Reviewerly/exHarmony huggingface dataset:
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks

The lottery ticket hypothesis (LTH) states that learning on a properly pruned network (the winning ticket) has improved test accuracy over the originally unpruned network. Although LTH has been justified empirically in a broad range of deep neural network (DNN) involved applications like computer vision and natural language processing, the theoretical validation of the improved generalization of a winning ticket remains elusive. To the best of our knowledge, our work, for the first time, characterizes the performance of training a sparse neural network by analyzing the geometric structure of the objective function and the sample complexity to achieve zero generalization error. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned, indicating the structural importance of a winning ticket. Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer. With a fixed number of samples, training a pruned neural network enjoys a faster convergence rate to the desirable model than training the original unpruned one, providing a formal justification of the improved generalization of the winning ticket. Our theoretical results are acquired from learning a sparse neural network of one hidden layer, while experimental results are further provided to justify the implications in pruning multi-layer neural networks.
Favourite Artificial Intelligence-based speaking applications for autonomous learning

For doing the learning process along Covid-19 pandemic, online learning is the most appropriate alternative. Nevertheless, this wisdom causes some challenges, especially for students in remote area. One of them is network. The alternative must be done so their main learning process can be supported. By practicing speaking through artificial intelligence-based speaking application, they are expected to learn speaking autonomously everywhere and everytime. This research aims to know the students’ favourite artificial intelligence-based speaking applications for learning speaking, describing the reasons why the students choose them and analyze the weaknesses of using artificial intelligence-based speaking applications. This qualitative research was conducted on the second semester students of Tadris Bahasa Inggris, Institut Agama Islam Negeri Kudus. Data was collected through questionnaire, interview and documentation. The are three results. First, among the other artificial intelligence-based speaking applications, the favourite ones are Cake and Talk. Second, the reasons why the students choose them because those speaking applications are free, easy to access, able to be used to practice speaking everywhere and everytime, become good alternative like partner and their speaking practice can be evaluated or assessed by the application. And the third, the weaknesses of the artificial intelligence-based speaking applications are limited on the topics and conversation served by the applications so it cannot be elaborated more. The speaking applications with good features are not free accessed and need more space in gadget. The students expect that good apps with complete features, like ELSA Speak, can be free accessed so all students can learn maximally.
Boyolali Opera House Dengan Pendekatan Arsitektur Metafora

Opera House is a place that is used as a venue for performances, both dance, music and drama. The design of this building is inseparable from the needs of Boyolali City for a performance building, along with the times and the development of art in Boyolali. In the process of designing the Boyolali Opera House, there are two things to consider, namely in terms of function and as a building that will become a new icon in the City of Boyolali. Based on the above conditions, it produces a phenomenon that in Boyolali requires a means of worshiping the actualization of art activities, especially those that have a character that is in accordance with the development of science and technology in modern times, in accordance with the concept of metaphorical architecture. Metaphor is an architectural style that is very suitable in the modern era because the taking of concepts can be taken from all aspects. The thought of the metaphor itself is far more creative and innovative going forward and far more advanced than its time, so as to create buildings that are environmentally friendly, and have aesthetic values so as to create a comfortable and safe impression.
Funny Yellow Dog Game

Author(s): Styles, Benjamin; Monica, Santiago; Jason, Rivero-Rivera | Abstract: Funny Yellow Dog Game features internet meme dog Cheems, who goes on a short adventure through a dog park. Navigate the mazes and fend off the vicious Henries with your trusty bonking bat and explosive cheeseburgers.
Predicitive Analysis of Breast Cancer using Multiple Classification Algorithms

The breast cancer is the vital cancer that plays the major role in the cancer types. These types of cancer are very dangerous to human's life especially females. Mostly females are affecting due to these types of cancer. In fact, we want to get precautioned to these cancers. The resources are taken from the machine learning repository that are available for the predictions of supervised machine learning technique. In this paper we have applied and analyzed the concept of the machine learning algorithms which have been evaluated for the cancer dataset. Based on the accuracy we have compared each algorithm. Upon the comparison we have calculated the good accuracy for the random forest and svm over 96 percent.
Educational Planning in Nigeria during COVID-19: Problems and Way Forward

Educational planning is the foundation of education. The development of education depend on effective planning. Educational planning is a continuous process and it covers all aspects of education. The COVID-19 outbreak disrupted the activities of educational planning in Nigeria. This article is aimed to discuss how COVID-19 clock down affected the planning of education in Nigeria. The researchers used secondary date sourced from both print materials and online to support some of the points raised in the article. The COVID-19 lockdown affected the educational planning in Nigeria in the following ways: suspension of implementation of planned programme, suspension of school planning, suspension of lesson planning at school level and suspension of data collection for educational planning. To prepare for the future and to ensure that even in the mist of pandemic planning of education continues, this article hereby suggests the following: Educational planners in all ministries and agencies of education should be train on how to use ICT facilities to carry out planning functions and ICT facilities should be provided for all the educational planners in the country etc.
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significantly when they stack many layers, because of the over-smoothing issue. Node embeddings tend to converge to similar vectors when GNNs keep recursively aggregating the representations of neighbors. To enable deep GNNs, several methods have been explored recently. But they are developed from either techniques in convolutional neural networks or heuristic strategies. There is no generalizable and theoretical principle to guide the design of deep GNNs. To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs. Based on it, a novel deep GNN framework -- EGNN is designed. It could provide lower and upper constraints in terms of Dirichlet energy at each layer to avoid over-smoothing. Experimental results demonstrate that EGNN achieves state-of-the-art performance by using deep layers.
Professional competence development when teaching computational informatics.

Bachelors and graduate students are offered in the course of teaching computational informatics, the ability to solve non-standard mathematical problems, which, as a rule, are not included in the content of teaching computational informatics. The article aimed to analyze the application effectiveness of non-standard mathematical problems in the course of teaching computational informatics, elaboration of constructive computational solution algorithms of inverse problems for differential equations, during which the bachelors and graduate students develop own professional competencies. The research conducted a review of previous literature on the topic. Formulation of the inverse problem for differential equations for the investigation of which the computational mathematics finite difference methods are applied, is presented. In the course of investigation, it was revealed that at elaborating the constructive computational algorithms of its solution, the bachelors and graduate students develop not only fundamental knowledge in the field of applied and computational mathematics, computational informatics methods, but also develop the professional competences, including computational thinking.&#x0D; Key words: professional competence; computational informatics; computational mathematics methods; non-standard.
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks

The general goal of text simplification (TS) is to reduce text complexity for human consumption. In this paper, we investigate another potential use of neural TS: assisting machines performing natural language processing (NLP) tasks. We evaluate the use of neural TS in two ways: simplifying input texts at prediction time and augmenting data to provide machines with additional information during training. We demonstrate that the latter scenario provides positive effects on machine performance on two separate datasets. In particular, the latter use of TS improves the performances of LSTM (1.82–1.98%) and SpanBERT (0.7–1.3%) extractors on TACRED, a complex, large-scale, real-world relation extraction task. Further, the same setting yields improvements of up to 0.65% matched and 0.62% mismatched accuracies for a BERT text classifier on MNLI, a practical natural language inference dataset.
Tingkat Pengetahuan dan Minat Mahasiswa Universitas Negeri Yogyakarta terhadap Media UKBI (Uji Kemahiran Berbahasa Indonesia)

UKBI is a test medium to measure a person's proficiency in the Indonesian language. The purpose of this study was to determine the knowledge and interests of UNY students in UKBI media. The research method used is descriptive qualitative and simple quantitative, namely calculating the percentage and describing the characteristics of the survey results. The results of the study showing that UNY students who did not know UKBI. Even so, UNY students have a high interest in UKBI. Therefore, it is recommended that related party continue to promote UKBI and provide training related to UKBI.
High dynamic range 3D measurement based on space-time speckle correlation and color multiplexing

In this paper, we propose a strategy to expand the measurement dynamic range taking advantage of color multiplexing speckle projection and space-time speckle correlation.
Editorial for “<scp>Cross‐Cohort</scp> Automatic Knee <scp>MRI</scp> Segmentation with <scp>Multi‐Planar U‐Nets</scp>”

Journal of Magnetic Resonance ImagingEarly View Editorial Editorial for “Cross-Cohort Automatic Knee MRI Segmentation with Multi-Planar U-Nets” Sokratis Makrogiannis PhD, Corresponding Author Sokratis Makrogiannis PhD smakrogiannis@desu.edu orcid.org/0000-0003-0316-3529 Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics, and Computer Science, Delaware State University, Dover, Delaware, USASearch for more papers by this author Sokratis Makrogiannis PhD, Corresponding Author Sokratis Makrogiannis PhD smakrogiannis@desu.edu orcid.org/0000-0003-0316-3529 Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics, and Computer Science, Delaware State University, Dover, Delaware, USASearch for more papers by this author First published: 10 November 2021 https://doi.org/10.1002/jmri.27990 Level of Evidence:: 5 Technical Efficacy Stage:: 2 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Smart College Camera Security System Using IOT

Features of an ordinary camera security system are enhanced with the help of computer vision and Machine Learning algorithms that treat images as input data. It is an inexpensive and unique way of providing all those functionalities that a conventional camera security system cannot provide. The system includes face recognition, object detection, and other computer vision related operations. This camera security system is developed as an IOT solution.
AEDA: An Easier Data Augmentation Technique for Text Classification

This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text. This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results. In addition, it keeps the order of the words while changing their positions in the sentence leading to a better generalized performance. Furthermore, the deletion operation in EDA can cause loss of information which, in turn, misleads the network, whereas AEDA preserves all the input information. Following the baseline, we perform experiments on five different datasets for text classification. We show that using the AEDA-augmented data for training, the models show superior performance compared to using the EDA-augmented data in all five datasets. The source code will be made available for further study and reproduction of the results.
A Unified Speaker Adaptation Approach for ASR

Transformer models have been used in automatic speech recognition (ASR) successfully and yields state-of-the-art results. However, its performance is still affected by speaker mismatch between training and test data. Further finetuning a trained model with target speaker data is the most natural approach for adaptation, but it takes a lot of compute and may cause catastrophic forgetting to the existing speakers. In this work, we propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation. For feature adaptation, we employ a speaker-aware persistent memory model which generalizes better to unseen test speakers by making use of speaker i-vectors to form a persistent memory. For model adaptation, we use a novel gradual pruning method to adapt to target speakers without changing the model architecture, which to the best of our knowledge, has never been explored in ASR. Specifically, we gradually prune less contributing parameters on model encoder to a certain sparsity level, and use the pruned parameters for adaptation, while freezing the unpruned parameters to keep the original model performance. We conduct experiments on the Librispeech dataset. Our proposed approach brings relative 2.74-6.52% word error rate (WER) reduction on general speaker adaptation. On target speaker adaptation, our method outperforms the baseline with up to 20.58% relative WER reduction, and surpasses the finetuning method by up to relative 2.54%. Besides, with extremely low-resource adaptation data (e.g., 1 utterance), our method could improve the WER by relative 6.53% with only a few epochs of training.
Do Transformer Modifications Transfer Across Implementations and Applications

The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption. In this paper, we comprehensively evaluate many of these modifications in a shared experimental setting that covers most of the common uses of the Transformer in natural language processing. Surprisingly, we find that most modifications do not meaningfully improve performance. Furthermore, most of the Transformer variants we found beneficial were either developed in the same codebase that we used or are relatively minor changes. We conjecture that performance improvements may strongly depend on implementation details and correspondingly make some recommendations for improving the generality of experimental results.
TransIns: Document Translation with Markup Reinsertion.

For many use cases, it is required that MT does not just translate raw text, but complex formatted documents (e.g. websites, slides, spreadsheets) and the result of the translation should reflect the formatting. This is challenging, as markup can be nested, apply to spans contiguous in source but non-contiguous in target etc. Here we present TransIns, a system for non-plain text document translation that builds on the Okapi framework and MT models trained with Marian NMT. We develop, implement and evaluate different strategies for reinserting markup into translated sentences using token alignments between source and target sentences. We propose a simple and effective strategy that compiles down all markup to single source tokens and transfers them to aligned target tokens. A first evaluation shows that this strategy yields highly accurate markup in the translated documents that outperforms the markup quality found in documents translated with popular translation services. We release TransIns under the MIT License as open-source software on https://github.com/DFKI-MLT/TransIns. An online demonstrator is available at https://transins.dfki.de.
Determinants of Job Satisfaction in the Lebanese Construction Sector

Job satisfaction is the fulfilment or enjoyment that a person derives from their job. Job satisfaction has been a focal point of social sciences studies since the early 1900s, due to its substantial impact on employees’ mental health, as well as its influence on the workplace atmosphere and productivity.This study aims to ascertain the determinants of job satisfaction in the Lebanese construction sector. The primary data was gathered through a quantitative structured questionnaire, while the sample amounted to 106 employees in the construction sector. The results of the study revealed a significant positive relation between payment (salary), financial benefits, promotion, and personal job satisfaction. In addition, this study revealed a non-significant connection between gender, age, package incentives, job security, rank, relationships with co-workers, stress/work–family conflict, designation (social recognition), and job satisfaction.
The Effectiveness of Using the Thinking Hand Strategy in the Achievement of Second Graders Average in Social and Mental Alertness

The Effectiveness of Using the Thinking Hand Strategy in the Achievement of Second Graders Average in Social and Mental Alertness
What can linearized neural networks actually say about generalization

For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization. However, for the networks used in practice, the empirical NTK represents only a rough first-order approximation of these architectures. Still, a growing body of work keeps leveraging this approximation to successfully analyze important deep learning phenomena and derive algorithms for new applications. In our work, we provide strong empirical evidence to determine the practical validity of such approximation by conducting a systematic comparison of the behaviour of different neural networks and their linear approximations on different tasks. We show that the linear approximations can indeed rank the learning complexity of certain tasks for neural networks, albeit with important nuances. Specifically, we discover that, in contrast to what was previously observed, neural networks do not always perform better than their kernel approximations, and reveal that their performance gap heavily depends on architecture, number of samples and training task. In fact, we show that during training, deep networks increase the alignment of their empirical NTK with the target task, which explains why linear approximations at the end of training can better explain the dynamics of deep networks. Overall, our work provides concrete examples of novel deep learning phenomena which can inspire future theoretical research, as well as provides a new perspective on the use of the NTK approximation in deep learning.
Detecting and Tracking the Moving Vehicles Based on Deep Learning

Aiming to address the problem of counting multi-target moving vehicles in the various complex traffic environments, this paper proposes a detecting and tracking method based on YOLO (You Only Look Once) and Deep Sort, and evaluates its performance with public dataset (TUA-DETRAC) and two self-collection datasets. The YOLOv4 algorithm is firstly used to detect each moving vehicles, and then Deep Sort algorithm is adopted to track multi-target vehicles. The experimental results show that moving vehicles can be effectively detected and tracked in real time under different traffic environments including daytime, nighttime, rainy and crowded scenes. The experimental results show that the proposed method can reach 93% average detection accuracy with 20fps of tracking speed, and is capable of dealing with different traffic and climate conditions.