented. This model relies on a new tagbased language called Social Identity Markup Language (SIML), allowing the rich specification of agents' social identities and relationships. A complementary model controls the simulation of interpersonal relationship development within small groups. The interactions of these two models create a complex system exhibiting emergent properties for the generation of meaningful sequences of social interactions in the temporal dimension. To address the issues related to the visualization of nonverbal interactions, results are presented of an evaluation experiment aimed at identifying the application requirements through an analysis of how real people interact nonverbally in virtual environments. Based on these results, a number of components for MPEG-4 body animation, AML — a tag-based language for the seamless integration and synchronization of facial animation, body animation, and speech — and a high-level interaction visualization service for the VHD++ platform are described. This service simulates the proxemic and kinesic aspects of nonverbal social interactions, and comprises such functionalities as parametric postures, adapters and observation behaviours, the social avoidance of collisions, intelligent approach behaviours, and the calculation of suitable interaction distances and angles.
Research Design and Statistical Analysis

Preliminary Considerations Samples and Populations Some Important Distributions Between-Subjects Designs - One Factor Between-Subjects Designs - Several Factors Contrasts Among Means Trend Analysis Repeated-Measures Designs Mixed Designs - Combining Between-Subject and Within-Subjects Factors Hierarchical Designs Latin Squares and Related Designs Bivariate Correlation and Regression Analysis of Covariance More About Correlation Multiple Regression Regression with Categorical Variables Appendices - Notation and Summation Operations, Expected Values and Their Applications, Matrix Algebra, Statistical Tables, Control Information for Computer Programmes for Statistical Analysis.
Intelligent system of selecting key search algorithms automatically

This paper proposes an automatic selection method for key search algorithms. The methodology has been implemented in a system called KESE2. Key search algorithms are selected according to user's requirements through conversation controlled by inferences performed upon an evaluation table. The evaluation table has values representing fitness between search algorithms and their characteristics, or properties, to the applications. The selection algorithm presented determines candidates of key search algorithms by reducing unsuitable methods step by step. The questions to be asked to the user are driven by inferences over the restricted set. The paper also proposes an assisting facility that consists of both a supporting function and a program synthesis function. Experimental results show that by using the selection algorithm, the number of questions to be asked in order to select the appropriate key search algorithm was less than half the number of questions without inferences.
Boosting: Foundations and Algorithms

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb. " A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
Using old memories to store new ones

This paper describes a connectionist model of the interaction between long and short term memory. The proposed model addresses both issues of old memory utilization and novel memory consolidation. It comprises two different auto-associator network components; one for short term memory (STM) and one for long term memory (LTM). The STM network component uses a Hebbian type fast learning algorithm, while the LTM component uses the relatively slow mean field theory algorithm (Peterson and Hartman 1989).
A Case Study in Optimizing Parsing Schemata by Disambiguation Filters

Disambiguation methods for context-free grammars enable concise specification of programming languages by ambiguous grammars. A disambiguation filter is a function that selects a subset from a set of parse trees the possible parse trees for an ambiguous sentence. The framework of filters provides a declarative description of disambiguation methods independent of parsing. Although filters can be implemented straightforwardly as functions that prune the parse forest produced by some generalized parser, this can be too inefficient for practical applications. In this paper the optimization of parsing schemata, a framework for high-level description of parsing algorithms, by disambiguation filters is considered in order to find efficient parsing algorithms for declaratively specified disambiguation methods. As a case study the optimization of the parsing schema of Earley’s parsing algorithm by two filters is investigated. The main result is a technique for generation of efficient LR-like parsers for ambiguous grammars disambiguated by means of priorities.
Data mining to support engineering design decisions

The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An association model was used to uncover hidden trends among parts involved in maintenance events. Classification techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to major financial loss was traced with the help of summarization techniques. Secondly this paper compares and evaluates the business intelligence approach to solve the problem of knowledge transfer with solutions available from the Semantic Web. The results obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data.