Miracle-dz/UniFAR-FTUs huggingface dataset:
La modellazione microstrutturale di materiali a struttura eterogenea: princìpi ed applicazioni

Molti problemi della Meccanica e Fisica dei Solidi e della Scienza dei Materiali, non sono facilmente risolvibili con gli approcci tradizionali. Oltre allo studio delle proprieta effettive dei solidi eterogenei, vi e la crescente necessita di incorporare un maggiore numero di informazioni sui meccanismi di deformazione e danneggiamento generati alla microscala, anche per i materiali abitualmente considerati omogenei. Micromeccanismi di cavitazione e concentrazioni locali di tensione e deformazione, sono indispensabili per spiegare fenomeni non-lineari come la rottura di fatica o il cedimento duttile, altrimenti non inquadrabili con approcci classici di tensioni e deformazioni medie. La micromeccanica si occupa della determinazione precisa, o di una stima accurata, di grandezze di campo microstrutturali locali. In questo lavoro sono illustrati i principi che sono alla base dell’approccio micromeccanico, come i concetti di multiscala, di distribuzione statistica delle fasi, di descrizione mediante volumi di riferimento e di omogeneizzazione e localizzazione, e, attraverso alcune applicazioni pratiche delle principali tecniche di modellazione, sono illustrati e discussi criticamente i risultati della ricerca effettuata su varie strutture di ghisa nodulare.
H-infinity control : an exploratory study

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Urdu-Hindi-Urdu Machine Translation: Some Problems

In this paper we discuss the problems in UrduHindi-Urdu Machine Translation at various levels. Though because of large common vocabulary it may sound that only transliteration can help to overcome the language barrier between Urdu and Hindi, the tendency of Urdu to use words from Persian and Arabic origin, and the tendency of Hindi to use words of Sanskrit origin, call for the use of proper Machine Translation System. However, we point out the problems at various levels of Machine Translation, and suggest an alternative approach. Following this alternative approach a working system has been built and is available at http://sanskrit.uohhyd. ernet.in/~anusaaraka/urdu/Urd u-Hindi-Translation.
Mining complex databases using the EM algorithm

This thesis focused on developing efficient data mining algorithms based on the Expectation-Maximization (EM) algorithm. The EM algorithm is a general numerical optimization method that solves many important statistical problems. In particular it can be used to perform clustering, and that is the aspect this work concentrated on. An algorithm to mine association rules from a collection of images segmented by EM was introduced. Experiments with synthetic images show the algorithm is reasonably accurate and fast. Then the problem of programming EM inside a relational DBMS was studied. Three efficient ways to implement the EM algorithm in the SQL language and important improvements were proposed. Then this work studied the general problem of clustering large data sets with high dimensionality. A fast and robust EM clustering algorithm was proposed for that purpose. This algorithm incorporates tunable initialization, sufficient statistics, cluster splitting, outlier handling and regularization techniques. Experimental evaluation shows the algorithm is fast and accurate. Finally, this thesis proved association rule mining can be solved as a clustering problem. The fast clustering algorithm was customized to build a statistical model for association rules. This model allows efficient approximate mining of association rules. The EM algorithm has had a proven success and it is still an active research topic in the Statistics and Machine Learning communities. It will be more widely used for Data Mining applications as it is better understood and optimized by database researchers. This work is a step in that direction.
An Algorithm for Query Folding with Functional Dependencies

Query folding is a technique for determining how a query may be answered using a given set of resources, which may include materialized views, cached results of previous queries, or queries answerable by other databases. Its applications include query optimization in centralized databases, query processing in distributed databases, and query answering in federated databases. The power of query folding can be considerably enhanced by taking into account integrity constraints that are known to hold on base relations. This paper describes an extension of query folding that utilizes functional dependencies to nd foldings of queries that would otherwise be overlooked. We describe a strategy for nding foldings in the presence of functional dependencies and present a basic algorithm that implements that strategy.
Some New Item Selection Criteria for Adaptive Testing

In this study some alternative item selection criteria for adaptive testing are proposed. These criteria take into account the uncertainty of the ability estimates. A general weighted information criterion of which the usual maximum information criterion and the proposed alternative criteria are special cases is suggested. A small simulation study was conducted to compare the different criteria. The results showed that the likelihood weighted information criterion is a good alternative to the maximum information criterion. Another good alternative is a maximum information criterion with the maximum likelihood estimator of ability replaced by the Bayesian expected a posteriori estimator.
The prediction of Taiwan government bond yield by neural networks

Numerous studies have demonstrated that the Neural Networks is an accurate and efficient prediction method in various financial applications. Yet research for the field of forecasting government bond yield is still short. Among these limited number of studies, Backpropagation network (BPN) is the most used method. However, suffering form the potential problems, such as slow training speed, long processing time, and possible local minimum, BPN may not be the omni-solution for all applications in practice. The purpose of this research is to provide an in-depth study of effects of on the performance of different neural networks in government bond yields forecasting. Four selected models with different structures, namely Resilient Propagation (RPROP), Radial Basis Function Neural Network (RBFN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Backpropagation network, are investigated and the results are analyzed and compared. The results indicate that (1) the number of nodes in the hidden layer is insensitive to the prediction. (2) The recommended number of input nodes is five. (3) Obviously, more training samples do enhance forecasting performance in our study. (4) The performance of RBFN is the best, followed by ANFIS and RPROP, SVR, and then BPN. (5) BPN is efficient but not the best approach. (6) Our result reveals that RBFN is a useful predicting approach in government bond yield, it performs better than other four models. The recommended combination of parameters for RBFN is five input nodes, six center nodes in the hidden layer, and one output node.
Source-level debugging of globally optimized code

Compiler optimizations play a crucial role in the performance of modern computer s ystems. Debugger technology, however, has lagged behind in its support of optimizations. Debugging the unoptimized translation is often impractical, so handling of optimized code by the debugger is necessary. But compiler optimizations make it difficult to provide sourcelevel debugger functionality: When a program is compiled with optimizations, mappi ngs between breakpoints in the source and object code become complicated, and values of variables may be either inaccessible in the runtime state or inconsistent w i h what the user expects at a breakpoint. Although researchers and implementors have long acknowledge d the need for source-level debugging of optimized code, compiler developers have gener ally avoided implementing this support. Designing a source-level debugger for global ly optimized code remains an open problem. This dissertation presents techniques that enable accurate source-level deb ugging of fully optimized code. These techniques are demonstrated in the context of a compiler that performs a complete set of global scalar optimizations for commonly used source la nguages such as C. The techniques presented in this dissertation are non-invasive — t hat is, hey do not constrain optimizations in any way and do not require instrumentation of the debugge d program. All interactions between the debugger and the user are in terms of the ori ginal source text. In general, it is impossible to hide the effects of optimizations from the user and to provide the same interactions as in unoptimized code. The approach presented in this disser tation makes the effects of optimizations transparent whenever possible. Only when optimiza tions cannot be hidden from the user does the debugger inform the user of the effects of optimizations on the expected execution behavior of the program. This dissertation analyzes in detail the problems caused by scalar optimiza tions such as partial redundancy elimination, register allocation, instruction scheduling, and m y others. This dissertation also presents the low-level algorithms necessary in the compiler and debugger to track the effects of optimizations,and discusses the limitat ions of the techniques. Finally, this dissertation presents measurements of how often a user is li kely to be affected by optimizations during debugging. These measurements are based on an implementati on of these techniques in the cmcc compiler, an optimizing C compiler developed as part of this dissertation.
An Approach of Bio-inspired Generalized Shape for Writer Identification

Writer identification is one of the areas that attract many researchers to work in. It has being focus in forensic and biometric application where the writing styles can be used as biometric features for authenticating an identity. The individualistic of handwriting styles can be adopted into bio-inspired generalized global shape in writer identification. This paper is discussed the problem overview and solution concept of generalized global shape in writer identification. Moreover, to explore the feasibility of this generalized global shape approach in this domain. An experiment based on the proposed framework has been conducted to proof the feasibility of this approach in off-line writer identification.
Traitement du signal et modélisation probabiliste du trafic internet

We present in this paper a probabilistic model for representing the bit rate rate of Internet traffic on a high speed backbone link of an IP commercial network. This probabilistic model is validated by using tools from digital signal processing (spectral densities, wavelet filters). The main conclusion is that the prevalence of peer-to-peer applications tends to smooth out traffic and that there is no evidence for self-similarity, even though the analysis of spectral densities may be misleading.
A Comparison of Three Cursor Control Devices on a Cursor Control Benchmark Task

A number of past studies have compared performance of cursor control devices. Conclusions regarding the "best" device for a particular application are difficult to draw because the tasks used in previous comparisons have differed from one another and have not included all of the factors affecting performance with the devices. The present study used a target acquisition task whose components were derived from analyzing the cursor control device movements of users while performing actual tasks. The components of the task were target size, target distance, direction, and mode (i.e., point-and-click vs. drag). Three cursor control devices, cursor keys, mouse, and trackball, were each used by 12 subjects to perform the task. Results indicate the mouse and trackball perform similarly in most cases. The cursor keys always performed more poorly than the other devices. Hence, the mouse and trackball are preferable to the cursor keys based on objective measure of user performance. In conditions where cursor keys are necessary, careful attention should be paid to aspects of the interface listed above in order to attain the highest level of performance possible.
Rank Computation Methods for Web Documents

We analyze the existing rank computation methods for the Web using a notion of rank flows and focusing on the methods for authority rank computation. We then propose a new model, called the voting model, for computing various ranks of Web pages that can be used by a search engine to better estimate the quality and relevance of the Web pages. The proposed model has a number of advantages over the surfing model used in the Google search engine [4]: it subsumes the surfing model providing natural trivially converging computation of more different types of ranks, as well as easier extensibility, faster personalization, and finer control over its parameters, due to its conceptual simplicity. The voting model also provides a new meaning for the parameters and the results of the Google's surfing model.
Automatic population of structured reports from narrative pathology reports

The aim of this project is to use the methods of natural language processing to extract pertinent information from free-text pathology reports to automatically populate structured reports. A processing pipeline has been developed cosseting of a combination of a supervised machine learning based approach using Conditional Random Fields for medical entity recognition and some rule-based methods. In total 477 narrative pathology reports of primary cutaneous melanomas were collected for evaluation. Evaluations on the training set show that system performance can be improved by about 8.7% by refinement of the rules. The overall micro-averaged precision, recall and F-score of end to end evaluation on the test set are 89.44%, 80.60% and 84.79% respectively. Our study indicates the feasibility of this approach to automate the population of structured template from narrative reports with promising results. Error analysis reveals that a single specimen report with standard headings and the presence of simple and concise statements is significantly associated with correct populations. In conclusion, the system can improve pathology reporting, and data mining for cancer registries, clinical audits and epidemiology research.
Instruction Cache Effects of Different Code Reordering Algorithms

While scientiic programs written in procedural languages like C and Fortran tend to have good instruction cache behavior, improving instruction cache performance continues to be an important issue for commonly used applications, such as compilers and document previewers, and for applications written using object oriented languages. This paper explores several code reordering algorithms that aim to improve instruction cache hit rates. We show the eeects of diierent levels of aggressiveness in applying the standard depth-rst algorithm and the eeects of diierent enhancements to this standard. We also show that code reordering becomes more important as cache line size increases.
Oops, something is wrong - error detection and recovery for advanced human-robot-interaction

A matter of course for the researchers and developers of state-of-the-art technology for human-computer- or human-robot-interaction is to create not only systems that can precisely fulfill a certain task. They must provide a strong robustness against internal and external errors or user-dependent application errors. Especially when creating service robots for a variety of applications or robots for accompanying humans in everyday situations sufficient error robustness is crucial for acceptance by users. But experience unveils that operating such systems under real world conditions with unexperienced users is an extremely challenging task which still is not solved satisfactorily. In this paper we will present an approach for handling both internal errors and application errors within an integrated system capable of performing extended HRI on different robotic platforms and in unspecified surroundings like a real world apartment. Based on the gathered experience from user studies and evaluating integrated systems in the real world, we implemented several ways to generalize and handle unexpected situations. Adding such a kind of error awareness to HRI systems in cooperation with the interaction partner avoids to get stuck in an unexpected situation or state and handle mode confusion. Instead of shouldering the enormous effort to account for all possible problems, this paper proposes a more general solution and underpins this with findings from naive user studies. This enhancement is crucial for the development of a new generation of robots as despite diligent preparations might be made, no one can predict how an interaction with a robotic system will develop and which kind of environment it has to cope with.
Hardware-Based Particle Filter with Evolutionary Resampling Stage

Autonomous systems require, in most of the cases, reasoning and decision-making capabilities. Moreover, the decision process has to occur in real time. Real-time computing means that every situation or event has to have an answer before a temporal deadline. In complex applications, these deadlines are usually in the order of milliseconds or even microseconds if the application is very demanding. In order to comply with these timing requirements, computing tasks have to be performed as fast as possible. The problem arises when computations are no longer simple, but very time-consuming operations. A good example can be found in autonomous navigation systems with visual-tracking submodules where Kalman filtering is the most extended solution. However, in recent years, some interesting new approaches have been developed. Particle filtering, given its more general problem-solving features, has reached an important position in the field. The aim of this thesis is to design, implement and validate a hardware platform that constitutes itself an embedded intelligent system. The proposed system would combine particle filtering and evolutionary computation algorithms to generate intelligent behavior. Traditional approaches to particle filtering or evolutionary computation have been developed in software platforms, including parallel capabilities to some extent. In this work, an additional goal is fully exploiting hardware implementation advantages. By using the computational resources available in a FPGA device, better performance results in terms of computation time are expected. These hardware resources will be in charge of extensive repetitive computations. With this hardware-based implementation, real-time features are also expected.
A Column Generation Approach for Support Vector Machines

The widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and those values which are critical for the classification. Its classification ability is comparable to the standard linear SVM and clearly better than Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. The method involves the optimization of a Linear Programming problem with a large number of decision variables, for which we use the well-known This work has been partially supported by projects MTM2005-09362-C03-01 of MEC, Spain, and FQM-329 of Junta
A biblioteca escolar na formação de comunidades de leitores-autores via web

The article makes an indentation in the scientific literature on the importance of reading in today's society and emphasizes the responsibility of the school and library in encouraging the practice of literary reading. Recommends the formation of community hybrid reader-writer for the school library, not only through the digital platform, but also with alliance and interaction between the various textualities. Considers that the use of participatory technologies, the school library can bring children and youth literature, since its main users are the digital natives and live normally and intuitive with the technological apparatus that pervades humanity.
Dynamic security policies

This thesis investigates the specification and the application of dynamic security policies in information-flow type systems. In traditional information-flow type systems, programmers specify security policies as type annotations, and the type checker guarantees the confidentiality of secret variables by ruling out information leaks at compile time. These security policies state simply that high-security data does not affect low-security computation. Because they offer an explicit and declarative means of writing down policies, these type systems are attractive for specifying strong security constraints on software. But practical systems interact with their dynamic environment via authentication and access control checks. Security policies in such systems necessarily involve principals and their privileges known only at run time. The challenge to existing security-typed languages is both practical and theoretical: What language constructs are necessary to connect the static information-flow analysis and the dynamic security environment? What formal theorems can be proved to guarantee the security properties of program written in the new language? This dissertation aims to study the foundation and the application of information-flow analysis involving dynamic principals and dynamic delegation. Towards this goal, the author has developed new language constructs, formalized their semantics, proved the soundness of the analysis, implemented the analysis for a practical Java subset, and, finally, built and verified a prototype of a medical information system. The main technical ideas behind the analysis are representation types and subtype refinement from type theory. A dynamic principal is represented with a special term that has a singleton type with an abstract type variable; a dynamic delegation is tested at a conditional branch so that the ordering of principals is statically refined at the success branch. These two ideas, combined with universal and existential quantification, allow the compile-time analysis to precisely track the principal identities and delegation constraints available at run time. The main theoretical contribution is a generalized version of the standard noninterference theorem to account for dynamic delegation and downgrading. The intuition is that any delegation or explicit downgrading is witnessed by an external form of justification, namely certificates in our case. By ruling out certificates that are independent of the data's label, the secrecy and the integrity of the data are still guaranteed. Furthermore, the standard noninterference theorem then follows as a simple corollary. Integrating the analysis with existing languages such as Java demands a balance between usability and compatibility. The key is to isolate system libraries such as credentials and database management in the trusted computing base, allowing the analysis to focus on application code. This work presents a design of the trusted interfaces with security types and a user application based on a realistic security model.