Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL TALKING CHIPS UNTUK MENGUKUR HASIL BELAJAR DAN KECERDASAN INTERPERSONAL

The aims of research is to find out the analysis of student learning result and interpersonal intelligence through talking chips learning model. The methods of this research is using experiment. The sample used was XI IPA 1 class and XI IPA 2 at SMA N 2 Kudus with sampling technique based on the consideration of the teacher concerned. The data taking is done using validation sheets, cognitive test instruments, observation sheets, student response questionnaires and documentation. The cognitive analysis results of the experimental class 1 pre-test score was 70 and the post-test was 100 while the experimental class 2 the pre-test score was 70 and the post-test was 95. The results of the affective analysis is the highest aspect of the experimental class 1 were aspects of cooperation while for the experimental class 2 is the aspect of curiosity. The indicators of empathetic processing, team building and listening to other owned by the experimental class students are very good, but the indicators for giving feedback and inquiry and questioning are still not enough and need to be improved. It can be concluded that talking chips learning can improve student learning result and interpersonal intelligence and make students more interested and active during the learning process.
Iterative Adaptation to Quantization Noise

Quantization allows accelerating neural networks significantly, especially for mobile processors. Existing quantization methods require either training neural network from scratch or gives significant accuracy drop for the quantized model. Low bits quantization (e.g., 4- or 6-bit) task is a much more resource consumptive problem in comparison with 8-bit quantization, it requires a significant amount of labeled training data. We propose a new low-bit quantization method for mobile neural network architectures that doesn’t require training from scratch and a big amount of train labeled data and allows to avoid significant accuracy drop.
Shared nearest-neighbor-based clustering internal index for high-dimensional datasets

针对因使用基于距离的相似性度量,传统聚类内部指标随着数据维数的增加而性能下降的问题,提出了一种基于共享近邻相似度的聚类内部指标.首先,利用共享近邻相似度和k最近邻(kNN)方法,估计数据点的密度,构建融合密度的共享近邻相似度图.然后,根据融合密度的共享近邻相似度图,利用最大流算法,计算出类内相似度和类间分离度,并结合两者计算出聚类内部指标.通过对人工数据集和真实数据集的测试表明,与9个基于距离的传统聚类内部指标相比,该指标能更准确评估数据集的最佳划分和预测数据集的最佳类数.因此,该指标处理复杂类结构和高维数据的能力优于所对比的其他聚类内部指标.
Reliability and quality of distance learning technical education in the context of the COVID-19 pandemic: practice and issues

Abstract The covid-19 pandemic, which was announced in March 2020, has made significant changes in all spheres of society, the state and the world in general. A quick restructuring of many structures, processes and mechanisms was required. The education system is no exception. The educational process was modernized, a large number of new technologies were introduced and the existing mechanisms were improved. So, in the conditions of the forced transition to training using distance educational technologies in the spring of 2020, it becomes necessary to assess the quality of the functioning of educational software in the higher technical education system. This article is aimed at researching and analysing the practical and theoretical aspects of the use of distance learning technologies in order to determine the correspondence of the quality of distance learning to the traditional form of full-time education. The authors of the article pay attention to the possible prospects for the development of distance learning within the technical education system in the Russian Federation, not only as a response to changing epidemiological conditions, but also as a potentially alternative form of education. The study assesses the success of using software systems, identifies the advantages and disadvantages of the existing system of distance education.
Extrinsic Camera Calibration by using a Nonuniform Subdivision Accumulator and Orthogonal Vanishing Points

In this study, we propose an accurate and efficient algorithm for extrinsic camera calibration that utilizes an accumulator on the sphere and the orthogonal constraint of vanishing points. We develop a nonuniform subdivision method to create an accurate accumulator for validating rotation matrix hypotheses. In contrast with the multiline method for generating rotation matrix hypotheses, we utilize the parallelism and orthogonality of two lines to develop hypotheses in the spherical coordinate. Moreover, we expand our method to the scene that lacks orthogonal information. Our method achieves 99.36% precision and 99.92% recall on the York Urban Database, and it is successfully applied to real-world scenes.
Translating Lambda Calculus into C++ Templates

The C++ template system is capable of performing arbitrary compile-time computations, which is typically exploited in generic programming libraries. However, the template language itself is syntactically cumbersome. A variety of tools, ranging from libraries to dedicated compilers, was created to alleviate this issue. One such approach is translating a functional program into a template metaprogram. In this work, we present a new way of translating functional programs based on lambda calculus into template metaprograms. The translation produces metaprograms with clearly defined lazy semantics and supports common functional features such as recursion and algebraic data types. We demonstrate its viability by providing a proof-of-concept implementation.
Chaotic Encryption Method of Color Image Based on Improved Convolutional Neural Network

In order to improve the chaotic encryption ability of color images, a chaotic encryption technology of color images based on improved neural network and chaotic random nonlinear feature mapping is proposed. The multi-pass filter detector is used to collect the feature information of color images, multi-dimensional phase space hierarchical fusion and feature evolution clustering analysis are carried out on the collected color images, the deep chaotic encryption method is used to realize the arithmetic mapping coding processing of color images, the random nonlinear mapping encryption chaotic key control scheduling model of color images is constructed, and the chaotic mapping coding sequence distribution diagram of color image chaotic encryption is established by combining the segmentation prior knowledge of multi-dimensional multi-view images. The shallow feature information and deep feature information of color image are analyzed, and the improved convolutional neural network technology is used to realize information fusion of feature information of each layer, and the chaotic encryption and recognition optimization of color image is realized by using the chaotic map coding sequence distribution map fusion algorithm. Simulation results show that this method has higher accuracy and lower error rate in chaotic encryption of color images, and improves the ability of color image recognition and detection.
Familiarization of Senior Preschool Children with Scientific Discoveries and Inventions

The article reveals the methodology for the formation of a cognitive interest in the creative activity of a person, his achievements in the field of science in older preschool children. The author proposes an original approach for acquainting preschoolers with the field of physical phenomena, scientific discoveries and technical inventions, which allows them to motivate children not only to acquire new knowledge, but also to make a significant contribution to their overall intellectual development, to provide great assistance in the formation of a personality capable of adapting to conditions of the rapidly changing modern world.
Live Video Action Recognition from Unsupervised Action Proposals

The problem of action detection in untrimmed videos consists in localizing those parts of a certain video that can contain an action. Typically, state-of-the-art approaches to this problem use a temporal action proposals (TAPs) generator followed by an action classifier module. Moreover, TAPs solutions are learned from a supervised setting, and need the entire video to be processed to produce effective proposals. These properties become a limitation for certain real applications in which a system requires to know the content of the video in an online fashion. To do so, in this work we introduce a live video action detection application which integrates the action classifier step with an unsupervised and online TAPs generator. We evaluate, for the first time, the precision of this novel pipeline for the problem of action detection in untrimmed videos. We offer a thorough experimental evaluation in Activi-tyNet dataset, where our unsupervised model can compete with the state-of-the-art supervised solutions.
A process tracing study of decision strategies and bad decisions

This chapter is based on experimental enemy research by Takemura et al. on decision strategy as a cognitive strategy in multiattribute decision-making processes. In the seventh and eighth chapters, I added various second-stage decision strategies to the analysis of Payne et al. and analyzed them based on various indicators of decision-making. It was also found that the second-stage decision strategy, lexicographic screening, and the additive evaluation on a few alternatives, is effective in avoiding worst case decisions. However, we examined whether such a second-stage strategy is really effective in actual decision-making tasks, and how it compares with compensatory strategies such as additive strategies, using the information monitoring method, a technique for tracking the decision-making process. First, using a compensatory decision-making strategy such as WAD in actual decision-making did not lead to the best decision. This is a decision-making strategy that is not recommended, especially in emergency situations. Second, a strategy such as DIS, which requires less information processing but tends to shift attention to less important attributes, is more likely to lead to bad decision-making and less likely to lead to better decision-making. Finally, it was suggested that a decision-making strategy such as LEX, which focuses on the most important attributes, requires less cognitive effort and makes it relatively easier to avoid the worst decision and choose the best option.
Mbb: A Multi-Scale Method For Data Based On Bit Plane Slicing

Multi-scale methodology can enhance the performance of the model in deep learning. The current multi-scale methodology focuses on changing the formation, which will increase the parameters and calculations of the network. This paper offers a multi-scale method for data based on bit plane slicing(MBB). This expands the receptive field of valid information in image data. It is done by multi-level fusing image with high bit planes. Our experimentation shows that by adding MBB in front of the backbone network, one can achieve a significant performance improvement. The MBB approach is widely applicable because it does not require changes to the structure of the backbone network.
On the Innovation of College Physical Education Teaching Mode under the Computer Multi-media Environment

Abstract Many students are in poor physical condition now. Although students’ academic performance is very good, the school should also find ways to help them improve their physical quality. Therefore, physical education has become a hot topic in the field of education. Nowadays, physical education in many universities is irregular. They don’t have a complete teaching model of their own. This leads to students in physical education class can not learn professional knowledge. The improvement of students’ physical quality cannot be guaranteed by professional knowledge. In recent years, the application of multi-media technique in the field of teaching is a new breakthrough in education mode [1]. Researchers believe that the combination of multi-media technique and physical education will greatly improve the effect of physical education in universities. This paper briefly analyzes the current situation of physical education in universities. In this paper, the implementation of multi-media teaching mode in universities is described.
Problems of the quality of Bachelor's degree training at the higher educational institution in the program track of "Vocal Art"

The high level of development of culture and art in modern Russia requires appropriate training of employees for the theater and concert complex. Well-trained specialists will be able to find a job and employment. In the labor market in the field of art and culture, there is creative competition. Therefore, the training of bachelors at the higher educational institutions should be guided by the requirements of the labor market in this area, and the higher educational institutions should produce competitive specialists in creative professions. In the higher education system of modern Russia, the issue of improving the quality of teaching of special (major) disciplines, as well as all subjects included in the training program, is on the agenda of the department collectives. The program trajectory, which is set through the Federal State Educational Standards, is associated with a certain experiment in modeling competencies based on accepted professional standards. An important component is the position of the higher educational institution and the right to develop competencies based on the experience, profile of the university and the composition of the teaching staff. Higher educational institutions have certain academic freedoms and use their opportunities to achieve a higher quality of bachelor's and master's degree training. The article deals with various aspects of vocal pedagogy, the formation of the necessary professional and general cultural competencies, and individual work with students. Such problems are raised by teachers of creative higher educational institutions. We considered it possible to enter into the discussion and conduct a friendly polemic.
Data Efficient Learning on Graphs

Prevailing methods of graph representation learning (GRL) usually rely on learning from "big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with "small'' labeled data as data annotation and labeling is always a time and resource consuming task. The fact overshadows GRL's capability and applicability for many real situations. Therefore, data efficient learning on graphs has become essential for many real-world applications and there have been many studies working on this topic in recent years. In this tutorial, we will systematically review recent studies of data efficient learning on graphs, in particular a series of methods and applications of graph few-shot learning and graph self-supervised learning. At first, we will introduce the overview of graph representation learning methods, conventional few-shot learning, and self-supervised learning techniques. Then, we will present the work of data efficient learning on graphs in terms of three major graph mining tasks at different granularity levels: node-level learning tasks, graph-level learning tasks, and edge-level learning tasks. In the end, we will conclude the tutorial and raise open problems and pressing issues in future research. The authors of this tutorial are active and productive researchers in this research area.
Removing Input Confounder for Translation Quality Estimation via a Causal Motivated Method

Most state-of-the-art QE systems built upon neural networks have achieved promising performances on benchmark datasets. However, the performance of these methods can be easily influenced by the inherent features of the model input, such as the length of input sequence or the number of unseen tokens. In this paper, we introduce a causal inference based method to eliminate the negative impact caused by the characters of the input for a QE system. Specifically, we propose an iterative denoising framework for multiple confounding features. The confounder elimination operation at each iteration step is implemented by a Half-Sibling Regression based method. We conduct our experiments on the official datasets and submissions from WMT 2020 Quality Estimation Shared Task of Sentence-Level Direct Assessment. Experimental results show that the denoised QE results gain better Pearson’s correlation scores with human assessments compared to the original submissions.
Improving Object Detection And Attribute Recognition By Feature Entanglement Reduction

We explore object detection with two attributes: color and material. The task aims to simultaneously detect objects and infer their color and material. A straight-forward approach is to add attribute heads at the very end of a usual object detection pipeline. However, we observe that the two goals are in conflict: Object detection should be attribute-independent and attributes be largely object-independent. Features computed by a standard detection network entangle the category and attribute features; we disentangle them by the use of a two-stream model where the category and attribute features are computed independently but the classification heads share Regions of Interest (RoIs). Compared with a traditional single-stream model, our model shows significant improvements over VG-20, a subset of Visual Genome, on both supervised and attribute transfer tasks.
Multi-Level Optical Flow Estimation Based On Spatial Partitioning

We address the problem of optical flow estimation between a stereo pair of images suitable for view interpolation applications. In recent years, deep learning based approaches are found to be capable of producing superior optical flow estimation results than that of traditional approaches. However, deep learning approaches generally suffer from large GPU memory constraints and hence their application is limited to low resolution images. Here, we present an approach to accurate optical flow estimation via the state-of-the-art deep learning based approaches that introduces fewer artifacts in view interpolation applications. We confirm the suitability of our approach through both qualitative and quantitative results.
CESumm: Semantic Graph-Based Approach for Extractive Text Summarization

The amount of textual data online is growing increasing rapidly, and it is a demanding task to condense the context of the source text indexed by any search. Manually reading, comprehending and summarizing the text requires huge task force and makes it a time consuming and expensive task. Text summarization system apprehends the topic of the source text and generates summary from it. This work aims to generate an extractive text summary for a single document by using graph-based approach which also preserves the cause and effect relation by constructing a semantic link network then generating the final extractive summary. Since the extractive summary is generated using an unsupervised method, it does not require training and can easily be ported to another document domain. The system also attempts to resolve the issue of previous graph-based approaches where the significance of noun word is not given with added weightage, rather equally weighed with other words in the sentence. The issue of biasing a dominant topic in source text is prevalent in previous graph-based approaches where the proposed system avoids biasing to the dominant topic and makes attempt to include all the topics. The system proposed is evaluated using standard benchmark datasets from DUC family—DUC 2001 and DUC 2002 using the standard evaluation tool: ROUGE, and it is shown that the proposed system achieves higher ROUGE score with improvement of 8–8.7% compared to the state-of-the-art extractive text summarization systems and contains more key phrases in summaries than other approaches and hence can be applied for outline generation, finding central theme and compressing the text without losing important context.
The Cultivation Path of Sports Spirit in Colleges and Universities Physical Education

Under the contemporary education background of comprehensively promoting quality education, college physical education is an essential part of it. Physical education in colleges and universities is an organic combination of the teaching of basic sports skills, basic skills and basic knowledge and the cultivation of sports spirit. It can effectively promote the overall development of students. The current college physical education teaching model still largely follows the traditional teaching model. Physical education in colleges and universities is the final stage of school physical education and has the characteristics of being connected with society. Physical education in colleges and universities should make full use of this approach of physical education to complete the cultivation of sportsmanship and personality of students, so that students can do a good job in their spiritual will. Prepare to complete the role conversion of natural persons to social persons and enter society smoothly. This article mainly uses the literature method, combined with the objective laws and characteristics of the formation and development of sportsmanship itself, to study the current situation of sportsmanship training in college physical education, in order to promote the overall physical and mental development of college students and promote sports itself rich.
Assessing technology-based learning: a case of “<i>Opon-Imo”</i> use for agricultural subjects in senior secondary schools of Osun State, Nigeria

Abstract The study assessed using an e-learning device, ‘ Opon-Imo ’ for agricultural subjects among secondary school students and teachers in Osun State, Nigeria. It determined the teachers’ and students’ extent of use of ‘ Opon-Imo ’ for agricultural subjects, ascertained their perception towards its value as a learning/teaching aid, and the challenges faced with its use. A multi-stage sampling technique was employed to select 150 students and corresponding 12 teachers instructing them on the agricultural subject(s) to give a total of 162 respondents sampled. Data were collected with a validated questionnaire and analysed using descriptive statistics. The results revealed that students occasionally used ‘ Opon-Imo ’ as reference material to seek answers to assignments (x̄= 3.1 ± 0.9) just as the teachers often utilised the modules as a reference point while explaining class notes to the students (x̄= 3.5 ± 0.8). (Most (58%) of the students were indifferent about the values of ‘ Opon-Imo ’ as a learning aid for agricultural subjects, whereas most of the teachers (58.3%) had a favourable disposition towards it for their teaching activities. As a challenge, teachers noted students’ misuse of the device for entertainment purposes instead of use as a learning aid (x̄= 3.16). It was concluded that teachers validly used ‘ Opon-Imo ’ to a greater extent for agricultural educational purposes and were more positively inclined to its educative usefulness than the students. As such, effective measures have to be developed to make the students aware of the value of this learning aid for their career in agriculture.