Reviewerly/exHarmony huggingface dataset:
Resilient Distributed Target Tracking Over Sensor Networks Against Misbehaving Nodes

This paper is concerned with the distributed target tracking for a moving target of discrete time-varying nonlinear dynamics over a wireless sensor network. A number of spatially distributed sensors are deployed to measure the state of the target, calculate local state predictions as well as local state estimates, and further exchange local information with their underlying neighboring sensors. Due to adversarial attacks, a subset of sensors are deliberately manipulated and thus misbehaving. Accordingly, information exchanges from the misbehaving sensors to their neighbors become antagonistic rather than cooperative as in normal operation. First, a novel distributed target tracking scheme in terms of local state predictors and state estimators is developed for each sensor over a partially misbehaving sensor network. Second, criteria for designing the desired distributed target tracking scheme and the time-varying adjacency matrix are derived such that two ellipsoidal prediction and estimation sets can be recursively computed. It is analytically proved that these two sets guarantee the containment of the true target state at every instant of time regardless of misbehaving sensors as well as unknown-but-bounded process and measurement noises. Third, based on the proposed design criteria, optimization methods are put forward to minimize the calculated ellipsoids. Finally, an application to vehicle tracking is given to show the effectiveness of the results.
Multiple Parameters Determination for Image and Video Reconstruction Using Genetic Algorithm and Generalized Stein’s Unbiased Risk Estimation

This paper proposes a heuristic multi-parameter optimization method for image and video reconstruction. The method is embedded in an evolutionary computation framework in which a set of parameters are encoded into chromosomes of an individual, and a generalized Stein's unbiased risk estimation (GSURE) is employed as a fitness function. By using the proposed method, the near-optimal parameters can be determined after several iterations, and with the use of these parameters, the ideal image can then be reconstructed. Furthermore, this method can be extended to video reconstruction. According to the similarity between adjacent frames of a video, the generation of population is vividly replaced by the frame index. We calculate the fitness based on the current frame, breed a new generation selectively, and use the new population to optimize continually based on the next frame. After several frames, the parameters can be optimized, and an ideal reconstructed frame can then be steadily output. Numerous experiments demonstrate the effectiveness and advantages of the proposed method.
Parallel Construction of Local Clearance Triangulations

The usage of navigation meshes for path planning in games and otherdomains is a common approach. One type of navigation mesh that recently has beendeveloped is the Local Clearance Triangulation (LC ...
Subject Guides: EXPS 410: Multiculturalism in School and Society: Getting started @ Mardigian Library

This guide is designed to support coursework in EXPS 410, which focuses on multiculturalism in school and society.
Social Media Engine: Extending our Methodology into other Objects of Scholarship

In this paper we describe our efforts towards building a framework that extends the functionality of an Open Access Repository by implementing processes that integrate the ongoing trends in social media into the context of a digital collection—while taking into account the potential of social media, the relevance of open infrastructures and the accessibility of open knowledge. We refer to these processes collectively as the Social Media Engine. The purpose of this paper is twofold: first, we propose to challenge some of the preconceived notions of digital libraries by making repositories more dynamic; and second, by challenging this notion we want to promote public engagement and open scholarship. As a work in progress, we believe that a real challenge lies in emphasizing the connections between documents that can be treated as objects of study as well.
A Study to assess the Knowledge and Attitude of staff Nurses regarding Umbilical Cord Stem cells before and after the Structured Teaching Programme at selected Hospitals, Coimbatore

Statement of the Problem: A study to assess the knowledge and attitude of staff nurses regarding umbilical cord stem cells before and after the structured teaching programme at selected Hospitals, Coimbatore. Study Objectives: a) To assess the knowledge of staff nurses regarding umbilical cord stem cells. b) To assess the attitude of staff nurses regarding umbilical cord stem cells. c) To deliver structured teaching programme regarding umbilical cord stem cells, collection, preservation and utilization. d) To reassess the knowledge regarding umbilical cord stem cells after delivering structured teaching programme. e) To reassess the attitude regarding umbilical cord stem cells after delivering structured teaching programme. f) To find out the correlation between the knowledge and attitude of staff nurses regarding umbilical cord stem cells. g) To find out the association between the post test knowledge with selected demographic variables of staff nurses regarding umbilical cord stem cells. h) To find out the association between the post test attitudes with selected demographic variables of staff nurses regarding umbilical cord stem cells. Methodology: One group pre test post test Experimental design was used for the study. The sample for the study consists of 100 staff nurses, selected by random sampling technique. Knowledge questionnaire and attitude scale was used to assess the knowledge and attitude of staff nurses regarding umbilical cord stem cells. Results: Inferential and Descriptive statistics were used to analyse the values. Conclusion: the study shows that the knowledge and attitude level of staff nurses were improved after the structured teaching programme.
Applying design system in cybersecurity dashboard development

This thesis evaluates the applicability of a particular Design System in the development of a dashboard that addresses the needs of cybersecurity teams. This work is motivated by the reduced knowledge of the specific needs that a dashboard for cyber security products could encounter and by the narrow understanding of the limitations and challenges that the applicability of a design system on such a targeted system could encounter. The context of research, development and testing is the multinational Ericsson which offer the opportunity to gain access to not only to experts from Finland but worldwide. The initial efforts were directed towards identifying and understanding the users, their needs and tasks, and the environment in which they operate. This qualitative data is obtained by performing a literature review on the state of the art and multiple interviews with experts from Security Operation Centres. After the requirements have been collected and by utilising the Design System, a design for the dashboard is presented and tested with experts. The primary outcome of this thesis comes in the form of a user-centred methodology for the extraction of expert knowledge and its conversion into requirements. The proposed solution constitutes a baseline approach towards identifying the needs of professionals in an environment in which the access to users is limited.
PROMOTING ENGLISH CLUB AS AN EXTRACURRICULAR ACTIVITY FOR YOUNG LEARNERS: SUGGESTED PROCEDURE

2013 Curriculum points out English is no longer recommended as a local content subject in elementary schools in Indonesia. This policy does not seem to make parents and practitioners stop introducing English for young learners in Indonesia. There are some reasons to keep providing these learners with English. One of themdeal with the need to prepare young learners with the ability to communicate in English earlier which in turn will facilitate them to cope with and compete in global communication. Realizing the importance of providing young learners with the ability to communicate in English, this paper promotes English Club as an extracurricular activity for young learners in order to enhance the learners’ ability to communicate in English. This paper introduces format that can be applied by teachers, tutors, facilitators and practitioners of English club for young learners and the procedure as well which will give the members of the club a wide range of practice which will contribute to the members’ ability to communicate in English. In order to give a clear picture how activities in English club improve young learners’ ability to communicate in English, a sample activity is also included in this paper .
Object detection of colored images using improved point feature matching algorithm

For computer vision, image matching is an essential trait which includes scene or object recognition. Detection using point feature method is much effective technique to detect a specific target instead of other objects or within clutter scene in an image. It is done by comparing correspondence points and analyzing between cluttered scene image and a target object in image. This paper presents novel SURF algorithm that is used for extracting, describing and matching objects in colored images. The algorithm works on finding correspondence points between a target and reference images and detecting a particular object. Speeded-up robust features (SURF) algorithm is used in this study which can detect objects for unique feature matches and which has non-repeating patterns. This approach of detection can robustly find specified objects between colored cluttered images and provide constriction to other achieving near real time performance.
Bezier curve based lattice planner and lateral controller for highway scenarios

The autonomous driving technology is transforming the road-based transport industry into a new era that will change the way we think about transport solutions. With advancements in computational po ...
THE EFFECTIVENESS OF AUDIO-LINGUAL METHOD ON STUDENTS’ PRONUNCIATION ABILITY AT THIRD SEMESTER IN THE UNIVERSITY OF PASIR PENGARAIAN

Abstract. Studying pronunciation is little bit hard than other skill cause need practice and need new method to engage students’ ability. For those the researcher apply audio-lingual method to engage students’ pronunciation ability. This research aims to see the effect of audio-lingual method to students’ pronunciation. This research is quasi experimental research; nonequivalent control group design. They are thirdly eight students. Twenty students are in regular class and eighteen students are in non-regular class. Experiment class is taught Audio-lingual method   and   Control   class   is   taught   conventional technique. The data will be analyzed by using independent t-test. The research finding is expected enrich theories about teaching method in pronunciation. Keywords. Audio-lingual method, Students; pronunciation, Third semester, University of Pasir Pangaraian
LibGuides: Academic Areas of Interest: Business, Entrepreneurship, & Information Technology

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Research of robot control by ambiguous instructions

Recently, AI equipped robots, smart speakers have appeared in homes, opportunities to come into contact with robots in general households and towns have increased, and various people also have a high possibility of controlling robots. To be able to control the robot smoothly, the robot side needs to understand human intention. In this paper, we propose a system that controls a robot from human ambiguous instructions and images of feet. In this system, Raspberry Pi, USB microphone and USB camera are attached to the mobile robot for control. Control of the robot is performed only with body direction estimated by feet image of the user and voice instructions without attaching special sensors. Ambiguity in speech recognition that do not limit the target using the directive are recognized. Experimental results of 93% success rate when we conducted 10 experiments of here shown the effectiveness of the proposed method.
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Quantum BCH and reed-solomon entanglement-assisted codes

Quantum error correcting codes play the role of suppressing noise and decoherence in quantum systems by introducing redundancy. Some resources can be used to improve the parameters of these codes, e.g., entanglement. Such codes are called entanglement-assisted quantum (QUENTA) codes. In this talk, a general method to construct QUENTA codes via cyclic codes is shown. Afterwards, the method is applied to BCH and Reed-Solomon codes.
Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages

To overcome the data sparseness in word embedding trained in low-resource languages, we propose a punctuation and parallel corpus based word embedding model. In particular, we generate the global word-pair co-occurrence matrix with the punctuation-based distance attenuation function, and integrate it with the intermediate word vectors generated from the small-scale bilingual parallel corpus to train word embedding. Experimental results show that compared with several widely used baseline models such as GloVe and Word2vec, our model improves the performance of word embedding for low-resource language significantly. Trained on the restricted-scale English-Chinese corpus, our model has improved by 0.71 percentage points in the word analogy task, and achieved the best results in all of the word similarity tasks.
THE PROJECT "ENGINEERING CLASS IN MOSCOW SCHOOL"

The issues of implementation of the project "Engineering class in Moscow school" are discussed. The results of the pre-professional examination in three areas of training are presented, the low level of pre-professional knowledge was noted. The research course of solving applied problems of engineering practice with the use of modern software has not been studied. Based on the analysis of the results of pre-professional examinations in the areas of research, technology and programming, conclusions about the weak information support of the project and the unwillingness of school graduates to expand the fields of training are obtained.
Practice of Social Implementation Robot Education through Development of Micro Intelligent Robot System

Since 1988, the Department of Electronic Control System Engineering at National Institute of Technology, Numazu College has conducted development of Micro Intelligent Robot System “MIRS” as a Project-Based Learning (PBL) into the curriculum for the education of system development. We are intended to provide a place of learning "process of system development" and "making education as a creative education" and "Proposal and management of competitions with social implementation" through the development of robot. In 2017, a new robot platform, which is provided for students at the beginning of this PBL program, was developed in order to improve technical factors of the robot system, and at the same time, increase students' motivation for learning. Since 2017 we have adopted social implementation education, and we are beginning to take the developed robot system to the work site and actually evaluate it based on the theme of "Robots in our lives". In this paper, we report about the Robots developed on the theme of "Robots in our college" that we tackled in 2018 and describe the effect of social implementation education. As a result, the team who got the developed robot on site and evaluated was a good result. We plan to promote social implementation education in the future.
Scaling Down: Efficient Inference for Convolutional Neural Networks

Author(s): Osajima, Jason Shiego | Advisor(s): Wu, Yingnian | Abstract: Convolutional neural networks achieve impressive results for image recognition tasks, but are often too large to be used efficiently for inference applications. In this paper, we explore several efficient architectures that satisfy a baseline accuracy on an image recognition task. For this task, accuracy is defined as the number of correctly identified images over total images. We train a NasNet-A convolutional neural network to an accuracy of 0.8034 that has 5.2M parameters, 662M multiplication operations, and 659M addition operations. When comparing this model against the baseline model WideResNet-28-10, it achieves a score of 0.1659 using the Micronet Challenge scoring scheme. The Micronet Challenge score is defined as the sum of the number of parameters and number of multiplications and additions, normalized by the number of parameters and multiplications and additions for the baseline model WideResNet-28-10.
An Automatic System for Flooded Area Detection: A Machine Learning Approach

Floods are some of the most destructive and catastrophic natural events, as well as the most recurrent. This works objective is to explore the capabilities of a powerful tool as Machine Learning in order to build an automatic system for the detection of flooded area analyzing geospatial data collected from satellitar missions. To achieve this, different systems (based on several Machine Learning algorithms) are built and tested, in order to make a comparison of their performances on the the data set. After the test of state of the art techniques as Convolutional Neural Networks, other white box models are experimented, in order to collect informations about the data set, and on the most important features towards the detection of flooded area. Moreover, different image processing tools were applied in pre-processing and post-processing phase and evaluated in relation to each methods performance variations. This Thesis contains the report of all the experiments performed, a deep analysis of the results collected, and the consequential considerations and evidences they have pointed out.
Analysis and Optimization of CNN-based Super Resolution with Filter Pruning

Recent studies using convolutional neural networks (CNN) have been conducted in the field of super resolution. These studies include Super-Resolution Convolution Neural Networks (SRCNN) and Very Deep Convolution Neural Networks Super-Resolution (VDSR). SRCNN and VDSR are known to be heavy in terms of parameter size and the inference speed is low to be applied in real-time. In order to overcome these issues, we apply filter pruning to optimize the network. Experimental results show that when the network is 71% pruned, the parameter size was reduced to 71%, the inference speed is enhanced by about 60%, and the inference quality is maintained at 94% compared to the original VDSR network.
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Development of rescue robot using hydraulic actuator

This paper describe a rescue robot with hydraulic actuator and wheel driving mechanism with an inner tube pressure control(ITPC). The developed robot is composed of two driving units which are enable to climb over obstacles by lifting the driving unit with a hydraulic driven arm. The ITPC has been developed to traverse the rough terrain. The ITPC system does not need an air compressor and an air tank which are heavy and bulky. The control system was constructed by Tinker Board (ASUS).
The Effect of Strategic Learning for Human Resources on Dynamic Capabilities of Airlines Companies in Kuwait

The aim of this study was to explore the impact of strategic learning on the dynamic capabilities of human resources in airline companies in Kuwait. The study sample consisted of all managers working at different levels in these airline companies. To collect the data, a questionnaire-based survey was used and the model was empirically tested using 110 responses from the airline companies in Kuwait and applying multiple regression tests using SPSS software. It was found that strategic learning had a significant impact on the dynamic capabilities of the human resources workforce in airline companies in Kuwait with regards to the four dimensions studied. Based on the study results, the researchers recommended that managers and decision makers in the airline industry in Kuwait should have the ability through various strategies to seize opportunities from companies that show great importance within the market because of their ability to increase the market share which was in turn reflected by an enhancement of their competitive position.
WHY DO THE STUDENTS DO DISRUPTIVE BEHAVIOR IN ENGLISH CLASSROOM? A CASE STUDY IN ONE OF SENIOR HIGH SCHOOLS IN INDONESIA

Abstract A conducive learning situation is essential in learning English. However, one problem that usually disturbs the learning situation is the appearance of disruptive behavior. Students' disruptive behavior is an inappropriate behavior conducted by students during the learning process which turns the classroom into not conducive. Exactly, there are several factors contribute to the occurrence of disruptive behavior in the classroom, especially on senior high school students. Thus, the current study was a case study aimed to investigate several causes of students' disruptive behavior in the English teaching-learning process in the classroom. The subjects of this study were seven English teachers and six selected classes from the tenth grade until the twelfth-grade students in a small town in Indonesia. The data were collected by using observation and interviews. The result of this study showed that students' disruptive behavior in the English classroom was caused by internal and external factors. Feeling boredom, feeling anxiety, and seeking attention was internal factors that became the occurrence of students’ disruptive behavior. While fatigue and the influence of media were the external factors that caused the emergence of students' disruptive behavior in the English teaching-learning process in the classroom.  Keywords: disruptive behavior, English teaching-learning process, senior high school students
ADVANCED COLOR COVERT IMAGE SHARING USING ARNOLD CAT MAP AND VISUAL CRYPTOGRAPHY

The demand for effective information security schemes is increasing day by day with the continual growth of the internet. Visual cryptography (VC) is a very important secret sharing scheme. The essential step behind this secret sharing scheme is to convert the color covert image into multiple indecipherable image shares so it cannot reveal the data within the color covert image unless combined along by some mathematical calculation. This paper proposes an advanced color covert image-sharing scheme using Arnold cat map (ACM) and VC. The random matrix-encoding scheme encodes the color covert image into an image matrix. ACM algorithm disrupts the high correlation among the pixels of the image matrix to generate an encrypted image. The generation of shares from this encrypted image is by VC that uses pixel reversal and random matrix generator. The shares one by one does not provide any information concerning the color covert image however put together they offer back the encrypted image. The projected paradigm offers 3 levels of security and through decipherment gives back the covert image without loss of information. Related examples and experimental results reveal the effectiveness of this scheme.