Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative study of two biorthogonal wavelet transforms in time series prediction

We present comparative results of time-series prediction preprocessed with two different wavelet transforms: (1) the compactly supported biorthogonal wavelet transform developed by Cohen, Daubechies, and Feauveau (1992), and (2) the semi-orthogonal wavelet transform, a class of biorthogonal wavelet transform, constructed by Cai and Wang (1996). Both theoretical and computational results of the two wavelet transforms are discussed. The major difference between the two wavelet transforms is the computational procedure. So far, only the semi-orthogonal wavelet transform of Cai and Wang can compute wavelet coefficients from a coarse scale level to a fine scale level, which makes the computation more flexible and cost effective. However, the compactly supported biorthogonal wavelet transform of Cohen et al. Has better decorrelation property. Thus, we found that the semi-orthogonal wavelet transform of Cai and Wang provides a faster computation process while the compactly supported biorthogonal wavelet transform provides better predicted wavelet coefficients in our experimental results. Based on the wavelet coefficients computed from signals, nonlinear prediction models utilizing recurrent neural networks are applied to predict wavelet coefficients at each scale level, Thus, the predicted signal is obtained from the reconstruction of predicted wavelet coefficients, In our experiments, the multi-step prediction using wavelet transforms gives much superior results than those obtained without using wavelet transforms. We applied our method to predict specific time series, intracranial pressure, acquired from head-trauma patients in the neuro intensive care unit at the University of Pittsburgh Medical Center.
Towards Abstractions for Web Services Composition

ing over service details (I simply write xi for a specific service) the alternative composition can be obtained by the pick statement. Pick allows nondeterministic execution of one of several paths based on an external event, this is the typical choice operator of the π-calculus. Let A be the alternative composition of A ′ and A , the specification in webπ∞ of A is the following: [[[pick ( (x1, ĩ1, A), (x2, ĩ2, A) ) ]]]yũ = x1(ĩ1). [[[A ′]]]yũ + x2(ĩ2). [[[A ′′]]]yũ
A SIMULATOR FOR PLANNING SOFTWARE PROJECT MANAGEMENT

In this paper, an attempt has been made to develop a Stochastic Simulator which helps in decision making to identify the critical activities which needs to be given due priorities to certain activities during the development of Software Project. In response to new information or revised estimates, it may be necessary to reassign resources, cancel optional tasks, etc. Project management tools that make projections while treating decisions about tasks and resource assignments as static will not yield realistic results. The usual PERT procedure may lead to overly optimistic results as many pass which are not critical but slightly shorter than critical on the basis of estimated activity duration or average durations. Due to randomness of durations, these pass under some combination of activity durations, could become longer then the average longest path. Such paths would be ignored while using the PERT technique on the basis of the average durations. In order to overcome this problem and be more reasonable, the said Stochastic Simulator has been designed by generating random samples from a specific probability distribution associated with that particular activity of SPM. The proposed stochastic simulator will be an asset in effort estimation to Project/Module Leaders to complete the assigned Software Development Task in stipulated time to enable the development house to release the product software in time to achieve the specific target by the consultancy unit
Robel: synthesizing and controlling complex robust robot behaviors

We briefly present the Robel supervision system3 which learns from experience robust ways to perform high level tasks. Each possible way to perform a task is modeled as a Hierarchical Tasks Network whose primitives are sensory-motor functions. The relationship between supervision states and the appropriate modality is learned through experience as a Markov Decision Process (MDP). This MDP is independent of the environment and characterizes the robot abilities for the task.
Spectromorphological analysis of sound objects: an adaptation of Pierre Schaeffer's typomorphology

Abstract The last fifty years has witnessed an enormous development with regard to sound production, and has opened a new world of novel aural experiences. In order to be able to articulate and discuss these experiences there is a need for a corresponding novel set of terms and concepts. Such a terminology would also be relevant for analytical and interpretive approaches to electroacoustic music, avant-garde Western music, and ethno music. Pierre Schaeffer's typomorphology, developed in the 1960s, proposed a variety of novel terms, but they have not been of widespread use, since they unfortunately did not lend themselves very well for practical analysis. The present paper intends to develop Schaeffer's approach in the direction of a practical tool for conceptualising and notating sound quality. While carefully reducing the sometimes-bewildering number of terms found in Schaeffer's work, it introduces a set of graphic symbols apt for transcribing electroacoustic music in a concise score. The analysis of sound objects calls for a specific listeners' intention, called reductive listening.
Developing Technical Training: Structured Approach for Developing Classroom and Computer‐Based Instructional Materials (2nd Edition)

Since it was first published almost twenty years ago, Developing Technical Training has been a reliable resource for both new and seasoned training specialists. The third edition of this classic book outlines a systematic approach called the Instructional Systems Design (ISD) process that shows how to teach technical content defined as facts, concepts, processes, procedures, and principles. Whether you teach “hard” or “soft” skills, or design lessons for workbooks or computers, you will find the best training methods in this book. Using these techniques, you can create learning environments that will lead to the most efficient and effective acquisition of new knowledge and skills. Throughout the book, Clark defines each content type and illustrates how to implement the best instructional methods for delivery in either print or e-learning media.
Heuristic and Formative Evaluation: A Case Study Illustration of a New Technique

The purpose of the present work was to carry out an evaluation of an interactive, instructional Website that teaches the basic tenets of human performance technology. The evaluation methodology was based upon a unique combination of heuristic and formative evaluation techniques. It involved measuring attitudinal reactions to the Website, learning gains from performance scores on practice exercises, and content, navigation, and design areas needing modification or revision. Evaluation data were gathered from five students pursuing graduate degrees in education at an urban university. Paper-based attitude surveys, think-aloud protocols, and heuristic response forms were utilized to collect data. Student evaluators found the content in the Website to be useful and interesting; however, in some instances the practice items were confusing. The site was found to be easy to navigate and, overall, evaluators enjoyed using it. The evaluation methodology was shown to be effective in assessing design, content, and attitudinal issues, although in the future think-aloud protocols may be optional because they do not provide sufficient data to warrant the time spent on their use. Data also revealed that measuring learning gains was critical to the accurate evaluation and educational effectiveness of instructional Websites.
Video salient event classification using audio features

The aim of this work is to detect the events in video sequences that are salient with respect to the audio signal. In particular, we focus on the audio analysis of a video, with the goal of finding which are the significant features to detect audio-salient events. In our work we have extracted the audio tracks from videos of different sport events. For each video, we have manually labeled the salient audio-events using the binary markings. On each frame, features in both time and frequency domains have been considered. These features have been used to train different classifiers: Classification and Regression Trees, Support Vector Machine, and k-Nearest Neighbor. The classification performances are reported in terms of confusion matrices.
The need and drive for open data in biomedical publishing

The concept of open data goes beyond making data freely available. Data must also be free to reuse and build upon without legal or technical impediments. Funder and journal policies for data sharing and the growing open science movement are helping open data to spread across biomedical sub-disciplines. Editors should embrace open data to ensure that their decisions can stand up to close scrutiny; journals need open data to help them fulfil their stated goals, and publishers should utilize open data and data publication to serve the growing sector of the scientific community requiring it as a service, and to continue developing novel forms of scholarly communication in an increasingly data-intensive scholarly communication environment.
Managing Cognitive Load in Adaptive Multimedia Learning

Research in multimedia and computer-based learning has entered a new phase with a focus on adapting instruction to characteristics of individual learners. Managing Cognitive Load in Adaptive Multimedia Learning provides theory- and research-based recommendations on information presentation techniques for multimedia and e-learning environments. Focusing on extensively researched principles and methodologies, this book offers readers comprehensive research and practical implications in the subject while providing concrete examples on adaptive multimedia learning.
Use of multilayer networks for the recognition of phonetic features and phonemes

Artificial neural networks capable of doing hard learning offer a new way to undertake automatic speech recognition. The Boltzmann machine algorithm and the error back‐propagation algorithm have been used to perform speaker normalization. Spectral segments are represented by spectral lines. Speaker‐independent recognition of place of articulation for vowels is performed on lines. Performance of the networks is shown to depend on the coding of the input data. Samples were extracted from continuous speech of 38 speakers. The error rate obtained (4.2% error on test set of 72 samples with the Boltzmann machine and 6.9% error with error back‐propagation) is better than that of previous experiments, using the same data, with continuous Hidden Markov Models (7.3% error on test set and 3% error on training set). These experiments are part of an attempt to construct a data‐driven speech recognition system with multiple neural networks specialized to different tasks. Results are also reported on the recognition performance of other trained networks, such as one trained on the E‐set consonants.
Megastore: structured storage for Big Data

Megastore es uno de los componentes principales de la infraestructura de datos de Google, elcual ha permitido el procesamiento y almacenamiento de grandes volumenes de datos (BigData) con alta escalabilidad, confiabilidad y seguridad. Las companias e individuos que usanesta tecnologia se estan beneficiando al mismo tiempo de un servicio estable y de altadisponibilidad. En este articulo se realiza un analisis de la infraestructura de datos de Google,comenzando por una revision de los componentes principales que se han implementado en losultimos anos hasta la creacion de Megastore. Se presenta tambien un analisis de los aspectostecnicos mas importantes que se han implementado en este sistema de almacenamiento y que le han permitido cumplir con los objetivos para los que fue creado. Abstract: Megastore is one of the building blocks of Google’s data infrastructure. It has allowed storingand processing operations of huge volumes of data (Big Data) with high scalability, reliabilityand security. Companies and individuals using this technology benefit from a highly availableand stable service. In this paper an analysis of Google’s data infrastructure is made, startingwith a review of the core components that have been developed in recent years until theimplementation of Megastore. An analysis is also made of the most important
Optimization of Kernel Function Parameters SVM Based on the GA

That Support Vector Machines applies to image recognition have good results，But the kernel function C and parameters of the SVM which influence the result and performance has not been decided. Against this question, this paper bring forward a new algorithm that combines SVM with GA to classify and uses GA to select excellent kernel function, the results of experiment show Image Recognition based on SVM and GA are effective.
Optical image acquisition system for colony analysis

For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems since they belong to a new technology product. One of the main problems is image acquisition. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. A digital camera in the top of the box connected to a PC computer with a USB cable, all the camera functions are controlled by the computer.
Selection of suitable human-robot interaction techniques for intelligent wheelchairs

There are many user interfaces already available to drive electric wheelchairs. These enable users to convey their intention explicitly to the wheelchair control system. However, we have observed users who find it nonetheless extremely difficult to do so. For some severely disabled users it is almost impossible to drive a conventional electric wheelchair in a safe way. This paper explores several ways of implicit communication to assist the user to perform daily manoeuvres. Some initial user trials have been performed at a hospital. The results are evaluated and conclusions are drawn on the selection of suitable human-robot interaction techniques.
Side information refinement using motion estimation in dc domain for transform-based distributed video coding

The key to good decoding performance in distributed video coding systems lies in the efficient prediction of frames using side information. Previously, this process has been performed without considering the progressive nature of the synthesis of the final frame. Observing the fact that incorrectly predicted areas of the current frame can be detected at different levels of final frame synthesis, an algorithm is proposed for refinement of side information synthesis based on an additional stage of motion estimation. The additional stage is performed on correctly decoded DC frames and is used to significantly improve the motion prediction of the final resolution frames, which leads to enhanced performance of the overall system.
Object tracking under nonuniform illumination with adaptive correlation filtering

A real-time system for illumination-invariant object tracking is proposed. The system is able to estimate at high-rate the position of a moving target in an input scene when is corrupted by the presence of a high cluttering background and nonuniform illumination. The position of the target is estimated with the help of a filter bank of space-variant correlation filters. The filters in the bank, adapt their parameters according to the local statistical parameters of the observed scene in a small region centered at coordinates of a predicted position for the target in each frame. The prediction is carried out by exploiting information of present and past frames, and by using a dynamic motion model of the target in a two-dimensional plane. Computer simulation results obtained with the proposed system are presented and discussed in terms of tracking accuracy, computational complexity, and tolerance to nonuniform illumination.
Power System Load Modeling Based on Genetic Programming

Genetic Programming (GP) is a new evolutionary algorithm based on genetic algorithm, which has selfadaptive, self-organizing, self-learning and other advantages, and has significant advantages in terms of symbolic regression to solve long-term problems of the model structure automatically recognizes the problem. In this paper, the genetic programming is introduced to the power system load modeling to solve long-standing problems of automatic identification model structure in the power system. KeywordsLoad Model; Genetic Programming; Model Structure; Model Identification; Simulation
Steady state analysis of nonlinear circuits using SPICE

A program for computing steady-state behavior for a nonlinear circuit with periodic inputs is described. Conventional simulators (like SPICE) use time-domain integration methods until the steady-state response is obtained and then are often very time-consuming. Other simulators use 'ad-hoc' methods for steady-state analysis but are not so widely used as SPICE. A program is presented that uses SPICE as a computational machine but is also able to implement some of those more efficient methods. <<ETX>>
Using a general purpose microcomputer to aid the searching of bibliographic databases

This paper describes a system designed to utilise a general purpose microcomputer for online literature searching in place of a coventional terminal. A program (LIB) was written to enable search profiles, which have previously been stored in disk files on the microcomputer, to be sent to the host computer at the speed of the communication line. In addition, a log of the response from the host is stored in another disk file by LIB. LIB is described together with a ‘typical’ search that was run using both the search program and a conventional terminal. The times taken to run the search by both methods are given. It was found that substantial reductions in connection time can be made, and that using a microcomputer in this way need not restrict the interactive nature of online searching. A final advantage of this approach is that a general purpose microcomputer has many other applications. 7 references.
Enantioselective Catalytic Asymmetric Hydrogenation of Ethyl Acetoacetate in Room Temperature Ionic Liquids.

Abstract Ethyl acetoacetate was chosen to evaluate the efficiency of hydrosoluble derivatives of 4,4′- and 5,5′-diamBINAP in enantioselective catalytic asymmetric hydrogenation in various room temperature ionic liquids (RTILs). Complete conversion and good selectivity were obtained. Recycling by simple extraction with pentane was also possible.
Recent Advances in Acquisition/Reconstruction Algorithms for Undersampled Magnetic Resonance Imaging

Several applications of Magnetic Resonance Imaging (MRI), in particular dynamic MRI and functional MRI (fMRI), require rapid acquisition to measure dynamic processes changes. Experimental data are collected in the k-space by following different trajectories to cover the whole space. Complete data acquisition necessitates waiting for a fixed time interval: a reduced number of collected trajectories allows acquisition time reduction but undersampling occurs, often producing artifacts. In what follows, a review of methods for sparse sampling acquisition and reconstruction is presented. In particular, a differentiation is done between sparse acquisition methods which do not use any restoration algorithm (artifacts are tolerated) and those methods for which a restoration algorithm is essential. The first class contains also methods where spatial information is shared between temporal images to reduce the collected data. In the second class of methods, a differentiation is done between those reconstruction/restoration methods that reduce artifacts independently of the sample shape, and those restoration methods that adapt their action by modifying the acquisition trajectories during the acquisition, i.e. the chosen trajectories (both in number and directions) are dependent on the sample shape. A third emerging class of methods, those including hybrid forms of the second class, are also reported.
Route planning based on uncertain information in transport networks

The goal of this paper is to find a solution for route planning in a transport network where the network type can be arbitrary: a network of bus routes, a network of tram rails, a road network or any other type of a transport network. Furthermore, the costs of network elements are uncertain. The concept is based on the Dempster-Shafer theory and Dijkstra's algorithm which helps with finding the best routes. The paper focuses on conventional studies without considering traffic accidents or other exceptional circumstances. The concept is presented by an undirected graph. In order to model conventional real transport, the influencing factors of traffic congestion have been applied in the abstract model using uncertain probabilities described by probability intervals. On the basis of these intervals, the cost intervals of each road can be calculated. Taking into account the uncertain values of costs, an algorithm has been outlined for determining the best routes from one node to all other nodes comparing cost intervals and using decision rules that can be defined by the end user, and if necessary, node by node. The suggested solution can be applied for both one type of network as well as for a combination of a few of those.
Semantic Middleware Architecture

Some of the most important features of future IT systems will come from the current research of Semantic Web technologies and distributed systems. Following this idea we set out to implement a middleware solution that builds upon the latest developments of research activity into Internet of Things and, more generally, context-aware systems. These directions where selected because they currently are the main drivers of the research into the applications of semantic technologies. Our focus was mainly on the aspects that we considered to be overlooked by other proposed semantic middleware solutions, such as the support of asynchronous, event based communication and ontology management in distributed systems. The developed middleware was used to build a test system in order to prove its advantages over similar systems that rely on currently used technologies.
Radio wave propagation in curved rectangular tunnels at 5.8 GHz for metro applications

The need for wireless communication systems is increasing in the transport domain. These systems have to be operational in every type of environment and particularly tunnels for metro applications. These ones can have rectangular, circular or arch-shaped cross section. Furthermore, they can be straight or curved. This paper presents a new method to model the radio wave propagation in straight tunnels with an arch-shaped cross section and in curved tunnels with a rectangular cross section. The method is based on a Ray Launching technique combining the computation of intersection with curved surfaces, an original optimization of paths, a reception sphere, an IMR technique and a last criterion of paths validity. Results obtained with our method are confronted to results of literature in a straight arch-shaped tunnel. Then, comparisons with measurements at 5.8 GHz are performed in a curved rectangular tunnel.