Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data Cognition: Keynote Lecture of “2015 Forum of Big Data Value Realization Road”

This paper deeply elaborated the big data cognitive methodology with simple explanation with the introduction of poetry writing,language translation,the voice recognition,insurance,face recognition and so on. Big data symbolizes the coming of new era of data-intensive science and the big data age is the era of little model,little's law and interdisciplinary. The breakthrough of big data cognitive lies in the cluster. Using the shape and depth of cloud and end to study is advocated and the research manufacturing of cloud robots which may like in spirit but do not like in form are advocated.He introduced the driving service cloud robots and robot language,and promoted development of brain machine driving,and demonstrated the research achievements of unmanned buses and the ongoing six class testing and assessment of the smart car of his team and advocated researchers to do more in practice,and less research in practice.
The influence of implantation of total hip endoprosthesis on the life quality

Introduction:Total hip endoprosthesis is one of the most frequent and effective methods that are used in the treatment of progressive degenerative hip changes. After the implantation of total hip endoprosthesis, patients perception on their postoperative improvement and health has big role in their contentment and thus in the success of the surgical procedure. The aim of this survey is to examine whether there are differences in the quality of life of people in third age before and after the implantation of total hip endoprosthesis.Material and methods:The survey included 100 examinees in the third age, above 65 years, of both genders, with the disease of hip joint that had been treated at the Clinics for orthopaedics and traumatology of UKCS by implanting the total endoprosthesis. The examinees were questioned before the surgery and after implanting the total hip endoprosthesis. The survey has covered the period from 1st January 2014 till 31st December 2016. As an instrument tool in the survey well validated SF-36 questionnaire for the estimation of life quality and total health status was used. Results: Evaluating all the elements in SF-36 questionnaire it was determined that statistically significant improvement of all the life quality elements that this questionnaire measured was detected, after the conducted surgery compared to the state before the surgery, p=0.001. Statistically biggest improvement has been detected in limitations that happened because of physical and emotional issues, and then in those that were related to physical and social functioning (p=0.001). Body pains and vitality are also statistically significantly better after the conducted surgery (p=0.001). Conclusion:Out of the results of the survey it derives that the quality of life of the people in third age is significantly better after the surgery of implanting the total hip endoprosthesis.
An implementation of short-timed speech recognition on layered neural nets

Static models in the form of state transition probability matrices representing short speech units such as syllables which correspond to Chinese utterance of isolated characters are adopted as learning patterns of layered Neural Nets, called MLP (multilayer perceptron). One of the major problems in neural net implementation of speech recognition is how an associative network usually with a fixed architecture, such as a number of input-output units and internal representation units, can deal with the sequential nature of speech. A static model is proposed as the input of a multilayer perceptron machine; a network architecture and learning algorithm are also introduced to implement isolated word recognition on MLP. <<ETX>>
Exemplar-based Recognition of Speech in Highly Variable Noise

Robustness against varying background noise is a crucial requirement for the use of automatic speech recognition in everyday situations. In previous work, we proposed an exemplarbased recognition system for tackling the issue at low SNRs. In this work, we compare several exemplar-based factorisation and decoding algorithms in pursuit of higher noise robustness. The algorithms are evaluated using the PASCAL CHiME challenge corpus, which contains multiple speakers and authentic living room noise at six SNRs ranging from 9 to -6 dB. The results show that the proposed exemplar-based techniques offer a substantial improvement in the noise robustness of speech recognition.
Analyzing Software Artifacts through Singular Value Decomposition to Guide Development Decisions

SHERRIFF, MARK STEPHEN. Analyzing Software Artifacts through Singular Value Decomposition to Guide Development Decisions. (Under the direction of Laurie A. Williams.) During development, programming teams will produce numerous types of software development artifacts. A software development artifact is an intermediate or final product that is the result or by-product of software development. Hidden relationships and structures within a software system can be illuminated through singular value decomposition using software development artifacts, and these relationships can be leveraged to help guide software development questions regarding the interactions among software files. The goal of this research is to build and investigate a framework called Software Development Artifact Analysis (SDAA) that uses software development artifacts to illuminate underlying relationships within a system. SDAA provides guidelines for selecting and gathering software development artifacts, discovering relationships, and then leveraging the insights gained through the analysis of those relationships. We use singular value decomposition (SVD) to generate the relationships from a matrix of software development
Teacher's Attitudes as Knowledge Predictors

Attitudes and behaviours towards the natural environment have been extensively studied in certain cultural settings during the last 40 years. In education, the teacher's ability to grasp the fundamentals of an academic subject may define his or her own attitudes towards that discipline; certainly the reverse is also valid. The correlations between affective and cognitive domains appear to play a significant role in teaching-learning dynamics. In this study we seek to assess whether the affective posture towards school sciences of a cohort of teachers in rural communities shows an association with their cognitive competence in the disciplines. The results of this study provide evidence that there is a statistically significant correlation between the cognitive and affective domains for in-service teachers. The affective domain, therefore, could serve as a predictor for cognitive competency and self-efficacy expectancies with respect to both content and career fulfilment.
Performance measurement and tuning of remote acquisition

The paper introduces the concept of forensic triage and the novel method of its application — the acquisition of iSCSI attached storage over Wide Area Network (WAN) link. Next, in the created lab environment, it tests the potential methods of I/O performance improvement (prefetching, parallelism). The gathered metrics show that for the links with RoundTrip Time (RTT) introduced, performance degraded. Finally, the author discusses flaws in the established method and argues about the future work in this area.
Protection Scheme for Platform Private Data in Direct Anonymous Attestation

Aiming at the problem in DAA(Direct Anonymous Attestation),the best theoretical solution for Identity Attestation of TPM(Trusted Platform Module) is suggested.And for the authentication secret— private data(A,e)is directly stored in plaintext on the platform,it could be easily attacked or destroyed.Based on the safe storage function of TPM,a protection scheme for platform private data in DAA is proposed.This scheme,in accordance with the protection key and for the user’s identity generation the authority data,could protect the platform private data,and the private data(A,e) is encrypted with the symmetric key of AES,and stored in hard disk.The theoretical analysis and experimental results indicate that the proposed scheme could effectvely protect the private data(A,e)and enhance the creditability of DAA is identity futhentication.
Visuospatial working memory and early math skills in first grade children

This study aimed to investigate the relationship between different components of active visuospatial working memory and math ability in young children. In a longitudinal study, we compared the contributions of active visual and spatial working memory (WM) tasks in early math performance at two times: the beginning of the first class of primary school (T1) and the end of the first class of primary school (T2). Two tests were conducted with 43 young participants to investigate active visual WM (Imaginative Puzzles) and active spatial WM (Corsi Backward). Measurements related to pre-math ability (BIN 4-6 test) at T1 and math skills (AC-MT 6-11 test) at T2 were accomplished. The relationship between visual and spatial WM and math ability was analyzed using a regression model in which the predictors were identified through a forward selection based on the use of the BIC index (Bayesian Information Criterion). Results show that at the beginning of primary school, basic knowledge of magnitude and numbers is strongly influenced by spatial WM. T1 pre-math performance is the sole predictor of mathematical performance at T2. These results suggest different implications of domain-general and domain-specific variable on early math performance, depending on the child’s development period. This finding brings additional evidence to the debate on the relationship between visuospatial WM and math ability in young children.
Fine-tunning and knowledge transfer for gender recognition using CNN

Automatic gender classification has gain popularity particularly with the increasing amount of social platforms and social media providers. Also, recently has been noted an increase use of CNNs to solve different classification tasks, one of which is gender recognition. Training and testing a CNN often involves a large amount of time and the classification results are not consistent across databases. In this work I use a pre-trained CNN with one data set, retrain it in order to obtain similar result when classifying another data set with much less effort involve in training. I evaluate the method using the Image of Group data set with a CNN trained with Adience data set.
Development of Best Practices for Large-scale Data Management Infrastructure

htmlabstractThe amount of available data for processing is constantly increasing and becomes more diverse. We collect our experiences on deploying large-scale data management tools on local-area clusters or cloud infrastructures and provide guidance to use these computing and storage infrastructures. In particular we describe Apache Hadoop, one of the most widely used software libraries to perform large- scale data analysis tasks on clusters of computers in parallel and provide guidance on how to achieve optimal execution time when performing analysis over large-scale data. Furthermore we report on our experiences with projects, that provide valuable insights in the deployment and use of large- scale data management tools: The Web Data Commons project for which we extracted all Microformat, Microdata and RDFa data from the Common Crawl web corpus, the largest and most up-to-date web corpus that is currently available to the public. SciLens, a local-area cluster machine that has been built to facilitate research on data management issues for data-intensive scientific applications. CumulusRDF, an RDF store on cloud-based architectures. We investigate the feasibility of using a distributed nested key/value store as an underlying storage component for a Linked Data server, which provides functionality for serving large quantities of Linked Data. Finally we describe the News Aggregator Pipeline, which is a piece of software to perform the acquisition of high volume textual streams, it’s processing into a suitable form for further analysis and the distribution of the data.
Image segmentation algorithm in the system focusing digital camera

The article discusses the segmentation algorithm of digital image based on the measurement contrast of fine detail in the normalized CIELAB system. Using normalized CIELAB system allows you to segment fine detail in accordance with the contrast sensitivity. Focusing the camera is carried out on the basis of the search of function maximum that determines the dependence of the number of segmented fine detail from the focal length. Proposed software as part of an interactive digital camera control system implements this function. The results of experiments and comparative analysis with other methods focus are given. It is shown that the proposed method improves the accuracy of the focus when photographing images in low light.
The Research of Diagnosis of Alzheimer's Disease Based on the Coherence Analysis of EEG Signal

After investigating the arithmetic of coherence for electroencephalograph (EEG), the arithmetic of coherence for EEG based on discrete sampling data is present here. The program was designed by using of C ++ language, and applied in clinic. The program for calculation coherence runs quickly and conveniently. The experimental facts indicate that there are apparently different coherences between Alzheimer's disease patients and the normal controls as such in the left-right frontals (F3-F4), left-right centrals (C3-C4), left-right parietals (P3-P4) and left-right temporals (T5-T6). It is suggested that methodology of coherence analysis of EEG data is a useful tool in diagnosing Alzheimer's disease.
Integration of Genome Databases Using a Deductive Object-Oriented Database

Recently, many genome databanks were developed as a result of growing genome project activities. Each of them consists of a large amount and variety of data , and they were d eveloped independently. Therefore, their integration and efficient management of the data are required. It is also necessary to develop a framework for easily building and testing biological hypotheses with the integrated database . We developed a deductive objectoriented database for searching an integrated database , acquiring new knowledge from it, and storing the knowledge in the database . It consists of an object-oriented database that integrates the conventional genome databases such as GenBank, and deductive language interface for genome analysis. In this paper, we present an overview of the system and examples of analyses using the database.
Investigating Potential Combinations of Visual Features towards Improvement of Full-Reference and No-Reference Image Quality Assessment

Objective assessment of image quality is the process of automatic assignment of a scalar score to an image such that the rating or score corresponds to the score provided by the Human Visual System (HVS). Despite extensive studies since the last two decades, it remains a challenging problem in image processing due to the presence of different types of distortions and limited knowledge of the HVS. Existing approaches for assessing the perceptual quality of images have relied on a number of methodologies that directly apply known properties of the HVS, construct hypotheses considering the HVS as a blackbox and use hybrid approaches that apply both of the techniques. All of these methodologies have relied on different types of visual features for Image Quality Assessment (IQA). In this dissertation, we have studied the problem of different types of IQA from the feature extraction point of view and showed that effective combinations of simple visual features can be used to develop IQA approaches having competitive performance with the state-of-the-art. Our work is divided into four parts each having the final goal to bring about performance improvement in the areas of Full-Reference (FR) and No-Reference (NR)-IQA. We have gradually moved from FR to NR-IQA in the works presented in this dissertation. First, we propose improvements in two existing FR-IQA techniques by introducing changes in the features used. Next, we propose a new FR-IQA technique by extracting image saliency as global features and combining them with the local features of gradient and variance to improve the performance. For NR-IQA, we propose a novel technique for sharpness detection in natural images using simple features. The performance of this method provides improvement over the existing methods. After working with the specific purpose NR-IQA, we propose a general purpose technique using suitable features such that no training with pristine or distorted images or subjective quality scores is required. This technique, despite
Design and Realization of Hadoop Distributed Platform

Hadoop provides a reliable shared storage and analysis system to meet the needs of the mass data of Internet users. We have designed the architecture of Hadoop distributed development platform. And under the environment of Ubuntu, the Hadoop distributed development platform is realized. The aim is to provide a parallel cloud computing system to a user in a template based and reusable way. Through the experimental test, the platform has the reliability and practicability, which provides the basis for the development of large data or cloud computing application services.
Sugar content and valid acidity detection of navel orange based on feed forward neural network

The paper investigates non-destructive detection methods of sugar content and valid acidity of Citrus sinensis Osbeck cv.Skaggs Bonanza based on machine vision and factors influencing detection accuracy.Images of Citrus sinensis Osbeck cv.Skaggs Bonanza acquired with machine vision system are cut and converted from RGB to HSI space and then removed backgrounds to be ready for analysis.Hue values H are as input of neural network to model sugar content and valid acidity of Citrus sinensis Osbeck cv.Skaggs Bonanza.Test results of 40 samples show,the correlation coefficients of measured values and predicted values of sugar content and valid acidity are 0.796 1 and 0.721 4 respectively.The correctness for accuracy±1.5°Brix of sugar content is75%,for valid acidity,correctness for accuracy±0.5pH is 90%. Sugar content and valid acidity correlate to color of fruit pericarp,so machine vision system can be used to estimate these two parameters non-destructively.One method to improve accuracy of estimation is to remove affects of insects and disease during image processing.
Active Server Pages for Dummies with Cdrom

From the Publisher: Completely updated to take advantage of the new active Server Pages 2.0 features, this book is targeted at Web masters, IS professionals, and programmers who want to know how to put the power and flexibility of Active Server Pages (ASP) to work for them. The CD-ROM includes sample scripts, ActiveX server components and sample Active Server Pages.
Security in Cloud computing Using Attribute Based Encryption: Proxy Re-encryption

In today’s world securing personal health record (PHR) is an up-coming model in sharing the information about health. PHR system offers different features to view health data, secure communication with the providers, new entry of data, securely transfer health data. Patients have whole control over their health record and share that with the system providers and users. Patients have control over their own health care record can effectively share their health data with the other users. A PHR service is operated by the semi-trusted cloud service providers. More privacy required for health record stored on cloud servers, because of increasing number circumstances happened in recent years with the cloud data. In present system, Encrypted PHRs are stored on semitrusted servers such that sensitive PHR’s does not accessed by the server. Patients can have rights to use their PHR by assigning attribute-based access, fine grained access constitutional rights to selected data users.
Exploring a Multithreaded Methodology to Implement a Network Communication Protocol on the IBM Cyclops-64

A trend of emerging large-scale multi-core chip design is to employ multithreaded architectures such as the IBM Cyclops-64 (C64) chip that integrates large number of hardware thread units, main memory banks and communication hardwares on a single chip. A cellular supercomputer is being developed based on a 3D connection of the C64 chips. This paper introduces our design, implementation, and evaluation of the Cyclops Datagram Protocol (CDP) for the IBM C64 multithreaded architecture and the C64 supercomputer system. CDP is inspired by the TCP/IP protocol. Its design is very simple and compact. The implementation of CDP leverages the abundant hardware thread-level parallelism provided by the C64 multithreaded architecture. The main contributions of this paper are: (1) We have completed a design and implementation of CDP that is used as the fundamental communication infrastructure for the C64 supercomputer system. It connects the C64 back-end to the front-end and forms a global uniform namespace for all nodes in the heterogeneous C64 system; (2) On a multithreaded architecture like C64, the CDP design and implementation effectively exploit the massive thread-level parallelism provided on the C64 hardware, achieving good performance scalability; (3) CDP is quite efficient. Its Pthread version can achieve around 90% channel capacity on the Gigabit Ethernet, even it is running at the user-level on a single processor machine; (4) Extensive application test cases are passed and no reliability problems have been reported.
Generalized interval caching policy for mixed interactive and long video workloads

In a video server environment, some video objects (e.g., movies) are very large and are read sequentially. Hence it is not economical to cache the entire object. However, caching random fractions of a multimedia object is not beneficial. Therefore, traditional cache management policies such as LRU are not effective. The sequential access of pages can be exploited by caching only the intervals between two successive streams on the same object, i.e., by retaining the pages brought in by a stream for reuse by a closely following stream and subsequently discarding them. In contrast to the movie-on-demand workload, an interactive workload is composed of many short video clips (e.g., shopping). Hence, concurrent access to the same video chip will be infrequent and interval caching policy will not be effective. In this paper, we propose a Generalized Interval Caching policy that caches both short video objects as well as intervals or fractions of large video objects. To study the efficacy of the GIC policy, we also propose a model for mixed short interactive and long video workloads. The proposed policy is shown to effectively reduce disk overload and hence to increase the capacity of the video server.