Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Deep Learning-Based Abstractive Text Summarization Techniques

In this digital era, a vast amount of information is generated; it is very difficult to get the information faster and more efficiently. In today’s time, everyone needs more information from existing data in a shorter amount of time. For this, a good mechanism is needed which will extract important information from raw data or provide an abstract view of the source document. Basically, in this survey, we have described different recent deep learning-based approaches used for abstractive text summarization. We explained different approaches with its working, effectiveness, and drawback. In this article, we analyzed different approaches based on its architecture, training algorithm, and dataset used. We further discussed how text summarization can be improved in the future.
A method to calculate the number of dynamic HDFS copies based on file access popularity

&lt;abstract&gt; &lt;p&gt;HDFS heterogeneous clusters usually have multiple storage media at the same time. How to efficiently read and write file copies and reasonably use various storage media is a problem to be solved. Dynamically adjusting the number of copies is important in HDFS, which can solve the problem of accessing a large number of hot files at the same time and improve the efficiency of cluster services. A method is introduced to calculate the number of dynamic HDFS copies based on file access popularity in this paper. Firstly, an algorithm was proposed to predict file popularity based on the cuckoo search optimization Markov model. The unbiased grey model is used to predict the accessing file's popularity at the next moment according to the recent access of the file. The cuckoo search is used to optimize the Markov model, and the prediction error is corrected. Then, the calculation method of the number of copies is designed based on the prediction of the popularity of the file to be accessed and the availability of the node. The experiment shows that the proposed method has a high fitting degree with the actual value, and the MAPE is 3.08%, and it is the smallest, compared with several commonly used prediction models. In CloudSim4.0 simulation platform, multiple users write 10 files to the cluster at the same time, and the change number of copies is calculated according to the predicted value at the next moment, so as to improve the user access efficiency.&lt;/p&gt; &lt;/abstract&gt;
The effect of edmodo based english learning to support students with hearing loss during the COVID-19 pandemic

Deaf students are students who experience hearing loss which results in their limitations in communicating and obtaining linguistic information through the sense of hearing. Therefore, in communicating, deaf students generally use sign language, lip reading, or use communication. Deafness has an impact on language development. One of the factors that can support the language skills of deaf students is vocabulary mastery. In general, students have difficulty in acquiring vocabulary, especially in English vocabulary. Therefore, it is necessary to have a special technique given to deaf children that is more effective in increasing their English vocabulary. The purpose of this study was to determine the effect of Edmodo-Based English Learning to support students with hearing loss during the Covid-19 pandemic. This research was quasi-experimental quantitative. The sampling in this research was carried out purposively sampling. The results showed that there was a relationship between Edmodo media and student learning outcomes with a value of 0.453 on the Pearson bivariate SPSS 21.0 which means the level of the relationship is moderate.
The glass that offers hope

Ukraine’s rich cultural heritage includes stained glass from mediaeval times to art inspired by the geometry of quantum computer chips.
How Powerful are Membership Inference Attacks on Graph Neural Networks?

Graph Neural Networks (GNNs) are Machine Learning models that operate on structured graph data. By leveraging their node/graphs classification and link prediction capabilities, they have been successfully applied in several domains such as community detection, drug discovery, and location sharing services. These successful applications and the immense availability of graphs in various fields have encouraged the adoption of GNNs in Privacy sensitive contexts (e.g., Healthcare and banking systems). Unfortunately, GNNs are prone to sensitive information leakage through principled attacks. Our focus is on Membership Inference Attacks(MIAs) that allow an adversary to infer the membership of a given sample to the training dataset. Our work focuses first on performing the MIAs on Graph Neural Networks and devises the impact of the used attack classifier on its success. Then, we propose new defense methods that decrease the accuracy of MIAs to 50% (i.e., random guess). Finally, we support our findings through extensive experiments on four largely used GNN models.
MEASURES TO MANAGE THE APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGY IN TEACHING IN HIGH SCHOOLS IN DON DUONG DISTRICT, LAM DONG PROVINCE TO MEET THE EDUCATIONAL DIGITAL TRANSFORMATION ERA

Recognizing the importance of applying information and communication technology in teaching to meet the digital transformation era of education, over the years, high schools in Don Duong district, Lam Dong province has implemented many measures to promote the application of information and communication technology and management of the application of information and communication technology in teaching, initially achieving certain results. By 2021, all high schools in Don Duong district, Lam Dong province will be equipped with multimedia classrooms with full equipment for teaching and learning of teachers and pupils to meet the era of digital transformation of education. However, the management of information and communication technology application in teaching still has many limitations. From that situation analysis, the article has proposed a number of management measures to apply information and communication technology in teaching in high schools in Don Duong district, Lam Dong province to meet the digital transformation era of education. Through testing the necessity and feasibility of seven proposed measures, it was found that: the measures are highly necessary and feasible. Keywords: Information and communication technology application management; Managing information and communication technology applications in teaching; High schools; Don Duong district, Lam Dong province; Digital transformation era of education.
Implementation

This RFID (Radio Frequency Identification) technology will help solve the problem of traffic light violation detection.
Research on information interoperability of grains and oil supply chain based on identification resolution system of industrial internet

The grains and oils supply chain has many characteristics, such as long life cycle, complex links, multiple sources of information, fragmented data, and many risk factors, etc. Based on the industrial Internet identification resolution system, the method and technology of information exchange in the whole supply chain of grains and oils are studied. Through the analysis of my country’s industrial Internet identification resolution system, design identification and resolution connection middleware architecture. The identification resolution system is the nerve hub that supports the interconnection of all things in industry. By opening up the data of the whole supply chain of grains and oils, it will realize the extensive interconnection of upstream and downstream industries and cross-fields. It breaks the "information island" and ensures the quality and safety of the whole life cycle of grains and oils. On this basis, a feasible information exchange framework for the whole supply chain of grains and oils is constructed. This has practical implications.
An Improved Privacy Protection Algorithm for Multimodal Data Fusion

With the rapid development of Internet technology, the use and sharing of data have brought great opportunities and challenges to mankind. On the one hand, the development of data sharing and analysis technology has promoted the improvement of economic and social benefits. On the other hand, protecting private information has become an urgent issue in the Internet era. In addition, the amount and type of information data are also increasing. At present, most algorithms can only encrypt a single type of small-scale data, which cannot meet the current data environment. Therefore, it is very necessary to study the privacy protection algorithm of multimodal data fusion. To improve the security of privacy protection algorithm, combined with the idea of multimode, this paper combines the improved traditional spatial steganography algorithm LSB matching method and the improved traditional transform domain steganography algorithm DCT with AES encryption algorithm after modifying the S-box and then combines it with image stitching technology, so as to realize a safe and reliable privacy protection algorithm of multimode information fusion. The algorithm completes the hidden communication of private information, which not only ensures that the receiver can accurately recover private information in the process of information transmission but also greatly improves the security of private information transmission.
Implementing a Data Communication Security Tokens Management System Using COSMOS, an Energy Efficient Proof-of-Stake Blockchain Framework

This paper proposes an energy efficient approach for securing inter-connecting IOT devices with servers. A scheme using blockchain and a second network for security management was previously described [1]. To overcome the drawbacks and shortcomings of using traditional smart contract with Ethereum, a new approach is proposed. The proposed approach ensures that the overall IoT network is “hardened” against attack using a framework which is environmentally sustainable and meets the stringent requirements of mission critical applications. This new approach also results in shorter latency, higher throughput and consuming less power, thus suitable for edge computing environment in mobile.
PressyCube: An Embeddable Pressure Sensor with Softy Prop for Limb Rehabilitation in Immersive Virtual Reality

In this demo, we present PressyCube, an embeddable pressure sensor that allows collecting physical force data from different types of exercises and body parts via Bluetooth for home-based rehabilitation. Currently, most home-based rehabilitation devices are focused on one specific body part only. Thus, we designed the pressure sensor that can be embedded with other props to extend its applications. We also demonstrate our work for fulfilling varying rehabilitation needs in the virtual environment with a head-mounted display by a swimming simulation exergame for players to work out using different exercises.
A Design-Based Research on Developing Accelerated Online English Language Arts Curriculum for Underrepresented High-Ability Students

The current study is part of a federally-funded three-year design-based research (DBR) project centering on creating and delivering two accelerated online English language arts (ELA) courses to high potential, socio-economically disadvantaged 7 th and 8 th graders from four school districts in a Midwestern state.Using a mixed-methods design, this study examined data regarding key components of the intervention during the first two-years of implementation.Quantitative data included students' school achievement in 6 th grade, their baseline scores on an above-grade-level ACT and ACT Engage test, and the performance of the first cohort of project students as 8 th graders on the district's ELA end-of-9 th -grade exam.Qualitative data included interviews with school staff regarding professional development, curriculum design and instructional issues, and an end-of-course student survey.Findings of this study will be valuable for iterative and continuous adjustment of the DBR project.
Blockchain Platforms, Architectures, and Consensus Algorithm

This chapter discusses about concepts such as blockchain architecture, blockchain platforms, and consensus algorithm. Literatures from the year the first blockchain was developed (2009) till date were reviewed to gather insight into these concepts. Blocks, nodes, and ledgers are the major features that make up the architecture of any Blockchain platform. There are other technologies and phenomenon blockchain relies on to gain its immutability such as cryptography, distributed computing, and smart contracts. The concepts that blockchain relies on have brought about a number of already-existing platforms such as the numerous cryptocurrencies, decentralized finance, and decentralized applications. A sequence is solving the problem of which transaction is legit or not is called the consensus algorithm. There are numerous consensus algorithms for different blockchain network. Understanding these concepts is the first step in developing sustainable and advanced applications using blockchain.
Design of Intelligent Logistics Delivery Experiment Teaching System based on Machine Vision

Logistics delivery is widely used in many fields. This paper designs an intelligent logistics delivery experimental teaching system based on STM32F103 microcomputer and machine vision sensor. The convolutional neural network model based on improved LeNet-5 is applied to identify information of the destination number. The navigation information can be identified and traced at the same time. All information can be sent to the host computer through the serial port for processing. The experimental results show that the intelligent logistics delivery system can quickly complete the target number recognition and achieve high accuracy. The system has the advantages of low cost, moderate difficulty and stable performance. It has a certain value of experimental teaching and practical application.
The growing need for digital document attestation

Advancing technology and the Covid-19 pandemic have prompted a rise in digital transactions. More sales, contracts and legal agreements have moved online than ever before and that number is only growing. But accompanying this is a rising concern about the security of digital documents, and particularly the robustness of digital document attestation.
Container attacks: a blast radius analysis

Containers may seem to offer greater security through segregation. But they are increasingly becoming a focus for attackers looking to escape the confines of containers in order to move laterally across the system. This is typically achieved through maliciously crafted container images, as recent research using honeypots has shown.
Plenary Panel 1 - Discovery Technology Foundations and Open Science

The ability to make rapid progress in science is more urgent than ever, as demanded by the tremendous societal challenges we are facing, from ensuring our health broadly to climate change to food shortages to energy to security.Scientific discovery -built on the scientific method, is our best tool to advance our knowledge and capabilities for solving to these challenges.At the same time, powered by technology advances in Artificial Intelligence, High Performance Computing, and Hybrid Cloud … with even further impact expected from Quantum Computing, we are entering a new era of Accelerated Discovery.New computational methods built on growing volumes of data are helping significantly to break through bottlenecks and speed up key steps in discovery, and enable new levels of automation and autonomy.This pace and scale is being further amplified through collaborative efforts that create and build on open data, open source, and open challenges.This panel will explore both the evolving landscape of discovery technologies and opportunities for further advancing open science.
Relative Belief and Combining Evidence

The problem of combining the evidence in several Bayesian inference bases is considered.Evidence is measured in each inference base using the relative belief ratio which gives an unambiguous prescription of whether there is evidence in favour of or against each possible value of an unknown such as a parameter.While there are many possible ways to combine the evidence, the method of linear pooling stands out as it preserves a consensus while others may not.There are constraints on this application, however, if one requires a formal Bayesian justification.In some applications where these restrictions do not hold, the approach can be generalized by allowing for the methodology known as Jeffrey conditionalization.
Loading Data from the Web and the Cloud

In this chapter, we will take a look at a subset of the fast-growing and wide-ranging set of data sources available over the Internet that you can use as a source of analytical data for Power BI Desktop. While the data sources that you will see in the following pages may be extremely diverse, they all have one thing in common: they are stored outside the enterprise and are available using an Internet connection.
MENDETEKSI PENYAKIT PARKINSON DENGAN OPENCV, COMPUTER VISION, DAN SPIRAL / WAVE TEST

ABSTRACT&#x0D; &#x0D; Parkinson's disease is the second most common neurodegenerative disease in humans after Alzheimer's disease. The disorder causes patients to experience a variety of symptoms, including intellectual and behavioral disturbances, dementia, memory loss, muscle weakness, stiffness (slow and stiff movements), and tremors.&#x0D; This study describes how to detect Parkinson's disease using Open CV and how geometric images can be used to detect and predict Parkinson's.&#x0D; We will then examine our image dataset collected from both patients with and without Parkinson's. After reviewing the dataset, I will teach you how to use the HOG image descriptor to scale the input image and then how we can train the Random Forest classifier over the extracted features.&#x0D; The expected result is that the system can detect and predict Parkinson's disease from a patient with an accuracy rate above 90%&#x0D; &#x0D; Keywords: Parkinson's detection, HOG, OpenCV, Deep learning.
APPLICATION OF RS AND GIS IN COASTAL ZONE MONITORING AND MANAGEMENT

The Coastal Zone is very much essential for economic growth as well as for environmental growth.The coastal area of tropical and subtropical region contains major diversity of flora and fauna.Nevertheless, the increasing anthropogenic activities in the coastal region, have hindrance to the natural growth of coastal ecosystem.Consequently, have severe effect on the overall ecosystem.These uncontrolled increasing anthropogenic activities, pressing the requirements for management of coastal zone.Remote sensing and GIS technology is very popular in this area.Those advanced computerized digital plat-forms and technologies promotes the proper management of a coastal zone with the help of satellite platforms like OCEANSAT I (IRS P4) OCM, LAND-SAT, SPOT, IRS LISS II amp; LISS III etc.The scaling methods are very much necessary for a better result.The 1:25,000-scale is generally accepted for the better results for the managing of the coastal zones.The classification of a geographical area is very much required for a better approach for the management strategies, the GIS based software's like ILWIS, JUMP GIS, GRASS GIS, Geosoft, Falcon View etc.The proper classification with the help of those modern and advanced techniques is very much time saving and ac-curate.The information of natural hazards like sedimentation into the delta region, cyclones, sea level rise, draught or excessive flood in coastal zone agricultural area are now easily accessible with the help of these two methods.
A Survey of Knowledge Representation Learning Based on Structure and Semantics

Knowledge representation methods have played an important role in the field of artificial intelligence especially in machine learning and deep learning. It converts useful information such as images, texts, and languages into low-dimensional and dense entity vectors, and provides NLP with better updated ideas and improves computational efficiency. In order to understand the current knowledge representation learning methods and status, this paper analyzes and categorizes the knowledge representation model based on structure and semantics, and finds that the knowledge represented by graph is easy to understand, but there are high complexity and long-tailed distribution, and semantic information of the relationship is difficult to obtain. Therefore, the semantic composition method of relation is adopted to solve this problem.
Application Research of Word Vector in Component Parsing

As we all know, syntactic analysis is one of the classic tasks in the field of natural language processing, and its goal is to analyze the input sentence and obtain the corresponding syntactic structure. The machine idea of ​​syntactic analysis came from the 1950s. Due to the importance of this task in natural language processing, syntactic analysis has become one of the very basic and very important tasks in the field of natural language processing, and this task has not only attracted a large number of the computer experts also attracted a large number of linguists, who made great contributions to the development of syntactic analysis. Since the development of syntactic analysis tasks, this research has made great progress, which has played a positive role in promoting the progress of natural language processing. However, there have always been two difficulties in the task of syntactic analysis: one is the accuracy of the analysis results; the other is the speed of analyzing the input sentences. From rule-based to statistics-based, from word vector models to pre-trained models, every technological innovation has contributed to the development of syntactic analysis tasks. Nonetheless, the heights achieved by the syntactic analysis task have not completely overcome the long-standing challenges of accuracy of results and speed of analysis. Therefore, syntactic analysis still has great value and research significance. This paper will start from three aspects: the development status of component parsing algorithms, the impact of word vector technology on component parsing, and syntactic parsing algorithms.
Digital library initiatives of nehru library, CCSHAU, hisar and behavioural approaches of new generation library users: A Study

In this changing environment, the thinking, behavior, and needs of human beings keep changing according to time and technology. In this study, the surveyor discusses digital library initiatives of Choudhary Charan Singh, Haryana Agriculture University (Hisar), Nehru Library (University Library), and the behavior of new generation users through a user-based survey study. With the help of ICT, today's digital library is playing an effective role in providing useful information to the academic community. With the emergence of internet access and electronic gadgets in the field of library and information science, there is no need for a user to visit the library. Today’s library users keep their essential information at their fingertips and in their pockets with the help of technology and electronic devices. Nowadays university libraries are spending a huge amount on e-services. So, it is necessary to examine users’ needs and satisfaction levels with digital services. For this users-based survey, the surveyor has selected 50 library users (10 faculty members, 20 researchers, and 10-10 PG-UG students). On the basis of findings, the study gives valuable suggestions and recommendations to the university library. This survey also presents the actual status of Nehru Library's digital services.