Reviewerly/exHarmony huggingface dataset:
THE USE OF SMART PHONES TO IMPROVE SPEAKING SKILLS AND TO DEVELOP INDEPENDENT LEARNERS

Speaking is one of the four language skills that is often considered the most difficult to master properly because it includes various components, namely accuracy, fluency, comprehensiveness and content. Nevertheless, the time allocated for speaking skills in English Study Programs is very limited, in addition the classes are usually large. Therefore, it was necessary to find a language teaching method that could overcome this condition. This research has implemented language learning with the help of cellphones or MALL (Mobile Assisted Language Learning). The research also introduced the concept of independent learning to the students to train them to be come independent learners. This would enable them to continue to improve their speaking English and other language skills after completing their university education. Independent learning would also help them keep up with the times and become life-long learners so that they are always able to keep up with the rapid development in this era. This research is a quasi experimental research in which the subjects were the students of two second semester classes of the English Study Program. The experimental class was introduced to independent learning and both classes used MALL for the class sessions. A pre-test and post test were used to measure the improvement of students' speaking proficiency. In addition, a questionnaire on their perceptions of using MALL in distance learning and independent learning.  This was supported by qualitative data obtained through, student journals, observations and interviews. The results showed that the students doing extra activities using MALL did not show significant increase in the post test results, nevertheless good improvement was made in pronunciation and intonation.
Fusing Node Embeddings and Incomplete Attributes by Complement-Based Concatenation

Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.
LibGuides: Music Resource Guide: MLA

A guide from Funderburg Library on how to use library resources to do research for your music classes.
Using Deep Reinforcement Learning to Train and Evaluate Instructional Sequencing Policies for an Intelligent Tutoring System

We present STEP, a novel Deep Reinforcement Learning solution to the problem of learning instructional sequencing. STEP has three components: 1. Simulate the student by fitting a knowledge tracing model to data logged by an intelligent tutoring system. 2. Train instructional sequencing policies by using Proximal Policy Optimization. 3. Evaluate the learned instructional policies by estimating their local and global impact on learning gains. STEP leverages the student model by representing the student’s knowledge state as a probability vector of knowing each skill and using the student’s estimated learning gains as its reward function to evaluate candidate policies. A learned policy represents a mapping from each state to an action that maximizes the reward, i.e. the upward distance to the next state in the multi-dimensional space. We use STEP to discover and evaluate potential improvements to a literacy and numeracy tutor used by hundreds of children in Tanzania.
LibGuides: Library Events: Citation Workshops

Want to know what is going on at the Library? Check out all of our past and upcoming events here! The Library and the TLC are working together to cover all citation styles!
Virus As A Living Object

Based on the study of living objects a new understanding of the functioning and of the general existence of the virus is proposed. The previous concept was a virus, which was a kind of chemical-technological pun, which fully exists only in the phase of transition from one host to another, its purpose of existence is only to spread, to infect and to destroy.
Perancangan Aplikasi Indentifikasi Kematangan Jambu Madu Dengan Metode Ekstraksi Ciri Statistik

Guava Honey is a fruit that can be consumed and has a wide market share. The similarity in texture of honey guava skin color between ripe and immature results in difficulty in identifying ripe honey guava in terms of fruit skin texture characteristics and subjective human assessments of the maturity level of Honey guava fruit causing the assessment of the maturity level of Guava Honey to differ from one with others. From these problems, so that research was carried out to identify the maturity of Guava Honey based on the texture of the fruit skin color. The purpose of this study was to apply the statistical feature extraction method with characteristic parameters, namely Mean (μ), Variance (σ2), Skewness (α3), Kurtosis (α4) and Entropy (H) as a method to recognize the ripeness of honey guava in terms of fruit skin texture. and to find out the accuracy value after the system is tested. The subject of the research was to build an application to identify the ripeness of honey guava to statistically detect the ripe fruit of the ripe honey guava in terms of the texture of the fruit skin. Based on this research, the authors designed an Android-based application of guava honey maturity identification using Eclipse Juno and Sqlite. To simplify the identification process in the application, the writer applies the Statistical Feature Extraction Method and the expected results in this study are an application that applies the Statistical Feature Extraction Method.
Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU

Binary convolutional networks have lower computational load and lower memory foot-print compared to their full-precision counterparts. So, they are a feasible alternative for the deployment of computer vision applications on limited capacity embedded devices. Once trained on less resource-constrained computational environments, they can be deployed for real-time inference on such devices. In this study, we propose an implementation of binary convolutional network inference on GPU by focusing on optimization of XNOR convolution. Experimental results show that using GPU can provide a speed-up of up to $42.61\times$ with a kernel size of $3\times3$. The implementation is publicly available at this https URL
Planning and Control of Uncertain Cooperative Mobile Manipulator-Endowed Systems under Temporal Logic Tasks

Control and planning of multi-agent systems is an active and increasingly studied topic of research, with many practical applications such as rescue missions, security, surveillance, and transporta ...
Trustworthiness in Mobile Cyber-Physical Systems

As they continue to become faster and cheaper, devices with enhanced computing and communication capabilities are increasingly incorporated into diverse objects and structures in the physical environment [...]
Technology of forming an intersubjective position in a high school student

The successful socialization of a school graduate requires development of their specific characteristics. The study aims to describe the technology of forming an intersubjective position in a high school student. The process includes the stages of intraposition, interposition, and sociocultural position; their substages are diagnostic, motivational, goal-setting, planning, content-technological, reflective-analytical. The questionnaire Criteria and indicators of the ISP determined the increased level of the intersubjective position and its individual criteria; reflection assessed each students achievements. The stage-by-stage implementation of the program My socially significant position contributed to the ISP formation. The intersubjective approach is considered new to the ISP formation.
Особенности применения метода биографии в духовно-нравственном воспитании школьников

The article discusses the theoretical substantiation of method of biography in the spiritual and moral education of schoolchildren and the experience of its application in an educational institution. The theoretical part presents an analysis of the concepts of spiritual and moral education, values, ideals, biography. It highlights the national values, regulated by modern standards of education. The approaches of Russian teachers to the concept and role of biography in the upbringing of the younger generation are considered. The structural components of the lesson that uses the method of biography, modules for compiling a biography in the context of spiritual and moral education have been determined. The empirical part is devoted to describing the experience of using the method of biography in the spiritual and moral education of primary schoolchildren, aimed at generating interest in science, in the events that took place in the lives of prominent Russian scientists, creating in students a sense of involvement in outstanding achievements and victories, developing the ability to set worthy goals, work, self-development and self-realization. Examples of lessons are given. The results of observations are presented and recommendations for further study are given.
Continual Memory: Can We Reason After Long-Term Memorization?

Existing reasoning tasks often follow the setting of ‘’end-to-end reasoning'', which has an important assumption that the input contents can be always accessed while reasoning. However, human beings frequently adopt another reasoning setting in daily life, referred to ‘’reasoning after memorizing''. Concretely, human beings have the ability to unconsciously memorize their experiences within limited memory capacity, from which they can recall and respond to subsequent tasks. In this setting, the input contents are no longer available during reasoning, thus we need to compress and memorize the input stream in one pass, trying to answer general queries that are unseen before. Memory augmented neural networks introduce a write-read memory to perform such human-like memorization and reasoning, but they continually update the memory from current information and inevitably forget the early contents, failing to answer the queries relevant to early information. In this paper, we propose the Continual Memory (CM) to explore this ability of reasoning after long-term memorization. To alleviate the gradual forgetting of early information, we develop self-supervised memorization training with item-level and sequence-level objectives. We demonstrate several interesting characteristics of our continual memory via synthetic data, and evaluate its performance by several downstream tasks, including long-term text QA, long-term video QA and recommendation with long sequences.
The Practice of English for Young Learner Classes during Pandemic

During pandemic all school are obligated to use online learning activity. And the terms of Online learning is all range of programs and media/tool using by teacher to teach English lesson which use the Internet to provide instructional materials and facilitate interactions between teachers and students. Based on that phenomenon, the researcher was interested in analyzing about how the practice of English for young learner classes during pandemic based on the theory of teaching English for Young learner.The researcher applied descriptive qualitative analysis to analyze the practice of teaching and learning process during pandemic. It presented three steps data analysis procedures, these are: reduction, display, and conclusion. From the data analysis the researcher conduct 5 components in the practice of teaching and learning English for Young learner classes during Pandemic those are: The practice of English in MIN 7 Tapin during pandemic, Teachers’ strategies and ideas that teacher used, The learning media & resources that teacher used, The functions of online media/tool that teacher used and The process of teaching English Skills through those media/tool.The results of this research conclude that the practice of English in MIN 7Tapin during pandemic is done through online activity by using WhatsApp. Then, the teachers’ strategy that teacher use is online chat, for the teachers’ ideas is encourage collaboration between teacher and students through WhatsApp Group Discussion. Then, the learning media and resource that teacher use are students’ worksheet/ LKS, developing material design by teacher and YouTube. WhatsApp application is used as the online media/tool to communicate and to instruct the students. And the features that are provided in WhasApp Application is used by theteacher as teaching technique to teach English Skills.
Outlier Preserving Distribution Mapping Autoencoders

State-of-the-art deep outlier detection methods map data into a latent space with the aim of having outliers far away from inliers in this space. Unfortunately, this often fails as the divergence penalty they adopt pushes outliers into the same high-probability regions as inliers. We propose a novel method, OP-DMA, that successfully addresses the above problem. OP-DMA succeeds in mapping outliers to low probability regions in the latent space by leveraging a novel Prior-Weighted Loss (PWL) that utilizes the insight that outliers are likely to have a higher reconstruction error than inliers. Building on this insight, OP-DMA weights the reconstruction error of individual points by a multivariate Gaussian probability density function evaluated at each point's latent representation. We demonstrate and provide theoretical proof that this succeeds to map outliers to low-probability regions. Our experimental study shows that OP-DMA consistently outperforms state-of-art methods on a rich variety of outlier detection benchmark datasets.
Discussion on the Strategy of High School Chemistry Teaching Based on Core Literacy Perspective——Taking Ganzi Tibetan Autonomous Prefecture in Sichuan Province as an Example

Based on the core literacy concept of chemistry subject proposed by the new curriculum standard, Combining the cultural diversity, geographical speciality and education status of Ganzi Tibetan Autonomous Prefecture. Exploring targeted teaching strategies from the four dimensions of core literacy. So as to achieve the purpose of improving teaching efficiency and cultivating students’ core literacy.
Scene Text-Line Extraction with Fully Convolutional Network and Refined Proposals

Texts appearing in images are often regions of interest. Locating such areas for further analysis can help to extract image-related information and facilitate many applications. Pixel-based segmentation and region-based object classification are two methodologies for identifying text areas in images and have their own pros and cons. In this research, a text detection scheme consisting of a pixel-based classification network and a supplemented region proposal network is proposed. The main network is a Fully Convolutional Network (FCN) employing Feature Pyramid Networks (FPN) and Atrous Spatial Pyramid Pooling (ASPP) to indicate possible text areas and text borders with high recall. Certain areas are further processed by the refinement network, i.e., a simplified Connectionist Text Proposal Network (CTPN) with high precision. Non-Maximum Suppression (NMS) is then applied to form appropriate text-lines. The experimental results show feasibility of the scheme.
교양중국어 쓰기 교육의 현황과 개선방안 - S대학의 사례를 중심으로-

This paper looks at the present situation and direction of improvement in the liberal Chinese writing education of S University. Classes are required to be taken during the second semester with two hours and two credits each semester, and the number of students is five classes with 191 students based on the first semester of 2019. We then analyzed the three kinds of textbooks we used over the next five years and the current ones. The writing section is mostly made up of controlled writing, which is the type where students lay the foundation, and the burden of writing can be reduced.The survey was conducted to find out the status of writing education for students. According to the survey, most of the students thought writing education was the most difficult part among the four language functions: listening, speaking, reading and writing, and they were also generally aware of the importance of writing education. And they want to spend more time on writing.According to the development direction for this, the number of classes should be expanded to two hours a week due to the difficulty of writing education, and about 15 students should be arranged in groups, meanwhile, the first and second semesters should use the same materials to maintain connectivity and uniformity.From an educational standpoint, the textbooks used by S universities consist mostly of controlled writing types, which can make students feel a little bored. Therefore, students should be encouraged to be confident in writing by guiding them both inductive writing and free writing types. Since students find writing education difficult, the professor should motivate them to actively participate in writing activities and make them interested in writing.
Introducing Sample Robustness

Choosing the right data and model for a pre-defined task is one of the critical competencies in machine learning. Investigating what features of a dataset and its underlying distribution a model decodes may enlighten the mysterious black box and guide us to a deeper and more profound understanding of the ongoing processes. Furthermore, it will help to improve the quality of models which directly depend on data or learn from it through training. In this work, we introduce the dataset-dependent concept of sample robustness, which is based on a point-wise Lipschitz constant of the label map. For a particular sample, it measures how small of a perturbation is required to cause a label-change relative to the magnitude of the label map. We introduce theory to motivate the concept and to analyse the effects of having similar robustness distributions for the training- and test data. Afterwards, we conduct various experiments using different datasets and (non-)deterministic models. In some cases, we can boost performance by choosing specifically tailored training(sub)sets and hyperparameters depending on the robustness distribution of the test(sub)sets.
Gap identification strategy for mobile robot navigation in static and dynamic environments

This paper presents a new strategy of mobile robot navigation inspired from Follow The Gap and Grouping Obstacles methods in static and dynamic environments. In this work, we have proposed a solution based on gap identification for the problem of obstacle avoidance. The mobile robot scans the surrounding through a laser sensor, then chooses the safest gap between the obstacles to reach the target. After that, the direction of the mobile robot is given by a fuzzy logic controller. This algorithm has shown its adaptability in cluttered environment and has produced good results comparing to the methods suggested in previous works. On the other hand, we have added a new fuzzy logic controller in the case of dynamic obstacles to command the linear velocity of the robot. This approach was tested in some simulations, and has shown its efficiency in generating shorter and optimal paths in a small time, which represents a great advantage.
Data reduction using cluster sampling

It is natural to use larger and more diverse datasets to get better performance in pose study. Learning with a large scale is essential to improve the model performance to a level similar to human recognition, but there is a problem with gradually increasing learning time and data redundancy. This can also lead to a lack of data storage. Our study proposes a new way to solve these problems: Data shaping Using Cluster Sampling (DUCS). In this paper, we propose a sampling framework that clusters a pose dataset and extracts only a small number of random frames from each cluster. To ensure the consistency of pose data, the data is normalized, and a preprocessing process of aligning the entire joint based on the pelvic joint is performed, and an optimal parameter search in DBSCAN is proposed to improve the performance of clustering. This process can greatly reduce the redundancy due to the specific posture bias. To demonstrate the effectiveness of our method, we trained a 3D pose estimation model with sampled datasets of Human3.6M and shown competitive results despite the drastic compression rate of over 95%.
Application and Practice of Blended Teaching in Computer Courses

The blended learning model is a new teaching method under the guidance of blended learning theory, using the network and information technology and other important channels. It is one of the important development trends of teaching innovation in universities today. By combining with the computer, the mixed teaching method is used to design and research the teaching mode of related courses.
Implementation of ETL-technology for the data lake of the ethnocultural bank of the indigenous minorities of the north

The article proposes a solution for the implementation of the ETL process and the data lake of the Ethnocultural Bank of the Indigenous peoples of the North. The Ethnocultural Bank is an aggregating information resource on the languages and cultures of indigenous peoples of the North, which can become the basis for using NLP technologies and automating the processing of texts in these languages.
Intelligent Wearable Sensor Band for Underground Working People

This paper presents an Intelligent wearable sensor band for underground working people. The security and soundness of laborers are significant for underground individuals. The proposed framework consolidates wearable sensors to quantify physiological and natural parameters. A passage is acquainted with giving information preparing, a neighborhood web server, and a cloud association. A wearable sensor on a laborer and natural sensor on a wanderer that can transmit the information to the client by means of a door for example server, gives offer notice and cautioning component for the clients. Live health examination taken for laborers who work in an underground like Tunnels, Shafts, etc., it has an Individual database of laborers and contrasts it and current essential tangible qualities separate to workplace information. Live update, will screen from the control room and it can direct the specialist if any medical problem occurs and furthermore can maintain a strategic distance from the undesirable passing.