Reviewerly/exHarmony huggingface dataset:
The Role of Knowledge Creation-Oriented Convolutional Neural Network in Learning Interaction

When convolutional neural network (CNN) applications have different tasks in the source domain and target domain, but both have labels, it is easy to ignore the difference between the source domain and target domain by using the current traditional method, and the recognition effect of image features is not ideal. This paper proposes a deep migration learning method based on improved ResNet based on existing research to avoid this problem. This method extracts high-order statistical features of images by increasing the number of network layers for classification when performing model transfer learning. The ImageNet dataset is used as the source domain, and a Deep Residual Network (DRN) is used for model transfer based on homogeneous data. Firstly, the ResNet model is pretrained. Then, the last fully connected layer of the source model is modified, and the final deep model is constructed by fine-tuning the network by adding an adjustment module. The impact of content differences between datasets on recognizing transfer learning features is reduced through model transfer and deep feature extraction. The deep transfer learning methods after improving ResNet are compared through experiments. The identification algorithm is based on Support Vector Machine (SVM), the deep transfer learning method on Visual Geometry Group (VGG)-19, and the deep transfer learning method based on Inception-V3. Four experiments are performed on MNIST and CIFAR-10 datasets. By analyzing the experimental data, ResNet's improved deep transfer learning method achieves 97.98% and 90.45% accuracy on the MNIST and CIFAR-10 datasets, and 95.33% and 85.07% on the test set. The accuracy and recognition accuracy on the training and test sets have been improved to a certain extent. The combination of CNN and transfer learning can effectively alleviate the difficulty of obtaining labeled data. Therefore, the application of a CNN in transfer learning is significant.
Bayesian Bivariate Cure Rate Models Using Copula Functions

Bivariate survival cure rate models extend the understanding of time-to-event data by allowing for a cured fraction of the population and dependence between paired units and make more accurate and informative conclusions. In this paper, we propose a Bayesian bivariate cure rate mode where a correlation coefficient is used for the association between bivariate cure rate fractions and a new generalized Farlie Gumbel Morgenstern (FGM) copula function is applied to model the dependence structure of bivariate survival times. For each marginal survival time, we apply a Weibull distribution, a log normal distribution, and a flexible three-parameter generalized extreme value (GEV) distribution to compare their performance. For the survival model fitting, DIC and LPML are used for model comparison. We perform a goodness-of-fit test for the new copula. Finally, we illustrate the performance of the proposed methods in simulated data and real data via Bayesian paradigm.
English Language Teaching and Globalization: to Support Economic Growth

The English language is no doubt significant for communication in the international community. It may not be the most widely spoken language globally, but it is the official language in many countries. This paper aims to analyze why the English language plays a significant role in supporting economic growth and the factors that influence the use of the English language in the era of globalization. A function that helps people communicate promotes nation-to-nation development and builds relationships with other countries. The survey design used a survey, the type of survey used a descriptive survey, and the equipment used was student observation. The results show that the economy is becoming increasingly global, and more and more countries are turning to the English language for business reasons. In conclusion, the English language is a significant contributor to sustainable development, so those who do not speak the English language do not stand a chance.
D-F of cyber security

Network SecurityVol. 2022, No. 3 The FirewallD-F of cyber securityKaren RenaudKaren RenaudUniversity of StrathclydeSearch for more papers by this authorKaren RenaudPublished Online:31 Mar 2022https://doi.org/10.12968/S1353-4858(22)70024-4AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. Human Factors in Diving, home page. Accessed Mar 2022. www.hf-in-diving-conference.com. Google Scholar FiguresReferencesRelatedDetails 1 March 2022Volume 2022Issue 3ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 31 March 2022 Published in print 1 March 2022 Information© MA Business Ltd.
DEVELOPING AND LEADING FOR INDUSTRY-EDUCATION INTEGRATION SERVICE IN VOCATIONAL AND TECHNICAL COLLEGES

The industry-education integration (IEI) is one of the strategies to develop vocational education. The concept of IEI is the basis for the development of vocational education proposed by the Ministry of Education of China in 2011, which mainly refers to the integration of industrial system and education system. The integration is an important measure to promote the reform and development of vocational education and an effective way to train high quality skilled personnel in vocational and technical colleges (VTC). Vocational and technical education should respond to the strategic education development programme of the Chinese government and comply with the basic requirements of IEI in vocational and technical education. This paper aims to develop the conceptual framework for IEI to serve the vocational and technical college students better. The VTC will focus on training social service personnel as the goal, through the reform of teaching content, inspire students to innovate learning mode in the learning process, the combination of demand-driven and IEI, to enrich students' practical experience and professional work ability. In the course of vocational and technical education, some training subjects of scientific research should be added to cultivate students' ability of independent work and innovation. Through the application of the Internet technology of modern science and technology to improve the teaching environment, to achieve the quality of talent training for the goal of teaching methods and teaching modules, to provide enterprises with elite talents. The students will most probably benefit from the industry-driven education initiatives.
Relationship Between Organisational Factors and Adoption of New Technology in Kenya: A Case of CEMASTEA

This research evaluated the relationship between organisational factors and adoption of new technology in education support organisations in Kenya based on the case of Centre for Mathematics, Science and Technology Education in Africa (CEMASTEA). The research design adopted for this study was descriptive correlational design. The study population was 214 employees of CEMASTEA. A stratified sample of 64 participants was drawn from the different departments within the institution. A structured questionnaire was developed for data collection. Data was analysed using descriptive statistics and regression analysis in SPSS. An R-Square of 0.749 with the standard error of estimate being 0.235 was found, implying that for any variation in adoption of new technology by one-unit, organisational factors explained 74.9% of such variation. This was a demonstration that organisational factors were a salient determinant of technology adoption success. Government and all relevant authorities should also formulate and implement policies that will assist organisations adopt and adapt to new technologies in a favourable and user-friendly environment.
Perbandingan Algoritma SVM, Random Forest Dan XGBoost Untuk Penentuan Persetujuan Pengajuan Kredit

Credit is an option for seeking funding for most economic activities. The demand for credit is currently growing very rapidly, in line with the increasing financial needs of the community, especially in developing countries such as Indonesia. Credit analysis needs to be carried out to achieve proper and safe lending. Credit analysis is an observation to see the feasibility of a credit problem. From this analysis, the creditworthiness of the recipient will be known. This study uses the CRISP-DM methodology which consists of 6 stages, namely Bussines Understanding, Data Understanding, Data preparation, Modeling Evaluation, and Deployment by applying the classification method by comparing the SVM, Random Forest, and XGBoost algorithms. This research uses an open source dataset obtained from Kaggle. The results of the research using the SVM, random forest, and XGBoost algorithms get the highest accuracy, recall, precision values in the XGBoost model with 82% accuracy, 70% recall, and 92% precision.
ADCSA-WSD: Adapted Discrete Crow Search Algorithm for Word Sense Disambiguation

In the field of natural language processing, the semantic disambiguation of words is beneficial to several applications, which helps us to identify the correct meaning of a word or a sequence of words according to the given context. It can be formulated as a combinatorial optimization problem where the goal is to find the set of meanings that contribute to improving the semantic relationship between target words. The Crow Search Algorithm (CSA) is a nature-inspired algorithm. It mimics the food foraging behavior of crow birds and their social interaction. CSA can deal with both continuous and discrete optimization problems. In this paper, the Word Sense Disambiguation (WSD) is modelled as a combinatorial optimization problem that is by nature a discrete problem. For this propose the discrete version of CSA has been adapted for solving the WSD problem and a DCSA-based WSD approach is proposed and called ADCSA-WSD. The proposed approach has been evaluated and compared with state-of-the-art approaches using three well-known benchmark datasets (SemCor 3.0, SensEval-02, SensEval-03). Experimental results show that ADCSA-WSD approach is performing better than other approaches.
Evidential Spammers and Group Spammers Detection

The online success of the brands, products or services depends upon the online reviews written by the consumers to share their experiences. These reviews deeply affect the buying decision of the new customers. For the purpose of performing their e-reputation, some companies rely on spammers to involve fraud reviews with the aim of gaining more profit. They can work individually or collaborate together to post various fake reviews trying to promote or demote target companies or products. These spammers and the group of spammers mislead the readers which make the e-commerce unsafe domain. To deal with this issue, we propose a new method having the objective to detect the spammers while taking into account both the group spammers and the individual spammers indicators. Our proposed method relies on the K-nearest neighbors algorithm under the belief function theory in order to handle the uncertainty in both the spammers and the group spammers indicators. Experiments are conducted on two labeled real datasets extracted from Yelp.com where our method achieves significant results.
Theoretical studies on quantum imaging with time-integrated single-photon detection under realistic experimental conditions

Abstract We study a quantum-enhanced differential measurement scheme that uses quantum probes and single-photon detectors to measure a minute defect in the absorption parameter of an analyte under investigation. For the purpose, we consider two typical non-classical states of light as a probe, a twin-Fock state and a two-mode squeezed vacuum state. Their signal-to-noise ratios (SNRs) that quantifies the capability of detecting the defect are compared with a corresponding classical imaging scheme that employs a coherent state input. A quantitative comparison is made in terms of typical system imperfections such as photon loss and background noise that are common in practice. It is shown that a quantum enhancement in SNR can be described generally by the Mandel Q-parameter and the noise-reduction-factor, which characterize an input state that is incident to the analyte. We thereby identify the conditions under which the quantum enhancement remains and can be further increased. We expect our study to provide a guideline for improving the SNR in quantum imaging experiments employing a differential measurement scheme with time-integrated single-photon detectors.
Detection of URL Based Phishing Websites Using Machine Learning in Django Framework

Abstract: In this modern world, Phishing website detection is one of the most critical tasks in the world. In the recent times, a lot of people have suffered phishing attack due to phishing website. Machine Learning plays an important role in prediction of phishing website in the network. The proposed method predicts the URL based phishing websites based on features and also gives maximum accuracy to predict the result. This method uses uniform resource locator (URL) features to detect. It identified features that phishing site URLs contain. The proposed method takes those features for phishing detection. Security of the phishing detection website is also a major concern which is solved by providing administration who can manage the phishing detection website. Keywords: Phishing site, Machine learning, Legitimate, Prediction
Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring

Monitoring, detection, and control of traffic is a serious problem in many cities and on roads around the world and poses a problem for effective and safe control and management of pedestrians with edge devices. Systems using the computer vision approach must ensure the safety of citizens and minimize the risk of traffic collisions. This approach is well suited for multiple object detection by automatic video surveillance cameras on roads, highways, and pedestrian walkways. A new Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74,108 data files and 74,108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. The data were captured from real road scenes using 50 video cameras from the leading video camera manufacturers at different road locations and under different meteorological conditions. The AVDL dataset consists of two directories, the Data directory and the Labels directory. Both directories provide the data as NumPy arrays. The dataset can be used to train and test deep neural network models for traffic and pedestrian detection, recognition, and counting.
Exploring EFL Graduate Students' Perspectives and Strategies to Improve English Speaking Skills

Even though Indonesian EFL learners have been studying English since elementary school, many may still consider speaking one of the most challenging language skills. Consequently, when they move to the advanced level, they are required to master more complex speaking skills. Indonesian EFL graduate students faced some problems during learning that can contribute to their speaking performance. Therefore, it is urgent for this study to investigate EFL graduate students' perspectives on their speaking challenges and strategies used to improve their speaking skills. This study used a qualitative method and a semi-structured interview to obtain deep insight into participants' perspectives. The participants were EFL graduate students who are taking an English Education Master's Program. Results showed that some factors influenced their speaking skills in an academic context, such as a lack of accurate pronunciation and lack of vocabulary knowledge and that they used effective speaking strategies for improving their ability in speaking. It is expected that the findings would assist EFL graduate students in uncovering their speaking challenges so that they could perform better when using their spoken English.
Morphological Analyzer using the BILSTM Model only for Japanese Hiragana Sentences

This study proposes a method to develop neural models of the morphological analyzer for Japanese Hiragana sentences using the Bi-LSTM CRF model. Morphological analysis is a technique that divides text data into words and assigns information such as parts of speech. In Japanese natural language processing systems, this technique plays an essential role in downstream applications because the Japanese language does not have word delimiters between words. Hiragana is a type of Japanese phonogramic characters, which is used for texts for children or people who cannot read Chinese characters. Morphological analysis of Hiragana sentences is more difficult than that of ordinary Japanese sentences because there is less information for dividing. For morphological analysis of Hiragana sentences, we demonstrated the effectiveness of fine-tuning using a model based on ordinary Japanese text and examined the influence of training data on texts of various genres.
Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity

Reading Indian scene texts is complex due to the use of regional vocabulary, multiple fonts/scripts, and text size. This work investigates the significant differences in Indian and Latin Scene Text Recognition (STR) systems. Recent STR works rely on synthetic generators that involve diverse fonts to ensure robust reading solutions. We present utilizing additional non-Unicode fonts with generally employed Unicode fonts to cover font diversity in such synthesizers for Indian languages. We also perform experiments on transfer learning among six different Indian languages. Our transfer learning experiments on synthetic images with common backgrounds provide an exciting insight that Indian scripts can benefit from each other than from the extensive English datasets. Our evaluations for the real settings help us achieve significant improvements over previous methods on four Indian languages from standard datasets like IIIT-ILST, MLT-17, and the new dataset (we release) containing 440 scene images with 500 Gujarati and 2535 Tamil words. Further enriching the synthetic dataset with non-Unicode fonts and multiple augmentations helps us achieve a remarkable Word Recognition Rate gain of over 33% on the IIIT-ILST Hindi dataset. We also present the results of lexicon-based transcription approaches for all six languages.
The Sanitised Platform

Feminist legal scholar Vicki Schultz argues that US law on sexual harassment has created a “sanitised workplace”, by encouraging employers to suppress any kind of sexual behaviour, while ignoring broader issues around gender equality. This paper employs Schultz’s concept of sanitisation as a frame to critique current trends in European social media regulation, focusing on the 2019 Copyright Directive, 2021 Terrorist Content Regulation and the Digital Services Act proposed in 2020. EU law incentivises the deletion of various broadly-defined types of illegal content, which is also likely to suppress large amounts of legal and harmless content. Evidence of how social media platforms moderate content suggests that this over-enforcement will disproportionately suppress marginalised users and non-mainstream viewpoints, while increasing the influence of platforms’ commercial goals on online communications. Yet at the same time, by focusing primarily on content (i.e. individual posts and uploads) over broader contextual and design factors, European regulation fails to effectively address many social harms associated with major social media platforms. Schultz’s approach not only draws our attention to these failings, but provides theoretical insights as to how private ordering heightens these problems, enforces dominant discourse norms and subordinates online communication to commercial priorities.
Hearthstone Battleground: An AI Assistant with Monte Carlo Tree Search

We are in the golden age of AI. Developing AI software for computer games is one of the most exciting trends of today’s day and age. Recently games like Hearthstone Bat- tlegrounds have captivated millions of players due to it’s sophistication, with an infinite number of unique interactions that can occur in the game. In this research, a Monte-Carlo simulation was built to help players achieve higher ranks. This was achieved through a learned simulation which was trained against a top Hearthstone Battleground player’s historic win. In our experiment, we collected 3 data sets from strategic Hearthstone Bat- tleground games. Each data set includes 6 turns of battle phases, 42 minions for battle boards, and 22 minions for Bob’s tavern. The evaluation demonstrated that the AI assis- tant achieved better performance — loosing on average only 9.56% of turns vs 26.26% for the experienced Hearthstone Battleground players, and winning 56% vs 46.91%.
Research data in the context of open science: Case study of the Republic of Moldova

At present, there are important changes in scientific research, changes that concern both the organization, realization and evaluation of research, as well as the use and dissemination of scientific results. These changes that are characteristic to the Open Science phenomenon are determined by the development of new technologies, by increasing the social role of scientific research in an institutional and political context. The Open Science concept represents a new approach to the way in which scientific research based on cooperation and new ways of disseminating knowledge is carried out and organized, using new digital technologies, new tools for collaboration, and RDI infrastructure to ensure open access to research data.&#x0D; The paper presents the results of the study on mapping research data in the Republic of Moldova. The study is based on results of two surveys conducted in January-February 2016 and May-July 2018. The research has highlighted the general concern of RDI actors about the retention and use of scientific data. A new challenge is needed to solve scientific data issues by creating new type of infrastructure to ensure data archiving, broad access for the purpose of their dissemination and reuse, and thus creating new research opportunities based on research data.
Classified Foster and Cauer Circuits for the Choice of a Model

Approximations of constant phase elements (CPE) or Warburg impedance are frequently made by equivalent electrical circuits. However, these approximating circuits generally do not take into account the boundary conditions involved by making such approximations. In this paper, we assess various models or possible topologies and classify them according to their asymptotic limits. We show that circuit topologies having the same number and the same types of elements has the same impedance (thus the same frequency behaviour). Having identical factorized impedance proves the equivalence between several proposed models (serial Foster, parallel Foster, serial Cauer and parallel Cauer). We conclude that the choice of a model for parameter identification needs to take into account the specific boundary conditions.
Big Data Analytics to Predict Breast Cancer

Abstract: Breast Cancer is the second cause of death among women. Early prediction of breast cancer will help with the survival of breast cancer patient. Machine Learning and Data Mining have been widely used in the prediction of breast cancer and on the early detection of breast cancer. This paper compares the machine learning techniques which are used for the prediction of breast cancer. Keywords: Breast Cancer, Malignant, Benign, Machine Learning, Big Data Analytics.