Reviewerly/exHarmony huggingface dataset:
Methods of Formation and Improvement on the Basis of an Innovative Approach to Improving the Pedagogical Skills of School Teachers

In the Twenty-First Century, significant changes are taking place in connection with the development of new scientific discoveries, information, globalization, Cosmonautics, robotics and artificial intelligence. This century is called the age of digital technology and knowledge. How will the school change in the new century? How does the theory of Education change? At the moment, you can hear a lot of criticism that the class has not changed significantly compared to the last century or even two centuries ago. Will teachers succeed in modern changes? The purpose of the chapter is to generalize the existing changes in didactics for the use of innovative teaching methods and to examine the understanding of change by the teachers. In this chapter, we will consider four areas: the expansion of pedagogical science, the environmental approach to teaching, the digital generation and the changes that are taking place and innovations in teaching. The theory of Education, figuratively speaking, has two levels. At the macro level, decentralization and diversification in the relations of Education-Society occur, internationalization of education and the introduction of digital technologies. At the micro level in the relationship teacher-pupil there is an active mixture of traditional and innovative methods, a combination of an activity approach with an EnergyInformation environment approach, cognition with constructivism and connotation.
Auto Encoding Explanatory Examples with Stochastic Paths

In this paper we ask for the main factors that determine a classifier's decision making process and uncover such factors by studying latent codes produced by auto-encoding frameworks. To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifier's decisions. These examples are generated through interpolations in latent space. We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature (data) space via latent code interpolations. We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifier's behaviour and find that the solution of the associated variational problem allows for highlighting differences in the classifier decision. Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.
Low-Cost Lipschitz-Independent Adaptive Importance Sampling of Stochastic Gradients

Stochastic gradient descent (SGD) usually samples training data based on the uniform distribution, which may not be a good choice because of the high variance of its stochastic gradient. Thus, importance sampling methods are considered in the literature to improve the performance. Most previous work on SGD-based methods with importance sampling requires the knowledge of Lipschitz constants of all component gradients, which are in general difficult to estimate. In this paper, we study an adaptive importance sampling method for common SGD-based methods by exploiting the local first-order information without knowing any Lipschitz constants. In particular, we periodically changes the sampling distribution by only utilizing the gradient norms in the past few iterations. We prove that our adaptive importance sampling non-asymptotically reduces the variance of the stochastic gradients in SGD, and thus better convergence bounds than that for vanilla SGD can be obtained. We extend this sampling method to several other widely used stochastic gradient algorithms including SGD with momentum and ADAM. Experiments on common convex learning problems and deep neural networks illustrate notably enhanced performance using the adaptive sampling strategy.
Hierarchical Routing Mixture of Experts

In regression tasks, the data distribution is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate our method's effectiveness compared to various other regression models.
Image Secret Sharing Based on MDS Code

Most of the current Shamir image secret sharing schemes are based on Lagrange interpolation, which uses interpolation polynomials to split and reconstruct the secret. There are some problems, such as high time complexity, low computational efficiency and insufficient utilization of shadow image. To solve the problem of appeal, the proposed scheme combines the bit matrix of MDS code to replace the original Lagrange interpolation method. Experimental results show that, in addition to the advantages of most current Lagrange-based interpolation schemes, this scheme does not increase the size of shared images and reduces the computational time complexity, accurately recovers the original secret images, and can be extended to audio and video data, so it is perfect.
Cross-Visual Attention Fusion Network with Dual-Constrained Marginal-Ranking for Visible-Infrared Person Re-Identification

Abstract Visible-Infrared Person re-identification(VI-REID) is extremely important for night-time surveillance applications. It is a challenging problem due to large cross-modality discrepancies and intra-modality variations caused by different illuminations, human poses, viewpoints, etc. In this paper, we propose a cross visual attention fusion dual-path neural network with dual-constrained marginal ranking(DCAF) to solve the problem. First, we utilize cross-visual attention to learn discriminative feature of high-level semantic information in their respective modals. Second, in order to establish the relationship between modals, we fuse attentional weight of two modals and add it into backpropagation to obtain those regions that are distinctive for classification. Third, a dual-constrained marginal-ranking loss is introduced to narrow the gap between different networks and to learn strongly the similarities between two modals. Extensive experiments demonstrate that the proposed approach effectively improves the performance of VI-REID task and remarkably outperforms the state-of-the-art methods.
Biologically Inspired Neural Behavioral Control of the Wheeled Mobile Robot

In this paper, to solve the task of neural behavioral control of a 2-wheeled mobile robot (WMR), a hierarchical structure is used. At higher levels of the hierarchic generate a desired trajectory of mobile robot motion based on the artificial potential field theory. The generated trajectory is a desired trajectory realized by the neural control algorithm, implemented on the lower level of the hierarchy. Correctness of the solution of the desired trajectory generator and the control system of the elementary robot behavior has been confirmed in numerical simulations.
Analysis on the Application of Cause Intelligence Financial Management System Based on Artificial Intelligence Environment

Abstract In recent years, artificial intelligence has made great progress in the field of science and technology, and has played a substantial role in the development of real life. At present, China’s financial management system is still in the basic stage, mainly relying on manual work, financial software informatization has achieved results, but the promotion of intelligence is relatively slow, cannot provide good prediction and decision-making scheme for people. This paper analyzes the current situation of the application of artificial intelligence technology in intelligent financial management system, and puts forward the application analysis of the cause intelligent financial management system based on the artificial intelligence environment. In this paper, through the investigation and analysis of 220 financial personnel and system R &amp; D personnel, as well as the comparative analysis of the specialization, rationalization and intellectualization of the financial management system after the application of artificial intelligence, and the discussion and analysis of the results, put forward the application advantages of artificial intelligence in the financial management system, which provides guarantee for the development of intelligent financial management system. The research in this paper is of great significance to the further integration and development of the two, to find problems and to put forward constructive suggestions on the application of artificial intelligence technology in the cause intelligent financial management system.
The BLEU Score for Automatic Evaluation of English to Bangla NMT

The BLEU Bilingual Evaluation Underscore is a very popular technique to evaluate machine translation(MT)systems since long. It mainly exploits the precision to evaluate the performance of translators. In this paper, we evaluate the performance of a very popular online automatic translator, google translate to translate English to one of the low resource languages Bengali. Bengali is a language which is widely spoken in eastern region of Indian subcontinent. The main focus of this paper is to evaluate the performance of google translate with automatic evaluator BLEU and analyze the outcome in terms of various parameters including adequacy and fluency.
Efficient Use of End-to-End Data in Spoken Language Processing

For many challenging tasks there is often limited data to train the systems in an end-to-end fashion, which has become increasingly popular for deep-learning. However, these tasks can normally be split into multiple separate modules, with significant quantities of data associated with each module. Spoken language processing applications fit into this scenario, as they usually start with a speech recognition module, followed by multiple task specific modules to achieve the end goal. This work examines how the best use can be made of limited end-to-end training for sequence-to-sequence tasks. The key to improving the use of the data is to more tightly integrate the modules via embeddings, rather than simply propagating words between modules. In this work speech translation is considered as the spoken language application. When significant quantities of in-domain, end-to-end data is available, cascade approaches operate well. When the in-domain data is limited, how-ever, tighter integration between modules enables better use of the data to be made. One of the challenges with tighter integration is how to ensure embedding consistency between the modules. A novel form of embedding-passing between modules is proposed that shows improved performance over both cascade and standard embedding-passing approaches for limited in-domain data.
Dictionary Learning Based on Structural Self-similarity and Convolution Neural Network

Aiming at insufficient detailed description problem caused by the loss of edges during a single low-resolution (LR) image’s reconstruction process, a novel algorithm for super resolution image reconstruction is proposed in this paper, which is based on fusion of internal structural self-similarity dictionary and external convolution neural network parameters learning model. Firstly, for solving training samples too scattered problem, besides external database, an internal database is constructed to learn a dictionary of the single image’s structural self-similarity by multi-scale decomposition approach. Secondly, nonlocal regularization constraint is calculated on the priori knowledge, which is obtained from the internal database of the single LR image. Thirdly, similar block pairs of high and low-resolution samples in the external database are input into a convolution neural network for learning the parameters of reconstructing model. After all, combined parameters learned and the internal dictionary, the single LR image is reconstructed, and by iterative back-projection algorithm its result is improved. Experimental results show that, compared with state-of-the-art algorithms, such as Bicubic, K-SVD algorithm and SRCNN algorithm, our method is more effective and efficient.
Smart Obstacle Recognition System using Raspberry Pi

Blindness is a major problem in the society which made difficult for the person to lead his/her day-to-day life. The proposed system will detect, track and analyze the approaching objects and alert them to avoid collision. The PI camera and the Ultrasonic sensor sense the type and distance between the person and the object. The contactless temperature sensor senses the temperature of the object contactless manner using infrared rays. The GPS sensor tracks the route and location of the blind and the ESP8266 connects with the cloud for Realtime monitoring of the blind. The Vibrating motor and the buzzer alert the person when the safety limit is exceeded. The proposed system uses Raspberry Pi and Arduino Uno microcontroller. The beeping and the vibration are varied according to the distance and temperature of the object. This proposed system will make the life of blind less difficult.
Toreutic Art - AS A National Value

The article discusses the use of modern educational forms, methods and tools of national values in the development of creative thinking in future professionals of higher education, their appropriate and targeted use is a modern requirement, which is good and effective in research.
RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning

Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.
Horizontal Dynamic Resource Scaling by Measuring the Impacts of Scheduling Interval in Cloud Computing

Cloud servers or Host machines are having a sufficient number of computing resources, and these resources will be shared among its client applications as per the requirement. The traffic on the cloud servers or Host machines is unpredictable, and the demand may increase or decrease with a great variations many times. Even there are lots of reasons of such variations in traffic, here we are not going to discuss such reasons. During the COVID-19 pandemic, most of the meetings, teaching–learning, documents exchanging, etc. are performed using online applications. It is noticed that the both data and processing load are increased in multiple times during the last six months on the social networking sites, and clients faced the problem of low connectivity and slow processing of applications. These cases show that there is very great requirement of load balancing of computing resources on cloud servers to maintain the smooth processing of tasks. In this paper, our main focus is to measure the impacts of scheduling interval on dynamic resource allocation and to calculate the demand of resources dynamically on the basis of dynamic creation or deletion of virtual machines to serve the cloudlet load efficiently. Dynamic resource scaling can be done either horizontally or vertically. In horizontal dynamic resource scaling, the Host machines are equipped with the more number of resources rather than increasing the number of Host machines. In cloud computing, the service provider’s intention is to provide a cost-effective and efficient service support to its clients. The resources and services in cloud computing are on the pay-per-use basis, and hence, the horizontal dynamic resource scaling provides a good cost-effective solution for the resource requirement.
Fontnet: On-Device Font Understanding and Prediction Pipeline

Fonts are one of the most basic and core design concepts. Numerous use cases can benefit from an in depth understanding of Fonts such as Text Customization which can change text in an image while maintaining the Font attributes like style, color, size. Currently, Text recognition solutions can group recognized text based on line breaks or paragraph breaks, if the Font attributes are known multiple text blocks can be combined based on context in a meaningful manner. In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font. Major contributions of this paper are three-fold: First, we developed a novel CNN architecture for identifying font style of text in images. Second, we designed a novel algorithm for predicting similar fonts for a given query font. Third, we have optimized and deployed the entire engine On-Device which ensures privacy and improves latency in real time applications such as instant messaging. We achieve a worst case On-Device inference time of 30ms and a model size of 4.5MB for both the engines.
Can data placement be effective for Neural Networks classification tasks? Introducing the Orthogonal Loss

Traditionally, a Neural Network classification training loss function follows the same principle: minimizing the distance between samples that belong to the same class, while maximizing the distance to the other classes. There are no restrictions on the spatial placement of deep features (last layer input). This paper addresses this issue when dealing with Neural Networks, providing a set of loss functions that are able to train a classifier by forcing the deep features to be projected over a predefined orthogonal basis. Experimental results shows that these `data placement' functions can overcome the training accuracy provided by the classic cross-entropy loss function.
Feature Embedding Based Text Instance Grouping for Largely Spaced and Occluded Text Detection

A text instance can be easily detected as multiple ones due to the large space between texts/characters, curved shape and partial occlusion. In this paper, a feature embedding based text instance grouping algorithm is proposed to solve this problem. To learn the feature space, a TIEM (Text Instance Embedding Module) is trained to minimize the within instance scatter and maximize the between instance scatter. Similarity between different text instances are measured in the feature space and merged if they meet certain conditions. Experimental results show that our approach can effectively connect text regions that belong to the same text instance. Competitive performance of our approach has been achieved on CTW1500, Total-Text, IC15 and a subset consists of texts selected from the three datasets, with large spacing and occlusions.
Visualizing Association in Exemplar-Based Classification

Recent progress in deep learning has enhanced image classification performance. However, classification using deep convolutional neural networks lacks interpretability. To solve this problem, we propose a novel method of explainable classification; this method uses images representing each image class, which we call exemplars. Our method comprises encoder-decoder models (association networks) and a classifier. First, the association networks transform each input image into an image that a deep neural network associates, which we call an associative image. Then, the image-level similarity between the associative images and the exemplars is used as a feature for classification. This similarity explains the decision of the classifiers. We conducted experiments using CIFAR-10, CIFAR-100, and STL-10 and demonstrated our classifier’s interpretability through the proposed visualization technique.
Data Augmentation for Foreign Material Discrimination using Deep Learning

The authors try to discriminate small foreign materials using a convolution neural network (CNN). In this paper, the authors verify the effects of data augmentation for discriminating small foreign materials. The authors experimented for three target foreign materials, woodchips, pieces of plastic and cymothoidaes. The authors measured accuracy of discrimination for foreign materials without data augmentation and with it. The results of the experiment proved that data augmentation contributed to improvement in accuracy of discrimination for small foreign materials.
Polynomial Universal Adversarial Perturbations for Person Re-Identification

In this paper, we focus on Universal Adversarial Perturbations (UAP) attack on state-of-the-art person re-identification (Re-ID) methods. Existing UAP methods usually compute a perturbation image and add it to the images of interest. Such a simple constant form greatly limits the attack power. To address this problem, we extend the formulation of UAP to a polynomial form and propose the Polynomial Universal Adversarial Perturbation (PUAP). Unlike traditional UAP methods which only rely on the additive perturbation signal, the proposed PUAP consists of both an additive perturbation and a multiplicative modulation factor. The additive perturbation produces the fundamental component of the signal, while the multiplicative factor modulates the perturbation signal in line with the unit impulse pattern of the input image. Moreover, we introduce a Pearson correlation coefficient loss to generate universal perturbations, for disrupting the outputs of person Re-ID models. Extensive experiments on DukeMTMC-reID, Market-1501, and MARS show that the proposed method can efficiently improve the attack performance, especially when the magnitude of UAP is constrained to a relatively small value.
Development of Aceh biodiversity information system

Abstract Aceh is rich in biodiversity, both flora, and fauna. Aceh’s biodiversity has not been well documented. This can be seen from the absence of an information system that is open to the public regarding biodiversity in Aceh. This study aims to develop a web-based Aceh Biodiversity Information System of Aceh Biodiversity. This Aceh Biodiversity Information System will collect data on biodiversity in Aceh from various parties, including researchers, academics, government, and the general public. The waterfall model as the Software Development Life Cycle (SDLC) model is used in the development of this information system. The System Usability Scale (SUS) is used to determine user opinions in using this information system as input for improvement. The results showed that this information system was good enough to accommodate the data collection process from various parties. Improvements to the existing features used in this information system need to be done. It is hoped that these Aceh Biodiversity Information System can be utilized as a data collector for the education, research, management, conservation, and other fields of a task, especially bioinformatics.