Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Machine Learning Algorithms for Big Data

With the explosive growth of the industry data,more and more attention is paid to big data. However,due to the volume,complex and fast-changing characteristics of big data,traditional machine learning algorithms for small data are not applicable. Therefore,developing machine learning algorithms for big data is a research focus. In this paper,the state-of-the-art machine learning techniques for big data are introduced and analyzed. As parallelism is a mainstream strategy for applying machine learning algorithms to big data,some parallelism strategies are described in detail as well. Finally,the challenges of applying machine learning to big data and some interesting research trends of machine learning in big data are pointed out.
Creation and Use of Internal Matrix Database Functions in VBA MS Excel Environment for Bulk Data Processing

A whole range of organisations use the MS Office package on different levels of their internal structure, either directly or as ITC support. The package includes the MS Excel spreadsheet processor. Excel is mostly used where the deployment of stronger tools such as SQL or other database tools is too work-intensive with regard to the strategic priorities of the company in the ITC area. There are several reasons for this. MS Excel is a rather intuitive and user-friendly tool and a functioning application can be created with its help with nearly zero knowledge and skills in programming by use of MS Excel internal functions and tools. Nevertheless, an internal inquiry performed in the years 2011 2014 in companies daily using MS Office has found out that the utilisation of VBA (an environment for the creation of own macros, functions and forms in Visual Basic for Application), integrated across the MS Office package, is nearly zero. This article therefore aims not only at pointing out the current status of bulk data processing in the MS Excel environment, but mainly at underlining (and comparing to the current status) the options with the help of which, using the VBA language, a more effective use of the MS Excel spreadsheet processor can be achieved in the area of bulk data processing, in particular in the creation of matrix functions simulating database functionality. I have used some of the existing internal functionalities of MS Excel for a comparison with my proposal.
Design and Implementation of CrossBit: Dynamic Binary Translation Infrastructure

Binary translation is the technique that translates binary program from one machine platform to another,which enables the binary code to migrate among heterogeneous machine platforms.This paper introduces the principles of binary translation,and focuses on the design and implementation of CrossBit dynamic binary translation infrastructure,including its system architecture and essential design philosophies.Experiment data are provided to prove binary translation’s performance advantage.
Two Sample Median Tests by Ranks

The median test is a statistical procedure for testing whether two independent populations differ in their measure of central tendency or location. That is median test enables us determine, whether it is likely that two independent or unrelated samples not necessarily of the same size have been drawn from two populations with equal medians. The median test may be used whenever the observations or scores obtained from the two populations are at least on the ordinal scale of measurement. In these situations, the assumptions of normality and homogeneity necessary for the valid use of the parametric ’t’ test may not be satisfied so that parametric tests may not here readily recommend themselves [1].
Improved AdaBoost Algorithm and Object Recognition Based on Haar-like Training

Author Xiuyang Li Title Improved AdaBoost Algorithm and Object Recognition Based on Haar-like Training Year 2016 Language English Pages 53 Name of Supervisor Yang Liu The main aim of this thesis is to introduce a new improved AdaBoost algorithm based on the traditional AdaBoost algorithm of improving the accuracy and speed of traditional AdaBoost algorithm. In this project, there are two part: Part 1 is the Improved AdaBoost algorithm, Part 2 is Training our own object detector. In improved AdaBoost algorithm section, two methods were used to improve the traditional AdaBoost algorithm: Weighting Parameter and limit weight expansion. Matlab was used to first emulate the traditional AdaBoost algorithm and the improved AdaBoost algorithm, then compare the accuracy and speed between these two algorithms. Part 2 introduces a method based on Haar-like features to train our own object detectors. In this thesis Orange was regarded as the target object. This process includes the preparation of positive samples and negative samples, setting the number of training stages. The thesis was mainly carried on by using Python programming language based on Window 10 operating system. OPENCV was used for the process images and training the objector detector. In conclusion, it will become a popular field to recognizing different kinds of objects, not only faces of human beings be recognized, but also any other objects in the real world can be recognized in the future.
A General Catalyst for the Asymmetric Strecker Reaction.

General: Fmoc-amino acids were purchased from Advanced Chemtech and used as received. TMSCN was purchased from Aldrich and distilled before use. TBSCN was purchased from Aldrich and used as received. (R,R)-1,2-Diaminocyclohexane was resolved by literature methods. Salicylaldehydes were synthesized according to published procedures. All solid supported coupling reactions were carried out in fritted 1.5 ml or 10 ml disposable chromatography columns. Reactions were filtered upon completion and rinsed with DMF, THF, CH2Cl2 and toluene unless otherwise indicated. The progress of all amino acid coupling reactions was monitored by UV by quantification of dibenzofulvene released from 2 mg resin samples upon Fmoc cleavage. Thiourea formation was monitored by IR for disappearance of isothiocyanate. All imines were prepared as desribed below except for 1v which was prepared using a literature procedure. Imines 1a, 1b, 1c, 1f, 1i, 1m, 1n, 1r,and 1s, have been reported previously.
Moving objects detection approach based on adaptive mixture Gaussian background model

An adaptive approach to detect moving objects with a static camera was proposed in this paper.Based on the assumption that the probability of the same pixel covered by the same intensity of the car is the least,the initial background was established and the number of Gaussians for each pixel on-line was chosen.In order to update the Gaussians parameters,the learning rate should be updated on-line according to the relationship between the pixel and its adjacent pixels.Lastly,the moving objects were detected by background subtraction.Compared to the moving objects detection approach based on conventional mixture Gaussian model,this approach has preferable adaptive performance,and can deal with changes in scenes rapidly.
Design of Remote Monitoring System for Automated Pharmacy Based on Internet of Things

In order to meet the hospital's management,modern automated pharmacies still have to first ensure the reliability.So present a design of remote monitoring system for automated pharmacy based on internet of things.The system can be divided into three tier structure in the architecture of the internet of things.Perception layer locates on the client node.This layer collects device information and gives it to the automated pharmacy terminal.Network transport layer will transmit the information from the automated pharmacy terminal to the server via GPRS.The application layer is located in the main server.Its main role is to process the information that is collected from the client and issue the feedback order.The system will help establish full quality monitoring and remote handling capability also saves the cost of the enterprise.
Runtime Systems and Scheduling Support for High-End CPU-GPU Architectures

In recent years, multi-core CPUs and many-core GPUs have emerged as mainstream and cost-effective means for scaling. Consequently, a trend that is receiving wide attention is of heterogeneous computing platforms consisting of both CPU and GPU. Such heterogeneous architectures are pervasive across notebooks, desktops, clusters, supercomputers and cloud environments. While they expose huge potential for computing, the state-of-theart software support lacks much of the desired features to improve the performance and utilization of such systems. Particularly, we focus on three important problems: (i) While machines consisting of both multi-core CPU and GPU are available, there is no standard software support that enables application to harness aggregate compute power of both CPU and GPU, (ii) Although GPUs offer very high peak performance, often, its utilization is low, which is an important concern in heavily shared cloud environments. While resource sharing is a classic way to improve utilization, there is no software support to truly share the GPUs, and (iii) In shared supercomputers and cloud environments, a critical software component is a job scheduler, which aims at improving the resource utilization and maximizing the aggregate throughput. Thus, we formulate and revisit scheduling problems for CPU-GPU clusters. For the first problem, we have developed a runtime system that will enable an application to simultaneously benefit from the aggregate computing power of available CPU and GPU. Starting from a high-level API support, the runtime system transparently handles the
Layout Algorithm Suitable for Structural Analysis and Visualization of Social Network

In information visualization of social networks,force directed layout algorithms,which enable the creation of node-link diagrams of huge-graphs,are the most popular,however,they are not quite suitable for structural analysis and visualization which often produce images where nodes clump together in the center of the screen,making it hard to discern structural features. This paper proposes a Subgroup Analysis Layout(SAL) algorithm to solve this problem,which plots out and analyses the subgroups in social networks through the analysis of roles and key attributes. Then,the results of subgroup analysis are used to improve the force directed layout algorithm in both 2D and 3D visualization. Results with the case of terrorist organization information show that SAL algorithm can be excellent in analyzing and displaying the structure of social network.
New Kids on the Block: Understanding and Engaging Elementary Readers and Writers in New Times

This collaborative study examined literacy instruction in a fifth grade classroom with particular reference to two case study students and the role of pedagogy in bridging the divide in their literacy practices. Grounded in the multiliteracies framework and perspectives consistent with third space theory, data were collected using a multi-site approach. Data were analyzed using the thematic analysis and constant comparison approaches. Findings suggest that the two children disengaged from most of school tasks because of the traditional approach to instruction, lack of recognition of their learning styles and interests, as well as the absence of digital literacies. However, they became engaged with school as the teacher implemented a more transformative approach to learning which included the integration of digital technologies and the creation of productive spaces for learning. Implications for literacy teaching and learning were discussed.
Talking out of order: task order and retrieval of grammatical gender and phonology in lexical access

ABSTRACT Despite early evidence that grammatical gender is retrieved prior to phonology in lexical access, more recent studies demonstrating task effects and non-converging evidence raise doubts about the extent to which this is a general feature of the language production system. We employed the dual-choice go/no-go paradigm with event-related potentials (ERPs) in order to further clarify the time course of retrieval of grammatical gender and phonology. Specifically, we examined how task order influences the relative timing with which these features are retrieved. Results find no clear evidence that grammatical gender is retrieved prior to phonology in a serial manner. Instead, the relative timing with which these features are retrieved is subject to task order, suggesting that prior estimates of lexical access obtained with this paradigm may be confounded by task effects. Overall, our result support parallel access models of feature retrieval during lexical access and suggest that attentional biases may modulate retrieval.
Simple Analytic Performance Models for Streaming Data Applications Deployed on Diverse

Modern hardware is inherently heterogeneous. With heterogeneity comes multiple abstraction layers that hide underlying complex systems. While hidden, this complexity makes quantitative performance modeling a difficult task. Designers of high-performance streaming applications for heterogeneous systems must contend with unpredictable and often non-generalizable models to predict performance of a particular application and hardware mapping. This paper outlines a computationally simple approach that can be used to model the overall throughput and buffering needs of a streaming application on heterogeneous hardware. The model presented is based upon a hybrid maximum flow and decomposed discrete queueing model. The utility of the model is assessed using a set of real and synthetic benchmarks with model predictions compared to measured application performance. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Simple Analytic Performance Models for Streaming Data Applications Deployed on Diverse Architectures Jonathan C. Beard, Roger D. Chamberlain and Mark A. Franklin Dept. of Computer Science and Engineering Washington University in St. Louis {jbeard,roger,jbf}@wustl.edu
BlastPhyMe: A toolkit for rapid generation and analysis of protein-coding sequence datasets

SUMMARY: We present BlastPhyMe (BLAST, Phylogenies, and Molecular Evolution) a new application to facilitate the fast and easy generation and analysis of protein-coding sequence datasets. The application uses a portable database framework to manage and organize sequences along with a graphical user interface (GUI) that makes the application extremely easy to use. BlastPhyMe utilizes several existing services and applications in a unique way that save researchers considerable time when building and analyzing protein-coding datasets. The application consists of two modules that can be used separately or together. The first module enables the assembly of coding sequence datasets. BLAST searches can be used to obtain all related sequences of interest from NCBI. Full GenBank records are saved within the database and coding sequences are automatically extracted. A feature of particular note is that sequences can be sorted based on NCBI taxonomic hierarchy before export to MEGA for visualization. The application provides GUIs for automatic alignment of sequences with the popular tools MUSCLE and PRANK, as well as for reconstructing phylogenetic trees using PhyML. The second module incorporates selection analyses using codon-based likelihood methods. The alignments and phylogenetic trees generated with the dataset module, or those generated elsewhere, can be used to run the models implemented in the codeml PAML package. A GUI allows easy selection of models and parameters. Importantly, replicate analyses with different parameter starting values can be automatically performed in order to ensure selection of the best-fitting model. Multiple analyses can be run simultaneously based on the number of processor cores available, while additional analyses will be run iteratively until completed. Results are saved within the database and can be exported to publication-ready Excel tables, which further automatically compute the appropriate likelihood ratio test between models in order to determine statistical significance. Future updates will add additional options for phylogenetic reconstruction (eg, MrBayes) and selection analyses (eg, HYPHY). BlastPhyMe saves researches of all bioinformatics experience levels considerable time by automating the numerous tasks required for the generation and analysis of protein-coding sequence datasets using a straightforward graphical interface. AVAILABILITY: Installation package and source code available from blastphyme.codeplex. com CONTACT: ryan.schott@utoronto.ca
Snorri, a software for speech sciences

Using tools for investigating speech signals is an invaluable help to teach phonetics and more generally speech sciences. For several years we have undertaken the development of the software WinSnorri which is for both speech scientists as a research tool and teachers in phonetics as an illustration tool. It consists of five types of tools: * to edit speech signals, * to annotate phonetically or orthographically speech signals. WinSnorri offers tools to explore annotated corpora automatically, * to analyse speech with several spectral analyses and monitor spectral peaks along time, * to study prosody. Besides pitch calculation it is possible to synthesise new signals by modifying the F0 curve and/or the speech rate, * to generate parameters for the Klatt synthesiser. A user friendly graphic interface and copy synthesis tools allows the user to generate files for the Klatt synthesiser easily. In the context of speech sciences Snorri can therefore be exploited for many purposes, among them, illustrating speech phenomena and investigating acoustic cues of speech sounds and prosody.
An Empirical Evaluation of Feature Selection Methods

The key objective of the chapter would be to study the classification accuracy, using feature selection with machine learning algorithms. The dimensionality of the data is reduced by implementing Feature selection and accuracy of the learning algorithm improved. We test how an integrated feature selection could affect the accuracy of three classifiers by performing feature selection methods. The filter effects show that Information Gain (IG), Gain Ratio (GR) and Relief-f, and wrapper effect show that Bagging and Naive Bayes (NB), enabled the classifiers to give the highest escalation in classification accuracy about the average while reducing the volume of unnecessary attributes. The achieved conclusions can advise the machine learning users, which classifier and feature selection methods to use to optimize the classification accuracy, and this can be important, especially at risk-sensitive applying Machine Learning whereas in the one of the aim to reduce costs of collecting, processing and storage of unnecessary data.
A Fast Human Face Detection Method From Color Images Under Complex Background

This paper proposes a fast approach for human face detection in color images under complex background.The approach first utilizes evolutionary agent computation to cluster the skin-like pixels and segment the possible face-like regions.After the face-like regions are located,wavelet decomposition is applied to each region to detect the possible facial features.Regions in which the wavelet features are distributed like a predefined human face model are recognized as human faces.Experimental results show that the proposed approach is fast and has a high detection rate.
Atomic Layer Deposition of Lead Sulfide Thin Films for Quantum Confinement.

Lead sulfide (PbS) thin films were deposited by atomic layer deposition (ALD) for the fabrication of quantum well structures. A linear growth rate of 0.9 A/cycle and pulse saturation behavior characteristic of ALD were observed. The stoichiometry of the films was confirmed using X-ray photoelectron spectroscopy (XPS) with no chemical contamination. The polycrystalline film morphology was observed with grain sizes ranging from 30 to 150 nm. Size quantization effects are shown on the bandgap by fabricating PbS quantum wells with a sub-10 nm thickness. Bandgap values were measured by tunneling spectroscopy (TS) using scanning tunneling microscopy (STM) and are matched to an effective mass model. The bandgap of the films was changed from 0.4 to 2.75 eV by varying only the number of ALD cycles.
Base Chunk Scheme for the Chinese Language

Chunk parsing is an important technique in the natural language processing research community,whose processing basis lies in a suitable and efficient chunk scheme.In this paper,we proposed a new topology-based base chunk scheme for the Chinese language.After introducing the lexical cohesion relationships to determinate three basic topological structures,we formed a better set of principles to analyze the content cohesion of a base chunk and built an efficient bridge to link its syntactic form and semantic meaning.Based on the chunk scheme,we can greatly simplify the processing procedure to automatically extract useful base chunk annotated corpora and corresponding lexical cohesion knowledge from a large scale Chinese syntactically annotated corpus TCT.All these research work will lay good foundations for the further explorations to develop Chinese base chunk parser and lexical cohesion knowledge acquisition tools.
A Safe and Efficient Route for the Preparation of Nitro-, Dinitro-, and Trinitrophenyl Chloroformates

Abstract A safe and simple method has been developed for synthesis of chloroformates derived from 2-nitrophenol, 4-nitrophenol, 2,4-dinitrophenol, 2,4,6-trinitrophenol, and pentafluorophenol, the yields being in the range of 72–96%. The method is based on a heterogeneous reaction of the substituted phenol with phosgene dissolved in dichloromethane catalyzed with solid anhydrous potassium carbonate. The synthesis of 2,4,6-trinitrophenyl chloroformate takes place only in the presence of a phase-transfer catalyst, in this case the easily recovered α,ω-dimethoxy poly(ethylene glycol) (PEG; M = 2 kDa). The only waste products of the suggested synthesis of the mentioned esters are sodium chloride and potassium chloride. The method markedly reduces the explosion risk connected with drying and handling of salts of mono-, di-, and trinitrophenols and is much more environmentally friendly than the currently used methods. Supplemental materials are available for this article. Go to the publisher's online edition of Synthetic Communications® to view the free supplemental file. GRAPHICAL ABSTRACT
Active Sampling Based on PureSVD Model for Collaborative Filtering

A parameter-change maximization sampling method is proposed to capture new user's preference in recommender system.This method produces an item list that maximizes model parameter change based on pure singular value decomposition(PureSVD). By querying new user with specific item list,the ratings are obtained for training the corresponding user's parameter in PureSVD model,it performs prediction for new users in return.A greedy approximation algorithm is presented to produce the item list with an acceptable time bound.Experiments show that the method can learn new user's preference efficiently with small sample size under Top-N metrics.
A NEW FORECAST METHOD OF THE MOVING OPTICAL OBJECT POSITION BASED ON THE PARALLEL HIERARCHICAL NETWORK

-This paper disc usses principles necessary to develop a forecast method based on the parallel-hierarchical (PH) network and an idea of the hyperbolic smoothing of empiric data. Mathematic models for forecasting dynamic image series, as well as software for forecasting moving optical object positions on the example of energy centers of laser beam spot images are developed. The developed method is more efficient and less time consuming for real-time systems in realization of the forecasting operation of energy center positions of laser beam spots.