Reviewerly/exHarmony huggingface dataset:
Analysis of website service quality with webqual 4.0 integration method

Abstract Universitas Sumatera Utara is one of the state universities that uses a website as an information system to support education. The Academic Portal and Student Directory are websites released by the USU Information System Centre to facilitate academic services for students. However, as a service provider, the Academic Portal website and student directory do not escape complaints from students’ complaints about the quality of service provided and make students feel that the quality of the website is not in accordance with what they expect and give dissatisfaction with the services provided. This research was conducted to analyze the quality of the Academic Portal website and the Student Director i-Student based on the Webqual 4.0 dimension. Analysis of website quality by calculating webqual index to determine the level of quality of the website based on the WQI coefficient interval. If the Academic Portal Dimension usability and Information are in the coefficient interval of 0.60-0.70, it can be said that the quality level of the Academic Portal Website is the Usability and Information dimension is “Good”. Whereas the interaction dimension is at an interval of 0.40-0.59, it can be said that the quality level of Portal A Website is academic.
Absolute measurement for a 300-mm-aperture vertical interferometer based on compensation of gravity-induced deformation

For the absolute measurement on 300-mm-aperture vertical Fizeau interferometer, the gravity-induced deformation of flats must be considered. In this paper, we proposed a three-flat method based on compensation of gravity-induced deformation. The large-aperture three-flat algorithm was derived. Then, the transient state changes of deformations were analyzed quantitatively by finite element method (FEM). The consistency of deformation between FEM and elastic theory was verified. Furthermore, to validate the proposed large-aperture three-flat method, we compared three-flat method and liquid reference method. Finally, the expanded uncertainty of three-flat method was deduced, which reached 1.47×10−2 λ (Peak Valley, PV) for a 300-mm aperture.
Secure Key Exchange by NFC for Instant Messaging

This study offers an alternative to current implementations of key exchange by utilizing NFC technologies within android mobile devices. Supporting key exchange protocols along with cryptographic algorithms are offered, which meet current security standards whilst maintaining a short key length for optimal transfer between devices. Peer-to-peer and Host Card Emulation operational modes are observed to determine the best suited approach for key exchange. The proposed model offers end to end encryption between Client-Client as opposed to the usual Client-Server encryption offered by most Instant Messaging applications.
An Evacuation Route In Bengkulu City Based On Fuzzy Dijkstra Algorithm

Abstract Bengkulu City is one area that has the potential to be hit by the tsunami disaster. Therefore an evacuation route is needed that can be used when a disaster strikes. In this study, a weighted graph is made by considering the node as a road intersection, the edge as the road connecting it, and weight as a fuzzy output obtained based on the length and width of the road. Furthermore, the evacuation route is obtained by calculating the smallest number of weights from each intersection to the cluster point using Dijkstra Algorithm. The result is an evacuation route that consider the length and width of the roads.
Correlation Power Analysis for SM4 based on EEMD, Permutation Entropy and Singular Spectrum Analysis

Signal-to-noise ratio of the power consumption curves collected during encryption has a greater impact on the bits of Secret key cracked and the efficiency of later analysis. Wavelet threshold noise reduction's parameters are difficult to determine. Method based on EEMD and correlation coefficients is hard to adjust. Therefore, a noise reduction algorithm combining EEMD, Permutation Entropy and Singular Spectrum Analysis is proposed. The algorithm decomposes the original power consumption curves using EEMD to obtain Intrinsic Mode Function components, selects noisy IMF on the basis of correlation coefficient and PE, and then uses SSA to extract useful signals. Original power consumption curves, power consumption curves processed by EEMD and correlation coefficient, and the power consumption curves obtained by the method proposed in this paper are compared, and the correlation coefficient is increased by 37.11% and 26.47% respectively. The proportion of curves consumed increased by 77.5% and 25.8% respectively.
Detecting Cyberbullying across Social Media Platforms in Saudi Arabia Using Sentiment Analysis: A Case Study

Abstract Twitter has become an open space for the users’ interactions and discussions on diverse trending topics. One of the issues raised on social media platforms is the misunderstanding of ‘freedom of speech’, which in turn, leads us to a new social and behavioral attack: cyberbullying. Cyberbully affects both individuals and societies. Despite tough sanctions globally and locally, cyberbullying is still a serious issue, which needs further consideration. Thus, this research aims to address this issue by proposing a framework, based on sentiment analysis, to detect cyberbullying in the tweets stream. The proposed framework in this paper extracts the tweets from Twitter. Then, the preprocessing through tweets tokenization was applied to remove noise from tweets and also symbols and phrases such as http, emoji faces, hash tag symbols, mention symbols and retweet. After data tokenization, the proposed system classifies the tweets, based on extracted keywords from both experts and potential victims, using deep-learning classification algorithm with 70% of dataset samples used for the training purpose, and 30% of the dataset samples used for the testing purpose. The experimental results show the ability of proposed methodology to detect cyberbullying effectively with accuracy 81%.
Design of Digital Museum Narrative Space Based on Perceptual Experience Data Mining and Computer Vision

Design of digital museum narrative space based on perceptual experience data mining and the computer vision is presented in this paper. The carrier of the traditional physical museum is the physical exhibition space inside the magnificent building. In contrast, the carrier of the network virtual museum is much more convenient and flexible. A computer server with powerful configuration can become its carrier. The proposed design utilizes the computer vision and data sensing framework to finish the efficient model. In the general terms of the time performance, segmentation based algorithm is generally faster than fuzzy clustering based algorithm. The data mining model is integrated here to estimate the performance. The simulation has provided the numerical understanding of the proposed method.
State-of-the-Art Applications of Graph Convolutional Neural Networks

Graphs are practiced widely in various real-world applications because of their structure modelling capability. Currently, deep neural networks have been employed to produce excellent outcomes in tasks such as classification. A graph convolutional network (GCN) is a deep learning model that operates on the graphs. We offer the general architecture of a GCN and its utilization in semi-supervised learning. We also investigate the application and performance of GCN in the classification of fake news. Finally, we present different application areas of GCN and open challenges for future research.
Towards Using Blockchain Technology for Microcredit Industry in Bangladesh

In the era of modernization, poverty and hunger is a curse for the developing and under developed countries. Micro- credit is a groundbreaking idea that can help in defending the problem. But there is a problem in accomplishing the task due to trust and security issues. In this critical situation of poverty, these issues hinder the rate of success. Central databases have many issues with security and trust. In this paper, a comprehensive approach to implement a system for the micro-credit distribution and management is described. Being one of the most emerging technologies, blockchain is used to ensure trust, security and reliability. NGO/MFIs and registered miners will be able to act as nodes. The data is completely secure, as each block is encrypted. There is reward for the miners which will enhance the field of employment. Borrowers have a defined credit which is deducted at the time of borrowing and refunded during returning the borrowed amount. Borrowers have a reputation point based on borrow and return. A group of well reputed borrowers will be able to form an MFI. A portion of interest will be deducted as network charge which can be deposited as VAT in the government fund.
Stacked Embeddings and Multiple Fine-Tuned XLM-RoBERTa Models for Enhanced Hostility Identification

Designing effective automated techniques for proper identification and categorisation of hostile speech is essential, especially for low resource Indian languages without established datasets. A majority of Indians use Hindi for their interactions on social media. Multiple dialects of Hindi spoken by the users in different parts of the country further exacerbate the challenge of identifying hostile speech as they imply diverse patterns of expression of hostility. In this work, we experimented with a wide range of neural network models for hostility identification in Hindi - pre-trained word embeddings, stacked word embeddings, and fine-tuned XLM-RoBERTa model. We also analyzed the effectiveness of back translation as a data augmentation technique to assist in fine-tuning of XLM-RoBERTa model for hostility identification. The experiments are carried out on the dataset provided by Constraint 2021 shared task. Our team's (Siva_Alfred on leader board) best neural network model achieves F1-weighted score of 0.90 for coarse-grained hostility detection and 0.54 F1-weighted score for fine-grained hostility identification.
Geovisualization of 3D Modeling Representation of Topographic Relief Based on Open Source GIS

Abstract 2-dimensional and 3-dimensional representations have been used in various physical, virtual, and geovisual surface models of the earth - to improve spatial thinking skills. Physical models such as relief maps have been shown to enhance understanding of topographic maps, helping to understand the relationship between 2D representations and 3D objects. Virtual models can support spatial learning, for example, by viewing objects at various angles, by providing an environmental context. This model makes it possible to combine physical models’ interactive benefits with virtual tools’ flexibility and diversity. The term geovisual is an overlay depiction of information scanned through a sensor, processed by a computer. Subsequently, the data obtained are presented to the media so that it is like the real world. It has been built and used in this Tangible research Landscape, which is a real-time geovisual system integrated into a computer connected to a 3D sensor, projector, and supported by GRASS GIS [1]. This geovisual system is then applied for modeling terrain 3-D terrain physically through media, scanned through sensors to a computer, and used to analyze the environment with various effects and graphical simulations. The initial model produced is topography from aerial photographs and DEM from LiDAR, which has a spatial resolution of up to 0.4 meters showing physical processes such as contour lines and water flow, and sediment transportation can be well visualized.
User opinions driven social recommendation system

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.
Do not ask me what I am looking for: Index deniable encryption

In this network era, the cloud storage service has become increasingly important. People have become used to storing data on the cloud and accessing them anytime, anywhere. However, due to privacy issues, data should be encrypted before being uploaded to the cloud. Thus, determining how to obtain the designated data from ciphertexts is important. In recent years, numerous searchable encryption schemes have been proposed. Most of these schemes do not consider the case wherein the search behavior is monitored, and there may come a time when users will be forced to open their search records and results. Thus, in this paper, an index deniable encryption scheme that supports both searchability and deniability is proposed to ensure user privacy in the cloud storage and the outside coercion case. We integrate the Bloom filter and the CP-ABE technique to construct a searchable encryption scheme. With the redundant keyword design, the search result can be opened to a fake data for the outside coercer. The proposed work has a constant ciphertext size to multiple deniable possibilities. • A searchable encryption scheme with deniability is proposed. • The faking decision is made at the searching moment instead of the encryption phase. • The searching queries are unlinkable. • Supporting the lightweight pre-shared key update feature.
PBL English micro-audio and video teaching model based on data mining algorithm

At present, PBL English (foreign language) micro audio and video teaching mode is gradually rising, and the application of intelligent algorithm in it has gradually attracted people’s attention. This paper studies PBL English micro audio and video teaching mode based on human-computer interaction and micro audio and video recognition and data mining algorithm. Through the extraction and selection of acoustic features of speech emotion, the relationship between emotion and each feature parameter is explored, and the acoustic feature with the strongest emotion discrimination ability is selected. Using the machine learning method of support vector machine, a speech emotion recognition model with high recognition rate is constructed and applied to the mobile speech emotion recognition system, and the algorithm is verified by experiments. The experimental results show that the prediction accuracy is 70%. The algorithm proposed in this paper has good recognition effect, strong human-computer interaction ability and good effect. The research of this paper will contribute to the development of English (foreign language) micro audio and video teaching mode, optimize the human-computer interaction mode of English (foreign language) micro audio and video teaching, and improve the teaching autonomy.
Enhancing the Educational Value of Tangible and Intangible Dimensions of Traditional Crafts Through Role-Play Gaming

Advances in Cultural Heritage (CH) representation and presentation technologies are explored concerning new potentials brought by the gaming industry. These include the use of digitisation technologies for the creation of realistic digital assets, educational gaming concepts, and immersive technologies. In this context, it is shown how the creative sector can exploit these potentials in novel educational and gaming experiences, inspired by CH. The aim is to enhance the way that cultural content is experienced in the digital world, to present, and to valorise intangible dimensions and, ultimately, exploit technological advances to enhance our understanding, appreciation, and preservation of tangible and intangible heritage.
Introduction to the special issue on socio-cultural role of technology in digital musical instruments

(2021). Introduction to the special issue on socio-cultural role of technology in digital musical instruments. Journal of New Music Research: Vol. 50, Special Issue: Socio-Cultural Role of Technology in Digital Musical Instruments. Guest Editors: Koray Tahiroğlu and Thor Magnusson, pp. 117-120.
S-CNN-ESystem: An end-to-end embedded CNN inference system with low hardware cost and hardware-software time-balancing

Abstract In recent years, a variety of dedicated hardware accelerators have been proposed using field programmable gate arrays (FPGAs) to accelerate convolutional neural networks (CNNs) on embedded platforms. However, the speed of dedicated hardware has always exceeded that of data acquisition/loading of software. Therefore, the hardware can stay idle due to a long wait for the software, which will lead to less benefits even when utilizing hardware acceleration. In this paper, a hardware-reusing strategy is proposed based on the structural characteristics of CNNs. Furthermore, a heuristic hardware/software time-balancing flow is introduced with the help of the hardware-reusing to narrow and even eliminate the imbalance of hardware-software time in the low-cost end-to-end embedded CNN inference system. On the premise that the system’s time and accuracy requirements are met, the hardware speed is optimized in hardware design to achieve the hardware/software time-balancing. Based on these two strategies, an end-to-end embedded CNN inference system with low hardware cost and hardware/software time-balancing (S-CNN-ESystem) is suggested. Evaluations have been performed on the Zynq xc7z020-clg400-1 platform and LeNet-5, AlexNet and VGG-16 used to validate our solution. Compared with the accelerators implemented by the pure FPGA, the hardware-to-software time consuming ratio is much closer to 1:1.
Social Data Analysis: Cyber Recruitment Analysis Spam Detection over Twitter Dataset Using SVM &amp; ARIMA Model

Social media has emerged as a vital mode of communication to friends as well as communities. Many communities have their presence on social media, as they provide non restricted communication with fewer authentications thus becomes the target for identity deception. Cyber recruitment is one of the spammer activities which is also being performed using the open platform social media. Thus analysis of data exchange and filtering them based on communication, alarming the network is the requirement. Cyber posts analysis is previously done by many approaches such as text filtering, key bag word analysis, pattern analysis etc. they are limited with their dataset size and requirement of pre-learning by programmers. Thus the requirement of an automated approach with auto learning of pattern with proper detection is needed. In this paper an Enhanced SVM CTM based approach is proposed, this approach gives the analysis of data taken from twitter posts. Performing the pre-process through NLP and further applying the SVM CTM approach over filtered data for spam analysis. Comparison analysis with traditional approaches shows the better prediction ratio.
Performance evaluation of health recommendation system based on deep neural network

Abstract Deep learning has developed as an innovative zone of machine learning and data mining exploration part. Controlled or unconfirmed methodologies which contain of a number of layers of handling which form a hierarchy are castoff for preparation in deep learning. Every succeeding layer mines an ever more intellectual depiction of the input data and shapes upon the depiction from the preceding layer, usually by calculating a nonlinear alteration of its input. The constraints of these alterations are adjusted by preparation of the prototypical on a dataset. A deep learning prototypical studies better depiction as it is delivered with more volumes of data. Key objective of using deep learning methods in recommender schemes is to lower time complexity and to increase the accurateness of formed expectations. In this paper, performance of planned HRS is evaluated by Arbitration Time, Latency Time, Jitter, Execution Time, Network Bandwidth Consumption, Power Consumption, Training Accuracy and Testing Accuracy.
Dense Connected Residual Generative Adversarial Network for Single Image Deblurring

Recently, deep convolutional learning has been applied to image deblurring, which greatly improves the performance of single-image blind deblurring algorithms. However, most deep image deblurring models based on convolutional neural networks do not make full use of the information of low-level spatial convolutions, so they achieve relatively poor performance. In this paper, we improved the generator of the Generative Adversarial Network (GAN) and proposed dense networks and residual networks to solve this problem in image deblurring. The network makes full use of the hierarchical features of all convolutional layers. Specifically, we extract the local features of the convolutional layer through the proposed densely connected convolutional layer. Moreover, the dense connections of the residual dense network can connect all layers from the previous state to the current state. Then using local jump connection and feature fusion, it can adaptively learn more effective features from the previous and current local features, making the trained network more extensive. After fully obtaining the dense local features, the global residual connection is used to learn the global feature information in an overall manner. Experimental comparison and verification have proved the effectiveness of our method and achieved excellent results both quantitatively and qualitatively.
Modulation Recognition of Communication Signals Based on Deep Learning Joint Model

Abstract In modern communication, it is often required for a non-cooperative party to identify the modulation mode when no prior knowledge is given to facilitate the subsequent demodulation and analysis. However, the traditional modulation recognition process requires cumbersome and uncertain manual signal-feature extraction, making it inapplicable to the complex communication environment. In order to overcome this limitation, this paper proposes a communication-signal modulation recognition model based on the dense connection network (DenseNet) and residual connection network (ResNet). The convolutional block attention mechanism (CBAM) is introduced into the DenseNet and ResNet structures, significantly enhancing the modulation recognition accuracy of the proposed global network model. Besides DenseNet and ResNet, the long short-time memory (LSTM) network is also adopted. The experimental results show the performance of the proposed model.