Reviewerly/exHarmony huggingface dataset:
Guides: Access Services: Stack Management

Guide to Access Services which include activities related to Circulation, Interlibrary Loan, Reserves, Stack Management and other library public services. Stack Management
Evaluation of the Encoding Accuracy of the PQ based HDR Content Delivery Formats

Recently, new formats like HDR10 and Dolby Vision have emerged and become the popular choice for delivering HDR content. HDR10 uses a bit-depth of 10 to quantize the data after applying a non-linear transformation operator known as the perceptual quantizer (PQ). It has been known that at this precision of just 10 bits, banding artifacts can be observed in the lower luminance regions. However, in general, HDR10 has good visual quality on HDR displays and is widely recognized by the display makers and content developers. This paper evaluates the accuracy of the content encoded in HDR10 format, especially when the dynamic range is high. It is observed that the accuracy of HDR10 is low when there are extremely dark and bright regions in the image. Therefore while it is the right choice for HDR display, other formats should be used to preserve the original dynamic range for applications such as machine-based analysis and tone-mapping for LDR display. We also propose a dual-layer codec using companding, which resolves the known drawbacks of two-layer backward compatible formats. The proposed format does not require metadata for the reconstruction of the HDR image, and therefore its decoding function has a low computational cost. Moreover, its encoding accuracy is higher than HDR10 and the existing state of the art popular formats like JPEG-XT and HDR-MPEG.
Bypassing the Random Input Mixing in Mixup

Mixup and its variants have promoted a surge of interest due to their capability of boosting the accuracy of deep models. For a random sample pair, such approaches generate a set of synthetic samples through interpolating both the inputs and their corresponding one-hot labels. Current methods either interpolate random features from an input pair or learn to mix salient features from the pair. Nevertheless, the former methods can create misleading synthetic samples or remove important features from the given inputs, and the latter strategies incur significant computation cost for selecting descriptive input regions. In this paper, we show that the effort needed for the input mixing can be bypassed. For a given sample pair, averaging the features from the two inputs and then assigning it with a set of soft labels can effectively regularize the training. We empirically show that the proposed approach performs on par with state-of-the-art strategies in terms of predictive accuracy.
4E cognition and musical instruments: Modes of Access

In this presentation, I will discuss how the role of instruments in musical practice can be under- stood, through a theoretical framework drawing upon the emerging paradigm of 4E cognition. Ani- mating the discussion are two studies, an interview study and a co-operative inquiry, together forming the empirical basis of my PhD-project. The case here is the simple-system flute, an in- strument that was primarily developed as a product of 19th century Western art music, and later became established in other genres and traditions. The theoretical centerpiece in this discussion is the concept of affordances (Gibson, 1979) through which the dichotomy of subject and object is collapsed in order to promote a focus on the reciprocal nature of perception, as a phenomenon depending on both. Affordances, already with a history of use within various forms of music research, is today in direct dialogue with a broa- der range of philosophical traditions and contributions. Although Gibson’s idea may seem intuitive and even simple at first, its implications for our understanding of human behavior and experience are profound. These implications are even more elaborate when taken as a core part of 4EC. Of particular interest in the discussion of the present paper are the extended (Clark & Chalmers, 1998) and the enacted (Noe, 2004) dimensions of cognition. Research in/through/about music can serve a double purpose in the theoretical and philosophical work that lies ahead, since such contributions may (i) highlight and apply these theoretical frameworks to a practice which ope- rates simultaneously in multiple time scales, as well as (ii) deepen our understanding of these relationships, thus shaping new artistic and pedagogical ideas. (Less)
LibGuides: Intro to Database Research for Elementary Students: Home

An overview of digital search techniques and an introduction to pertinent databases for upper elementary students
Managing language learning data in mobile apps

On the journey of learning a language we are exposed to countless words that are easily forgotten and subsequently difficult to find again. This study investigates how to design a personal data man ...
Robust Learning for Congestion-Aware Routing

We consider the problem of routing users through a network with unknown congestion functions over an infinite time horizon. On each time step t, the algorithm receives a routing request and must select a valid path. For each edge e in the selected path, the algorithm incurs a cost cet=fe(xet)+ηet, where xet is the flow on edge e at time t, fe is the congestion function, and ηet is a noise sample drawn from an unknown distribution. The algorithm observes cet, and can use this observation in future routing decisions. The routing requests are supplied adversarially. We present an algorithm with cumulative regret O~(|E|t2/3), where the regret on each time step is defined as the difference between the total cost incurred by our chosen path and the minimum cost among all valid paths. Our algorithm has space complexity O(|E|t1/3) and time complexity O(|E|log⁡t). We also validate our algorithm empirically using graphs from New York City road networks.
Intrinsically Guided Exploration in Meta Reinforcement Learning

Deep reinforcement learning algorithms generally require large amounts of data to solve a single task. Meta reinforcement learning (meta-RL) agents learn to adapt to novel unseen tasks with high sample efficiency by extracting useful prior knowledge from previous tasks. Despite recent progress, efficient exploration in meta-training and adaptation remains a key challenge in sparse-reward meta-RL tasks. We propose a novel off-policy meta-RL algorithm to address this problem, which disentangles exploration and exploitation policies and learns intrinsically motivated exploration behaviors. We design novel intrinsic rewards derived from information gain to reduce task uncertainty and encourage the explorer to collect informative trajectories about the current task. Experimental evaluation shows that our algorithm achieves state-of-the-art performance on various sparse-reward MuJoCo locomotion tasks and more complex Meta-World tasks.
Research Guides: Linking to articles, eBooks, and streaming media: OneSearch Links

Information about finding permanent links to articles, eBooks, and streaming media in the library's licensed databases to add to your course in Blackboard or Canvas.
Mobile applications: Incorporation into teaching processes in times of COVID-19

Since the pandemic declared by the World Health Organization in March 2020 as a result of the COVID-19 virus, applications that allows remote communication, such as WhatsApp, have risen their use around the world Around ninety percent of the world’s classrooms closed their doors, forcing the use of distance learning strategies to carry out the educational process The objective of this work is to reflect on the use of instant messaging applications for the development of the educational process in the midst of the pandemic The research corresponds to a theoretical documentary and/ or bibliographic review The results showed that these platforms allow the mobility of teaching, the separation of the educational process from an entrenched space, as well as the use of new forms of language linked to the technological culture of mobile devices It is concluded that the use of technological applications such as WhatsApp in the learning process offers several advantages, but at the same time, they show deep technological gaps, both in access to technology and in the knowledge for its use © 2021, Universidad del Zulia All rights reserved
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity

The first deep RL algorithm, DQN, was limited by the overestimation bias of the learned Q-function. Subsequent algorithms proposed techniques to reduce this problem, without fully eliminating it. Recently, the Maxmin and Ensemble Q-learning algorithms used the different estimates provided by ensembles of learners to reduce the bias. Unfortunately, these learners can converge to the same point in the parametric or representation space, falling back to the classic single neural network DQN. In this paper, we describe a regularization technique to maximize diversity in the representation space in these algorithms. We propose and compare five regularization functions inspired from economics theory and consensus optimization. We show that the resulting approach significantly outperforms the Maxmin and Ensemble Q-learning algorithms as well as non-ensemble baselines.
Cataloging Children’s Materials: Issues and Solutions

Library catalogs remain challenging for children to use, especially because children have difficulty with multi-step processes, have less semantic and technical knowledge, and often search differently from adults. Child-friendly catalogs should have clear, simple protocols and visual guides that are standardized yet include flexible options for differentiated manipulation. Materials should be described accurately and in ways that connect meaningfully to children. More fundamentally, cataloging children’s materials needs to be done in light of children as potential users and limitations of the integrated library management system itself. Getting children’s feedback in the process can optimize the results.
THE USE OF SCANNING TECHNIQUE TO IMPROVE READING COMPREHENSION IN NARRATIVE TEXT OF GRADE VIII STUDENTS AT SMP N 3 TOLITOLI

The objective of the research is to prove whether the use of scanning is effective to improve students’ reading comprehension in narrative text. This pre-experimental study uses 35 students as a sample. They are grade VIII A of SMP N 3 Tolitoli. The procedure of this research consists of pretest, treatment, and posttest. The result of the analysis shows that scanning technique contributes greatly to the students’ comprehension in narrative text. It can be seen by the difference of the mean score between pre-test and post-test. The mean score of pre-test is 67.49 while in the post-test improved up to 79.03. The data of this research are analyzed by using SPSS Program version 16.0. Then the data of both tests are analyzed by using T-test, and the result shows that the T-test value 11.81 is high than T-table value 2.032. It can be inferred that scanning technique is very effective to improve students’ reading comprehension in narrative text. Keywords: Improving, reading comprehension, narrative text, scanning technique.
Development and formation of research activities in primary school students in russian language classes

The article is devoted to the method of research activity as one of the most effective pedagogical technologies. The article discusses the concept of research methods for primary school students, its tasks, and the principles of organization that allow students to develop their own skills and apply them to develop creative and cognitive abilities in Russian language lessons.
FastHand: Fast Hand Pose Estimation From A Monocular Camera.

Hand gesture recognition constitutes the initial step in most methods related to human-robot interaction. There are two key challenges in this task. The first one corresponds to the difficulty of achieving stable and accurate hand landmark predictions in real-world scenarios, while the second to the decreased time of forward inference. In this paper, we propose a fast and accurate framework for hand pose estimation, dubbed as FastHand. Using a lightweight encoder-decoder network architecture, we achieve to fulfil the requirements of practical applications running on embedded devices. The encoder consists of deep layers with a small number of parameters, while the decoder makes use of spatial location information to obtain more accurate results. The evaluation took place on two publicly available datasets demonstrating the improved performance of the proposed pipeline compared to other state-of-the-art approaches. FastHand offers high accuracy scores while reaching a speed of 25 frames per second on an NVIDIA Jetson TX2 graphics processing unit.
ECOLOGICAL CONSCIOUSNESS AND THINKING AS THE BASIS OF ECOLOGICAL COMPETENCE IN STUDENTS

the article is devoted to ecological consciousness and thinking, which is currently considered necessary for conducting a healthy lifestyle in an unstable nature and environment, ensuring the health and safe future of a person. In the world of seriousness, a necessary condition is the development of relations between Nature-Man and society on the basis of conscious thinking, the development of environmental competence of specialists in this field.
Research Guides: Authority: Resources

This guide will help you better understand authority in the context of research and information and provide you with methods of evaluating an information source to determine its authority.
Machine Learning-Future of Quality Assurance

Machine learning models represent a data framework that takes data from a particular set and makes assumptions about the new observation through learning from the data. Machine learning techniques are developed to operate on the current data set and predict the existing trends. Machine learning uses neural networks for quality inspection. Neural networks are made out of a set of structured algorithms modified as per the process of learning. To create outcomes and then compare them with set outcomes, the learning process needs data inputs. Additionally, to produce fast and reliable performance, programs use technology to extract data trends and interpret the overwhelming data volume. Machine learning uses AI technology to provide programs to understand dynamically without specific scripting or human interaction. Experience-based applications and test automation can boost and continuously read information, check up with it, learn from the findings, and enhance the detection methods. Concerning the potential of machine learning testing and hence smart Quality assurance, there is certainly the opportunity of becoming the next important hit, and everybody should keep a close eye on future technologies. Many software development organizations are of the view that they do not test effectively. They know that the influence of quality flaws is important, and they spend significantly on quality assurance, but they don't get the outcomes they expect. This is not attributable to a shortage of creativity or hard work; instead, software testing assistance technology is not successful. It has poorly served the market. Machine learning (ML), which many businesses have disrupted and enhanced, is now beginning to find its entry into application development. Heads are spinning, and for an excellent purpose: never again can the market be doing the same. Although machine learning continues to develop and expand, it is increasingly used by the software industry, and its effect is beginning to dramatically alter the way software development can be conducted as the technology progresses. This research paper will review machine learning development and investigate how machine learning techniques are changing the industry of quality assurance radically.
Children’s Face Recognition Based on Convolutional Neural Network

Abstract Nowadays, the application of deep learning is more and more widely, and children’s face recognition has made great progress. However, in the existing framework of children’s face recognition based on deep learning, many tasks (including face recognition, authentication and attribute classification) are relatively independent, and its overall algorithm is inefficient and time-consuming. In order to solve this problem, this paper proposes a deep convolution network based on keras framework. Taking children’s face recognition, authentication and attributes as the parameters of the network, the whole deep convolution network can be trained step by step. The network can complete three tasks at the same time without further steps. The network model is trained and learned on the training samples. It is an efficient neural network model. The network structure scheme of this method can improve the classification accuracy It has a certain positive impact. The purpose of this paper is to construct a network model structure, which can make the children face recognition effect better and still obtain good performance under the limited data support.
Color figures from the Open Handbook of Linguistic Data Management

These images are from the chapter by Hiroyuki Akama, Managing, sharing and reusing fMRI data in computational neurolinguistics in the Open Handbook of Linguistic Data Management, edited by Andrea L. Berez-Kroeker, Bradley McDonnell, Eve Koller and Lauren B. Collister. Cambrige, MA: MIT Press Open.
Transformational Leadership and its Impact on Exploratory and Exploitative Innovation: Field Study of Jordanian Detergent Manufacturing Companies

Abstract The study objective to examine the impact of the transformational leadership on the explorative innovation and exploitative innovation. The study adopted three dimensions for the transformational leadership namely: Individual considerations, inspirational impact and the intellectual stimulation whilst employee’s two innovation dimensions were adopted for innovation which the exploratory innovation and exploitative innovation. The study was conducted on a sample consisting of (167) employees in (10) Jordanian Detergents Manufacturing Companies, from whom the necessary particulars were collected for the study through a questionnaire method which was developed by the researchers subsequent to review of numerous previous studies. The study used the analytical descriptive method and using the SPSS statistical program. The study concluded the existence of high levels for utilizing the transformational leadership dimensions by the leaders in these companies in addition to the existence of high and balanced levels of attention in the exploratory and exploitative innovation together with the existence of a higher proportionate attention in favor of the exploratory innovation. The study recommended of the necessity of continued and balanced attention of the two types of exploratory and exploitative innovation. Key Words: Transformational Leadership, Exploratory Innovation, Exploitative Innovation, Jordanian Detergents Manufacturing Companies
LibGuides: Marketing Research And Planning For Your Business: MORE HELPFUL RESOURCES

A guide introducing you to the basics of marketing and recommending which databases to use for effective research results.
LibGuides: ART 122: Home

This guide is to assist students with navigating the library databases and other resources.
Understanding Adversarial Attacks on Autoencoders

Adversarial vulnerability is a fundamental limitation of deep neural networks which remains poorly understood. Recent work suggests that adversarial attacks on deep neural network classifiers exploit the fact that non-robust models rely on superficial statistics to form predictions. While such features are semantically meaningless, they are strongly predictive of the input’s label, allowing non-robust networks to achieve good generalization on unperturbed test inputs. However, this hypothesis fails to explain why autoencoders are also vulnerable to adversarial attacks, despite achieving low reconstruction error on clean inputs. We show that training an autoencoder on adversarial input-target pairs leads to low reconstruction error on the standard test set, suggesting that adversarial attacks on autoencoders are predictive. In this work, we study the predictive power of adversarial examples on autoencoders through the lens of compressive sensing. We characterize the relationship between adversarial perturbations and target inputs and reveal that training autoencoders on adversarial input-target pairs is a form of knowledge distillation, achieved by learning to attenuate structured noise.
Learning the Connections in Direct Feedback Alignment

Feedback alignment was proposed to address the biological implausibility of the backpropagation algorithm which requires the transportation of the weight transpose during the backwards pass. The idea was later built upon with the proposal of direct feedback alignment (DFA), which propagates the error directly from the output layer to each hidden layer in the backward path using a fixed random weight matrix. This contribution was significant because it allowed for the parallelization of the backwards pass by the use of these feedback connections. However, just as feedback alignment, DFA does not perform well in deep convolutional networks. We propose to learn the backward weight matrices in DFA, adopting the methodology of Kolen-Pollack learning, to improve training and inference accuracy in deep convolutional neural networks by updating the direct feedback connections such that they come to estimate the forward path. The proposed method improves the accuracy of learning by direct feedback connections and reduces the gap between parallel training to serial training by means of backpropagation.
China Word: Vocabulary Quiz Game for Promoting Chinese Vocabulary Memory among 10th Grade Students

The main purpose of this research is to develop a mobile application game, China Word, to promote the memorization of Chinese vocabulary for 10th grade students.The comparison between students' scores before and after learning with the game is used to determine success or failure in this study.The sample group was randomly collected from 10th grade students at a volunteer school in Suphanburi Province, Thailand.Sixty students participated in this study.Sixty students were separated in two groups, first thirty students group was the control group and second thirty students group was the experimental group.The control learned Chinese with lecture while the experimental group learned Chinese with the China Word game.In addition, an achievement test and a satisfaction questionnaire were deployed in this experiment as tools to measure students' accomplishments.Then, simple statistics, such as averages, percentages, standard deviations, and t-test independence, were calculated.The result indicates that the experimental group attains higher learning achievement than the control group at a 95% confidence level.
LibGuides: Getting Started with Library Research: Database Tutorials

This LibGuide provides a comprehensive set of 'How-To' information for the MSUB Library. Review These Tutorials for Enhanced Database Searching
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness

Convolutional Neural Networks (CNNs) are vulnerable to unseen noise on input images at the test time, and thus improving the robustness is crucial. In this paper, we propose DJMix, a data augmentation method to improve the robustness by mixing each training image and its discretized one. Discretization is done in an unsupervised manner by an autoencoder, and the mixed images are nearly impossible to distinguish from the original images. Therefore, DJMix can easily be adapted to various image recognition tasks. We verify the effectiveness of our method using classification, semantic segmentation, and detection using clean and noisy test images.
Study Guides: Information Technology: Home

This guide contains resources and guidance to help students studying Computer Science and Information Systems.