Reviewerly/exHarmony huggingface dataset:
Text Complexity and Images of Huineng

8.1 Introduction 8.2 Text complexity as measured in SFL 8.2.1 Grammatical intricacy 8.2.2 Lexical density 8.3 Methodology 8.4 Analysis and discussion: how does Huineng speak? 8.4.1 Analysis of grammatical intricacy 8.4.2 Analysis of lexical density 8.5 Interpretation: why does Huineng speak in this way? 8.6 Conclusion
Artificial Neural Networks and their Applications in Modern Networks Technology

The purpose of this chapter is to introduce a powerful class of mathematical models: the artiicial neural networks.This is a very general term that includes many different systems and various types of approaches, both from statistics and computer science.The analogy is not very detailed, but it serves to introduce the concept of parallel and distributed computing.Neural networks explain and give the proper demonstration for the applications of neural networks and wireless network.The artiicial network and wireless network have strong connection and can be investigated and explained properly.There are also some models which are called mathematical models which can explain and demonstrate the artiicial and wireless networks in detail.There are many organizations which are using artiicial neural networks by using sensor technology for wide range of purpose.Then we analyze in detail a widely applied type of artiicial neural network: the feed-forward network with error back-propagation algorithm.We illustrate the architecture of the models, the main learning methods and data representation, showing how to build a typical artiicial neural network.Our aim is not to examine them all (it would be a very long discussion), but to understand the basic functionality and the possible implementations of this powerful tool.We initially introduce neural networks, by analogy with the human brain.
Ensuring Cloud Security Scheme with Hybrid Encryption Technique using AES_RSA for Fragmented Data

Cloud storage provides the enormous benefits to the cloud user. Especially the data accessibility feature that allows the user to access their data anywhere any time. But cloud computing maintain the distributed environment for storing the data which increase the risk of data leakages. A suitable solution to reduce the security risk is to deploy encryption mechanism. Ensuring security on cloud storage is still a challenging issue. In this paper a hybrid encryption techniques is proposed with three powerful algorithms such as Advanced Encryption Standard, Rivest–Shamir–Adleman (RSA) and MD5. The Encryption scheme executes between the fragmentation and replication process. The proposed hybrid framework strength the security of cloud data. If any successful attacks occurs the attackers does not able to discover the plaintext. The implementation was carried out with the help of Clousim framework. The performance of this study is compared with prior encryption techniques.
Leveraging Virtual Learning Environments to Strengthen Pre-Service Early Childhood Teacher Preparation: Program Pivots Due to COVID-19 Empowers Future Practice

This critical reflection illuminates the planning, delivery, and evaluation of the ‘pivots’ an early childhood teacher preparation program made in response to the shift to virtual learning necessitated by the lock down measures prompted by the COVID-19 pandemic. The challenge was to shift two undergraduate courses from in person to a virtual format, including an inperson internship to an entirely virtual experience while maintaining a high-quality program which meets the certification requirements and professional standards. The process of moving the courses and internship to a virtual setting gave us the unique opportunity to step back and reflect on the essential conceptual knowledge and understandings necessary for teacher candidates to meet professional standards leading to certification and to becoming competent and caring professionals. Results of our critical reflection include lessons learned by the two instructors and department chair on the effective leveraging of technology to meet the challenges posed by COVID-19 leading to modifications to our program post pandemic.
Imaging teacher learning

Based on a review of the relevant literature, this chapter provides an overview of the field. It opens with a description of the development of analog towards digital video applications for teacher development. Then, a variety of digital applications is described and the specific affordances of digital video as a medium for teacher learning are identified. On this basis, different functions and features of digital video for teacher development are delineated and concepts and terminology introduced. Also, the main paradigms underlying the research reviewed are characterised. The main conclusion is that visual feedback can make teacher learning more effective by helping teachers relate thought and action. To conceptualise which conditions, activities and processes are involved, the Visual Teacher Learning model is introduced. This model also covers effects on pupil learning.
Julian Dodd. Being True to Works of Music

The concept of being true to works of music and how true a performer is to the composer’s work has been an ongoing debate for many years. The idea of authenticity in the performance of musical works raises many questions, such as: what does it mean exactly to be authentic in music; how can one be authentic; to what exactly is one being faithful; which rules and conceptions should one follow to realise that authenticity; and can different performances of the same work both be authentic? Julian Dodd’s Being True to Works of Music is a short but insightful book about musical authenticity and musical meaning. It will be an important source not only for philosophers and musicologists, but also for students, and, most importantly, performers and listeners. It may also be of interest to general music readers…
COMPREHENSION OF CREATIVITY IN THE CONTEXT OF THE CORRELATION OF PLATO’S PHILOSOPHY, THEORIES OF FRACTALS AND ARCHETYPES

The study examines the creative process using the logical and methodological analysis of Plato’s concepts. It presents the modern scientific research related directly or indirectly to his philosophical views (the structural analogy method, the theory of archetypes and fractals and many others). A number of modern studies and concepts, such as the theory of fractals, evolutionary epistemology, the concept of autopoiesis, and others, confirm Plato’s views on the structure of the world and creativity. For this reason, the authors define creativity as the activity of a rational and social subject to produce a qualitatively new thing based on universal patterns of the fractal and archetype nature in accordance with the ideal. This activity needs in creativeness which is the state of love as a creative force arising from social interaction as a desire to create and expand space for life, connecting space inside the subject of creativity and outside it, creating a resonance between the creative self and other persons.
A unified Link Prediction architecture applied on a novel heterogenous Knowledge Base

Link Prediction (LP) aims at addressing incompleteness of Knowledge Graph (KG). The goal of LP is to capture the distribution of entities and relations present in a KG and utilise these to predict probability of missing information. State-of-the-art LP approaches rely on latent feature models for this purpose. The research focus has predominantly been on application of LP to triple based datasets (e.g. Freebase, YAGO). However, with growing adoption of KGs, it is common to see more heterogeneous property graphs being used, examples of common properties are temporal and weight data. The contributions of the following work are two fold. First, we introduce a novel framework which is the first to provide support for latent feature model LP on heterogeneous Knowledge Bases (KBs). Second, we utilise a novel KB — Refinitiv Knowledge Graph, to produce a heterogeneous dataset with which capabilities of the framework are examined.
A New Operator for ABox Revision in DL-Lite

In this paper, we propose a new operator for revising ABoxes in DL-Lite ontologies. We present a graph-based algorithm for ABox revision in DL-Lite, which implements the revision operator and we show it runs in polynomial time
Image Encryption Using Chaotic Maps in Hybrid Domain 

All image encryption systems are in the single domain. Hybrid domain (frequency and time domain) system is proposed to make the system more secured. We use the chaotic maps to generate pseudo random images. Since the chaotic maps have very good properties ,it will help us to encrypt the image in more secured manner. In our proposed system we employs two units: Transformation and substitution unit. We perform dft and dwt for the image. Transformation unit uses the tent map and substitution unit uses Bernoulli map.
Recycled Fuel Performance In The Sr 30 Gas Turbine

An SR-30 gas turbine engine has been run with biodiesel fuel (referred to henceforth as biodiesel) to broaden the educational experience of the engineering students in various classes.The preparation of biodiesel from new and used vegetable oil and pre-testing of the fuel is also described.Basic characteristics and operation of the engine have not been covered here since they are very well presented in the TTL publications and in Dr Callinan's ASEE paper [1]. BackgroundJohn Brown University (JBU) purchased an SR-30 gas turbine engine in 1999.Two years later we purchased the cutaway version since our use was for classroom and laboratory applications.The first applications of the engine were done with no deviations from the Turbine Technologies suggested practice.We found the engine to be an excellent device for demonstration and a definite morale builder.In the fall of 2000 we were making biodiesel from unused cooking oil for testing in piston diesel engines and decided to try that fuel in our SR-30.Based on that success, in 2001 we prepared SR-30 fuel from used cooking oil.A variety of fuels are specified by Turbine Technologies Ltd. , manufacturer of the SR-30 Turbojet Engine®.Throughout the history of the gas turbine one of the most interesting features has been the theoretical ability for them to use a variety of fuels.Some early authors even anticipated performance independent of fuel type.In the extreme, Tickell [2] reports applications for piston engines using used cooking oil 'straight', after a startup period with conventional fuel.Commercial airplane engines do not have the liberty of using compounded vegetable fuels, due mainly to their auxiliary equipment, temperatures and pressures, standards and guaranteed performance, Mattingly [5].The university laboratory is not bound by these considerations and we continue to explore the diversity of vegetable oil based fuels to broaden the engineering students' design mindset.
A Prototype Model to Predict Human Interest: Data Based Design to Combine Humans and Machines

In this paper, the possibility of quantifying a person's interest using data-driven algorithms is investigated. In doing so, interest estimation problem is formulated as a latent state estimation problem, and an answer is deduced via Bayesian Inference. First, a Subjective-Objective approach is used to measure activity. Through this calculated activity, the method indirectly infers human latent state values. A formulation of interest is then presented by drawing inspiration from the Ornstein-Uhlenbeck (OU) process in Physics. Moreover, concepts of stochastic volatility are employed to vary the instantaneous volatility of the OU process. This is done to further improve the performance. Subsequently, the convergence speed of the OU process is varied with time. A novel statistical framework is discussed that dynamically transforms interest into activity. Each of these individual contributions is combined to present a solution via Monte Carlo Simulations. To demonstrate the efficacy of the proposed method, numerical simulations are performed on real datasets. Lastly, a prototype is engineered and the method is implemented as a RESTful Web service. The prototype is hosted as a Web service on several Virtual Machines to demonstrate the practical feasibility of the framework in cloud-based deployment scenarios.
Why Come To Class? Posting Notes From Tablet Pc Lectures

When I first used a tablet PC in teaching I had colleagues who posted their notes for their students.My first reaction was, "Why would I do that?The students will not see any point in coming to my class."Furthermore, I was concerned that my students would not learn to be good note takers, instead relying on the posted notes.In this work, we are focusing on how students used posted notes and the impact this had on student attendance, learning, and perceptions.
Stad as A Strategy of Teaching Reading Comprehension in Junior High School

This research investigated whether there was a significant difference on students’ achievement in reading comprehension taught using STAD and lecture method. The sample was the eighth-graders of SMPN 3 Banjarmasin. A quasi-experimental design was employed in this research. The experimental group was taught using STAD while control group was taught using lecture method. A reading comprehension test as the instrument of collecting data was administered for both groups. Based on the research result using t-test, it was found that the t value was higher than t-table (2.39315 &gt; 2.00) which means that there is a significant difference of achievement between the experimental group and control group. From these findings it can be interpreted that the use of STAD is more effective than lecture method in teaching reading comprehension for junior high school students. It is suggested for English teachers in Junior High School to use STAD in teaching reading since it is useful in improving students’ reading comprehension achievement.
An XML transformed method to improve effectiveness of graphical password authentication

Graphical passwords are already examined as the reliable authentication scheme over the textual passwords. These passwords promise the better robustness and memorability. Various available applications use the image and graphical passwords based authentication systems. But, such authentication systems suffer from the problem of managing pictorial or image data. To map the user input image or graphical password over the larger database, slow down the authentication process. In this present work, XML based schema is proposed to represent the graphical image. As a password image with the graphical pattern is loaded by the user, the server processed the pattern and verifies it for valid pattern based on stroke length and drift. Various forms of graphical patterns are obtained on input graphical pattern by applying the numerous transformations. The extracted pixel values from these practices are kept in an XML pattern DB. Then the server also updated the pattern bits in input image using LSB steganography approach and returns it back to the user as password image. Each time, user input the password image, the password pattern extraction is performed, and mapping is done on XML pattern DB. The implementation of the presented model is done as Mobile and Desktop application. The comparative performance evaluation signifies that the method is more effective than other image password mappers. As the complete information is extracted from the query password pattern image, the 100% accuracy in password mapping is obtained. The applied qualitative measures also verify that the proposed model has improved the reliability and robustness against various aspects.
Virtual guitar: using real-time finger tracking for musical instruments

Kinect, a 3D sensing device from Microsoft, invokes the human-computer interaction research evolution. Kinect was implemented in many areas including music. One was implemented in a virtual musical instrument (VMI) system, which uses natural gestures to produce synthetic sounds similar to real musical instruments. From related work, it was found that the use of a large joint such as hand, arm and leg is inconvenient and limits the way of playing VMI. Thus, this study proposes a fast and reliable finger tracking algorithm suitable for virtual musical instrument playing. In addition, a virtual guitar system application is developed as an implementation of the proposed algorithm. Experimental results show that the proposed method can be used to play many varieties of songs with an acceptable quality. Furthermore proposed application could be used by a beginner who does not have any experience in music or on playing a real musical instrument.
A Modular Reinforcement Learning Framework for Interactive Narrative Planning

A key functionality provided by interactive narrative systems is narrative adaptation: tailoring story experiences in response to users’ actions and needs. We present a data-driven framework for dynamically tailoring events in interactive narratives using modular reinforcement learning. The framework involves decomposing an interactive narrative into multiple concurrent sub-problems, formalized as adaptable event sequences (AESs). Each AES is modeled as an independent Markov decision process (MDP). Policies for each MDP are induced using a corpus of user interaction data from an interactive narrative system with exploratory narrative adaptation policies. Rewards are computed based on users’ experiential outcomes. Conflicts between multiple policies are handled using arbitration procedures. In addition to introducing the framework, we describe a corpus of user interaction data from a testbed interactive narrative, CRYSTAL ISLAND, for inducing narrative adaptation policies. Empirical findings suggest that the framework can effectively shape users’ interactive narrative experiences.
Constraining kernel estimators in semiparametric copula mixture models

A novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models is presented. The standard kernel density estimator is replaced by a weighted version that permits to take into account the constraints put on the underlying marginal densities. Lower misclassification error rates and better estimates are obtained on simulations. The pointwise consistency of the weighted kernel density estimator is established under an assumption on the rate of convergence of the sample maximum.
A Massively Parallel Implementation of a Modular Self-Organizing Map on FPGAs

This paper describes the architecture design of novel massively parallel self-organizing map (SOM) neural networks. The proposed architecture, referred to as the planar SOM (PSOM), is described as a soft IP core synthesized in VHDL. The SOM neural network’s size and the input data vectors’ dimension are adjustable parameters. In this work, several SOM architectures are synthesized and their performance is evaluated for Xilinx Virtex-7 FPGAs. The presented hardware architecture allows online learning and can be easily adapted to a large variety of SOM topologies without a considerable design effort. A [Formula: see text] SOM hardware is validated through the FPGA implementation and its performances with an estimated working frequency of 297[Formula: see text]MHz for a 23-element input vector will reach 21,970 MCUPS in the learning phase and 35,902 MCPS in the recall one.
Fencing Tactics Analysis in Broadcast Video: A Point-by-Point Analytical System

In this paper, a system for tactic analysis of fencing video is developed. The proposed system uses three novel views for presenting the tactic and critical distance of fencers in a match point-by-point. Since fencers fight each other directly, identifying the moment of launching an attack is crucial for analyzing fencing tactics. A criterion function in terms of the speed and acceleration of a fencer and the tempo of the fencer's footwork is proposed for identifying that moment. In addition, a rule-based approach based on the fencer speed and distance is proposed for recognizing the offensive tactic used to score a hit. Three experiments on the video clips for finals and semi-finals of individual events of the World Fencing Championships in 2013-2015 were conducted for evaluating the proposed system. The experiment also included objective and subjective assessments of twelve fencers. The experimental results have demonstrated the feasibility of the proposed approach.
Viewpoint Assessment and Recommendation for Photographing Architectures

This paper studies the problem of how to assess the quality of photographing viewpoints and how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from photographs of world famous landmarks that are available on the Internet and their viewpoint quality ranked by online user annotation. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 15 world famous architectures as well as their 3D models from the Internet. Viewpoint recovery for images is carried out through an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users' viewpoint preferences when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues and perform viewpoint recommendation by learning from both 2D and 3D features using a specifically designed SVM-2K multi-view learner, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. The experiments also demonstrate that our system can be used to recommend viewpoints for rendering textured 3D models of buildings for the use of architectural design, in addition to viewpoint evaluation of photographs and recommendation of viewpoints for photographing architectures in practice.
Nonasymptotic control of the MLE for misspecified nonparametric hidden Markov models

Finite state space hidden Markov models are flexible tools to model phenomena with complex time dependencies: any process distribution can be approximated by a hidden Markov model with enough hidden states. We consider the problem of estimating an unknown process distribution using nonparametric hidden Markov models in the misspecified setting, that is when the data-generating process may not be a hidden Markov model. We show that when the true distribution is exponentially mixing and satisfies a forgetting assumption, the maximum likelihood estimator recovers the best approximation of the true distribution. We prove a finite sample bound on the resulting error and show that it is optimal in the minimax sense–up to logarithmic factors–when the model is well specified.
Trigger performance verification of the FlashCam prototype camera

FlashCam is a camera proposed for the medium-sized telescopes of the Cherenkov Telescope Array (CTA). We compare camera trigger rates obtained from measurements with the camera prototype in the laboratory and Monte Carlo simulations, when scanning the parameter space of the fully-digital trigger logic and the intensity of a continuous light source mimicking the night sky background (NSB) during on-site operation. The comparisons of the measured data results to the Monte Carlo simulations are used to verify the FlashCam trigger logic and the expected trigger performance.