Miracle-dz/UniFAR-FTUs huggingface dataset:
DIVINE - The Distributed Verification Environment

We present basic concepts and the current state of a general distributed verification environment (DiVinE). The environment is meant to support the development of distributed enumerative model checking algortihms, to enable unified and credible comparison of these algorithms, and to make the distributed verification available for public use in a form of a distributed verification tool.
Robust parametric fault detection and isolation for nonlinear systems

Although most practical failure situations require nonlinear modeling, quite often the nonlinearity associated with the fault function is partially known based on past experience of plant technicians, with the uncertainty arising due to unknown parameters. Motivated by such practical considerations, this paper presents a robust fault diagnosis scheme for parametric faults in nonlinear dynamical systems. A detection and approximation observer is used for online health monitoring. Once a fault is detected, a bank of nonlinear adaptive observers are activated for the purpose of fault isolation. A key contribution is the derivation of robust isolability conditions for the class of nonlinear systems under consideration. The effectiveness of the proposed methodology is illustrated by simulations on a benchmark example of a three-tank system.
The IMM Face Database, An Annotated Dataset of 240 Face Images

A machine for screwing down the spoke nuts of a bicycle wheel while controlling the torque applied to each nut in response to the position of the nut on the wheel in relation to the order of tightening of the nuts. The torque applied is progressively greater from nut to nut starting from the first until a maximum torque is applied after the spokes over half the wheel circumference have been tightened.
Effects of Structural Matching and Paraphrasing in Question Answering

In this paper, we propose an answer seeking algorithm for question answering that integrates structural matching and paraphrasing, and report the results of our empirical evaluation conducted with the aim of examining effects of incorporating those two components. According to the results, the contribution of structural matching and paraphrasing was not so large as expected. Based on error analysis, we conclude that structural matching-based approaches to answer seeking require technologies for (a) coreference resolution, (b) processing of parse forests instead of parse trees, and (c) large-scale acquisition of paraphrase patterns. key words: question answering, structural matching, paraphrasing, paraphrase space
Advanced Structural Dynamics Analysais by LS-DYNA3D Code

The good features included in the explicit ‘LS-DYNA3D’ code such as ALE, non-reflecting boundaries, constraint joints, contacts, multi-materials, initial volume fraction or geometry features and the flexibility in creation and application of load curves are explored and highlighted in the investigation. The fact that the focus is on structural engineering does not necessarily prevent from highlighting related issues which are crucial at the design stage and therefore reflected as factors directly influencing the analysis process. The mathematical background, time integration, drawbacks and superior features of the code are thoroughly explained in a fashion intended to enrich the knowledge of new engineers.
Predicting chaotic time series with fuzzy if-then rules

The authors continue work on a previously proposed ANFIS (adaptive-network-based fuzzy inference system) architecture, with emphasis on the applications to time series prediction. They show how to model the Mackey-Glass chaotic time series with 16 fuzzy if-then rules. The performance obtained outperforms various standard statistical approaches and artificial neural network modeling methods reported in the literature. Other potential applications of ANFIS are also suggested. <<ETX>>
Had we but world[s] enough, and time?

This paper presents ongoing research into user requirements for 3-dimensional "virtual worlds" to be used as a means of information retrieval. There is a brief review of the literature in the fields in which design of 3-dimensional virtual spaces has been carried out, and the conclusion is reached that this design has usually been done without regard to user preferences. This study has used grounded theory to establish user preferences, initially amongst a group of about fifty postgraduate information management students. Initial results of open coding of these interviews are described, and the next steps in the study are laid out. The Virtual Reality Modelling Language (VRML) will be used as a world design medium.
Machine Learning across the WSN Layers

Wireless sensor networks (WSNs) have seen rapid research and industrial development in recent years. Both the costs and size of individual nodes have been constantly decreasing, opening new opportunities for a wide range of applications. Nevertheless, designing software to achieve energy-efficient, robust and flexible data dissemination remains an open problem with many competing solutions. In parallel, researchers have effectively exploited machine learning techniques to achieve efficient solutions in environments with distribution and rapidly fluctuating properties, analogous to WSN domains. Applying machine learning techniques to WSNs inherently has the potential to improve the robustness and flexibility of communications and data processing, while simultaneously optimizing energy expenditure. This chapter concentrates on applications of machine learning at all layers in the WSN network stack. First, it provides a brief background and summary of three of the most commonly used machine learning techniques: reinforcement learning, neural networks and decision trees. Then, it uses example research from the literature to describe current efforts at each level of the stack, and outlines future opportunities.
Scheduling Optimization in a Cloth Manufacturing Factory Using Genetic Algorithm with Fuzzy Logic for Multi-Objective Decisions

The purpose of this work is the scheduling of trousers collection orders in the manufacturing factory of the development department of an important cloth manufacturing society in Tunisia. The scheduling of collection orders differ to the scheduling of production orders. These collections orders are small and average orders of various types of trousers for different international customers. From the anticipated customer information about the collection orders such us the anticipated dates of starting orders and exporting them, the quantities of each order and the combined importance of customer and style (fashion or classic style), we have studied the best orders scheduling solution which take in account all these information and constraints. We have for the first step studied the development department method for scheduling orders (classical method) which take in account only the priority rules either by anticipated beginning dates or by export dates. For a second step, we have used the fuzzy logic to include an importance factor to each order so that the scheduling problem becomes multi-objective. In the goal to optimisation of the result, we have used the genetic algorithm. The efficiency of this method was studied by comparing the fuzzy logic scheduling solution to the classical scheduling solution for the anticipated planning of the manufacturing factory.
Mobile Distance Learning with PDAs: Development and testing of pedagogical and system solutions supporting mobile distance learners

The article discusses basic teaching-learning philosophies and experiences from the development and testing of mobile learning integrated with the online distance education system at NKI (Norwegian Knowledge Institute) Distance Education. The article builds on experiences from three European Union (EU) supported Leonardo da Vinci projects on mobile learning: From e-learning to m-learning (2000-2003), Mobile learning – the next generation of learning (2003-2005), and the ongoing project, Incorporating mobile learning into mainstream education (2005-2007).
Separation filtering of aerogmagnetic data using filter-banks

Filter-bank transforms analyse the local frequency content of a signal. This information can be used to design matched filters that adapt to local properties of the data. Matched filters can be applied to aeromagnetic data to separate the responses of depth-varying magnetic layers and are known in this case as separation filters. The performance of filter-bank separation filters is demonstrated on two synthetic data sets, each with a deep and shallow magnetic layer with differing amounts of depth variation. The modelled data were analysed with a Gaussian filter-bank and conventional global Fourier methods. The Gaussian filter-bank method was more effective than the Fourier method when the depth to the layers was highly variable because the filter-bank was able to adapt to the changing local frequency properties of the data. The numerical accuracy of the Fourier method helped it perform better when the layers were at approximately constant depths because the observed data closely fitted the Fourier model. These results give a preliminary indication that the filter-bank approach to separation filtering provides an effective method for separating the anomalies of magnetic layers that vary significantly in depth. The method also automatically produces crude depth estimates of the layers that may be useful as a first-pass interpretation.
Technology transfer: Research Utilization and User Stimulation

This paper presents an analysis of technology transfer that considers the fact that the word "innovation" is a noun and is also a verb. The verb concept is expanded and developed. A third concept of technology transfer, capacity building, is then introduced. A paradigm or check list of micro factors helpful in capacity building is given.
Databases Model for Determining of Technological Features Based on the Theory of Technological Similarity

The main achievements presented in this paper are developed method orientated on computer aiding and computer program method, both used in designing process of the new technical features. Analyzed methods develop algorithmisation of selection features and support integration with the process of the preparation of the production. Further analyses will be carried on in order to specify the theory of the technological similarity in the new forms of computer aiding (relational databases). The represented programs were being developed on the practical examples of creating the module systems of hydraulic cylinders used in mining, slag cars used in metallurgy and gears series of types. Programs and data bases presented in the paper are basis of selection of the technological features in the process series of types of the technology creating. All of these applications support the intensive development of the types of technical features and affect on their competitive on the ready market.
Composing complex EXAFS problems with severe information constraints

In recent work, a model for the structural environment of Hg bound to a catalytic DNA sensor was proposed on the basis of EXAFS data analysis. Although severely constrained by limited data quality and scant supporting structural data, a compelling structural model was found which agreed with a similar but less detailed model proposed on the basis on NMR data. I discuss in detail the successes and limitations of the analytical strategy that were implemented in the earlier work. I then speculate on future software requirements needed to make this and similarly complex analytical strategies more available to the wider audience of EXAFS practitioners.
Measurement of snow and sea ice surface temperature and emissivity in the Ross Sea

The snow and sea ice surface temperature in the Ross Sea was measured aboard the NSF Research Vessel Nathaniel B. Palmer during two cruises in 1998 and 1999, including a winter cruise in May and June 1998, and a summer cruise in January and February 1999. The main objectives of the cruises were to collect sea ice surface temperature data sets using a variety of instruments, and to devise schemes for future validation of the satellite data sets to be derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Attempts for estimation of emissivity of snow surface in the thermal infrared region were also made. The authors collected surface temperatures on 37 individual ice floes. Three sets of instruments were available, including 20 Optic StowAway thermistors, a Heintronics Model KT 19.82 infrared radiation pyrometer, and an Exergen Model DJOI-RS infrared microscanner. Each thermister has its own automatic data logger, and has been calibrated separately at the factory. The Heintronics thermal infrared pyrometer has an 8-14 /spl mu/m spectral response. The brightness temperature measurements can be read manually off the instrument screen, although a data logger with voltage output is also connected to the pyrometer for recording time series automatically and continuously. The Exergen infrared microscanner has a spectral response between 2-20 /spl mu/m. It has an automatic emissivity compensation mechanism for most real surfaces of unknown emissivity, except for metal surfaces.
Beyond the Gaussian Model in Diffusion-Weighted Imaging: The Package dti

Diffusion weighted imaging (DWI) is a magnetic resonance (MR) based method to investigate water diffusion in tissue like the human brain. Inference focuses on integral properties of the tissue microstructure. The acquired data are usually modeled using the diffusion tensor model, a three-dimensional Gaussian model for the diffusion process. Since the homogeneity assumption behind this model is not valid in large portion of the brain voxel more sophisticated approaches have been developed. This paper describes the R package dti. The package offers capabilities for the analysis of diffusion weighted MR experiments. Here, we focus on recent extensions of the package, for example models for high angular resolution diffusion weighted imaging (HARDI) data, including Q-ball imaging and tensor mixture models, and fiber tracking. We provide a detailed description of the package structure and functionality. Examples are used to guide the reader through a typical analysis using the package. Data sets and R scripts used are available as electronic supplements.
Using feedforward networks to distinguish multivariate populations

It is shown how feedforward neural networks can be used to construct convenient and informative tests for nonspecific differences between populations with multivariate attributes. The key to the power of these tests is of independent interest: under mild conditions, feedforward neural networks have the universal approximation property when parameterized by weights in arbitrarily small neighborhoods. <<ETX>>
Medical data mining : improving information accessibility using online patient drug reviews

We address the problem of information accessibility for patients concerned about pharmaceutical drug side effects and experiences. We create a new corpus of online patient-provided drug reviews and present our initial experiments on that corpus. We detect biases in term distributions that show a statistically significant association between a class of cholesterol-lowering drugs called statins, and a wide range of alarming disorders, including depression, memory loss, and heart failure. We also develop an initial language model for speech recognition in the medical domain, with transcribed data on sample patient comments collected with Amazon Mechanical Turk. Our findings show that patient-reported drug experiences have great potential to empower consumers to make more informed decisions about medical drugs, and our methods will be used to increase information accessibility for consumers. Thesis Supervisor: Dr. Stephanie Seneff Title: Senior Research Scientist
Enhanced Reality Visualization in a Surgical Environment

Enhanced reality visualization is the process of enhancing an image by adding to it information which is not present in the original image. A wide variety of information can be added to an image ranging from hidden lines or surfaces to textual or iconic data about a particular part of the image. Enhanced reality visualization is particularly well suited to neurosurgery. By rendering brain structures which are not visible, at the correct location in an image of a patient''s head, the surgeon is essentially provided with X-ray vision. He can visualize the spatial relationship between brain structures before he performs a craniotomy and during the surgery he can see what''s under the next layer before he cuts through. Given a video image of the patient and a three dimensional model of the patient''s brain the problem enhanced reality visualization faces is to render the model from the correct viewpoint and overlay it on the original image. The relationship between the coordinate frames of the patient, the patient''s internal anatomy scans and the image plane of the camera observing the patient must be established. This problem is closely related to the camera calibration problem. This report presents a new approach to finding this relationship and develops a system for performing enhanced reality visualization in a surgical environment. Immediately prior to surgery a few circular fiducials are placed near the surgical site. An initial registration of video and internal data is performed using a laser scanner. Following this, our method is fully automatic, runs in nearly real-time, is accurate to within a pixel, allows both patient and camera motion, automatically corrects for changes to the internal camera parameters (focal length, focus, aperture, etc.) and requires only a single image.
Optical Image Subtraction

There are several methods for optically subtracting one image from another in order to detect differences between scenes or between photographs of scenes. There are many applications for such a technology, including earth resource studies, meteorology, automatic surveillance and/or inspection, pattern recognition, urban growth studies, and bandwidth compression. This paper presents an overview of several techniques for obtaining optical image subtraction, including holographic, interferometric, coding, and positive-negative superposition methods. As part of this review, a table is presented summa-rizing and comparing the characteristics of more than twenty-five approaches. An extensive bibliography is also given.
Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks

Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken by a Webcam with CCD sensor. These photos were converted to gray scale in Matlab. Afterwards, these photos were converted to binary photo format using Otsu’s Method. Then this data was used to train multi-layered neural network to complete grading. Matlab was used for both image processing and artificial neural networks. Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.
Bis­(dabconium) vanadium arsenate octahydrate, (N2C6H14)2[V4As6O30H6]·8H2O

The title compound contains a network of [V4As6O30H6]4− clusters, each of which is accompanied by two dabconium (doubly protonated 1,4-di­aza­bi­cyclo­[2.2.2]­octane) cations and eight water mol­ecules. A complex hydrogen-bonding network stabilizes the crystal packing.
An Information Theoretic Approach to Speaker Diarization of Meeting Recordings

In this thesis we investigate a non parametric approach to speaker diarization for meeting recordings based on an information theoretic framework. The problem is formulated using the Information Bottleneck (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, the IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. The distance between speech segments is selected as the Jensen-Shannon divergence as it arises from the IB objective function optimization. In the first part of the thesis, we explore IB based diarization with Mel frequency cepstral coefficients (MFCC) as input features. We study issues related to IB based speaker diarization such as optimizing the IB objective function, criteria for inferring the number of speakers. Furthermore, we benchmark the proposed system against a state-of-the-art systemon the NIST RT06 (Rich Transcription) meeting data for speaker diarization. The IB based system achieves similar speaker error rates (16.8%) as compared to a baseline HMM/GMM system (17.0%). This approach being non parametric clustering, perform diarization six times faster than realtime while the baseline is slower than realtime. The second part of thesis proposes a novel feature combination system in the context of IB diarization. Both speaker clustering and speaker realignment steps are discussed. In contrary to current systems, the proposed method avoids the feature combination by averaging log-likelihood scores. Two different sets of features were considered – (a) combination of MFCC features with time delay of arrival features (b) a four feature stream combination that combines MFCC, TDOA, modulation spectrum and frequency domain linear prediction. Experiments show that the proposed system achieve 5% absolute improvement over the baseline in case of two feature combination, and 7% in case of four feature combination. The increase in algorithm complexity of the IB system is minimal with more features. The system with four feature input performs in real time that is ten times faster than the GMM based system.
Communication through B2MML - is that possible?

Many of you have certainly heard about the ISA S95 standard, and you know that it treats the subject of how Enterprise/business systems such as e.g., SAP, Baan, Movex etc should be integrated with manufacturing and control systems You might also be aware that of B2MML (Business to Manufacturing Markup Language), a set of XML schemas corresponding to the S95 object models. B2MML is intended to be used for data exchange between a business system and a manufacturing system.This paper describes experiences of using the B2MML schemas in real applications. What happens if the Business system does not export the data in a format compliant with the schemas? What about security for the data being exchanged? Were extensions or restrictions needed on the schemas? Which are the reasons for choosing to use B2MML? Is it practically possible to communicate using B2MML? Benefits, difficulties, compromises, challenges etc of using the B2MML schemas in practical applications will be described in the paper. (Less)
Validity and reliability of web search based predictions for car sales

The first chapter is an introduction to social media predictions. In the next chapter the term prediction will be discussed, and four perspectives on prediction will be highlighted. Subsequently, in the third chapter previous studies and literature on social media- and web-search based predictions will be discussed. In the fourth chapter the research design for this thesis will be presented, with the associated hypotheses. The following section covers the data collection and methods of analysis. In chapter five, the analysis and results of the collected data will be elaborated and determine whether the hypotheses are supported or not. The last chapter describes the key findings and contains a brief discussion about previous studies. Subsequently, limitations are and recommendations for future research and predictions models are addressed.