Reviewerly/exHarmony huggingface dataset:
DEVELOPING AN EXPERIENTIAL LEARNING ACTIVITY TO LEARN ABOUT ROBOTICS

This paper focuses on the results obtained by developing an experiential learning activity (ELA) to transmit the basic concepts of robot programming.ELAs are a powerful tool for developing meaningful learning of several concepts, working with transversal competences and maintaining student interest and motivation on the subject.This paper shows how instructors can develop simple experiences to make a real change in the way they teach in order to adapt to the requirements of today's educational environment.We demonstrate how teachers can use a simple board game to help students test and learn about robot programming concepts, developing motivating sessions which will result in meaningful learning.Through this activity, the foundation is built to acquire basic knowledge related to robot programming and related processes in a structured way.Within this study, we develop the experience and the instructions to guarantee the correct application and effectiveness in the class session.Results on the outcome of the activity are encouraging: students were able to relate aspects covered in subsequent sessions to the situations and knowledge acquired through the activity.Furthermore, students were able to recall the basic principles and concepts experienced during the activity.
PRESERVASI  SEBAGAI UPAYA UNTUK MENJAGA KELESTARIAN DAN MEMPERPANJANG USIA ARSIP STATIS KONVENSIONAL

Observation or preservation of archives is a process and effort in protecting the physical archive against damage or destructive elements and restoration / resvarasi damaged part of the archive, which is caused by factors from the (intrinsic) archive itself as well as factors from the outside (extrinsic) physical archive itself. The importance of preservation of archives in each institution is for the institution’s long-term interests. Because the archive preservation program will be able to avoid more severe damage, loss of material and information. The purpose of preservation in the archive itself is to maintain its sustainability and extend the age of its use. So that it can survive and be more broadly useful, For this reason there is an effort to divert the provision of archives such as efforts to digitize archives so that they remain stored in electronic form to save space and maintain them, because many archival conditions are not feasible but have very important values. Keywords: preservation, intrinsic, extrinsic, digitizing archives
LibGuides: Finding Textbooks: Other Free Resources

A resource to help students explore their options when finding, renting, and buying textbooks.
Tensor Decomposition for Concept Recognition

Author(s): Wahba, Ahmed | Advisor(s): Wang, Li-C. | Abstract: The yield optimization data analytic process is an iterative search process. Each iterationcomprises of two main steps: data preparation, and result evaluation. In this thesis, wefocus on the result evaluation step, where the analyst evaluates the result from runninga certain analytic task looking for some specic concept. For example this concept canbe in the form a pattern formed by the failing dies on a wafer.A wafer pattern may hint a particular issue in the production by itself or guide theanalysis into a certain direction. In this thesis, we show how Generative AdversarialNetworks (GANs) can be used to build concept recognizers, we present the architecturechosen for the convolutional neural networks, we show how the network is trained, andwe show how the discriminator network can be used for concept recognition.However, the main focus of this work is on rules of Tensor decomposition in theautomated concept recognition ow. We introduce a novel concept recognition approachbased on Tucker decomposition. Tensor-based concept recognizers are combined withGANs-based recognizers to prevent escapes, also known as adversarial examples.In this work, we are concerned with two main aspects: (1) automation of the conceptrecognition process, and (2) ensuring robustness. Two tensor methods are introduced toaddress both aspects.The rst tensor method is based on clustering and is used to automatically extractconcepts that might be of interest to the analyst as well as choose the set of wafers to beused in training for each concept.Our second tensor method is to ensure robustness of the GANs-based recognizersby introducing the containment check to continuously check for GANs performance andreport to the expert if a problem occurs.We also address other challenges, such as automating the training process for GANs.We also introduce a collaboration view where the machine learning expert is assumedto be a separate entity. Hence he is not allowed to have access to protected informationsuch as yield. we present a tensor-based transformation for training wafers such that theprotected information is hidden.Our automated and robust software is applied to two high reliability automotiveproducts, with 8300 and 7052 wafers respectively.
Dynamic Control of CPU Cap Allocations in Stream Processing and Data-Flow Platforms

This paper focuses on Timely dataflow programming model for processing streams of data. We propose a technique to define CPU resource allocation (i.e., CPU capping) with the goal to improve response time latency in such type of applications with different quality of service (QoS) level, as they are concurrently running in a shared multi-core computing system with unknown and volatile demand. The proposed solution predicts the expected performance of the underlying platform using an online approach based on queuing theory and adjusts the corrections required in CPU allocation to achieve the most optimized performance. The experimental results confirms that measured performance of the proposed model is highly accurate while it takes into account the percentiles on the QoS metrics. The theoretical model used for elastic allocation of CPU share in the target platform takes advantage of design principals in model predictive control theory and dynamic programming to solve an optimization problem. While the prediction module in the proposed algorithm tries to predict the temporal changes in the arrival rate of each data flow, the optimization module uses a system model to estimate the interference among collocated applications by continuously monitoring the available CPU utilization in individual nodes along with the number of outstanding messages in every intermediate buffer of all TDF applications. The optimization module eventually performs a cost-benefit analysis to mitigate the total amount of QoS violation incidents by assigning the limited CPU shares among collocated applications. The proposed algorithm is robust (i.e., its worst-case output is guaranteed for arbitrarily volatile incoming demand coming from different data streams), and if the demand volatility is not large, the output is optimal, too. Its implementation is done using the TDF framework in Rust for distributed and shared memory architectures. The experimental results show that the proposed algorithm reduces the average and p99 latency of delay-sensitive applications by 21% and 31.8%, respectively, while can reduce the amount of QoS violation incidents by 98% on average.
Learning Natural Language Interfaces using Deep Neural Networks

Author(s): GUR, IZZEDDIN | Advisor(s): Yan, Xifeng | Abstract: Automating user tasks with natural language utterances, such as answering questionsover Wikipedia or booking flight tickets on the Web, is a key component in designingintelligent systems. Natural language is usually preferred as a unified interface for thesesystems and requires no domain expertise for users; however, understanding wide range ofdiverse inputs and resolving errors that occur during this process are still open challengesand the topics of this thesis.Traditional machine learning systems for natural language interfaces usually requirelarge-scale labeled datasets with handcrafted rules to train and evaluate the performancesof the respective models. Firstly, the handcrafted design constrains the scope to a limitedset of domains and prevents the adaptation to new tasks. Additionally, large-scale labeleddata collection is generally domain dependent, costly, and time consuming. These systemsfurther assume that the underlying database, such as Freebase, is accessible and can bequeried indefinitely which is prohibitive when learning from constrained user interfaces,such as Web pages. Last but not least, current systems focus on training offline in aclosed loop where users are excluded from the system inference process. They lack thecapabilities to continuously learn from users.In this thesis, we address the drawbacks of the existing systems and propose dataefficient and user-centric solutions. We classify the natural language inference problembased on two different perspectives: Accessiblity of the system functions – unconstrainedor constrained user interfaces, and nature of user involvement during inference – non-interactive or interactive user interfaces. We first develop neural network based systemsfor non-interactive and unconstrained users interfaces with different data types (i.e. structured and unstructured). The system is trained to learn a continuous representation ofuser utterance, generate and rank candidate answers from underlying database usingthis representation. We augment these systems with an extractive candidate refinementframework by integrating task-oriented human-machine dialogues. Our system is able tounderstand, point, and refine the error in candidates by asking users validation questionsand offering alternatives. We also address the limitations of unconstrained user interfaces and propose reinforcement learning methods to develop policies that are capable oflearning from more constrained web interfaces. The policies are trained on a variety ofweb pages, such as flight booking and social media interaction, with task-based rewardsignals and no human supervision. We test the performance of our models with simulated as well as real users. Empirical results show that the proposed models are able tolearn from limited supervised data and have successful dialogues with users. We observeimprovements in answer prediction accuracy, task success rate, and real user ratings.
LibGuides: UBorrow Guide: Most Common UBorrow Workflow errors

This guide is the beginnings of comprehensive information about UBorrow and the AlephILL client.
Study on application of entropy averaging method in airborne gamma-ray spectrum for local disturbance elimination

Abstract This paper briefly describes the basic principle and the entropy averaging method for local disturbance elimination in airborne gamma-ray spectrum. This method was employed to process t boundary of different lithologies and the identification of metal ore points. The results showed that applying the entropy averaging method to eliminate the interference of the airborne gamma-ray spectrum data, and selecting a larger window width, achieve more significant results compared with the ground gamma ray spectrum.
Improving Students’ Grammatical Skills Using Pictures And Images Learning Media At SMA N 1 Banyudono in 2019/2020 Academic Year

Nandi Prasetio Aditomo/ A320140270. Improving Students’ Grammatical Skills Using Pictures And Images Learning Media at SMA N 1 Banyudono in 2019/2020 Academic Year. Research Paper. School of Teacher Training and Education. Muhammadiyah University of Surakarta. November, 2019.This study aims to examine how a teacher uses pictures and images as learning media to improve grammatical skills and to find out how pictures and images as learning media can improve the students’ grammatical skills at SMA N 1 Banyudono. This research is a qualitative descriptive study. The subjects in this study were two 10th grade classes, namely MIPA 1 and IPS participated by 3 female and 3 male students at SMA N 1 Banyudono. The data were collected from observation, interview and document review. The results showed that for the implementation of pictures and images the teacher used picture for specific teaching purposes, used in picture, avoided excessive words which could make creative statements. The students’ responses to the implementation of pictures and images on students’ grammatical skills at SMA N 1 Banyudono were they were interested in the media, easily understood the materials, and enjoyed the activities in the implementation of using pictures and images as learning media.
Designing Songs for Teaching and Learning English: A Literature Review

This paper presents the theoretical backgrounds covering the theories of material design, which is useful for understanding of the effective material and the steps to produce it, and mentioning the theories of learning, which include foci on the language, the learners, and learning process.The paper aslo addresses a relationship among factors that contribute to teaching and learning process by using songs as English language teaching material.The authors review the benefits of songs and music under the lights of the cognitive, linguistic, and pedagogical levels through the material design model suggested by Hutchinson and Waters (1987).This paper contributes to the understanding of designing songs as a tool for teaching and learning English.
Eternal Bibliotheca : Library as a multigallery / Faculty of Art & Design, UiTM Puncak Alam

Library is actually where all the input and output are produced. All the knowledge that people need have in a library whether for children, teenagers, college students, adults or senior citizen. Unfortunately, nowadays there are no more people like to go to the library.
Pitch-shifting algorithm design and applications in music

Pitch-shifting lowers or increases the pitch of an audio recording. This technique hasbeen used in recording studios since the 1960s, many Beatles tracks being producedusing analog pitch-shifting e ...
Cybercrime in the IoT Era

Durante los ultimos anos, el desarrollo al que se ha visto sometido la tecnologia ha crecido exponencialmente. Cualquier tipo de dispositivo con acceso a Internet puede comunicarse con otros dispositivos. Estamos viviendo en un mundo hiperconectado que facilita el intercambio de servicios e informacion, pero tambien implica algunos riesgos. Organizaciones criminales tienen como objetivo atacar a estos equipos, los cuales muchos de ellos contienen capacidad de monitorizacion y videovigilancia, para atacar a otras empresas o simplemente con un proposito economico recogiendo, comprando y vendiendo informacion. Es un area de estudio novedosa y por tanto hay mucho trabajo de investigacion todavia por hacer. En el mercado existen una gran cantidad de fabricantes que intentan sacar los dispositivos lo antes posible, dejando de lado la aplicacion de controles de seguridad. Ademas, la heterogeneidad de estos dispositivos junto con la limitacion de recursos los hace mas vulnerables ante cualquier ataque. En este trabajo se analiza el estado actual de la seguridad los dispositivos que forman parte del Internet of Things, relacionandolo con actividades criminales o no eticas como son el espionaje o cyberstalking. Estos dispositivos son capaces de recoger y generar una gran cantidad de informacion. El desarrollo de una arquitectura secura para proteger confidencialidad e integridad es requerida en el proceso del flujo de datos. En este trabajo se ha realizado un analisis del proceso de comunicacion llevado a cabo por un Sistema de Reporte de Crimenes Nacional de la Policia Nacional. Ademas, se propone una serie de controles de seguridad para proteger el flujo de datos siguiendo un framework de controles de seguridad.---ABSTRACT---Technology has experienced a fast improvement during the last decade. Any type of device with network access capabilities can communicate with each other. A hyperconnected world is emerging easing the exchange of services and information, but also bringing some backwards. Criminal organization are targeting Internet of Thing devices, equipped with monitoring and surveillance capabilities, to perform attacks against other organizations or to collect, buy and sell information. There is still a lot of research to do in this area. The develop of new devices with different manufacturers competing to put any device as soon as possible in the market is lacking required security controls. In addition to the heterogeneity and limited resources, it is making them more vulnerable. Internet of Things are capable of collect and generate a high amount of data. Development of secure architecture that protect confidentiality and integrity is required through the data flow process. The communication process from a National Crime Reporting System for a Latin American country’s National Police is assessed. Moreover. security controls to protect the data and the information system are proposed following the recommendations provided by a security framework.
Research Guides: Scholarly Communication for Librarians: Home

This guide contains information and resources shared at the Scholarly Communication Boot Camp sessions.
Hand and Face Segmentation with Deep Convolutional Networks using Limited Labelled Data

Segmentation is a crucial step for many classification problems. There are many researchers that approach the problem using classical computer vision methods, recently deep learning approaches have been used more frequently in different domains. In this paper, we propose two segmentation networks that mark face and hands from static images for sign language recognition using only a few training data. Our networks have encoder-decoder structure that contains convolutional, max pooling and upsampling layers; the first one is a U-Net based network and the second one is a VGG-based network. We evaluate our models on two sign language datasets; the first one is our Ankara University Turkish Sign Language dataset (AU-TSL) and the second one is Montalbano Italian gesture dataset. Datasets contain background and illumination variations. Also, they are recorded with different signers. We train our models using only 400 images that we randomly selected from video frames. Our experiments show that even when we reduce the training data in half, we can still obtain satisfactory results. Proposed methods have achieved more than 98% precision using 400 frames with both datasets. Our code is available at https://github.com/au-cvml-lab/Hands-and-Face-Segmentation-With-Limited-Data.
Guides: Video Resources: Logging Into Streaming

RRC Library offers thousands of educational videos, documentaries and feature films. Learn more about our video collection here. Explains the two types of login screens that appear with streaming videos
The Implementation Of Team-Games Tournament (TGT) To Enhance Students’ Motivation In Learning English (A Classroom Action Research at the Eighth Grade Class of SMP N 16 Surakarta the 2017/2018 Academic Year)

The objective of this research is to describe the implementation of Team-Games Tournament in learning English in SMP N 16 Surakarta. The kind of reasearch is a mix to collecting data of the qualitative and quantitative. In this research the writer involving students grade of VIII. The researcher collect the data by means of system learning process in class. The result of the research indicated that the implementation of engineering the learning process in class can increase to interest the students in learning English process in SMP N 16 Surakarta; 1) Students are more active in learning process, 2) The Train students indepencency in a group discussion, 3) Material can easy to be received by using Team-Games Tournament.
Detecting domain-specific information needs in conversational search dialogues

As conversational search becomes more pervasive, it becomesincreasingly important to understand the user's underlying needs when they converse with such systems in diverse contexts. We report on an insitu experiment to collect conversationally described information needs in a home cooking scenario. A human experimenter acted as the perfect conversational search system. Based on the transcription of the utterances, we present a preliminary coding scheme comprising 27 categories to annotate the information needs of users. Moreover, we use these annotations to perform prediction experiments based on random forest classification to establish the feasibility of predicting the information need from the raw utterances. We find that a reasonable accuracy in predicting information need categories is possible and evidence the importance of stopwords in the classfication task.
Hur påverkas systemutvecklingsprocessen av implementeringen av GDPR

On May 25th 2018 a new general data protection regulation called GDPR took effect. The regulation contains rules regarding usage, storage and handling of data from individuals resident within The E ...
Synthesis of High-Performance Window Functions Using Minimization of Difference Between Its Waveform and Spectrum

A method for synthesis of high-performance window functions is under consideration. The method is based on the minimization of difference between the temporal and spectral shapes of window function. Such window may be represented by a set of cosine components. This method includes the cases with both odd and even number of cosine half-periods in window duration. A number of window functions have been calculated using the method under consideration. Depending on the number of cosine components the maximum relative difference between the waveform and spectrum of window varies from 0.17 to $$1.3\times 10^{-12}$$. Main parameters of synthesized windows have been calculated showing high performance of such windows. Maximum sidelobe level decreases from $$-23$$ dB to $$-275$$ dB while overlap correlation for 0.5 overlapping decreases from 32% to 0.01%. The waveforms, spectra and their differences are illustrated for the windows with the number of cosine components varying from 2 to 21.