Miracle-dz/UniFAR-FTUs huggingface dataset:
Detectability performance properties of learning-based nonlinear fault diagnosis

The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fault diagnosis scheme based on the learning approach. First, conditions are derived to characterize the range of detectable faults. Then, non-conservative upper bounds are computed for the detection time of incipient and abrupt faults. Finally, it is shown that the detection time decreases monotonically as the values of certain design parameters increase.
A Web-based Personalised Textfinder for Language Learners

This thesis describes the theoretical foundations for, and the implementation of, an application designed to help language learners or teachers on their behalf to find texts geared to individual ability and interests. For the purpose of providing learners with texts of suitable difficulty, a new formula is developed which measures both the ability of a learner and the readability of texts. A search-engine enables the efficient supply of texts from an XML database according to a learner’s query. The database was created specifically for this application by downloading texts from a diverse range of online newspapers. An experimental evaluation of the application is carried out to identify its strengths and weaknesses with the hope of eliciting improvements in future work in this field.
Meronymy Relation Extraction Based on 3-Motif in Wikidata

Meronymy relation extraction is playing an important role in knowledge mining and it is a huge challenge to extract meronymy relation from a large amount of complicated unlabeled data. In Wikidata, it is found that meronymy relations appear in some specific network topologies of the hyperlinks. Using this characteristic of Wikidata, we proposed a new approach to extract meronymy relations: based on all 13 different three-node motifs, we build a 13-dimensional feature vector for each hyperlink to be classified with different classification algorithms. We used part of labeled data in Wikidata to train models and used the left data to judge the quality of this classification model. With some of the classification algorithms, our new approach can extract meronymy relations in higher accuracy and works well in diverse fields.
Reasons not to Parallelize TCP Connections for Fast Long-Distance Networks

For utilizing fast long-distance networks effectively, parallel TCP was proposed, and has been employing. However, as high-speed transport-layer protocols appear, it is necessary to reinvestigate the performance of parallel TCP. In this paper, we use mathematical analysis to explore its performance. Analysis results show that the open issue of choosing the number of TCP connections is difficult to be solved in practice. Despite the mechanism that adjusts the number of TCP connections during data transfer is proposed, some potential problems still remain. In contrast, it is a better choice to use high-speed TCP in your future applications.
An Investigation of Methods for Missing Data in Hierarchical Models for Discrete Data

Hierarchical models are applicable to modeling data from complex surveys or longitudinal data when a clustered or multistage sample design is employed. The focus of this thesis is to investigate inference for discrete hierarchical models in the presence of missing data. This thesis is divided into two parts: in the first part, methods are developed to analyze the discrete and ordinal response data from hierarchical longitudinal studies. Several approximation methods have been developed to estimate the parameters for the fixed and random effects in the context of generalized linear models. The thesis focuses on two likelihood-based estimation procedures, the pseudo likelihood (PL) method and the adaptive Gaussian quadrature (AGQ) method. The simulation results suggest that AGQ is preferable to PL when the goal is to estimate the variance of the random intercept in a complex hierarchical model. AGQ provides smaller biases for the estimate of the variance of the random intercept. Furthermore, it permits greater flexibility in accommodating user-defined likelihood functions. In the second part, simulated data are used to develop a method for modeling longitudinal binary data when non-response depends on unobserved responses. This simulation study modeled threelevel discrete hierarchical data with 30% and 40% missing data using a missing not at random (MNAR) missing-data mechanism. It focused on a monotone missing data-pattern. The imputation methods used in this thesis are: complete case analysis (CCA), last observation carried forward (LOCF), available case missing value (ACMVPM) restriction, complete case missing value (CCMVPM) restriction, neighboring case missing value (NCMVPM) restriction, selection model with predictive mean matching method (SMPM), and Bayesian pattern mixture model. All three restriction methods and the selection model used the predictive mean matching method iii to impute missing data. Multiple imputation is used to impute the missing values. These m imputed values for each missing data produce m complete datasets. Each dataset is analyzed and the parameters are estimated. The results from the m analyses are then combined using the method of Rubin (1987), and inferences are made from these results. Our results suggest that restriction methods provide results that are superior to those of other methods. The selection model provides smaller biases than the LOCF methods but as the proportion of missing data increases the selection model is not better than LOCF. Among the three restriction methods the ACMVPM method performs best. The proposed method provides an alternative to standard selection and pattern-mixture modeling frameworks when data are not missing at random. This method is applied to data from the third Waterloo Smoking Project, a seven-year smoking prevention study having substantial non-response due to loss-to-follow-up.
Knowledge Management and the Creation of Business Value

Knowledge management (KM) initiatives that create business value are a major focus for many organizations. While academics often couch KM success in terms of process outcomes, some argue that the organizational outcomes perspective of KM success held by many practitioners is clearer, where KM success is tied to its impact on organizational performance. This study proposes an integrated model that incorporates both perspectives, in which the business value of KM is the dependent variable and effective KM process implementation is an important antecedent. We also hypothesize that two contextual factors, top management support and organizational culture, are positively related to business value. Data collected from a U.S. national survey of 268 information technology (IT) executives support the significance of these relationships. Furthermore, this research identifies key KM barriers as well as benefits that influence top management support of KM, KM process implementation, and indirectly business value. Practical and research implications are discussed.
Performance Analysis of BLDC Motor for Sinusoidal and Trapezoidal Back-Emf using MATLAB/SIMULINK Environment

The Brushless DC Motor find variety of applications in domestic and industrial applications. BLDC Motor has some important characteristics like low and high power density and ease of speed control. This paper presents a three phase inverter fed Brushless DC motor. The process considering the development of BLDC Motor Model in MATLAB/SIMULINK environment with sinusoidal and trapezoidal back-Emf waveform and also includes a comparison study for the harmonic analysis for sinusoidal and trapezoidal back-Emf models.
Learning Directional LBP Features and Discriminative Feature Regions for Facial Expression Recognition

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition
A corpus of real-life questions for evaluating robustness of QA systems

Many evaluation campaigns on question answering (QA) systems have been organized for years. The international TREC 1 conference proposes a track about it , the European CLEF 2 campaign proposes cross evaluation in eight languages, NTCIR 3 includes a QA track in three languages and the French Technolangue EQUER 4 evaluation focused on 500 corpus based questions. But the questions asked in those campaigns are checked for being well formed and enough complete. We aim to test QA systems on their ability to answer questions spontaneously typed by people without thinking deeply to the grammatical and lexical forms they might use. This is designed to test QA system robustness in more real life uses. Related experiments already done in document retrieval field test the robustness of search engine with automatic transcription of spoken queries (Crestani, 2000) or with automatically degraded text entries (Ruch, 2002). Moreover, we aim to test our QA system (Gillard et al., 2006) with questions written by dyslexic adults or children, or non-native speakers. This public is intuitively the most concerned with these problems of robustness. 1 Corpus constitution A web-based approach was used to acquire data for the corpus. The motivation for such an approach is two fold. Firstly, it lets users make the experiment in relaxing conditions, when and where they wish to. Secondly, it permits to collect data from a wide population, especially for dyslexics individuals already solicited for psycholinguistics experiments. It removes geographical constraints often restraining the quantity of data in this area. The experiment is composed of 20 questions selected from EQUER French evaluation campaign. The selected questions were some right answered by SQuALIA (Gillard et al., 2006). 8 of them contain proper nouns. 2 of them contain foreign low frequency proper nouns. The covered focuses are: person name (5 questions), number (5 questions), date (3 questions), location (2 questions), money, distance, age, journal name and military grade.
Imposing Password Restrictions for Multiple Accounts: Impact on Generation and Recall of Passwords

The most commonly used method of identification and authentication for many Web sites is username-password combinations. However, this is a notoriously weak security method because users tend to generate passwords that are easy to remember but also easy to crack. The method of proactive password checking has been proposed to improve security at little cost to memorability, because it allows users to generate their passwords but imposes restrictions to make the passwords more resistant to cracking. The present study evaluated the time and accuracy needed to generate unique passwords that satisfy different restrictions for multiple accounts, as well as the time and accuracy at recalling these passwords. Results showed that password restrictions do not necessarily improve the security of the password generated by users by making them more resistant to cracking because cracking software have become increasingly sophisticated. Although users show good recall of unique passwords generated with restrictions for multiple accounts when the number of accounts is small, the memorability for the multiple passwords decreases as the number of possible accounts increases. One way to improve the memorability of passwords for multiple accounts is to have users generate them several times, at different points of time, prior to allowing them to exit the system.
Computing resources placement in a multi-CDN context

As sensitive contents like video become more popular; coping with these contents constraints requires a more sophisticated computing of adequate resources placement. In this document, we motivate this problem and we propose, for solving it, a multi-steps framework that allows, based on static and dynamic inputs, moving from a single CDN to a multi CDN placement of required resources. Strengths of proposed framework lie in the granularity of resources fetching and in the dynamicity of resources computing over time.
Graphical Models Inferring Cellular Networks Using Probabilistic

http://www.sciencemag. org/cgi/content/full/303/5659/799 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag. org/cgi/content/full/303/5659/799/DC1 can be found at: Supporting Online Material found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag. org/cgi/content/full/303/5659/799#related-content http://www.sciencemag. org/cgi/content/full/303/5659/799#otherarticles , 12 of which can be accessed for free: cites 20 articles This article 70 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag. org/cgi/content/full/303/5659/799#otherarticles 15 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag. org/cgi/collection/comp_math Computers, Mathematics : subject collections This article appears in the following
Color image edge extraction using memristor-based CNN

Because of the locally connected lattice structure and high-speed parallel processing, cellular neural network (CNN) have been widely used in image processing. Traditional processing methods typically employ fixed templates, which impose significant limitations on practical complex image processing. However, the hardware implementation of large-scale CNNs becomes impossible due to the bottleneck of traditional CMOS technology. In this paper, a new threshold-adaptive algorithm is proposed by considering pixel space distributions based on human visual perception, which can overcome the aforementioned limitation. Then, the memristor, a two-terminal nonlinear device with unique high-speed switching, nonvolatility, and nanometer scale is used to solve the circuit realization problem. Specifically, we design a spintronic memristor-based CNN (SMCNN) to facilitate the proposed threshold-adaptive algorithm. Finally, by using the example of color image processing, the effectiveness of the proposed SMCNN is demonstrated by means of numerical simulations and comparative analysis.
Towards Efficient Packet Classification Algorithms and Architectures

Towards Efficient Packet Classification Algorithms and Arch itectures Omar Ahmed University of Guelph, 2013 Advisor: Professor Shawki Areibi Packet classification plays an important role in next genera tion networks. Packet classification is important to fulfill the requirements for many applic ations including firewalls, multimedia services, intrusion detection services, and differentiat ed services to name just a few. Hardware solutions such as CAM/TCAM do not scale well in space. Curren t software-based packet classification algorithms exhibit relatively poor performance, p rompting many researchers to concentrate on novel frameworks and architectures that employ bot h hardware and software components. In this thesis we propose two novel algorithms, PacketClassification withIncrementalUpdate (PCIU) andGroupBasedSearch packet classification Algorithm (GBSA), that are scalable and demonstrate excellent results in terms of preprocessing an d cl ssification. The PCIU algorithm is an innovative and efficient packet clas sification algorithm with a unique incremental update capability that demonstrates po werful results and is accessible for many different tasks and clients. The algorithm was further improved and made more available for a variety of applications through its implementation in hardware. Four such implementations are detailed and discussed in this thesis. A hardware accele rator based on an ESL approach, using Handel-C, resulted in a 22x faster classification than a pure software implementation running on a state of the art Xeon processor. An ASIP implementation ach ieved on average a 21x quicker classification. We also propose another novel algorithm, GBSA, for packet cl assification that is scalable, fast and efficient. On average the algorithm consumes 0.4 MB of mem ory for a 10k rule set. In the worst case scenario, the classification time per packet is 2 μs, and the pre-processing speed is 3M Rule/sec, based on a CPU operating at 3.4 GHz. The proposed al gorithm was evaluated and compared to state-of-the-art techniques, such as RFC, HiCut, T uple, and PCIU, using several standard benchmarks. The obtained results indicate that GBSA outper forms these algorithms in terms of speed, memory usage and pre-processing time. The algorithm , furthermore, was improved and made more accessible for a variety of applications through i mplementation in hardware. Three approaches using this algorithm are detailed and discussed in this thesis. The first approach was implemented using an Application Specific Instruction Proc essor (ASIP), while the others were pure RTL implementations using two different ESL flows (Impu lse-C and Handel-C). The GBSA ASIP implementation achieved, on average, a 18x faster runn ing speed than a pure software implementation operating on a Xeon processor. Conversely, th hardware accelerators (based on the ESL approaches) resulted in 9x faster processing.
Quantile Regression Models and Their Applications: A Review

Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.
File system simulation: hierachical performance measurement and modeling

File systems are very important components in a computer system. File system simulation can help to predict the performance of new system designs. It offers the advantages of the flexibility of modeling and the cost and time savings of utilizing simulation instead of full implementation. Being able to predict end-to-end file system performance against a pre-defined workload can help system designers to make decisions that could affect their entire product line, involving several million dollars of investment. This dissertation presents detailed simulation-based performance models of the Linux ext3 file system and the PVFS parallel file system. The models are developed using Colored Petri Nets. A performance study, using the models, shows that the obtained results are close to the expected behavior of the real file system. The model shows that file system parameters have significant impact on the performance of the I/O when compared to the parameters of the disk subsystem.
Efficient simulation of constant Q using coarse-grained memory variables

Improvements in computing speed have progressively increased the usable bandwidth of seismic wave-field simulations computed with time-stepped numerical schemes (e.g., finite difference, finite element, pseudospectral). As computational bandwidth increases, anelastic losses become increasingly significant for some important applications such as earthquake ground-motion modeling, whole earth seismogram simulation, and exploration seismic profile modeling, and these losses need to be included in the simulations. As bandwidth increases, however, the memory variables necessary to incorporate realistic anelastic losses account for an increasing proportion of total computational storage requirements, a consequence of the broad relaxation spectrum of typical earth materials. To reduce these storage requirements, we introduce a new method in which the memory variables are coarse grained, that is, redistributed in such a way that only a single relaxation time is represented at each node point (and therefore a single memory variable per stress component is required). Guided by a perturbation analysis, we effect this redistribution in such a way that spatial variability of this single relaxation time simulates the full relaxation spectrum. Such coarse graining reduces memory-variable storage requirements by a factor of 8 for 3D problems or a factor of 4 for 2D problems. In fourth-order finite-difference computations for the 3D acoustic-wave equation, the method simulates frequency-independent Q within a 3% tolerance over 2 decades in frequency, and it is highly accurate and free of artifacts over the entire usable bandwidth of the underlying finite-difference scheme. These results should also hold for the elastodynamic equations. The method is readily generalized to approximate specific frequency-dependent Q models such as power laws or to further reduce memory requirements. In its present implementation, the main limitation of the method is that it generates artifacts at wavelengths equal to 4 grid cell dimensions and shorter, which may, in some limited circumstances, overlap the usable bandwidth of very high-order finite-difference and/or pseudospectral schemes.
A scheduling problem for two competing queues with finite capacity and non-negligible setup times

We consider a setup scheduling problem for a manufacturing system with finite buffers. Some analytical results for the infinite capacity case are also included. The system comprises a non-perfect, reliable single machine processing two part types, modeled by means of a fluid approximation. The objective of the control is to minimize an index cost under steady and transient conditions. Dynamic programming is used to derive the structure of the optimal policy and some initial analytical results are given. A symmetry assumption on part types is considered to simplify the problem.