Reviewerly/exHarmony huggingface dataset:
A systematic investigation on Geometric Transformation

Geometric Transformation refers to the process of registering a digitized image using control points and equations or process of map onto a coordinate system. Three major components of Geometric Transformation are spatial transformation, image interpolation and anti-aliasing. Spatial transformation is of different types which may include mappings of affine, perspective as well as transformations of polynomial and piecewise. Geometric Transformation is commonly applicable for many fields which include image registration especially for medical field, remote sensing and changing video resolution. This paper explores a detailed review on Geometric Transformation techniques.
Some Computational Aspects of a Noncentral Dirichlet Family

The Dirichlet distribution is arguably the most well-known multivariate distribution for implementation on the unitary simplex. Different generalizations exist, one of which is the noncentral counterpart. The noncentral representation depends on the noncentrality parameters through the confluent hypergeometric function of several variables and admits both singly and doubly noncentral representations. This chapter explores the computational aspects when the estimation of this singly and doubly noncentral Dirichlet is of interest. It investigates to what degree the additional parameter(s) and their effect on the doubly noncentral Dirichlet, compared to the singly alternative, affects the practical implementation of the model. Real data examples are used for this investigation by using maximum likelihood estimation for the parameters and further strengthened by simulation studies.
THE EFFECTIVENESS AND QUALITY OF THE FORMATION OF STUDENTS’ COMPETENCIES

The article analyzes the relationship between the concepts of efficiency and quality of competence formation among students in a modern university. These concepts are revealed based on the analysis of psychological and pedagogical literature. It is shown that the modernization of higher education has determined the need to develop innovations and their introduction into the educational process of universities. The article identifies the problem of the dependence of the effectiveness and quality of the formation of students' competencies and the use of innovative pedagogical technologies. Further directions of scientific research include the development of criteria for determining the quality and effectiveness of the formation of students' competencies.
Memory Management

Most programs work on more data than a typical processor can hold in its registers, so data needs to be stored in memory or long-term store (such as disk storage). Most programming languages use memory as their primary data storage, relying on input/output or virtual memory to access long-term store.
Blockchain-based cable supply chain traceability system

The traditional cable supply chain industry has problems such as difficulty in product traceability, production monitoring, rights protection and evidence collection, and data sharing, which seriously restrict the further development of the cable supply chain industry. A blockchain-based cable supply chain traceability system is designed using blockchain technology to integrate, share and supervise cable supply chain data. This system includes five functional modules: user management, data upload, two-way traceability, data sharing, and complaint handling. According to the characteristics of the cable traceability cycle, the storage module adopts a combination of blockchain, Inter-planetary File System (IPFS) and MySQL to meet different business needs. The experimental results show that the system basically meets the performance requirements and has good practicability.
An Optimized Physical Reservoir Computing Through Genetic Algorithm

Reservoir Computing (RC), a compact recurrent neural network (RNN), is an efficient artificial neural network suitable for processing timing signals. In our previous work on a physical reservoir consisting of single-walled carbon nanotubes (SWCNTs) network, its computing performance was presented to be improved by phosphomolybdic acid-modification on CNTs. In this paper, a genetic algorithm was used to search some hyperparameters related to its computing architecture, including input gain, input position, auxiliary input, regularization coefficient and leak rate, for two test benches, i.e., NARMA10 time series prediction and epileptic seizure EEG classification. The parametric setting for improved computing capability of this physical system were found. The best performances were achieved at normalized root-mean-square error of 0.0877 for NARMA10 and an accuracy of 98.33% for EEG classification, respectively.
Planning and practice of IPv4/IPv6 dual-stack campus network's security threat monitoring and handling system

With the continuous advancement of information science and technology and the continued severe international competition and cooperation situation, colleges and universities are faced with increasing demand for information technology service capabilities and increased security standards. Here, we give brief introduction for the current demand of campus network security threat monitoring and handling and the current state of technology. Then, combining the actual requirements of network security protection management in colleges and universities, we propose a campus network security threat monitoring and handling system that meets the needs of supporting IPv6 protocol next-generation internet deployment and protection at this stage.
English Education Students' Attitude toward Autonomous Learning Amidst the Abundant Online Learning Sources

The development of information and communication technology has provided various learning resources that can be accessed by students anytime and anywhere, including for learning languages. This should provide benefits for improving studentsâ€™ language skills. This article explored several things related to the use of online learning resources by students majoring in English Education, including three problem statements, namely: (1) what kind of online learning activities did the students do for autonomous learning purposes? ; (2) what kind of improvement did they obtain from autonomous online learning activities? ; (3) what were the challenges they found in using online learning sources for autonomous learning activities? This study utilized quantitative and qualitative data to provide detailed description. Furthermore, the data was analyzed thematically. This study revealed that students tended to use social media, Youtube, and university LMS for their autonomous learning. Their reported that their autonomous learning improved their English skills, especially in listening, speaking and vocabulary mastery. It was influenced by their interest in certain skills and the learning resources they accessed. The challenges associated with online autonomous learning were unstable connections, lack of clear learning goals, and low student motivation. Several implications and recommendations are also discussed in this study.
Code clone detection based on Doc2vec model and Bagging

For software analysis and maintenance, the code clone detection has an important role. In order to improve the detection rate of code clone, this paper proposes a code cloning detection method based on Doc2vec model and bagging. Firstly, method converts the code into token sequence and abstract syntax tree sequence. Then, the Doc2vec model is used to learn the lexical and grammatical information of the code respectively. Finally, bagging algorithm is used to detect the cloning of code pairs. The results show that the accuracy and recall of the code clone detection method based on Doc2vec model and bagging is better than that of sourcerercc and tree LSTM on bigclonebench data set.
REQUIREMENTS, SHOWED TO QUALITY OF PREPARATION OF TEACHERS BY RESULTS OF STUDYING OF OPINION OF EMPLOYERS

In article on the basis of the lead questioning heads of educational establishments the problem of definition of requirements to graduates of pedagogical high schools. It is established, that requirements to professional competence have by graduates practical-guided character, with obligatory knowledge of rule-legal bases of activity of the teacher and necessary theoretical knowledge.
Pengembangan LKS Biologi Berbasis Inkuiri Terintegrasi Flipbook Maker pada Materi Sistem Pernapasan Manusia Kelas VII DI SMP Iceya Ndaha

The lack of learning media, especially inquiry-based worksheets, causes low student learning motivation. The purpose of this study was to determine the development of Flipbook Maker's Integrated Inquiry-Based Biology Worksheet on the material of the seventh grade human respiratory system at Iceya Ndaha Junior High School. The research method used in this research is the 4D development research model. The 4-D development model is: defining, designing, developing, and distributing. The results of the study show, (1) from the material expert validators the feasibility of the material gets 90.625, the validator score is 58, and the maximum score is 64, these results can show that the feasibility of the human breathing learning worksheet is very feasible to use, (2) the results of the assessment from the media expert validator, the feasibility of the media gets 96.875%, the validator score is 62, the maximum score is 64, with these results it can be seen that the learning worksheet media is very feasible to use (3) The results of the assessment of the linguist validator, the feasibility of the linguist. The conclusion of this study is that the development of an inquiry-based worksheet based on an integrated flipbook maker on the material of human respiratory system is very feasible to use
Dynamic Inference on Graphs using Structured Transition Models

Enabling robots to perform complex dynamic tasks such as picking up an object in one sweeping motion or pushing off a wall to quickly turn a corner is a challenging problem. The dynamic interactions implicit in these tasks are critical towards the successful execution of such tasks. Graph neural networks (GNNs) provide a principled way of learning the dynamics of interactive systems but can suffer from scaling issues as the number of interactions increases. Furthermore, the problem of using learned GNN-based models for optimal control is insufficiently explored. In this work, we present a method for efficiently learning the dynamics of interacting systems by simultaneously learning a dynamic graph structure and a stable and locally linear forward model of the system. The dynamic graph structure encodes evolving contact modes along a trajectory by making probabilistic predictions over the edges of the graph. Additionally, we introduce a temporal dependence in the learned graph structure which allows us to incorporate contact measurement updates during execution thus enabling more accurate forward predictions. The learned stable and locally linear dynamics enable the use of optimal control algorithms such as iLQR for long-horizon planning and control for complex interactive tasks. Through experiments in simulation and in the real world, we evaluate the performance of our method by using the learned interaction dynamics for control and demonstrate generalization to more objects and interactions not seen during training. We introduce a control scheme that takes advantage of contact measurement updates and hence is robust to prediction inaccuracies during execution.
Local Correlation Integral (LOCI)

Chapter 10 introduced us to one multivariate clustering technique in Connectivity-Based Outlier Factor (COF). COF is a popular technique for multivariate outlier detection, but it does come with the downside that there is little immediate guidance outside of “higher outlier scores are worse.” We lack solid guidance on what, exactly, represents a sufficiently large outlier score. In this chapter, we introduce a complementary technique to COF that does come with its own in-built guidance: Local Correlation Integral.
Multi-Scale Attribute-Based Attention For Fine-Grained Zero-Shot Learning

Zero-shot recognition achieves the classification of unseen classes by aligning the visual features and semantic information of images, while the global features extracted by zero-shot learning method are not enough to classify fine-grained classes. Firmed at the foregoing shortages, we put forward a multi-scale attribute-based attention network that captures local feature differences between fine-grained categories and improves the extraction efficiency of different scale attributes. Instead of aligning global visual features with the associated class semantic features, we achieve this by embedding each attribute which aims to pay more attention to the regions most related to the attribute. To this end, we adjust the contribution to prediction of each attribute by using multi-layered attention mechanism. Moreover, we design a new joint calibration loss to solve the distribution deviation problem caused by ignoring the clustering between coarse-grained classes in the fine-grained classification process. We perform experiments on several popular classical datasets of CUB, SUN and AWA2, demonstrating that our proposed framework works better than existing methods.
A Cooperative Federated Learning Mechanism for Collision Avoidance using Cellular and 802.11p based Radios Opportunistically

Reinforcement learning (RL) is a powerful learning framework which can be used in complex environments such as autonomous driving. Generally, in autonomous driving, vehicles run RL algorithm locally. However doing so will not give a desirable performance as each vehicle will only consider its own environment. So in autonomous driving it is very important that the vehicles make actions with the knowledge learned by other vehicles as well which can be received using V2X technology. However, relying on a single radio or V2X mode of communication is not desirable. In the absence of communication infrastructure on the road side, one can depend on technologies such as 4G/5G for V2N (Vehicle-to-Network) communication and Wi-Fi Direct for V2V (Vehicle-to-Vehicle) communication. Vehicles can depend on cellular technologies for indirect mode of communication (V2N), if direct V2V communication is not possible with other vehicles present in the close vicinity. To reap in the benefits of both federated learning and V2X, we present a federated learning architecture with support from V2X, where all the participant agents make their actions with the knowledge received using V2X, even when they are acting in very different environments. Effectiveness of the proposed V2X federated learning system is demonstrated using collision avoidance application using Flow, Veins, and SUMO simulators. Simulation results suggest that it important to use a federated learning to significantly improve the reliability of of the collision avoidance application.
Synopsis of Video Files Using Neural Networks: Component Analysis

The following paper provides a detailed analysis of the components of a novel framework for generating synopsis of CCTV videos. A synopsis is a video file obtained by overlaying the main objects from a source video on a single scene. This allows for a file length reduction and optimization of the storage of such files. This paper extends the presented work based on convolutional neural networks by discussing the effect that such algorithms may have on the final synopsis result which in turn helps in understanding how they can be further improved for this task. For the purposes of the component analysis presented in this paper, specialized datasets and metrics were selected to quantify the quality of the algorithms of the video synopsis framework.
Research on the Application of Computer Virtual Reality Technology in Oral English Courses

Aiming at the problems of imperfect functions and poor control performance of the current related mobile English listening and speaking learning software, this paper designs and implements a set of English listening and speaking autonomous learning software based on Android system. In addition to the regular audio file playback function, this software is designed and implemented., and also realize the functions of audio file repetition and subtitle matching, spoken language recognition and spoken language matching, and lock screen control. The research shows that the college oral English test platform has achieved the expected effect of the college English oral test to a certain extent. How to use the computer-aided oral test platform to achieve the test close to the real language environment needs further research.
Sequential Short-Text Classification from Multiple Textual Representations with Weak Supervision

The amount of news generated on the internet has increased significantly in recent years. As a trend, text data has gained attention from industry, government, academia, and the financial market. This information is potentially valuable to assist domain experts in decision making. Therefore, related applications based on machine learning have been widely available in several areas of knowledge. However, for supervised learning tasks, the availability of annotated texts in quantity and quality is a recurring problem. This work proposes a time-series-driven approach to labeling chronologically arranged documents. Our proposal categorizes short texts for a particular domain according to the level and trend patterns of a given time series. We use the obtained weak labels with the understanding that they are imperfect but still useful for building predictive text models. Documents and agribusiness commodity price series were employed to assess performance in four classification scenarios. The experimental evaluation considered nine textual representations and different learning paradigms. Neural language-based models demonstrated better classification performance than traditional ones. The results indicate that the proposed approach can be an alternative for automatically labeling a large news volume.
The Content of Education

The student is a co-author of education that expands the possibilities for the development of his personality. He independently and with the support of a teacher not only repeats the path of mankind but also discovers the laws of nature, society and personality, invents methods and algorithms, realizes big ideas – the basis for his orientation in the world. He masters emerging cultural practices and participates in their creation. Knowledge, skills and life skills will be possessed by the expanded personality of the student. In the future, the need for “memorization” and the time spent on achieving dexterity in work will be greatly reduced. Readiness for perception and creation of a new, independent search and application of knowledge and mode of action are the basis for creative development and pre-adaptation of the individual. The laws and algorithms independently discovered by the student, the facts found are preserved by him in the expanded developing personality and are used as cultural tools. The school develops systemic and critical thinking, universal actions and digital skills for the 21st century, helps the child to choose and master from the treasures of human culture what he likes, whether it is calligraphy, programming, acrobatics or versification.
Revisiting Intermediate-high Cold Reading Curriculum on Design and Practice

The phrase “cold reading” originates from marketing psychology, but for teaching Chinese as a second language, it refers to one of many strategies in the reading curriculum. Schema theory tells us those good readers are adept at comprehending interactions between bottom-up and top-down processes (Rumelhart, 1980). According to Liu Songhao et al. (2015), there is a close correlation between reading level and teaching strategy. Research on reading education for Chinese as a second language has largely focused on how educators should teach, but little discussion can be found with regards to how a student engages with reading material. Although the breadth of Chinese vocabulary likely differs from English, it remains clear that non-native readers are limited by their language level and must strive harder to develop the skill and psychological ability to adapt to challenges. In cold reading courses, the student becomes the lead actor, while the teacher adopts a supportive role.BR ICLP(NTU) started up the cold reading curriculum in 2016 for a new trial. However, as opposed to “hot reading”, unlike courses designed for common reading, classes dedicated to cold reading lack translations or vocabulary lists. Students are only provided texts in class, most of which come from authentic first-hand sources. The goal is to help students escape their psychological reliance on the classroom and to develop the ability to approach material they might encounter in daily life. Since students cannot prepare for cold reading courses in advance and lack supplemental material while in class, it better reflects real-life circumstances and forces students to draw upon their foundation of linguistic and background knowledge to guess or infer meaning.BR Through this method, students are expected to build a preliminary cognitive model of the text, which is then analyzed level-by-level in tandem with the teacher’s instruction to either confirm or adjust the student’s mental connotations, eventually resulting in comprehension. The support of the teacher’s knowledge and the initiation of the student’s metacognitive processes also play important roles. A few appropriate tests after an independent skimming reading can evaluate a learner’s level of comprehension and can help clarify problem areas. At the same time, students can also improve their listening, speaking and writing abilities through class discussion and homework assignments. After-class review and making their own vocabulary list can further solidify learning gains and become the knowledge foundation for the next cold reading assignment (Chen Hsiangyu, 2017).BR Base on the discussion of cold reading theory, curriculum design, and practice, this article aims at examining 1) Reading and cognitive theory, 2) Theories on teaching and learning Chinese reading for non-native students, 3) Approaches to the cold reading curriculum, 4) Limitations of and reflections on cold reading courses, and 5) Aspects of further research.
Food for Thought: Solid Liquid Gas Plasma Cake

If you ask chemical engineering students about the phases of matter, they will happily tell you that things are solids, liquids, gasses (or, sometimes, plasmas, apparently the letter “y” of matter) and go on to give you practical definitions of the same. And if you ask how substances tend to transform, students will add with absolute confidence that it’s nearly all about temperature - at low temperature, materials are solids, and as you go upwards they tend to become liquids then gasses, although at some pressures for some substances, sometimes you do not pass liquid (or collect $200) and go straight to gas. But a place where this all goes sideways is when you ask the same students what phase “cake” is.