Reviewerly/exHarmony huggingface dataset:
Node classification without features using graph convolutional network

Graph convolutional network (GCN) is proposed to deal with graph-structured data, and is applied to many fields successfully, such as computer vision, natural language processing and biology. Most current graph convolutional networks and its derivative methods need node features for classification. In fact, it is a formidable task to obtain node feature or other information. Therefore, we mainly focus on improving accuracy of GCN on the graphs with node features. In order to resolve the issue, we propose a local shortest path GCN (LpGCN) in this work. The proposed method only regard q-hop shortest path distance as node feature, and then employ GCN to realize node classification. The experiment results show that the proposed method can significantly improve the accuracy of node classification in the networks without node features.
A Novel Approach to Detect Face Mask in Real Time

COVID-19, a deadly virus outbreak in the entire world, infected countless number of people and leads to death of millions of people. Economy of countries halted, people are stuck up, the situation is becoming worse day by day that no one expected. The COVID-19 can be spread through airborne droplets, aerosols and other carriers. So, the guidelines are released which promotes 3 key points for its prevention: (a) maintain social distancing, (b) sanitization, and (c) most important, wearing of mask in public places. But unfortunately, people are avoiding these measures leading to spread of the disease. So, there is need of some sort of security guards on ground that ensures people would strictly follow the guidelines. But, it's too risky for the lives of security guards. Fortunately, we live in 21st century. There are so powerful technologies like Machine Learning, Artificial Intelligence, Deep Learning and many more. So, it is possible to develop a way where machines will help us to ensure the guidelines to be followed. There is no need for a physical person to watch over crowd. This research paper proposed a work to implement machine-learning algorithm to ensure people wear the proper face mask or not. It can be used in public places like airports, railway stations or at main gate of societies to ensure that no one without mask may enter the society and similarly can be used at stores.
Blockchain technology and socially sustainable supply chains—A valuation perspective

Blockchain technology, the distributed electronic ledger-based information technology, is disrupting business practices. Consensus capabilities and characteristics of blockchain technology include transparency-traceability of information, immutable and reliable information, smart contracts, and incentivization mechanisms. Global interest in blockchain technology continues to increase across sectors including governments, corporations and industry, civil society, and communities. This chapter explores the blockchain interrelationship with social sustainability and supply chains. It introduces the integration of these concepts to improve organizations and their supply chains. We also introduce how valuation—using economic market and non-market valuation—relates and can support to these major integrated issues. The combination of these topics can aid researchers, policymakers, and practitioners while contributing to strategic organizational and supply chain justification, performance measurement, and sustainability. This chapter will address a critical concern of sustainability valuation and blockchain technology; how they relate. Areas for future research are also described.
Improved Butterfly Optimization Algorithm Fused with Beetle Antennae Search

Aiming at the problems that the butterfly optimization algorithm has low optimization accuracy and easy to fall into the local optimal solution, this paper proposes an improved butterfly algorithm fused with the beetle search (BAS-LBOA). Firstly, chaotic map initialization is used to replace random initialization, so that the butterfly individuals are more evenly distributed in the solution space. At the same time the chaotic mapping function is used to replace the random function to generate the parameters that control the global and local search. Then the Levy flight rule is added which can prevent the search from falling into the latter part of the iteration. Afterwards, inspired by the beetle search algorithm, the beetle antennae search is used to improve the random search of the butterfly optimization algorithm. Finally, the BAS-LBOA algorithm is compared with multiple intelligent algorithms, and the results prove that the BAS-LBOA algorithm has better optimization performance.
Research on the Effects of Online Teaching and Influencing Factors in Primary and Secondary Schools

With the rapid development of emerging information technology, &#x201C;Internet +&#x201D; has been integrated into all aspects of education and teaching, changing the traditional teaching concept, teaching form, teaching environment, and teaching methods. Today, we have entered the post-epidemic era, but education should not and will not return to the state of teaching before the outbreak of the epidemic. Online teaching has become a necessary means of teaching. This paper investigates the effects of online teaching in primary school, junior high school and senior high school. In general, the satisfaction of online teaching in primary schools is higher than that in middle schools, and the satisfaction of students is higher than that of teachers. This paper further analyzes the Effects of Online Teaching and Influencing Factors in Primary and Secondary Schools, in order to provide reference for online teaching research.
Comparative Analysis of Urban and Rural Teachers' Classroom Teaching Behavior

In this paper, urban and rural teachers' teaching classes are se-lected as the re-search object. Based on iFIAS interactive analysis system, the teaching process of urban and rural teachers is compared and analyzed by slice obser-vation. It is found that there are similarities and differences between the two. The similarity is that both urban and rural teachers' classes are open classes, which are student-centered, giving full play to students' autonomy, and students speak actively in class. Teachers encourage students most or adopt their opinions. The difference between them lies in the classroom technology application be-tween urban and rural teachers. This paper focus-es on the comparative analysis of urban and rural teachers' classroom teaching behavior to help them improve their classroom teaching and provide a reference for later teaching.
Randomized Iterative Methods for Matrix Approximation

Standard tools to update approximations to a matrix A (for example, Quasi-Newton Hessian approximations in optimization) incorporate computationally expensive one-sided samples $$A\,V$$ . This article develops randomized algorithms to efficiently approximate A by iteratively incorporating cheaper two-sided samples $$U^\top A\,V$$ . Theoretical convergence rates are proved and realized in numerical experiments. A heuristic accelerated variant is developed and shown to be competitive with existing methods based on one-sided samples.
Comparative Analysis of NLP Text Embedding Techniques with Neural Network Layered Architecture on Online Movie Reviews

In NLP world, there is a need to convert the text data into numerical form in a smart way of text embedding with the machine learning architecture. In this research, the comparative text embedding methods of Binary Term Frequency, Count Vector, Term Frequency - Inverse Document Frequency, and Word2Vec is used for converting text to meaningful representations of vectors, containing numerical values. In order to analyze the performance of the various text embedding techniques, Neural Network Layered Architecture is designed for movie review's polarity classification to include input layer, dense layers followed by the ReLU (Rectified Linear Unit) activation layers and Sigmoid activation function to make classifications on the basis of training–testing performance. Word2Vec text embedding scored the highest training and testing accuracy among all the text embedding techniques of Binary Term Frequency, Count Vector, Term Frequency - Inverse Document Frequency, and Word2Vec with 89.75% and 86.94% respectively with $$\pm { }1.0\,\,{\varvec{error}}$$ for the online movie reviews.
A Spatial Common Datasets for Linkage between Dataset Values

We present a methodology for constructing a spatial ontology-based datasets navigation model to allow cross-reference navigation between datasets. We defined the structure of the dataset as metadata, the field names, and the actual values. We defined the relationship between datasets as 3-layer such as metadata layer, field names layer, and data value layer. The relationships in the metadata layer was defined as the correspondence between metadata values. We standardized the field names in dataset to discover the relationships between field names. We designed a method to discover the relationship between data values based on common knowledge datasets for each domain. To confirm the validity of the presented methodology, we applied our methodology to implement an ontology-based knowledge navigation model for actual disaster-related processes in operation. We built a knowledge navigation model based on spatial common knowledge.
Wide Residual Lightweight Network Using Simplified Adaptive Parameter Rectifying Units

Aiming at the problems of complex network structure, long training time and insufficient feature learning ability for deep learning, a lightweight network structure is designed. A kind of new activation function (namely rectifying linear unit) whose adaptive parameter is achieved by simplified training is proposed. The activation function is inserted into convolutional neural network to improve the feature learning ability by making each input signal has its own set of nonlinear transformation. Compared with traditional convolutional neural network, the number of network parameters is reduced by 51.61%, while the structure remains the ability of feature extraction before simplification. The proposed network structure can greatly reduce the network training time and improve the target recognition speed. The experiments on CIFAR-10 and CIFAR-100 datasets respectively show that the accuracies reach 95.26% and 76.54%, which are 1.67% and 3.76% higher than those of the traditional convolutional neural network.
Convergence and Near Optimality of Q-Learning with Finite Memory for Partially Observed Models

Q learning algorithm is a popular reinforcement learning method for finite state/action fully observed Markov decision processes (MDPs). In this paper, we make two contributions: (i) we establish the convergence of a Q learning algorithm for partially observed Markov decision processes (POMDPs) using a finite history of past observations and control actions and show that the limit fixed point equation gives an optimal solution for an approximate belief-MDP. We then provide bounds on the performance of the policy obtained using the limit Q values compared to the performance of the optimal policy for the POMDP, where we also present explicit performance guarantees using recent results on filter stability in controlled POMDPs. (ii) We apply these results to fully observed MDPs with continuous state spaces and establish the near optimality of learned policies via quantization of the state space, where the quantization is viewed as a measurement channel leading to a POMDP model and a history of unit window size is considered. In particular, we show that Q-learning, with its convergence and near optimality properties, is applicable for continuous space MDPs when the state space is quantized.
Action-Conditioned Frame Prediction Without Discriminator

Predicting high-quality images that depend on past images and external events is a challenge in computer vision. Prior proposals have tried to solve this problem; however, their architectures are complex, unstable, or difficult to train. This paper presents an action-conditioned network based upon Introspective Variational Autoencoder (IntroVAE) with a simplistic design to predict high-quality samples. The proposed architecture combines features of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) with encoding and decoding layers that can self-evaluate the quality of predicted frames; no extra discriminator network is needed in our framework. Experimental results with two data sets show that the proposed architecture could be applied to small and large images. Our predicted samples are comparable to the state-of-the-art GAN-based networks.
Availability in Openstack: The Bunny that Killed the Cloud

The use of cloud is on the rise and is forecast to keep increasing exponentially in the coming years. Openstack is one of the major contributors to the cloud space and there are lot of providers using Openstack for providing cloud solutions. Many organizations run their entire network in the cloud and the availability of the cloud is of paramount importance. Openstack is the popular choice of implementation by many organizations. Openstack is an integration of many projects that make up the platform. There are many advantages to open source modularization and many essential services required to run the infrastructure. One such service is AMQP message broker service and the default one for Openstack is RabbitMQ. Our experimentation shows that it is possible to inject random messages to the queue of RabbitMQ bottling the resource of the main controller. This eventually leads to the entire cloud infrastructure crashing.
Truth Detection Algorithm in Social Media Tweets Using Similarity Measures

In the social media environment, many tweets are posted by users. It is amongst the way of living with social media culture in this era. Internet and Online Media are an important part of communication these days. Sometimes fake news can create a lot of issues that may not be expected by the users. Therefore, it is regulated by cyber-crime authorities to regulate the news and general rules are created by social media websites. This makes it necessary for a proper truth detection algorithm. In this paper, a java implementation of a modified Jaccard Algorithm is applied which then classifies news from the dataset as true or false. The dataset is initially classified on sentiments which are done through the Stanford CoreNLP library in Java. The results show good accuracy for truth detection. This is used in big data truth discovery algorithm to prevent misinformation spread.
Epicentral Region Estimation Using Convolutional Neural Networks

Recent works have assessed the capability of deep neural networks of estimating the epicentral source region of a seismic event from a single-station three-channel signal. In all the cases, the geographical partitioning is performed by automatic tessellation algorithms such as the Voronoi decomposition. This paper evaluates the hypothesis that the source region estimation accuracy is significantly increased if the geographical partitioning is performed considering the regional geological characteristics such as the tectonic plate boundaries. Also, it raises the transformation of the training data to increase the accuracy of the predictive model based on a Projected Coordinate Reference (PCR) System. A deep convolutional neural network (CNN) is applied over the data recorded by the broadband stations of the Venezuelan Foundation of Seismological Research (FUNVISIS) in the region of 9.5 to 11.5N and 67.0 to 69.0W between April 2018 and April 2019. In order to estimate the epicentral source region of a detected event, several geographical tessellations provided by seismologists from the area are employed. These tessellations, with different number of partitions, consider the fault systems of the study region (San Sebastián, La Victoria and Morón fault systems). The results are compared to the ones obtained with automatic partitioning performed by the k-means algorithm.
Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations

The healthcare sector is placing great emphasis currently on modern machine learning techniques and advancements, which are happening on an almost daily basis. The power of these algorithms helps in data-centric operations while also minimizing the cost and time. The probabilistic approaches are utilized in order to minimize the diagnostic cost, alert patients and doctors, and help in early and accurate diagnosis. The big data support and probabilistic schemes such as reflexive testing, naïve Bayes and Bayesian network have been very useful in identifying potentially dangerous diseases in a shorter time and at less cost. The purpose of this chapter is to discuss current technological advancements to develop a solution for the salient cost of health care by maintaining both the institutions' and patients' perspectives, but keeping an emphasis on patient health in making health care more affordable to the general public. In the chapter, the authors discuss a knowledge-based predictive system that can assist medical practitioners in forecasting disease status using the existing clinical data of patients with high accuracy as a clinical analytical method which can further help us minimize the cost of healthcare diagnosis thus making it more economical. The authors discuss the Bayesian neural network (BNN) in addition to Markov chain Monte Carlo and variational inference (VI). They demonstrate this approach by predicting breast cancer occurrence. Around 97.3% accuracy was observed using this model with 0.4 as the decision boundary.
The Application Of The Model System The Achievement Of The Concept As An Attempt To Overcome The Misconceptions Language Indonesia

This research was conducted in class X MA Subulussalam Sumberjo. District Torgamba, Kabupaten Labuhanbatu Selatan. Object in this research is : the Application of the model system the achievement of the concept as an attempt to overcome the misconceptions of Indonesian Language, the Subject of the research is MA Subulussalam Sumberjo. The average value of student learning outcomes before the applied method of the System the achievement of concept is at 36,83% with the criteria is very low. Means the average value of the Indonesian Language students should be improved in the next cycle by applying the method of System achievement concept. The average value of learning outcomes of Indonesian students after applied System method is the achievement of the concept in the first cycle of 64,58 % with the criteria of being and in classical 63%. means the value obtained by the students have not yet reached the kertuntasan classical amounting to &gt; 85%. The average value of learning outcomes of Indonesian students after applied System method is the achievement of the concept in the second cycle of 72,42% with very high criteria and in classical 88%. mean value learning outcomes the student has attained mastery in the classical. The increase in the average value of learning outcomes of Indonesian students from before action to the cycle of 27,75 %, and from cycle I to cycle II by 7.83 %. In cycle I, the average results of observation of the learning activities of students by 2,81 (70,24%) with good category, and in cycle II the average results of observation of the learning activities of students by 3,57 (89,29%) with very good category. In cycle I, the average observation of teacher activity is 3,83 with good category, and in cycle II the average results of the observation of teacher activity by 3.75 with a very good category
Power-based Attacks on Spatial DNN Accelerators

With proliferation of DNN-based applications, the confidentiality of DNN model is an important commercial goal. Spatial accelerators, which parallelize matrix/vector operations, are utilized for enhancing energy efficiency of DNN computation. Recently, model extraction attacks on simple accelerators, either with a single processing element or running a binarized network, were demonstrated using the methodology derived from differential power analysis (DPA) attack on cryptographic devices. This article investigates the vulnerability of realistic spatial accelerators using general, 8-bit, number representation. We investigate two systolic array architectures with weight-stationary dataflow: (1) a 3 × 1 array for a dot-product operation and (2) a 3 × 3 array for matrix-vector multiplication. Both are implemented on the SAKURA-G FPGA board. We show that both architectures are ultimately vulnerable. A conventional DPA succeeds fully on the 1D array, requiring 20K power measurements. However, the 2D array exhibits higher security even with 460K traces. We show that this is because the 2D array intrinsically entails multiple MACs simultaneously dependent on the same input. However, we find that a novel template-based DPA with multiple profiling phases is able to fully break the 2D array with only 40K traces. Corresponding countermeasures need to be investigated for spatial DNN accelerators.
A Secured Framework for Offline File Synchronization using ANN and Storage Optimization Schemes

Advancement in the area of computing and mobile technology have led to the occurrence of a new computing atmosphere by utilizing the different categories of small sized mobile devices such as smart phones PDA, Handheld PCs (HPC) and Pocket PCs. Synchronization between a server and an android device helps the users to use applications more effectively. Synchronization is possible if the user is connected to the internet, if user is not connected to the internet the user task is down till connection regains. To overcome such difficulties in task completion, offline data synchronization scheme is adopted and can save data locally and remotely this can be managed by ANN using classification technique. Offline data synchronization is one of the techniques that helps user to design an application that works without an internet connection and protection of confidential and secure information is a concern for all dealing with such information, Breach in security state for those data result in data loss this can be overcome by validating the user through speech recognition while at login and encrypting the data before uploading to the cloud server. While at the server side before storing the encrypted data into cloud data is optimized by removing the duplicate and unnecessary space bits then stored into the cloud server.
Modeling and Predicting Online Learning Activities of Students: An HMM-LSTM based Hybrid Solution

With significant increase in popularity of online education, need for educators to get to know about learning experiences of students and to provide feedback to students has also been increasing. In this paper, we propose an HMM-LSTM based hybrid solution to model and predict online learning activities of students using online learning management systems (LMS) or platforms and provide real-time feedback to students. Our solution is a smart classifier empowered by and based on the Markov-Chain (MC) approach, specifically a Hidden Markov Model (HMM), with the Long Short-Term Memory (LSTM) neural network. The novelty is in the use and treatment of hidden data and metrics to raise flags that indicate outlier online student behavior based on historical data from the same online session and other sessions in the past. We propose a design of a system in which we utilize HMM and LSTM, and the LSTM component of the model is ‘advised’ by the HMM probability used in the metrics that drive the outlier detection process. The system relies on the LSTM prediction to perform early detection of patterns.