Reviewerly/exHarmony huggingface dataset:
Continuous data imputation applied to massive instances

La presencia de datos faltantes es, mas que una excepcion, una regla. Existentes en la mayoria de datasets que contienen datos reales, el tratamiento de estos datos desconocidos es practicamente inevitable en proyectos basados en el uso de datos extraidos del mundo real. Pese a la comoda inclinacion hacia metodos de eliminacion de estos datos, evitando un tratamiento mas concienzudo de estos, la literatura existente recomienda en la gran mayoria de los casos intentar inducir el valor que tendrian dichos datos si no fueran desconocidos mediante tecnicas de imputacion. Debido a la falta de trabajos en el campo de la imputacion sobre datasets que contienen un gran numero de instancias, este projecto fin de master propone un estudio aplicativo, en el cual se usan y evaluan tanto tecnicas de imputacion como de regresion con el objetivo de obtener un dataset completo a partir de uno incompleto el cual contiene 556.950 instancias con 31 variables continuas a imputar. Con el proposito de un estudio sistematico y diverso de como se comportan las diferentes tecnicas usadas, se propone un marco de trabajo donde se evalua la calidad de la imputacion bajo diferentes porcentajes de instancias conteniendo valores faltantes y diferentes mecanismos por los cuales estos se producen.---ABSTRACT---The presence of missing values is more a rule than an exception. Existing in the majority of datasets containing real data, the treatment of these unknown values is all but inevitable in projects based on data extracted from real life. Despite the seemingly tantalizing option of removing such data, which would prevent any further treatment, the existing literature on the matter suggests to infer the value they would take if they were not unknown through the use of imputation methods. Given the relative scarcity of research on imputation over datasets containing a large amount of instances, this master’s thesis puts forth an applicative study in which both, imputation and regression techniques are used to impute the missing values over an incomplete dataset containing 556,950 instances with 31 continuous variables to be imputed. Intent on expounding on the behaviour of the different selected methods in a systematic and diverse study, a validation framework is designed with the purpose of evaluating imputation quality under different amounts of incomplete instances and missing data mechanisms.
IoT-Based Environmental Data News Generation System for Healthcare

This paper designs and develops an Environmental Data News Generation (EDNG) system of Internet of things data acquisition and generation that can automatically collect data such as environmental temperature, humidity and light intensity in a region and automate broadcast by data news for healthcare. The system is based on digital data collection, Internet of things, embedded development and other technologies. Through designing hardware and software such as the design of networking data acquisition devices, the establishment of cloud forwarding servers, the development of terminal WeChat mini-programs and data news acquisition systems, the problem of automatic data collection, fusion generation and accurate and efficient reporting of regional environmental data news is solved. At the same time, through the research of regional environmental data news gathering and generation technology, the functions of automation of environmental data collection and real evolution of news broadcast were realized. The accuracy of data acquisition and the speed of news reporting are improved, and an effective strategy is provided for the automatic generation of other data news.
Lets Vocab at STEM helps enhance pupils command of English vocabulary

The Centre for Modern Languages & Human Science (CMLHS) at Universiti Malaysia Pahang (UMP) recently coordinated a field trip for 100 primary school pupils from around Pekan to the university to participate in a programme called Let's Vocab @ STEM.
From the initial acquisition of relativization of simple sentences to the interpretive approach of Noun Phrase Accessibility Hierarchy

Based on the high correlation between the child language acquisition and linguistic theories, this study concerns the interpretive approach of the implicational universals of relativization of simple sentences and the constraints on its acquisition via the initial acquisition of it. The children should firstly acquire the subject relative clause(SRC), then the object, and then the oblique, according to the implicational universals of relativization of simple sentences proposed by Keenan & Comrie(1977), but the sequence of the acquisition of RC types cross-linguistically does not entirely support this prediction. The naturalistic and experimental data of Chinese children in this study show that they firstly acquire ORC, then SRC. However, in both types of data of English children, the acquisition of SRC is significantly earlier than that of ORC. This paper predicts the accessibility of subject and object extractions in different word order languages according to two cognitive principles by Lu(2005), and concludes that noun phrase accessibility hierarchy(NPAH) should be refined into two principles based on the positions of RC and word order among S, V and O. The refined NPAH can explain not only the features of the early acquisition of Chinese and English children, but also the reasons of the existence of NPAH.
General quantum Bernoulli factory: framework analysis and experiments

The unremitting pursuit for quantum advantages gives rise to the discovery of a quantum-enhanced randomness processing named quantum Bernoulli factory (QBF). This quantum enhanced process can show its priority over the corresponding classical process through readily available experimental resources, thus in the near term it may be capable of accelerating the applications of classical Bernoulli factories, such as the widely used sampling algorithms. In this work, we provide the framework analysis of the QBF. We thoroughly analyze the quantum state evolution in this process, discovering the field structure of the constructible quantum states. Our framework analysis shows that naturally, the previous works can be described as specific instances of this framework. Then, as a proof of principle, we experimentally demonstrate this framework via an entangled two-photon source along with a reconfigurable photonic logic, and show the advantages of the QBF over the classical model through a classically infeasible instance. These results may stimulate the discovery of advantages of the quantum randomness processing in a wider range of tasks, as well as its potential applications.
Improving Students’ Reading Comprehension In Descriptive Text Through Reciprocal Teaching Technique At Tenth Grade Of SMA Cerdas Murni Tembung

This study was aimed to improve students‟ reading comprehension in descriptive text through reciprocal teaching technique. The subjects of this study were 34 students at tenth grade of SMA Cerdas Murni Tembung. This research was conducted by using classroom action research method that consisted of two cycles. The data were analyzed quantitatively and qualitatively.The result of this research in pre test cycle I showed that the mean of students score was (56,23) in which only there were who passed the standard minimum score or KKM. Then in post test I, there were who being improved and the mean score was (69,70). Then in the post test of cycle II, there were passed the standard minimum score (75,14). It indicated that reciprocal teaching technique could improve students‟ reading comprehension in descriptive text. This result is being reinforced by the observation and interview that done by research. The result of the observation is students become more active and enjoy in doing reading activity. Based on the result, it could be concluded that Reciprocal Teaching technique is effective in improving students‟ reading comprehension in descriptive text.
Student vehicle registration system with QR code

Safety factor in the university is very important. To make sure the safety of the university and campus areas, UMP Security Department will need a system to record the information of student including their vehicle’s information. Vehicle registration system is developed to ensure the information of all student’s vehicles are registered in the campus. The objective of this project is to design, develop an online student vehicle registration system and produce the official university vehicle’s QR code sticker. This project will focus on managing all student’s vehicle information and generate QR code on the vehicle sticker based on the information that is registered in the system such as student name, student matric number, contact number, session, residence and their vehicle plate number. The problem statement of this project is some students do not register their vehicle and misuse other vehicle’s sticker. The system is expected to help the security staff to detect whether the vehicle is registered or unregistered by scanning the QR code on the sticker and match the information with the owner of vehicle and matric number. This system will be used by UMP students, UMP security management will register the student’s vehicle and print the QR Code sticker. The student vehicle registration is developed using Visual Studio 2017 and Microsoft Access to save student’s information. The methodology used in developing this system is Iteractive Waterfall model. The Iteractive Waterfall model consists of six phases which are planning, analysis, design, implementation and testing. After implementation process, the system is tested by the Head Officer of the Security Management to ensure that all elements in that system is working without any error. It also to ensure that the system has achieve the objectives and can help the Security Guard to block the unregistered vehicles.
Constructing multipartite Bell inequalities from stabilizers

Bell inequality with a self-testing property has played an important role in quantum information processing with both fundamental and practical significance. However, it is generally challenging to find Bell inequalities used for self-testing multipartite states, and actually, there are not many known candidates. In this work we propose a systematic framework to construct Bell inequalities from stabilizers which are maximally violated by general stabilizer states, with two observables for each local party. We show that the constructed Bell inequalities can self-test any stabilizer state if and only if these stabilizers can uniquely determine the state in a device-dependent manner. This bridges the gap between device-independent and device-dependent verification methods. Our framework can provide plenty of Bell inequalities for self-testing $N$-party stabilizer states. Among them, we give two families of Bell inequalities with different advantages: (1) a family of Bell inequalities with a constant ratio of quantum and classical bounds using $2N$ correlations, and (2) single pair inequalities improving on all previous robustness self-testing bounds using $N+1$ correlations, which are both efficient and robust for realizations in multipartite systems.
Increasing Student’s Vocabulary Mastery Through Star Wrest Game at Boarding Darul Hikmah Modern on Aacademic Year 2018/2019

The research aimed to determine the effort to increase students’ vocabulary by Star Wrest Game. This research WAS conducted by using Classroom Action Research (CAR). The subject of this research was grade X-B of Boarding Darul Hikmah Modern, the study involved in 33 students. The principle problem was only one that is to effort students’ vocabulary by Star Wrest Game. This researcher was conducted in two cycles which each consists’ of planning, acting, observing, and reflecting. The data were gathered through qualitative and quantitative data. The qualitative data were gained by analyzing the interview and observation result. Then, quantitative data were obtained from the students’ vocabulary score of pre-test and post-test. The pre-test was conducted in experimental and controlled class to know whether the students of boarding school will be well or not. Post-test was conducted in the experimental and controlled class to find out the significance different between experimental and controlled class. The result of this research shown that the Star Wrest Game is effective method to increase students’ vocabulary, because that is can be seen by from the score of pre-test were 51.1, the mean score of post-test I was 75.4, and the mean score of . The results of the observation show the students were motivated in teaching-learning process.Based on the finding and discussion of the research, the researcher suggested that Star Wrest Game is recommended in teaching students’ vocabulary. Use Star Wrest Game was effective method and interesting technique in teaching vocabulary for students.
Red neuronal convolucional con extracción de características multi-columna para clasificación de imágenes

algorithms: Collaborative and Content Based Filter, using a Bayesian Classifier with techniques of Natural Language Processing.Besides, the user experience is improved by applying your GPS location as a filter to the recommendations.To measure the performance of the proposed system, we experimented with a set of data extracted from the Foursquare and TripAdvisor Websites.
Library Guides: 2019-Coronavirus Research Guide: Current Situation

This guide is under construction. If you are viewing it on your mobile device, scroll down to see results.
On Contemporary State of Islamic Phobia based Hate Crimes inCzech Republic

In our contribution we present some of our ideas on currentissue that is rising in the Czech Republic. This issue isusually called as hate crime or bias-motivated crime. Hatecrime has generally very wide range. It can be targeted againstwomen, sexual minorities, ethnic groups and – last but notleast – religious minorities. We have decided to choose onespecific area of hate crime that we consider actual in connectwith current anti-Islamic temper in part of Czech society. Itis hate crime based on Islamic phobia. In our contribution, weprovide definition of hate crime and related term hate speech,comment on possible issues of delimitation of these terms,analyse origins of Islamic phobia based hate crime and itsspecific projections in the Czech Republic. Finally, we presentsome basic criminological conclusions that might be helpful inorder to prevent or to efficiently struggle with this specifictype of criminality.
Attuning to ambiances

This presentation intends to explore urban ambiances through focusing on the world of sounds. What can be learned about an ambiance when we just listen to it? How and under which conditions is it possible to develop a sonic paradigm of urban ambiances? The basic argument is to consider sound as a particularly efficient medium to investigate and develop an account of urban ambiances. Various ideas will be explored in order to answer this question, involving theoretical, epistemological and methodological arguments. Three main directions are accentuated: the first one relates to the tuning into an ambiance, the second relates to the unfolding of an ambiance, and the third relates to the situating within an ambiance.
Research Guides @ Fordham: Digital Collections Metadata: Metadata Standards

This guide's primary function is to explain what metadata is and what metadata should be used for Fordham Libraries' Digital Collections. Information on Metadata Standards.
Multi-scale oriented object detection in aerial images based on convolutional neural networks with global attention

Object detection is a fundamental yet challenging problem in natural scenes and aerial scenes. Although region based deep convolutional neural networks (CNNs) have brought impressive improvements for object detection in natural scenes, detecting oriented objects in aerial images still remains challenging, due to the complexity of the aerial image backgrounds and the large degree of freedom in scale, orientation, and density. To tackle these problems, we propose a novel network, composed of backbone structure with global attention module, multi-scale object proposal network and final oriented object detector, which can efficiently detect small objects, arbitrary direction objects, and dense objects in aerial images. We utilize pyramid pooling blocks as a global attention module on the top of the backbone structure to generate discriminative feature representations, which provide diverse context information and complementary receptive field for the detector. The global attention module can help the model reduce false alarms and incorrect classifications in the complex aerial image backgrounds. The multi-scale object proposal network aims to generate object-like regions at different scales through several intermediate layers. After that, these regions are sent to the detector for refined classification and regression, which can alleviate the problem of variant scales in aerial images. The oriented object detector is designed to generate predictions for inclined box. The quantitative comparison results on the challenging DOTA dataset show that our proposed method is more accurate than baseline algorithms and is effective for objection detection in aerial images. The results demonstrate that the proposed method significantly improves the performance.
Trajectory representation learning for Multi-Task NMRDP planning

Expanding Non Markovian Reward Decision Processes (NMRDP) into Markov Decision Processes (MDP) enables the use of state of the art Reinforcement Learning (RL) techniques to identify optimal policies. In this paper an approach to exploring NMRDPs and expanding them into MDPs, without the prior knowledge of the reward structure, is proposed. The non Markovianity of the reward function is disentangled under the assumption that sets of similar and dissimilar trajectory batches can be sampled. More precisely, within the same batch, measuring the similarity between any couple of trajectories is permitted, although comparing trajectories from different batches is not possible. A modified version of the triplet loss is optimised to construct a representation of the trajectories under which rewards become Markovian.
Κατασκευή ρομποτικής πλατφόρμας αποστείρωσης χώρων μέσω ακτινοβολίας UVC

The present thesis focuses on the design and development of a robotic platform with the purpose to fully automate the sterilization process of spaces by UVC (Ultra Violet radiation Type C) radiation. The robot is made up of a base with drive mechanism, position sensors, motion detectors and UVC light sources. The robot is fully automated using a smart Arduino microcontroller while the user will be deposited to the space which wants to sterilize and implemented with an intelligent algorithm. Future improvements could be the installation of better drive mechanisms so that the robotic platform can sterilize harder surfaces or install smarter position sensors for better perception of space to be sterilized.
On the torsion method for measurement of out-of-plane shear properties

Torsional loading with combined out-of-plane compressive loading has been studied for its viability in measurement of out-of-plane shear properties. Paperboards of two qualities were evaluated, nam ...
A Comparison of Several Word Clustering Models

Sparse-data problem is a main issue that influences the performances of statistical language models; statistical language model based on word classes is an effective method to solve sparse-data problems. This paper presents a definition of word similarity by utilizing mutual information of adjoining words, and gives the definition of word set similarity based on word similarity, and puts forward a bottom-up hierarchical word clustering algorithm which can get global optimum. Experimental results show that the word clustering algorithm is of high executing speed and have good clustering performances. We then interpolated the class-based models with the word-based models and found that it mitigates remaining sparse-data problems of statistical language models.
Control of a two-wheeled robot using interval type-2 fuzzy logic with particle swarm optimization

Two-wheeled robot self-balancing has gained much interest of researchers due to its nonlineardynamics. This project is aimed to design an Interval Type-2 Fuzzy Logic Controller to controla two-wheeled LEGO EV3 robot self-balancing to keep it in the upright position. In this project, two-wheeled LEGO EV3 robot is modelled using SimWise 4D software and integrated with Simulink. The robot stability performance and output response are observed at the same time when the Simulink is executed. System identification is used to get the mathematical model of the system in state space based on input and output from SimWise 4D motion to compare both results. The state space is used during optimization of IT2FLS using Particle Swarm Optimization (PSO). The performances of Interval Type 2 Fuzzy Logic Controller (IT2FLC) and optimized IT2FLS are compared. The robustness of IT2FLS is observed during disturbance rejection by injecting different direction of 0.8N and 1.0N torque to the robot in first 15 seconds.
Design and Optimization of Crowd Behavior Analysis System Based on B/S Software Architecture

With the development of society and economy, the importance of crowd behavior analysis is increasing. However, the system often requires a large amount of computing resources, which is often difficult to meet for personal computers in traditional client/server architecture (C/S architecture). So based on the existing local analysis system [5], we construct a crowd behavior analysis system based on browser/server architecture (B/S architecture). Then we optimize many aspects of this B/S system to improve its communication capability and stability under high load. Finally, the acceleration work of the CGAN-based crowd counting module is carried out. The generator of CGAN (Conditional Generative Adversarial Network) was optimized such as residual layer pruning, upsampling optimization, and instance normalization layer removing, and then deployed and INT8 quantized in TensorRT. After these optimizations, the inferring speed on the NVIDIA platform is increased to 541.6% of the original network with almost no loss of inference accuracy.