Reviewerly/exHarmony huggingface dataset:
Improving the Quality of Education in the Development of Algorithmic and Critical Thinking of Students of “Applied Informatics” – Case Study

Improving the quality of education is not an easy task, and we believe that it is strongly linkedto student motivation. Student motivation is at the centre of events because, without students’internal motivation, it is not possible to effectively achieve educational goals and, ultimately,positive educational results. For this reason, it is necessary to modify the method of educationwith regard to the current needs of students, and as we demonstrate in the article, it is necessaryto continuously control, evaluate and reflect on changes. The introduction of a complexheterogeneous system of education that takes into account the different learning styles ofstudents as well as their individual characteristics proves to be an effective tool. Together witha comprehensive coverage of the issue based on the mediation of materials in various formsof processing, it appears to be a solution.
Meningkatkan Motivasi dan Hasil Belajar Siswa pada Mata Pelajaran IPS melalui Model Pembelajaran Berbasis Proyek

The low motivation and learning outcomes of students in social studies subjects at Kanisius Gayam I Elementary School challenge teachers to develop innovative approaches related to models, methods, media, learning resources, and evaluation. This study aims to describe the implementation of a project-based learning model to enhance motivation and improve learning outcomes in grade V social studies at Kanisius Gayam I Elementary School. This study falls under the category of Classroom Action Research and involves thirty fifth-grade students at Kanisius Gayam I Elementary School during the 2022/2023 school year. The research focuses on assessing the impact of the project-based learning model on motivation and learning outcomes in social studies. The research employed various instruments, including observation sheets, student learning motivation questionnaires, and tests. Data analysis involved both quantitative and descriptive qualitative methods. The results of the study are as follows: 1) Efforts to enhance motivation and learning outcomes in fifth-grade students at Kanisius Gayam I Elementary School were made through the implementation of a project-based learning model. The steps involved fundamental questions, project planning, schedule preparation, project activeness and development monitoring, testing results, project evaluation, and project outcomes. 2) The study noted an increase in learning motivation from an initial average of 65.66 to 76.26 in cycle I and a further increase to 83.66 in cycle II. 3) Learning outcomes also showed improvement, with an initial average of 54.82 rising to 62 in cycle I and further increasing to 83.66 in cycle II.
Pengembangan Game Edukasi Etnomatematika Tenun Ikat Bandar pada Materi Transformasi

This paper is a Research and Development to make an educational game learning called Puzzle TEKAD (Tenun Ikat Bandar), which is aimed at transforming material for 9th grade students. The software developed using Construct 2 by applying the ADDIE development model which includes 5 main stages, namely Analysis, Design, Development, Implementation, and Evaluation. The tools used in this paper include expert verification forms, practitioner questionnaires, black box tests, student response questionnaires, and student assessment forms. The results show that the product validity is in a very good category, with an average score of 4.95 for media experts, 4.15 for materials experts, and 4.66 for field practitioners. Based on group experiments, educational games fall into the utility category, as all aspects of functional testing using black-box testing work well. The product effectiveness aspect falls under the effective category based on student assessment results, which shows that all scores are higher than KKM. It can be concluded that the TEKAD educative game meets the aspects of feasibility, practicality, and effectiveness in teaching transform materials in 9 school grade.
Methodology for Assessing the Digital Transformation of Libraries

Due to the paucity of comprehensive studies in the field of library digitalization, a brief description of the methodology for assessing the digital transformation (DT) of libraries is presented. The problem is considered according to various components. Domestic and foreign approaches to the study of the level of DT are described. The stages of assessing the level of digital maturity of libraries (analysis of the socio-professional background of digital maturity, functional benchmarking of digital maturity of specific libraries, intellectual data analysis and replication of best practices, data-driven sectoral management with the use of artificial intelligence and revision of regulations) are substantiated. The levels of digital maturity of libraries (readiness for DT, activity of DT, innovativeness of DT) have been defined. The aim of the study is to develop a methodology for comprehensive assessment of libraries’ digital maturity at the general and corporate levels, and to propose tools for assessing the level of digital maturity of specific libraries. Recommendations for processing the results obtained using modern software and hardware tools are given. For the first time a formula for quantitative assessment of digital maturity of libraries using indices is proposed. The presented variant of the matrix for assessing digital maturity of technological processes includes the main library processes as components of digital maturity and expands the understanding of functional benchmarking aimed at comparing the results of digital maturity of specific libraries. Based on the study, the following conclusions are drawn. 1. To manage DT, it is necessary to organize continuous monitoring of digital maturity of libraries, i.e. their readiness for DT, activity of DT, innovativeness of DT expressed in criteria and indicators agreed with the professional community. 2. The results of monitoring should become an important component of management decision-making based on digital data (data-driven approach) and forecasting of both positive and negative trends as a result of intelligent data analysis. 3. The analysis of the digital maturity level of libraries will make it possible to create appropriate rankings, as well as dashboards (interactive visualisation dashboards), which have increased visibility, decision-making power and motivate libraries to achieve goals in DT.
Design and Development of Multidimensional Chaotic Maps with Genetic Operator

Security of a digital image can be achieved in number of ways including image Encryption and Decryption. Encryption technique tries to convert a plain image into the cipher image which is hard to understand. The decryption technique tries to convert a cipher image into the plain image. The image encryption is done inorder to provide security from attacks. In this work we aim to develop an image encryption technique based on multidimensional chaotic map with genetic operator. One of the powerful features of genetic operator is crossover which is used to confuse the pixels of the image. A combination of multidimensional chaotic maps, namely, Logistic, Henon and Chebyshev will be used to generate pseudorandom sequence which will be XOR'ed to obtain an unpredictable sequence. This sequence will be then applied to the Crossover unit and upon XOR'ed to obtain an encrypted image. Later the same upredictable sequence is generated while decrypting the image. By combining the entire different dimensional chaotic map namely Logistic, Henon, Chebyshev map along with the gentic operator called crossover will enhance the extra security to the digital image.
Sentiment analysis model for Airline customers’ feedback using deep learning techniques

Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.
Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs

We developed a new approach comprised of different visualizations for the comparative spatio-temporal analysis of displacement processes in porous media. We aim to analyze and compare ensemble datasets from experiments to gain insight into the influence of different parameters on fluid flow. To capture the displacement of a defending fluid by an invading fluid, we first condense an input image series to a single time map. From this map, we generate a spatio-temporal flow graph covering the whole process. This graph is further simplified to only reflect topological changes in the movement of the invading fluid. Our interactive tools allow the visual analysis of these processes by visualizing the graph structure and the context of the experimental setup, as well as by providing charts for multiple metrics. We apply our approach to analyze and compare ensemble datasets jointly with domain experts, where we vary either fluid properties or the solid structure of the porous medium. We finally report the generated insights from the domain experts and discuss our contribution's advantages, generality, and limitations
Novel Optimized Implementations of Lightweight Cryptographic S-Boxes via SAT Solvers

An optimized implementation of S-boxes has a significant impact on the performance of cryptographic primitives. SAT-based methods can find optimal implementations for moderately sized S-boxes but their efficiency decreases when handling complex S-boxes. To improve the efficiency of the implementations, we propose two different methods, namely OR-encoding and IF-encoding, to encode the implementations of S-boxes. Furthermore, we also simplify the encoding of the outputs of logic gates and introduce new SAT-based search methods to optimize the implementations of S-boxes. Finally, to get a better trade-off between the search results (optimized implementations of S-boxes) and the search efficiency (in terms of time complexity), an encoding scheme using local solutions is proposed. Compared to the previous methods, our algorithms are relatively simple and more efficient. For instance, when a serial software implementation is considered, then the S-boxes of Sycon, ASCON, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi$</tex-math> </inline-formula> function in Xoodyak, require 6, 1, and 2 fewer programming instructions, respectively, than the best known methods. Similar improvements are obtained for hardware implementations of S-boxes in some cryptographic primitives (e.g. LBlock, RECTANGLE, PRESENT/PHOTON-Beetle, TWINE, and ASCON), with the saving of gate equivalent (GE) that range from 1.67GE to 5.34GE compared to the current best implementations. Furthermore, our model can be applied to 6-bit, 7-bit, and 8-bit S-boxes, when the considered S-boxes are of low complexity.
Peningkatan Kualitas Masyarakat Melalui Penerapan “Say CHSE” Dan Pembuatan Masker Tie Dye Di Era Covid-19, Kampung Situ Bolang

Kampung Wisata Situ Bolang is a community-based tourism area. In the past, Situ Bolang was a natural lake that became a tourist destination for community. Unfortunately, due to the impact of the toll road construction, the springs are blocked so there is no supply of lake water; the lake is converted into rice fields; now the community has lost their source of clean water; The occurrence of land conversion for housing, road access, and industry has changed the habit pattern of the farming community into unskilled labour. Behind the irony, it turns out that there is an opportunity that can be developed which is currently guarded by the community, namely Martial Arts of Nampon style. Nampon martial arts being so special because the first students are Ir. Soekarno (the first President of the Republic of Indonesia) and Moch. Natsir. In addition to martial arts, there are several people who like to paint, even the paintings have been marketed to the ASEAN region. As an effort to preserve community-based culture in the era of the COVID-19 Pandemic, it is necessary to increase community capacity through training in the application of the Cleanliness, Health, and Environmental Sustainability (CHSE) concept in the Situ Bolang Tourism Area, otherwise to push the potential of people who like to paint, it can be utilized in making painted masks/tie dye motifs as an alternative souvenirs from Situ Bolang.&#x0D; Keywords: Community Enhancement; Situ Bolang; Nampon Martial Arts; CHSE; Tie Dye Masks
Demonstrating SurfaceCast: Ubiquitous, Cross-Device Surface Sharing

Real-time online interaction is the norm today. Tabletops and other dedicated interactive surface devices with direct input and tangible interaction can enhance remote collaboration, and open up new interaction scenarios based on mixed physical/virtual components. However, they are only available to a small subset of users, as they usually require identical bespoke hardware for every participant, are complex to setup, and need custom scenario-specific applications. We present SurfaceCast, a software toolkit designed to merge multiple distributed, heterogeneous end-user devices into a single, shared mixed-reality surface. Supported devices include regular desktop and laptop computers, tablets, and mixed-reality headsets, as well as projector-camera setups and dedicated interactive tabletop systems. This device-agnostic approach provides a fundamental building block for exploration of a far wider range of usage scenarios than previously feasible, including future clients using our provided API.
Noise filter with hyperparameter recommendation: a meta-learning approach

Applying Machine Learning (ML) algorithms to a dataset can be time-consuming. It usually involves, not only selecting and fine-tuning the algorithm, but also other steps, such as data preprocessing. To reduce this time, the whole or a subset of this process has been automated by Automated ML (AutoML) techniques, which can include Bayesian Optimization, Genetic Programming, and Meta-Learning techniques. However, despite it often being a necessary stage, preprocessing is commonly not well handled in AutoML tools. In this work, we propose and experimentally investigate the use of meta-learning to recommend noise detection algorithms and the values for their hyperparameters. The proposed approach produces a ranking of the best noise filters for a given dataset, reducing the development cost of ML-based solutions and improving their predictive performance. To validate the process, we generated 10740 noisy datasets, which we describe using 97 meta-features. For each dataset, we applied 8 noise filters, which increased to 27 when we added variations of hyperparameter values. Next, we applied 4 ML algorithms to this data and created a performance ranking, which we used as a meta-target to induce 3 meta-regressors. We compared these 3 meta-regressors and the results with and without hyperparameters for the noise filters. According to the experimental results, the introduction of hyperparameter recommendation resulted in a higher gain in the F1-Score performance metric. However, it came at the cost of lower accuracy in the Top-K ranking evaluation.
Domestic Cyberterrorism &amp;amp; Strategic Communications: Literature Review

Cyberterrorism poses a significant threat to the national security of the United States of America (USA), with critical infrastructure, such as commercial facilities, dams, emergency services, food and agriculture, healthcare and public health, and transportation systems virtually at risk. Consequently, this is due primarily to the country’s heavy dependence on computer networks. With both domestic and international terrorists increasingly targeting any vulnerabilities in computer systems and networks, information sharing among security agencies has become critical. Cyberterrorism can be regarded as the purest form of information warfare. This literature review examines cyberterrorism and strategic communications, focusing on domestic cyberterrorism. Notable themes include the meaning of cyberterrorism, how cyberterrorism differs from cybercrime, and the threat posed by cyberterrorism to the USA. Prevention and deterrence of cyberterrorism through information sharing and legislation are also key themes. Finally, gaps in knowledge are identified, and questions warranting additional research are outlined.
Application of above &amp; below the water 3D measurement technology in reservoir capacity curve calibration

The reservoir capacity curve is a key quantity for the safe operation of reservoirs, and the reservoir capacity may change during the operation of reservoirs due to various factors, so timely calibration is required. The topography of the reservoir area is measured in three dimensions using an unmanned aircraft and an unmanned boat, respectively. The data results establish a digital elevation model (DEM) of the reservoir area, and the DEM square grid method is used to calculate the reservoir capacity, thus realising the calibration of the reservoir capacity curve. The technical method was successfully applied in Lujiayao Reservoir with high accuracy of results.
Enhancing Real-Time Super Resolution with Partial Convolution and Efficient Variance Attention

With the increasing availability of devices that support ultra-high-definition (UHD) images, Single Image Super Resolution (SISR) has emerged as a crucial problem in the field of computer vision. In recent years, CNN-based super resolution approaches have made significant advances, producing high-quality upscaled images. However, these methods can be computationally and memory intensive, making them impractical for real-time applications such as upscaling to UHD images. The performance and reconstruction quality may suffer due to the complexity and diversity of larger image content. Therefore, there is a need to develop efficient super resolution approaches that can meet the demands of processing high-resolution images. In this paper, we propose a simple network named PCEVAnet by constructing the PCEVA block, which leverages Partial Convolution and Efficient Variance Attention. Partial Convolution is employed to streamline the feature extraction process by minimizing memory access. And Efficient Variance Attention (EVA) captures the high-frequency information and long-range dependency via the variance and max pooling. We conduct extensive experiments to demonstrate that our model achieves a better trade-off between performance and actual running time than previous methods.
Multi-scale Conformer Fusion Network for Multi-participant Behavior Analysis

Understanding and elucidating human behavior across diverse scenarios represents a pivotal research challenge in pursuing seamless human-computer interaction. However, previous research on multi-participant dialogues has mostly relied on proprietary datasets, which are not standardized and openly accessible. To propel advancements in this domain, the MultiMediate'23 Challenge presents two sub-challenges: Eye contact detection and Next speaker prediction, aiming to foster a comprehensive understanding of multi-participant behavior. To tackle these challenges, we propose a multi-scale conformer fusion network (MSCFN) for enhancing the perception of multi-participant group behaviors. The conformer block combines the strengths of transformers and convolution networks to facilitate the establishment of global and local contextual relationships between sequences. Then the output features from all Conformer blocks are concatenated to fusion multi-scale representations. Our proposed method was evaluated using the officially provided dataset, and it achieves the best and second best performance in next speaker prediction and gaze detection tasks of MultiMediate'23, respectively.
JoVE Video Dataset

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.
Diffusion Models in Generative AI

Diffusion models have shown impressive capabilities in the generative AI space. These models have the capability to create images in a variety of styles from photorealistic and futuristic to many more artistic styles by simply using text prompts. This tutorial aims to introduce the underlying mechanisms that make these models successful along with hands-on exercises. The tutorial will start with explaining the diffusion concept with forward and reverse processes. Then, it will cover the fine-tuning process and the control procedures such as guidance and conditioning. The provided hands-on exercises will help apply these concepts on some real-world problems.