Reviewerly/exHarmony huggingface dataset:
Applying Slack to Help Teach Computer Science and Computer Engineering Courses

Abstract Communication is one of keys for classroom teaching and project management. To help provide better communication, three things are desirable: (1) Prompt communication. For example, in a class exercise, if a student comes up with a good solution for coding implementation, it is desired that the student can share the example with the instructor and other students in a simple and quick way. (2) Group communication. Course projects and senior design projects require an easy, prompt, and convenient method to communicate beyond face-to-face meetings. (3) Forum. Similar to well-known stack overflow, students want to have an online forum to get their questions answered, like “virtual office hours.” We, as instructors to computer science and computer engineering courses, apply an online tool, called Slack, to provide better communication to our students at XXX University. Slack enables a convenient way for instantly messaging, centralizing notifications, discussing solutions, sharing information, and collaborating work. Moreover, the free version of Slack allows ten thousand searchable messages, ten applications and integrations, and one-to-one video calls, which is sufficient for classroom teaching, course projects, and senior design projects. So far, there are more than ten million daily active users for Slack, and 65 of the Fortune 100 companies use Slack. We apply Slack to teach two computer science courses, two computer engineering courses, and two senior design projects during Fall 2019. We explore Slack in the following five perspectives: (1) Sharing information. In each course, we created a Slack workspace and used a dedicated channel to provide class announcements, important class notes, and examples from students during class exercises. (2) Answering questions. Similar to forum, students can post their questions in Slack and get answers from the instructor, the teaching assistant, or other students. (3) Collaborating projects. For course projects or senior design projects, a channel is used for project discussions, progress reports, and file shares. For example, in one course, 27 students use Slack to share their “weekly scrum,” instead of using class time. (4) Providing examples. In the Web Design course, we use Slack as an excellent example to show concepts or implementations, such as HTTP, front-end pages, and WebSocket. (5) Promoting helping each other. Slack makes conversations between students more visible to instructors, so that we can encourage students to help each other and recognize students who contribute more to the courses. To measure the effectiveness of applying Slack in our teaching, we plan to conduct several surveys at the end of Fall 2019 semester. We will ask students their opinions about five perspectives in the above and get comments or feedback on the impact of Slack on their learning. We believe that Slack serves as an excellent online tool for improving the communication between students and instructors and among students in a project.
Challenges and Countermeasures of artificial intelligence in classroom teaching

With the application of artificial intelligence in the field of education gradually from imagination to reality, classroom teaching will usher in the reform and challenge of the coexistence of human teachers and artificial intelligence.Artificial intelligence is helpful to expand classroom teaching resources, dynamically understand students' learning situation, provide personalized teaching conditions and optimize classroom teaching relationship.But at the same time, artificial intelligence also brings challenges to classroom teaching, such as how to cooperate with "double teacher" teaching, how to effectively regulate and integrate teaching content, how to avoid the tendency of technicalism in teaching evaluation, and how to avoid weakening the relationship between teachers and students.In the face of this situation, we should correctly understand the value and role of artificial intelligence, reasonably standardize the teaching content, realize the diversification of teaching evaluation subjects, and give full play to the "educating" role of teachers.
Adversarial Training using Contrastive Divergence

To protect the security of machine learning models against adversarial examples, adversarial training becomes the most popular and powerful strategy against various adversarial attacks by injecting adversarial examples into training data. However, it is time-consuming and requires high computation complexity to generate suitable adversarial examples for ensuring the robustness of models, which impedes the spread and application of adversarial training. In this work, we reformulate adversarial training as a combination of stationary distribution exploring, sampling, and training. Each updating of parameters of DNN is based on several transitions from the data samples as the initial states in a Hamiltonian system. Inspired by our new paradigm, we design a new generative method for adversarial training by using Contrastive Divergence (ATCD), which approaches the equilibrium distribution of adversarial examples with only few iterations by building from small modifications of the standard Contrastive Divergence (CD). Our adversarial training algorithm achieves much higher robustness than any other state-of-the-art adversarial training acceleration method on the ImageNet, CIFAR-10, and MNIST datasets and reaches a balance between performance and efficiency.
Implementation of “Outbound Green Class” Program as a Way to Increased English Language Ability and interest of reading activity for Children and Adolescents at Makassar City

Community Service Activities (PKM) about “Outbound Green Class” program was one of the newest programs that the author applied in her course place. She carried out this activity in collaboration with several her friends and who have same profession with her that was lecturer. Outbound Our Green Class program provided a brief description of the guidance process increased learning skills and interest to do reading activity with used the principle English was cheap and could be learned anywhere and anytime. In this community service (PKM) activity, was one of the fields of knowledge that would be specialized or studied. Children and adolescents who came from poor parents' economies were the second material to be specialized or studied in this community service activity (PKM) about tutoring using the principle English was cheap and could be learned anywhere and anytime. Some of the goals or functions of this community service activity (PKM) were in accordance with the principle English was cheap and could be learned anywhere and anytime, namely: the first purpose or function, to show the world, especially to the wider community, especially children and adolescents who speak English. was not difficult, and not as scary as a monster. The second purpose or function was to help children and adolescents who came from poor parents' economies to learn at no charge and help prepared themselves to implement the AFTA program, which provided a great opportunity for all Indonesian citizens to work. And the third purpose or function was to foster enthusiasm of learning for children and adolescents in Makassar city.
Comprehension The Significance Of Information Center Automation And E-Information Center

Information center autonetics, expressed in single term, is the use of pcs and usage of pc based itemand administrations in the exhibition of various information center activities and capacities inarrangement of different administrations and creation of yield items. There is an extraordinary effectof pcs and data innovation and its applications on the libraries because of which a cycle of incrediblechange is occurring in libraries. Current innovation's having a tendency to adjust profoundly the ideaof our general public and influence the predominant monetary, political and social worth andlibraries are likewise simultaneously. This paper endeavors to record the significance of informationcenter autonetics and e-information center
Home art therapy techniques as nursing care for older adults in contingency by covid-19

Objective: To present a brief description of home art therapy techniques as Nursing care for older adults in contingency due to COVID-19 Development: Faced with the pronouncement of the health measures for COVID-19, older adults as the highest risk group require strict adherence to the isolation recommendations, for which some art therapy techniques are proposed as Nursing care, among which drawings, mandalas, painting, collages and mask technique stand out;which can favor the mental health of the older adults at theirnomes, as well as favor cognitive stimulation, mobility and expression of emotions;thus, its benefits involve the physical, social, emotional and spiritual well-being of the older adults Final considerations: Plastic arts as therapeutic strategies can be incorporated by the nurse in home activities for the older adults, favoring cognitive functions, preventing cognitive deterioration, maintaining mobility, and improving emotional state © 2020, Universidade Federal do Parana 1 All rights reserved
Multi-task learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis

Deep learning models are traditionally trained purely in a data-driven approach; the information for the model training usually only comes from a single source of the training data. In this work, we investigate how to supply additional clinical knowledge that is associated with the training data. Our goal is to train deep learning models for breast cancer diagnosis using mammogram images. Along with the main classification task between clinically proven cancer vs negative/benign cases, we design two auxiliary tasks each capturing a form of additional knowledge to facilitate the main task. Specifically, one auxiliary task is to classify images according to the radiologist-made BI-RADS diagnosis scores and the other auxiliary task is to classify images in terms of the BI-RADS breast density categories. We customize a Multi-Task Learning model to jointly perform the three tasks (main task and two auxiliary tasks). We test four deep learning architectures: CBR–Tiny, ResNet18, GoogleNet, and DenseNet and we investigate the benefit of incorporating such knowledge over ImageNet pre-trained models and in the case of randomly initialized models. We run experiments on an internal dataset consisting of screening full field digital mammography images for a total of 1,380 images (341 cancer and 1,039 negative or benign). Our results show that, by adding clinical knowledge conveyed through the two auxiliary tasks to the training process, we can improve the performance of the target task of breast cancer diagnosis, thus highlighting the benefit of incorporating clinical knowledge into data-driven learning to enhance deep learning model training.
Bathroom Anomaly Detection System Using Temperature Sensor Array

The number of Japanese elderly people is increasing every year and the demand for life monitoring systems is also increased. Accidents in bathroom such as heat shock, slipping and falling become particularly serious in recent years. Therefore, this paper proposes an anomaly detection system in bathroom using a temperature sensor array and machine learning. The proposed system extracts the feature values of human region and detects various abnormal behaviors by One-Class SVM. In addition, the effect of improved One-Class SVM with virtual outliers is also verified. Some experiments to demonstrate the effectiveness of the proposed anomaly detection system are conducted and the results show that the high detection accuracy of at least about 90% can be realized in both the washing place and bathtub.
An Exploration of Computational Software in Organic Chemistry teaching

Organic chemistry is an important course for students majoring in chemical engineering.The knowledge such as molecular orbitals, reaction mechanism and stereochemistry, etc. are the key and the difficult points.The model demonstration or simple animation simulation in traditional teaching cannot reveal the chemical reaction process in essence and also cannot really solve poor spatial imagination of students.In recent years, with the emergence of more and more quantum chemistry software, computational chemistry has shown its strong application advantage in organic chemistry teaching.In this paper, Gaussian software is introduced to assist teaching.Introduction of computational chemistry can make students understand stereochemistry, reaction and molecular orbitals deeply; and also, a new way to learn organic chemistry is offered.
OT-LLP: Optimal Transport for Learning from Label Proportions

Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning. Existing deep learning based LLP methods pursue an end-to-end learning fashion and construct the loss using Kullback-Leibler divergence, which measures the difference between the prior and posterior class distributions in each bag. However, unconstrained optimization on this objective can hardly reach a solution in accordance with the given proportions at the bag level. In addition, concerning the probabilistic classifier, it probably results in high-entropy conditional class distributions at the instance level. These issues will further degrade the performance of instance-level classification. To address these problems, we propose to impose the exact proportions on the classifier with a constrained optimization, and firstly apply the optimal transport algorithm to solve LLP. With the entropic regularization, our formulation allows to solve a convex programming efficiently and further arrive at an integer solution that meets the proportion constraint strictly. More importantly, our framework is model-agnostic, and demonstrates compelling performance improvement in extensive experiments, when it is incorporated into other deep LLP models as a post-hoc stage.
An artificial intelligence-based method for judging the quality of spoken Chinese pronunciation

Chinese has increasingly become a global language. Compared with the popular international languages such as English, French, and Spanish, traditional classroom teaching methods are often used in the promotion of Chinese. In particular, oral pronunciation requires face-to-face guidance from teachers, which is not conducive to students’ self-study. In response to this problem, this paper proposes to use the most widely used deep convolutional neural network in artificial intelligence technology to judge the standard of the spoken pronunciation of non-Chinese students of native language, and according to the “Mandarin Test Grade Standard” jointly issued by the National Language and Writing Committee and other departments The Chinese phonetic standards in, automatically grade the correctness of students’ spoken pronunciation. The activation function of the deep convolutional neural network(DCNN) uses the ReLU function, the loss function uses the cross-entropy cost function, and the training algorithm for the DCNN is also given here. Practical experiments on 100 speech fragments sent by 10 students show that the proposed DCNN is feasible and effective.
7 Backpropagation Through Time Algorithm in Temperature Prediction

In deep learning, data is transmitted through a number of layers in the feedforward network between input and output layers. In a recurrent network, data may propagate through a layer several times. Backpropagation through time (BPTT) technique is used to train recurrent networks (RNN). The underlying idea of BPTT is to transform a recurrent network into an unfolded feedforward network (multilayer network) where conventional backpropagation learning is used for gradient calculation. Here, each layer of the unfolded network represents a time step. The objective of this study is to integrate concept of BPTT in the framework of fuzzy time series prediction. The model takes sequence of previous values as input (fuzzy inputs) to the different layer of the unfolded network and produces fuzzy output. Temperature dataset is used to evaluate the performance of the model and prediction accuracy of BPTT is better than that of backpropagation neural network model.
Learning Visual Representation from Human Interactions

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. For this study, we collect a dataset of human interactions capturing body part movements and gaze in their daily lives. Our experiments show that our self-supervised representation that encodes interaction and attention cues outperforms a visual-only state-of-the-art method MoCo (He et al.,2020), on a variety of target tasks: scene classification (semantic), action recognition (temporal), depth estimation (geometric), dynamics prediction (physics) and walkable surface estimation (affordance).
Flow R-CNN: Flow-Enhanced Object Detection

This work addresses the problem of multi-task object detection in an efficient, generic but at the same time simple way, following the recent and highly promising studies in the computer vision field, and more specifically the Region-based Convolutional Neural Network (R-CNN) approach. A flow-enhanced methodology for object detection is proposed, by adding a new branch to predict an object-level flow field. Following a scheme grounded on neuroscience, a pseudo-temporal motion stream is integrated in parallel to the classification, bounding box regression and segmentation mask prediction branches of Mask R-CNN. Extensive experiments and thorough comparative evaluation provide a detailed analysis of the problem at hand and demonstrate the added value of the involved object-level flow branch. The overall proposed approach achieves improved performance in the six currently broadest and most challenging publicly available semantic urban scene understanding datasets, surpassing the region-based baseline method.
Single Image Super-resolution Reconstruction Based on Enhanced Residual Network

Abstract High-resolution images present richer detailed information and have stronger information expression capabilities. The increase of the network depth does not guarantee that the reconstructed image has a higher quality, and may cause problems such as overfitting. So this article proposes an enhanced residual network, which can fully extract input low-resolution image features and reconstruct high-resolution images. This paper introduces a deconvolution operation based on the residual module to expand the size of input features, and the connection between different modules promotes feature fusion, obtains more high-frequency details from the input low-resolution image. The objective experimental results show that the proposed method has improved the indicators PSNR and SSIM. In terms of visual effects, it can reconstruct clearer and more detailed images.
Application of Game Teaching Method in Children's English Teaching

With the improvement of the reform and opening up in China, more and more attention has been paid to the subject of English.When teaching English to children, it is necessary to do a good job in curriculum design and improve the application of games in the curriculum.Only in this way can the difficulty of English learning be reduced, children's enthusiasm for English learning be stimulated, laying a solid foundation for their future development.Through the analysis of the application of games in children's English classroom, this paper explored more teaching forms and analyzed their functions in order to improve the games teaching quality of children's English teaching and to meet the children's English learning needs.
LibGuides: Library Feedback: Home

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Penggunaan Preposisi Dalam Paragraf Narasi Siswa Kelas X-1 SMA PGRI 2 Bandung

Abstract, article, entitled the use of Prepositions in Narrative paragraph of Grade X-1 SMA PGRI 2 Bandung school year 2010/2011 examines four types of prepositions, such as preosisi singular form of word basic and noun, preposition forms compounds side by side and compounds are correlated. Methods this study used a qualitative descriptive. The results showed that in singular form of word preposition basics are just some of students who can use prepositions in accordance with rules. On prepositions of singular form of word derivative not found one datapun that uses preposition. On a compound preposition is attached also none of data found. On a compound preposition correlates of students can use in accordance with rulesKeywords: basic word preposition, noun, plural side by side, and compounds are correlated
Image Restoration Menggunakan Metode Lucy-Richardson Pada Citra Underwater

Image restoration is a step to get a clearer image of a degraded image by only knowing some of the degradation factors of that image. Image restoration is different from improving image quality (image enhancement) even though both of them aim to improve image quality. Image enhancement is more related to the sharpening of certain features in the image, while image restoration utilizes knowledge about the process of degradation to recover the original image. The image restoration aims to eliminate or minimize defects in the image. The goal is almost the same as image repair operations, the difference being the restoration of the image that causes image degradation is known. For the image restoration using the Lucy-Richardson method for image restoration and the method can be the solution to the above problems
Developing a Framework for Mobile Assisted Language Learning through Learning Analytics for Self-Regulated Learning

Mobile Assisted Language Learning through Learning Analytics (MALLAS) is a conceptual framework to aid learning designers in developing effective support for second- and foreign language (L2) learners through the application of learning analytics to facilitate self-regulated language learning across learning settings. Designing sound support mechanisms to develop adult L2 learners’ ability to self-regulate their language learning process is important since many of them have limited opportunities to participate in language classes. MALLAS can be used to assist in design choices when developing theoretically underpinned mobile assisted language learning applications and/or services.
Pseudo Random Number Coding for Lift Car Position Sensing

Aiming at the disadvantages of the traditional positioning for lift car applications, a novel binary bar coding and sensing technology is presented in this paper. It studies the relationship of the addressable length and the number of digits to be sensed. A more systematic approach is demonstrated to generate the pseudo random code more efficiently than the random sorting method used in the example case. Finally, a function of vector distance is developed to measure the difference of any two groups of the digits to be sensed along the complete code sequence. By searching the maximum of the vector distance, the optimal sequence can be achieved to reduce the chance of decode error.