Reviewerly/exHarmony huggingface dataset:
Automatic Exam Paper Generator

Exam Paper Generator provides an answer to decide on challenging, well-framed queries and generate it easy for the instructor to get it at intervals a brief amount of your time. This will be done in some faucets of the hand because it is an associate degree golem application, therefore accessible at any time and place. It contains various modules that allow the system to trot out all queries simply. The modules like admin module, user module, and question entry and question management make it a simple task. From the entered input the paper is generated and saved as a .pdf file that can be complete for own or distributed as per the user or admin requirements.
Doing Work in the Background

In the previous chapter, we looked at all sorts of ways to make and send notifications. It’s an invaluable tool for large applications and more so for applications that need to perform tasks in the background.
Homomorphic Computation in Reed-Muller Codes.

With the ongoing developments in artificial intelligence (AI), big data, and cloud services, fully homomorphic encryption (FHE) is being considered as a solution for preserving the privacy and security in machine learning systems. Currently, the existing FHE schemes are constructed using lattice-based cryptography. In state-of-the-art algorithms, a huge amount of computational resources are required for homomorphic multiplications and the corresponding bootstrapping that is necessary to refresh the ciphertext for a larger number of operations. Therefore, it is necessary to discover a new innovative approach for FHE that can reduce the computational complexity for practical applications. In this paper, we propose a code-based homomorphic operation scheme. Linear codes are closed under the addition, however, achieving multiplicative homomorphic operations with linear codes has been impossible until now. We strive to solve this problem by proposing a fully homomorphic code scheme that can support both addition and multiplication simultaneously using the Reed-Muller (RM) codes. This can be considered as a preceding step for constructing code-based FHE schemes. As the order of RM codes increases after multiplication, a bootstrapping technique is required to reduce the order of intermediate RM codes to accomplish a large number of operations. We propose a bootstrapping technique to preserve the order of RM codes after the addition or multiplication by proposing three consecutive transformations that create a one-to-one relationship between computations on messages and that on the corresponding codewords in RM codes.
Regression Network for Real-Time Pedestrian Detection

This paper presents a real-time robust pedestrian detection algorithm based on Convolutional Neural Network (CNN). With the success of CNN in image recognition, CNN has also been used in pedestrian detection, but these methods are still difficult to use the case of real-time application. The proposed method makes full use of the powerful feature representation and faster recognition ability of CNN to meet the real-time request for pedestrian detection. To do so, we regard the pedestrian detection as a regression problem, and a CNN model is employed to solve it. Since a new loss function is introduced, the training can be completed end-to-end, and the trained CNN can directly map an image to the location and confidence of pedestrian bounding box without feature abstraction. We verify the proposed method on the common pedestrian detection dataset of Caltech, and experimental results show that it owns lower miss rate and higher detection frame rate compared with some popular methods.
An Annotation Framework for Luxembourgish Sentiment Analysis

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view.
Extração de conhecimento através de data mining: Obtenção de regras de associação num dataset público

EnglishThis paper presents a research that consisted of a Market Basket Analysis through the application of data mining techniques to a large dataset. The aim is to find sets of items commonly purchased together and then generate Association Rules, which can be valuable for promotional campaigns or recommendation systems. The CRISP research methodology was adopted, which is especially designed for data mining research. Its various phases are described, focusing on the exploratory data analysis (to understand better the data), the preparation of data for application and configuration of the Apriori algorithm and the assessment of the model behaviour, measured by the increase and decrease of the number of generated Association Rules, according to the variation of the parameters and the selected data portuguesEste artigo apresenta um trabalho de investigacao que consistiu na Analise de Carrinhos de Compras (Market Basket Analysis) atraves da aplicacao de tecnicas de data mining a um conjunto de dados de dimensao significativa. Pretende-se encontrar conjuntos de itens habitualmente comprados em conjunto e dai gerar Regras de Associacao, as quais podem ser valiosas para campanhas promocionais ou sistemas de recomendacao. Foi adotada a metodologia de investigacao CRISP, especialmente vocacionada para data mining, e sao descritas as diversas fases da mesma, com foco na analise exploratoria dos dados (para os conhecer), a preparacao dos dados para aplicacao e configuracao do algoritmo Apriori e o estudo do comportamento do modelo obtido, aumento e diminuicao do numero de Regras de Associacao geradas, de acordo com a variacao dos parâmetros e dos dados utilizados.
Effluvial States - 1st Mvmnt. - Spectral Spaces

This work for trumpet and electronics was collaboratively devised to explore the potentials of trumpet timbre and the spatial possibilities in 3OA.Just as the sonic materials seek to dissolve the boundaries between live and projected trumpet, the physical choreography of the work, in which the performer walks in and around the ambisonic space, blends the distinction between audience and music. The audience should feel like they are inside the piece, indivisible from it.In this way the piece seeks to create a shared and immersive experience for audiences.
Diagnostic value of body surface electrocardiogram in myocardial infarction related artery

ObjectiveTo investigate the diagnostic value of electrocardiogram (ECG) on infarct related artery (IRA) in patients with acute myocardial infarction (AMI).MethodsFrom February 2017 to February 2018, 151 patients with AMI in the People's Hospital of Rugao were selected. All the patients were examined by 18 lead ECG immediately after admission to the hospital. The patients were treated with percutaneous coronary intervention (PCI), and the results of coronary angiography (CAG) were used to analyze the value of IRA for the diagnosis of AMI patients with body surface ECG.ResultsIRA was performed in 41 cases of LAD, 22 cases of LCX, 39 cases of RCA, 28 cases of LAD with LCX, and 11 cases of LAD with RCA2.The high predictive value of ECG for RCA infarction was STⅢ↑>STⅡ↑, STavL↓>STⅠ↓. The sensitivity and specificity were 88.02%, 86.31%, 92.25% and 82.01%, respectively. The ECG with high predictive value for LCX infarction was STv3↓/ STⅢ↑>1.2, STavL↑/STⅠ↑, with the sensitivity and specificity of 85.12%, 82.02%, 96.25% and 89.02%, respectively. The ECG of patients with LAD infarction was STv2↑≥3 mm, STv3↑≥2 mm, STⅡ, Ⅲ, avF↓≥1 mm, and the sensitivity of the diagnosis of LAD infarction was 65.02%, 77.25%, and 40.25%, respectively. The ECG characteristics of patients with LCX infarction group were STⅠ, v1-v6↓, STⅡ, Ⅲ, avF↑, STavL, avR↓, and LAD combined RCA infarction group, and the ECG characteristics were STⅠ, v1-v6↑, STⅡ, Ⅲ, avF↓, STavL, avR↑.ConclusionThe ECG of different IRA infarction body surface is different. The single branch of IRA is not difficult to make judgement with the characteristics of ECG, but the two or more IRA ECG is lack of specific manifestation. The clinical attention should be paid more attention to the two or more IRA ECG.Key words: Electrocardiography; Myocardial infarction; Coronary occlusion; Anterior descending branch; Circumflex branch
Research on Intelligent Algorithm for Image Quality Evaluation Based on Image Distortion Type and Convolutional Neural Network

In order to detect different images under different distortion conditions, this paper classifies image distortion types and proposes video image distortion detection and classification based on convolutional neural networks. By segmenting the input video image to obtain a small block image, and then using the active learning feature of the convolutional neural network, the positive and negative cases are equalized, the adaptive learning rate is slowed down and the local minimum problem is solved. The type of distortion is mainly predicted by the SoftMax classifier image, and then the video image prediction type obtained by the majority voting rule is used. The objective quality evaluation algorithm based on image distortion type and convolutional neural network is analyzed. Using LIVE (simulation standard image library) and actual monitoring video library, the final accurate results of the two performance tests are not much different. The overall classification accuracy rate is significantly higher than other algorithms. After the positive and negative case equalization and adaptive learning rate are introduced, it is found that the CNN classification accuracy can be significantly improved. The final test results also confirm that this method can actively learn image quality features, and improve the accuracy of video image classification detection. It is applicable to image quality detection of all video image distortion conditions, and has strong practicability and robustness.
Double-Base Chains for Scalar Multiplications on Elliptic Curves.

Double-base chains (DBCs) are widely used to speed up scalar multiplications on elliptic curves. We present three results of DBCs. First, we display a structure of the set containing all DBCs and propose an iterative algorithm to compute the number of DBCs for a positive integer. This is the first polynomial time algorithm to compute the number of DBCs for positive integers. Secondly, we present an asymptotic lower bound on average Hamming weights of DBCs \(\frac{\log n}{8.25}\) for a positive integer n. This result answers an open question about the Hamming weights of DBCs. Thirdly, we propose a new algorithm to generate an optimal DBC for any positive integer. The time complexity of this algorithm is \(\mathscr {O}\left( \left( \log n\right) ^2 \log \log n\right) \) bit operations and the space complexity is \(\mathscr {O}\left( \left( \log n\right) ^{2}\right) \) bits of memory. This algorithm accelerates the recoding procedure by more than 6 times compared to the state-of-the-art Bernstein, Chuengsatiansup, and Lange’s work. The Hamming weights of optimal DBCs are over 60% smaller than those of NAFs. Scalar multiplication using our optimal DBC is about 13% faster than that using non-adjacent form on elliptic curves over large prime fields.
A Study on Applicability of Assessment Tools for the TOSEL Basic through the Examination of the Achievement Standards in 2015 Revised National Curriculum

The TOSEL Basic intended for 6th graders was newly developed by International TOSEL Committee. This study closely examined the TOSEL Basic through the achievement standards in 2015 Revised National Curriculum. Results showed that the TOSEL Basic was adequately mapped in relation to the achievement standards in 2015 Revised National Curriculum for reading (50%) and writing (40%), but not for listening (28.5%) and speaking (28.5%). Overall, results of this study showed that the TOSEL Basic can be used as the alternative solution to the NAEA. However, the TOSEL Basic should develop new items of skimming such as main idea and purpose in listening and reading sections in order for it to widely cover achievement standards in 2015 Revised National Curriculum. Results of this study could be a useful guide for various stakeholders including elementary school teachers, parents, and policy makers who would like to use the TOSEL Basic to evaluate 6th graders’ English achievement. Some practical suggestions for the TOSEL Basic item writers are also presented to refine the test specifications.
Installing CPAN Modules

The Comprehensive Perl Archive Network (CPAN) is a repository of Perl modules (among other Perl "things") that developers throughout the world have created. Most of the code provided on CPAN is free for any programmer’s use.
Corpus generation for voice command in smart home and the effect of speech synthesis on End-to-End SLU

Massive amounts of annotated data greatly contributed to the advance of the machine learning field. However such large data sets are often unavailable for novel tasks performed in realistic environments such as smart homes. In this domain, semantically annotated large voice command corpora for Spoken Language Understanding (SLU) are scarce, especially for non-English languages. We present the automatic generation process of a synthetic semantically-annotated corpus of French commands for smart-home to train pipeline and End-to-End (E2E) SLU models. SLU is typically performed through Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) in a pipeline. Since errors at the ASR stage reduce the NLU performance, an alternative approach is End-to-End (E2E) SLU to jointly perform ASR and NLU. To that end, the artificial corpus was fed to a text-to-speech (TTS) system to generate synthetic speech data. All models were evaluated on voice commands acquired in a real smart home. We show that artificial data can be combined with real data within the same training set or used as a stand-alone training corpus. The synthetic speech quality was assessed by comparing it to real data using dynamic time warping (DTW).
Best Burner Phone Apps For Android And iPhone

Burner phones have become extremely popular within the past few years as a result of popular T.V shows such as Breaking Bad and The Wire. In this blog ...
Three-Dimensional Shape Recognition and Classification Using Local Features of Model Views and Sparse Representation of Shape Descriptors

In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm.
Development Based on Cloud Accounting as Accountant Reconciliation Media on Collage

In the process of academic activities, has staff that are structured in an organizational structure to provide excellent service. With the aim of succeeding all activities that are running, and having their respective functional duties, one of them is the head of finance. The head of finance is obliged to report all income and expenses that are needed by collages. Where the financial statements are reported monthly to top management such as the Director of Perguruan Tinggi Raharja The financial report can be said to be accurate if it has passed the final process of reconciliation. Reconciliation is an adjustment of the balance between bank balances and transaction data that we have. There are 8 (eight) financial statement processes until finally Reconciliation is carried out. However, the flow of the reconciliation process can be said to be still semi-conventional and lack the use of existing technology. In this study, 3 (three) problems were identified regarding the reconciliation process which had an impact on the accuracy of financial statements. Where in the reconciliation process accountants enter payment data input and data matching manually so that it requires a long time in the balance balance amount. With financial reports that are faster and more accurate, of course the work can be assessed as effective and efficient according to the time specified. The use of cloud accounting in the reconciliation process is expected to better assist accountants in real terms. So that in the process of reconciliation the accountant gets a financial report that is accurate and effective and efficient in time. This research is of course motivated by accountants in reconciliation activities that are still manually at College. Problems like this, with the use of cloud accounting in financial reports, are a new breakthrough for Perguruan Tinggi Raharja. By conducting 5 (five) literature reviews on reconciliation and cloud accounting to help the performance of accountants in obtaining financial reports. The final result of this study is the implementation of a cloud accounting system at collages in reconciliation activities by accountants.
Application of software restoration system in computer room management

The computer laboratory of colleges and universities plays a key role in the scientific research and talent training of colleges and universities. The maintenance and management of computer room is relatively heavy. In recent years, the computer motherboard supports the development of restore function and network transmission function, which brings great convenience to the management and maintenance of computer room in colleges and universities. Among them, the representative is Tsinghua Tongfang's network with the transmission technology and Lenovo's network transmission technology, but in order to achieve the network with the transmission of this function, also need to meet a lot of conditions. This paper will mainly explore the management of college computer room cannot use the hardware network to restore the computer function.
How To Charge Your Phone Without A Charger (Best Methods)

If you dont have a phone charger, or youve misplaced it, or forgotten it somewhere and you need to charge your phone. I will tell you about some ...
보육교사의 직무스트레스가 보육교사의 이직의도에 미치는 영향에서 원장-교사 교환관계의 매개효과

The purpose of this study was to investigate the effect of childcare teachers’ job stress with respect to turnover intention through the mediation of director-teacher exchanges in the process of affecting the turnover intention. The subjects of this study were 277 childcare teachers working in child care centers located in Jeollanam province and Gwangju city. Data were collected with questionnaire surveys. The analysis of the data was conducted with PASW 21.0. The results of this study are summarized as follows: The first area concerned the influence of job stress of childcare teachers on their turnover intention., and the influence on job stress experienced by childcare teachers with director-teacher exchanges. The influence of the director-teacher exchanges on the turnover intention of the childcare teachers was statistically significant. Second, when the job stress of childcare teachers influenced the turnover intention, the turnover intention of the teachers decreased when the director - teacher exchange was input as mediation. The results of this study suggest that it is necessary to study the ways to bring about positive leadership, and teacher exchange such as with this research about the leadership of the director and their levels of communication in childcare centers in order to lower the turnover intention of childcare teachers.
Employment of Optimal Approximations on Apache Hadoop Checkpoint Technique for Performance Improvements

The Checkpoint and Recovery (CR) technique is widely used due to its fault tolerance efficiency. The Apache Hadoop framework uses this technique as a way to avoid failures in its distributed file system. However, determining the optimal interval between successive checkpoints is a challenge, mainly inside Hadoop as it does not allow real-time modifications. The Dynamic Configuration Architecture (DCA) was created to solve this issue by enabling changes in the checkpoint period without any interruption of the Hadoop services. This paper presents improvements for the DCA through the configuration of the Hadoop checkpoint period in real-time based on optimal period approximations that were already endorsed by the literature. The proposed improvement depends on the tracking of the system resources. The data collected from these resources are stored in a history of attributes: a tree of monitored elements where data is updated as new observations are experienced in the system. This feature enables the user to estimate system factors so that our solution computes the checkpoints costs and the mean time between failures (MTBF). For the validation, experiments with transient failure in the NameNode were created and the usage of the history of attributes was tested in different scenarios. The evaluation results show that an adaptive configuration of checkpoint periods reduces the wasted time caused by failures in the NameNode and improves Hadoop performance. Also, the history of attributes demonstrated its value by providing an efficient way to estimate the system factors.
GHZ states as near-optimal states for reference frame alignment.

Let two coordinate systems, in possession of Alice and Bob, be related to each other by an unknown rotation $R\in SO(3)$. Alice is to send identical states $|\psi_0\ra$ to Bob who will make measurements on the received state and will determine the rotation $R$. The task of Bob is to estimate these parameters of the rotation $R$ by the best possible measurements. Based on the Quantum Fisher Information, we show that Greenberger-Horne-Zeilinger (GHZ) states are near optimal states for this task. Compared to the optimal states proposed before, the advantage of $GHZ$ states are that they can be more easily prepared experimentally, and more importantly, we show concrete measurements which will allow Bob to determine the rotation $R$. We also study the robustness of these states in keeping their encoded information, against common sources of noises.