Reviewerly/exHarmony huggingface dataset:
Asymptotic Convergence Rate of Dropout on Shallow Linear Neural Networks

We analyze the convergence rate of gradient flows on objective functions induced by Dropout and Dropconnect, when applying them to shallow linear Neural Networks (NNs) ---which can also be viewed as doing matrix factorization using a particular regularizer. Dropout algorithms such as these are thus regularization techniques that use {0,1} -valued random variables to filter weights during training in order to avoid coadaptation of features. By leveraging a recent result on nonconvex optimization and conducting a careful analysis of the set of minimizers as well as the Hessian of the loss function, we are able to obtain (i) a local convergence proof of the gradient flow and (ii) a bound on the convergence rate that depends on the data, the dropout probability, and the width of the NN. Finally, we compare this theoretical bound to numerical simulations, which are in qualitative agreement with the convergence bound and match it when starting sufficiently close to a minimizer.
The Constrained Interaction Network for Aspect-level Sentiment Classification Task

The purpose of aspect-level sentiment classification is to predict the sentiment polarity of specific aspect words in a sentence. Recently many works exploit LSTM models based on the attention mechanism. However, the prior work only attends to using the aspect terms to capture the aspect-specific sentiment information in the text. It may cause the mismatch of sentiment when the aspect words are extracted incorrectly. To solve this problem, we propose a simple but effective framework called the Constrained Interaction Network(CIN), which consists of the context-aspect level interaction layer(CAI-Layer), the long and short-term memory network layer(LSTM-Layer), and Constraint Attention layer(CA-Layer). CIN can extract the sentiment features of specific aspects with the assistance of LSTM-Layer and CAI-Layer, which greatly share the attention layer. The experiment conducted on three widely used data sets in SemEval 2014 and Twitter shows that the constrained attention mechanism is always better than other existing attention mechanisms, which also confirms that the CA- Layer can indeed help LSTM to extract the specified aspect-level sentiment characteristics.
Automated Consulting for Cloud Native Architectures

The rapidly growing interest in cloud native applications means more users than ever are needing guidance on how to design and deploy their systems. The Cloud Native Computing Foundation (CNCF) has developed a landscape map that provides the most current collection of both open source and proprietary tools and services, but the breadth of options and the complex relationships between various tools and services can be quite overwhelming. Most users interested in exploring the cloud native space will need to hire an experienced consultant to guide them through the choices, but this can be both cost prohibitive and logistically challenging. This paper describes our work to automate this consulting process and enable users to make intelligent, informed decisions about their architecture design and then deploy the architecture for immediate use.
Application of Machine Learning and Digital Information Technology in Volleyball

Since the 1980s, machine learning has attracted extensive attention in the field of artificial intelligence. Following the expert system, it opened a precedent for the application of machine learning in the field of artificial intelligence and became one of the important topics of artificial intelligence. However, in the field of volleyball, the application of machine learning and information technology in volleyball is extremely limited. Volleyball has not developed widely in society nor has it become a common event in people’s daily life. Therefore, the development of volleyball in China lags behind. Unlike other sports, volleyball requires both strong skills and playing tactics. While taking into account the technical and tactical aspects, the requirements for the comprehensive quality and learning ability of both sides of the teaching are relatively strict. If the application of modern information technology is neglected, it may affect the teaching effect of volleyball and hinder the long-term spread of volleyball. The article starts with the serving, landing, and blocking of two groups of volleyball players with different sports levels. Through the application of machine learning and digital information technology in volleyball, as well as the use of artificial neural networks and genetic algorithms, the reaction time and accuracy of judging serving, landing, and blocking are improved, and specific application strategies are further proposed. According to the influence of athletes of different levels on the cognition of volleyball landing points, it can be seen that there are three parts that account for 40% of the allocation.
Human-Cascaded network for Robust Detection of Occluded Pedestrian

Occlusion is a key challenge in real-world on-road pedestrian detection task. Due to constrained viewpoint geometry, a pedestrian is very likely to be obstructed by other pedestrians and/or other objects such as cars and bicycles. For Advanced Driver Assistance System (ADAS), heavily occluded pedestrians are as important as reasonable pedestrians because they may burst out from crowds or roadside obstacles. In this paper, a human-cascaded network is proposed for robust detection of heavily occluded pedestrians. Specifically, a sharp-response proposal network (SRPN) is designed to refine the feature responses in a narrow area to handle crowded pedestrian detection, followed by outputting the head and full body proposals for diverse occlusion situations. After RoI-pooling, a visible-guided attention (VGA) module is developed to leverage the head and visible area information. The VGA module also suppresses the feature noise of occluded area to enhance the feature representation learning of the backbone network. Finally, a head-cascade RCNN (HRCNN) network is proposed to predict the pedestrian bounding box from the head proposal. The proposed approach is validated through a widely used pedestrian detection dataset: CityPersons. Experimental results show that our approach achieves promising detection performance (log-average miss rate, MR) improvement of 11.4% on heavy occlusion subset, compared to the baseline detector.
Let’s get to learn the particulate structure of matter with augmented reality!: a jigsaw IV technique lesson plan

The present study aimed to develop, introduce, and conduct an activity for the integration of augmented reality to the jigsaw IV technique in a planned and systematic manner. The activity developed for secondary school students is related to the “Pure Substances” subject which it is included in the “Particulate structure of matter” topic. The activity was arranged as; an attractive introduction, expert group studies, home team studies and assessment stages. Whereas augmented reality was used during the “home team study” stage for the jigsaw IV technique. Students are given the opportunity to examine a lot of resources during the “expert group study” and “home team study” stages to encourage them to be educated as science literate individuals. Whereas the students are subject to individual examinations during the assessment stage. This activity is an example for increasing the usage area of augmented reality in the education environment. Moreover, it is also considered that the activity will provide benefits in ensuring meaningful learning by embodying abstract concepts and helping students in improving 21st-century skills (e.g., cooperative study skills, learning to learn, communication, entrepreneurship, adaptation, leadership, social skills, and responsibility, etc.).
FORMATION OF PROFESSIONAL COMPETENCES OF STUDENTS IN THE DIRECTION "PEDAGOGICAL SCIENCES"

This article discusses the key professional competencies: communicative and organizational. The essential characteristics, structure and interdependence of these competencies have been studied. The results of an empirical study are presented, the purpose of which was to determine the level of formation of communicative and organizational competencies among students in the direction of "Pedagogical Sciences".
An infinite perfect-secrecy system with non-uniformly distributed keys

Based on some recent results derived from the Shannon theory of secrecy systems, this paper develops a new mathematical model for an infinite cryptosystem with perfect secrecy, and establishes a sufficient condition for a cryptosystem of perfect secrecy with non-uniformly distributed keys based on several groups of orthogonal Latin squares. The new model has the following features and advantages. First, the model with non-uniformly distributed keys, where the number of keys can be many more than the number of messages in a secrecy system, greatly improves the common one with finite perfect secrecy based on modulo additions in the current literature. Second, the model can be regraded as a theoretical base for designing practical block cipher algorithms. Third, since the number of basic cryptosystems in the new model is many more than that of the basic cryptosystems available in the current literature, it helps design new stream and block cipher algorithms with practical security and better resistance to known plaintext attacks. Finally, the present research on infinite secrecy systems of the new model is theoretically valuable and practically useful, as illustrated by an example of designing new basic cryptosystems under the conditions of the new model.
A Nearest Neighbor Open-Set Classifier based on Excesses of Distance Ratios

This paper proposes an open-set recognition model that is based on the use of extreme value statistics.For this purpose, a distance ratio is introduced that expresses how dissimilar a target point is from the known classes by considering the ratio of distances locally around the target point.It is shown that the class of generalized Pareto distributions with bounded support can be used to model the peaks of the distance ratio above a high threshold.The resulting distribution provides a probabilistic framework to perform open-set recognition.Furthermore, we describe a numerical procedure to estimate the hyperparameters of our model.This procedure is based on a new objective function that considers both the fit of the generalized Pareto distribution and the misclassification error of the known classes.Our method is applied to three image data sets and an audio data set showing that it outperforms similar open-set recognition and anomaly detection methods.
Bakpia Emoticon dengan Selai Jambu Biji

Birthday celebrations are usually done by holding a party or thanksgiving. There are several things that need to be considered by parents to prepare for a child's birthday party, namely: Party Place and Decoration, Birthday Cake, Invitations, Events, Costumes, Souvenirs and Food and Drinks. Food and drinks cannot be separated from a party, because the banquet is a sign of how to appreciate the guests who attend the event. Emoticon bun is a dish that is made interesting by combining the shape of a round bun using an emoticon decoration that describes an expression. The application of emoticons in buns gives an interesting impression if the dish is specially made for children. Therefore, emoticon buns with guava jam can be used as an additional dish at a child's birthday party. The method used in this research is an experiment supported by literature studies and real work. From the results of the trial, it was obtained that emoticon bun filled with guava jam.&#x0D; &#x0D; Keywords: Birthday dish, emoticon bakpau
Is it possible to turn the "upbringing" from Cinderella into a princess?

The article discusses the situation with education in modern culture and pedagogy, raises the question of the reasons for the decline in interest in education and the possibility of its revival in the near future. The meanings of education (widely understood education, controlled development, inclusion in culture) are considered. Two conditions of the conceivability of the traditional understanding of education are analyzed. Firstly, it is the principle of "epistemological transparency" (according to it, the teacher knows the laws of student development and the requirements that will be imposed on him at the exit of education-upbringing), which allows to determine the goals and content of education-upbringing, as well as methods of education. Secondly, the "deterministic" principle: the development of a young person can be directed in the direction desired by the state, society and school, while blocking the student's activity leading in the wrong direction. It is argued that currently both of these conditions are not met, which forms a problem for education. Social reality is increasingly thought of as pluralistic, complex and uncertain; and determination in the field of education is becoming less and less, on the contrary, the principles of variability, the need to take into account different types of personality and divergent trajectories of their development and evolution are rising to the shield. The installations of a new concept of education, taking into account modern challenges of the time and sociality, are considered. According to the author, education should satisfy the "environmental approach", "personalistic" and "reflexive-conceptual".
Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study

A backdoor attack controls the output of a machine learning model in 2 stages. First, the attacker poisons the training data set, introducing a back door into the victim's trained model. Second, during test time, the attacker adds an imperceptible pattern called a trigger to the input values, which forces the victim's model to output the attacker's intended values instead of true predictions or decisions. While backdoor attacks pose a serious threat to the reliability of machine learning-based medical diagnostics, existing backdoor attacks that directly change the input values are detectable relatively easily.The goal of this study was to propose and study a robust backdoor attack on mortality-prediction machine learning models that use electronic health records. We showed that our backdoor attack grants attackers full control over classification outcomes for safety-critical tasks such as mortality prediction, highlighting the importance of undertaking safe artificial intelligence research in the medical field.We present a trigger generation method based on missing patterns in electronic health record data. Compared to existing approaches, which introduce noise into the medical record, the proposed backdoor attack makes it simple to construct backdoor triggers without prior knowledge. To effectively avoid detection by manual inspectors, we employ variational autoencoders to learn the missing patterns in normal electronic health record data and produce trigger data that appears similar to this data.We experimented with the proposed backdoor attack on 4 machine learning models (linear regression, multilayer perceptron, long short-term memory, and gated recurrent units) that predict in-hospital mortality using a public electronic health record data set. The results showed that the proposed technique achieved a significant drop in the victim's discrimination performance (reducing the area under the precision-recall curve by at most 0.45), with a low poisoning rate (2%) in the training data set. In addition, the impact of the attack on general classification performance was negligible (it reduced the area under the precision-recall curve by an average of 0.01025), which makes it difficult to detect the presence of poison.To the best of our knowledge, this is the first study to propose a backdoor attack that uses missing information from tabular data as a trigger. Through extensive experiments, we demonstrated that our backdoor attack can inflict severe damage on medical machine learning classifiers in practice.
Automated Grading and Feedback of Programming Assignments

Over the last few years, Computer Science class sizes have increased, resulting in a higher grading workload. To manage this workload, universities often use multiple graders to deliver the grades and associated feedback quickly. While using multiple graders enables the required turnaround times to be achieved, it does come at the cost of consistency and feedback quality. Automating the process of grading and feedback could help solve these issues. This project will look into methods to fully or partially automate grading and feedback, such as machine learning and natural language processing, to improve grade uniformity and feedback quality.
EFL Students’ Perceptions on Using Edmodo in Online Learning During Covid-19 Pandemic

Covid-19 pandemic has been spread out in the entire world in 2020. Many aspects of life get the impact directly. In education, there are several impacts that must be face toward the situation, one of them is about the implementation online class in this new era. From kindergarten until university have their own ways to provide an online class effectively. The research has aim to give the perceptions from EFL students’ on using Edmodo in Critical Discourse Analysis online class. The researcher will give their point of view about this case also. This research will conduct 31 EFL students’, especially in Critical Discourse Analysis class, point of view about online class during pandemic Covid-19, they are from seventh semester in State Islamic Institute of Kediri. The results of the research are expected to provide the data from students’ perceptions about online class and how should this be, in order to improve the online class implementation in the further.
Body-of-Knowledge development by using Artificial Intelligence

This paper proposes and overview of the role artificial intelligence (AI) can play to solve issues related to knowledge management in the field of systems-of-systems (SoS). While the principles presented in the paper apply to both technological and socio-technical systems, we will mostly refer to Product Service Systems (PSS), a particular type of socio-technical systems, to illustrate our purpose. The application of artificial intelligence can bring high-value to the field of PSS since it confers to an organization the means to manage a body of knowledge in an effective and comprehensive way. The present paper presents the expected benefits of using AI in the field of knowledge management for PSS as illustrated by examples of use cases and AI technologies that can be applied.
Response Measures of States in the Field of Higher Education in the Context of Coronavirus

As a result of the sudden onset of a pandemic around the world many problems have arisen that no one has met before. One of these problems was the threat of liquidation of higher education as a social institution. Therefore, this article presents the results of a study of possible actions on the part of states, academic communities and all interested parties, including administrative and financial resources in the context of a sudden outbreak of a pandemic around the world. The consistent measures that the states should take when pursuing a certain state educational policy are analyzed.
Towards utilization of rule base structure to support fuzzy rule interpolation

Abstract Fuzzy rule interpolation (FRI) offers a reliable approach for providing an interpretable approximate decision with a sparse rule base, when a new observation does not match any existing rules. As the mainstream application of a fuzzy rule base is to extract valuable approximate information from each individual rules, existing FRI methods typically work by postulating that the more rules used to implement the interpolation the better the reasoning outcomes. Yet, empirical results have shown that using a large number of rules in an FRI process may adversely lead to worsening the accuracy of the inference outcomes, not just degrading efficiency. The objective of this work is to set a firm theoretical foundation for the eventual establishment of a novel FRI approach. It achieves this goal by mapping the structural patterns within a given fuzzy rule base onto a mathematically isomorphic data space, such that the essential information embedded in the original rule base can be effectively captured, represented and analysed. The resulting mathematically mapped patterns enable the production of a theorem that determines the upper limit of the number of rules required to effectively and efficiently perform FRI. The experimental investigations reported herein demonstrate that the number of required rules to perform FRI obeys the theorem discovered in this work.
Transferable adversarial examples based on global smooth perturbations

Although the attack rate and the imperceptibility of perturbations are two main concerns of adversarial attacks, the transferability of adversarial examples is an emerging topic due to the need for applications. It is known from previous work that adversarial examples with low-frequency perturbations have better transferability than those with high-frequency perturbations. In this paper, we propose a method to generate global smooth low-frequency perturbations with parameterized smooth functions, unlike previous pixel-wise local methods. We optimize perturbations by minimizing a proposed loss function that fulfills the attack task and meets the requirement of imperceptibility for perturbations. The global smoothness of perturbations ensures the spectrum of low-frequency and hence increases adversarial examples’ transferability. In the implementation, the Gaussian mixture model is used as the prototype of parameterized smooth functions to evaluate the proposed method. Extensive experiments on ImageNet and CIFAR-10 demonstrate that our method significantly improves the transferability by 10–20% over other state-of-the-art methods with a comparable attack rate.
An intelligent data analysis system for fault diagnosis of marine monitoring sensors

In order to improve the accuracy of the monitoring data of marine monitoring sensors, diagnose various potential faults of the sensors and repair them in time, an intelligent data analysis system for diagnosing the faults of the marine monitoring sensor is proposed and developed. Three kinds of data analysis methods based on principle of statistics, including the Grubbs Criterion, the PauTa Criterion and the Dixon Criterion, are used to realize the automatic detection of abnormal data and the corresponding algorithm workflow is designed and implemented with Python intelligent computing modules. Taking the wave sensor data as an example, a set of experiments are conducted to verify the effectiveness of the intelligent system. The results indicate that the system ensures the effectiveness and accuracy of monitoring data properly and it can be used to monitor and analyze the abnormal data of wave sensors for fault diagnosis.