Reviewerly/exHarmony huggingface dataset:
Content-Based Image Retrieval Using Discrete Wavelet Transform and Convolutional Neural Network

Due to the development of internet and digital media techniques the size of digital image collection is increasing rapidly. Various techniques for storing, searching and retrieving images become essential for large image archives. This leads to an existence of Image Retrieval System. Content-Based Image Retrieval (CBIR) is a process of retrieving the expected images from large image databases based on the features of the query image and this is performed by various feature extraction techniques. In this work, two different methods of CBIR have been proposed. The first method is CBIR using Discrete Wavelet Transform (DWT), which extracts the texture feature of an image and histogram extracts the color feature of an image. And also the results will be ranked according to the chi-square distance calculation. The second method is CBIR using Convolutional Neural Network (CNN). In CNN method the features are extracted using VGG16 pre-trained model and canny edge detection is used for detecting the edges of an image. Finally, the performances of DWT and CNN based methods are analyzed and compared.
Construction method and architecture of integrated energy service ecological platform based on support vector machine

research-article Construction method and architecture of integrated energy service ecological platform based on support vector machine Share on Authors: Yaolin Liang Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., China Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., ChinaView Profile , Xuejin Huang Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., China Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., ChinaView Profile , Zhe Chen Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., China Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., ChinaView Profile , Jiaqi Li Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., China Planning and Development Department, Dongguan Power Supply Bureau, Guangdong Power Grid Co. Ltd., ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 347Pages 1–5https://doi.org/10.1145/3469213.3471379Online:18 August 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The effects of pair-programming in introductory programming courses with visual and text-based languages

— This full research to practice paper investigates the setting of pair-programming focusing on its effects on students’ learning to program when using a visual versus text-based programming language. In particular, we are interested whether there are any differences in learning outcomes and social/motivational factors to keep in mind when learners’ make their initial steps in programming in a visual versus text-based language while employing a pair-programming approach. First a systematic literature review (SLR) was conducted, encompassing searches in the ACM Digital Library, IEEE Xplore/Electronic Library, LNCS (Springer), and Google Scholar. The results of the SLR informed the second step, namely the design of a qualitative study consisting of the evaluation of students’ achievements, a questionnaire, and focus interviews with students at secondary level (K10). Students worked in pair-programming (PP) and solo-programming (SP) mode while using a visual language (MIT App Inventor) and a text-based language (Python), respectively. In a nutshell, we found that regarding achievement, students tended to earn more points in the PP setting. This tendency was more distinct in the text-based condition compared to visual programming. In both conditions, students responded that in the PP setting they had more fun solving the assignments and the PP setting had helped to solve problems faster. Aside of the scientific findings, the paper describes the didactic concepts used in order to inspire educators to engage in similar practice and research. With this contribution we aim to enrich educators’ and researchers’ repertoire of making introductory programming lessons more engaging and successful.
Labelability of Elliptical Properties : Evidence from Fragmentary Questions in Korean

In this paper, I have presented some properties of FQs in Korean, the implication pointed by connectivity effects would seem to be : (i) there exist syntactic structure inside ellipsis sites, but it is unpronounced. (ii) Remnants in FQs in Korean are considered as a Contrastive Topic. In accordance with Takita et al. (2016), den Dikken (2008), and Ko (2011), I propose there exist a functional head H mediating the remnant (Contrastive Topic) and elided material in FQs in Korean that can salvage remnants from labeling failure at narrow syntax (NS). I also scrutinized to explore the nature of the interaction between narrow syntax (NS) and two interfaces with the goal of achieving an explanation of how elided FQ is realized. In Chomsky (2013, 2015), every syntactic object needs to be labeled for interpretation at the interfaces since those labels play certain roles in semantic and phonological computations. This paper aims to present the possibility that ellipsis should be licensed in narrow syntax. Furthermore, labels play a crucial role in ellipsis phenomena, specially in FQ in Korean.
China-Africa Stock Market Linkages and the Global Financial Crisis

In this paper, we investigate the long-run linkages and short-run dynamics among China and five major African stock markets (South Africa, Morocco, Egypt, Nigeria and Kenya), with emphasis on the impact of the global financial crisis on the linkages and transmission mechanisms. In the pre-crisis and crisis era, we find convincing linkages among the stock markets, evidenced by the presence of co-integration and stronger dynamic price transmission. Our results show that the influence of China on the African stock markets became more noticeable during the crisis. Interestingly, after the crisis, the co-integration was lost as the China-Africa stock market linkages became significantly weakened. The influence of China on Africa’s stock markets - which was not in doubt prior to and during the crisis – became eroded after the crisis. Thus, our results suggest that Africa’s major stock markets have become less connected with China’s stock market post crisis, even though China’s trade and other economic influence on Africa has grown – an outcome we attribute to a possible disconnect between Africa’s real economy and stock markets. Meanwhile, the decoupling of African and Chinese stock markets in the post crisis period suggests that gains from international diversification exist in Africa for portfolio investors and asset managers with nontrivial exposure to China.
Integration of Odoo Learning Management System with Zoom Video Conferencing for K-12 Students

Many educational institutions use the Learning Management System as an e-learning application. However, the e-learning method is less effective to use because students cannot interact directly with the teacher, so it takes longer to get feedback. The purpose of this research is to make it easier for students and teacher to interact directly (synchronous learning) within the scheduled time according to the e-learning material. The system development method used in this study uses the waterfall methodology with the stages of communication, planning, modelling, construction, and deployment. The framework used is Odoo and Zoom application as video conferencing media. The expected result from developing this system is that students get the optimal benefits of e-learning by making face-to-face online learning schedules with instructors in an integrated Learning Management System (LMS).
Bi- Classification Semi Supervised Algorithm Using Deep Fuzzy C-Mean Clustering

Deep Fuzzy C-Means algorithm is applied to decide the hidden structure in the data set. It is commonly utilized when data limits are not characterized, and additional boundaries are expected to reduce the statistical closeness. In this paper, we propose a semi-supervised fuzzy C-Means algorithm that accommodates this elusiveness. It applies to a machine learning strategy that depends on algorithmic stream for dynamic data. With statistical data gave as a collection of numerical data set of two classes, in particular, named and unlabeled, the semi-supervised fuzzy c-means clustering gives a comparison and answer for a given data set. The clustering approach sees enrollment functions for fuzziness. The proposed structure for semi-supervised data set finds supervised data and isolates it from unsupervised data. Here, the expression deep characterizes the closeness in ℜ2 space, which is utilized to improve precision along with the centres.
Effect of J48 and LMT Algorithms to Classify Movies in the Web—A Comparative Approach

Social Media websites such as Facebook, YouTube, twitter, etc., are the convenient platforms to share one’s views about the multimedia. Videos getting uploaded on YouTube every day are millions in number. Videos can be of different category such as comedy video, sports video, news, advertisement, movie trailer video, etc. Nowadays, data mining researchers are attracted towards different classification techniques of data mining to discover hidden information as well as to discover knowledge from huge video data. The goal of this research is, classifying and predicting movies trailer videos as poor movie, good movie, very good movie and excellent movie based on the meta data such as likes, dislikes, comments, ratings, budget, etc. An attempt is made in the present work to provide an effective mining result about classifying Social Media movies. These movies are labelled based on a particular class and other related attributes of the same dataset. 10 folds cross-validation test is applied on J48 and LMT decision tree algorithm, and comparison analysis is made based on confusion matrix and accuracy rate.
Optimal allocation of time resources for phased array radar multi-target tracking based on BP neural network

Aiming at the different threat levels under phased array radar multi-target tracking, the Bayesian Cramer-Rao Lower Bound (BCRLB) of the target position estimation is used as the allocation criterion, this paper establishes a multi-target tracking time resource allocation optimization model based on threat degree. The model based on threat degree to track the target can be divided into two categories, different types with different time resource allocation method. Due to the time-consuming operation of this model and optimization algorithm, this paper also proposes a multi-target tracking time resource fitting method based on BP neural network. Computer simulation shows that the model and method can keep the target tracking in the best state, and the BP neural network will reduce the time consumption by thousands of times.
Impact of Sodium Chloride Crystallization on Saliva Droplets Spreading

It is well known that SARS–CoV–2, can be transmitted through airborne diffusion of saliva droplets which travels into atmospheric air through a thermo-fluid dynamic interaction with it. In order to limit SARS-CoV-2 spread, social distancing is crucial. However, in this context is really important to emphasize that available knowledge is largely inadequate to make predictions on the airborne spreading of infectious droplets emitted during a cough and/or sneezing. The main aim of our research activity is to provide a contribution to thermo-fluid dyamic modeling of saliva droplets diffusion produced by coughing. In particular in this paper, several efforts were devoted to the analysis of impact of saliva chemical composition on the their spreading in space.
Impact of Single Window Service Center Characteristics on Achieving Organizational Commitment, Analytical Research at The Ministry of Interior/Directorate of General Passports/Iraq

The purpose of this paper is to identify and explores the concept of one Window Service Center(WSC) as one of contemporary models that in field of business management, received the attention of marketing services researchers according to available resources of the organization, in balanced ways to meet the needs of its customers, Therefore provide facilities to them as well as reduce costs and attempting to fulfill the organization's obligation towards it's customers because the Organizational Commitment (OC) has very important impact as one of important active concepts in the field of administration to achieve the efficiency as well as, effectiveness of organization and achieve congruence between the goals of the organization, The research found that there is a weak rapid response by the research organization to citizens' requests, this calls for the need to follow up the organization and its' interest in this aspect to achieve the organization's goal, which is to provide the best and fastest services to customers and on time.
The Effectiveness of Using Pictures for Enhancing Vocabulary of ESL Learners at Elementary Level Students

Vocabulary is a difficulty for children, especially young learners. Young children's capacity to learn and recall words is limited. Vocabulary development is a challenging task for teachers due to the limitations of children's skills.  The teacher can use practical, inspiring, and effective classroom teaching methods to help students to enhance their vocabulary knowledge. Using pictures to teach vocabulary can help teachers focus students' attention and encourage them to learn more efficiently. The study aims to investigate whether using pictures as a medium can help elementary school students learn new words. This study examined elementary ESL students who had divided into two groups: experimental and control. Images were delivered to the Experimental group, while the Control group received directions without pictures. The data for the study is collected using a pre-test and a post-test. Before administering the treatments, the researcher conducts a pre-test for both groups.  After providing the students with the knowledge for two weeks, the researcher performs a post-test to see how their vocabulary knowledge had improved. The working hypothesis of this study is that teachers who utilize pictures get much better grades than those who do not. The difference in scores demonstrates the effectiveness of using images to teach vocabulary skills to students. As a result, English teachers can utilize pictures to teach vocabulary to their students.
Image Captioning using Deep Learning based System

Annotation of images is a time-consuming task. In recent times captioning of images is required in many Artificial Intelligence applications. Captioning Images intends to produce a sound and thorough portrayal that sums up the contents of a picture. Traditional approaches cannot handle the complexity and difficulties of image captioning as well as deep learning-based approaches. In this paper an encoder-decoder version with an attention mechanism integrating the picture feature that generates a semantic and syntactically sound description. One of the available benchmark datasets, Flickr8k is used to evaluate the model. The system incorporates an EfficientNet B3 encoder that draws out features from an image. It uses the mapped caption of images to produce the corpus. For the caption generation process, the model implements the attention and Gated Recurrent Unit (GRU) model to effectively bring about the image description. The various cosine similarity and N-Gram feature extraction means have been used for generating the blue score for the entire testing dataset and show the effectiveness of the proposed system.
Overcoming the Obstacles of Implementing the Educational Leadership Standards in the Distance Learning Program in the Light of Corona Pandemic from the School Principals’ Perspectives in the Directorate of Education of the Northern Mazar District / Jordan

This study aimed at overcoming the obstacles of implementing educational leadership standards in the distance learning program in light of Corona pandemic from the school principals’ point of view in the Directorate of Education of the Northern Mazar District / Jordan. To achieve the aims of the study, the researcher purposefully selected the research sample (44 principals) in the Directorate of Education of the Northern Mazar. The descriptive-analytical approach was used. The study emphasized the importance of implementing the educational leadership standards by school principals in the Directorate of Education of the Northern Mazar. Moreover, the study revealed that there were no statistically significant differences at (α ≤ 5.0) between the responses of the study sample in the practice of school principals to the following educational leadership standards: leading the development of the school, the directorate development program, the community school. In addition, the study concluded that the leadership in the educational system is based on standards in the Directorate of Education of the Northern Mazar in the distance learning program according to the Gender variable. The study recommended that the standards of educational leadership should be implemented in distance learning programs, as an advanced technological system, to meet the challenges of the educational process. Keywords: Standards, educational leadership, obstacles, distance education program, school principals
Design And Development Of Security Frameworks For Performance Enhancement Of Public Key Crytogrphy Using Genectic Algorithm

Cryptography is an imperative tool for guaranteeing and securing data. Security gives prosperity and steadfast quality. A Genetic Algorithm (GA) is ordinarily used to get solutions for development and search issues. data development secure transmission of data is a significant test. Symmetric and unbalanced cryptosystems are not reasonable for a critical level of security. Present-day hash work-based structures are better than traditional systems yet the confounding algorithms of generating invertible limits are very time-consuming. In traditional structures data is being encrypted with the key yet simultaneously there are possible results of tuning in the key and changed text. Thus, the key ought to be strong and surprising, so a strategy has been proposed which abuses the theory of natural selection.
Ph. D. in Information Assurance: An Indian Context—A Policy Framework Based on UGC Regulation

Information Assurance is an emerging field in Information Technology related subjects and responsible for the privacy and security. Information Assurance is broad and interdisciplinary than Information security, Information Technology security. And IT Security consist with few areas viz. areas of Web Security, Database Security, Network security and latest Cloud Security, Mobile Security, Multimedia Repository Security etc. The theories as well as practice of collection, organization, assuring and managing of information and knowledge are the core of Information Assurance field. Information Assurance deals with two sides viz. technological and manual information security and privacy solutions. Information Assurance is dedicated to the uses, processing, storage, and transformation of information with proper practice and principles. Information Assurance as a field of study available in developed countries. In United States itself it is available as Certificate, Diploma to Bachelors, Masters Program and even Doctoral Program. In US and many western countries availability of super specialty areas program i.e. the emerging and sub field of IT become common and important. Similar to this approach, Information Assurance is also available as a Doctoral Program. In India, academic program/major in the sub fields and emerging areas of Information Technology is little rare and also in Doctoral program. Though the Ph. D. in Information Assurance has huge potentials. The paper highlighted the potentiality of Ph. D. in Information Assurance with model curricula based on Indian Ph. D. regulation 2016.
MARIND (MALIND–ANIM) LANGUAGE PRESERVATION IN MERAUKE REGENCY, PAPUA, INDONESIA

Indonesia is the largest island country with huge language diversity in the world. The latest data shows that over 700 indigenous languages are spoken across 34 provinces of the country. Statistics show that among those numbers, the largest number of indigenous languages exists in Papua province , which covers more than 300 languages. This qualitative study was conducted in Merauke Regency, Papua, the largest regency in Indonesia. It is located in the easternmost tip of Papua island. The study aimed to discover the existence of the Marind language or in local dialect known as Malind - Anim, one of the indigenous languages spoken by the native tribe of Merauke, and to find out the preservation effort that has been conducted in Merauke. The result of the study revealed that there have been many efforts conducted by the local government of Merauke Regency and the stakeholders to maintain the existence of Marind (Malind – Anim) Language. It was conducted through (1) Education al policy , (2) Marind (Malind – Anim) language habituation in public spaces such as announcement at the airport, Marind (Malind – Anim) language usage in the public banner, and greeting in Marind (Malind – Anim) Language in every formal meeting, (3) Broadcasting media, and (4) Language documentation in forms of songs and dictionary.
Recommendation System for High Average Utility Itemset using Deep Learning Techniques

Data Mining is the technology that blends the traditional Data Analysis method with the sophisticated algorithms for processing large amount of data. Recommendation system enables the ability to use the various types of user-preference and user-requirement data to make recommendation. There many issues faced by the recommendation system among that major issue is that they need large data to effective make the recommendations. The main problems that are raised are changing the data, changing user preferences and unpredictable items. To overcome, in the proposed work High Average-Utility Mining is used. The two Deep learning techniques namely Recurrent Neural Network and fusion of Recurrent Neural Network-Convolutional Neural Network have been used. For this, dataset have been obtained from online of super market. For preprocessing, normalization is done on the actual dataset followed Long Format Data Frame is implemented to find the High Average Utility Itemset Mining. The preprocessed data is given as the input to Convolutional Neural Network which is activated by the sigmoid function. On the other hand the sigmoid Function is replaced with Recurrent Neural Network. 2-Bidirectinal LSTM is used which classifies the Predicted Products and the top k recommended products. From the above experiments, it is found that CNN-RNN fusion model gives the satisfactory results.