Reviewerly/exHarmony huggingface dataset:
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification

Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, which may be unrealistic. To cope with those cases where the query shots of each class are nonuniform (i.e. nonuniform few-shot learning), we propose a Task-Prior Conditional Variational Auto-Encoder model named TP-VAE, conditioned on support shots and constrained by a task-level prior regularization. Our method obtains high performance in the more challenging nonuniform few-shot scenarios. Moreover, our method outperforms the state-of-the-art in a wide range of standard few-shot image classification scenarios. Among them, the accuracy of 1-shot increased by about 3\%.
Relevance of Sri Aurobindo’s philosophy of education to national education policy 2020

The National Education Policy (NEP) 2020 aims to turn India into a ‘vibrant knowledge-driven society’ by designing a ‘India-Centric’ education ecosystem capable of disseminating high-quality education to all in a sustainable way. In this pursuit, it is necessary to review the educational philosophy of great Indian philosophers. The paper presents the relevance of Sri Aurobindo’s educational philosophy to NEP 2020. In this vein, Prime Minister, Narendra Modi informed to Indian people about Aurobindo’s vision of national education on which NEP 2020 is founded during his monthly radio broadcast on Mann ki Baat , on 29th November 2020. From this perspective the present paper explored, examined, and explicated the Aurobindo’s educational philosophy regarding NEP 2020 themes.
A Supervised Learning Approach for Classification of Medical Image Retrieval

Abstract The advancement in biomedical engineering have been significant to the medical or healthcare industry. However, it faces issues like how it can be applied to the medicine and biology for healthcare aspects. Recently, quick advances of programming and equipment innovation have made simple, the issue of keeping up beneficial images accumulations. Visual elements like shading, and shape and composition are actualized for image retrieval. Conventional strategies for image indexing have been demonstrated neither reasonable nor effective regarding space and time so it set off the advancement of the new approach. A new concept called Content-based image retrieval (CBIR) is beneficial for the different sort of medical images having dissimilar imaging modalities, anatomic areas with diverse directions and biological schemes is projected. Classification of the medical image retrieval is the major concern for group of medical image. Hence, support vector machine (SVM) classifier can be favorable for grouping forecast of query and database images based on similarity matching. It is very difficult to detect the features of the compared images effectively for all the different types of queries. Hence, the proposed SVM-MIR aims to classify and retrieval of biomedical images using SVM classifier method. The SVM-MIR based classification considers numerous groups of medical images for analysis. The outcomes of the proposed SVM-MIR approach achieve better performance compared to the existing approach.
Enhancing EFL students’ reading learning process in COVID-19 pandemic through Nearpod

This research aims to enhance students’ reading learning process using a genre-based approach during the COVID-19 pandemic through Nearpod. Action research with four stages was applied as a research design. The qualitative data were collected through interviews and teacher’s field notes, while the quantitative data were obtained by students’ reading pre-test and post-test. The qualitative data were analyzed by assembling the data, coding the data, building interpretations, and reporting the outcomes. Meanwhile, the quantitative data were analyzed using descriptive quantitative analysis. Findings demonstrated that students learned how to determine the main idea, make an inference, and distinguish detailed information from the texts. They also considered Nearpod as an accommodating and encouraging tool in learning. The teacher addressed how to reinforce the students’ reading learning process by deep consideration, preparation, and reflection to ensure that Nearpod worked properly. The statistical report of students’ scores for the pre-test and post-test showed improvement from 64.7 to 84.8. The average students’ post-test score was greater than the pre-test. This study revealed that the Nearpod program successfully supported the reading learning process for students because of its user-friendliness and it enabled teachers to monitor students’ progress during the teaching process.
Efficient Human Pose Estimation via 3D Event Point Cloud

Human Pose Estimation (HPE) based on RGB images has experienced a rapid development benefiting from deep learning. However, event-based HPE has not been fully studied, which remains great potential for applications in extreme scenes and efficiency-critical conditions. In this paper, we are the first to estimate 2D human pose directly from 3D event point cloud. We propose a novel representation of events, the rasterized event point cloud, aggregating events on the same position of a small time slice. It maintains the 3D features from multiple statistical cues and significantly reduces memory consumption and computation complexity, proved to be efficient in our work. We then leverage the rasterized event point cloud as input to three different backbones, PointNet, DGCNN, and Point Transformer, with two linear layer decoders to predict the location of human keypoints. We find that based on our method, PointNet achieves promising results with much faster speed, whereas Point Transfomer reaches much higher accuracy, even close to previous event-frame-based methods. A comprehensive set of results demonstrates that our proposed method is consistently effective for these 3D backbone models in event-driven human pose estimation. Our method based on PointNet with 2048 points input achieves 82.46mm in MPJPE3D on the DHP19 dataset, while only has a latency of 12.29ms on an NVIDIA Jetson Xavier NX edge computing platform, which is ideally suitable for real-time detection with event cameras. Code is available at https://github.com/MasterHow/EventPointPose.
A Multi-Label Classification of Al-Quran Verses Using Ensemble Method and Naïve Bayes

Al-Quran is the holy book as a guide and also a source of law for muslims. Thus, understanding and studying Al-Quran is very important for muslims. To make it easier for muslims to understand and study the Qur'an, it is necessary to classify the verses of the Al-Qur'an. This study built a system that can perform multi-label classification of Al-Quran verses. Multi-label means that the classification will divide each verse of the Al-Quran into more than 1 topic. The model is built using the ensemble method by combining several Naïve Bayes algorithms. The ensemble method was chosen because research with different datasets can obtain good performance. The naïve Bayes algorithm was also chosen because it has a simple calculation so it requires a fairly short computation time. The preprocessing step is also carried out to see the comparison of performance results. To measure the performance of the system that has been built, the calculation of hamming loss is used. Based on the experimental results with several testing scenarios, the best performance results are obtained by combining Multinomial NB and Bernoulli NB with a hamming loss value of 0.1167. Thus, the use of the ensemble method can improve performance compared to without the ensemble method. This research can also of course build a multi-label classification model for the verses of Al-Quran with the ensemble method
GESTURE BASED MONITORING SYSTEM FOR PARTIALLY PARALYSED PATIENTS

The electronic document contains details on a device termed Gesture based monitoring system for partially paralysed patients.he device created and the idea originally belongs to RAY HealthTech. The concept of paralysis is explained initially, this is followed by the technical explanation of the device created, the electronic components and the physics of the Monitoring system.
Application of Auxiliary and Alternative Communication System Based on Big Data Analysis in English Teaching

In order to improve the teaching effect of modern English, this paper combines the auxiliary and alternative communication systems based on big data analysis to improve English teaching, improve the effect of English teaching, and change the traditional English teaching mode. Moreover, this paper applies the Internet of Things and big data technology to English classroom teaching, and proposes a clustering routing protocol suitable for high dynamic environments. The LEACH algorithm is used for cluster head election and maintenance to ensure the stability of the network. In addition, this paper uses a driver protocol for business transmission between clusters, constructs an auxiliary and alternative communication system based on big data analysis, and evaluates the system in combination with teaching evaluation experiments. According to the teaching evaluation research, the auxiliary and alternative communication system based on big data analysis proposed in this paper has good application effect in English teaching, and can effectively improve the effect of English teaching.
Prediction of Bus Passenger Traffic using Gaussian Process Regression

The paper summarizes the design and implementation of a passenger traffic prediction model, based on Gaussian Process Regression (GPR). Passenger traffic analysis is the present day requirement for proper bus scheduling and traffic management to improve the efficiency and passenger comfort. Bayesian analysis uses statistical modelling to recursively estimate new data from existing data. GPR is a fully Bayesian process model, which is developed using PyMC3 with Theano as backend. The passenger data is modelled as a Poisson process so that the prior for designing the GP regression model is a Gamma distributed function. It is observed that the proposed GP based regression method outperforms the existing methods like Student-t process model and Kernel Ridge Regression (KRR) process.
A closed-form alternative estimator for GLM with categorical explanatory variables

The parameters of generalized linear models (GLMs) are usually estimated by the maximum likelihood estimator (MLE) which is known to be asymptotically efficient. But the MLE is computed using a Newton-Raphson-type algorithm which is time-consuming for a large number of variables or modalities, or a large sample size. An alternative closed-form estimator is proposed in this paper in the case of categorical explanatory variables. Asymptotic properties of the alternative estimator is studied. The performances in terms of both computation time and asymptotic variance of the proposed estimator are compared with the MLE for a Gamma distributed GLM.
A Zero Trust Framework Security to Prevent Data Breaches and Mitigate the Cloud Network Attacks

Abstract: Due to new modes of communication, we have seen a surge in the use of wireless networks in recent years. Online safety has become a contentious issue in the community. People want to be able to access all of your applications and resources at any time and from any location. As the use of cloud computing and the Internet of Things grows, so does the number of linked devices, increasing the number of cyber crime targets. A simple shift in mindset can help secure data and the network as a whole. This article defines a Zero Trust Network and illustrates some of the concepts that underpin this architecture/philosophy. Everything inside or outside the network is not reliable unless it is confirmed, according to the Zero Trust design. Zero Trust is a sophisticated security approach in which all users, both inside and outside an organization's network, must be authorized, authenticated, and validated of their security posture and configuration on a continuous basis before being granted access to the network, data, and applications. To validate a user's identity while ensuring tight security, this strategy employs high-end security technologies such as multi-factor authentication, next-gen endpoint security, and identity &amp; access management. Keywords: Cyber security, Zero Trust Network,logical components, security solutions, Google Beyond Corp.
The experiences of biology education master students in web 2.0 content development

In this study, the effect of the activities carried out via Web 2.0 tools on the biology education master students’ content development self-efficacy beliefs and students’ views were examined. The mixed model was used in the study. The study group consisted of 15 students who took the course “Web Supported Material Design in Biology Teaching” in the last three years in the Master's program of Biology Education of a public university in Turkey. The data of the research were obtained through the “Web 2.0 Fast Content Development Self-Efficacy Belief Scale” and an interview form containing 5 open-ended questions. During the implementation process, students developed content with eight web 2.0 tools (Camtasia Studio, Prezi, Thinglink, etc.). As a result of the implementation, it was seen that the self-efficacy belief levels of the students increased. When the students’ views were examined, the advantages of web 2.0 tools such as being interesting and entertaining, increasing retention in learning and providing rich content came to the fore. Regarding the limitations of these tools, issues such as lack of internet connection and hardware, technical problems, high cost and time management are highlighted. In addition, suggestions for the effective use of Web 2.0 tools in biology teaching, experiences in daily/business life and views about their favourite tools are also included.
Online graph learning for time‐varying graphs

In this paper, the is focus on the online graph learning problems in time-varying environment. Traditional graph learning methods always assume that the underlying graph is static and that enough training data are available. However, in many real world applications, the underlying graph always changes slowly and only a small batch of data can be accessed at each time step. Under the assumption that the variation of the adjacency matrix is smooth, an online graph learning method is proposed, which improves the performance by leveraging the prior from the previous estimations. Experiments show that the proposed method performs best in both dynamic and static settings.
In Need of Development, Learning and Research?

A growing body of initiatives aims to connect school improvement with external actors, such as universities, by means of networks and collaborative partnerships of different kinds. Simultaneously, many schools have difficulties in assessing or predicting their needs associated with the digitalization of a specific local school practice given their lack of existing tools to articulate those needs. This has made it difficult to study digitalization in a complementary and symmetrical way between academia and practice. In this study, we used a quantitative instrument to generate findings and development needs relevant to both research and school development. The instrument, which we distributed to all school leaders in one municipality, measures perceptions of three overall areas: (a) levels of digitalization, (b) organizational digital maturity, and (c) notions of leadership. The data shows, for example, that digitalization, in this municipality, was a concern or issue on an individual level. Achieving a more complex view of digitalization as school development—a collegial approach and mindset together with leadership and organization that focuses on strategy and common goals—appears to be a high priority for research and practice. To conclude, the results generated from the instrument used in this study can contribute to a shared understanding of the findings and the needs relevant to both research and school development.
Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification

Indonesia is a tropical country that has various skin diseases. Tinea versicolor, ringworm, and scabies are the most common types of skin diseases suffered by the people of Indonesia. The classification of the three skin diseases can be automatically completed by artificial intelligence and deep learning technology because the classification process using an expert will require a lot of money and time. The challenge in classifying skin diseases is in the process of collecting data. Because health data cannot be obtained freely, there must be approval from the patient or hospital. Therefore, to overcome the limited amount of data, Pre-Trained CNN is used. The Pre-Trained CNN model has many patterns from thousands of images, so we do not need many images to train the model. In this study, a comparison of five pre-trained CNN models was conducted, namely VGGNet16, MobileNetV2, InceptionResNetV2, ResNet152V2, and DenseNet201. The aim is to find out which CNN model can produce the best performance in classifying skin diseases with a limited amount of image data. The test results show that the ResNet152V2 model has the best classification ability with the highest accuracy, precision, recall, and F1 score values, namely 95.84%, 0.963, 0.96, and 0.956. As for the training execution time, the ResNet152V2 model has the fastest time to achieve 95% accuracy. That's happened because the addition of the dropout parameter is 20%.
comparative analysis of consensus algorithms in the health care sector using block chain technology

As Blockchain is a distributed digital ledger system, it focuses on various sectors such as bitcoin, the banking sector, the corporation sector, the real estate and the healthcare sector. Each block in the blockchain contains the hash value, timestamp and transaction data of their previous block. The consensus algorithms plays a major role in the blockchain framework. This consensus algorithm maintaining the safety and efficacy of blockchain. The consensus protocols determines how the agreement to add the updated block to all nodes in the network works. Every consensus protocols has its own set of performance and scalability features. It is essential to technically compare each consensus mechanism by highlighting their strengths and weaknesses. Consensus algorithms in blockchain can be divided into two types. They are Proof based consensus and voting based consensus. The Proof based consensus shows that they are more qualified than others to do mining work. Voting-based consensus explains that nodes are needed in a blockchain network to exchange decisions for mining a new block or transaction before reaching a final conclusion. Their effectiveness can be enhanced by manipulating the suitable consensus algorithm in the blockchain. Blockchain technology is recently implement in many domains, especially for healthcare Industry.
A Survey of Awareness of Social Engineering Attacks to Information Security Management Systems: The Case of Kibabii University Kenya

Computer based systems are socio-technical systems in nature. The security of the system depends both on technical aspect and also social aspect. The social aspect refers to people in contact with system commonly referred to as wetware. To attack the system you may consider to target the technical or wetware. Social engineering is based on exploiting human traits that make human susceptible to these attacks. The aim of this paper was establish how aware the staff of Kibabii University were of these attributes and how these attributes could be used by social engineers to penetrate the Information Security Management systems at the institution. A survey research was adopted with a questionnaire being developed using Google application, and was administered online to all staff members of Kibabii University. A descriptive analysis was carried out on feedback. The finding was that to a large extent the sampled staff are aware of these traits but there is need for awareness training to enhance the information security management system of Kibabii University
A Sports Video Behavior Recognition Using Local Spatiotemporal Patterns

These days, many researchers are interested in sports video behavior recognition (SVBR), which has emerged as a core research domain for visual understanding and analysis of athlete performance. Despite significant results in simple scenarios, SVBR remains a difficult task due to numerous challenges such as independence, obstruction, and interclass visual appearance in real situations. Recognizing the sports video behavior is one of the crucial challenges in computer vision for sports video behavior data, with implementation in monitoring, mental illness diagnostics, video information extraction, and multitarget tracking. This paper provides a local pattern activity discrimination model for the detection and localization of active individuals in a video to address issues of multitarget tracking in video behavior recognition caused by mutual occlusion of targets and the complexity of ambient background. This work solves the problem of low recognition accuracy due to incomplete extraction of trajectories or overly complex backgrounds using a trajectory-based approach. Our model is applied to the video segments of 38 matches associated with goal events in Euro 2012, and an average accuracy of 91.3% is obtained. The experimental results verify the high accuracy and applicability of our method for the recognition of target object behavior in the videos.
SalyPath360: Saliency and scanpath prediction framework for omnidirectional images

This paper introduces a new framework to predict the visual attention of omnidirectional images.The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus.The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps.In addition, an auxiliary network is employed to generate probable viewport center fixation points through the So f tArgMax function.The latter allows deriving fixation points from feature maps.To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpathbased saliency heatmap.The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset.The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks.