Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic Models for Speech Recognition: - Hidden Markov Network -

Acoustic modeling is one of the most important aspects of a speech recognition system. In recent years, most research has focused on probabilistic models, namely hidden Markov models. For achieving the recognition of sponωneous speech from unrestricted speakers, precise and robust acoustic modeling becomes even more important. This paper describes a j均meworkforachieving precise and robust hidden Markov modeling, and the most recent research at ATR for refining the framework and achieving evenfiner acoustic models.
A Multi-Objective Airport Service Cars Location/Allocation Problem with Spatiotemporal constraints

The continuous growth of air traffic implies an increase of the number of vehicles that provide service to aircrafts. Increasing traffic in airport causes decreasing efficiency of airport operations and need to improve service operations. Location-allocation models are improved in two ways: 1) the allocation rule is developed to more accurately reflect customer choice processes; and 2) the objective function is developed to incorporate future changes. In this paper we propose a covering problem in a discrete space where the covering radius will be determined according to the flight time. In this problem the best locations of airport service cars located, so that servicing efficiency improved. Allocating of each aircraft to the service car regarding the covering time is done by finding optimum service car(s) location at minimum travel distance. This problem solved for airport providing service in different situations. There are some spatiotemporal constraints in airport field that must be considered. Some parameters including network restrictions, departure constraint and runway limitation which must be considered in developed model. Proposed method implemented for Imam Khomeini international airport and the numerical result shows that using A,B,C stations reduced traveling distance about 400 meters. In addition, service cars allocation has been implemented in a mobile information system.
The I-MAC Teaching Model for Beginning Family Science Instructors

A model for effective teaching is presented to guide new instructors in famity science courses. The I-MAC Teaching Model includes: (I) Introduction, (2) Mini-Iecture, (3) Application, and (4) Conclusion. Examples of the authors' application of the model, the model's flexibility, and student evaluation of its effe ctiv ene s s are de s cribed. "Ideal teachers are those who use themselves as bridges over which they invite their students to cross; then having facilitated their crossing,joyfully collapse, encouraging them to create bridges of their own. " --Nikos Kazantzakis The skill of teaching has, at times, been underappreciated in academia. lt is common for graduate students and new faculty to be given course assignments with little guidance on how to teach course material to promote optimal learning. Many make the unfortunate assumption that if a person has expertise in a particular area that such knowledge will be adequately taught to an audience of students. As we enter our fourth and sixth years, respectively, as professors, we have come to appreciate the art and skill of teaching undergraduate and graduate courses. The purpose of this paper is to share a model of teaching, the I-MAC model, to assist new family science instructors. This model has helped us organize our classes to increase the likelihood that our students will learn the course material and remain engaged with the information being presented. It has been especially applicable to family science courses because of its emphasis on cooperative learning. Students arrive in family science courses with Direct correspondence to the first author: Marriage and Family Therapy Program, School ofEducation, Univenity ofSan Diego,5998 Alcala Park, San Diego, CA 92110 (! edwald! .@3Aurd€du). Second author is in the Departrnent of Management, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061. The authors wish to thank the anonymous reviewers for their helpful comments on earlier drafts of this manuscript.
Gender Report 2017: Participation, Performance, and Attitudes Towards Mathematics, No. 2 – 2017

With a focus on Australian and Choose Maths data, this report provides a comprehensive analysis of Australian primary and secondary mathematics education trends in relation to gender difference and its impact on Year 12 participation, students' performance and attitudes over the last 10 years. Australian mathematical capability remains under threat. While the overall total number of Year 12 students enrolling in mathematics has increased, participation in intermediate and advanced mathematics remains at historic lows. The gender gap also remains, with more boys enrolled in intermediate and, particularly advanced, mathematics than girls. The very low participation of girls in advanced mathematics threatens female participation in many STEM professions including mathematics teaching. We do note however, the proportion of girls in advanced mathematics has reached a decadal high due to a slight increase in female participation since 2012 and the recent decline in male participation.
Constrained Optimization of Multicast Routing for Wide Area Control of Smart Grid

Wide area control (WAC) is highly desirable for maintaining the stability of large-scale smart grid. It requires the support of real-time data communications with low latency, which leads to the consideration of the multicast technique for routing of data packets of various measurements, control signals, and commands. While the feasibility of multicast in WAC communications has been investigated, there is a lack of a general framework to formulate and solve the multicast routing problem, particularly for WAC. To tackle this problem, this paper presents a systematic approach to constrained optimization of multicast routing in WAC over wide area networks (WANs) with a publish-subscribe architecture. To fulfil the requirements of real-time WAC communications, the optimization is formulated to minimize the end-to-end delay over WANs subject to bandwidth constraints. Then, the constrained optimization is solved theoretically by using Lagrangian Relaxation. The theoretic results are implemented in an iterative bandwidth-constrained minimum Steiner tree algorithm. Simulations are conducted to demonstrate the approach presented in this paper.
Feature Fusion for Efficient Object Classification Using Deep and Shallow Learning

Bag-of-Features (BoF) approach have been successfully applied to visual object classification tasks. Recently, convolutional neural networks (CNNs) demonstrated excellent performance on object classification problems. In this paper we propose to construct a new feature set by processing CNN activations from convolutional layers fused with the traditional BoF representation for efficient object classification using SVMs. The dimension of convolutional features were reduced using PCA technique and the bag-of-features representation was reduced by tailoring the visual codebook using a statistical codeword selection method, in order to obtain a compact representation of the new feature set which achieves increased classification rate while requiring less storage. The proposed framework, based on the new features, outperforms other state-of-the-art approaches that have been evaluated on benchmark datasets: Xerox7, UIUC Texture, and Caltech-101.
Seasonal water quality variations in a river affected by acid mine drainage : the Odiel River ( South West Spain )

This paper intends to analyse seasonal variations of the quality of thewater of the Odiel River. This river, together with the Tinto River, drains the Iberian Pyrite Belt (IPB), a region containing an abundance of massive sulphide deposits. Because of mining activity dating back to prehistoric times, these two rivers are heavily contaminated. The Odiel and Tinto Rivers drain into a shared estuary known as the Rı́a of Huelva. This work studies dissolved contaminant data in water of the Odiel River collected by various organisations, between October 1980 and October 2002, close to the rivers entry into the estuary. Flow data for this location were also obtained. The most abundant metals in the water, in order of abundance, are zinc (Zn), iron (Fe), manganese (Mn) and copper (Cu). Arsenic (As), cadmium (Cd) and lead (Pb) are also present but in much lower quantities. The quality of the river water is linked to precipitation; the maximum sulphate, Fe, Zn, Mn, Cd and Pb concentrations occur during the autumn rains, which dissolve the Fe hydroxysulphates that were precipitated during the summermonths. In winter, the intense rains cause an increase in the river flow, producing a dilution of the contaminants and a slight increase in the pH. During spring and summer, the sulphate and metal concentration (except Fe) recover and once again increase. The Fe concentration pattern displays a low value during summer due to increased precipitation of ferric oxyhydroxides. The arsenic concentration displays a different evolution, with maximum values in winter, and minimum in spring and summer as they are strongly adsorbed and/or coprecipitated by the ferric oxyhydroxides. Mn and sulphates are the most conservative species in the water. Relative to sulphate, Mn, Zn and Cd, Copper displays greater values in winter and lower ones in summer, probably due to its coprecipitation with hydroxysulphates during the spring and summer months. Cd and Zn also appear to be affected by the same process, although to a lower degree than Cu, experiencing a slight reduction in summer with respect to Mn and sulphates. D 2004 Elsevier B.V. All rights reserved.
Survey on Algorithms for High Utility Itemset Generation

: Data mining is becoming a popular research topic with its frequent applications in online e-business, web click stream analysis and cross marketing. Mining high utility itemsets from a transactional database is concerned with the discovery of itemsets with high utilities like profits or gains. Efficient discovery of the frequent and useful itemsets in huge datasets is a crucial task in data mining. In the recent years, many methods have been proposed for generating high utility patterns. Owing to this, there are few problems such as, if the minimum utility threshold value is set too low, huge amount of itemsets are generated. But, if the minimum utility threshold is set too high, very few or no high utility itemsets will be generated. In high utility itemset mining, the profit values or utility value for every item and the number of units of each item is taken into consideration. We hereby present the study of issues related to the different structures used and algorithms for mining the high utility itemsets.
Critical thinking and clinical decision making in anaesthesia

As anaesthesiologists, we are constantly striving to improve the perioperative care of our patients. Over the past several decades, the specialty has been lauded for improving perioperative safety through efforts to improve intraoperative monitoring and management. Recent studies suggest that the risk of death directly from anaesthesia is of the order of 1 in 250,000 cases, although one author has questioned this value [1]. However, perioperative complications and death from those complications continue to occur at high rates. This lecture will demonstrate how care decisions made by anaesthesiologists may have an impact on outcome and that appropriate utilization of best evidence can aid decision making and lead to improved patient outcomes [2].
Accelerating a Computer Vision Algorithm on a Mobile SoC Using CPU-GPU Co-processing - A Case Study on Face Detection

Recently, mobile devices have become equipped with sophisticated hardware components such as a heterogeneous multi-core SoC that consists of a CPU, GPU, and DSP. This provides opportunities to realize computationally-intensive computer vision applications using General Purpose GPU (GPGPU) programming tools such as Open Graphics Library for Embedded System (OpenGL ES) and Open Computing Language (OpenCL). As a case study, the aim of this research was to accelerate the Viola-Jones face detection algorithm which is computationally expensive and limited in use on mobile devices due to irregular memory access and imbalanced workloads resulting in low performance regarding the processing time. To solve the above challenges, the proposed method of this study adapted CPU–GPU task parallelism, sliding window parallelism, scale image parallelism, dynamic allocation of threads, and local memory optimization to improve the computational time. The experimental results show that the proposed method achieved a 3.3~6.29 times increased computational time compared to the well-optimized OpenCV implementation on a CPU. The proposed method can be adapted to other applications using mobile GPUs and CPUs.
Corpulence des enfants et des adultes en France métropolitaine en 2015. Résultats de l'étude Esteban et évolution depuis 2006.

Selon l’Organisation mondiale de la santé (OMS), l’obésité a atteint les proportions d’une épidémie mondiale conduisant à une augmentation des maladies chroniques associées. Par conséquent, il est nécessaire de promouvoir des politiques de santé publique pour réduire ce phénomène. C’est l’un des objectifs du Programme national nutrition santé (PNNS), mis en place en 2001 par le ministère de la Santé. Cet article décrit l’évolution de la corpulence des enfants et adultes (6-74 ans) en France entre 2006 et 2015 à partir de deux études nationales transversales constituées par un plan de sondage probabiliste à trois degrés : ENNS-2006 (N=3 566) et Esteban-2015 (N=3 702).
SIMCALC : Accelerating Students’ Engagement With the Mathematics of Change

SimCalc: Accelerating StudentsÕ Engagement with the Mathematics of Change Jeremy Roschelle SRI International James J. Kaput, and Walter Stroup University of Massachusetts, Dartmouth To appear as: Roschelle, J., Kaput, J., & Stroup, W. (in press). SimCalc: Accelerating student engagement with the mathematics of change. In M.J. Jacobsen & R.B. Kozma, Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments. Hillsdale, NJ: Earlbaum.
Analysis of forest vegetation in Binsar Wildlife Sanctuary, Kumaun Himalaya, Uttarakhand, India

Forest vegetation and regeneration were studied along different aspects and altitudinal gradient in Binsar wildlife sanctuary Almora district of Uttarakhand Himalaya (India) using standard phytosociological random sampling quadrat method. A total of six stands were randomly laid. The result reveals that along different altitudes and aspects a total of ten tree species were recorded. Across the stands, the total tree density ranged between 300-560 Ind/ha, sapling density between 20 to 380 Ind/ha and seedling density from 640 to1880 Ind/ha. The shrub density varied from 540 to 8520 Ind/ha. Pinus roxburghii was the dominant species on western, southern, eastern and western facing aspects with IVI 135.32, 218.41, 214.63 and 167.06 respectively. While Lyonia ovalifolia and Quercus leucotrichophora were the most dominant species on the northern and eastern facing aspects respectively. Across the stands, total basal area and diversity of tree layer ranged from 26.66-117.41 m /ha and 0.45-1.57 respectively. The regeneration of Pinus roxburghii and Myrica esculenta was 2 excellent. The Pinus roxburghii was shown good regeneration even in northern and eastern facing aspects. The occurrence and increasing regeneration of Pinus roxburghii in these aspects might be due to global warming as this species requires warm and drier slopes for growth. Whereas The Quercus leucotrichophora had a complete absence of saplings and decrease of young tree classes, but greater proportion of individuals in seedling class, indicates that seedlings from recent past had failed to attain sapling class, if the trend continues; the populations of species will decrease in near future.
3D reconstruction from non-uniform point clouds via local hierarchical clustering

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.
Using Artificial Neural Network for Classification of High Resolution Remotely Sensed Images and Assessment of Its Performance Compared with Statistical Methods

Image classification is always one of the most important issues in remote sensing and obtained ‎information from image classification is most widely used in this field and other applications like urban ‎planning, natural resource management, agriculture and etc. The purpose of this study is to assess the ‎performance of multi-layer perceptron neural networks to classify high resolution IKONOS image ‎which covers mainly urban area of Shahriar city which is located in Tehran Province of Iran. The output of a neural network classifier has been compared with the results of support vector machine with the Gaussian kernel function and Maximum Likelihood Classification (MLC) algorithm which is most commonly used in ‎statistical approach image classification. In the best situation, the classification outputs indicate that ‎neural network algorithm, including 0.8775overall accuracy and 0.82 Kappa Coefficient is more ‎accruable and reliable than both the support vector machine with 0.8557 and 0.8197 and maximum likelihood with 0.7836 and 0.7295 overall accuracy and ‎Kappa Coefficient Respectively. Also results indicate that in these three methods, training data and model parameters play ‎important roles in the classification accuracy. ‎
Empirical study of algorithms for qualitative temporal or spatial constraint networks

Representing and reasoning about spatial and temporal information is an important task in many applications of Artificial Intelligence. In the past two decades numerous formalisms using qualitative constraint networks have been proposed for representing information about time and space. Most of the methods used to reason with these constraint networks are based on the weak composition closure method. The goal of this paper is to study some implementations of these methods, including three well known and very used implementations, and two new ones.
Scaling Big Data Cleansing

Scaling Big Data Cleansing Zuhair Yarub Khayyat Data cleansing approaches have usually focused on detecting and fixing errors with little attention to big data scaling. This presents a serious impediment since identifying and repairing dirty data often involves processing huge input datasets, handling sophisticated error discovery approaches and managing huge arbitrary errors. With large datasets, error detection becomes overly expensive and complicated especially when considering user-defined functions. Furthermore, a distinctive algorithm is desired to optimize inequality joins in sophisticated error discovery rather than näıvely parallelizing them. Also, when repairing large errors, their skewed distribution may obstruct effective error repairs. In this dissertation, I present solutions to overcome the above three problems in scaling data cleansing. First, I present BigDansing as a general system to tackle efficiency, scalability, and ease-of-use issues in data cleansing for Big Data. It automatically parallelizes the user’s code on top of general-purpose distributed platforms. Its programming interface allows users to express data quality rules independently from the requirements of parallel and distributed environments. Without sacrificing their quality, BigDansing also enables parallel execution of serial repair algorithms by exploiting the graph representation of discovered errors. The experimental results show that BigDansing outperforms existing baselines up to more than two orders of magnitude. Although BigDansing scales cleansing jobs, it still lacks the ability to handle sophisticated error discovery requiring inequality joins. Therefore, I developed IEJoin as an algorithm for fast inequality joins. It is based on sorted arrays and space efficient 5 bit-arrays to reduce the problem’s search space. By comparing IEJoin against wellknown optimizations, I show that it is more scalable, and several orders of magnitude faster. BigDansing depends on vertex-centric graph systems, i.e., Pregel, to efficiently store and process discovered errors. Although Pregel scales general-purpose graph computations, it is not able to handle skewed workloads efficiently. Therefore, I introduce Mizan, a Pregel system that balances the workload transparently during runtime to adapt for changes in computing needs. Mizan is general; it does not assume any a priori knowledge of the graph structure or the algorithm behavior. Through extensive evaluations, I show that Mizan provides up to 84% improvement over techniques leveraging static graph pre-partitioning.
Use of Metrics and their Visualisations in Software Engineering Projects

In this paper, we describe an observational investigation that explores whether creating visualisation systems using software metrics promotes reflection. We presented metric visualisations to six groups, in separate sessions, from an undergraduate software engineering project course, each consisting of 7-8 students. The results and feedback from these sessions were used to build upon the visualisation system to improve them. Feedback and recorded statements from the students showed that the students reflected but were primarily focused on performance in their course. Delayed questions to the groups showed that the engagement was increased in all groups. It was found that for the visualisations to be of use to the students they must provide direct information about the student’s code base, such as telling students where to look to find possible faults. This information is then used to create a visualisation system which looks to address the issues students had with the initial implementation.
Approximate greatest descent in neural network optimization

Numerical optimization is required in artificial neural network to update weights iteratively for learning capability. In this paper, we propose the use of Approximate Greatest Descent (AGD) algorithm to optimize neural network weights using long-term backpropagation manner. The modification and development of AGD into stochastic diagonal AGD (SDAGD) algorithm could improve the learning ability and structural simplicity for deep learning neural networks. It is derived from the operation of a multi-stage decision control system which consists of two phases: (1) when local search region does not contain the minimum point, iteration shall be defined at the boundary of the local search region, (2) when local region contains the minimum point, Newton method is approximated for faster convergence. The integration of SDAGD into Multilayered perceptron (MLP) network is investigated with the goal of improving the learning ability and structural simplicity. Simulation results showed that two-layer MLP with SDAGD achieved a misclassification rate of 9.4% on a smaller mixed national institute of national and technology (MNIST) dataset. MNIST is a database equipped with handwritten digits images suitable for algorithm prototyping in artificial neural networks.