Miracle-dz/UniFAR-FTUs huggingface dataset:
Speaker Change Detection via Binary Segmentation Technique and Informational Approach

This paper deals with problems of speaker change detection in acoustic data. The aim is to identify the optimal number and position of the change-points that split the signal into shorter sections belonging to individual speakers. In particular we focus on so-called binary segmentation technique, which is well-known in mathematical statistics, but it has never been used in speaker change detection task. We prove its applicability on this task in simulated tests with artificially mixed utterances ans also in tests done with 30 hours of real broadcast news (in 9 languages). Further we review commonly used approach to speaker change detection via Bayesian Information Criterion and we suggest theoretically more tenable solution.
A high-level Petri nets-based approach to verifying task structures

As knowledge-based system technology gains wider acceptance, there is an increasing need to verify knowledge-based systems to improve their reliability and quality. Traditionally, attention has been given to verifying knowledge-based systems at the knowledge level, which only addresses structural errors such as redundancy, conflict and circularity in rule bases. No semantic errors (such as inconsistency at the requirements specification level) have been checked. In this paper, we propose the use of task structures for modeling user requirements and domain knowledge at the requirements specification level, and the use of high-level Petri nets for expressing and verifying the task structure-based specifications. Issues in mapping task structures onto high-level Petri nets are identified, e.g. the representation of task decomposition, constraints and the state model; the distinction between the "follow" and "immediately follow" operators; and the "composition" operator in task structures. The verification of task structures using high-level Petri nets is performed on model specifications of a task through constraint satisfaction and relaxation techniques, and on process specifications of the task based on the reachability property and the notion of specificity.
Response Modeling with Non-Random Marketing Mix Variables

Sales response models are widely used as the basis for optimizing the marketing mix or for allocation of the sales force. Response models condition on the observed marketing mix variables and focus on the specification of the distribution of observed sales given marketing mix activities. These models fail to recognize that the levels of the marketing mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that, contrary to standard assumptions, the marginal distribution of the marketing mix variables is not independent of response parameters. We expand on the standard conditional model to include a model for the determination of the marketing mix variables. We apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. We do not assume, a priori, that details are set optimally but, instead, infer the extent to which sales force managers have knowledge of responsiveness and use this knowledge to set the level of sales force contact. We find that physicians are not detailed optimally; high volume physicians are detailed to a greater extent than low volume physicians without regard to responsiveness to detailing. In fact, it appears that unresponsive but high volume physicians are detailed the most.
Event detection and summarization in sports video

We propose a general framework for event detection and summary generation in broadcast sports video. Under this framework, important events in a class of sports are modeled by "plays", defined according to the semantics of the particular sport and the conventional broadcasting patterns. We propose both deterministic and probabilistic approaches for the detection of the plays. The detected plays are concatenated to generate a compact, time compressed summary of the original video. Such a summary is complete in the sense that it contains every meaningful action of the underlying game, and it also servers as a much better starting point for higher-level summarization and/or analysis than the original video does. We provide experimental results on American football, baseball, and sumo wrestling.
Hybrid Geometric Feedback Control of Three-Dimensional Bipedal Robotic Walkers with Knees and Feet

Hybrid Geometric Feedback Control of Three-Dimensional Bipedal Robotic Walkers with Knees and Feet. (May 2011) Ryan Wesley Sinnet, B.S., California Institute of Technology Chair of Advisory Committee: Dr. Aaron D. Ames This thesis poses a feedback control method for obtaining humanlike bipedal walking on a human-inspired hybrid biped model. The end goal was to understand better the fundamental mechanisms that underlie bipedal walking in the hopes that this newfound understanding will facilitate better mechanical and control design for bipedal robots. Bipedal walking is hybrid in nature, characterized by periodic contact between a robot and the environment, i.e., the ground. Dynamic models derived from Lagrangians modeling mechanical systems govern the continuous dynamics while discrete dynamics were handed by an impact model using impulse-like forces and balancing angular momentum. This combination of continuous and discrete dynamics motivated the use of hybrid systems for modeling purposes. The framework of hybrid systems was used to model three-dimensional bipedal walking in a general setup for a robotic model with a hip, knees, and feet with the goal of obtaining stable walking. To achieve three-dimensional walking, functional Routhian reduction was used to decouple the sagittal and coronal dynamics. By doing so, it was possible to achieve walking in the two-dimensional sagittal plane on the three-dimensional model, restricted to operate in the sagittal plane. Imposing this restriction resulted in a reduced-order model, referred to as the sagittally-restricted model. Sagittal control in the form of controlled symmetries and additional control strategies was used to achieve stable walking on the sagittally-restricted model.
Power dissipation analysis and optimization of deep submicron CMOS digital circuits

This paper introduces a simple analytical model for estimating standby and switching power dissipation in deep submicron CMOS digital circuits. The model is based on Berkeley Short-Channel IGFET model and fits HSPICE simulation results well. Static and dynamic power analysis for various threshold voltages is addressed. A design methodology to minimize the power-delay product by selecting the lower and upper bounds of the supply and threshold voltages is presented. The effects of the supply voltage, the threshold voltage, and /spl eta/, which reflects the drain induced barrier lowering, are also addressed.
Stochastic algorithms for motif discovery: a comparison of sampling strategies

The stochastic EM motif discovery algorithm has several potential advantages over algorithms using deterministic EM. To explore the choice of sampling method in stochastic EM, we implement a Metropolis independence sampler and a roulette wheel sampling method to compare their performance and computational costs. The effects of varying the number of Monte Carlo samples and EM iterations are also investigated. The surprisingly good performance of the Metropolis independence sampler indicates its potential; however, more efficient sampling strategies are required in practical applications. Background The EM algorithm [1] is the basis of a number of algorithms for motif discovery (notably the popular MEME algorithm [2]). Stochastic versions of EM have been shown to alleviate limitations of EM in theory [3] and have been implemented in a motif discovery context by Bi as the SEAM algorithm [4]. SEAM replaces the computation and maximisation of the complete-data log-likelihood function with a sampling step, which estimates the posterior distribution for each input sequence and simulates a ‘pseudo-sample’ from it. This is equivalent to the weighted ‘roulette wheel selection’ method (Figure 1). Figure 1: Using the roulette wheel sampling method requires scoring all sites in each sequence The model parameters are updated based on the set of samples. The drawback of this approach is that the probability of each site in a sequence being a motif occurrence needs to be calculated for all positions at every EM iteration. This requires considerable computation and raises the question of whether it is possible to sample from an input sequence without evaluating each site? Method and Results We propose using Markov Chain Monte Carlo (MCMC) to sample from each input sequence; the simplest MCMC strategy is the Metropolis algorithm with an independence sampler. It is well known that the Metropolis independence sampler converges to a target distribution in general; we show that this result holds in the context of motif discovery, with large sample sizes. We implement the original SEAM algorithm and a new version, which uses the Metropolis independence sampler to sample from each input sequence (Figure 2). Both sampling methods were tested on a small collection of datasets containing previously characterised E. coli TFBS motifs. Initial tests of the Metropolis method with large sample sizes returned similar results to the Figure 2: Using a Metropolis independence sampler only requires scoring k sites in each sequence roulette wheel method; in some cases, results were improved. Further tests carried out with smaller sample sizes showed the maximum value of the motif energy function to decrease, indicating worse performance, as sample size decreases. Increasing the number of EM iterations typically increases performance with larger sample sizes. Motif E. coli Ada E. coli MetR Method Roulette Metropolis Roulette Metropolis MC samples 1000 200 20 1000 200 20 EM iterations 50
A Modern Web Interface for Medical Image Retrieval

This paper discusses the need for retrieval systems and interfaces in today’s rapidly growing medical imaging datasets. It then presents Shambala, a web-based front-end for an image retrieval system called ParaDISE (Parallel Distributed Image Search Engine). The Shambala application aims to provide a user-friendly interface for both novice and expert users and takes advantage of modern web technologies to make the user experience interactive and engaging. The interface can be extended to other retrieval systems and is in the process of being integrated with alternative human-computer interaction methods (such as motion sensors or speech recognition) to make it suitable for use in a sterile environment.
Beginning DirectX 11 Game Programming

Discover the latest and most popular technology for creating next-generation 3D games: DIRECTX 11! BEGINNING DIRECTX 11 GAME PROGRAMMING is an introductory guide to learning the basics of DirectX 11 that will help get you started on the path to 3D video game programming and development. Written specifically for the beginner programmer, this book uses step-by-step instructions to teach the basics of DirectX 11 and introduces skills that can be applied to creating games for PCs and game console platforms such as the Xbox 360. Updated for all the newest DirectX 11 technology, this book includes coverage of improved professional coding practices, an overview of the latest DirectX components and tools, sprites, text and font rendering, 3D character rendering, cameras, audio, shaders and effects, and much more. By the time you reach the end of this book, you will have had enough experience with DirectX 11 that you should be able to explore making simple video games and demos. From there, you can progress toward making more complex games and demos until you find yourself able to complete and release your own PC or console games.
Genera lized K- Harmonic Means - - Boosting in Unsupervised Learnin g

We propose a new class of center-based iterative clustering algorithms, K-Harmonic Means (KHMp), which is essentially insensitive to the initialization of the centers, demonstrated through many experiments. The insensitivity to initialization is attributed to a boosting function, which increases the importance of the data points that are far from any centers in the next iteration. The dependency of the K-Means’ and EM’s performance on the initialization of the centers has been a major problem. Many have tried to generate good initializations to solve the sensitivity problem. KHMp addresses the intrinsic problem by replacing the minimum distance from a data point to the centers, used in K-Means, by the Harmonic Averages of the distances from the data point to all centers. KHMp significantly improves the quality of clustering results comparing with both K-Means and EM. The KHMp algorithms have been implemented in both sequential and parallel languages and tested on hundreds of randomly generated datasets with different data distribution and clustering characteristics.
Optimum number of errors corrected before releasing a software system

Software release problems based on Jelinski-Moranda and decreasing-failure-rate models are considered. The following software release policy is considered: after a preassigned number, n, of errors are removed, the test is terminated and the software system is released to the operational phase. The average gain is used as a criterion; there exists a unique optimal value of n. Numerical examples indicate that the software release policy based on the number of errors detected can be a good alternative to the existing policy. >
Rethinking buffer management in data center networks

Data center operators face extreme challenges in simultaneously providing low latency for short flows, high throughput for long flows, and high burst tolerance. We propose a buffer management strategy that addresses these challenges by isolating short and long flows into separate buffers, sizing these buffers based on flow requirements, and scheduling packets to meet different flow-level objectives. Our design provides new opportunities for performance improvements that complement transport layer optimisations.
An Efficient Density-based Clustering Algorithm Combined with Representative Set ⋆

Cluster analysis is a key technique of data mining and has been applied widely. The paper presents a novel clustering algorithm DCURS, which incorporates the principle of proximity and limited area into the density-based clustering method to improve clustering accuracy and stability. In addition, DCURS introduces the representative set to accelerate the running speed of the algorithm. Experimental results verify that DCURS has better performance than traditional density-based algorithms of DBSCAN and DBRS.
Q-analysis Based Clustering of Online News

With online publication and social media taking the main role in dissemination of news, and with the decline of traditional printed media, it has become necessary to devise ways to automatically extract meaningful information from the plethora of sources available and to make that information readily available to interested parties. In this paper we present a method of automated analysis of the underlying structure of online newspapers based onQ-analysis and modularity optimisation. We show how the combination of the two strategies allows for the identification of well defined news clusters that are free of noise (unrelated stories) and provide automated clustering of information on trending topics on news published online.
Bayesian estimation of soil parameters from remote sensing data

Considers the problem of finding a mathematically optimal algorithm to estimate soil parameters based on radar and/or other measurements. Specifically, given measurements m/sub 1/, m/sub 2/, . , m/sub J/ representing radar cross-sections of a given resolution element at different polarizations and/or different frequency bands, and given an approximate model expressing the dependence of these measurements on the dielectric constant /spl epsiv/ and the r.m.s. surface height h of the corresponding resolution element, the authors would like to make an "optimal" estimate of the actual /spl epsiv/ and h that gave rise to the particular m/sub 1/, m/sub 2/, . observed. By "optimal" they mean that their algorithm should produce estimates that are, on average, as close as possible to the actual values. To obtain such an algorithm, they assume that they have at their disposal a data catalogue consisting of careful measurements of the soil parameters /spl epsiv/ and h, on one hand, and the corresponding remote sensing data m/sub 1/, m/sub 2/, etc., on the other. They also assume that they have used this data to write down, for each j, an approximate formula which computes an average value of m/sub j/ to associate to the corresponding values of /spl epsiv/, h. Rather than throw away the data catalogue at this stage, and use the average formulas in a deterministic fashion to solve the inverse problem, they propose to use the data catalogue more fully and quantify the spread of the measurements about the average formula, then incorporate this information into the inversion algorithm. This paper describes how they accomplish this using a Bayesian approach. <<ETX>>
A Walloon spatial simulation model of risks in environment and health

To develop a spatial and quantitative analysis of health problems related to the environment is a challenging exercise combining different factors of risk and many actors. Using geographic information systems (GIS), multi-criteria decision theory and fuzzy logic, our risk simulation model aims to support decision makers to better understand, analyse and manage the health of the population in relation to environmental pollutions. This tool will be used within an exchange platform between the responsible authorities such as data providers and experts in health and environment to set up priorities in managing the complex system of health and environment at the level of the Walloon Region. This paper describes the purpose of this simulation model of risks, its technical development with a first set of simulation data and the proposed way of using this tool by authorities.
Latent semantic analysis as a tool to improve automatic speech recognition performance

This thesis explores the use of Latent Semantic Analysis to augment an N-gram language model to improve the accuracy of a large vocabulary speech recognition system. This thesis discusses possible solutions to three problems presented when integrating LSA with an N-gram model. First, two approaches to deriving a probability from a semantic distance are examined. Numerous parameters are introduced and optimal values found. Second, because the N-gram and LSA model have different strengths, it is necessary to develop confidence metrics that indicate when to rely more strongly on a particular model. Several confidence metrics are developed and used. Lastly, the problem of combining the two probability models is explored. Several different approaches to combining the models, including geometric mean and a decision tree were evaluated. Experimental results compared to a standard trigram model showed a reduction in perplexity of approximately 14%, and a significant reduction in the word error rate of a speech recognizer by 0.5%, 3.0% relative. Furthermore, the word errors that remained were on average of less semantic import than the original set of word errors.
GMDH algorithms for complex systems modelling

At present, GMDH algorithms give us a way to identify and forecast economic processes in the case of noised and short input sampling. In contrast to neural networks, the results are explicit mathematical models, obtained in a relatively short time. For ill-defined objects with very big noises, better results are obtained by analog complexing methods. Nets with active neurons should be applied to increase accuracy. Active neurons are able, during the self-organizing process, to estimate which inputs are necessary to minimize the given objective function of the neuron. In the neuronet with such neurons, we have a twofold multilayered structure: neurons themselves are multilayered, and they will be united into a multilayered network. SelfOrganize! is an easy-to-use modelling tool which realizes twice-multilayered neu-ronets and enables the creation of time series, single input/single output, multi-input/single output and multi-input/multi-output systems (system of equations). Successful applications are show.
Supporting language diversity of European MOOCs with the EMMA platform

This paper introduces the cross-language support of the EMMA MOOC platform. Based on a discussion of language diversity in Europe we introduce the development and evaluation of automated translation of texts and subtitling of videos from Dutch into English. The development of an Automatic Speech Recognition (ASR) system and a Statistical Machine Translation (SMT) system is described. The resources employed and evaluation approach is introduced. Initial evaluation results are presented. Finally, we provide an outlook into future research and development.
Application of GIS for the evaluation of human exposure to magnetic field in the vicinity of power lines

A geographical information system (GIS) application with a statistical model has been used to evaluate the magnetic field exposure of a population living near a high-voltage transmission line. The paper presents how the GIS application interacts with the statistical model. Also, two sites have been chosen to validate the model. At these sites, some measurements have been taken and the calculations have been compared with the measurements.
Literature Survey on Various Frequent Pattern Mining Algorithm

Data mining discovers hidden pattern in data sets and association between the patterns. In this association rule mining is one of the technique used to achieve the objective of data mining. These rules are effectively used to uncover unknown relationships producing result that can give us a basis for forecasting and decision making. To discover these rules we have to find out the frequent item sets because these item sets are the building blocks to obtain Association rules with a given confidence and support. In this paper we theoretical analyses the extraction for frequent item sets and compare these algorithm.
Techniques for intrusion-resistant ad hoc routing algorithms (TIARA)

This paper presents a set of design techniques, collectively called TIARA (Techniques for Intrusion-resistant Ad Hoc Routing Algorithms), to protect ad hoc networks against denial of service attacks. The TIARA mechanisms described seek to limit the damage sustained by ad hoc networks from intrusion attacks and allow for continued network operation at an acceptable level during such attacks. These mechanisms are designed to handle attacks on the routing traffic as well as the data traffic in ad hoc networks thereby providing a comprehensive defense against intruders. TIARA is routing algorithm independent. That is, TIARA may be viewed as providing general design principles and techniques the can be incorporated within a number of existing ad hoc routing algorithms to make them robust to intrusion attacks.
A theory of typed hygienic macros

We present the λm-calculus, a semantics for a language of hygienic macros with a non-trivial theory. Unlike Scheme, where programs must be macroexpanded to be analyzed, our semantics admits reasoning about programs as they appear to programmers. Our contributions include a semantics of hygienic macro expansion, a formal definition of α-equivalence that is independent of expansion, and a proof that expansion preserves α-equivalence. The key technical component of our language is a type system similar to Culpepper and Felleisen’s “shape types,” but with the novel contribution of binding signature types, which specify the bindings and scope of a macro’s arguments.