Reviewerly/exHarmony huggingface dataset:
A 3D GAN Architecture for Volumetric Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) is used extensively in underwater imaging for visualizing the seafloor and objects present on it. However, processing SAS images can be time-consuming and tedious, with machine learning techniques being ineffective due to the lack of available data. In particular, automated target recognition (ATR) with 3D SAS data for machine learning is challenging in many ways due to the complexity with working with 3D volumetric data. Recently, researchers have introduced generative adversarial networks (GANs) to help perform 2D SAS image generation for data augmentation. Following this line of work in this paper, we introduce a 3D-GAN architecture to generate photorealistic 3D SAS data which matches the fidelity of real data. In particular, we discuss novel latent space sampling and normalization to help 3D GANs overcome mode collapse for generating volumetric SAS information. Experimental results are shown on real 3D SAS data, showing the potential of using 3D GANs for dataset augmentation in the future.
Identification of Suitable FMRP Using Inverse Ant Algorithm for Caraga Region

The Inverse Ant Algorithm w/ Mutable Path Pheromone Concentration is an enhanced inverse algorithm that mimics biological ants to find food from their mounds to the food itself. It uses constraints in defecting actual events like road capacity, speed limit, car length, and traffic light delays. One of the Inverse Ant Algorithms' purposes is to optimize performance in traffic management, especially in the stagnation issue. On the other hand, Farm to Market Road (FMR) is a government initiative that connects production centers to the market. It helps farmers easily access roads that ease the burden of bringing their farm produce to the consumers. Its goal is to eliminate or mitigate food waste due to limited transportation. FMR was chosen through prioritization criteria that the Department of Agriculture-Bureau of Agricultural and Fisheries Engineering (DA-BAFE) evaluates through physical inspection. The requirements were developed using the Analytic Hierarchy Process (AHP), which produces a prioritization list of FMR for possible government funding. Prioritization criteria like existing agricultural production area, potential production area, existing livestock production area, distance to the production area, distance to market, and the average daily traffic of vehicles passing the road are considered in creating a priority list. These criteria are converted as constraints in applying the inverse ant algorithm in locating the suitable road from the area of production to the market or network links in identifying the suited FMR projects as the priority for implementation.
Blockchain-Based Access Control Model for IoT Applications

In Internet of things (IoT) domains, safe and easy access to the data and resources is a big challenging issue. The data and resources are also vulnerable to availability, integrity, security, and privacy threats. To overcome such issues, access control models were proposed. But, most of the traditional access control models are not providing complete security in terms of integrity and correctness to the IoT applications as well as the risk of privacy leakage exists. To overcome such problems, a blockchain-based access control model is proposed which can secure the IoT applications (data and resources) as well as provide guarantee the auditability and correctness of access control policies. Smart contracts are deployed in the blockchain that will provide the correct access to correct users in a distributed manner. The security analysis of the proposed model ensures that the blockchain-based access control model will provide safe and easy access to the data and resources to the IoT applications.
Multi-language Sentiment Analysis – Lesson Learnt from NLP Case Study

The aim of this paper is to present the use of sentiment analysis in both Polish and English languages. This goal is related to the fact that the authors of this article have observed many sentences in both Polish and English, used in social media and on websites in Poland. The paper presents the principles of various inflectional forms that should be used in the preparation of the training dataset being the subject of the analysis. Therefore, one of the goals of this article is to identify possible problems that an analyst of sentiment analysis machine learning methods may misinterpret. The motivation of the study was to see if the same methods could be used to analyse sentiment in different languages. We decided to evaluate the possibility of using one sentiment evaluation mechanism, assuming the use of similarly prepared training sets. In addition, the article shows the principles and differences between these languages, including in terms of the possibility of gender identification based on the text. We presented the results of a case study that showed how machine learning tools treat unstructured data to find the right sentiment and what problems can be identified when delivering text in these two languages. The conducted study also showed the possibility of using Big Data sources, such as comments in the form of comments on websites or social media, in order to correctly identify the sentiment, which is not always the case if the training set is not prepared properly.
How Do University L2 Learners Make Use of Mobile Dictionary Apps to Become Autonomous Learners?

The ability to make good use of mobile dictionary applications (apps) is a key factor of autonomy in language learning. This study investigates how L2 learners use dictionary apps to become autonomous learners when engaging in mobile assisted language learning (MALL). A questionnaire was designed to collect data from more than 200 university students to explore second language (L2) learners' personal experiences with mobile dictionary apps. The results show that dictionary apps not only facilitate students' academic studies, but also become essential to these students' successful language learning. Students enjoy the autonomy of choosing which dictionary app to use, as well as when and where to do so. However, discrepancies exist between the dictionary functions learners use and those they desire, suggesting that future apps could integrate more functions to better fit learners' needs and help them become autonomous. Some pedagogical implications are proposed to improve learner autonomy with regard to using mobile dictionary apps.
Why Do People Not Install Corona-Warn-App? Evidence from Social Media

This study investigates why the Corona-Warn-App, which was meticulously designed in Germany to interrupt the COVID-19’s chain of infection, was not installed by the majority of the population and therefore failed to achieve what it was created to do. We collect natural language data by scraping 70,529 related comments from Twitter, and apply sentiment analysis to understand the content. We distinguish negative comments into two categories: technical issues, e.g. crashes and errors, and trust-related issues, e.g. concerns about privacy protection. After a more detailed manual check, we find that some criticisms of the app are not accurate. Surprisingly, more than 40% of trust-related denunciations are based purely on misinformation spread by users. For example, a user complains about a violation of data privacy, when, in fact, the app is fully GDPR-compliant. Our study provides evidence for the intentional promulgation of misinformation to lower trust in life-saving technologies during a pandemic, and calls for a more careful evaluation of the technology’s performance.
Nonparametric empirical Bayes estimation based on generalized Laguerre series

In this work, we delve into the nonparametric empirical Bayes theory and approximate the classical Bayes estimator by a truncation of the generalized Laguerre series and then estimate its coefficients by minimizing the prior risk of the estimator. The minimization process yields a system of linear equations the size of which is equal to the truncation level. We focus on the empirical Bayes estimation problem when the mixing distribution, and therefore the prior distribution, has a support on the positive real half-line or a subinterval of it. By investigating several common mixing distributions, we develop a strategy on how to select the parameter of the generalized Laguerre function basis so that our estimator possesses a finite variance. We show that our generalized Laguerre empirical Bayes approach is asymptotically optimal in the minimax sense. Finally, our convergence rate is compared and contrasted with several results from the literature.
Resourcing of Schools for an Inclusive Education System

Assistive technologies (AT) are revolutionising and reinventing learning practices for learners with special needs. Opportunities have been created to embrace and accommodate those who were previously marginalised in mainstream educational settings. The 4IR is still an improbable idea in developing countries, where the ravages of poverty create inequalities and social exclusion amongst people with disabilities. Years after decolonisation, African countries are still struggling to reap the benefits of information and communication technologies (ICT) due to social, economic, and political challenges. The digital divide is exacerbated daily, as resources are limited to enhance access to inclusive education. This chapter examines the phenomenological principle of “Ubuntu” to curb and solve indigenous problems that are difficult to remedy through Westernised methods. Ubuntu must guide technological transformation and direct educational experts in designing curriculum and learning practices relevant to the African context.
Understanding the Effects of the Digital Divide on Remote Learning in Elementary Education

As many sectors in the world have been affected by the COVID-19 pandemic, education has also been affected. Regardless of the readiness of teachers and students, they were forced to suddenly switch to remote instruction not to interrupt education. In this process, especially students who do not have access to appropriate technologies have fallen behind their friends due to the digital divide. This educational barrier especially affected elementary students as this is the initial step in education where these younger students should receive education synchronously with their teachers and friends in the classroom. Moreover, if families cannot provide adequate opportunities or access to their children due to their socio-economic status, students who are deprived of this education fall behind from education at this critical age, and this is something that can negatively affect their entire lives. For this reason, this chapter raises awareness about the digital divide and to propose ways for teachers to overcome this obstacle, especially in primary education.
Monitoring Negative Sentiment-Related Events in Open Source Software Projects

Open source software (OSS) development is a highly collaborative process where individuals, groups and organizations interact to develop, operate and maintain software and related artifacts. The developers&#x0027; sentiment in this process can have an impact on their working willingness and efficiency. Monitoring sentiment factors can help to improve OSS development and management. However, no method has been proposed to dynamically monitor the sentiment phenomena during the OSS development process. In this paper, an approach to detect Negative Sentiment-related Events (NSE) is proposed. It consists of two steps. The first step is to identify the burst interval of negative comments from open source projects, which corresponds to a NSE. The second step is to annotate this NSE with its event type. To support this approach, the types of NSEs in OSS projects are defined through an empirical study and classifiers are trained to annotate event types automatically. Moreover, conversation disentanglement techniques are employed to make the comments extracted more complete. Finally, the factors that have an influence on NSEs in the OSS project are studied.
DFCatcher: A Deep CNN Model to Identify Deepfake Face Images

In recent years, advancement in the realm of machine learning has introduced a feature known as Deepfake pictures, which allows users to substitute a genuine face with a fake one that seems real. As a result, distinguishing between authentic and fraudulent pictures has become difficult. There have been several cases in recent years where Deepfake pictures have been used to defame famous leaders and even regular people. Furthermore, cases have been documented in which Deepfake yet realistic pictures were used to promote political discontent, blackmail, spread fake news, and even carry out false terrorism attacks. The objective of our model is to differentiate between real and Deepfake images so that the above mentioned situations can be avoided. This project represents a deep CNN model with 13000 images divided in two segments that are: Training and Testing. The dataset was prepared using necessary image augmentation techniques. A total of 2 categories are considered (real image category and fake image category). Our suggested model was successful in achieving 98.77% accuracy. The model shows promising results in the case of detecting real and DeepFake images than all the other models used before.
A Method for CTCS-3 Knowledge Extraction of Unstructured Data

With the rapid development in AI technology, constructing the nationally independent intelligent railway is a trend at present in Chinese rail transit industry. As CTCS-3 is one of the core technologies for Chinese high-speed railway, making machines understand the CTCS-3 knowledge efficiently and concretely is becoming an important topic. Knowledge extraction is one of the most significant parts. Therefore, we proposed a method to extract CTCS-3 knowledge from unstructured data by combining BERT and BiLSTM-CRF. We built a 407936-word labeled dataset about CTCS-3 equipment for model training. Using such small dataset, we completed the experiments of CTCS-3 knowledge extraction including entity recognition and relationship extraction. The experiment show that the F1 score is 75.89%. By the method, we can get some entity relationship triples which are the foundation to achieve cognitive intelligence of CTCS-3. In summary, it extracts CTCS-3 entity relation triples with a small number of rail transit industry dataset.
Integration and data portability in the programming of digital analytical services of the education system

It analyzes the development trends of information and communication technologies for the analysis of big data in the process of digital transformation of education - the integration of various levels of the educational data analytics system to conduct the necessary analysis and forecast the behavior of the components of the education system as a whole. The problem of the current state of information systems and services in education is analyzed - the presence of a large number of discrete software products, each of which accumulates digital traces of the learning process and generates output in unsystematized. The formats methods of data analysis in integration electronic services and systems are analyzed: big data mining, data fusion and integration, genetic algorithm, predictive analytics, spatial analysis, split testing, Integration digital applications based on system interoperability and data portability, using class software middleware and open systems methodology. Modern systems of the middleware class are able to process data based on universal formats and provide multi-channel data transfer between all application components. The methodology of open systems for analyzing big data in education is analyzed. The essence of the methodology of open systems is characterized, consisting in the fact that during their construction, docking should be provided using standard interfaces between all components of the systems. The paper analyzes the management of creating a specification for a protocol for the interaction of multi-format systems based on the use of XML technology. The prospects of using an open system methodology, building integration using standard interfaces using Application Program Interface (API) standards and External Environment Interface (EEI) standards are described to solve the problems of system interoperability, data portability. The study uses methods of genetic data analysis, modeling of digital integration of data portability applications.
Best technical abstract – Thomas LA van den Heuvel

a postdoctoral fellow at the Radboud university medical center in the Netherlands.His research focuses on combining point-of-care ultrasound devices with deep learning solutions to facilitate diagnosis in resource limited countries.During his PhD, he developed a deep learning software that automatically detects twin pregnancies, estimates gestational age and determines fetal presentation.The system is so easy to operate that any health care worker can use the device after two hours of training.Currently, the methods are implemented in Android based smartphones to further facilitate widespread use of this technology.
Analysis of relationship between economic sectors of Dki Jakarta towards land use changes and erosion in Ciliiwung Hulu watershed, Bogor Regency

Abstract Land resources are increasingly important as the population continues to grow, which will put pressure on demand, so land use is increasingly competitive. The purpose of this study is to determine the interrelationships between economic sectors between DKI Jakarta and Bodetabek and the impact of DKI Jakarta’s economic development on the economy and changes in land use in Bodetabek. Research using IRIO approach and analysis of Landsat imagery. The sector in DKI Jakarta that has an effect on Bodetabek is the trade sector. Food industry, building, restaurant and hotel, communication, building and service business,. On the other hand, the sectors in Bodetabek that affect DKI Jakarta are the chemical, wood, and metal goods industries. According to the IRIO table, an area of 2,561.86 ha of built land is needed in DKI Jakarta and 251,167.57 ha in DKI Jakarta and 251,167.57 ha while the results of the interpretation of TM7 images in 2010 and 2019 mean that the built area of Bodetabek has changed from 183,681.86 ha to 249,867.91 ha. or has increased to 66,186,05 ha. There is a difference in calculations with the IRIO table and image interpretation of 1,299.66 ha. The land cover of the Ciliwung Hulu watershed was obtained through analysis of Landsat imagery data and 2019 was dry land agriculture of 4435 ha in 2019. A sizeable reduction also occurred in paddy fields and forest cover of 2% each. The reduction in forest area to dry land agriculture causes disruption of hydrological functions and higher erosion
Mineralogy and trace elements geochemistry of the RoudMajan kaolin deposit, northwest of Torbat-e Heydarieh, NE Iran

Mineralogy and trace elements geochemistry of the RoudMajan kaolin deposit, northwest of Torbat-e Heydarieh, NE Iran
Partial Reversible Data Hiding Scheme Using Graphical Code

Nowadays, data hiding has been received much attention for information security due to the rapid growth of the Internet and multimedia technologies. Good steganography techniques can be used to achieve a high level of security. In this paper, a new image steganography technique, namely partial reversible data hiding using graphical code (PRDHGC), has been proposed taking the advantage of graphical error-correcting codes and a secret shared key. It is mainly a partial reversible data hiding scheme, and comparison with various methods has been shown that the proposed scheme performs better than the existing schemes in terms of PSNR, embedding efficiency and embedding payload.
Chemostratigraphy Enables Correlations and Reservoir Characterization with High Resolution Elemental Data

Abstract Previous studies have shown that by applying multivariate statistical analysis to chemostratigraphy, indistinct sequence stratigraphic correlations can be enhanced. Chemofacies and correlatable chemozones can be defined within highly homogenous strata, using specially designed statistical algorithms. In this study, we first investigated the better performing of linear and non-linear dimensionality reduction techniques in analyzing geochemical datasets for chemofacies and chemozones development. The general applicability of this conceptual model for sequence stratigraphic correlations, was subsequently tested. The results show that the linear method was able to account for 63% of input data variance while the non-linear technique accounted for 100% of the variance. In addition, the linear techniques are better utilized to establish chemofacies, whereas the non-linear techniques considerably perform better in establishing correlatable chemozones, while also improving accuracy.
Research on a method of completeness index based on complex model

Abstract The completeness of the complex model is an important index to evaluate the pros and cons of the complex model. It is of great significance to study the objective evaluation under different indicators and construct an indicator system to ensure the integrity of the complex. The construction of complex system indicators requires instructors who can fully describe the functions of complex models. This study designs a complex index E X , proves its authenticity through comparison with the real world, illustrates its similarity through conflict and comparison with the coverage of the target problem, and describes its intelligence level through confrontation and comparison with other complex models. Research on complex model problems based on the derivation process of domain theory proves that the quantified complete distance E X can calculate the similarity between the model and the actual environment to evaluate ‘true and false’. The experiment proved to be a reliable and complex model verification indicator.
Chromaticity Preserving Contrast Enhancement for Low Illumination Images

In order to improve the visibility of low-illumination images, a new image enhancement method based on image decomposition is proposed. The method of intrinsic image decomposition based on a single RGB-D image is used to decompose the image into enhancement gray template and shading map, where the depth map is replaced by gray scale map. Experiment results show that the gray scale of low-illumination images can be well enhanced with enhancement gray template. The illumination of some images can be well balanced with high light removed.
Discussion on the Security Mechanism of Energy Internet in 5G network

Abstract With the coming of 5G network, it is possible for energy Internet to achieve high bandwidth and low delay interconnection. At the same time, it also brings new security problems. The core elements of 5G - virtualization, cloud migration, network slicing, Internet of Things, 5G gateway and even related software development may become security vulnerabilities. Aiming at the possible security problems in the application of 5G in the energy Internet, including the overall security architecture, industry security subdivision, Internet of Things security operation and maintenance, and the application of new security mechanisms, this paper expounds the relevant concepts and threats. This paper puts forward that in security architecture, we should make full use of security mechanisms such as logical isolation and access control to achieve in-depth defense; in industry segmentation, we should have customized security capability response for different application scenarios of different industries; in the security operation and maintenance of the Internet of Things, we should aim at the complex network environment and topology of mass terminals. In the new security mechanism, it should be combined with large data analysis and AI intelligent processing to enhance the ability of security early warning. Finally, combined with the application scenario of the energy internet, this paper proposes a comprehensive solution to the energy Internet Security in the 5G.