Reviewerly/exHarmony huggingface dataset:
Research on the training path of first-class architectural talents in high-level research universities from the perspective of educational information: —Take Chinese, American and Korean universities as an example

Under the current international “Internet + education” development concept, a high-level research university architecture first-class talent training system from the perspective of education informatization is an inevitable measure to promote the development of Chinese architectural education. When formulating a high-level research university architecture first-class talent training program, we must combine it with social needs and fully understand the basic standards of employers. High-level research universities must also strengthen the cultivation of students' learning skills, innovation abilities and comprehensive quality abilities to meet the needs of the society for high-quality professional talents. This paper proposes the method and path of “China's top-level research university architecture first-class talent training system” through a comparative analysis of the first-class architecture talent training models of Chinese, American and Korean universities. This research breakthrough has laid a theoretical foundation for China to build a new era of high-level research university architectural education system.
Introduction: plot( ), qplot( ), and ggplot( ), Plus Some

R provides many ways to visualize data. Graphics in the R language are generated by functions. Some functions create useful visualizations almost instantly. Other functions combine together to create highly coded sophisticated images. This book shows you how to generate both types of objects.
Graphical Representation Skills in Online Learning During COVID-19 Pandemic Through Augmented Reality Assisted Student Worksheets

This study aims to determine the student's graphical representation skill in online learning through Augmented Reality Assisted student worksheets in Simple Harmonics Motion during COVID-19 pandemic.This study is quasi experiment using Post-test Control Group Design.The population is all students of X grade in SMA Negeri 1 Kalasan.The sampling technique used was simple random sampling.The sample in the study was X MIPA 1 as experimental class and X MIPA 3 as control class.Assessment of graphical representation skill was obtained by giving essay questions.There are three indicators of the graphical representation skill that are measured, namely, the student's ability to find the spring's constant from the F-x graph, student's ability to analyses the order of spring's constant from largest to smallest based on the slope and student's ability to find a spring's displacement when the force is changed based on the graph.The analysis used is descriptive statistics to determine the mean value, standard deviation, standard error mean and independent sample T test.It can be concluded that there is no significant difference between experiment class and control class based on the student's graphical representation skill in online learning during COVID-19 pandemic.
Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

The latest studies have compellingly argued that Neural Networks (NN) classification and prediction are the right direction for forecasting. It has been proven that NN are suitable models for any continuous function. Moreover, these methods are superior to conventional methods, such a Box–Jenkins, AR, MA, ARMA, or ARIMA. The latter assume a linear relationship between inputs and outputs. This assumption is not valid for skimmed milk powder (SMP) forecasting, because of nonlinearities, which are supposed to be approximated. The traditional prediction methods need complete date. The non-AI-based techniques regularly handle univariate-like data only. This assumption is not sufficient, because many external factors might influence the time series. It should be noted that any Artificial Neural Network (ANN) approach can be strongly affected by the relevancy and “clarity” of its input training data. In the proposed Convolutional Neural Networks based methodology assumes price series data to be sparse and noisy. The presented procedure utilizes Compressed Sensing (CS) methodology, which assumes noisy trends are incomplete signals for them to be reconstructed using CS reconstruction algorithms. Denoised trends are more relevant in terms of NN-based forecasting models’ prediction performance. Empirical results reveal robustness of the proposed technique.
Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions

This paper proposes a method to relax the conditional independence assumption of connectionist temporal classification (CTC)-based automatic speech recognition (ASR) models. We train a CTC-based ASR model with auxiliary CTC losses in intermediate layers in addition to the original CTC loss in the last layer. During both training and inference, each generated prediction in the intermediate layers is summed to the input of the next layer to condition the prediction of the last layer on those intermediate predictions. Our method is easy to implement and retains the merits of CTC-based ASR: a simple model architecture and fast decoding speed. We conduct experiments on three different ASR corpora. Our proposed method improves a standard CTC model significantly (e.g., more than 20 % relative word error rate reduction on the WSJ corpus) with a little computational overhead. Moreover, for the TEDLIUM2 corpus and the AISHELL-1 corpus, it achieves a comparable performance to a strong autoregressive model with beam search, but the decoding speed is at least 30 times faster.
Adaptive edge-based bi-cubic image interpolation

Image interpolation is a technique of creating new pixels by using old pixels. Nowadays image interpolation systems are widely used in many digital signal processing applications like reconstructing medical images and increasing the resolution of satellite and multimedia images. Bi-cubic interpolation is one of traditional and high-performance scheme as compared with other conventional interpolations like nearest neighbour and bilinear. However, the traditional interpolation produces image artefacts like jagging and blurring. To avoid these kinds of drawbacks, several adaptive bi-cubic schemes are introduced in the past decade. This work presents an edge-based adaptive bi-cubic image interpolation using clamp filter and sigmoidal edge detection technique. The clamp filter avoids aliasing artefacts and it smoothes edge information by performing low-pass filtering. The experimental result shows that the proposed edge-based bi-cubic outperforms other related bi-cubic image interpolation schemes.
The analysis of the tendencies in the application of artificial intelligence technologies in the educational sphere

The article discusses the main trends in the field of artificial intelligence in relation to the educational sphere. The analysis of the use of artificial intelligence technologies and their impact on the higher education system was carried out. The attitude of the citizens of the Russian Federation to artificial intelligence in education was investigated. The main reasons for the fears of the respondents about the use of intelligent information systems in the educational sphere were identified. The proposals for the implementation of the models and methods of artificial intelligence in the higher education system of the Russian Federation were developed. The potential of the possibilities of the influence of new information technologies on the modern society was indicated.
Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems.

Computer vision approaches are widely used by autonomous robotic systems to sense the world around them and to guide their decision making as they perform diverse tasks such as collision avoidance, search and rescue, and object manipulation. High accuracy is critical, particularly for Human-on-the-loop (HoTL) systems where decisions are made autonomously by the system, and humans play only a supervisory role. Failures of the vision model can lead to erroneous decisions with potentially life or death consequences. In this paper, we propose a solution based upon adaptive autonomy levels, whereby the system detects loss of reliability of these models and responds by temporarily lowering its own autonomy levels and increasing engagement of the human in the decision-making process. Our solution is applicable for vision-based tasks in which humans have time to react and provide guidance. When implemented, our approach would estimate the reliability of the vision task by considering uncertainty in its model, and by performing covariate analysis to determine when the current operating environment is ill-matched to the model's training data. We provide examples from DroneResponse, in which small Unmanned Aerial Systems are deployed for Emergency Response missions, and show how the vision model's reliability would be used in addition to confidence scores to drive and specify the behavior and adaptation of the system's autonomy. This workshop paper outlines our proposed approach and describes open challenges at the intersection of Computer Vision and Software Engineering for the safe and reliable deployment of vision models in the decision making of autonomous systems.
Necessary and sufficient conditions for the convergence of positive series

We provide new necessary and sufficient conditions for the convergence of positive series developing Bertran-De Morgan and Cauchy type tests given in [M. Martin, Bull. Amer. Math. Soc. 47(1941), 452-457] and [L. Bourchtein et al, Int. J. Math. Anal. 6(2012), 1847-1869]. The obtained result enables us to extend the known conditions for recurrence and transience of birth-and-death processes given in [V. M. Abramov, Amer. Math. Monthly 127(2020) 444-448].
The university library within Russian State Agrarian University – Moscow Timiryazev Agricultural Academy

Academic information space is seen as an element of educational environment to facilitate student social and professional adaptation. The students’ perspective of the information component of educational environment was studied at Russian State Agrarian University – Moscow Timiryazev Agricultural Academy in 2019–2020 as the pilot project. The role of the academic library in the life of university educational environment is analyzed. The long-standing characteristics of Zheleznov Central Scientific Libraries are discussed. The Library’s information products and external information resources (special databases, www-sites and webservers of leading agricultural institutions with the access provided) as well as the Library’s information services and factors to define professional information demands are discussed. The efficiency of collaboration between librarians and the faculty is emphasized. The role of the Library in educating information culture in specialists-to-be and influencing their satisfactory information behavior is characterized; the Library’s cultural and educational activities and events (lectures, arts exhibition, etc.) are discussed. The conclusions are made and prospects for developing favorable environment for students’ social adaptation and for educating professional competences are outlined.
“Reading Pictures Instead of Looking”: RGB-D Image-Based Action Recognition via Capsule Network and Kalman Filter

This paper proposes an action recognition algorithm based on the capsule network and Kalman filter called “Reading Pictures Instead of Looking” (RPIL). This method resolves the convolutional neural network’s over sensitivity to rotation and scaling and increases the interpretability of the model as per the spatial coordinates in graphics. The capsule network is first used to obtain the components of the target human body. The detected parts and their attribute parameters (e.g., spatial coordinates, color) are then analyzed by Bert. A Kalman filter analyzes the predicted capsules and filters out any misinformation to prevent the action recognition results from being affected by incorrectly predicted capsules. The parameters between neuron layers are evaluated, then the structure is pruned into a dendritic network to enhance the computational efficiency of the algorithm. This minimizes the dependence of in-depth learning on the random features extracted by the CNN without sacrificing the model’s accuracy. The association between hidden layers of the neural network is also explained. With a 90% observation rate, the OAD dataset test precision is 83.3%, the ChaLearn Gesture dataset test precision is 72.2%, and the G3D dataset test precision is 86.5%. The RPILNet also satisfies real-time operation requirements (&gt;30 fps).
Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation

Abstract Due to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image denoising. Experimental results on several test images were taken on Magnetic Resonance Imaging (MRI), X-RAY and Computed Tomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio (PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM), and Correlation Coefficient (CC) were computed. The results shows that Contourlet based Medical image denoising methods are achieving significant improvement in association with Neighsureshrink thresholding technique.
Improve Critical Thinking Ability Through Augmented Reality Assisted Worksheets

Worksheets have a complex arrangement that can support the learning process.Augmented reality technology can also be integrated in worksheets for physics concepts.The population in this study was Class X SMAN 1 Kalasan and obtained XI MIPA 1 as the experimental class and XI MIPA 3 as the control class.Quasi-experimental with post-test control group design was used to measure the critical thinking skills of the group.Ability to think critically with instrument assessment essay questions with inference indicators, analysis, evaluation and assessment.The hypothesis of analysis used independent t-test with the help of SPSS and it was found that there were differences in critical thinking skills in the two sample groups.This shows that the use of worksheets with augmented reality integration can be used to train and improve physics critical thinking skills in physics harmonic material.
Sentiment analysis of German Twitter

The immense popularity of online communication services in the last decade has not only upended our lives (with news spreading like wildfire on the Web, presidents announcing their decisions on Twitter, and the outcome of political elections being determined on Facebook) but also dramatically increased the amount of data exchanged on these platforms. Therefore, if we wish to understand the needs of modern society better and want to protect it from new threats, we urgently need more robust, higher-quality natural language processing (NLP) applications that can recognize such necessities and menaces automatically, by analyzing uncensored texts. Unfortunately, most NLP programs today have been created for standard language, as we know it from newspapers, or, in the best case, adapted to the specifics of English social media.This thesis reduces the existing deficit by entering the new frontier of German online communication and addressing one of its most prolific forms—users’ conversations on Twitter. In particular, it explores the ways and means by how people express their opinions on this service, examines current approaches to automatic mining of these feelings, and proposes novel methods, which outperform state-of-the-art techniques. For this purpose, I introduce a new corpus of German tweets that have been manually annotated with sentiments, their targets and holders, as well as lexical polarity items and their contextual modifiers. Using these data, I explore four major areas of sentiment research: (i) generation of sentiment lexicons, (ii) fine-grained opinion mining, (iii) message-level polarity classification, and (iv) discourse-aware sentiment analysis. In the first task, I compare three popular groups of lexicon generation methods: dictionary-, corpus-, and word-embedding–based ones, finding that dictionary-based systems generally yield better polarity lists than the last two groups. Apart from this, I propose a linear projection algorithm, whose results surpass many existing automatically-generated lexicons. Afterwords, in the second task, I examine two common approaches to automatic prediction of sentiment spans, their sources, and targets: conditional random fields (CRFs) and recurrent neural networks, obtaining higher scores with the former model and improving these results even further by redefining the structure of CRF graphs. When dealing with message-level polarity classification, I juxtapose three major sentiment paradigms: lexicon-, machine-learning–, and deep-learning–based systems, and try to unite the first and last of these method groups by introducing a bidirectional neural network with lexicon-based attention. Finally, in order to make the new classifier aware of microblogs' discourse structure, I let it separately analyze the elementary discourse units of each tweet and infer the overall polarity of a message from the scores of its EDUs with the help of two new approaches: latent-marginalized CRFs and Recursive Dirichlet Process.
A Study on Coding Education for Non-Computer Majors Using Programming Error List

When carrying out computer programming, the process of checking and correcting errors in the source code is essential work for the completion of the program. Non-computer majors who are learning programming for the first time receive feedback from instructors to correct errors that occur when writing the source code. However, in a learning environment where the time for the learner to practice alone is long, such as an online learning environment, the learner starts to feel many difficulties in solving program errors by himself/herself. Therefore, training on how to check and correct errors after writing the program source code is necessary. In this paper, various types of errors that can occur in a Python program were described, the errors were classified into simple errors and complex errors according to the characteristics of the errors, and the distributions of errors by Python grammar category were analyzed. In addition, a coding learning process to refer error lists was designed to present a coding learning method that enables learners to solve program errors by themselves.
DMSPool: Dual Multi-Scale Pooling for Graph Representation Learning

Graph neural networks (GNNs) have recently become a powerful graph representation technique for graph-related tasks. However, the existing GNN models mainly focus on generalizing convolution and pooling operations in a pre-defined unified architecture, limiting the model’s ability to capture meaningful information of nodes or local structures. Besides, the importance of subgraphs at various levels has not been well-reflected. To address the above challenges, we propose Dual Multi-Scale Pooling (DMSPool), which uses multiple architectures concurrently to integrate graph convolution and pooling modules in an end-to-end fashion. Specifically, these modules adopt multiple GNN architectures to learn node-level embeddings and nodes’ importance from different aggregation iterations. Additionally, we employ attention mechanism to adaptively determine the contribution of subgraphs’ representations at varying levels to graph classification and integrate them to perform the cross-scale graph level representation. Experiment results show that DMSPool achieves superior graph classification performance over the state-of-the-art graph representation learning methods.
A Survey and Analysis of Crowd Anomaly Detection Techniques

In recent years, Crowd Anomaly detection has become a field of critical importance, especially so after the recent surge in technological development in the modern era. An increasing number of situations or places require surveillance which is also required to detect anomalies so as to prevent unforeseen accidents. Crowd Anomaly detection usually works by analyzing the surveillance scene in real time using various concepts and techniques such as AI, ML, optical flow analysis, streak flow analysis etc. to identify anomalies in a crowd of people. If an anomaly is found, the relevant authorities are immediately alerted. In this survey paper, few of these methodologies are analyzed by comparing their merits and demerits.
A Practical Approach for Ranking Software Warnings from Multiple Static Code Analysis Reports

Static analysis tools examine source code to look for software flaws and potential vulnerabilities. It is a common practice to use multiple tools so that we do not overlook code which truly has a problem. However, the problem of using multiple bugs finding tools is they not only detect similar software defects but also generate new warning messages. The excessive warnings make code analysis time consuming and expensive. In this paper, we describe our methods to merge software warning categories from different bugs finding tools from two popular programming languages such as Java and C++, and prioritize the files consolidated warning messages by building an analytical model using principal component analysis. Results have shown that files real software defects occupied top of the list, and false positives occupied the bottom slots.
A Coprocessor-based Introspection Framework via Intel Management Engine

During the past decade, virtualization-based (e.g., virtual machine introspection) and hardware-assisted approaches (e.g., x86 SMM and ARM TrustZone) have been used to defend against low-level malware such as rootkits.However, these approaches either require a large Trusted Computing Base (TCB) or they must share CPU time with the operating system, disrupting normal execution.In this article, we propose an introspection framework called NIGHTHAWK that transparently checks system integrity and monitor the runtime state of target system.NIGHTHAWK leverages the Intel Management Engine (IME), a co-processor that runs in isolation from the main CPU.By using the IME, our approach has a minimal TCB and incurs negligible overhead on the host system on a suite of indicative benchmarks.We use NIGHTHAWK to introspect the system software and firmware of a host system at runtime.The experimental results show that NIGHTHAWK can detect real-world attacks against the OS, hypervisors, and System Management Mode while mitigating several classes of evasive attacks.Additionally, NIGHTHAWK can monitor the runtime state of host system against the suspicious applications running in target machine.