Reviewerly/exHarmony huggingface dataset:
SELF-REPRESENTATION THROUGH ONLINE IDENTITIES: A CDA OF OFFICIAL WEBSITES OF PAKISTANI UNIVERSITIES

Virtual world of Internet has provided a dynamic virtual space to the organizations like universities to represent themselves to a wider audience in a fast manner as well as construct desired online identities. In this article, we examined linguistic features in Pakistani universities’ websites and explored the ways Pakistani universities make use of virtual space to construct certain preferred online identities in and around organization. The use of digital affordances such as anonymity and visibility in these websites add to the preferred self-representation of the universities. Eight Pakistani universities’ websites are selected for analysis using purposive sampling technique. The study employs critical discourse analysis and follows a three-dimensional analysis including textual analysis, process analysis and social analysis. The findings indicate that Pakistani universities’ websites are highly affected due to social changes such as globalization and commercialization of education. These websites are developed with a common purpose of constructing global identity, display international standard and high reputation among other universities as well as attract potential students. The analysis further revealed that these websites are a medium to construct various desired individual and collective online identities. Such diversified web-based constructs of university identities allow themselves to portrayal of both prominent as well as distinguished among all. As an outcome, the choice of website audience (who is not limited to students rather faculty, employees, and donors), regarding their future endeavors is influenced. Keywords: Critical discourse analysis, online identity construction, Pakistani universities websites, homepage, linguistic features, visibility through digital affordances.
Exploration and practice of payment system for campus card

The campus card platform is an essential part of the university's information-based public service platform. With the continuous development of internet technology, mobile payment has greatly enriched the daily lives of teachers and students, and also led to great expectations regarding the campus card platform. Focusing on the prevalent problems of the existing campus card payment system, the present thesis explores, both as a practicaI example and from the perspective of service, the emergency guarantee scheme of the campus card of Zhejiang University, to discuss the various methods of the payment system for the campus card. Our research aims to provide reference and specimen for the invaluable critical review by peers from fraternal universities.
The Cultivation of Students' Thinking Capacity through English Classroom Questioning

Thinking capacity, which is one of the core elements of English, plays a crucial role in students' development.However, many teaching activities are superficial and do not really play a role in developing students' thinking quality.Based on Bloom's taxonomy of educational objectives and the English Curriculum Standards of General Senior High School (2017 Edition 2020 Revised) (hereinafter referred to as the English Curriculum Standards), this paper takes a reading class A Day in the Clouds as an example, aiming to explore the cultivation of students' thinking quality through English classroom questioning.Some suggestions on English classroom questioning are put forward: Attach importance to students' level and ask questions related to reality; focus on the differences of students and ask different types of questions; pay attention to the hierarchy of questions and ask questions from easy to difficult; balance the proportion of various questions.
New Language Models for Spelling Correction

Correcting spelling errors based on the context is a fairly significant problem in Natural Language Processing (NLP) applications. The majority of the work carried out to introduce the context into the process of spelling correction uses the n-gram language models. However, these models fail in several cases to give adequate probabilities for the suggested solutions of a misspelled word in a given context. To resolve this issue, we propose two new language models inspired by stochastic language models combined with edit distance. A first phase consists in finding the words of the lexicon orthographically close to the erroneous word and a second phase consists in ranking and limiting these suggestions. We have applied the new approach to Arabic language taking into account its specificity of having strong contextual connections between distant words in a sentence. To evaluate our approach, we have developed textual data processing applications, namely the extraction of distant transition dictionaries. The correction accuracy obtained exceeds 98% for the first 10 suggestions. Our approach has the advantage of simplifying the parameters to be estimated with a higher correction accuracy compared to n-gram language models. Hence the need to use such an approach.
Personalized Extractive Summarization with Discourse Structure Constraints Towards Efficient and Coherent Dialog-Based News Delivery

In this paper, we propose a method to generate a personalized summary that may be of interest to each user based on the discourse structure of documents in order to deliver a certain amount of coherent and interesting information within a limited time, primarily via a spoken dialog form. We initially constructed a news article corpus with annotations of the discourse structure, users’ profiles, and interests in sentences and topics. The proposed summarization model solves an integer linear programming problem with the discourse structure of each document and the total utterance time as constraints and extracts sentences that maximize the sum of the estimated degree of user’s interest. The degree of interest in a sentence is estimated based on the user’s profile obtained from a questionnaire and the word embeddings of BERT. Experiments confirm that the personalized summaries generated by the proposed method transmit information more efficiently than generic summaries generated based solely on the importance of sentences.
PENGARUH LEMBAR KERJA PESERTA DIDIK (LKPD) BERBASIS HIGHER ORDER THINKING SKILL (HOTS) TERHADAP HASIL BELAJAR TEMATIK PESERTA DIDIK

This research is motivated by the importance of HOTS-based student worksheets in thematic learning. HOTS-based student worksheets were used in this study with the aim of actively interacting between teachers and students as well as students and students as well as improving learning outcomes and increasing students' higher-order thinking skills. The goal to be achieved in this study is to find out the thematic learning outcomes of students before and before being treated with HOTS-based student worksheets. This study uses experimental quantitative research on fourth grade students at State Elementary School. Dr.Sutomo V/327 Surabaya with a population of 281 students evenly distributed in 8 classes. The sampling technique in this study used a post-test only control group design technique. The research sample was students in grades IV A - IV H. The experimental class of learning using HOTS-based thematic worksheets and the control class were given lessons as is usually done in schools in the form of lectures and given demonstrations. The research was conducted at the public elementary school of Dr. Sutomo V/327 Surabaya. The research instrument used is a test (posttest). The results showed that the learning outcomes of students had increased by using the HOTS-based student worksheets in thematic learning Theme 3 Sub Theme 2 Learning 1 State elementary school students Dr. Sutomo V/327 Surabaya based on One Way Anova test with sig. less than 0.05.&#x0D;
Classify the Sentiments of email Contents using Novel Bidirectional Encoder Representation for Transformation over Naïve Bayes Algorithm

Aim: The aim of the study is to detect sentiment analysis from the good ones and improve the false positivity rate by using the proposed Novel Bidirectional Encoder Representation for Transformation (NBERT) over Naïve Bayes Algorithm.Materials and Methods: Sample groups that are considered in the project can be classified into two, one for NBERT over Naïve Bayes Algorithm, which are tested using 0.80 for G-power to determine the sample size is 20 and for t-test analysis.Enron email content dataset that data collected from email.Results: The automatic feature selection of NBERT algorithm splits the data with best fit, which has an average accuracy of 87%, which by far seems to be better than the Traditional Feature Extraction Method of Naïve Bayes Algorithm which gives 76%.The significance is around 0.0267 (p<0.05) and therefore there is a statistically insignificant difference among the study group.Conclusion: NovelBERT seems to be better in finding the Sentiment in enron email dataset over the Naïve Bayes Algorithm.
Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning (Extended Abstract)

We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems. RSL uses regression to select relevant sets of states at a range of different distances from the goal. RSL then formulates a Supervised Learning problem to obtain the parameters that define the NN heuristic, using the selected states labeled with exact or estimated distances to goal states. Our experimental study shows that RSL outperforms, in terms of coverage, previous classical planning NN heuristics functions while requiring a fraction of the training time.
Project confronts online sexual harassment

Children and Young People NowVol. 2019, No. 3 Special ReportProject confronts online sexual harassmentJoanne HorneJoanne HorneSearch for more papers by this authorJoanne HornePublished Online:1 Jul 2019https://doi.org/10.12968/cypn.2019.3.36aAboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 March 2019Volume 2019Issue 3ISSN (print): 1755-8093ISSN (online): 2515-7582 Metrics History Published online 1 July 2019 Published in print 2 March 2019 Information© MA Education LimitedPDF download
Use of Graphics Processors in Data Compression Algorithms

Query co-processing on graphics processors (GPUs) has become an effective means to improve the performance of main memory databases. However, this co-processing requires the data transfer between the main memory and the GPU memory via a lowbandwidth PCI-E bus. The overhead of such data transfer becomes an important factor, even a bottleneck, for query co-processing performance on the GPU. In this paper, we propose to use compression to alleviate this performance problem.
Analysis the Growth &amp; Linkage b/w Crypto-currency and Stock Market Indices: Evidence from the US and Indian Stock Market

In this paper, the researcher studies the growth of cryptocurrency trading in different countries and also investigates the speculative properties of three significant cryptocurrencies Bitcoin (BTC), Binance Coin (BNB) and Ethereum (ETH), and their conditional correlation with six indices of the stock market namely; IXIC, Standard & Poor's 500, NYSE Composite, DJI, Nifty 50 and BSE Sensex from the period of April 2, 2018, to May 31, 2022.The result of this paper shows the following findings: First-the cryptocurrency market is extremely volatile as compared to indices of the stock market.Second-the stock market and cryptocurrency market are shows highly correlated values in the United Stateindices of the stock marketas compared to the Indian indices of the stock market.
A comparison of Chaotic Key Sequence and Blockchain Hasher for the Privacy Protection of the Internet of Things Image Data

The internet of things (IoT) is a system that requires interconnected computing devices with unique identifiers which can perform data communications without human involvement. In an integrated network of computer systems, IoT devices such as smart cameras and image sensors play a critical role. In the internet of things, images generated by these sensors are constantly at risk of disclosure and privacy invasion (IoT). Because of the presence of third parties in IoT environments, traditional options for securing sensitive data fade. To prevent such malicious tampering image encryption system, while being safe and easy to implement provides a high level of protection to the shared media files. Among them a new image encryption has been derived using chaotic sequence. In this study, a comparison of hybrid image encryption algorithms of a chaotic system based Logical map is described to encrypt and decrypt IoT image data for security purposes using various quality measures.
Krill herd algorithm (KHA), patter search algorithm (PSA), salp swarm algorithm (SSA) and gradient based algorithm (GBA) - Optimization methods – A critical review

This paper discusses on the applications of non-traditional method. There is different non-conventional optimization are reviewed to solve the optimization problems. In this survey, the methods we are going to review are Krill Herd Algorithm (KHA), Pattern Search Algorithm (PSA), Sal Swarm Algorithm (SSA) and Gradient Based Algorithm (GBA). These methods are approach to find the optimization methods can solve the linear and non-linear optimization problems and results the global values. These methods are broadly reviewed.
Programming Learning Considering Sustainable Society

Japanese K-12 schools are asked to cultivate builders of sustainable society. The authors have engaged in educational practices of fostering human resources capable of respect for others and for working in collaboration with a huge variety of people while making use of ICT and programming effectively, hoping to enhance educational effect. This practice was […]
Comparative Analysis of Different Controllers for Tracking of Manipulator

Engineers have struggled to control robots since the 1950s, when the PID controller was first used to regulate complex systems. Since its release, this controller has been a top pick among manufacturers due to its low price and ease of assembly. For nonlinear systems, fuzzy logic controllers have been used by researchers and scientists to overcome the drawbacks of PID. However, as time goes on, new controlling techniques develop that are more powerful in terms of control than the previous ones. Complex, nonlinear, and dynamically ununderstood systems need neuro logic controllers, which, when paired with standard controls, comprise the neuro PID controller or neuro Fuzzy PID controller. This paper takes a comprehensive look at the many control systems utilised in the functioning of robotic manipulators, from the first controllers to the present.
Semantic Web and Ontologies to Preserve Gastronomic Knowledge

Ontologies are the main components of the Semantic Web. Since its origin and evolution over time, ontologies have been used in various domains of knowledge. As a concrete application of this technology, an experimental system is described, still evolving in phases, where concepts of cooking recipes are intertwined with ontologies, Semantic Web and good Software Engineering practices. This article is based on a completed thesis work. The knowledge domain of Cuisine and Gastronomy was chosen, making an ontological design for recipes in view of the need to preserve the knowledge of the Master Chef and support the teaching-learning process in a haute cuisine school. The original design was done by Wright State University. Taking this design as a reference, the proposed design for the project was created, taking advantage of the reuse of knowledge that ontologies allow. Adjusted the layout for cooking recipes that allow alternate ingredients in a recipe. A combined Cascade/Unified Process Methodology was used for Software Engineering, as well as the Ontology Development 101 methodology. The OWL 2 XML, Serialization (OWL/XML) and RDF/XML standards/syntax were used, in addition to objectoriented programming with the Java language and different frameworks. Finally, the results obtained with the functional prototype are provided and future actions to be carried out are outlined.
ACCOUNTING FOR PROFESSIONAL STANDARDS AS THE BASIS FOR THE FORMATION OF EDUCATIONAL PROGRAMS OF HIGHER EDUCATION

The object of the study is the relationship between the sphere of work and the education system through professional standards and educational programs of higher education (Bachelor's degree). The subject of the article is the mechanisms of taking into account the structural elements of professional standards in the formation of educational programs of higher education (Bachelor's degree). The paper aims to substantiate and clarify the mechanisms for taking into account professional standards as the basis for the formation of the professional component of educational programs of higher education in terms of their content and planned results. The novelty consists in studying the professional standard as a system object, the elements of which are the basis for the development of various structural parts of educational programs; in identifying the degree of consideration of the main structural elements of the professional standard in the formation of the professional component of Bachelor's degree programs; in using, along with the system, a process approach that considers the development of educational programs based on professional standards as a chain of interrelated processes. The peculiarities of taking into account the provisions of professional standards in the formation of a list of tasks of professional activity of graduates, a list of professional competencies for the main professional educational programs of higher education, indicators of achievement of competencies, matrix of competencies. The proposed scientific and methodological approaches can be used in the formation of the main professional programs for other levels of higher education - specialist and Master's degree.
Effect of spectral resolution on the segmentation quality of magnetic resonance imaging data

The majority of the machine learning methods employed for brain tissue or tumor segmentation from multi-spectral MRI data, especially the ensemble learning methods, use hundreds of attributes for the characterization of the pixels, which leads to enormous storage space requirement. Dealing with hundreds of volumetric records under such circumstances also represents a severe computational burden. To facilitate the establishment and deployment of such data processing frameworks, this paper proposes to investigate, which is the best trade-off between segmentation accuracy and necessary storage space, via manipulating with the spectral resolution used for the attributes of the pixels. Three machine learning methods, two multi-spectral brain MRI datasets, and five statistical indicators of segmentation accuracy were involved in the experimental study, which revealed that an 8-bit color depth or spectral resolution of the feature data is sufficient to obtain the finest achievable segmentation accuracy, while allowing for up to 50% reduction of the memory required by the segmentation procedure, compared to the commonly deployed feature encoding techniques.
ANALISIS TINGGI MUKA AIR PADA KAWASAN SUNGAI CILIWUNG MT. HARYONO – PINTU AIR MANGGARAI DENGAN PROGRAM HEC-RAS 5.0.3

Flooding in DKI Jakarta Province has become a routine that occurs every year. One of the causes of flooding in Jakarta is the overflow of the Ciliwung river. On February 20, 2021, flooding occurred around the MT herd. Haryono – Manggarai sluice as a result of overflowing water from the Ciliwung river. This study aims to identify the water level in the Ciliwung MT river area. Haryono – Manggarai Sluice using the HSS Nakayasu method and hydraulics analysis using the HEC-RAS 4.1.0 application. Based on the results of the analysis and studies that have been carried out in the Ciliwung MT river area. Haryono – Manggarai Sluice, the design flood discharge is obtained as follows: flood discharge 2 years return period = 198.08 m3/second, 5 year return period = 287.87 m3/second, 10 year return period = 351.12 m3/second, 25 year return period = 427.94 m3/sec, 50 year return period = 490.28 m3/sec, and 100 year return period = 563.44 m3/sec. The results of these calculations were analyzed using the HEC-RAS 4.1.0 program so that it can be seen that several points have the elevation of the water level exceeding the elevation of the river bank being targeted.
Optimizing data locality by executor allocation in spark computing environment

Data locality is an important concept in big data processing. Most of the existing research optimized data locality from the aspect of task scheduling. However, as the execution container of tasks, the executors started on which nodes can directly affect the locality level achieved by the tasks. This paper tries to improve the data locality by executor allocation for reduce stage in Spark computing environment. Firstly, we calculate the network distance matrix of executors and formulate an optimal executor allocation problem to minimize the total communication distance. Then, when the network distance between executors satisfies the triangular inequality, an approximate algorithm is proposed; and when the network distance between executors does not satisfy the triangular inequality, a greedy algorithm is proposed. Finally, we evaluate the performance of our algorithms in a practical Spark cluster by using several representative micro-benchmarks (Sort and Join) and macro-benchmarks (PageRank and LDA). Experimental results show that the proposed algorithms can decrease the execution time of tasks for lower data communication.
Surface Reconstruction Method of Color 3D Image Based on Independent Adjustable Sparse Coefficient

Color 3D images play an important role in many fields. The segmentation effect of traditional 3D image surface reconstruction methods is poor, resulting in unreliable reconstruction results. Therefore, a new surface reconstruction method of color 3D image is designed based on the independent adjustable sparse coefficient. Firstly, the color 3D image is enhanced, and then the point cloud registration algorithm is designed based on the independent adjustable sparse coefficient. The surface reconstruction of the color 3D image is realized by the point cloud registration algorithm. The experimental results show that the reconstruction effect of this method is good, which proves that it has high application value.
Application Specific Interface to Control and Calibrate Programmable Photonic Integrated Circuits

A custom in-house designed interface to provide accurate driving voltage for a 4 × 4 Mach-Zehnder interferometer based photonic processor is presented providing the required tool to assess and calibrate both electrical and optical crosstalk.
Filter Estimation in a Convolutional Neural Network with Type‐2 Fuzzy Systems and a Fuzzy Gravitational Search Algorithm

We propose the adaptation of parameters (number of filters (NF)) of each convolution layer of a convolutional neural network (CNN). Type 2 fuzzy logic is used, which works together with the Fuzzy Gravitational Search Algorithm (FGSA) method, which It is based on the Gravitational Search Algorithm method also known as GSA, which is inspired in Newton's second law and the gravity. The Fuzzy Gravitational Search Algorithm method will help the construction the membership functions of a Type-2 fuzzy system, which we propose for the search and adaptation the filter numbers of each the convolutional layers the convolutional neuron network. The uncertainty between the upper and lower membership functions the Type-2 fuzzy system is manually modified with values of 0.02, 0.05 and 0.08. This work is to demonstrate that Type-2 fuzzy logic used in conjunction with a bioinspired algorithm and working together with the convolutional neural network, obtain better results than applying Type-1 fuzzy logic in image recognition.