Reviewerly/exHarmony huggingface dataset:
Closed-Loop Residual Attention Network for Single Image Super-Resolution

Recent research on single image super-resolution (SISR) using convolutional neural networks (CNNs) with the utilization of residual structures and attention mechanisms to utilize image features has demonstrated excellent performance. However, previous SISR techniques mainly integrated extracted image features within a deep or wide network architecture, ignoring the interaction between multiscale features and the diversity of features. At the same time, SISR is also a typical ill-posed problem in that it allows for several predictions for a given LR image. These problems limit the great learning ability of CNNs. To solve these problems, we propose a closed-loop residual attention network (CLRAN) to extract and interact with all the available diversity of features features efficiently and limit the space of possible function solutions. Specifically, we design an enhanced residual attention block (ERA) to extract features, and it dynamically assigns weight to the internal attention branches. The ERA combines multi-scale block (MSB) and enhanced attention mechanism (EAM) base on the residual module. The MSB adaptively detects multiscale image features of different scales by using different 3 × 3 convolution kernels. The EAM combines multi-spectral channel attention (MSCA) and spatial attention (SA). Therefore, the EAM extracts different frequency component information and spatial information to utilize the diversity features. Furthermore, we apply the progressive network architecture and learn an additional map for model monitoring, which forms a closed-loop with the mapping already learned by the LR to HR function. Extensive experiments demonstrate that our CLRAN outperforms the state-of-the-art SISR methods on public datasets for both ×4 and ×8, proving its accuracy and visual perception.
Anticheat System Based on Reinforcement Learning Agents in Unity

Game cheating is a common occurrence that may degrade the experience of “honest” players. It can be hindered by using appropriate anticheat systems, which are being considered as a subset of security-related issues. In this paper, we implement and test an anticheat system whose main goal is to help differentiate human players from AI players. For this purpose, we first developed a multiplayer game inside game engine Unity that would serve as a framework for training the reinforcement learning agent. This agent would thus learn to differentiate human players from bots within the game. We implemented the Machine Learning Agents Toolkit library, which uses the proximal policy optimization algorithm. AI players are implemented using state machines, and perform certain actions depending on which condition is satisfied. Two experiments were carried out for testing the agent and showed promising results for identifying artificial players.
Red Blood Cells Detecting Depending on Binary Conversion at Multi Threshold Values

Automatic detection of Red Blood Cells (RBCs) by using image-processing techniques is important in determining blood disorders and diseases. This study proposes an algorithm for automatic detection of red blood cells in optical microscopy images of blood smears. The proposed method depends on binary conversion at multi-threshold and includes a morphological operation as erosion and image fill according to certain conditions. In this study, we used 50 images from IDB data of blood samples taken with an optical microscope. The suggested method is compared with other modern techniques based on the two accuracy coefficients, namely, detection and false alarm rates. Results show that the proposed method has a high detection accuracy compared to other methods.
Providing A Hands-on Advanced Persistent Threat Learning Experience Through Ethical Hacking Labs

Advanced persistent threats are causing several serious cybersecurity events due to their highly stealthy characteristics, advanced technology and tools, and complicated attacking strategies, making them an imminent challenge to cybersecurity professionals. To conquer such a challenge, a thorough and dedicated defense plan must be addressed, and we believe engaging advanced persistent threat learning experiences to computer science and cybersecurity students in the early stages of their college education will be the most important part of the plan. Since there is a lack of promising approaches for engaging students in learning of advanced persistent threats, it is now an emerging issue for cybersecurity educators and researchers to investigate and develop doable and affordable advanced persistent threat learning platforms. Hands-on learning has been adopted by several fields and demonstrated promising performance improvements in the learners. Therefore, integrating hands-on learning knowledge and experiences in advanced persistent threat training for computer science and cybersecurity students will be a potential solution for mitigating such an issue. In this research, we recognize the importance of improving students’ learning of advanced persistent threats. To develop a learning platform for students to learn the knowledge, skills, and abilities of advanced persistent threats, we adopt the NDG ethical hacking lab series with appropriate supplemental lectures to each stage of the lifecycle of an advanced persistent threat. We ensure our model could comply with the required knowledge units listed on NICE Cybersecurity Workforce Framework. Students are expected to connect their advanced persistent threat learning experiences to real world cybercrime cases once they have successfully completed the learning process.
Improving Machine Translation Quality with Denoising Autoencoder and Pre-Ordering

The problems in machine translation are related to the characteristics of a family of languages, especially syntactic divergences between languages. In the translation task, having both source and target languages in the same language family is a luxury that cannot be relied upon. The trained models for the task must overcome such differences either through manual augmentations or automatically inferred capacity built into the model design. In this work, we investigated the impact of multiple methods of differing word orders during translation and further experimented in assimilating the source languages syntax to the target word order using pre-ordering. We focused on the field of extremely low-resource scenarios. We also conducted experiments on practical data augmentation techniques that support the reordering capacity of the models through varying the target objectives, adding the secondary goal of removing noises or reordering broken input sequences. In particular, we propose methods to improve translat on quality with the denoising autoencoder in Neural Machine Translation (NMT) and pre-ordering method in Phrase-based Statistical Machine Translation (PBSMT). The experiments with a number of English-Vietnamese pairs show the improvement in BLEU scores as compared to both the NMT and SMT systems.
The Challenges of Digitalization in the Public Sector: Cloud Computing

Information technology is a reality of our days, and the future means digitalization.The public sector cannot be absent from this natural evolution of things.Cloud computing technology is defined as a service that uses cloud accounting software available to users and can be accessed from anywhere and does not require hardware, and its development brings a new evolution in the information-accounting system: cloud accounting.This article focuses on the implementation of cloud computing in the public sector, and qualitative research highlights the benefits and challenges of integrating this technology in the public domain.The result of the study can be a starting point for the implementation of cloud computing technology in the public sector.
Anomaly Detection using Autoencoder with Gramian Angular Summation Field in Multivariate Time Series Data

Uncertainty is ubiquitous in data and constitutes a challenge in real-life data analysis applications.To deal with this challenge, we propose a novel method for detecting anomalies in time series data based on the Autoencoder method, which encodes a multivariate time series as images by means of the Gramian Angular Summation Field (GASF).Multivariate time series data is represented as 2D image data to enhance the performance of anomaly detection.The proposed method is validated with four time-series data sets.Experimental results show that our proposed method can improve validity and accuracy on all criteria.Therefore, effective anomaly detection in multivariate time series data can be achieved by combining the methods of Autoencoder and Gramian Angular Summation Field.
Academic perception of barriers to the adoption of technological innovations during the covid-19 pandemic

The objective of this research was to identify barriers to the adoption of a technological innovation perceived by professors at a public university in southeastern Mexico, in which a strategy for the use of virtual classrooms was implemented as a measure to address the social distancing forced by the covid-19 pandemic. A descriptive, non-experimental, cross-sectional, quantitative descriptive study was carried out in which two groups of incident factors were considered: intrinsic (attitudes and beliefs; knowledge and skills) and extrinsic (available resources and educational system). A questionnaire with 30 items was designed and measured with a Likert-type scale and applied to 805 teachers. The results showed that teachers perceive extrinsic barriers as the most important in adopting technological innovations, especially those related to equipment and connectivity, while intrinsic barriers are are mainly focused on the beliefs, so the early adopters and early majority profiles are distinguished.
The Development of Mobile Augmented Reality: 3-Dimensional Video

Augmented reality has become one of the learning processes applicable through mobile devices, called Mobile Augmented Reality (MAR). Learning and service experiences can be enhanced using mobile devices such as those offered by MAR, an innovative learning space providing active interaction of superimposing digital content into the real world. Previous studies have discussed the importance of the MAR system in various domains, especially in education, as it led students to achieve higher levels of engagement in their learning process. However, there are limitations in highlighting the importance of the development process for MAR. Therefore, this present study describes the development process of MAR through the AREM model, which aimed to exhibit the systematic guidelines in producing a practicable MAR. It also highlights the preferable learning content and media component based on the respondents’ responses in the survey questionnaire. Data gathered from questionnaires were analysed by using descriptive statistics. The guideline in developing MAR can benefit students and educators in producing a MAR for learning purposes.
Peran Sistem Informasi Manajemen dalam Pengambilan Keputusan di Madrasah Ibtidaiyah Darussalam Pacet Mojokerto

This article aims to describe: 1) explain the management of the Management Information System, 2) explain the decision making based on data in the Management information system. This research is a qualitative descriptive study. Data was obtained through observation, interviews, and documentation. All collected data were then analyzed using data in a few stages from reducing data, presenting data, to drawing evaluation conclusions. Research informants were the Headmaster of MI Darussalam, the School operator, and Teachers. The results showed that. The Role of Management Information System in Decision Making in MI Darussalam: (1) The components in the Management Information System has been fulfilled, (2) management of management information systems centered on two people, (3) The flow of MIS management in MI Darussalam is the process of data input, data processing, and data storage, (4) The head of the Madrasah puts forward the principle of deliberation to make decisions. (5) The Management Information System is used as a database for making decisions and making program activities. (6) There is a Decisive Support System (DSS) that is used, but it is still constrained.
Lightweight ANU-II block cipher on field programmable gate array

Nowadays the number of embedded devices communicating over a network is increasing. Thus, the need for security appeared. Considering various constraints for the limited resources devices is very important. These constraints include power, memory, area and latency. A perfect environment for satisfying requirements of security in limited resources devices is lightweight cryptography. A recent lightweight algorithm that has a low area and high throughput which is the ANU-II block cipher. Many technologies like the internet of things (IoT) needed lightweight hardware architectures to provide security for it. In IoT issues like the size of memory, power consumption and smaller gate counts need to take care of by using lightweight cryptography. This paper presents hardware lightweight data path implementation for the ANU-II algorithm using field programmable gate array (FPGA). This paper presents a hardware implementation of a 64-bit ANU-II block cipher. Also, this research presents comparisons based on various design metrics among our data path for the ANU-II cipher and other existing data path designs. The result of the proposed design shows a high throughput of 1502.31, 1951.86, and 2696.47 Mbps. Also, it shows the high efficiency of 7.0201, 31.9977, and 10.6579 Mbps/slice as compared to other ciphers in this paper.
Hubungan Pergaulan Teman Sebaya dengan Prestasi Belajar Siswa Kelas IV SD

The problem in this study is that grade IV students have a high intensity of meeting with friends because most students have a distance of residence that is not far from one another. So that students tend to spend time gathering together in a place where there is wifi that makes it easier for them to play online gamesand access various sites that are still not suitable for the development of students' ages. Activities carried out by these students, of course, can hinder the learning process. This research is a quantitative research with correlational type which aims to determine the relationship between peers and the learning achievement of fourth graders of SD Cluster IV. The results of the study show that there is a significant relationship between peer association and student achievement fourth grade Cluster IV with a value of t arithmetic (3.794) is greater (&gt;) than t table (1.66757) which means H0 is rejected and H1 is accepted. Based on the analysis of the data, it can be concluded that peer association can affect student achievement. So, the better the peer association carried out by students, the better the learning achievement obtained by students. From these conclusions, the impact resulting from this study is known that good peer relationships will improve student achievement.
The Use of ICTs in Teaching at the Normal Superior School “Profr. Moisés Sáenz Garza”: An Exploratory Study

The use of ICTs in the classroom is studied in a descriptive exploratory way to favor the professional training process in the Normal Superior School “Profr. Moisés Sáenz Garza” based on the 2018 study plan. A survey was applied to teachers of eight specialties from different semesters. The questionnaire evaluated the frequency dimensions of the use of ICTs, the use of technological resources in teaching, the teacher’s communication process with students, and the usefulness of ICTs in class for teachers. The main results express that the use of ICTs is systematic and conceived as permanent support for the training path. The main means of teacher-student communication are email and the Moodle educational platform, while the most common activities carried out on the platform are exams, guidelines and forums. The established teachers agree in accepting and recognizing the need for ICTs to make the teaching-learning process more efficient. In general, the use of ICTs in the school studied is satisfactory, which is explained by the fact that the institution has the necessary technological infrastructure for academic activity.
Itinerario escolar de la pandemia. Doble recuento personal de los efectos de la enseñanza distante durante el encierro provocado por el covid-19

This is a testimony of the impact caused by the Covid-19 pandemic in educational and family settings and affairs, from the perspective of the teacher and the student: lockdown, distance learning, the isolation effects on family and school health. Was it a general deterioration/ cognitive impairment or a challenge to learn in some other way?
Entropy Analysis for Modbus Traffic over TCP/IP in Industrial Control Systems

Anomalies in network traffic are usually detected by measuring unexpected deviation from what constitutes a baseline. Several statistical techniques have been proposed to create baselines and measure deviation. However, simply looking at traffic volume to find anomalous deviation may result in increased false positives. Traffic feature distributions need to be created, and deviations need to be measured for these features. An effective approach to finding anomalous deviations starts with entropy analysis on these features. In this paper, we presented an initial entropy analysis on an industrial control system network using selected features with datasets obtained from an HVAC system. We started with the fundamental question: whether a preliminary entropy analysis on Modbus-over-TCP data using only a few TCP/IP features without going into the Modbus traffic itself gives us information about an anomaly in the network. We acknowledge that the initial entropy analysis provides only a starting point that would lead to several questions and investigating relevant issues resulting in an optimal system design and implementation. *
ENHANCING STUDENTS’ LISTENING SKILL THROUGH QUIZIZZ AUDIO FEATURE IN ONLINE LEARNING AT JIARAWANON-UTIS 4 SCHOOL

The most challenging skill for EFL learners, especially in online learning is listening. As a result, an effective teaching tool is required to improve student's listening skills and motivation. This research aim is to describe how second graders students at Jiarawanon-Utis 4 School in Loei Province, Thailand used the Quizizz Audio Feature to enhance their listening skills. Eight students from the first semester of the school year 2021-2022 took part in this study. CAR (Classroom Action Research) was conducted in this study divided into two cycles. Each cycle consists of three meetings and four steps; planning, action, observation, and reflection. Data collection were collected from listening test, quizziz, observation, and questionnaires. Then triangulation is needed to evaluate the data. The result showed that there was an improvement in students’ listening skills. It is proven from the result of the average of both cycles. The average is 70,81% in the first cycle and 90,24% in the second cycle, so the enhancement is 19,43%. Furthermore, the findings of the questionnaires revealed that students gave excellent feedback regarding the use of the Quizizz Audio Feature to enhance their listening skills, as evidenced by their responses to the survey
Exploring Coherence among Sri Lankan CFL Learners in Chinese-English Translation: Decoding and Interpreting of Culture-loaded Content

Modern translation has shifted from its traditional approach of merely translating linguistic features towards a more culturally sensitive approach, which further considers the sociolinguistic spaces of the source text in producing the target text. This significant theoretical transformation has demanded the translator to play a more comprehensive role, which involves intercultural and sociolinguistic competencies apart from language competence. Subsequently, maintaining cohesion and coherence within and among texts also demands a heavier effort from the translator with neologisms being created frequently. The present study has been an attempt to identify the core issues pertaining to lexical coherence in translating from Chinese sources in Sri Lankan context using sample texts translated by Sri Lankan CFL (Chinese as a Foreign Language) learners. 20 culture-loaded lexical items were examined for coherence using a plagiarism checker and compared with Google and Baidu translation outputs of the source text. The results manifest that there is significant coherence among the target texts but coherence is a result of excessive use of machine translation, especially Google translate(?=94.67). 5-10 clusters were the most frequent clusters with an average of 21.73 clusters. &lt; 10 word clusters have recorded an average of 19.07 whereas&gt; 5 word clusters have recorded an average of 12.60.The highest number of clusters is recorded in text 5 with 44 clusters of &lt;10 words. The highest number of translated variants of the same cultural term of the selected word list (n=20) is 4 while 70% (n=14) of the selected terms were only translated into either 1 or 2 variants. While machine translation is perceived as a constructive instrument under proper management, vigilant use of footnotes and end notes is perceived as a viable solution for reducing misinterpretation, vagueness and confusion in translating culture-loaded lexical items.
Reform of University English Teaching and Examination Based on Stratified Teaching Method and Mobile Computing

Teachers should change the traditional mode of teaching with the same content, schedule, and method, take note of individual differences among university students, implement hierarchical teaching, and use mobile computing platforms to improve teaching quality, in light of the “polarization” phenomenon that university students are currently experiencing in English learning. The hierarchical teaching mode, as a new type of teaching mode, has the potential to improve the quality and efficiency of English reading instruction in junior high schools. The stratified teaching method, as a teaching mode that can effectively demonstrate the principles of teaching university students according to their intelligence and graded guidance, plays a very important role in improving university students’ English learning level and learning enthusiasm. Teachers must abandon the traditional teaching mode, actively apply the hierarchical teaching method to English reading instruction, and design teaching content that is reasonable in relation to the physical realities of university students in order to contribute to the improvement of junior high school English instruction and the reading ability of university students. To continue to adapt to the development and needs of today’s society, university English teaching should implement hierarchical teaching, cultivate university students’ interest in learning in a variety of ways, and constantly strengthen the exploration and implementation of classroom teaching models.