Reviewerly/exHarmony huggingface dataset:
Single image super-resolution reconstruction of remotely sensed images using MRF-2D phase congruency model

Recently, the deep learning (DL)-based solution for single image super-resolution reconstruction has been extensively used. Though these methods have shown exceptional results, the computational requirement is very high and requires high-end graphic processors. Motivated to establish an efficient method without such requirement, we propose a modified Markov random field (MRF) model and two-dimensional (2D) phase congruency-based single-image super-resolution reconstruction method for remote sensing images. The 2D phase congruency-based feature extraction method is used to compute features such as edge and texture maps to achieve higher accuracy in finding similar example patches in feature space. To address an essential aspect of successful texture reconstruction, we have incorporated a texture prior for the computation of joint probability in our work. Image Euclidean distance (Ieuc) is integrated to achieve higher accuracy in finding the similarity between image patches in feature space and modeling compatibility functions. The experimental results demonstrate that the results of the proposed method are at par with the DL-based methods and outperform other state-of-the-art methods in perceptual quality as well as peak signal-to-noise ratio and structural similarity index parameters. Moreover, it shows significant improvement in the texture regions while reconstructing sharper edges for ×4 upscaling.
Forensic Whatsapp Investigation Analysis on Bluestack Simulator Device Using Live Forensic Method With ACPO Standard

This study aims to conduct a forensic analysis of the WhatsApp application on the Bluestacks android simulator device. BlueStacks App Player is designed to allow Android apps to run on PCs running Microsoft Windows and Apple's macOS. In this study, the scenario was carried out using two devices as Whatsapp communication media. The first device is a laptop device that uses the Bluestacks android simulator with the SM-G955F device type, and the second is a smartphone device as opposed to communication. This study uses the ACPO standard which consists of several stages such as Plan, Capture, Analysis, Present. Pada tahap Capture, teknik yang digunakan dalam melakukan  pencarian bukti pada aplikasi BlueStacks adalah live forensik. Hasil penelitian ini menunjukan bahwa analisis forensik pada perangkat android simulator Bluestacks dapat dilakukan sesuai prosedur ACPO. From the procedure carried out, information related to communication on the WhatsApp application was obtained. The source of this information is obtained from the WhatsApp database file msgstore.db.crypt12 which has been decrypted using the SQLite Browser application with a combination of the WhatsApp Key file contained in the cloned digital evidence. From the results of the decryption that has been carried out, then an explanation is carried out through the WhatsApp viewer application to make it easier to understand from the display side.
Expectation, Perception, and Accuracy in News Recommender Systems: Understanding the Relationships of User Evaluation Criteria Using Direct Feedback

Typically, a user-focused approach of evaluation of recommender systems requires the users to recollect their experiences, exposing study results to memory biases. In this paper, we describe a study conducted to test a framework, that allows recommender systems to be used and evaluated simultaneously. In this study, we asked 140 participants about their expected, perceived, and actual quality of the recommendations. We compare the performance of two recommender systems. The singular value decomposition recommendation system was able to correctly predict more than half of all evaluations and performed better than participants expected. However, users were more satisfied with the suggestions of the user-based collaborative filtering recommendation system. Our approach allows to compare actual item ratings, expected quality, and perceived quality of recommendations. Serendipity was found to be an important influencing factor for better item ratings by users. Participants rated both recommendation systems better when they perceived higher quality.
The FEMTI guidelines for contextual MT evaluation: principles and resources

A large number of evaluation metrics exist for machine translation (MT) systems, but depending on the intended context of use of such a system, not all metrics are equally relevant. Based on the ISO/IEC 9126 and 14598 standards for software evaluation, the Framework for the Evaluation of Machine Translation in ISLE (FEMTI) provides guidelines for the selection of quality characteristics to be evaluated depending on the expected task, users, and input characteristics of an MT system. This approach to contextual evaluation was implemented as a web-based application which helps its users design evaluation plans. In addition, FEMTI offers experts in evaluation the possibility to enter and share their knowledge using a dedicated web-based tool, tested in several evaluation exercises.
Time Classification Algorithm Based on Windowed-Color Histogram Matching

A web-based search system recommends and gives results such as customized image or video contents using information such as user interests, search time, and place. Time information extracted from images can be used as a important metadata in the web search system. We present an efficient algorithm to classify time period into day, dawn, and night when the input is a single image with a sky region. We employ the Mask R-CNN to extract a sky region. Based on the extracted sky region, reference color histograms are generated, which can be considered as the ground-truth. To compare the histograms effectively, we design the windowed-color histograms (for RGB bands) to compare each time period from the sky region of the reference data with one of the input images. Also, we use a weighting approach to reflect a more separable feature on the windowed-color histogram. With the proposed windowed-color histogram, we verify about 91% of the recognition accuracy in the test data. Compared with the existing deep neural network models, we verify that the proposed algorithm achieves better performance in the test dataset.
Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining

Aiming at the problems of traditional method of exercise recommendation precision, recall rate, long recommendation time, and poor recommendation comprehensiveness, this study proposes a personalized exercise recommendation method for English learning based on data mining. Firstly, a personalized recommendation model is designed, based on the model to preprocess the data in the Web access log, and cleaning the noise data to avoid its impact on the accuracy of the recommendation results is focused; secondly, the DINA model to diagnose the degree of mastery of students’ knowledge points is used and the students’ browsing patterns through fuzzy similar relationships are clustered; and finally, according to the clustering results, the similarity between students and the similarity between exercises are measured, and the collaborative filtering recommendation of personalized exercises for English learning is realized. The experimental results show that the exercise recommendation precision and recall rate of this method are higher, the recommendation time is shorter, and the recommendation results are comprehensive.
A Comprehensive Review of Cryptographic Algorithms

Abstract: The internet has revolutionized advancements, it’s conveniences and uses come at the price of new perils. To be safe from being the victim of fraud, theft and other damage security and vigilance is critical. Cryptography plays an important role in securing information and communications using a set of rules, it ensures the integrity of our data. It maintains confidentiality by protecting the privacy of personal information stored in enterprise systems. Hence Encryption is the only way to ensure that your information remains secure while it is stored and being transmitted. Cryptographic Algorithms mathematically maintain the integrity, confidentiality and authenticity of sensitive information by preventing data disclosure, data tampering and repudiation. The three main types of cryptography are Symmetric Key Cryptography, Asymmetric Key Cryptography and Hash Functions. In this Paper, several important algorithms used for encryption and decryption are defined and analysed, the algorithms are DES, AES, ECC, RSA, MD5 Keywords: Cryptography, Encryption, Decryption, AES, DES, ECC, RSA, Blowfish, MD5
Blessed Are The Lawyers, For They Shall Inherit Cybersecurity

This paper considers which types of evidence guide cybersecurity decisions. We argue that the “InfoSec belongs to the quants” paradigm will not be realised despite its normative appeal. In terms of progress to date, we find few empirical results that can guide risk mitigation decisions. We suggest the knowledge base about quantitative cybersecurity is continually eroded by increasing complexity, technological flux, and strategic adversaries. Given these secular forces will not abate any time soon, we argue that legal reasoning will increasingly influence cybersecurity decisions relative to technical and quantitative reasoning. The law as a system of social control bristles with ambiguity and so legal mechanisms exist to resolve uncertainties over time. Actors with greater claims to authority over this knowledge base, predominantly lawyers, will accrue decision making power within organisations. We speculate about the downstream impacts of lawyers inheriting cybersecurity, and also sketch the limits of the paradigm’s explanatory power.
Big Seismic Data Visualization on Cloud: A Case Study Collaborating with Industry

Scientific and industrial data have grown too big to efficiently process and visualize using the current desktop solution.Cloud computing becomes a feasible solution to tackle the big data challenge.However, how to efficiently visualize big data sets, such as the oil and gas industry seismic volume, on the cloud remotely remains a challenge.It is not feasible to send big data back to the client side to visualize them.The limitations of Internet latency and bandwidth require all data rendering and processing are carried on the cloud side in order to achieve real-time big data visualization.The paper presents a collaborative work with Thermo Fisher Scientific, our industry partner, to design and implement a cloud-based visualization software system toward to providing a feasible solution for the big data visualization.Our system comprises of four parts including a scalable seismic data processing package, a data communication library, a data visualization module, and a web portal.The work builds on top of open source packages including Apache Spark, Hadoop, and a commercial visualization package named Open Inventor 3D Visualization toolkit from the Thermo Fisher Scientific for rendering images.The paper outlines the bricking data format and the level-of-details algorithm used to make it feasible to visualize big data sets near real-time.The work demonstrates the superior performance to the prior work using the same data sets.We implemented this work on the PVAMU big data cloud platform using large seismic volume data to evaluate the performance.
Impact of Virtual Reality on Healthcare Provider Empathy for Older Adults With Sensory Impairment

Abstract Virtual reality (VR) is an innovative technology that can simulate dual sensory impairment so that healthcare providers and others can experience this affliction common in older adults. This study investigated whether VR simulation could increase empathy among healthcare workers. Healthcare providers experienced a 7-minute scenario from the viewpoint of “Alfred”, a 74-year-old with macular degeneration and high frequency hearing loss on a commercial VR headset (Oculus Rift). Using a one-group pre/post-test study design, we measured knowledge, changes in empathy, and assessed participants’ self-reported behavior change. Results showed that participants increased their knowledge and that 9 of 14 empathy items had statistically significant increases. Additionally, 97% of participants agreed or strongly agreed that they would utilize the information learned in their work with patients. In conclusion, evidence suggests VR is an effective intervention to increase empathy and positively change behavior to support persons with sensory impairment.
The Austrian EOSC Mandated Organisation / The EOSC Support Office Austria

This report offers some reflections for a better understanding of the realization of the European Open Science Cloud (EOSC) at a local level, describing the implementation of the newly established Austrian EOSC Mandated Organisation and the EOSC Support Office Austria.
Firefly Optimization Based Noise Additive Privacy-Preserving Data Classification Technique to Predict Chronic Kidney Disease

With the continuous advancements in Information and Communication Technology, healthcare data is stored in the electronic forms and accessed remotely according to the requirements. However, there is a negative impact like unauthorized access, misuse, stealing of the data, which violates the privacy concern of patients. Sensitive information, if not protected, can become the basis for linkage attacks. Paper proposes an improved Privacy-Preserving Data Classification System for Chronic Kidney Disease dataset. Focus of the work is to predict the disease of patients’ while preventing the privacy breach of their sensitive information. To accomplish this goal, a metaheuristic Firefly Optimization Algorithm (FOA) is deployed for random noise generation (instead of fixed noise) and this noise is added to the least significant bits of sensitive data. Then, random forest classifier is applied on both original and perturbed dataset to predict the disease. Even after perturbation, technique preserves required significance of prediction results by maintaining the balance between utility and security of data. In order to validate the results, proposed method is compared with the existing technology on the basis of various evaluation parameters. Results show that proposed technique is suitable for healthcare applications where both privacy protection and accurate prediction are necessary conditions.
Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cantconsequences in coastal operations, such as naval engineering and navigation. Conventional sealevel predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elementsand require long-term historical sea level data. In this paper, two deep learning approachesare applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), andthe second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (InexpensiveDevice for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained themodel for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1stMay to 1st August. We compared forecasting results with RNN and LSTM with the results of theconventional method, namely tidal harmonic analysis. The LSTM’s results showed better performancethan the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 andan RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodatenon-tidal harmonic data such as sea level anomalies.
Development of the blockchain architecture of the Industrial Internet of Things system of the enterprise

The development of the digital economy in the modern world requires solving the issue of security of Industrial Internet of Things (IIoT) applications. A large number of distributed, network-based, IIoT devices managed by intelligent programs (software agents) require protection. A successful attack on any IIoT device will lead to hacking of the IIoT application and to large financial losses, as well as to the termination of the IIoT application, therefore, the research topic is relevant. The purpose of this article is to radically solve the security problem of the IIoT application by developing a blockchain architecture of the application. The authors were tasked with investigating all aspects of the blockchain system that ensure the security of IIoT application devices. The peculiarity of the blockchain system is that its participants are software agents that control the application devices. As a result of the research, the concept of the blockchain architecture of the IIoT application is proposed. He mechanisms of consensus of intelligent programs of IIoT devices as equal active participants of the blockchain network are investigated. The consensus mechanism and the cryptographic system of the distributed registry of the blockchain network increase the information security of the IIoT application. The synergistic effect of the blockchain system and intelligent systems of software agents of IIoT application devices significantly increases the efficiency of the solution. Intelligent systems of software agents and IIoT applications are effectively trained on the blockchain platform, and as a result, we get a decentralized supercomputer in the form of a blockchain system.
Modern University As A Driver Of Growth Of The Regional Digital Economy

In education, as in many other spheres of life, significant changes are observed as a response to external threats caused by the processes of economic integration and global digitalization of society, new technologies and innovations, new thinking and a new attitude towards the ongoing changes.The economy and society as a whole need cadres -thinking and competent, capable of continuous renewal and reasonable change of their competences and types of activity.This requires progressive teaching methods, practical skills and professional competencies to feel free in the digital world.All this can be clearly seen in modern life and, in turn, leads to a change in the philosophy of modern education for a successful and stable future in the Russian digital economy.In this regard, the university is also changing its role -becoming not so much an educational organization as a full-fledged actor of economic space, an active subject of territorial development.A modern university occupies a leading position not only in the regional educational sphere, but also in the region as a whole, setting the main trends of its industrial, innovative, and cultural development.
The Science Teachers’ Optimism Response to the Use of Marker-Based Augmented Reality in the Global Warming Issue

During the COVID-19 pandemic, almost all regions of Indonesia, including Sikka, East Nusa Tenggara, and Indonesia, have implemented e-learning. However, this method has not been conducted in all schools due to the problem of uneven Internet access. Disconnection from the Internet makes it difficult to conduct this process effectively even though students have smartphones. Furthermore, the expectation of education quality improvement in sciences also gets higher with the development of today’s world technology, and this cannot be avoided. Augmented reality (AR) gives a variety of opportunities to be utilized as science learning media embedded on smartphones even without an Internet connection. Therefore, this study aims at conducting an intervention towards the teachers through the training and workshops on the use of augmented reality for science learning on the topic of Global Warming. This research was an action research approach. While considering this problem, the intervention of teachers’ behavior and perception was conducted through training and workshops on the use of augmented reality for science learning using a one-shot case study research design. This program had four stages: need identification, strengthening the understanding of using AR, training and workshop implementation, and evaluation. The participants of this program were 24 science teachers from 10 schools at junior high schools in Sikka Regency, Indonesia (17 females, 7 males). The marker-based AR was developed based on learning media need assessment provided by teachers. Teacher optimism was measured using a questionnaire with a Likert scale. The program’s implementation led teachers to understand the use of AR in learning, significantly to develop HOTs (higher-order thinking skills) in science learning. After experiencing training and workshops, the teachers showed high optimism to use AR in science learning. The results of this study imply for the development of school policies to establish digital learning media used without the Internet on various learning issues in rural areas.
Deep Learning Technology Based on Intelligent Teaching in Social Psychology Courses

Social psychology is a comprehensive course which is an interdisciplinary of sociology and psychology. Most colleges and universities have currently taken social psychology as a general course to popularize psychological knowledge to students, which has great significance to cultivating their quality. Previous teaching did not combine this course with social needs, the optimization of students’ psychological needs and enhancement of their abilities to solve problems, on the contrary, pure theoretical interpretation was the focus, resulting in students’ lacking of proactivity. Therefore, a new teaching mode for improvement is urgent. Based on deep learning theory and comprehensively taking teaching environment, teaching resources and technical strategy support into consideration, and intelligence as the core, the paper has designed an intelligent teaching mode including intelligent preview, intelligent classroom, intelligent promotion and intelligent evaluation. Meanwhile, combined with courses of social psychology, video, audio and text teaching resources and multimedia courseware, multimedia resources were integrated, and problem discussion-based collaborative learning was adopted for teaching. To accurately evaluate the teaching effect under the intelligent teaching environment, guided by the appeal of intelligent teaching and based on analysis of accurate teaching objectives, the paper has established an accurate teaching evaluation index system, aiming at providing an idea for "How to evaluate the effect of accurate teaching in the intelligent teaching environment". At last, the paper has applied the new teaching mode into the practice of social psychology, finding that the new mode can stimulate students’ proactivity, improve their performance, which is worth promotion.