Miracle-dz/UniFAR-FTUs huggingface dataset:
On analyzing maintenance process data at the global and the detailed levels: a case study

Concerns the empirical analysis of data collected during the maintenance phase of a large operating system. The data concern notices that are issued to the commercial users of the system, detailing how to avoid, circumvent or patch the error that it describes until the latter can be properly corrected in a subsequent official release of the operating system. Notices had been collected, compiled, and statistically analyzed for more than three years, first at the global level to determine whether they conformed to previous observations, and then at a sublevel, that of each successive release for the article, in an attempt to determine whether patterns exist at sublevels, how such patterns determine those, if any, at the global level, and whether they can be utilized for the creation of models to predict the behaviors of individual components of the system. The analysis shows that maintenance data observed at the global level tend to show few, if any, patterns. It appears that the number of components and the concealment of their individual patterns, and hence the strength of the global invariance and dynamics, are functions of the system's complexity. >
Optimal Asset Pricing

We describe an R package for determining the optimal price of an asset which is “perishable” in a certain sense, given the intensity of customer arrivals and a time-varying price sensitivity function which specifies the probability that a customer will purchase an asset offered at a given price at a given time. The package deals with the case of customers arriving in groups, with a probability distribution for the group size being specified. The methodology and software allow for both discrete and continuous pricing. The class of possible models for price sensitivity functions is very wide, and includes piecewise linear models. A mechanism for constructing piecewise linear price sensitivity functions is provided.
Incremental learning of decision rules based on rough set theory

With the changes of databases, the former rule sets obtained from the data set require updating. We require that the algorithms of rule generation are incremental learning methodology for modification of the existing decision rules and their numerical measures when new objects are appended to the database, instead of running the whole learning process again. In this paper, based on the rough set theory, the concept of /spl part/-indiscernibility relation is put forward in order to transform an inconsistent decision table to one that is consistent, called /spl part/-decision table, as an initial preprocessing step. Then, the /spl part/-decision matrix is constructed. On the basis of this, by means of a decision function, an algorithm for incremental learning of rules is presented. The algorithm can also incrementally modify some numerical measures of a rule.
Analysis of equine gait through strain measurement

This work describes the analysis of equine gait data using Principle Component Analysis, Wavelets, and various Artificial Neural Networks. Strain data is collected from the hoof of the animal and is analyzed in an attempt to identify characteristics of the horse's gait. These include if the animal is shod, the rate at which it is moving, and if it is not walking with a healthy gait. A comparison is made between the success of different Neural Network architectures and other techniques which are used to classify the data.
Homophonic coding of cryptographic sources

We present a new algorithm for the homophonic coding of a message source for cryptographic purposes. The purpose of homophonic coding is to transform the output of a nonuniformly distributed message source into a uniformly distributed source sequence. This is achieved by randomly mapping each source symbol into one of a set of homophones. The selected homophones are then encoded by means of arithmetic coding. A new arithmetic coding algorithm is introduced, and simulation results show that, as expected, homophonic coding increases the entropy by less than 2 bits per symbol.
Algorithms and Applications for Spatial Data Mining

Due to the computerization and the advances in scientific data collection we are faced with a large and continuously growing amount of data which makes it impossible to interpret all this data manually. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area “knowledge discovery in databases”. Knowledge discovery in databases (KDD) has been defined as the non-trivial process of discovering valid, novel, potentially useful and ultimately understandable patterns from data, a pattern is an expression in some language describing a subset of the data or a model applicable to that subset (Fayyad et al., 1996). The process of KDD is interactive and iterative, involving several steps such as data selection, data reduction, data mining, and the evaluation of the data mining results. The heart of the process, however, is the data mining step which consists of the application of data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data (Fayyad et al., 1996).
3D face recognition by profile and surface matching

In this paper, we presented an approach for automatic face verification from range data. The method consists of profile and surface matching. The profile is extracted on the basis of symmetry of human face, and a global profile matching method based on k-th Hausdorff distance is used to align and compare profiles, without detection of fiducial points that is often unreliable. For each individual, a statistical model of facial surface is built to represent the distinct discriminative capability of the different parts in the facial surface. Then the model is incorporated into a weighted distance function to measure similarity of surfaces. Finally two experts are combined to give a decision. The comparable experimental results are obtained on a database with 180 pieces of range data of 30 individuals.
A review on software reliability: metrics, models and tools

With the ever increasing dependency on software, its reliability has become a major concern and a key attribute in determining software quality. Estimation and prediction of software reliability are fundamental to develop reliable software systems. In this direction, various techniques concerning quantitative assessment and analysis through metrics, models and tools have been proposed in the last four decades. However, there still exist many challenges. In this paper, we review software reliability focusing on its characteristics, available metrics, modeling techniques and tools.
Partial column FFT pipelines

This paper presents the development of two efficient FFT implementation algorithms which allow for more parallelization than the standard pipeline. M=2/sup q/ radix r parallel computational elements are allocated per column of the FFT flowgraph, and the constant geometry FFT is used for uniform stages. The first method solves the interstage data shuffle problem by decomposing the perfect shuffle matrix into the product of four matrices, with a memory grouping resulting in a reduction of switching from M/sup 2/ to 2M. The second method decomposes to the perfect shuffle into the product of two matrices, and the memory is partitioned such that multiport elements may be used. All required switching is accomplished via addressing of the multiport elements requiring no external switching elements. Finally implementations are presented which allow for a varied amount of parallization by using uniform modules and merely modifying interconnect wiring. >
Trust management mechanism for Internet of Things

Trust management has been proven to be a useful technology for providing security service and as a consequence has been used in many applications such as P2P, Grid, ad hoc network and so on. However, few researches about trust mechanism for Internet of Things (IoT) could be found in the literature, though we argue that considerable necessity is held for applying trust mechanism to IoT. In this paper, we establish a formal trust management control mechanism based on architecture modeling of IoT. We decompose the IoT into three layers, which are sensor layer, core layer and application layer, from aspects of network composition of IoT. Each layer is controlled by trust management for special purpose: self-organized, affective routing and multi-service respectively. And the final decision-making is performed by service requester according to the collected trust information as well as requester' policy. Finally, we use a formal semantics-based and fuzzy set theory to realize all above trust mechanism, the result of which provides a general framework for the development of trust models of IoT.
Analysing Film Content: A Text-Based Approach

There is a lot of books, user manual, or guidebook that related to Reading Multimodal Translational Webs Dialogue Of The PDF, such as : quantitative techniques in management one source facility solutions inc suzuki quad sport 50 owners manual 5th grade engage ny module 3 answer keys small engine warehouse inc jibananda das bengali poet get talking and keep talking norwegian pack auditing information systems 2nd edition 1994 mercedes e320 operators manual turn that corner
Questioning the Author: An Approach for Enhancing Student Engagement With Text

"Questioning the Author" (QtA) is an interactive teaching strategy that helps students comprehend what they are reading. When elementary students read in a QtA lesson, they learn to question the ideas presented in the text while they are reading, making them thinkers, not just readers.Questioning the Author: An Approach for Enhancing Student Engagement with Text presents many examples of QtA in action. The authors analyze the transcripts of actual class discussions to show the important tools that differentiate QtA from other approaches to reading comprehension and engagement.
Gradience and categoricality in phonological theory

Within phonological theory, important roles are assigned to the notions of “gradience” and “categoricality. ” The opposition qualifies sounds and sound patterns, and is crucial both for the definition of the phonological and the phonetic components of generative grammar, and for the development of alternative types of grammatical models. This chapter discusses the assumptions generative phonology and its direct successors (including Optimality Theory) have made about the role of gradience. Moreover, it presents data supporting or contradicting these assumptions, and discusses new models accounting for the conflicting data. Keywords: phonology; functional grammar; generative grammar
An Exploration Of Gender And Trust In Mentoring Relationships

To explore the role of gender and trust in mentoring, fifteen interviews with mentors were conducted. Eight mentors were male and seven were female; eight were involved in cross-gender mentoring. Subjects were asked to discuss the levels of trust they had developed in their relationships, and what had influenced its development. Interviews were conducted by telephone, transcribed, and analyzed independently by the two authors. This paper will present these findings and discuss future avenues of research. Practical implications to mentoring program designers are also discussed.
Electronic Circuit and System Simulation Methods

This comprehensive volume reveals how, using basic principles of elementary circuit analysis along with familiar numerical methods, readers can build up sophisticated electronic simulation tools capable of analyzing large, complicated circuits. It describes in clear language an especially broad range of uses to which circuit simulation principles may be put-from running general applications, to understand why SPICE works in some cases and not in others.
The effects of linguistic mediation on the identification of environmental sounds

Recent studies have suggested that environmental sound recognition shares many of the same processing demands - and possibly neural resources - as language comprehension. Some investigators have suggested that the tight correlations between linguistic and environmental sound deficits observed in aphasic patients - as well as the spatial overlap in functional activation patterns shown by fMRI - may be due to linguistic mediation of environmental sound processing. Here, we show that covert naming of environmental sound recognition exerts an additional processing load above and beyond that used for recognition alone. Furthermore, naming does not increase recognition accuracy above the levels for recognition alone. Thus, linguistic mediation of environmental sound recognition appears not to be an important or even natural component of most participants’ processing strategies.
The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques

From the Publisher: Well-designed graphical user interfaces (GUIs) for business systems can greatly increase user productivity, but designing them can be difficult and time consuming. This book walks developers through the basics of good interface design, using real-world examples from systems that are proven successes. * Galitz is an internationally recognized consultant, author, and instructor with many years of experience with information systems and user interface design * Written especially for developers who may be designing user interfaces for the first time, but also extremely useful for any developer involved in GUI or Web site design * Revised to reflect the profound enhancements in interface design, specifically how Web page design has revolutionized interface design * New information covers a variety of platforms, both traditional and Web-based
Effects Of The Atmosphere And Surface Emissivity On The Thermal Infrared Spectral Signature Measured From Modis-n

We simulate effects of the atmosphere and surface emissivity on the thermal infrared spectral signature in six proposed thermal channels, for measurements of surface temperature, of MODIS-N (Moderate Resolution Imaging Spectrometer Nadirmode). Conditions for the simulations include a variety of atmospheric profiles over a wide elevation range, with difkrent visibilities near the Earth’s surface under cloud-free conditions. Calculated or measured spectral emissivities for various land surfaces-snow, clay, coarse sand and fine sand-range from 0.55 to 1.0. The upwelling band-averaged radiance at the top of the atmosphere can be exprcssed with three terms: surface emittance, reflection of the downward radiance at the surface, and the atmospheric path radiance. The model separates effects of surface temperature and emissivity from those of the atmosphere; it also includes effects of solar radiation on the medium wavelength infrared bands from 3.5 to 4.lp. m. This parameterized model is useful for development of practical algorithms for deriving surface temperature and emissivities from multi-channel MODIS-N thermal data.
Interactive Exploration of Multitask Dependency Networks

Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there su cient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input from the domain scientist to refine the objective function. This dissertation introduces a framework to incorporate domain knowledge in transfer learning to facilitate the exploration of solutions. The framework is a generalization of existing algorithms for multiple network structure identification. Solutions produced with human input narrow down the variance of solutions to those that answer questions of interest to domain scientists. Patterns, such as identifying di↵erences between networks, are learned with higher confidence using transfer learning than through the standard method of bootstrapping. Transfer learning may be the
Visual Content-Based Segmentation of Talk and Game Shows

Abstract In this article we present a method to remove commercials from talk and game show videos and to segment these videos into host and guest shots. In our approach, we rely mainly on information contained in shot transitions, rather than analyzing the scene content of individual frames. We utilize the inherent difference in scene structure of commercials and talk shows to differentiate between them. Similarly, we make use of the well-defined structure of talk shows, which can be exploited to classify shots as host or guest shots. The entire show is first segmented into camera shots based on colour histogram. Then we construct a data-structure (shot connectivity graph) that links similar shots over time. Analysis of the shot connectivity graph helps us to automatically separate commercials from program segments. This is done by first detecting stories and then assigning a weight to each story based on its likelihood of being a commercial. Further analysis on stories is done to distinguish shots of the hosts from shots of the guests. We have tested our approach on several full-length shows (including commercials) and have achieved video segmentation with high accuracy. The whole scheme is fast and works even on low- quality video (160x120 pixel images at 5 Hz).
The SARS Case Study: An Alarm Clock?

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. The SARS Case Study: An Alarm Clock? Gabriel Turinici, Antoine Danchin
Moving horizon optimal quantizer for audio signals

By analyzing the quantization of audio signals as a deterministic finite-set constrained quadratic optimization problem, a new scheme, called moving-horizon optimal quantizer (MHOQ), is developed. The MHOQ includes a model of the ear's sensitivity to low-level noise power and minimizes directly the perceived error over a finite prediction horizon. Feedback is incorporated by means of the moving-horizon principle. With a prediction horizon equal to 1, the MHOQ reduces to the psychoacoustically optimal noise-shaping quantizer, widely used in practical applications. Larger prediction horizons outperform the noise shaper at the expense of only a small increase in computational complexity.
A modified Elman neural network model with application to dynamical systems identification

In this paper, an overview of the structure and learning algorithm of the Elman neural network is first presented. A modified Elman network is then proposed by adding new adjustable weights that connect the context nodes with output nodes. Convergence speed of the two network structures are compared. A parallel dynamic system identification scheme based on the modified Elman network is set up as well. Theoretical analysis and simulation results show that our improved neural network-based identification method has the advantage of identifying both linear and nonlinear dynamic systems without any prior knowledge of their orders and structures.
The case for application-specific operating systems

Recent evidence suggests that trends will require rethinking the traditional role of operating systems. The challenge to operating systems designers is to deliver to applications the performance available now only from dedicated hardware, combined with the ease of sharing resources and data among multiple applications and the simpler programming model found in general-purpose operating systems. An application-specific structure is proposed where as much of the operating system as possible is pushed into runtime library routines linked with each application. The operating system kernel is stripped to its bare minimum functionality. At a minimum, the kernel must adjudicate among application requests for physical resources, and it must enforce hardware protection boundaries by operating system code running as library routines in each application. The key is that the operating system must notify each application of changes in its resource allocation, to allow the application on the chance to adapt to make best use of whatever resources are available to it. <<ETX>>
Interpreting Evidence-of-Learning: Educational research in the era of big data

Abstract In this article, we argue that big data can offer new opportunities and roles for educational researchers. In the traditional model of evidence-gathering and interpretation in education, researchers are independent observers, who pre-emptively create instruments of measurement, and insert these into the educational process in specialized times and places (a pre-test or post-test, a survey, an interview, a focus group). The ‘big data’ approach is to collect data through practice-integrated research. If a record is kept of everything that happens, then it is possible analyze what happened, ex post facto. Data collection is embedded. It is on-the-fly and ever-present. With the relevant analysis and presentation software, the data is readable in the form of data reports, analytics dashboards and visualizations. We explore the methodological consequences of these developments for research methods.
Stability of MaxWeight-({\alpha},g)

We consider a single-hop switched queueing network. Amongst a plethora of applications, these networks have been used to model wireless networks and input queued switches. The MaxWeight scheduling policies have proved popular, chiefly, because they are throughput optimal and do not require explicit estimation of arrival rates. In this article, we prove the same throughput optimality property for a generalization of the MaxWeight policy called the MaxWeight-({\alpha},g) policy. These throughput optimal, myopic scheduling policies allow for scheduling choices similar to those found in bandwidth sharing networks -- a further well studied model of Internet congestion.
Statistical Theory on Reliability

Abstract : Considerable progress was made by the Principal Investigator Professor Asit Basu and his collaborators on the areas of tests for exponentiality, component life length estimation, sequential and influential methods, and Bayesian approaches for reparable systems. All of these results contribute to a better understanding of reliability principles and better techniques for applied reliability practice.
Throughput analysis of p-CSMA based LonTalk protocols for building management systems

We analyse the feasibility of using a building management system (BMS) with multimedia communications over an automation network, focusing our attention on the requirements and limitations for digital video transmission. First, we address the communications requirements of BMS video information, selecting a quarter of intermediate format (QCIF). We choose an 1.25 Mbit/s LonTalk bus as the BMS communications subnetwork. The LonTalk medium access delay is analysed. Performance figures are obtained for a case study scenario, and we conclude that the selected bus fits our requirements.