Miracle-dz/UniFAR-FTUs huggingface dataset:
Phrase Structure Identification and Classification of Sentences using Deep Learning

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/ by/4.0) ABSTRACT Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN-LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP.
AiAds: Automated and Intelligent Advertising System for Sponsored Search

Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it's crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers. In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser's campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several of key challenges. Finally, online A/B test and long-term grouping experiment demonstrate the advancement and effectiveness of this system.
The Influence of an Online Elementary Mathematics Pedagogy Course on Teacher Candidates' Performance.

The number of candidates seeking and completing online courses and programs in teacher education continues to increase in order to meet the licensing flexibility demands of adults pursuing teacher certification. Using the theoretical constructs of learner-centered instruction and zones of proximal development, this paper provides an overview of the design principles, analysis of qualitative evaluation data, and description of the refinement of an asynchronous online mathematics pedagogy course for graduate students seeking a teaching license in elementary education (children ages 5-12). Inductive qualitative analysis indicated that course revisions led to an increase in the frequency of teacher candidates’ high-level tasks and quality lesson plans. Implications for the design and refinement of online teacher education courses are also shared.
A introdução do relatório de estágio supervisionado: uma análise retórica

Considerando a diversidade de possibilidades de analise dos generos, neste estudo, optamos pela abordagem sociorretorica, que se caracteriza, em linhas gerais, por se interessar pela natureza social do discurso. Dentre os teoricos que se inserem nessa linha, selecionamos os estudos de Swales (1990), uma vez que o trabalho desse pesquisador se volta para analises de generos em contextos profissionais e academicos. O interesse de Swales, no que se refere ao contexto academico, e fazer com que os alunos desenvolvam o conhecimento de generos e aprendam a produzir textos, que apresentem, de forma bem sucedida, as caracteristicas do genero. Nessa perspectiva, este artigo tem como objetivo identificar qualitativamente a organizacao retorica do genero relatorio de estagio supervisionado, produzido por alunos dos cursos de Letras e de Agronomia da Universidade Federal de Uberlândia (UFU). O aparato teorico-metodologico adotado tem como base as contribuicoes de Swales (1990) no que se referem a analise, ao ensino de genero e a aplicacao do modelo CARS ( Create a Research Space ). Concluimos que apesar de haver uma flexibilidade nas informacoes retoricas nos relatorios de estagios supervisionados nos dois cursos pesquisados, o que demonstra estilos particulares utilizados pelos autores, existe uma padronizacao (quase imutavel) dessas informacoes.
Development algorithm- computer program of digital mammograms Segmentation for detection of masses breast using Marker-Controlled Watershed in MATLAB environment

Breast cancer detection images are the standard clinical practice for the diagnosis and prognosis of breast cancer. Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD) systems that will improve detection of abnormalities in mammogram images. In this paper we present a fully algorithm for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI). We are using medio-lateral oblique (MLO) view of mammograms. We have proposed method Marker-Controlled Watershed Segmentation Algorithm (MCWSA). The algorithms proposed are fully autonomous, and are able to isolate and abnormal regions in the breast tissue, If any abnormalities are present it gets accurately highlighted by this algorithm thus helping the radiologists to further investigate these regions, a task very few existing mammogram segmentation algorithms can claim
Interactive Specification of Context-Sensitive Displays in Humanoid

User interface design can be viewed as a search in a space of design alternatives. During the early conceptualization stages of application development designers are concerned with identifying the functionality that an application should deliver, the overall style of interactions provided by the interface, and global policies for generating displays. For example, designers might decide what objects and what attributes of those objects ought to be presented to the user, but not how. Later on, designers refine the design to identify the windows that the interface provides, and which objects are displayed in which windows. Finally, designers decide exactly how objects will be presented, how commands will be invoked, and how the various windows are sequenced. Typically, the design process does not proceed in a strict top-down fashion. Designers might work on the details of a presentation before finalizing the conceptual design of the application. The process is iterative [Buxton80], with redesigns based on feedback from representative end-users interacting with early prototypes.
Complexity and uncertainty in software design

The authors focus on two modes of operation, namely, rational and experimental, and on two means of expression, namely, specifications and prototypes, as key features of approaches to software design. The discussion is based on an examination of relevant trends in software development and on reappreciation of well-known principles of human problem-solving. They conclude that the effectiveness of a design effort is restricted by the principle of limited reduction: relying on rational behavior to reduce complexity introduces new sources of uncertainty and this requires experimental countermeasures. Correspondingly, relying on experimental behavior to reduce uncertainty introduces new sources of complexity requiring rational countermeasures. One important implication of this principle is that effective software design requires a systematic effort combining rational and experimental modes of operation, independently of whether specifications or prototypes are used. <<ETX>>
Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks

Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the graphical processing unit capabilities. Increasing number of the neuron sizes at each layer and hidden layers is directly related to the computation time and training speed of the classifier models. The classification parameters including neuron weights, output weights, and biases need to be optimized for obtaining an optimum model. Most of the popular DL algorithms require long training times for optimization of the parameters with feature learning progresses and back-propagated training procedures. Reducing the training time and providing a real-time decision system are the basic focus points of the novel approaches. Deep Extreme Learning machines (Deep ELM) classifier model is one of the fastest and effective way to meet fast classification problems. In this study, Deep ELM model, its superiorities and weaknesses are discussed, the problems that are more suitable for the classifiers against Convolutional neural network based DL algorithms.
Implementation of Computational Algorithms using Parallel Programming

Parallel computing is a type of computation in which many processing are performed concurrently often by dividing large problems into smaller ones that execute independently of each other. There are several different types of parallel computing. The first one is the shared memory architecture which harnesses the power of multiple processors and multiple cores on a single machine and uses threads of programs and shared memory to exchange data. The second type of parallel computing is the distributed architecture which harnesses the power of multiple machines in a networked environment and uses message passing to communicate processes actions to one another. This paper implements several computational algorithms using parallel programming techniques namely distributed message passing. The algorithms are Mandelbrot set, Bucket Sort, Monte Carlo, Grayscale Image Transformation, Array Summation, and Insertion Sort algorithms. All these algorithms are to be implemented using C#. NET and tested in a parallel environment using the MPI.NET SDK and the DeinoMPI API. Experiments conducted showed that the proposed parallel algorithms have faster execution time than their sequential counterparts. As future work, the proposed algorithms are to be redesigned to operate on shared memory multi-processor and multi-core architectures.
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition

We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.
Designing Thermal Feedback for Notifying Users About Stress

Though many new technologies have been adopted for stress detection, communicating stress to the users is still experienced primarily through visual or auditory channels. However, these commonly used feedback channels are already associated with smartphone notifications. Instead, we focus on thermal feedback yielding the advantage to preserve privacy due to its unobtrusiveness. By this work, we contribute an investigation of thermal feedback for notifying users about stress comprising the exploration of the preferred temperature level, rate of change, and body location. Accordingly, we compared different stimuli for each for those in a user study involving 21 participants. From their quantitative and qualitative feedback, we found that cold stimuli are preferred in general, showing that ± − 0.5◦C is the optimal rate of change and preferably when presented at the lower back. We conclude with discussing our findings from both the quantitative and qualitative data and finally present our research agenda paving the way for thermal feedback as a stress notifier.
PEDESTRIANS TEND TO LOOK AT SCENES WITH HIGHER LUMINANCE AND GREATER SALIENCY AT NIGHT

The mechanism of luminous environmental parameters influencing pedestrian visual behavior is unknown. Field experiments using mobile eye tracking glasses device (ETG) were carried out in outdoor night streets in this study. Two luminous parameters of visual scene, luminance and saliency, were examined by coded image processing and statistical analyses as main influencing factors. The results indicate that distribution of fixations has a clear tendency to higher brightness (68.45%) and greater saliency (78.21%) comparing with chance level (50%). It is suggested that pedestrians at night time tend to look at areas with higher luminance and greater saliency regardless of context of the scene.
A Parallel Greedy Randomized Adaptive Search Procedure Metaheuristic for the Resource-Constrained Task Scheduling Problem

The Task Scheduling Problem (TSP) consists, basically, of a group of tasks that should be executed respecting precedence constraints and minimizing the execution time of all tasks. For every task, processing units must be chosen to execute a particular task and at what moment that will happen. This work presents a parallel implementation, through multicore architectures, to solve Resource-Constrained TSP, based on a known model called dynamic resource constrained task scheduling problem (DRCTSP) and using a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic. In this approach, the moment a task is activated until the last considered time period, an amount of resources (called pro t), associated with each activated task, is provided for each period. Thus, the amount of resources available in a given period will depend on what tasks have been activated by then and when this occurred. Based on the OpenMP standard, this work shows the bene ts of using parallel methods to speed-up the overall time spent to nd a feasible solution without compromising the its quality. Also, a comparative analysis is done based on the empirical probability distributions of the random variable time to target solutions.
Bayesian Networks for Clinical Decision Making : Support, Assurance, Trust

Bayesian networks have been widely proposed to assist clinical decision making. Their popularity is due to their ability to combine different sources of information and reason under uncertainty, using sound probabilistic laws. Despite their benefit, there is still a gap between developing a Bayesian network that has a good predictive accuracy and having a model that makes a significant difference to clinical decision making. This thesis tries to bridge that gap and proposes three novel contributions. The first contribution is a modelling approach that captures the progress of an acute condition and the dynamic way that clinicians gather information and take decisions in irregular stages of care. The proposed method shows how to design a model to generate predictions with the potential to support decision making in successive stages of care. The second contribution is to show how counterfactual reasoning with a Bayesian network can be used as a healthcare governance tool to estimate the effect of treatment decisions other than those occurred. In addition, we extend counterfactual reasoning in situations where the targeted decision and its effect belong to different stages of the patient’s care. The third contribution is an explanation of the Bayesian network’s reasoning. No model is going to be used if it is unclear how it reasons. Presenting an explanation, alongside a prediction, has the potential to increase the acceptability of the network. The proposed technique indicates which important evidence supports or contradicts the prediction and through which intermediate variables the information flows. The above contributions are explored using two clinical case studies. A clinical case study on combat trauma care is used to investigate the first two contributions. The third contribution is explored using a Bayesian network developed by others to provide decision support
Coupling SUMO with a Motion Planning Framework for Automated Vehicles

Testing motion planning algorithms for automated vehicles in realistic simulation environments accelerates their development compared to performing real-world test drives only. In this work, we combine the open-source microscopic traffic simulator SUMO with our software framework CommonRoad to test motion planning of automated vehicles. Since SUMO is not originally designed for simulating automated vehicles, we present an interface for exchanging the trajectories of vehicles controlled by a motion planner and the trajectories of other traffic participants between SUMO and CommonRoad. Furthermore, we ensure realistic dynamic behavior of other traffic participants by extending the lane changing model in SUMO to implement more realistic lateral dynamics. We demonstrate our SUMO interface with a highway scenario.
Agglomerative User Clustering and Cluster Scheduling for FDD Massive MIMO Systems

Two-stage precoding is a promising transmission strategy for frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with significant overhead reduction in both downlink training and feedback. In this paper, we propose a new agglomerative clustering method to significantly simplify the user clustering process. In order to suppress the residual inter-cluster interference in realistic two-stage precoding transmission, we propose an average signal-to-leakage-plus-noise ratio (SLNR)-based iterative cluster scheduling and outer precoder design scheme to achieve a balance between providing high multiplexing gain and improving per-user rate. For uniform linear arrays (ULAs), a fast implementation of the scheme with discrete Fourier transformation (DFT) approximation is proposed. The numerical results demonstrate the performance improvement of the proposed methods over the existing methods.
Research on Power Data Storage Scheme and Settlement Scheme Based on Blockchain

In view of the privacy leakage problem existing in the centralized storage mode of power grid data, the blockchain technology can protect user privacy to a certain extent, but the blockchain has insufficient data storage capacity. In this paper, the blockchain technology is used to design the electricity data storage chain model, and the existing blockchain scalable capacity scheme is improved. Through comparison experiments, it can be shown that the scheme can minimize the total amount of storage under the premise of ensuring certain redundancy of data, and can achieve self-backup after being attacked and restore the original transaction verification performance. After that, this paper proposes a block-based power settlement model. This model uses the MPT tree + Electricity Consumption Information Storage Blockchain (ECISB) structure to settle the power consumption and verify the correctness. At the same time, the feasibility of the model is proved by experiments.
Early Detection of Health Kindergarten Student at School Using Image Processing Technology

The health early detection system for kindergarten students helps teachers to monitor the health conditions of students. For this reason, the purpose of this system is to propose a system that can detect the health of kindergarten students so that teachers can concentrate more on teaching. This paper presents a technique of combining facial recognition and movement classification for health classifications. For expression recognition, this system uses the PCA method to extract features, then the Euclidean distance algorithm is used to calculate between Eigen Face images and test images. Classification of movements, this study uses two classifications namely active and inactive. For recognition of facial expressions, this system obtains an accuracy of 83.75%.
DOES A SINGLE LED BIN REALLY REPRESENT A SINGLE LED TYPE?

In a recent H2020 European wide research project (Delphi4LED, www.delphi4LED.eu) extensive study for generating multi-domain compact LED models based on measurement data was concluded. In this context combination of the latest LED testing standards and recommendations of JEDEC and CIE have been applied in testing and new models and modelling methods have been developed in order to capture the multi -domain operation of LEDs for the purpose of simulation. A valid question is: how representative a model is for a type of an LED package, how to select a nominal LED device for modelling and how to capture the inherent variation of LED operating parameters. We had specific tasks dedicated to measure and model these variations. Findings related to capturing, quantifying and modelling of the thermal features of the mechanical structure of LED packages have already been published. In this paper we report about the measured variances of the multi -domain chip level operation of LEDs and the way how to model them. During this analysis we were confronted with the issue of having various parameter distributions in a LED population purchased from the same bin. This raises the question: LEDs belonging to a given binning class really represents a single LED type if their multi -domain behaviour is different.