Reviewerly/exHarmony huggingface dataset:
Chinese Sentence-Initial Indefinites: What Corpora Reveal

The sentence-initial position in Chinese* is generally associated with givenness and definiteness (Li and Thompson 1976, 1981 and subsequent literature). However, observations have been raised against this association, e.g., with informationally new sentence-initial referents (Paul, 2015) or even indefinite ones (Bisang, 2016; B. Lu, Zhang, &amp; Bisang, 2015). Furthermore, literature has emerged that offers contradictory findings, including Fan (1985) and subsequent studies on so-called ‘indefinite subject sentences’ (无定主语句). However, little is known of the statistical relevance of the phenomenon of sentence-initial indefinites (SIIs), as well as its interaction with features connected to linear order (e.g., animacy). Crucially, corpus-based studies on the topic remain the minority and are usually conducted on relatively small, genre-specific corpora. This paper adopts corpus methodologies and tools to investigate the phenomenon of SIIs, with a particular focus on determining (i) the statistical relevance of SIIs of the type of ‘一CLF N’ in big-size corpora, and (ii) whether there is an interaction with the semantic feature of animacy. To this end, it proposes the results of quantitative and qualitative analyses conducted on three major big-size, generalised corpora, namely the PKU CCL corpus (Peking University, 470 million characters), the BCC corpus (Beijing Language and Culture University, 15 billion characters), and the ZHTenTen (Stanford Tagger) corpus mounted at Sketch Engine (13,5 billion characters). The analysis highlights that SIIs are not only possible, but also statistically relevant. Furthermore, it shows that the semantic trait of animacy plays an important role, as animate indefinites are significantly more likely to occur sentence-initially. Finally, it singles out and discusses a new pattern featuring a proper noun introduced by ‘一CLF’.
Three-dimensional dynamic tracking learning algorithm for pedestrians on indefinite shape base based on deep learning

In order to improve the three-dimensional dynamic tracking and recognition ability to pedestrians, a three-dimensional dynamic tracking learning algorithm for pedestrians on indefinite shape base based on deep learning is proposed in this paper. First, the indefinite shape base mesh of body imaging is segmented to extract three-dimensional dynamic similarity features of pedestrians, and the three-dimensional feature points are marked; the deep learning method is adopted for fusion of gray pixel value and extraction of difference feature to images during three-dimensional dynamic tracking. Then a motion vector library is constructed based on the extraction results, and the template matching equation of three-dimensional dynamic feature points of pedestrians is obtained. The simulation results show that this method can accurately track moving bodies in three-dimensional dynamic tracking and recognition and can provide good robustness in moving body target extraction with accuracy up to 100% at maximum and detection time of 48.83ms at maximum.
Perancagan Sistem Infomasi Administrasi Berbasis Web (Studi kasus: Fakultas Teknologi Informasi Universitas Flores)

Abstract. The very rapid development of information technology has made various fields of human activity begin to take advantage of this information technology to help ease the activities carried out, one of which is providing information and processing data, as well as the education sector in managing its administration effectively and efficiently. The website, which is one of the most widely used internet technologies to facilitate administrative management because it can be accessed easily anywhere and anytime, through the use of web-based information systems, problems regarding data redundancies at faculty, cool archives, errors in the process of faculty financial payments are slow report generation can be overcome. In overcoming this problem, through designing a website-based administrative information system using the waterfall-based system development method, it is hoped that it can increase the effectiveness and efficiency of the performance of the program administration system. After the development of the system has been tested, the result is that the administration system can be operated by the information system study program to help the work process more efficiently and optimally.Keywords: Information System; Administrative management; Data flow; Data archive; WEB.
Serialist Boot Camp

SUMMARY. The presenters gave an overview of the role of serials librarianship in the larger information chain. The audience was introduced to acquisitions, cataloging, public services, publishers and publishing, and vendors and vending. A lively exchange brought forth a variety of perspectives on these distinct but related points in the information chain. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
No. 52091. International Bank for Reconstruction and Development and Morocco

Loan Agreement (First Capital Market Development and small and medium-sized enterprise finance development policy loan) between the Kingdom of Morocco and the International Bank for Reconstruction and Development (with schedules, appendix and International Bank for Reconstruction and Development General Conditions for Loans, dated 12 March 2012). Rabat, 27 May 2014
Image Deblurring

In the previous chapter we talked about image colorization, which is done using tools like Photoshop. Now let’s talk about another task that Photoshop is normally used for, but which we can automate using neural networks. In this chapter we will talk about image deblurring. We are going to use a generative adversarial network (GAN) combined with a VGG-16 for this project. First we will take a look at what a GAN is and how it works. Then we will take a closer look at what image deblurring is.
A Phonetic Level Analysis of Dysarthria in Kannada Language

Dysarthria is a motor speech disorder and the most common neurodegenerative disease characterized by low volume in precise articulation, poor coordination of respiratory and phonatory subsystems, irregular pauses and so on. The challenging factor in improving the communication with dysarthria is speech intelligibility. The intelligilibility of speech can be determined by measuring the acoustic characteristics and phonetic structures of speech. In our analysis, we have concentrated on bisyllabic words in the Kannada language. The analysis is carried out with respect to basic acoustic parameters and spectrogram to extract fundamental frequency, formant frequency, jitter, shimmer, HNR and Standard Deviation using PRAAT tool. These results are helpful to identify and differentiate the complex natural frequency of the vocal tract functions with respect to normal subjects and dysarthria subjects. This study provides inputs to increase efficacy in the clinical assessments for monitoring and diagnosing the speech disorders for improving communication among human beings.
Data Mining Performance of Toddler Nutrition Classification Based on Family Nutrition Awareness and Human Development Index

Nutrition problems that occurred in districts/cities of Central Java province from 2015-2017 were only 1 district city that did not have nutritional problems (good category) in 2015.The rest had acute, chronic or acute chronic nutrition problems. The search for the most influential attributes in toddler nutrition problems using data mining is expected to help health workers to focus more on solving problems based on classification in the area.Therefore, improving the nutritional status of the community can be accelerated. The best parameter search from the selection of features and data mining algorithm using the Optimize Parameters (Grid) operator found in Rapidminer.The feature selection models used are Backward Elimination, Forward Selection, and Optimize Selection. The datamining algorithm used is Naive Bayes, Decision Tree, k-NN, and Neural Network.The merging of the feature selection model and the datamining algorithm resulted in 12 algorithm models used in this study.The best model that was processed using test data with the highest accuracy of 74.19% was obtained from backward-neural network elimination. The attribute that is not very influential based on the model obtained is the condition of the mother who died.
CBIR using SIFT&amp; FDCT with Relevance Feedback Mechanism

Content-based image retrieval is a technique which uses visual contents to search images from large scale image databases according to users' interests, has been an active and fast advancing research area since the 1990s. During the past decade, remarkable progress has been made in both theoretical research and system development. However, there remain many challenging research problems that continue to attract researchers from multiple disciplines. Some of the techniques used in CBIR are Query by example, Semantic retrieval, content comparison techniques etc. Most of the existing works were done on spatial domain which is not so efficient. To overcome the difficulties of the existing works, a new algorithm is planned. And the proposed approach is based on the frequency domain for the content based Image retrieval systems.A new image retrieval technique which will retrieve images from image databases based on their contents in frequency domain to get better results. And a relevance feedback method is used for improving the retrieval efficiency. Many techniques are there in this computer vision and image processing field. But using low level features as a basis and retrieve features with good efficiency is problem of the study. The proposed work relates Feature extraction using both frequency domain as well as spatial domain. For spatial domain SIFT and for frequency domain FDCT techniques are applied and results were compared to find better information retrieval.
Two-Way Interactions In Multiple Regression

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
Emotional Instruments on Reading Novel using Social Media Among Indonesian Students

Emotional reading instruments are tools to uncover variables or objects to obtain information about a person's feelings or thoughts while reading. The aim of the study was to develop a set of emotional instruments for novel readers. The research method uses product development research begun by instrument testing process to some respondents using social media in order to get the appropriate instruments in terms of the validity and reliability. The results obtained were emotional instruments reading literary works, in this case, novels, which are tested for validity and reliability, can be used to measure novel works. So, a novel reader can know his or her emotions toward the plot, characterization, point of view, background, message, and theme.
Convolutional neural network for image classification based on transfer learning technique

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.
Secure and Error-Free Data Storage on Cloud via Deniable CP-ABE Scheme

Cloud storage services are quickly increasing and more prevalent. CSP-cloud storage providers offer storage as a service to all the users. It is a paid facility that allows association to outsource their confidential data to be stored on remote servers. But, identity privacy and preserving data from an untrusted cloud is a difficult concern, because of the successive change of the members. CSP has to be secured from an illegitimate person who performs data corruption over cloud servers. Thus, there is a need to safeguard information from the individuals who don’t have access by establishing numerous cloud storage encryption plans. Every such plan implemented expects that distributed storage suppliers are protected and can't be hacked; however, practically speaking, a few powers will compel distributed storage suppliers to render client details and secret information on the cloud, in this manner inside and out bypassing stockpiling encryption plans. In this paper, a new scheme is introduced to protect user privacy by a deniable CP_ABE(Cloud Provider_ Attribute Based Encryption) scheme which implements a cloud storage encryption plan. Since coercers cannot specify whether privileged insights are valid or not, the CSP ensures privacy of the user.
An Analysis of the Pattern of Korean Chinese Learners" Acquisition of Modal Adverbs Denoting ‘Probable’

초록·키워드 목차 오류제보하기 The purpose of this research paper is to understand the Pattern of Korean Chinese Learners" Acquisition of Modal Adverbs Denoting ‘Probable’ and to offer preliminary data for related education. As one of particular Chinese adverbs, Modal Adverbs expresses psychological states such as speakers" emotions or attitudes, which makes it hard to define a meaning into a single word. This adverb entails the position in a sentence and co-occurrence relations with different vocabulary. Taking it into account, Modal Adverbs is counted as one of the most difficult categories among Chinese grammar and vocabulary elements. In particular, Modal Adverbs Denoting ‘Probable’ is learned by learners, at a relatively early period, and thus, it needs to involve preliminary education research in relation to it. In this regard, this paper examined intermediate language corpus to understand how Korean learners acquaint themselves with the Chinese language Modal Adverbs Denoting ‘Probable’. Based on preliminary research, this research paper considered the basic meaning and feature of Modal Adverbs Denoting ‘Probable’, and then carried out an analysis of this adverb, built on "The HSK Dynamic Composition Corpus" of Beijing Language and Culture University. To do it, each 281 proper sentence and 67 improper sentence in corpus was collected to understand the status of acquisition of the language, based on those proper sentences. Error types are classified into five elements, substitution, abuse, ommission, word order, the Chinese Character to analyze contextual types and the cause of error occurrence. #Modal Adverbs Denoting ‘Probable’ #Acquisition #The HSK Dynamic Composition Corpus #Error Analysis #Chinese Education 1. 서론2. 이론적 배경3. 추측 어기부사 습득 양상4. 결론參考文獻ABSTRACT
Underlying Direction

Abstract Our musical concerns first and foremost should drive the direction of our reed work.
On Evolving Text Centroids

Centroid terms are single, descriptive words that semantically and topically characterise text documents and thus can act as their very compact representation in automated text processing tasks that strongly rely on the semantic similarity of texts. Algorithms to classify and cluster them make use of this information. In this book, the novel, brain- and physicsinspired concept of centroid terms is introduced and deeply discussed. Furthermore, their unique properties and practical usage in major natural language processing and text mining tasks are covered. In this regard, a new graph-based method for their fast calculation is presented as well. In contrast to methods relying on the bag-of-words model, the derived centroid distance measure can uncover
Recognition of Online Handwritten Isolated Kannada Characters using PCA and DTW

Handwriting is a natural means of documentation and communication for several years. Human beings communicating with computers through handwritten input would be the best and easiest way of exchanging the information. It is difficult to input data for computers for Indian language scripts because of their complex typing nature. This paper focuses on exploring performance of Principal Component Analysis (PCA) and Dynamic Time Wrapping (DTW) approaches for recognizing online handwritten isolated Kannada characters. Methodology proposed in this paper is writer independent model which recognizes basic 50 Kannada characters including 16 vowels and 34 consonants.
Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

This paper provides a platform for analyzing and summarizing the sentiments expressed by users or customers in the field of online tourism. The objective of this research is to analyze online reviews of all the users to propose a new optimized business model to improve present services of business organization to enhance profit and customer satisfaction. The proposed system filters tourism online reviews and classifies them using sentimental technique with the help of deep learning technique. Deep learning technique will not only identify the polarity of online reviews but also recognizes relevant patterns deeply to find the hidden reviews details. After applying the deep learning technique, the results will be generated through which we can find the inferences. These inferences would provide a great help for improvisation of the subject. In this research a new optimized business model will be implemented using deep learning technique so that we would be able to compare new business model with the present system [1]. The relevance of this research lies in helping tourism industries to understand the social sentiment of their brand, product or service while monitoring online conversations. It helps in enhancing business profits by running online websites throughout by giving best services to the online users or customers.
USE OF RFID TECHNOLOGY IN THE IDENTIFICATION, STORAGE, CONTROL AND MOVEMENT OF ELECTRONIC URNES

This article brings an important part ofElectoral Justice: the Electronic Ballot Box. The importance of the Electronic Ballot Box in the Electoral process is of great importance, as it provides a change in the process of receiving votes (representing the will of the voter), providing greater speed and security, in addition to the totalization and dissemination of those elected within a few hours,which used to take days. Despite this evolution, the identification and storage of Electronic Ballot Boxes takes place in a traditional way, stacked on pallets and discarded in deposits in the Electronic Ballot Box Section. What makes a search for a specific urn, a work task and collapsed. In this item, we propose the use of radio frequency identification -RFID as an alternative for the identificationand control of electronic voting machines, promoting intelligent storage that allows, in addition to the identification and location of the electronic ballot box, an umbrella of useful information from the electronic ballot box
Pupil Detection Algorithm Based on Feature Extraction for Eye Gaze

Exact real-time pupil tracking is an essential step in a live eye gaze. Since pupil centre is a base point’s reference, eye centre localization is essential for many applications. In this research, we extract pupil eye features exactly within different intensity levels of eye images, mostly with localization of determined interest objects and where the human is looking for. Since it’s a digital world and digital transformation, everything is becoming virtual. Hence this concept has a huge scope in e-learning, class room training and analyzing human behaviour. This research covers eye tracking technology to track and analyze the learners' behavior and emotion on e-learning platform like level of attention and tiredness. Harr’s cascade classifier was used to first locate the eye’s area, and once found and support vector machine (SVM) for classification with the trained datasets. We also include the state of emotions, facial landmarks of the salient patches on face image using automated learning-free facial landmark detection technique.Experimental results help in developing learner eye gaze detection in system using Pycharm and hardware output using Raspberry Pi. In Raspberry Pi is given with the input image captured using external webcam and based on the engagement level of the learner content 1 or 2 would be displayed in the Raspbian OS environment
Tune Field

This paper introduces Tune Field, a 3-dimensional tangible interface that combines and alters previously existing concepts of topographical, field sensing and capacitive touch interfaces as a method for musical expression and sound visualization. Users are invited to create experimental sound textures while modifying the topography of antennas. The interfaceâs touch antennas are randomly located on a box promoting exploration and discovery of gesture-to-sound relationships. This way, the interface opens space to playfully producing sound and triggering visuals; thus, converting Tune Field into a sensorial experience.
Improving Privacy and Security of User Data in Location Based Services

Location Based Services (LBS) expose user data to malicious attacks. Approaches, evolved, so far, for preserving privacy and security, suffer from one or more anomalies, and hence the problem of securing LBS data is far from being resolved. In particular, accuracy of results vs. privacy degree, privacy vs. performance, and trust between users are open problems. In this article, we present a novel approach by integration of peer-to-peer (P2P) with the caching technique and dummies from real queries. Our approach increases efficiency, leads to improved performance, and provides solutions to many problems that have existed in the past. In addition, we offer an improved way of managing cache. Simulation demonstrates superiority of our approach over earlier ones dealing with both the ratio of privacy and that of performance.
A Novel Encryption Algorithm Based on Pseudo Random Sub Blocks of Data Blocks

In this paper, we propose data encryption algorithm based on randomized sub blocks XORing of data blocks. The data is divided into number of square blocks (in terms of bits) of equal size. The last block is padded with zeros if required. The proposed algorithm uses pseudo random keys to generate the order of sub blocks of data blocks for encryption. The encryption is done on sub blocks and therefore much faster and highly sensitive for small changes in the keys.