Reviewerly/exHarmony huggingface dataset:
The teaching of sports science of track and field-based on nonlinear mathematical equations

Abstract This article uses the gray system theory of nonlinear mathematical equations to predict the best results of China's track and field events in the 2021 Olympic Games. And compare the above prediction information with the track and field performance of the 2021 Olympic Games. The article uses this to guide the teaching of Chinese track and field sports and analyze the relevant factors affecting Chinese track and field training.
Blockchain

The growth of innovative technology by leaps and bounds also attracts an equal amount of speculation. Blockchain technology is no exception to this phenomenon. Still, in its exploratory stages, blockchain is not just limited to cryptocurrency; contrary to this popular perception, it extends far beyond that. One cannot help but draw comparisons with the advent of cloud technology and the myths and misconceptions around it when it was touted to be just a bubble, susceptible and volatile. And yet, slowly, it gained momentum and captured the entire virtual world. Like the cloud process, blockchain has several positive aspects, but there is also a great deal of initial misunderstanding and scepticism about its intent. This chapter attempts to unravel the DNA of the most common misconceptions that persist about blockchain, distinguish the myths from reality, and contribute to a greater understanding of this impending technology, which can no longer be kept at bay.
Current Issues of Teacher Education in the Era of Digital Transformation: A Theoretical Review

The review paper aims to identify the most significant issues in the field of digital transformation of teacher education, which are studied by modern scientists - teachers, psychologists, sociologists. The paper determines the relevant topics and traces the dynamics of changes in the content of teacher education caused by digital transformation processes, according to the analysis of Russian and foreign academic pedagogical publications over the past 5 years (2017-2021). An increase in Russian researchers’ publication activities in the field of the digital transformation of teacher education is noted. The work presents a theoretical and methodological review of authors’ positions and interpretations in solving basic issues and matters related to changes in teachers’ role and functions in the digital information educational environment. The advantages and potential risks of the process of global digitalisation of education are assessed. It is the first time that the consequences of the digital transformation of the teacher education system are being considered in the context of an interdisciplinary approach as a global modernisation process covering all areas of training and educational activities. The research is novel in that carrying out a contextual analysis of a target sample containing texts of psychological and pedagogical nature that shed light on the impact of digitalisation on various aspects of future teachers’ professional training, it identifies the key changes in the forms, methods and organisation of the educational process in undergraduate and graduate teacher training programmes that meet the global challenges of the information society. Those changes include actively introducing digital educational technologies aimed at stimulating students’ creative mental actions and cognitive abilities; updating teacher education management on a digital basis; developing principles of digital didactics; reassessing professional pedagogical values. As a result, clusters of significant practice-oriented topics, keywords, as well as the most cited publications on the topics under consideration have been formed; possible prospects for the reconstruction of teacher education taking into account modern digital realities have been described.
An Distributed CA System : Identity authentication system in transnational railway transportation based on blockchain

With the introduction of "One Belt, One Road" initiative, transnational railway container transportation has played an increasingly important role in the field of international trade and logistics. The transnational railway container transportation business involves multiple entities at home and abroad. It is difficult for decentralized transportation entities to achieve mutual trust, and it is difficult for document data to flow and share. The blockchain-based transnational railway transportation data exchange system combines the immutable and traceable characteristics of blockchain technology, and uses blockchain smart contracts and consensus mechanisms to propose a specific technical route for the sharing and exchange of document data based on blockchain. Aiming at the issue of identity authentication among the subjects, this paper proposes a distributed CA scheme based on blockchain to realize mutual recognition of transnational identities, and ensure the sharing of transnational document data and the protection of privacy and security.
Effect of service quality on customer satisfaction in Tanzanian energy industry: A case of TANESCO residential customers in Nyamagana District

AI Publications is an open access peer review international journal to publish the article in the field of Agriculture, Horticulture, Food science, Rural Development, Environment and Health Research, Chemistry, Mathematics,Physics.
Hybrid Decision Tree for Machine Learning: A Big Data Perspective

In recent years, machine learning (ML) is adopted everywhere in a wide range of massive and complex data-intensive fields like medicine, astronomy, education, biology, etc. The performance increases when it deals with big data. ML algorithms challenged by big data make more accurate and timely predictions and unveil more concrete patterns than ever before. A decision tree is one of the most successful models in data science, especially when big data are to be handled. It is a top-down, inductive, tree-structured classifier consisting of high-level, easy-to-interpret classification rules. The decision tree is a non-parametric supervised machine learning model used for both classification and regression. The rules constructed are then used to predict the values for new instances. The performance of the decision tree increases profoundly when it is hybridized with techniques like genetic algorithms, neural networks, etc. The application of hybrid decision trees for machine learning from a big data perspective will be an interesting topic for future research. Various hybridization of decision trees along with other machine learning models like Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are emerging models in exploring hidden patterns from big data. In some of the applications like healthcare and banking sector, hybridization of decision trees with evolutionary computing plays a pivotal role in providing solutions to various problems. In this chapter, we have discussed some of the techniques used for hybridization of the decision tree, which provides elegant solutions for big data problems.
Scaling up instance annotation via label propagation

Manually annotating object segmentation masks is very time-consuming. While interactive segmentation methods offer a more efficient alternative, they become unaffordable at a large scale because the cost grows linearly with the number of annotated masks. In this paper, we propose a highly efficient annotation scheme for building large datasets with object segmentation masks. At a large scale, images contain many object instances with similar appearance. We exploit these similarities by using hierarchical clustering on mask predictions made by a segmentation model. We propose a scheme that efficiently searches through the hierarchy of clusters and selects which clusters to annotate. Humans manually verify only a few masks per cluster, and the labels are propagated to the whole cluster. Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76× compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets. Code, data, and models are available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Evaluating the Scalability and Elasticity of Function as a Service Platform

Function as a Service (FaaS) is a new software technology with promising features such as automated resource management and auto-scaling. Since these operational aspects are transparent, software engineers may not fully understand the scaling characteristics as well as limitations of this technology and this lack of information can lead to undesired performance results. To address these concerns, we perform a study to characterize FaaS' scalability with intensive workloads on three popular FaaS cloud platforms, namely Amazon AWS Lambda, IBM and Azure Cloud Function. We also study a workload smoother design pattern to examine if it enhances FaaS overall performance. The results show that different FaaS platforms adopt distinct scaling strategies and by applying a workload smoother, software engineers can achieve 99 - 100% success rates compared to 60 - 80% when FaaS' system is saturated.
Study on Ecological Risk Evaluation and Optimum Selection of Desert Expressway Schemes Based on the Two-Dimensional Cloud Model

Aimed at the fragile ecological environment in desert, once the resources along routes are destroyed, it will be difficult to restore the original ecological equilibrium in a short time. This paper studies the ecological-equilibrium-oriented risk evaluation of desert motorway schemes in order to choose route schemes reasonably and prevent the sandstorm aggravation caused by ecological damage. Having insight into the special geographical environment and road construction characteristics of Tengger Desert, the research establishes an index system for the ecological risk evaluation in desert areas from five aspects: the interference of living environment of animals and plants, the destruction of natural ecological landscapes, the pollution of water and soil resources, the development and utilization of natural resources, and the sand fixation of plants and the subsequent maintenance; then it confirms each evaluation index’s weight by using the set-valued statistics method to transform the description of the evaluation indexes to an interval value and furthermore determines the comprehensive ecological risk evaluation level of the route scheme in virtue of the two-dimensional cloud image MATLAB outputs which takes the ecological damage and remediation cost as the basic variables. By constructing the model under the method above, the ecological risk evaluations of two route schemes in Zhongwei sand crossing section of Wuhai-Maqin Expressway have been made. The result shows that the western route scheme’s evaluation grade is “good,” is better than the southern route scheme, is within the scope of acceptable risk, agrees with the practical project condition, and verifies the applicability and effectiveness of the model for ecological risk evaluation in desert areas.
Real Time Object Detection using SSD and MobileNet

Abstract: This Accurate Real-time object detection needs faster computation power to identify the object at that specific time. The accuracy of object detection has increased drastically with the advancement of deep learning techniques. We incorporate a stateof-the-art method for object detection to achieve high accuracy with real-time performance. The state-of-the-art methods are subdivided into two types. The first is one-stage methods that prioritize inference speed, and example models include YOLO, SSD, and RetinaNet. The second is two-stage methods that prioritize detection accuracy, and its example models include Faster R-CNN, Mask R-CNN, and Cascade R-CNN. Among all these, Faster-RCNN and SSD have better accuracy, while YOLO performs better when speed is given preference over accuracy. A major challenge in many of the object detection systems is that it is dependent on the other computer vision techniques for helping the deep learning-based approach, which results in slow and non-optimal performance. In this paper, we have used a deep learning-based approach to solve the matter of object detection in an end-to-end fashion. Deep learning combines SSD and Mobile Nets to perform the efficient implementation of detection and tracking.SSD eliminates the feature resampling stage and combined all calculated results as a single component. MobileNet is a lightweight network model that uses depth-wise separable convolution for the places which lacks computational power like mobile devices (eg: laptop, mobile phones, etc). This algorithm performs efficient object detection while not compromising on the performance The main purpose of our research is to elaborate the accuracy of an object detection method SSD and the importance of the pre-trained deep learning model MobileNet. The resultant system is fast and accurate, thus aiding those applications which require object detection Keywords: Object Detection, CNN, TensorFlow object detection API, SSD with MobileNet
Incremental and compressible kernel null discriminant analysis

Kernel discriminant analysis (KDA), the nonlinear extension of linear Discriminant Analysis (LDA), is a popular tool for learning one or multiple categories in nonlinear data sets. However, in most modern pattern recognition applications such as video surveillance, data are collected in flow and require sequential processing. In this context, KDA is faced two critical issues: an original formulation unsuited to the dynamic nature of the data and an increasing memory requirement for the kernel matrix storage. Motivated by the state-of-the-art performance reported by the null KDA, we propose in this paper a new solution to solve the null KDA (NKDA) in the context of data streams. Compared to previous works, our contribution is based on three points: first, we develop an exact incremental scheme which guarantees accurate solutions. Secondly, we develop a compression mechanism based on the following observation: rger the size of the training data set more the distances in the null space contract This property of the null space leads to formulate an indicator of redundancy in the training data set. This criterion is the cornerstone of our incremental KNDA because it authorizes incremental learning on large-scale data sets. Third, the problem of novelty detection in multi-class and one-class scenarios is addressed. More precisely, the fact that distances in the null space change over the training period leads us to define adjustable novelty thresholds. Lastly, numerous experiments based on various publicly available data sets and state-of-the-art classifiers show that the proposed method is effective both for multi-class and one-class real applications.
Knowledge guided learning: Open world egocentric action recognition with zero supervision

Advances in deep learning have enabled the development of models that have exhibited a remarkable tendency to recognize and even localize actions in videos. However, they tend to experience errors when faced with scenes or examples beyond their initial training environment. Hence, they fail to adapt to new domains without significant retraining with large amounts of annotated data. In this paper, we propose to overcome these limitations by moving to an open-world setting by decoupling the ideas of recognition and reasoning. Building upon the compositional representation offered by Grenander's Pattern Theory formalism, we show that attention and commonsense knowledge can be used to enable the self-supervised discovery of novel actions in egocentric videos in an open-world setting, where data from the observed environment (the target domain) is open i.e., the vocabulary is partially known and training examples (both labeled and unlabeled) are not available. We show that our approach can infer and learn novel classes for open vocabulary classification in egocentric videos and novel object detection with zero supervision. Extensive experiments show its competitive performance on two publicly available egocentric action recognition datasets (GTEA Gaze and GTEA Gaze+) under open-world conditions.
Intelligent Deep Detection Method for Malicious Tampering of Cancer Imagery

In recent years, deep generative networks have reinforced the need for caution while consuming different formats of digital information. One method of deepfake generation involves the insertion and removal of tumors from medical scans. Significant drains on hospital resources or even loss of life are the consequences of failure to detect medical deepfakes. This research attempts to evaluate machine learning algorithms and pre-trained deep neural networks' (DNN) ability to distinguish tampered data and authentic data. Moreover, this research aims to classify cancer scans based on DNN. The experimental results show that the proposed method based on using DNN can enhance performance detection. Furthermore, the proposed system increased the detection accuracy rate and reduced the number of false alarms.
Initialization of Random Vectors to Enhance Defense-GAN for Image Classification

Deep learning has been successfully applied to a wide spectrum of applications. Nevertheless, its performance is seriously degraded by adversarial attacks, an elaborately designed small perturbation to input data that can mislead the model prediction. Specifically, life-critical deployments of deep learning such as autonomous vehicles are a major concern of these attacks. Defense-GAN is considered a state-of-the-art defense method that is robust to both black-box and white-box adversarial attacks. Its defense framework generates new data close to the unperturbed ones from a given adversarial sample before feeding to the predictive model. But Defense-GAN takes time to determine reconstructed data at inference time due to the iterative nature of gradient descent. In this work, the improvement of Defense-GAN to rapidly generate well-reconstructed data from adversarial samples is proposed using carefully crafted initialization of random vectors. The experimental results demonstrate that the proposed method can outperform both classification performance and inference time compared with the Defense-GAN on the benchmark datasets.
Arduino based Dual Axis Solar Tracker

Our life is dependent on various forms of energy. Solar energy is one of the renewable sources of energy generation and does not leave any harmful residue waste, making it clean. It can be smoothly exercised by using solar photovoltaic (PV) panels. But it is observed that most of the solar panels are placed at fixed angles. To achieve the maximum duration of incident solar energy collected by a solar panel, the solar tracking device is used to position the solar panel facing the sun throughout the day, improving the performance ratio of the system. This research paper covers the development and design of dual-axis solar panel tracker & practical study of dual-axis solar tracker compared to the defined solar panels in terms of performance improvement. Solar energy is rapidly growing in importance as a source of renewable energy. Heat and light are the two basic types of solar energy. The environment transforms and absorbs sunlight and heat in a variety of ways. The purpose of this paper is to offer methods for collecting solar energy in more amounts as compared to the traditional method.
Further Studies on State Estimation of Discrete-Time Nonlinear Circuits Based on a Switching-Type Multi-Instant Fuzzy Observer

This brief proposes further studies on fuzzy state estimation of discrete-time nonlinear circuits. In order to give a less conservative result over the most recent one, a new switching-type multi-instant fuzzy observer is introduced and thus different groups of gain matrices can be dispatched in the light of the updated mode about the normalized fuzzy weighting functions. For each possible mode, its corresponding gain matrices are deduced by using a new time-varying balanced matrix method. Compared with the existing time-invariant balanced matrix method, much more freedom can be provided with the aid of the powerful analysis tool of multi-instant homogenous polynomials. As a result, the conservatism is reduced and better performance of fuzzy state estimation is capable of being obtained in this brief. Finally, two advantages of our proposed results are validated via the benchmark example of this field.
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language

Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language.Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model's logical reasoning process.Currently, these black-box models generate both the proof graph and intermediate inferences within the same model and thus may be unfaithful.In this work, we frame the deductive logical reasoning task by defining three modular components: rule selection, fact selection, and knowledge composition.The rule and fact selection steps select the candidate rule and facts to be used and then the knowledge composition combines them to generate new inferences.This ensures model faithfulness by assured causal relation from the proof step to the inference reasoning.To test our framework, we propose FAIRR (Faithful and Robust Reasoner) where the above three components are independently modeled by transformers.We observe that FAIRR is robust to novel language perturbations, and is faster at inference than previous works on existing reasoning datasets.Additionally, in contrast to black-box generative models, the errors made by FAIRR are more interpretable due to the modular approach.1 1 The source code of FAIRR has been made available at https://github.com/
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing

We introduce two techniques, length perturbation and n-best based label smoothing, to improve generalization of deep neural network (DNN) acoustic models for automatic speech recognition (ASR). Length perturbation is a data augmentation algorithm that randomly drops and inserts frames of an utterance to alter the length of the speech feature sequence. N-best based label smoothing randomly injects noise to ground truth labels during training in order to avoid overfitting, where the noisy labels are generated from n-best hypotheses. We evaluate these two techniques extensively on the 300-hour Switchboard (SWB300) dataset and an in-house 500-hour Japanese (JPN500) dataset using recurrent neural network transducer (RNNT) acoustic models for ASR. We show that both techniques improve the generalization of RNNT models individually and they can also be complementary. In particular, they yield good improvements over a strong SWB300 baseline and give state-of-art performance on SWB300 using RNNT models.
mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling

Zero-shot slot filling has received considerable attention to cope with the problem of limited available data for the target domain. One of the important factors in zero-shot learning is to make the model learn generalized and reliable representations. For this purpose, we present mcBERT, which stands for momentum contrastive learning with BERT, to develop a robust zero-shot slot filling model. mcBERT uses BERT to initialize the two encoders, the query encoder and key encoder, and is trained by applying momentum contrastive learning. Our experimental results on the SNIPS benchmark show that mcBERT substantially outperforms the previous models, recording a new state-of-the-art. Besides, we also show that each component composing mcBERT contributes to the performance improvement.