Miracle-dz/UniFAR-FTUs huggingface dataset:
NLP in social Networking: An Overview

: Today social network is so popular. So Analysis in this area is so important . social Network analysis refers to the process of discover distributed structures through the use of network and graphs. All the information on the social network is unstructured and we have need to get the structured information that will helpful for making use of the important information. Natural language processing improve the accuracy in envisage the structured information that is speckled over social network. The basic overview of NLP and its technique are showing in this paper. This paper shows various text mining systems which is the foundation of NLP to analyze social network information.
MEETING THE DISTANCE EDUCATION CHALLENGE: A GUIDE FOR DESIGNING ONLINE CLASSROOMS

iii CHAPTER ONE: INTRODUCTION . 1 CHAPTER TWO: ACADEMIC PERSPECTIVES . 6 Andragogy . 7 Transformative Learning Theory . 10 Teacher Immediacy . 11 CHAPTER THREE: QUALITY . 17 Quality Online Course Initiative . 18 Quality Matters . 20 Quality Online Learning and Teaching . 21 CHAPTER FOUR: CONCLUSION . 24 APPENDIX A: QUALITY ONLINE TEACHING: EFFECTIVE COURSE DESIGN STRATEGIES . 26 REFERENCES . 97
Databases Demystified

Learning DATABASE fundamentals just got a whole lot EASIER! Now you can design, build, and manage a fully functional database with ease. Thoroughly updated to cover the latest technologies and techniques, Databases Demystified, Second Edition gives you the hands-on help you need to get started. Written in a step-by-step format, this practical guide covers methods that can be used with any database, including Microsoft Access, MySQL, Microsoft SQL Server, and Oracle. You'll learn about relational database components, database queries, SQL, the database life cycle, logical database design using normalization, and physical database design. Data and process modeling, database security, Online Analytical Processing (OLAP), and XML are also covered. Detailed examples and concise explanations make it easy to understand the material, and end-of-chapter quizzes and a final exam help reinforce learning. It's a no-brainer! You'll find out how to: Create and run database queries using the forms-based query tool in Microsoft Access Write SQL statements and queries Use entity relationship diagrams (ERDs) for data modeling Design physical tables Connect databases to users, computer systems, and applications Secure database data Handle cursor processing, transaction management, and performance tuning Integrate XML documents and objects into databases Simple enough for a beginner, but challenging enough for an advanced student, Databases Demystified, Second Edition is your self-paced guide to learning universal database concepts.
Who Gets to Talk About NGO Images of Global Poverty?

Abstract Ademolu and Warrington are social science researchers who have used photographs and films to explore the experiences and perspectives of those absent from the debates on visual representations of global poverty. This article engages in a discussion about their respective research projects, The People in the Pictures (Warrington and Crombie 2017) and Rethinking Audiences (Ademolu 2018). It explores the backgrounds to their projects and their experiences of using images as a research tool, and considers how their research contributes to ongoing debates about NGO communications and representations of global poverty. The impetus, experience and findings of their respective research seek to provoke those involved in NGO image making (practitioners and researchers) to ask themselves if the perspectives of all those involved are being considered, especially those communities who are the subject of representation, either directly in images or more indirectly through diaspora connections to the represented communities. It argues for a shift in both the debates and NGO image making itself, towards more inclusive dialogue and practice.
Method of Moving Region Detection for Static Camera

The moving object detection from a stationary video sequence is a primary task in various computer vision applications. In this proposed system; three processing levels are suppose to perform: detects moving objects region from the background image; reduce noise from the pixels of detected region and extract meaningful objects and their features (area of object, center point of area etc.). In this paper; background subtraction techniques is used for segments moving objects from the background image, which is capable for pixel level processing. Morphology operation (Erosion and dilation) are used to remove pixel to pixel noise. In last level, CCL algorithm is used for sorts out foregrounds pixels are grouped into meaningful connected regions and their features.
New Prediction Methods of Indicators and Costs Capable of Increasingthe Efficiency of Pharmacoeconomic Studies for Health Decision Analysis

Like most of the resources we have in the world, health-care resources are also scarce. Thus, pharmacoeconomics is very important to aid health systems into this process. It is noteworthy that new methods are being created to improve pharmacoeconomic analysis efficiency. This paper brings comments about three new techniques incorporated in modelling to measure probabilities: Risk scale, Delphi method, and Bayesian statistics. They represent new methods that can provide the development of pharmacoeconomic studies with a better design due to shortening study time and complexity, ensuring robust results. In this way it should lead to resource savings and increased efficiency in pharmacoeconomic research.
Discovering thematic visual objects in unconstrained videos

Over the last decade, with the popularization of camera-equipped devices, there has been an explosive growth of video data. Despite the diverse visual contents, there are usually some thematic objects in these videos. As the key objects to be presented, thematic objects appear frequently and occupy highlighted positions in the video scenes, thus retain our impression after watching the videos, such as the bride and the groom in wedding ceremony videos, the birthday girl in birthday party videos, or product logo in commercial videos. Automatically discovering and localizing these thematic objects can benefit many real-world applications, such as video summarization, search, and labeling. However, this task is challenging as there is no prior information or initialization about the thematic objects. Moreover, there is usually background clutter, occlusions, or camera motions accompanying the targets. In this thesis, a systematic study is conducted on the automatic discovery and localization of thematic objects in videos. We have studied this problem under various settings, including automatic discovery and localization of the thematic object in single videos, automatic discovery and segmentation of the thematic object in single videos, and automatic thematic action discovery and localization in collections of videos. In the absence of category-specific supervision and manual initialization, various category-independent cues have been explored to discover and localize the thematic objects. These include the spatiotemporal saliency to highlight ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Edge-Preserving Texture Transfer Using the Laplacian Pyramid

Texture plays an important role in our perception of photographic images. It can be used to differentiate objects, draw the eye to salient regions, and convey information about a subject. However, post processing photographs in image editing software can be time consuming, often requiring advanced skills to achieve convincing effects. We introduce a multi-scale method, based on the Laplacian pyramid, to introduce auxiliary textures into photographic images. The final result retains the structural characteristics from the input, including edges, colour, and high-contrast existing texture, while enhancing the image with fine-scale details. In addition, we extend patch-based texture synthesis to include a guidance channel so that texture structures are aligned with an orientation field, obtained through the image structure tensor. Both the original structure tensor calculation and its subsequent smoothing are aided with geodesic knowledge so that our orientation fields are both edge preserving and smooth.
Multimodal Deep Learning methods for person annotation in video sequences

In unsupervised identity recognition in video sequences systems, which is a very active field of research in computer vision, the use of convolutional neural networks (CNN's) is currently gaining a lot of interest due to the great results that this techniques have been shown in face recognition and verification problems in recent years. In this thesis, the improvement of a CNN applied for face verification will be made in the context of an unsupervised identity annotation system developed for the MediaEval 2016 task. This improvement will be achieved by training the 2016 CNN architecture with images from the task database, which is now possible since we can use the last version outputs, along with a data augmentation method applied to the previously extracted samples. In addition, a new multimodal verification system is implemented merging both visual and audio feature vectors. An evaluation of the margin of improvement that these techniques introduce in the whole system will be made, comparing against the State-of-the-Art. Finally some conclusions will be exposed based on the obtained results will be drawn along with some possible future lines of work.
Rise-Fall-Rise as a Marker of Secondary QUD s

In the literature, English rise-fall-rise (RFR) intonation is known both as a marker of secondary information and as a marker of topics. �is paper aims to make plausible that these two uses can be derived from a common core, which in turn can be derived from a recent theory of intonational meaning more generally, according to which rises and falls indicate (non-)compliance with the maxims (Westera 2013, 2014, 2017). �e core meaning of RFR, I propose, is that the main question under discussion (Q��) is not compliantly addressed, while some secondary Q�� is. Several more concrete predictions are derived from this core meaning, pertaining to secondary information, topic marking, exhaustivity, and discourse strategies. �e resulting account is shown to generate certain ingredients of existing accounts, while also doing some things di�erently in ways that may be empirically accurate. If the proposed account is on the right track, it provides an important new intonational window on Q��s.
Social pertinence of a doctorate in administration under the guidelines of the PNPC

The objective of this research, of a descriptive type, of documentary analysis and field study is to analyze the social pertinence of a new Doctorate in Administrative Sciences in Mexico according to the National Postgraduate Quality Program of the National Council on Science and Technology of Mexico. The results show that the formation of doctors in the subject will remedy the need of the public and private sector to correct the actual problem of inadequate work structures.
How classifiers facilitate predictive processing in L1 and L2 Chinese: the role of semantic and grammatical cues

ABSTRACT This study examines long-standing claims that L2 learners rely more on non-grammatical than on grammatical information during sentence processing compared to native speakers. Nominal classifiers in Mandarin Chinese offer an ideal opportunity to test this claim, as they simultaneously encode semantic as well as grammatical form-class cues about co-occurring nouns. This paper reports findings from a visual world eye-tracking experiment with L1 and L2 speakers of Mandarin, which was designed to assess listeners’ relative reliance on these two concurrently available cues when creating expectations about an upcoming noun in a sentence. Results show that L2 listeners experienced greater competition than L1 listeners from nouns that were grammatically incompatible with the classifier they heard but shared semantic features associated with it. The greater reliance on semantic cues observed in L2 processing is argued to be an effect of adaptation to the relative reliability of information, serving to maximise L2 processing efficiency.
On the Automaticity of Familiarity in Short-term Recognition: A Test of the Dual-Process Assumption with the PRP Paradigm

Dual-process models of recognition often assume that one retrieval process, generating a familiarity signal, is automatic, whereas the other, recollection, is controlled. Four experiments are presented to test for automaticity of familiarity in a short-term recognition task. The experiments use the Psychological Refractory Period (PRP) paradigm to assess whether familiarity requires central processing capacity. Task 1 was an oral tone-classification task. Task 2 was a local-recognition task, in which participants decided whether a probe matched a particular item in the memory set, identified by its screen location. Intrusion probes, matching an item of the memory set in a different location, were slower and more difficult to reject than new probes. The size of this intrusion cost reflects the influence of familiarity on recognition. In all four experiments the size of the intrusion cost was additive with the stimulus-onset-asynchrony (SOA) of Task 1 and Task 2, demonstrating that extraction of familiarity requires central capacity. In addition, Experiment 2 showed additive effects of memory set size and serial position with SOA, confirming that recollection, too, requires central capacity. Experiments 3A and 3B compared a condition including new probes to one including only positive and intrusion probes; in the latter condition the familiarity signal was completely uninformative. Participants showed some ability to reduce the influence of familiarity when it was completely uninformative, but only when they were explicitly told to do so (Experiment 3B). To conclude, by one criterion familiarity is a controlled process: It demands central processing capacity. It might also be controlled by another criterion: People can intentionally reduce the influence of familiarity on recognition decision, but they fail to do so spontaneously even when it would be advantageous. All raw data are available on the Open Science Framework: osf.io/7pr72.
Grid-connected converters with virtual electromechanical characteristics: experimental verification

Grid-connected power converters, which are frequently used to link renewable generation plants with the grid, are required to provide a better functionality for large scale integration of renewables. They are expected to be grid-friendly, or even grid-supportive, instead of simply grid-feeding or grid-demanding. This paper designs a synchronous power controller for grid-connected converters in detail, emulating the electromechanical characteristics of synchronous machines and improving even its actual performance, as it is based on a virtual approach. Based on this design, the grid-interfacing units are capable of showing inertia, damping, and droop characteristics as synchronous machines and presenting thus a grid-supporting behavior. The detailed control design and experimental validation on a 10 kW laboratory setup acts as the main contribution of this paper, compared with the existing studies on generator emulation controls.
A curriculum-based approach for feature selection

Curriculum learning is a learning technique in which a classifier learns from easy samples first and then from increasingly difficult samples. On similar lines, a curriculum based feature selection framework is proposed for identifying most useful features in a dataset. Given a dataset, first, easy and difficult samples are identified. In general, the number of easy samples is assumed larger than difficult samples. Then, feature selection is done in two stages. In the first stage a fast feature selection method which gives feature scores is used. Feature scores are then updated incrementally with the set of difficult samples. The existing feature selection methods are not incremental in nature; entire data needs to be used in feature selection. The use of curriculum learning is expected to decrease the time needed for feature selection with classification accuracy comparable to the existing methods. Curriculum learning also allows incremental refinements in feature selection as new training samples become available. Our experiments on a number of standard datasets demonstrate that feature selection is indeed faster without sacrificing classification accuracy.
QoS-Aware Routing for Multi-Sink WMSNs

With the development of Wireless Multimedia Sensor Networks (WMSNs), it is difficult to meet the various QoS requirements in single sink network. Multi-Sink nodes in the network can effectively reduce the average hop count and balance network energy. A QoS aware routing algorithm based on improved ant colony is proposed to meet the QoS requirements. Compared with other protocols, simulation results show that the new algorithm can effectively balance the traffic load and extend the network lifetime.
Rule-based named entity recognition for drugrelated crime news documents

Drug abuse pertains to the consumption of a substance that may induce adverse effects to a person. In international security studies, drug trafficking has become an important topic. In this regard, drug-related crimes are identified as an extremely significant challenge faced by any community. Several techniques for investigations in the crime domain have been implemented by many researchers. However, most of these researchers focus on extracting general crime entities. The number of studies that focus on the drug crime domain is relatively limited. This paper mainly aims to propose a rule-based named entity recognition model for drug-related crime news documents. In this work, a set of heuristic and grammatical rules is used to extract named entities, such as types of drugs, amount of drugs, price of drugs, drug hiding methods, and the nationality of the suspect. A set of grammatical and heuristic rules is established based on part-of-speech information, developed gazetteers, and indicator word lists. The combined approach of heuristic and grammatical rules achieves a good performance with an overall precision of 86%, a recall of 87%, and an F1-measure of 87%. Results indicate that the ensemble of both heuristic and grammatical rules improves the extraction effectiveness in terms of macro-F1 for all entities.
Assessment of Particles of Varied Soil By Grain Size Analysis – A Case Study in Jabalpur M.P

Grain size distribution plays a vital role for characterization of soil. Particle size distribution (PSD) in a soil mass is a character which gives a major idea about bearing capacity of soils and bearing capacity of the soil is a key parameter to design foundation of any civil engineering structure. Indian Standards has classified the soils as per particle size gradation. As per I S Code range of the particle size varies from boulder to clay. This paper discusses about the results of particle size distribution of varied soil stratum at a construction location in Jabalpur M.P. Present paper also suggests some recommendations about types of foundation and suitable methods for ground improvement .
Training Korean second language speakers on English vowels and prosody.

This study trained 36 Korean L2 speakers on vowel identification and prosody recognition (focus and lexical stress), with the aim of investigating the extent to which training improves general speech perception abilities or specific underlying processes. Vowel training was accomplished with a high-variability identification training technique (multiple talkers and words), and prosody training was accomplished using a category discrimination task in which they needed to choose sentences based on focus or words based on syllable stress. The results demonstrated that both trainers reduced vowel epenthesis and improved syllable stress perception, vowel training improved vowel identification more, and prosody training better improved focus perception in sentences. Both types of training can thus work in a complementary fashion to improve overall speech recognition.
Sistem Informasi Pengelolaan Pelaksanaan Dana Desa Berbasis Responsive Web Design Di Kabupaten Kudus

Manajemen Keuangan adalah sesuatu yang tidak dapat dipisahkan dari proses manajemen dalam suatu intansi, misalnya seperti di pedesaan, yang memiliki beberapa pendapatan pendapatan dalam hal pendapatan yang berbeda. Untuk memfasilitasi dalam hal mengelola pelaksanaan dana desa dengan baik kebutuhan sistem informasi. Dengan pengelolaan sistem informasi pelaksanaan dana desa ini, diharapkan dapat membantu proses pengelolaan dana desa dari membuat implementasi anggaran, merekam masuk atau keluar dana, hingga pembuatan laporan. Hasil penelitian menunjukkan bahwa perancangan sistem informasi manajemen pelaksanaan dana desa berdasarkan desain web yang responsif dapat membantu pemimpin desa dalam mengelola pelaksanaan dana desa di masing-masing daerah dan masyarakat dapat melihat informasi dana desa, sistem implementasi meliputi implementasi perangkat lunak, perangkat keras, antarmuka aplikasi yang dihasilkan. Tahap akhir dalam perancangan sistem adalah menguji aplikasi menggunakan metode White Box.
Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification

Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, 𝜃 to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.