Reviewerly/exHarmony huggingface dataset:
Keynote 3: Blockchain – IoT – AI Trust Nexus

Blockchain is the underlying technology behind Bitcoin and it is finding its way in the development of a variety of new software systems. With its innovative approach to decentralized computation and assets management, the blockchain has not only potential to solve a number of system scalability, reliability, security, and privacy issues, but also to revolutionize software systems development in a way similar to what the Internet did. For example, the integration of the blockchain, artificial intelligence (AI), and the Internet of Things (IoT) provides a promising new research and development direction that has generated significant research funding and venture capital investments. However, the lack of explanation when it comes to the decisions made by today's AI algorithms is a major drawback with respect to trust in such systems, e.g., deep neural networks do not offer control or reasoning over their internal processes. In this keynote talk, we present the trust issues common to blockchain – IoT – AI systems and their integration. We present some directions for achieving more trustworthy and explainable blockchain – AI – IoT system integration by leveraging features of blockchain, smart contracts, trusted oracles, and decentralized storage. We discuss a framework for complex AI systems in which the decision outcomes are reached based on decentralized consensuses of multiple AI predictors. Finally, we discuss few key application areas with practical use cases.
Teaching EFL in Digital Age

Since the beginning of the 21st century, science and technology have been developing and innovating continuously, and people have entered the digital era. Digitalization is an important trend of global economic development. The popularization of digital technology makes it a trend that digital technology can assist EFL teaching. In the current EFL teaching, how to apply advanced digital technology to EFL classroom and how to carry out teaching activities better with digital equipment has become a hot issue for EFL teachers. This study introduces several main auxiliary teaching techniques in the digital age and puts forward some specific strategies for EFL teaching, in order to further improve the teaching level of EFL courses so as to meet students’ personalized development needs.
Subject and Course Guides: Resource Acquisition and Management Department : For LHS Sites

Are you curious of what happens behind the scene? This guide will help you learn more about RAM Department This page will focus on procedures and workflows associate with liaison librarians
Mining International Political Norms from the GDELT Database

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society. This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events extracted from news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.
Emoji and the History of Human Communication

places emoji within the context of the evolution of language and communications technology. Since language first evolved some one hundred thousand years ago, allowing people to communicate complex ideas to each other, each new technology has extended its reach. The last few decades have seen the speed of technological change increase rapidly, with the computer, the internet and mobile devices making communication across distances ever easier and cheaper. But these inventions have also presented challenges for how we communicate, especially around the important issue of empathy and emotional distance. Various innovations, from punctuation marks to emoticons, have been used in an attempt to provide solutions for these challenges. In explaining the trajectory of this evolution, the chapter looks at how emoji relate to and differ from earlier forms of written language, such as hieroglyphics, and why they’ve emerged to become so popular at this particular juncture in history.
A CORPUS BASED STUDY OF SIMPLIFICATION IN THE LEGAL TRANSLATED DOCUMENTS OF PAKISTAN

Simplification has been reckoned among translational universals since Baker’s (1996) incipient contentions. This study aims to investigate the relevance of simplification hypothesis in translated authentic legal documents. The text of the Constitution of Pakistan and its Urdu translated draft served the purpose of data in this corpus based study. Word list and key word list tools available in Wordsmith6 have been employed to measure the simplification indicators. The analysis has suggested that the text understudy does not verify the simplification hypothesis. However, the generic conventions, the speech act theory and the less flexible nature of translated texts situated on LSP cline provide sufficient justification for contrary to hypothesis findings. The findings of this study can be further strengthened by conducting the similar studies on other legal text types and other language pairs. The study would provide insight to legal translators, trainers, trainees, editorsand genre analysts.
A Bayesian approach for initialization of weights in backpropagation neural net with application to character recognition

Convergence rate of training algorithms for neural networks is heavily affected by initialization of weights. In this paper, an original algorithm for initialization of weights in backpropagation neural net is presented with application to character recognition. The initialization method is mainly based on a customization of the Kalman filter, translating it into Bayesian statistics terms. A metrological approach is used in this context considering weights as measurements modeled by mutually dependent normal random variables. The algorithm performance is demonstrated by reporting and discussing results of simulation trials. Results are compared with random weights initialization and other methods. The proposed method shows an improved convergence rate for the backpropagation training algorithm.
Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of twelve state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters. Finally, it introduces a new and efficient semantic weighting method (using semantic segmentation feature maps on each video frame) to reduce the drift problem. Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128 and VOT-2018 visual tracking datasets demonstrate that the proposed method effectively outperforms state-of-the-art methods in terms of precision and robustness of visual tracking.
A Study on the Direction of the Elderly Theatre in Aged Society: Around the ‘Hoechun Circus’ in Dangjin

본 연구의 목적은 문화지원 측면의 노인복지에 속하는 노인연극 프로그램의 한 사례를 분석하여 노인연극의 지향점을 고찰하는 데 있다. 이를 위해 충남 당진시 할머니들로 구성된 ‘회춘유랑단’의 활동 사례를 조사하고, 그 공연 내용을 분석하였다. 연구 방법은 사례 연구와 연행 이론을 사용하였으며, 동료 검토법(peer examination)의 전략을 사용하여 연구의 타당도를 높였다. 연구 결과, 노인연극 프로그램을 통해 할머니들은 첫째, 어린이와 청소년들과의 소통을 지향했고, 둘째, 몸이 불편한 다른 노인들과 소통하기를 원했으며, 셋째, 청년이나 중년은 물론 모든 주민과 소통하길 바랐고, 넷째, 연극이라는 새로운 장르를 체험하여 자신의 정체성을 확인하고 성취감을 높이면서 노년을 즐겁고 유익하게 보냈다는 것을 알 수 있었다. 이런 연구 결과를 바탕으로 아래와 같은 제언을 하였다. 첫째, ‘회춘유랑단’을 밴치마킹한 노인연극 프로그램을 확대해야 한다. 둘째, 노인연극에 관한 다양한 사례를 연구하고, 이런 노인연극을 지원하는 정책 연구도 필요하며, 노인연극 전문가를 양성하는 교육 과정도 개발해야 한다. 셋째, 노인연극 프로그램이 지역적인 한계를 벗어나 국내와 국외의 단체들과 교류하는 방안도 모색해야 한다. 넷째, 연극치료 분야에서도 새로운 길을 모색할 여지가 있고, 소시오드라마나 사이코드라마 프로그램 개발이 대안이 될 수 있다.The purpose of this study is to examine the direction of the elderly theatre by analyzing an example of the elderly theatre program which is attempted in a way of the welfare of senior citizens in terms of cultural support. To this end, the ‘Hoechun Circus’ composed of elderly women in Dangjin, South Chungcheong Province, was investigated and analyzed. Research methods uses a case study, performance theory, and peer review strategies to increase the validity of the study. The results of the study are as follows: Firstly, the elderly women intended to communicate with children and adolescents through performance; Secondly, they wanted to communicate with other physically uncomfortable elderly people; Thirdly, they also attempted to communicate with young or middle-aged as well as all residents; Fourthly, they experienced a new genre of theatre to identify themselves in a different ways and enhance their sense of accomplishment and spent their old age pleasantly and informally. Based on these findings, the following suggestions were made. First, we should expand the theatre programs of senior citizens which benchmark the ‘Hoechun Circus’. Second, various cases of elderly theater should be studied and policy research to support such elderly theatre is necessary. Besides, educational courses to train elderly theatre experts should also be developed. Third, it should also seek ways for the program to move beyond regional limits and interact with organizations at home and abroad. Fourth, there is room to explore new ways in the field of theater therapy, and the development of sociodrama or psychodrama programs can be an alternative.
A Swiss German Dictionary: Variation in Speech and Writing

We introduce a dictionary containing forms of common words in various Swiss German dialects normalized into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same dialect. To alleviate the uncertainty associated with this diversity, we complement the pairs of Swiss German - High German words with the Swiss German phonetic transcriptions (SAMPA). This dictionary becomes thus the first resource to combine large-scale spontaneous translation with phonetic transcriptions. Moreover, we control for the regional distribution and insure the equal representation of the major Swiss dialects. The coupling of the phonetic and written Swiss German forms is powerful. We show that they are sufficient to train a Transformer-based phoneme to grapheme model that generates credible novel Swiss German writings. In addition, we show that the inverse mapping - from graphemes to phonemes - can be modeled with a transformer trained with the novel dictionary. This generation of pronunciations for previously unknown words is key in training extensible automated speech recognition (ASR) systems, which are key beneficiaries of this dictionary.
Steganographic-Algorithm and Length Estimation Classification on MP3 Steganalysis with Convolutional Neural Network

Steganography is a method of embedding secret messages into a cover file in the form of text, audio, picture or video, so that the message is not suspected by those who are not authorized to open the message. The technique to find out whether the cover media is a stego file or not is steganalysis. In this study, detection of hidden messages focused on MP3 files inserted by the MP3Stego algorithm and Equal Length Entropy Codes Substitution to classify based on algorithms and the estimated length of the message and detect cover files. In conducting this research, it is necessary to know the audio features of MP3, build suitable deep learning methods and the performance of the models that have been produced. The proposed solution for these two problems is to use the QMDCT audio feature and deep learning architecture with Convolutional Neural Network. The results of this study are the best algorithm classification model with an accuracy performance of 91.78% and F1-Score 92.22% and the best classification model for message length estimation has an accuracy performance of 24.16% and F1-Score 21.40%. Thus, the proposal of deep learning architecture is good in classifying algorithms and covers, but still poor in classifying the estimated length of the message.
A Survey on Depth Estimation for 3D Reconstruction of Images

Depth estimation plays an important role in many applications of computer vision.  Depth can be inferred from multiple images of same scene taken from different station points or from monocular cues associated with a single image. Stereo vision, an extensively used method for gathering depth information from 2D scene endures with view synthesis, which is 3D scene reconstruction. The automated systems such as robotics, constructing a 3D spatial model of a scene and tracking object in 3D space are some of the applications that rely on stereo vision. Stereo vision concept is exploited by many vision based remote control systems to access the machine in a touch-free environment. Most of the existing approaches use multiple images to perceive depth such as stereopsis, structure from motion, depth from defocus/focus, deep learning, machine learning.
Metamorphosis An Action-Adventure Shooter Game

Video Game is an electronic game that involves interaction using different electronic mediums such as computers, consoles and mobile devices. Video Games are playing a big part in the entertainment industry as a form of leisure, advertisement, treatment, training and simulation, education, and a new form of art and literature. Video games are the new form of adventure books, where people will play a character in a story that will immerse them to a whole different reality. They are also a form of escape, where people tend to go to in times of despair, boredom, or simply bonding with others. Playing video games can enhance someone’s hand and eye coordination, it can let anybody experience a new story, sometimes it let’s people discover something new and learn from it, and maybe pick up a moral lesson and use it as an inspiration or philosophy in life. Video Games can tell a story and the player can play the part of the main character by giving him life trough decisions and actions. Metamorphosis is an action-adventure shooter game, where the player will follow a narrative and discover the story behind the main character, Abeo, the Mega Bunny. The game will revolve around a storyline telling a tale about the adventures of Abeo and his quest to defend the citizens and stop evil to promote peace. The game’s story is in a fictional setting where animals are depicted as human like beings. This project will be published in the Personal Computer (PC) platform. It is a combination of 2 dimensional (2D), 2.5 dimensional (2.5D), and 3 dimensional (3D) arts, where the main levels are created in 2.5D, visuals and cut scenes are drawn in 2D, and some mini games or side quests are in the 3D space. Other features of the game includes a visual novel representation of the metamorphosis story depicted in the cut scenes and some choices that the player can make in game, mini games or side quest where the player can earn more currency points that can be used on enhancing the main character’s equipments and status, a combat system that is mild and non violent yet immersive and challenging, and a plot twist that can add fun to the game’s funativity.
LibGuides: Remote Resources for College Students: About

Conducting research remotely? The New York Public Library has you covered. This guide includes resources to support your research 24/7 no matter where you are.
Can the Guarantee Instrument Fight Poverty? : A Minor Field Study in the Morogoro Region in Tanzania

The role of access to finance for economic development has received extensive attention recently, which has brought the issue to the top of the policy agenda. The growing attention has resulted in  ...
Finding magic squares with Douglas—Rachford algorithm

The first author was supported by MINECO of Spain and ERDF of EU, as part of the Ramon y Cajal program (RYC-2013-13327) and Grants MTM2014-59179-C2-1-P and PGC2018-097960-B-C22.
Sentido Otorgado A Las Tecnologías Por Los Maestros

This document is a product of the research “Reasons that underlie teachers' resistance to incorporate the tic into their teaching work. Educational Institution Our Lady of Carmen de La Dorada (La Dorada, Caldas, Colombia) ”. In this research, we sought to “Understand the reasons underlying the resistance of teachers to incorporate ICT into their teaching work”.Phenomenology was used as a method of inquiry, processing and analysis of the data collected. This is characterized by having two methods or moments for the treatment of information; On the one hand, it was the epoje as the placement in parentheses of the researcher in the face of his prejudices, answers and explanations regarding the studied phenomenon, in order to give voice to the informants and unveil the meaning of the phenomenon from a subjective and intersubjective bet; On the other hand, the phenomenological reduction allows the organization and treatment of information in a way that deepens more and more in the subject and manages to find its true essence. For this, the human-technology relationships proposed by Ihde (in Van Manen, 2016) are used: (1) Encarnacion Relationship; (2) Hermeneutic Relationship; (3) Otherness relationship and (4) Background relationship.The information was collected from interviews that inquired about the experiences, conceptions and relationships with the technology of 20 teachers of the educational institution Nuestra Senora del Carmen of the municipality of La Dorada, Caldas (Colombia); These teachers were intentionally selected, 2 from each area of ​​academic training: Spanish Language, Foreign Language, Social Sciences, Arts Education, Ethics and Values, Religious Education, Physical Education, Mathematics, Technology and Systems and Natural Sciences.In the end, the conclusions give account of three (3) central themes; (1) Technology as a tool for life; (2) the technology that transforms and transforms and (3) the benefits of technology.
Medical image Synthesis with improved Deep Convolutional Bi-Generative Adversarial Network Aided Genetic Algorithm

In different clinical applications, important role is played by Medical imaging. Acquisition of various image models are limited by radiation lose and cost considerations. Desired image modality are estimated by medical image synthesis without original scan. To address this issues, generative adversarial method is proposed in this research. For a given source image, target image is generated by training fully convolutional network (FCN).Better model of FCN is obtained by using adversarial learning method. Mapping of source to target imaging is done in an effective way by this and highly accurate target images are produced by this. Generation of target images with blurring is avoided by incorporating loss function with image-gradient-difference in the design of FCN. Network is trained using Long-term residual unit. Deep convolutional adversarial network with context awareness is created by applying Auto-Context Model (ACM).Deep Convolutional GA connected with bi-generative adversarial network (DC-Bi-GAN) is implemented for synthesizing images. From source images, target images are accurately synthesized by proposed model and it is more robust as shown by results of experimentation. From MRI, CT is generated and from 3T MRI, 7T MRI are generated using this model with three datasets. In all task and dataset, superior results are produced by proposed model.
Design and Implementation of an Automatic Summarizer Using Extractive and Abstractive Methods

Chattopadhyay, Adhiraj Dey, MonalisaCommunication via a natural language requires two fundamental skills: producing ‘text’ (written or spoken) and understanding it. Here, the two terms, natural language processing (NLP) and natural language generation (NLG) become important. NLU, also known as natural language understanding, is where the system understands to disambiguate the input sentences (in some human or natural language) to produce the machine representation language. NLG, on the other hand, is the technique of generating natural language from a machine representation system (a database/logical form). An example of a simple NLG system is the Pollen Forecast for Scotland system (Turner et al., in generating spatio-temporal descriptions in pollen forecasts 2006, [1]) that could essentially be a template. NLG system takes as input six numbers, which predicts the pollen levels in different parts of Scotland. From these numbers, a short textual summary of pollen levels is generated by the system as its output. In this paper, our aim is to test and/or improve the preexisting algorithms for our NLG system and if required, to develop our own algorithms for the said system. The paper starts, first defining the stages and components of the NLG task and their distinctive roles in accounting for the coherence and appropriateness of natural texts. It then sets out the principal methods that have been developed in the field for building working computational systems. Thereafter, the attempts to define a new method for the application being developed are shown. Finally, the problem faced in developing an NLG system and potential applications are discussed.
Smart Automation Attendance System using Neural Networks

A Traditional Attendance system will be a huge burden on teachers. After marking down it manually, they have to upload it to some database to maintain the student’s records. When there is a batch of students more than 500, doing it manually is imaginary. To resolve this problem, smart and auto attendance management system is utilized. But authentication is very important issue in this system. The smart attendance system is generally done with the help of biometrics. Face recognition is one of thebest biometric methods to improve this attendance system. Being a prime feature of biometric verification, facial recognition is used in several applications like video monitoring. By Utilizing this system, the problem of proxies and students being marked present even though they are not physically present in the class can be easily solved. The main implementation step used in this system is face detection and recognizing the detected face. This paper proposes a model for implementing an automated attendance management system using ANN and CNN. After this the student’s faces are mapped to their USN(University serial number) or ID. When the student face is recognized and if they are present in current location of the teacher, automatically the USN mapped to that student is marked as present in the database. In this model teacher need to hosts the system through web application or android application. While hosting teachers current location is taken. This model will be successful technique to manage the attendance and records of students.