Miracle-dz/UniFAR-FTUs huggingface dataset:
Cité créative et District culturel ; une analyse des thèses en présence

The paper analyses the theoretical bases of the theses of the Creative Class and Creative City. The objective is to highlight the contributions of authors that preceded Richard Florida’s work and which put forward the factors susceptible to contribute to a city becoming a creative place. This can help in defining the conditions which can define an urban planning model centered on creativity and innovation. After the issue of the creative city, we thus develop on the creative class and then on the critics brought forward in regard to these two concepts. In the last part of the article, we examine an original form of spatial organization which is based on the thesis of the districts, and we highlight the work of Pier Luigi Sacco (Sacco et Blessi, 2005a,b) on cultural districts. This work appears important, while little known outside Italy. The objective of the paper is to illustrate the richness of writings on the importance of creativity and culture, while much of the debate is centered on Richard Florida’s work, which presents a particular, and limited, vision.
Implementation of Breiman's Random Forest Machine Learning Algorithm

This research provides tools for exploring Breiman’s Random Forest algorithm. This paper will focus on the development, the verification, and the significance of variable importance. Introduction A classical machine learner is developed by collecting samples of data to represent the entire population. This data set is usually subdivided into two or more dataset. Part of the dataset set is commonly use for developing the machine learner, and the remaining data is use for evaluation. Often this data set is imbalanced; the data consists of only a very small minority of the data. Imbalanced machine learners tend to perform poorly with the classification of fraud detection, network intrusion, rare disease diagnosing, etc [1, 2]. This is due to imbalanced sampling during developing the machine learner. During the testing phase these rare cases are unseen during the training phase and are usually misclassified. Leo Breiman, a statistician from University of California at Berkeley, developed a machine learning algorithm to improve classification of diverse data using random sampling and attributes selection. This project involved the implementation of Breiman’s random forest algorithm into Weka. Weka is a data mining software in development by The University of Waikato. Many features of the random forest algorithm have yet to be implemented into this software.
The Data Model Resource Book, Vol. 1: A Library of Universal Data Models for All Enterprises

A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful First Edition, while adding a companion volume to take care of more specific requirements of different businesses. Each volume is accompanied by a CD-ROM, which is sold separately. Each CD-ROM provides powerful design templates discussed in the books in a ready-to-use electronic format, allowing companies and individuals to develop the databases they need at a fraction of the cost and a third of the time it would take to build them from scratch. Updating the data models from the First Edition CD-ROM, this resource allows database developers to quickly load a core set of data models and customize them to support a wide range of business functions.
Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment.
Progressive partial memory learning

PROGRESSIVE PARTIAL MEMORY LEARNING Marcus A. Maloof George Mason University, 1996 Dissertation Director: Ryszard S. Michalski A learning methodology called Progressive Partial Memory Learning (PPML) is presented. PPML takes a partial memory approach to progressive inductive learning problems in which training examples are distributed over time. The partial memory approach retains and uses representative examples and induced concept descriptions for future learning. Representative examples are those training examples that maximally expand and constrain concept descriptions in the representation space. Mechanisms such as the selection of representative examples, forgetting, and aging allow the system to efficiently learn concepts over time and to track changing concepts through a representation space. Key components of the methodology are implemented in an experimental system called AQ-Partial Memory (AQ-PM), which is based on the AQ15c inductive learning system. AQ-PM is experimentally validated against a baseline AQ15c learning algorithm using both synthetic and real-world data sets. Synthetic data sets include problems in which concepts change or drift in the representation space. Real-world problems include applications to dynamic knowledge bases (i.e., computer intrusion detection), intelligent agents (i.e., email sorting), and computer vision (i.e., blasting cap detection in x-ray images). Results demonstrate that the methodology is able to perform well on a variety of problems. Specifically, the method considerably improves memory requirements and learning time with slight decreases in predictive accuracy when compared to the baseline learner. Furthermore, the method is also able to track concept drift. Results are presented for the STAGGER concepts in which AQ-PM achieves predictive accuracies comparable to the FLORA systems, but requires much less memory. Comparisons are made to the AQ11 and GEM incremental learning systems using the blasting cap detection and computer intrusion detection problems in which they learned more predictive, but more complex concept descriptions than AQ-PM. Partial memory learning is also conducted using the incremental learning algorithms of AQ11 and GEM for the blasting cap detection and computer intrusion detection problems. Results for the partial memory versions of these learners, when compared to the unmodified versions, show slight decreases in predictive accuracy and concept complexity with considerable decreases in learning time.
Power efficient transmission policies for multimedia traffic over wireless channels

The current and future wireless systems need to support a multitude of services with a wide range of data rates and reliability requirements. The limited battery resource at a mobile terminal coupled with the hostile multipath fading channel makes the problem of providing reliable high data rate services challenging. In this thesis we develop a new framework for the design of power efficient transmission mechanisms that support multimedia traffic having different delay constraints. First, we present optimal design techniques that substantially reduce the transmission power (exceeding 60% in certain cases) for small increases in average communication delays for a single user scenario thereby providing another avenue for mobile devices to save on battery power. The power minimizing schedulers adapt the transmission rate and power based on the queue and channel state. We also construct a variable rate modulation scheme to show the benefits of the proposed formulation in a practical system. Power optimal schedulers with absolute packet delay constraints are also studied and their performance is evaluated via simulations. Second, we design power efficient schedulers that satisfy average delay bounds for multiple users in a broadcast channel. Optimal schedulers that jointly allocate rate and power to the different users based on the different buffer and channel conditions are presented in code-division multiple access (CDMA) and time-division multiple access (TDMA) regimes used commonly in current cellular standards. We show that joint scheduling gains over non-cooperative single user schedulers are largest when the users belong to different delay classes. We also show the superiority of CDMA over TDMA in terms of a larger set of achievable delays and at consistently lower powers. Near optimal low complexity schedulers are introduced in which computational complexity increases gracefully with increasing number of users. We also compute achievable power regions under average delay constraints in a multiple access channel with global and local queue state information. Finally, we develop a framework for designing power limited delay bounded transmission schemes that minimize the outage probability in fading channels with feedback. We explicitly construct schemes that quantitatively demonstrates the outage reduction with increasing delays and amounts of feedback information.
Business Information Management: Improving Performance Using Information Systems

Traditionally, introductory information systems texts have used a largely descriptive approach. Typical questions answered in many previous texts include: What is hardware? What is software? What types of information systems are there? Where can they be applied in organizations? What are the types of local-area network or database systems? What are the merits of sequential file access, token ring networks, hierarchical databases or chip design? Yet most business and management students will never be directly involved with the implementation of such technologies. What all will be involved with is managing information to improve their personal performance and the performance of the work area they are responsible for. This will involve assessing the quality of information available and working with colleagues to develop processes and introduce applications to make better use of this information. This book does not focus on the hardware and software technology for managing information technology – the ‘T’ in ‘IT’; instead the emphasis is on the ‘I’ in ‘IT’. It acknowledges the major problems with implementing information systems which deliver value to the business. It explores the reasons for these problems and management solutions to reduce these problems.
The effects of error frequency and accounting knowledge on error diagnosis in analytical review

SYNOPSIS AND INTRODUCTION: The performance of audit tasks has been modeled as a function of the auditor's ability, knowledge and experiences (Libby 1993). Thus, an important aspect of assigning audit tasks is identifying the levels of knowledge and types of experience an auditor must have to achieve a sufficiently high level of performance (Abdolmohammadi and Wright 1987). An objective of the current study is to determine whether auditors' knowledge of basic accounting principles and error frequencies improves over the course of their early careers so as to enhance performance of a common analytical procedure, ratio analysis. Research in psychology suggests that two characteristics of a task (say, analytical procedures) could diminish the accuracy with which auditors learn error frequencies from experience and apply their knowledge to a task. First, auditors' memories of financial statement errors are encoded while they perform other information-processing activities. These competing task demands could use enough of an auditor's information-processing capacity to diminish both the accuracy with which memory traces of errors are encoded and the accuracy of their knowledge of error frequency (Naveh-Benjamin and Jonides 1986). Second, auditors must consider a variety of evidence when performing analytical procedures. In diagnostic tasks like ratio analysis, inordinate attention is given to evidence that is highly diagnostic of low-frequency events, causing an "inverse base rate effect" in which auditors consider such events as more likely (Medin and Edelson 1988). Assessing the extent to which either of these characteristics
Assessing Teacher-Made Tests in Secondary Math and Science Classrooms.

ABSTRACT A model for use in identifying assessment needs in association with teacher-made mathematics and ncience tests at the secondary level was developed. The model focuses on the identification and narrowing of discrepancies between teachers' perceptions of their testing practices and actual practice. All 19 math and 16 science teacilers at a 4-year high school in a mixed suburban/rural school district participated in the project. Subjects' involvement consisted of completing a brief survey instrument and supplying their most recently administered unit or quarter test. Data from 35 questionnaires and 34 tests containing more than 1,400 items were analyzed. Results cover the nature of classroom assessment, characteristics of teacher-made tests, item formats, cognitive levels treated, quality of items, and teachers' confidence in testing skills. Topics identified as izlortant for inclusion in in-service activities for teacher test construction are the use of tables of specifications, development of higher order items, item formatting, and the empirical analysis of test results. Instruments developed during the study--the Test Analysis Guide and Teacher Testing Questionnaire--are provided. Three data tables and two bar graphs are included. (TJH)
Book Reviews: Pattern Grammar: A Corpus-Driven Approach to the Lexical Grammar of English

This book describes an approach to lexis and grammar based on the concept of phraseology and of language patterning arising from work on large corpora. The notion of 'pattern' as a systematic way of dealing with the interface between lexis and grammar was used in Collins Cobuild English Dictionary (1995) and in the two books in the Collins Cobuild Grammar Patterns series (1996; 1998). This volume describes the research that led to these publications, and explores the theoretical and practical implications of the research. The first chapter sets the work in the context of work on phraseology. The next two chapters give several examples of patterns and how they are identified. Chapters 4 and 5 discuss and exemplify the association of pattern and meaning. Chapters 6, 7 and 8 relate the concept of pattern to traditional approaches to grammar and to discourse. Chapter 9 summarizes the book and adds to the theoretical discussion, as well as indicating the applications of this approach to language teaching. The volume is intended to contribute to the current debate concerning how corpora challenge existing linguistic theories, and as such will be of interest to researchers in the fields of grammar, lexis, discourse and corpus linguistics. It is written in an accessible style, however, and will be equally suitable for students taking courses in those areas.
Security design in distributed computing applications

The software developer designing a security architecture for a distributed application is often faced with practical constraints that further complicate an already difficult task. These include limited resources and conflicting requirements. The goal will often be to simply provide as much effective security as possible, targeted at the end-user security needs. To achieve this goal, the developer must be able to systematically determine where security problems exist, understand the impact of security mechanisms as they are designed, determine which problems have and have not been addressed, explore alternative designs, and build on the architecture in the future. Current approaches to secure system design do not meet these requirements. Although much is understood about many aspects of computer security, little attention has been given to the the issue of how to integrate this knowledge into a design process; of how to generate and maintain a security architecture in a systematic, predictable manner. In this dissertation, this issue is examined in the context of a distributed information retrieval system. The security design problem is analyzed, defining and characterizing its underlying causes and the tools desirable to address it. This is followed by a classification and analysis of current security design approaches in relation to this problem, including a detailed case history of our efforts to employ risk management paradigms, demonstrating their strengths and limitations. A new security design methodology is then presented, which provides the desired tools. All aspects of the application, supporting software, hardware, physical environments, and attack scenarios are modeled in a unified, object-oriented manner. An information flow analysis is applied to automatically discover security violations. Safeguards can then be modeled and added to the flow analysis to readily assess their effects and interrelationships. Although the developer must create the application-specific models, the remaining models such as those for the operating system, hardware, environments, and attack scenarios are application-independent, and can evolve over time to represent generic security knowledge bases. These can be utilized by any developer employing this design methodology, regardless of their security background.
OCULAR VERGENCE RESPONSE OVER ANAGLYPHIC STEREOSCOPIC VIDEOS

The e ﬀ ect of anaglyphic stereographic stimuli on ocular vergence response is examined. An experiment is performed comparing ocular vergence response induced by anaglyphic stere-ographic display versus standard monoscopic display. Two visualization tools, synchronized three-dimensional scanpath playback and real-time dynamic heatmap generation, are developed and used to subjectively support the quantitative analysis of ocular disparity. The results of a one-way ANOVA indicate that there is a highly signiﬁcant e ﬀ ect of anaglyphic stereoscopic display on ocular vergence for a majority of subjects although consistency of vergence response is di ﬃ cult to predict.
Peut-on parler de recherche en pédagogie universitaire, aujourd’hui, en France ?

Definir la pedagogie universitaire et savoir ce qu’elle recouvre en France en termes de recherche constitue un enjeu. En effet, le manque de visibilite et de lisibilite de ce champ en limitent probablement la portee a l’heure ou la notion de qualite semble se faire plus pressante dans le contexte universitaire francais. En partant d’une definition operationnelle de ce que l’on peut entendre par pedagogie universitaire en France et en s’appuyant sur une recherche documentaire effectuee a partir de la base de donnees Francis ainsi que de la consultation de programmes complets de differents colloques, cet article vise a mettre en perspective et a illustrer les principaux axes de recherche qui s’en sont degages.
Surface color perception and light field estimation in 3D scenes

Previous research on surface color perception has typically used Mondrian stimuli consisting of a small number of matte surface patches in a plane perpendicular to the line of sight. In such scenes, reliable estimation of the color of a surface is a difficult if not impossible computational problem (Maloney, 1999). In three-dimensional scenes consisting of surfaces at many different orientations it is at least in theory possible to estimate surface color. However, the difficulty of the problem increases, in part, because the effective illumination incident on each surface (the light field) now depends on surface orientation and location. We review recent work in multiple laboratories that examines the degree to which the human visual system discounts the light field in judging matte surface lightness and color and how the visual system estimates the flow of light in a scene.