Reviewerly/exHarmony huggingface dataset:
Information Platform for Classroom Teaching Quality Evaluation and Monitoring Based on Artificial Intelligence Technology

The purpose of this paper is to study the construction and implementation of an information platform for classroom teaching quality(TQ) evaluation and monitoring based on artificial intelligence(AI) technology. In this paper, the classroom teaching of teachers is taken as the object of TQ evaluation and control for in-depth and detailed research, to find a more reasonable TQ evaluation method, so as to provide accurate feedback information for the improvement of TQ. In this paper, 18 teachers were taken as test objects, and a team of 5 experts made an objective evaluation of each teacher's TQ index at all levels, and the evaluation score was taken as the original data. Through the simulation experiment of the data source, the error of training the TQ evaluation index by using BP neural network is not more than 0.002.
Analyzing Economic Growth and Its impact on Poverty Reduction in Africa

This study currently examines some economic growth variables that interact to reduce poverty in West, East, South, Central, and North African. The review uses panel data from 1998 to 2017 based on the unit root, co-integration, fully modified ordinarily least squares (FMOLS), and granger causality to effect estimations. The study examines the association between these variables of economic Growth received and how they affect poverty. The results find economic growth promotes certain aspects of poverty reduction with the size of population and employment has been a significant factor. The granger result shows the uniqueness of the variables with bi-direction running between gross domestic product and poverty, gross capital formation and broad money, population, and broad money, and population and poverty do not Granger cause each other in the long run. It was in-line with most studies by other researchers that state these main variables has been very significant when it comes to poverty reduction in most developed countries. The study emphasized that, in the contest of Africa, these main variables helps to reduce poverty but at a minimal level.
Understanding youths' perception and factors advancing cybercrime (yahoo-yahoo) in Ado-Ekiti, Ekiti State, Nigeria

Previous studies on cybercrime in Nigeria have largely been conducted with the intention of understanding its influence on economy and country's reputation, with little attention devoted to youths' perception and issues promoting cybercrime. This study seeks to address this gap by understanding youths' perception and factors advancing cybercrime with a special focus on Ado-Ekiti, Ekiti State, Nigeria. A total of 20 respondents were purposively selected from the identified youth population and the semi-structured interview type was employed to elicit qualitative data from the identified respondents. While the NVivo (v.12) qualitative software was used to identify themes from the transcript interview, the content analysis technique was employed to make sense of the themes. The Rational Choice Theory was used as a theoretical leaning to understand the presuppositions raised in the study. Findings show falsification of identity and impersonation, the use of diabolical powers/charms to hypnotize victims, and the belief system that cybercrime is seen as routine work where wage is expected as perception of cybercrime. Similarly, there is an evidence to support the claim that social media tools are robustly employed to perpetrate cybercrime. On the question of factors advancing cybercrime, the study found poor economic performance and youths‘ unemployment, weak law enforcement agencies and the challenge of corruption, and environmental and peer group influence as factors promoting cybercrime. The study recommends a robust investment on youth empowerment programmes, wealth creation and value orientation in addition to responsive and corrupt-free law enforcement agencies as critical to addressing the scourge of cybercrime.
Stacked Denoising Autoencoder: A Learning-Based Algorithm for the Reconstruction of Handwritten Digits

This paper delivers a strategy to build a deep neural network, established by heaping layers of autoencoder, which in turn consists of both encoder and decoder layers, which are generally being locally trained to denoise the corrupted inputs and reconstruct an approximation to the original input. The outcome as an algorithm is a candid variation by stacking the ordinary autoencoder. It is basically a classification problem of machine learning yielding to obtain less classification error, and therefore spanning the performance gap with deep belief neural networks and in majority of the cases surpassing it. Results show that the reconstruction of the inputs depend upon the training parameters such as the upsurge of the epoch and batch size will increase the training period, thus increasing the accuracy in representing the denoised reconstruction.
On Limitations of Abstraction-Based Deadlock-Analysis of Service-Oriented Systems

Deadlock-analysis of concurrent service-oriented systems is often done by P/T-net-based approaches. We show that there is a concurrent service-oriented system with synchronous (stack behavior) and asynchronous procedure (concurrent behavior) calls with a deadlock that is not discovered by classical P/T-net-based approaches. Hence, P/T-net-based approaches lead to false statements on absence of deadlocks. We propose an approach based on Mayr’s Process Rewrite Systems to model both, concurrent and stack behavior while the deadlock problem remains decidable.
A Novel Cosine-based Internal and External Validation metrics to assess twitter Data Clustering using Hybrid Topic Models

Abstract In document clustering labeled and unlabeled documents are organized into a desired number of coherent and meaningful sub-clusters. Topic models are useful in extracting cluster tendency from Twitter-based data document clusters. Evaluating cluster tendency and performance with a reliable metric is one of the unsolved problems in topic document clustering. In the previous study cluster validity metrics have been proposed under Euclidean distance measure, these metrics underperform in topic models when dealing with numerical databases and for large corpus datasets. In this paper, to assess Twitter data clustering a novel cosine based internal and external validity indices used by considering closeness between documents, the lexical similarity and cluster classification metrics for each topic separately using Hybrid topic models. Experimentally proved the effectiveness of cosine based internal and external validity metrics and results compared with Euclidean metrics using benchmark and Twitter-based datasets.
Swapping the API with Local Storage

We now know how to write an adapter and serializer from scratch as we learned in the last chapter. Imagine we’ve been tasked with swapping our backend to Local Storage. We know that adapters are responsible for figuring out how to talk to a backend via a RESTful API. They figure out how to retrieve data and where to send data for storage. In this chapter, we will swap out our custom RESTAdapter with our own Local Storage adapter, and our application should continue to work seamlessly.
Urine Red Blood Cells Generation Using StyleGAN2 Network

With the development of computer hardware and deep learning theory, the application of deep learning in the field of digital medicine is very popular. Deep learning technologies use neural networks to build the relationship between medical data and disease diagnosis from large amounts of data. The acquisition and labeling of medical data has always been very time-consuming and laborious. In addition, the law states that patient data belongs to personal privacy, so medical data is rarely disclosed. Based on the urine red blood cell dataset (URBC), we train the recently proposed StyleGAN2 network to generate a public dataset S2RBC-256. It effectively avoids the strict regulations on patient ethical privacy and hospital data. This dataset can further help future scientific research in the related fields of red blood cell. In terms of digital metrics (SSIM, PSNR), the images we generate achieve good results. Through the review of medical experts, our generated images are visually very consistent with real images. Therefore, the public dataset we generate can be used for training of deep neural networks for the purpose of reducing the labor and material consumption of annotation.
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization

Loss landscape analysis is extremely useful for a deeper understanding of the generalization ability of deep neural network models. In this work, we propose a layerwise loss landscape analysis where the loss surface at every layer is studied independently and also on how each correlates to the overall loss surface. We study the layerwise loss landscape by studying the eigenspectra of the Hessian at each layer. In particular, our results show that the layerwise Hessian geometry is largely similar to the entire Hessian. We also report an interesting phenomenon where the Hessian eigenspectrum of middle layers of the deep neural network are observed to most similar to the overall Hessian eigenspectrum. We also show that the maximum eigenvalue and the trace of the Hessian (both full network and layerwise) reduce as training of the network progresses. We leverage on these observations to propose a new regularizer based on the trace of the layerwise Hessian. Penalizing the trace of the Hessian at every layer indirectly forces Stochastic Gradient Descent to converge to flatter minima, which are shown to have better generalization performance. In particular, we show that such a layerwise regularizer can be leveraged to penalize the middlemost layers alone, which yields promising results. Our empirical studies on well-known deep nets across datasets support the claims of this work
Enhance The Awareness Of Food Waste Management Through The Digital World

Food waste is one of many environmental issues in Indonesia that need to be solved. One of the solution to the problem is to manage the food waste and turn it to be something useful such as biogas and organic fertilizer. Yayasan Rumah Energi, an Indonesian NGO, is focusing themselves in this matter, they already found the way to manage the food waste, but then, they still need help to spread the awareness to the society. To help the organization, the team then using the digital world, to reach more people in the society. Another reason of using the digital channel is that in the time of pandemic, it is not possible to host an off-air event. The activity starting from analyzing the problem of the communication, setting the goals and objectives, preparing the strategy, coaching the communication personnel, and hosting digital event. Through the digital event, the team has raised awareness and get more people interested in the matter of food waste management
Deep Recognition-based Character Segmentation in Handwritten Syriac Manuscripts

Ancient Syriac manuscripts represent a valuable cultural heritage for a large community, mainly in the region of the Middle East. Digitization of these manuscripts plays a major role in preserving this heritage and making it publicly available without the need to access the original paper versions. In this context, this paper presents a recognition-based algorithm for character segmentation in handwritten Syriac manuscripts. A convolutional neural network is used as a classifier for character recognition in a variable-size sliding window that spans individual word images. The classifier also provides likelihood values that are then used to determine suitable segmentation points for individual characters. Since the classifier is a core component of the segmentation algorithm, the proposed approach allows for joint character segmentation and recognition, thus providing a means for automated document indexing and content-based search and retrieval.
P-FideNet: Plasmodium Falciparum Identification Neural Network

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. The identification of the parasitized blood cells is a laborious and challenging task as it involves very complex and time consuming methods such as spotting the parasite in the blood and counting the number of the parasites. This examination can be arduous for large-scale diagnoses, resulting in poor quality. This paper presents a new Convolutional Neural Network (CNN) architecture named P-FideNet aimed at the detection of Malaria. The proposed CNN model can be used to solve image classification problems of blood cells infected or not by parasite X. This tool makes the process of analysis by the specialist faster and more accurate. Comparative tests were carried out with state-of-the-art works, and P-FideNet achieved 98.53% recall, 98.88% accuracy and 99% precision.
DONE: Distributed Newton-type Method for Federated Edge Learning.

There is growing interest in applying distributed machine learning to edge computing, forming \emph{federated edge learning}. Compared with conventional distributed machine learning in a datacenter, federated edge learning faces non-independent and identically distributed (non-i.i.d.) and heterogeneous data, and the communications between edge workers, possibly through distant locations with unstable wireless networks, are more costly than their local computational overhead. In this work, we propose a distributed Newton-type algorithm (DONE) with fast convergence rate for communication-efficient federated edge learning. First, with strongly convex and smooth loss functions, we show that DONE can produce the Newton direction approximately in a distributed manner by using the classical Richardson iteration on each edge worker. Second, we prove that DONE has linear-quadratic convergence and analyze its computation and communication complexities. Finally, the experimental results with non-i.i.d. and heterogeneous data show that DONE attains the same performance as the Newton's method. Notably, DONE requires considerably fewer communication iterations compared to the distributed gradient descent algorithm and outperforms DANE, a state-of-the-art, in the case of non-quadratic loss functions.
Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes

We propose a newly annotated dataset for information extraction on recipes. Unlike previous approaches to machine comprehension of procedural texts, we avoid a priori pre-defining domain-specific predicates to recognize (e.g., the primitive instructionsin MILK) and focus on basic understanding of the expressed semantics rather than directly reduce them to a simplified state representation (e.g., ProPara). We thus frame the semantic comprehension of procedural text such as recipes, as fairly generic NLP subtasks, covering (i) entity recognition (ingredients, tools and actions), (ii) relation extraction (what ingredients and tools are involved in the actions), and (iii) zero anaphora resolution (link actions to implicit arguments, e.g., results from previous recipe steps). Further, our Recipe Instruction Semantic Corpus (RISeC) dataset includes textual descriptions for the zero anaphora, to facilitate language generation thereof. Besides the dataset itself, we contribute a pipeline neural architecture that addresses entity and relation extractionas well an identification of zero anaphora. These basic building blocks can facilitate more advanced downstream applications (e.g., question answering, conversational agents).
Traditions in teaching physics at the regional Kaliningrad University

Abstract The article discusses the features of the functioning of the physical education system at the regional University of Königsberg (East Prussia) and the University of the Kaliningrad region. The article discusses the specifics of training physicists at each stage of the University’s development. It is shown that the scientific and educational processes are inextricably linked with each other when teaching physics. Participation of the faculty of physical departments of the University in research work and involvement of students in it is the key to successful training of highly qualified specialists. At various stages of the development of the physical education system, special attention was paid to the training of physics teachers. The tradition of teaching physics, laid down in Albertina, is continued at the modern University of Kaliningrad.
Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging

End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since unique label histories correspond to distinct models states, such models are decoded using an approximate beam-search process which produces a tree of hypotheses. In this work, we study the influence of the amount of label context on the model's accuracy, and its impact on the efficiency of the decoding process. We find that we can limit the context of the recurrent neural network transducer (RNN-T) during training to just four previous word-piece labels, without degrading word error rate (WER) relative to the full-context baseline. Limiting context also provides opportunities to improve the efficiency of the beam-search process during decoding by removing redundant paths from the active beam, and instead retaining them in the final lattice. This path-merging scheme can also be applied when decoding the baseline full-context model through an approximation. Overall, we find that the proposed path-merging scheme is extremely effective allowing us to improve oracle WERs by up to 36% over the baseline, while simultaneously reducing the number of model evaluations by up to 5.3% without any degradation in WER.
Evaluation of fringe pattern magnification for lens dioptric power measurement

Dioptric power measurement is an essential part of the characterization and quality control of lens manufacturing. In this framework, a fairly simple and potentially cost-effective method for lens characterization by interferometric fringe evaluation is reported. This technique consists of acquiring images of straight and parallel fringes projected onto a planar screen through the lens to be tested and in the regions surrounding the lens. Upon phase evaluation procedures, two apparent three-dimensional shapes of the screen are obtained, namely, the background contour and the one visualized through the test lens. The lens power is obtained as a function of the ratio between the two shapes and the parameters of the optical setup. The fringe pattern evaluation was performed by four-stepping and phase unwrapping methods. The results obtained by this technique were compared with the ones obtained by a commercial focimeter in the measurement of unifocal and progressive power lenses.
An End to End Thyroid Nodule Segmentation Model based on Optimized U-Net Convolutional Neural Network

For current clinical diagnosis of thyroid nodules, thyroid ultrasound is one of the most valuable imaging examinations to evaluate thyroid diseases. There are many improved ultrasound equipment whose imaging mechanism will cause large imaging noise, blurred borders, complicated background, which certainly bring great challenges to the nodule segmentation. As a consequence, there will be disadvantages of poor segmentation accuracy or high model complexity when using the ordinary image segmentation methods. This paper proposes an Optimized U-Net convolutional neural network model of thyroid nodule segmentation method whose structure is mainly based on U-Net model and combines the advantages of residual network. The segmentation method is also combined with the TTA (test time segmentation) method, that is, the output is the weighted average of all prediction results of the input image after enhancement. The network model trained on 544 thyroid nodule images not only achieves the end-to-end segmentation output, but also can achieve a dice coefficient of 89.50% in the final verification set.
The Use of Multimedia Technologies in the Teaching Process

Nowadays, multimedia has a great importance - the use of multimedia teaching in the field of education helps to improve the efficiency of teaching English and stimulates learning activities. The applications of multimedia technologies have been accelerated in economics, science, technology, education, healthcare, culture, media, entertainment, music and other fields. Multimedia technologies have already become one of the dominant technologies in the information society, and the combination of multimedia technologies and networked communication technologies has led to the emergence of video, and so on. The use of these technologies has changed the way of life and habits of people and continues to have an impact on the public and private life of a person, especially has a huge impact on the educational system of the state. The teaching staff is interested not so much in high attendance of classes, but in the transfer of relevant knowledge to students, stimulating their scientific and educational activities, and, as a consequence, increasing the efficiency of the educational process for everyone, so that the content of information is transmitted quickly, and at the same time scientific reasonably.
Signature Detection and Identification Algorithm with CNN, Numpy and OpenCV

The purpose of the article is to present a simple signature detection algorithm and its subsequent signature identification using a deep learning model for processing images based on a convolutional neural network. To solve the task of the image recognition, a binary classification has been performed to predict text or signature and signature classifications to determine the author of this signature. The proposed algorithm is interesting in the preliminary processing of scanned documents with signatures in order to extract the area with the signature and transfer it to the trained model. The research results are presented for documents of the same type, in which the signature is located in the same place. To select a specific element in the document we are using the tensor-slicing operations on Numpy arrays. To extract areas with text and signature, OpenCV tools are used. The results on the ready-made neural network model studies on a small dataset are presented in this article. Good results have been achieved in recognizing the famous writers’ signatures. The proposed algorithm demonstrates the possibility of using the classical convolution network model for solving specific practical problems. The studies can be recommended to students in the study of neural networks to understand the basics of deep learning and apply a ready-made model as a template for solving practical problems in the field of computer vision.