Reviewerly/exHarmony huggingface dataset:
Ensemble Learning for Encrypted Malware Detection and Classification

The widespread use of network encryption in communication technologies is becoming the standard of communication due to its privacy preservation capability. On the contrary, this method of hiding data from unintentional parties introduces another problem in the communication system. Malware authors utilize this technique for their intention of malicious code deployment and infiltration. In this study, we used machine learning algorithms to detect and classify encrypted malware. We analyze raw encrypted traffic from different sources to study and extract different feature sets that can discriminate malicious flows from benign flows. This includes handshake, certificate, inter-arrival time and length of packets, statistical features, Meta connection features, and the cipher suite used. We preprocessed 14152 unique flows and extracted 304 features. Two ensemble learning models, namely extreme gradient boosting (XGBoost) and random forest (RF) models, were used for detection and classification. The detection model achieved 99. 78% precision, 0. 467% false detection rate, 99. 39% precision, 99. 33% recall and 99. 63% F1 score. However, for a family classification model, the accuracy was 98.65%, 98.28% Fl-score, 98.39% precision, and 98.16% recall.
Surveying the Landscape: Compound Methods for Aspect-Based Sentiment Analysis

Aspect-based sentiment Analysis (ABSA) has emerged as a critical research area in natural language processing, facilitating a deeper understanding of user opinions and sentiments expressed in text. This review article comprehensively surveys the landscape of deep learning approaches in ABSA, focusing on triplet and quadruplet ABSA. We delve into the significance of ABSA in diverse domains and present an overview of the critical components and challenges associated with compound ABSA tasks. The review analyzes state-of-the-art models, encompassing pipeline-based and generative-based solutions. Comparative analysis demonstrates the advantages and limitations of these approaches, including their performance, generalizability, and efficiency. Additionally, we explore the domains and datasets used in ABSA research and highlight the crucial factors contributing to ABSA task solutions’ effectiveness. This comprehensive review highlights current challenges and fosters further advancements in compound ABSA tasks.
Creation of WebQuests for Cultural Education and Refugee Integration in Germany: On the Example of Cologne

Purpose. This research aims to determine the methodology of creating and conducting web quests for people who have left their homes and settled in another country (especially emigrants and refugees). The study includes a clear definition of the sequence of creation and implementation of web quests for families who emigrated to Germany.&#x0D; Methodology. The research was conducted in the format of a single case study with a description of the creation of a specific web quest for a seed audience of emigrants and refugees. The methodology presented in the study included a description of the process, principles and means necessary for the organizer and other interested parties in case of reproduction of this web quest for a similar audience and in similar conditions.&#x0D; Results. The main results presented in the study include a clear algorithm for conducting the WebQuest “Family creative and educational quest in Cologne”, aimed at cultural adaptation and education in the field of history and iconic places of Cologne; «excursion program»; communication between different generations of families; a simple divergent task, proposed at each station of the WebQuest to creatively reinterpret the received excursion information; increasing the level of digital literacy.&#x0D; The article provides recommendations for increasing the effectiveness of WebQuest in the context of cultural adaptation of refugees and emigrants living in Germany.&#x0D; Conclusions. It was determined that creating and conducting WebQuests is a complex and time-consuming process, the goal of which is to maximize interactivity and involvement of participants. Therefore, the creators of such a digital product need to consider a number of features of the participants and digital tools used in the process. &#x0D; The format of web quests allows people to involve their whole family with the process, which significantly increases their cohesion in a new place and enables integration and adaption with not solely individual family members, but the whole family, which expands the level of awareness of the culture and history of their new home.
A High-Throughput and Compact GOST-28147-89 Accelerator for RISC-V System-on-Chip

Taking advantage of the royalty-free, open-source RISC-V Instruction Set Architecture (ISA), various RISC-V System-on-Chip (SoC) designs have been proposed to be applied in securing customized embedded applications. In this work, we propose a high-throughput and compact GOST-28147-89 block cipher accelerator that is suitable for integrating into these RISC-V SoC designs. The GOST-28147-89 block cipher accelerator is capable of operating at the maximum frequency of 251.32 MHz, yielding a high throughput of 487.4 Mbps. Meanwhile, the hardware utilization of the proposed GOST-28147-89 accelerator is extremely small at 273 Slice Look-up-Tables (LUTs) and 138 Slice Registers, which is 0.27% and 0.07% of available Slice LUTs and Slice Registers in a Xilinx Kintex-7 XC7K160T FPGA device, respectively. When attached to the Berkeley's-Rocket-processor-based RISC-V SoC, the proposed accelerator improves the GOST-28147-89 cryptographic operations by 20.43 folds.
3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

In processing the point cloud of three-dimensional (3D) reconstruction, various point cloud noises would generate, resulting in decreasing the accuracy of 3D point cloud. In order to improve the accuracy of 3D point cloud, this paper proposes a point cloud denoising method based on 3D point cloud segmentation. This method can be divided into four steps. Firstly, the K Nearest Neighbor (KNN) algorithm is used to construct a KNN look-up table for 3D point cloud. Secondly, the look-up table is used to segment the 3D point cloud to obtain the noise point cloud and the noise-free point cloud. Then, using the relationship between the noise point cloud and the noise point of the absolute phase, the reference noise-free phase is established to restore the absolute phase of the noise-free point. Finally, 3D reconstruction is performed according to the recovered absolute phase to obtain a noise-free 3D point cloud. This proposed method only calculates the KNN once for the initial 3D point cloud and only uses a KNN look-up table to segment the point cloud, which also accelerates the speed of 3D point cloud segmentation. The experimental results show that this method not only removes the noisy point cloud, but also restores part of the noisy point cloud into a noise-free 3D point cloud, improving the accuracy of the 3D point cloud.
3D reconstruction of structured light against texture interference based on feedback modulation projection method

To enhance the measurement accuracy and robustness of 3D reconstruction systems for obtaining precise shape and position information of object surfaces, including resistance to texture interference, we propose an anti-interference structured light measurement system based on scene surface. This system addresses limitations in stripe-encoded structured light 3D measurement, such as abrupt surface reflectance changes and camera defocusing, while investigating methods for phase retrieval and surface reflectance-based phase compensation. By adjusting the projection modulation intensity of the projector, we control the intensity of the reflected light on the measurement surface, reducing the sensitivity of the camera defocusing blur coefficient to texture variations. We propose an adaptive modulation intensity adjustment method to minimize phase errors between neighboring pixels, enabling high-precision phase retrieval. Experimental results demonstrate that in simulated experiments, the root mean square errors before and after anti-interference processing are 0.2124 rad and 0.0371 rad, respectively. In real-world scenarios, the errors of different measured surfaces decrease by approximately 60%. Comparative experiments validate the effectiveness and feasibility of the proposed method, indicating a significant improvement in measurement accuracy. The method enhances the performance of the coded structured light in applications such as industrial measurement and precise sorting, exhibiting high robustness, accuracy, and anti-interference capabilities.
The Effectiveness of Powtoon Animation Media on Student Learning Outcomes in Human Respiratory System Subject

Powtoon is an online application that can be used as a learning medium. This study aims to determine the effectiveness of using powtoon animation media on student learning outcomes in the human respiratory system in class VIII SMP Negeri 3 Pontianak. The method used in this research is Quasi Experimental with Pre-test Post-test Control Group Design. The samples used were 2 classes, namely the experimental class with 31 students and the control class with 32 students, which were taken using the intact group technique. The instrument used was a multiple choice test totaling 20 questions. Learning outcome data was analyzed using the independent sample t-test. The average post-test result for the experimental class was 76.56, which was higher than the control class, which was 69.53. The effect size calculation obtained is 0.70 which is included in the medium category and contributes 25.80% in learning, so it can be concluded that powtoon animation media influences student learning outcomes.
Demystifying Sonar Tool Estimates in the Contexts of Familiar and Unfamiliar Software Projects: An Empirical Study with Junior Developers

Developing a high-quality software product requires complete familiarity with the software product requirements and constraints. However, developers are often assigned to projects they are unfamiliar with, making project properties hard to understand for them. Sonar tool is one of the most popular Automated Static Analysis Tools that nowadays has become an integral part of software development process. The objective of this study is to evaluate the usefulness of the Sonar tool in the context of familiar and unfamiliar projects. More specifically to investigate the accuracy of Sonar’s estimates while the remaining three assess the perceived refactoring difficulties, Sonar issue and fixing instructions as possible reasons for differences in estimates. A repeated measurements experiment in the context of a university course was designed and involved two sessions during which the subject refactored Sonar issues identified in familiar and unfamiliar projects. In total 60 students completed assignments, who in two sessions combined refactored 5,179 Sonar issues by violating 138 different Sonar rules. This study showed that estimates provided by Sonar are less accurate for familiar projects. Plausible explanations are that the refactoring and understandability of Sonar tool issue description seem to be more challenging in unfamiliar, compared to familiar projects.
Innovative chaotic dragon fractal (ChDrFr) shapes for efficient encryption applications: a new highly secure image encryption algorithm

Abstract In this paper, the generation of new dragon fractal shapes with chaotic iteration parameters is introduced as the main component of a new efficient approach for different cryptographic applications. This process involves applying a chaotic map, which is considered the initiator pattern, to generate different chaotic dragon fractal (ChDrFr) shapes in lieu of lines (which are classically used to generate dragon fractals). This is the new concept of this paper. The used chaotic maps are sensitive to their initial conditions and are characterized by randomness; hence, the resulting scheme is highly secure. As the resulting ChDrFr shapes have sparse structures, the spaces are packed with random values generated from another 5D hyper chaotic map. For encryption applications based on the substitution approach, one of the five generated ChFrDr shapes can be used to construct a chaotic fractal (ChFr) S-Box, while the other four ChDrFr shapes can be used for diffusion purposes. As an application to these new ChDrFr shapes and the ChFr S-Box, we introduce in this paper a new highly secure image encryption algorithm. A Henon chaotic map is used as the initiator of the ChDrFr shapes. The integer wavelet transform (IWT) is used to generate an approximation and three detail sub-bands for the original image. As the approximation sub-band contains a considerable amount of information about the original image, the above-described ChFr S-Box is used as a replacement for each pixel’s value in this sub-band. Then, the resultant substituted image is diffused with one of the generated ChFrDr shapes. The other three ChDrFr shapes are XORed with the details sub-images. Numerical simulation is applied to ensure the efficacy of encrypted images against different attacks. In particular, the correlation coefficient between the initial and the generated images is shown to be nearly zero. Moreover, tests reveal that the information entropy of the encrypted images and UACI were close to their optimum values. The properties of the newly proposed ChDrFr-based encryption algorithm are compared to the ones obtained by other encryption algorithms, and the results prove the superiority of this newly proposed algorithm to other types of encryption methods.
Perkembangan Literasi Digital dalam Pendidikan: Sebuah Tinjauan Bibliometrik

The development of digital literacy in education in Indonesia is an integral part of efforts to prepare the younger generation to face the growing digital era. This study aims to provide a comprehensive bibliometric analysis of literature related to the development of digital literacy in education in Indonesia. This bibliometric analysis uses the Vosviewer tool. From the Google Scholar database, we identified 995 literature sources published between 2012 and 2023.The results show that the number of publications has increased every year in the 2012-2023 time frame. Network visualization shows that research trends related to the development of digital literacy in education in Indonesia during the period include keywords such as Literacy, Technology, Literacy Ability, Data, and Teachers. The analysis results also resulted in 16 clusters, Cluster 1 (17 items), Cluster 2 (12 items), Cluster 3 (11 items), Cluster 4 (9 items), Cluster 5 (8 items), Cluster 6 (8 items), Cluster 7 (8 items), Cluster 8 (6 items), Cluster 9 (6 items), Cluster 10 (6 items), Cluster 11 (5 items), Cluster 12 (5 items), Cluster 13 (5 items), Cluster 14 (4 items), Cluster 15 (4 items), Cluster 16 (3 items). This study provides information to education researchers related to digital literacy in education which is expected to strengthen further research topics
Review of Object Detection Algorithms for Sonar Images based on Deep Learning

Background: Deep learning object detection algorithm is widely used in the field of image classification and has become an indispensable part. With the improvement of image classification accuracy, sonar image target detection algorithm based on deep learning has gradually become the focus of more and more people's research. Objective: This article aims to provide a summary and analysis of deep learning-based sonar image object detection algorithms, with the hope of offering insights for future research in the field of sonar target detection technology. Method: This paper systematically summarizes sonar image target detection algorithms based on deep learning. According to the method principle, the existing deep learning target detection algorithms are divided into four categories: target detection algorithm based on candidate region, deep target detection method based on regression, Anchor Free deep learning target detection algorithm, and search-based target detection and recognition algorithm. Then, the performance of algorithms based on COCO data sets is compared, and the standard sonar data sets and formats are introduced. Results: The sonar image object detection algorithm based on deep learning has made significant progress. The combination of deep learning and object detection methods has been applied to sonar images, resulting in the emergence of excellent performing algorithms. However, most algorithms are still in the developmental stage and face challenges in practical applications. Subsequently, several invention patents have been developed based on the aforementioned algorithms, including a feature extraction method for side-scan sonar images based on fully convolutional neural networks, an underwater sonar image target detection method based on improved YOLOv3-tiny, and more. Conclusion: Sonar image object detection technology based on deep learning has a wide range of application needs but also faces many difficulties and challenges, we still need to continue to learn and explore in future research, and we believe that we can make greater breakthroughs in the future.
DEVELOPMENT OF ICT LEARNING MEDIA BASED ON EXPERIENTIAL LEARNING APPROACH TO IMPROVE LEARNINGOUTCOMES CLASS V PKN SD SWASTA PAB 23 PATUMBAK

Basically this research is intended to see the level of feasibility and effectiveness of developing ICT learning media in the form of interactive power points on PKN material. This research was conducted at PAB 23 Patumbak Private Elementary School. The subjects of this study were all fourth grade students at PAB 23 Patumbak Private Elementary School. This research method is Borg &amp; Gall development research. The results showed (1) Based on the results of the analysis of ICT learning media products from experts it was declared very effective and feasible to use with the criteria of validity of material experts 85.00%, design experts 91.23%, and linguists 87.34%; 2) ICT learning media based on experiential learning is categorized as practical because ICT learning media based on experiential learning results from student responses have increased; and 3) ICT learning media based on experiential learning is said to be effectively used in learning with the assumption that students' understanding is higher when using ICT learning media based on experiential learning in the PKN learning process.
An IoT-based intelligent smart energy monitoring system for solar PV power generation

Abstract As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.
Hubungan antara Dukungan Sosial Teman Sebaya dengan Kemandirian Belajar Siswa Kelas VIII MTs Negeri 2 Medan

The purpose of this study was to determine the "Relationship Between Peer Social Support and Learning Independence of Grade VIII Students at MTs Negeri 2 Medan Academic Year 2022/2023". This study uses a quantitative approach with the correlation method. In this study, the population consisted of 106 students. Sampling in this study used random sampling, which was taken based on the slovin formula, namely as many as 84 students. The data collection technique used in this study was a questionnaire using a Likert scale. The validity of the instrument was obtained by using the product moment correlation formula, while the reliability of the instrument was obtained by using the Cronbach Alpha formula. Test the hypothesis using the Chi Square test with the help of SPSS IMB 25 for windows. The results of the analysis showed an Asymp.sig (2-sided) value of 0.000 for 84 respondents, so that the value of p &lt;0.05, which means that there is a significant correlation between the variables of peer social support and learning independence
Hubungan Antara Kecerdasan Emosional dengan Perkembangan Sosial Siswa Kelas XI IA MAN 2 Model Medan Helvetia

The purpose of this study was to determine the relationship between emotional intelligence and social development of class XI IA MAN 2 Model Medan Helvatia in the 2022/2023 Academic Year. The method in this study is a quantitative method with a correlational research design. The population in this study were students of class XI IA MAN 2 Model Medan. Sampling in this study used random sampling using the slovin formula. The sample size is 85 students. The data collection tool used is a questionnaire. The validity of the questionnaire was obtained by product moment correlation and the reliability test was obtained by the Cronbach alpha test. Test the hypothesis using the product moment formula from pearson with the help of SPSS22 For Windows. The results of the analysis showed r = 0.628 and Rsquare = 0.386 and a significant value of 0.000 (p &lt;0.05). Based on these results, the research hypothesis was put forward: "There is a relationship between emotional intelligence and students' social development
6 Desiring-infrastructures in the crypto economy

This chapter examines the interface between desire and money infrastructures in the new crypto economy. Focusing on NFTs, utility tokens, and interoperability technologies, it argues that economic investment in monetary technologies is tantamount to a libidinal investment in technological designs and the forms of capitalisation they enable.
Diffractive Optical Networks &amp; Computational Imaging Without a Computer

We will present our recent work on deep learning-designed diffractive materials for optical computing and computational imaging.