Miracle-dz/UniFAR-FTUs huggingface dataset:
Sennacherib's Palace at Nineveh: The primary sources for Layard's second campaign

During his second expedition to Assyria, October 1849 to April 1851, Layard excavated extensively at both Nimrud and Nineveh, as in his first campaign of November 1845 to June 1847, but now principally in Sennacherib's palace on Kuyunjik. The two London institutions, the British Museum and the British Library, house a rich corpus of primary material, in the form of drawings, sketches, plans, notebooks, diaries and journals, letters and other manuscript documents, which not only add much information and detail to our knowledge of Layard's achievements, as published in his second book, Nineveh and Babylon (1853), but also give a vivid and colourful picture and almost day-by-day account of his excavations. These sources are here described and discussed, and in a subsequent article will be used to trace the course of Layard's second campaign at Sennacherib's palace on Kuyunjik. Since this article deals for the most part with the primary sources and only secondarily with Layard's published account, Nineveh and Babylon, the rooms and courtyards of Sennacherib's palace are here referred to as Chamber A, etc., as found in these sources and also in Nineveh and its Remains (1849). It was only in Nineveh and Babylon that Layard changed to the system of designating rooms by Roman numerals (Fig. 1). Concordances for converting the chamber or room letters to numbers and vice versa will be found on p. 213.
Automatic text categorisation of racist webpages

Automatic Text Categorisation (TC) involves the assignment of one or more predefined categories to text documents in order that they can be effectively managed. In this thesis we examine the possibility of applying automatic text categorisation to the problem of categorising texts (web pages) based on whether or not they are racist. TC has proven successful for topic-based problems such as news story categorisation. However, the problem of detecting racism is dissimilar to topic-based problems in that lexical items present in racist documents can also appear in anti-racist documents or indeed potentially any document. The mere presence of a potentially racist term does not necessarily mean the document is racist. The difficulty is finding what discerns racist documents from non-racist. We use a machine learning method called Support Vector Machines (SVM) to automatically learn features of racism in order to be capable of making a decision about the target class of unseen documents. We examine various representations within an SVM so as to identify the most effective method for handling this problem. Our work shows that it is possible to develop automatic categorisation of web pages, based on these approaches
Analysis of software vulnerability

Software vulnerability is the fault that can be viciously used to harm security of software system. In order to decrease the harm, vulnerability analysis can be used to find security problems of software system as early as possible, and related measures, such as correctness, avoidance, may be planed. Vulnerability analysis is divided into two types, one is static analysis of vulnerability, the other is dynamic analysis of vulnerability. In this paper, we will introduce libFunction, which is a kind of dynamic analysis of vulnerability. LibFunction runs on redhat linux. It analyzes vulnerabilities related to library function. The main functions of libFunction are testing the behavior of application program when the return value of the called function is abnormal, and assessing the behavior.
Le frottement cinétique : analyse des raisonnements des étudiants

Le travail presente ici etudie les modes de raisonnement d'etudiants de differents pays, confrontes a des situations physiques simples de frottement cinetique. La nature des raisonnements rencontres, en presence de frottement solide sec, differe profondement de celle que l'on rencontre avec des situations de mecanique habituelle. Les caracteristiques principales de ces raisonnements seront decrites et analysees. Les consequences d'un tel travail, tant au niveau des perspectives de recherche qu'au niveau pedagogique, seront evoquees.
The Role of User-Interface in a Medical Expert System.

Abstract Expert systems have been designed for numerous medical applications. Although many of these systems have performed very well in experimental testing, very few are in actual clinical use. One continuing issue in the design of these systems is the type of user interface, and consequently the ease of use of the system. EMERGE, a rule-based expert system designed for analysis of chest pain, was originally designed with two types of user interface: a questioning mode in which the user answers questions with y, n, or? , and a data-driven mode in which information in free-format phrases can be entered directly. EMERGE was tested both retrospectively and prospectively. During the prospective analysis, it was found that a third, menu-driven mode of user interface was desirable. In this mode, the user can choose selections from a menu in a manner similar to many microcomputer applications. In this paper, the use and design of these user interfaces is discussed.
Enterprise 2.0 : How Social Software Will Change the Future of Work

Contents: Introduction. Part 1 Social Media and Social Software: The social media explosion The birth of social software Social software in the enterprise. Part 2 The 4Cs Approach: Communication Cooperation Collaboration Connection. Part 3 Implementing Social Software in the Enterprise: Models for success (and failure) Implementation and adoption. Part 4 Social Software Outside the Enterprise: Join the conversation Afterword. Appendix References Index.
Les Fouilles Recentes du Pech de L'Azé IV (Dordogne)

Le gisement mousterien du Pech de l’Aze IV se situe a Carsac (Dordogne) en Perigord, pres de Sarlat, dans le sud-ouest de la France. Fouille par F. Bordes durant les annees 1970, il n’a jamais ete veritablement publie si l’on excepte l’etude que nous avons consacree en 2000 au materiel issu de ses fouilles. Les travaux de terrain effectues dans ce gisement entre 2000 et 2003 avaient pour but de faire une nouvelle lecture stratigraphique, de preciser les processus de formation du site et de dater la sequence. Nous presentons ici les premiers resultats concernant la geologie, ainsi qu’une description rapide des industries et les premieres dates ESR, le programme engage etant toujours en cours. L’etude microscopique et macroscopique detaillee des huit principales couches stratigraphiques revele que les depots sableux proviennent d’un meme et vaste systeme de grottes englobant probablement l’ensemble des sites mousteriens du Pech de l’Aze (I, II et IV). Le niveau inferieur, depot sombre et organique, est particulierement interessant. Il renferme de multiples surfaces de combustion souvent pietinees et peut-etre des vidanges de foyers. Bien que deposes sous un climat tempere, les sediments d’origine anthropique sont bien conserves. Si, dans la sequence, l’observation macroscopique a permis d’identifier des phenomenes de solifluxion, l’analyse microscopique n’a montre pratiquement aucun indice d’un climat froid. En ce qui concerne les industries, les nouvelles series sont globalement comparables aux anciennes. Toutefois, quelques nouveaux elements sont apparus, notamment la presence d’une technologie Quina dans la partie superieure de la sequence.
Availability Studies in Libraries.

Des etudes sur la disponibilite des documents ont ete realisees sur les 50 dernieres annees pour evaluer les performances des bibliotheques. L'auteur reprend une quarantaine de ces etudes dans une enquete comparative et fonde son analyse sur les techniques de calcul de probabilites de De Prosp et Kartor
Applications of parametric and semi-parametric models for longitudinal data analysis

Title of dissertation: APPLICATIONS OF PARAMETRIC AND SEMI-PARAMETRIC MODELS FOR LONGITUDINAL DATA ANALYSIS Hisham Talukder, Doctor of Philosophy, 2014 Dissertation directed by: Professor Héctor Corrada Bravo Department of Computer Science A wide range of scientific applications involve analyses of longitudinal data. Whether it is time or location, careful considerations need to be made when applying different statistical tools. One such challenge is to correctly estimate variance components in observed data. In this dissertation, I apply statistical tools to solve problems involving longitudinal data in the field of Biology, Healthcare and Networks. In the second chapter, I apply SSANOVA models to find regions in the genome that have a specific biological trait. We introduce a direct approach of estimating genomic longitudinal data of two different biological groups. Using SSANOVA we then produce a novel method to estimate the difference between the two groups and find regions (location or time) where this difference is biologically significant. In the third chapter, we analyze longitudinal network data using an overdispersed Poisson model. We build a network of musical writers yearly for a 42 year period. Using statistical models, we predict network level topology changes and find covariates that explain these changes. Network level characteristics used for this chapter include average node degree, clustering coefficient and network density. We also build a visualization tool using R-Shiny. The fourth chapter uses data partitioning to study the difference between insured patients and uninsured patients in health outcomes. There is a disparity in health outcomes depending on an individual’s type of insurance. The level of risk for an injury is the longitudinal aspect of this dataset. We partition the data into four pre-defined risk categories and evaluate the disparity between insured and uninsured patients using logistic regression models. APPLICATIONS OF PARAMETRIC AND SEMI-PARAMETRIC MODELS FOR LONGITUDINAL DATA ANALYSIS
Numerical assessment of bed-load discharge formulations for transient flow in 1D and 2D situations

Two-dimensional (2D) transient flow over an erodible bed can be modelled using shallow-water equations and the Exner equation to describe the morphological evolution of the bed. Considering the fact that well-proven capacity formulae are based on one-dimensional (1D) experimental steady flows, the assessment of these empirical relations under unsteady 1D and 2D situations is important. In order to ensure the reliability of the numerical experimentation, the formulation has to be general enough to allow the use of different empirical laws. Moreover, the numerical scheme must handle correctly the coupling between the 2D shallow-water equations and the Exner equation under any condition. In this work, a finite-volume numerical scheme that includes these two main features will be exploited here in 1D and 2D laboratory test cases. The relative performances of Meyer-Peter and Muller, Ashida and Michiue, Engelund and Fredsoe, Fernandez Luque and Van Beek, Parker, Smart, Nielsen, Wong and Camenen and Larson formulations are analysed in terms of the root mean square error. A new discretization of the Smart formula is provided, leading to promising predictions of the erosion/deposition rates. The results arising from this work are useful to justify the use of an empirical sediment bed-load discharge formula among the ones studied, regardless of the hydrodynamic situation.
Ross Ice Shelf (Antarctica) in situ radio-frequency attenuation

Abstract We have measured the in situ average electric field attenuation length, 〈Lα 〉, for radio-frequency signals broadcast vertically through the Ross Ice Shelf, Antarctica. We chose a location, Moore Embayment, south of Minna Bluff, known for its high reflectivity at the ice–sea interface. We confirmed specular reflection and used the return pulses to measure the average attenuation length from 75–1250 MHz over the round-trip distance of 1155 m. We find 〈Lα 〉 to vary from ∼500 m at 75 MHz to ∼300 m at 1250 MHz, with an experimental uncertainty of 55–15 m.
Molecular Cloud Evolution

I describe the scenario of molecular cloud (MC) evolution that has emerged over the past decade or so. MCs can start out as cold atomic clouds formed by compressive motions in the warm neutral medium (WNM) of galaxies. Such motions can be driven by large-scale instabilities, or by local turbulence. The compressions induce a phase transition to the cold neutral medium (CNM) to form growing cold atomic clouds, which in their early stages may constitute thin CNM sheets. Several dynamical instabilities soon destabilize a cloud, rendering it turbulent. For solar neighborhood conditions, a cloud is coincidentally expected to become molecular, magnetically supercritical, and gravitationally dominated at roughly the same column density, $N \sim 1.5 \times 10^21 \psc \approx 10 \Msun$ pc$^{-2}$. At this point, the cloud begins to contract gravitationally. However, before its global collapse is completed ($\sim 10^7$ yr later), the nonlinear density fluctuations within the cloud, which have shorter local free-fall times, collapse first and begin forming stars, a few Myr after the global contraction started. Large-scale fluctuations of lower mean densities collapse later, so the formation of massive star-forming regions is expected to occur late in the evolution of a large cloud complex, while scattered low-mass regions are expected to form earlier. Eventually, the local star formation episodes are terminated by stellar feedback, which disperses the local dense gas, although more work is necessary to clarify the details and characteristic scales of this process.
A framework for the static and dynamic analysis of interaction graphs

Data originating from many different real-world domains can be represented meaningfully as interaction networks. Examples abound, ranging from gene expression networks to social networks, and from the World Wide Web to protein-protein interaction networks. The study of these complex networks can result in the discovery of meaningful patterns and can potentially afford insight into the structure, properties and behavior of these networks. Hence, there is a need to design suitable algorithms to extract or infer meaningful information from these networks. However, the challenges involved are daunting. First, most of these real-world networks have specific topological constraints that make the task of extracting useful patterns using traditional data mining techniques difficult. Additionally, these networks can be noisy (containing unreliable interactions), which makes the process of knowledge discovery difficult. Second, these networks are usually dynamic in nature. Identifying the portions of the network that are changing, characterizing and modeling the evolution, and inferring or predicting future trends are critical challenges that need to be addressed in the context of understanding the evolutionary behavior of such networks. To address these challenges, we propose a framework of algorithms designed to detect, analyze and reason about the structure, behavior and evolution of real-world interaction networks. The proposed framework can be divided into three components: (1) A static analysis component where we propose efficient, noise-resistant algorithms taking advantage of specific topological features of these networks to extract useful functional modules and motifs from interaction graphs. (2) An event detection component where we propose algorithms to detect and characterize critical events and behavior for evolving interaction graphs (3) A temporal reasoning component where we propose approaches wherein one can make useful inferences on events, communities, individuals and their interactions over time. For each component, we propose either new algorithms, or suggest ways to apply existing techniques in a previously-unused manner. Where appropriate, we compare against traditional or accepted standards. We evaluate the proposed framework on real datasets drawn from clinical, biological and social domains.
Le processus global d'audit : source de développement d'une gouvernance cognitive ?

Cette contribution defend la these qu’une forte interaction entre les trois acteurs du processus global d’audit permettrait une production de connaissances de plus grande qualite a destination des administrateurs de l’entreprise. Ce volume d’informations pourrait alors constituer un socle de discussion dans les lieux de decision, pour inciter l’entreprise a saisir les opportunites d’investissement les plus pertinentes. Le processus global d’audit ne pourra etre source de developpement d’une gouvernance cognitive, que s’il existe un systeme de relations formalisees entre comite d’audit, audit interne et audit externe. Afin de caracteriser les modes d’interaction, nous avons mene une enquete inspiree de la methode Delphi, en constituant un groupe d’experts francais, compose a parite par les acteurs du processus global d’audit. Notre etude exploratoire montre que le comite d’audit n’assume pas pleinement le role de coordinateur et d’animateur du processus global d’audit, qu’il devrait jouer. De surcroit, les relations entre auditeurs internes et auditeurs externes relevent plus d’une consultation non systematique que d’une cooperation. La mobilisation de la theorie dynamique de l’action collective (Hatchuel 1994) pour expliquer la faible intensite des interactions nous conduit a constater que les conditions ne sont pas reunies, dans le contexte francais, pour produire une information commune et utile aux administrateurs, et que le systeme de relations bloque la revision du modele d’interaction. La transposition de la 8e Directive Europeenne en droit francais est susceptible d’ameliorer les relations, en dotant le comite d’audit d’un pouvoir institutionnel et d’une certaine legitimite. Neanmoins, des questions, susceptibles d’entrainer un nouveau dysfonctionnement de l’action collective, demeurent en suspens.
Self-organization in large-scale wireless networks

Large-scale wireless networks such as ad-hoc networks and sensor networks have attracted considerable attention in the last few years, both in academia and industry. One of the main reasons for their popularity can be attributed to the various applications that they make possible. Habitat monitoring, environmental observation and forecasting, organ and health monitoring and target tracking are just a few examples of the many applications of such wireless networks. A characteristic requirement of these wireless networks is that they be "self-organizing" i.e., the wireless nodes organize themselves in order to efficiently perform the tasks required by the application. Self-organizing networks are attractive since they do not require human intervention and thereby reduce the cost of installation. In this thesis, we consider several tasks that arise commonly in the process of self-organization of a wireless ad hoc network, viz. topology formation, power control and leader election. We first consider the problem of neighbor discovery in wireless networks, the first step in bootstrapping a wireless network. We propose probabilistic algorithms that allow nodes to rapidly discover their neighbors, thereby expending less energy during neighbor discovery. In the second part of our thesis, we focus our attention on techniques to reduce the energy consumption of a wireless network. In particular, we consider the problem of optimal power assignment in wireless networks with transmitter-receiver tradeoffs. In these networks, the transmitter power can be increased to reduce the receiver power and consequently, the overall power consumption. We propose algorithms to assign power to nodes so that the lifetime of the network is maximized. Another aspect of self-configuration that arises frequently in wireless networks is the problem of leader election amongst a set of nodes. Arbitrary topological changes caused by node movement makes this problem significantly challenging. In the final part of this thesis, we propose a provably correct and highly efficient leader election algorithm that can tolerate arbitrary topological changes caused due to node movement.
A note on data access patterns in Standard ML of New Jersey

Thermosetting low temperature cure latex coating compositions are prepared by emulsion polymerization in aqueous medium from non-hydroxyl bearing acrylic monomers, acrylamide or methacrylamide and a major proportion of a vinyl unsaturated hydrocarbon. When hot or cold blended with a glycoluril cross-linker and applied to a wood substrate, the coating exhibits excellent gloss properties and MEK resistance.
Parameterizing phrase based statistical machine translation models: an analytic study

The goal of this dissertation is to determine the best way to train a statistical machine translation system. I first develop a state-of-the-art machine translation system called Phrasal and then use it to examine a wide variety of potential learning algorithms and optimization criteria and arrive at two very surprising results. First, despite the strong intuitive appeal of more recent evaluation metrics, training to these metrics is no better than the older traditional approach of training to BLEU. Second, the most widely used learning algorithm for training machine translation systems, called minimum error rate training (MERT), works no better than standard machine learning algorithms such as log-linear models. This result demonstrates that machine translation does not require using a special purpose learning algorithm, but rather can be approached in a manner similar to other natural language processing and machine learning tasks. These results have a number of important implications. Contrary to existing beliefs, work on improving machine translation evaluation metrics and then training to the improved metrics will not in itself result in improved translation systems. Even more significantly, the widespread usage of MERT has limited the sort of models that can be used for machine translation, as it does not scale well to large numbers of features. If it is not necessary to use MERT to train competitive systems, machine translation can be treated similarly to any other natural language processing task with models that include arbitrarily large feature sets.
Face Recognition using Weighted Facial Feature Values of Eyes, Nostrils and Mouth Areas

This paper describes a face recognition algorithm that extracts the eyes, nostrils and mouth features from cumulative distribution function (CDF) by applying Otsu thresholding. The algorithm, which is inspired by the probability of white pixels of binary facial image and the weighted facial feature values, has been tested using the BioID frontal face large database in different illuminations, expressions and lighting conditions. Illumination and lighting variations are addressed using a selective equalization technique. The experimental results have confirmed an average recognition rate of 96.45%.