Reviewerly/exHarmony huggingface dataset:
Multipurpose Watermarking Approach for Copyright and Integrity of Steganographic Autoencoder Models

With the great achievements of deep learning technology, neural network models have emerged as a new type of intellectual property. Neural network models’ design and training require considerable computational resources and time. Watermarking is a potential solution for achieving copyright protection and integrity of neural network models without excessively compromising the models’ accuracy and stability. In this work, we develop a multipurpose watermarking method for securing the copyright and integrity of a steganographic autoencoder referred to as “HiDDen.” This autoencoder model is used to hide different kinds of watermark messages in digital images. Copyright information is embedded with imperceptibly modified model parameters, and integrity is verified by embedding the Hash value generated from the model parameters. Experimental results show that the proposed multipurpose watermarking method can reliably identify copyright ownership and localize tampered parts of the model parameters. Furthermore, the accuracy and robustness of the autoencoder model are perfectly preserved.
Code Mixing in EFL Classroom: Views from English Teachers Side

The objectives of this research were to find out the category of code mixing used by the English teacher and the code mixing category dominantly used by the English teacher in teaching EFL (English as a foreign language) classroom based on the category of code mixing according to Muysken (2000). This research employed descriptive qualitative research design to analyze the teacher’s code mixing in teaching EFL at SMK Negeri 1 Makassar. The participant of this research was one of all the English teacher of SMK Negeri 1 Makassar, this participant was taken by using purposive sampling technique. The instruments of this research were observation and audio recording.The result showed that (1) The English teacher of SMK Negeri 1 Makassar used all of the categories that categorized by Muysken (2000, cited in Liu, 2008: 6) in teaching EFL classroom, they were: Insertion, Alternation and Congruent Lexicalization. There were seventeen examples of Insertion category, two examples of Alternation category and there were also two examples of congruent lexicalization category used by the English teacher; (2) The category of code mixing dominantly used by the English teacher of SMK Negeri 1 Makassar was insertion category in teaching EFL classroom.Therefore, it can be a positive input of the English teachers to enrich their English vocabulary to avoid using code mixing in teaching EFL classroom.
An Empirical Study on the Precise Employment Situation-Oriented Analysis of Digital-Driven Talents with Big Data Analysis

This paper conducts an in-depth research analysis on the precise employment of college graduates in the context of big data using a number-driven approach. The textual information of the study is obtained by using in-depth interviews, and the evaluation index system of college students’ employment quality is constructed by combining the step-by-step coding method with rooting theory. The research on the current situation of employment recommendation platform research and the application status of big data in the employment recommendation platform is explored by using a bibliometric approach. And the innovative use of web crawler technology is used to comprehensively understand the recommendation function and status quo of the same type of recommendation platform, which provides a reference for the research of this platform. Based on the preliminary analysis of platform requirements and overall design, the overall design and functional implementation of the big data employment recommendation platform are carried out by using big data crawler technology, big data architecture technology, text mining technology, database technology, etc. The construction of a recommendation module based on user history information, a recommendation based on real-time user online behavior data, and hybrid recommendation carried out on the recommendation module to grasp all-round the platform is built based on a stakeholder perspective. Based on the platform construction, the initial platform operation and maintenance management mechanism was established from the stakeholder’s perspective. The Pearson correlation coefficient is used to objectively evaluate the current situation of talent supply in universities and talent demand in enterprises from the perspective of image and data. In the research on the development status of the big data education industry, the Lorenz curve and Gini coefficient are used to match the status of new big data majors with their college construction volume in each province and provide data support for the reasonable adjustment of majors setting in each province according to the education level.
ANALISIS PERSEPSI GURU SAAT PEMBELAJARAN DARING DI MASA PPKM (PEMBERLAKUAN PEMBATASAN KEGIATAN MASYARAKAT)

Learning activity during public activity restrictions at Muhammadiyah Elementary School, Taman Sub-district, Sidoarjo Regency, is carried out through online learning; however, teachers at SD Muhammadiyah Taman continue to face a number of challenges in this teaching and learning activity. This research aims to examine online learning implementation during the public activity restrictions. There are still many teachers who lack an understanding of science and technology, let alone old or traditional teachers who make online learning uninteresting. This research applied the survey research method. Data were collected using an open questionnaire accessible via Google Forms. The data was analyzed using descriptive statistics. There were 97 teachers at SD Muhammadiyah Taman who filled out the questionnaire. Based on the questionnaire responses, 70.1% had implemented online learning. The issue during online learning was that many students did not have their own mobile phones or laptops because their siblings were still using them for online learning or their parents were still using them for work, so teachers felt that the material presented could not be fully absorbed by the students. Meanwhile, parents struggled with not being able to control their children when learning at home, which differed from face-to-face learning at school
Self-Supervised Representation Learning for Document Image Classification

Supervised learning, despite being extremely effective, relies on expensive, time-consuming, and error-prone annotations. Self-supervised learning has recently emerged as a strong alternate to supervised learning in a range of different domains as collecting a large amount of unlabeled data can be achieved by simply crawling the internet. These self-supervised methods automatically discover features relevant to represent an input example by using self-defined proxy tasks. In this paper, we question the potential of commonly employed purely supervised training (starting either from ImageNet pretrained networks or pure random initialization) in contrast to self-supervised representations that can be learned directly using self-supervised representation learning methods on large document image datasets. For this purpose, we leverage a large-scale document image collection (RVL-CDIP) to train ResNet-50 image encoder using two different self-supervision methods (SimCLR and Barlow Twins). Employing a linear classifier on top of self-supervised embeddings from ResNet-50 results in an accuracy of 86.75% as compared to 71.43% from the corresponding ImageNet pretrained embeddings. Similarly, evaluating on Tobacco-3482 dataset using self-supervised embeddings from ResNet-50 yields an accuracy of 88.52% in contrast to 74.16% from the corresponding ImageNet pretrained embeddings. We show that in the case of limited labeled data, this wide gap in performance between self-supervised and fully supervised models persists even after fine-tuning pretrained models. However, a significant reduction in this gap is observed with an increasing amount of data including the case where the model is trained from scratch. Our results show that representations learned using self-supervised representation learning techniques are a viable option for document image classification, specifically in the context of limited labeled data, which is a usual restriction in industrial use cases.
Detection of Cytopathic Effects Induced by Influenza, Parainfluenza, and Enterovirus Using Deep Convolution Neural Network

The isolation of a virus using cell culture to observe its cytopathic effects (CPEs) is the main method for identifying the viruses in clinical specimens. However, the observation of CPEs requires experienced inspectors and excessive time to inspect the cell morphology changes. In this study, we utilized artificial intelligence (AI) to improve the efficiency of virus identification. After some comparisons, we used ResNet-50 as a backbone with single and multi-task learning models to perform deep learning on the CPEs induced by influenza, enterovirus, and parainfluenza. The accuracies of the single and multi-task learning models were 97.78% and 98.25%, respectively. In addition, the multi-task learning model increased the accuracy of the single model from 95.79% to 97.13% when only a few data of the CPEs induced by parainfluenza were provided. We modified both models by inserting a multiplexer and de-multiplexer layer, respectively, to increase the correct rates for known cell lines. In conclusion, we provide a deep learning structure with ResNet-50 and the multi-task learning model and show an excellent performance in identifying virus-induced CPEs.
The factor of the risk and risk management In information security

Risk is the consequence of events and dangers. To rephrase it, an event that will cause damage and deprivation and this happening can be described via the term of information. The word risk means foreseeable dangers or being under the threat of possible damage. It defines the occurrence of an event that could lead to damage or loss. This term is used for events that are synonymous with danger and are expected to occur, but it is not clear whether they will occur or not. Thus, risk management means managing this uncertain environment. Risk Information Security Management system requires a risk-based approach. Information security policy emerges and is formed based on the results of risk analysis. Therefore, risk management is essential. As well as the systematic use of information contributes to identifying sources and forecasting risks. Information systems analyze the value of assets, threats and shortcomings in risk analysis. Here, the risks are assessed depending on the severity of the potential impact on the confidentiality, integrity and reliability of information systems. Everything that has value for an organization is called information availability. The standards define the availability of information as information that is valuable to the organization and should always be protected. &nbsp;&nbsp;&nbsp;&nbsp; Keywords: risk management, risk processing, risk assessment, risk monitoring, risk reassessment, risk review, Information security risk
Fast process for classifying structural image pairs using Weibull parameters extracted from undersampled Local Dissimilarity Maps

In previous works, the Local Dissimilarity Map (LDM) was proposed to compare two binary and grayscale images. This measure is based on a Hausdorff distance, which allows to quantify locally the dissimilarities between images. In this paper, we proposed the two-parameter Weibull distribution to model the LDM and the undersampled LDMs for two structural images. To classify structural image pairs, we used the two parameters of Weibull distribution for each LDM as descriptors. They are relevant to discriminate image pairs into similar and dissimilar classes. Experiments were made on the MNIST image dataset and in our own old print image dataset. The results shown our approach is more accurate than the other measures in the literature.
A machine learning domain ontology to populate knowledge base to support intelligent agents working in autonomous systems domains of the Internet infrastructure

This paper describes the creation of a domain ontology to represent knowledge to populate a knowledge base to be used by agents, in the environment of Internet Infrastructure routing domains. Protégé 5 was used, which produces results suitable for both software-developed agents and humans. The knowledge created with Protégé is explicit and Protégé has itself inference machines capable of producing implicit knowledge. The resources available in Protégé 5 are presented and the ontology is made available for public use.The content produced with Protégé 5 will be used to populate the knowledge base of the Structure for Knowledge Acquisition, Use, Learning and Collaboration (SKAU), an environment to support intelligent agents over Internet Autonomous Systems domains.
Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5

Congestion seems to be a daily occurrence in the Indonesian city of Jakarta. As a consequence, the rider has access to essential information regarding traffic conditions at all times, which is beneficial. Through social media platforms such as Twitter, this information is readily available to the public. On the other hand, the information offered on Twitter is still uncategorized text. DKI Jakarta, as a consequence, developed a congestion classification system that included data mining techniques, a classification approach based on the decision tree technique, and C4.5 as a component. This C4.5 method transforms a large amount of information into a decision tree that shows the rules. Geocoding will be utilized to illustrate the locations that have been gathered, and a data split with a confusion matrix will be used to assess how well the categorization process has worked. According to the study's results, the average accuracy rate is 99.08 percent, the average precision rate is 99.46 percent, and the average recall rate is 97.99 percent.
Evaluation of Logistics Service Quality: Sentiment Analysis of Comment Text Based on Multi-Level Graph Neural Network

The world is severely impacted by the coronavirus (COVID19). During the epidemic, logistics service, an often-overlooked pillar of the modern society, steps into the spotlight. However, the service capability is inevitably weakened by the epidemic. The fatigued service providers are increasingly unable to meet the high expectations of users, who therefore leave harsh comments on logistics services. It is important for managers to find information that helps to improve management, out of the biased and angry comments. Text sentiment analysis is a fundamental work in natural language processing (NLP). In recent years, graph neural network (GNN) has achieved excellent performance in various NLP tasks. Nevertheless, GNN only considers the adjacent words, as it updates graph nodes. The model thereby emphasizes local features over global features, and misses the intent of the comment text. This paper constructs a triple graph neural network (TGNN) to serve the sentiment analysis of service texts. Firstly, the corresponding node connection windows were applied on different network layers to consider both local and global features. Next, the graph attention network (GAT) was adopted as the message delivery mechanism to fuse the features of all word nodes in the graph. Experimental results show that, the TGNN can evaluate the comment texts on logistics service quality more accurately than the other models.
Synthesis and Comparison of Improved Edge Detection Technique Based on Metaheuristic and Intelligent Algorithm Optimization

The edge is the most significant information in image processing applications. Moreover, and the accurate and continue edge commonly leads to accurate related steps like object tracking and region clustering. In fact, it is the first step of image analysis and understanding. The accuracy edge detection results have an impact on the comprehension machine system. In this paper we present various improved edge detection techniques by our research team, of similar color and grey level images, using the information theory approach based on other energy information inspired from Shannon entropy and utilizing as well the metaheuristic and intelligent method combined with multilevel thresholding approach in various color spaces, and like the ant colony optimization with the graph cut approach for indexing images before the segmentation step. In addition, particular swarm optimization is done, and finally the fuzzy technique is used. The effectiveness and accuracy of these approaches are evaluated by many metric measurements and compared with the common operators. The PR metric, has a significant mean value (about 20) than PR of Canny operator (about 9). And also, we can denote that all improved techniques achieve significant results with ameliorated running time.
A Brief Overview of Context Aware System

Context Awareness emerged as a concept from Ubiquitous computing, which is becoming a reality by emphasizing the integration of the data space and the physical space. With its aid, people may receive and analyze data at any time and from any location using a device that can connect to the internet. As a result, it can reduce the complexity of using the gadget and make people's life easier and more efficient. Context aware systems (CAS) are an effective approach for dealing with day-today tasks. Context aware frameworks provide up completely new possibilities for applications developers and end users by collecting contexts data and changing system behaviour respectively. This survey presents an overview of context aware systems. This study analyzes the concept of context aware systems, network architecture, application, and user interface, as well as thorough information of every layer of context aware system. As a consequence of the survey, a general procedure layout in CASs is provided, and the architectural contemplations of CASs are clarified
IMPROVING STUDENTS’ LISTENING SKILL USING PODCASTS

The purpose of this study is to discover the use of podcasts in improving students’ listening skills. The quantitative technique was used in this research: the data obtained from the pre-experimental technique. This research was conducted to find out the information about the improvement of students listening skills on using podcasts. This study involved 15 students in the fourth semester in the Department of English Language Education of Universitas Islam Negeri Ar-Raniry Banda Aceh. The sample was chosen by using purposive sampling who are qualified the certain criteria; sixth semester-students in Department of English Language Education of Universitas Islam Negeri Ar-Raniry Banda Aceh, and The students who have passed Basic Listening subject, Intermediate Listening subject, and Advanced Listening subject. However, the students faced difficulties in comprehending audio at the first meeting, after giving a treatment by using podcasts, the finding revealed that podcasts help students to improve their listening skills. It was proven by the improvement of the mean score of students’ pre-test and post-test (36.3 to 63.3). Overall, this study suggests that developing English listening skills can be boosted with Podcasts
Estimation of soil organic carbon in agricultural fields: A remote sensing approach

Aim: In view of the importance of Soil Organic Carbon (SOC) in agricultural management, a study was conducted to develop a digital SOC map using remotely sensed spectral indices. The present study was conducted on the Tawdeehiya Farms, located in the central region of Saudi Arabia between Al-Kharj and Haradh cities. Methodology: Landsat-8 (L8) and Sentinel-2 (S2A) satellite images were used for the characterization of SOC stocks in the topsoil layer (0-10 cm) of the experimental fields. Soil samples were randomly collected from six (50 ha each) agricultural fields and analyzed in the laboratory for SOC (SOCA) following Walkley and Black method. While, vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI), NDVIRedEdge, Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), and Reduced Simple Ratio (RSR) were computed and subsequently used for the development of SOC prediction models. Results: Univariate linear regression technique was employed for the recognition of a suitable band/VI for SOC (SOCP) mapping. The SWIR-1 band of both L8 (R2 = 0.86) and S2A (R2 = 0.77) data was promising for predicting SOC with 16% (S2A) and 18% (L8) of BIAS. Interpretation: The NDVI and BSI (for L8 data) and BSI and RSR (for S2A data) were found most suitable VI in the prediction of SOC. The R2 values of linear regression models were 0.68 (BSI) and 0.78 (RSR), indicating that nearly 68% and 78% of the SOC could be predicted through L8 and S2A datasets, respectively.
How Do Socio-Demographic Patterns Define Digital Privacy Divide?

Digital privacy has become an essential component of information and communications technology (ICT) systems. There are many existing methods for digital privacy protection, including network security, cryptography, and access control. However, there is still a gap in the digital privacy protection levels available for users. This paper studies the digital privacy divide (DPD) problem in ICT systems. First, we introduce an online DPD study for understanding the DPD problem by collecting responses from 776 ICT users using crowdsourcing task assignments. Second, we propose a factor analysis-based statistical method for generating the DPD index from a set of observable DPD question variables. In particular, the DPD index provides one scaled measure for the DPD gap by exploring the dimensionality of the eight questions in the DPD survey. Third, we introduce a DPD proportional odds model for analyzing the relationship between the DPD status and the socio-demographic patterns of the users. Our results show that the DPD survey meets the internal consistency reliability with rigorous statistical measures, e.g., Cronbach’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha =0.92$ </tex-math></inline-formula> . Furthermore, the DPD index is shown to capture the underlying communality of all DPD variables. Finally, the DPD proportional odds model indicates a strong statistical correlation between the DPD status and the age groups of the ICT users. For example, we find that young users (15-32 years) are generally more concerned about their digital privacy than senior ones (33 years and over).
An Adaptive Island Model of Population for Neuroevolutionary Ship Handling

Abstract This study presents a method for the dynamic value assignment of evolutionary parameters to accelerate, automate and generalise the neuroevolutionary method of ship handling for different navigational tasks and in different environmental conditions. The island model of population is used in the modified neuroevolutionary method to achieve this goal. Three different navigational situations are considered in the simulation, namely, passing through restricted waters, crossing with another vessel and overtaking in the open sea. The results of the simulation examples show that the island model performs better than a single non-divided population and may accelerate some complex and dynamic navigational tasks. This adaptive island-based neuroevolutionary system used for the COLREG manoeuvres and for the finding safe ship’s route to a given destination in restricted waters increases the accuracy and flexibility of the simulation process. The time statistics show that the time of simulation of island NEAT was shortened by 6.8% to 27.1% in comparison to modified NEAT method.