Reviewerly/exHarmony huggingface dataset:
Efficient Implementation of a Fully Analog Neural Network on a Reconfigurable Platform

This paper investigates the potential of floating gate field-effect transistors (FETs) as primitives for subthreshold computation in analog neural networks. By leveraging the inherent properties of these transistors, we demonstrate their suitability for constructing neural network activation functions, such as sigmoid and rectified linear units (ReLUs), as well as winner-take-all (WTA) circuits for softmax activation. Our end-to-end analog implementation successfully classifies the concentric circles problem, illustrating the advantages of maintaining an analog signal chain throughout the process.
Design and Implementation of Certificateless Cryptography for IoT Applications

This work introduces a cryptographic module for IoT devices that addresses the security vulnerabilities that come with their widespread adoption. Four core cryptographic modules are implemented, including data confidentiality, message integrity, authentication, and secure communication channels. Specifically, the SHA-256 hashing and AES128-CBC/GCM cipher modules are very efficient, with an execution time of just a few µs. For the key exchange functionality, we opted to leverage Elliptic Curve Cryptography (ECC) and, in particular, the BLS12-381 curve, because it enables the implementation of certificateless public-key cryptography. We demonstrate the performance of the Hash to Curve and pairing operations that are required by both the BLS12-381 digital signature scheme and the session key agreement protocol. The pairing operation consists of two main steps, namely, the Miller loop and the final exponentiation. On a 10 MHz clock frequency (simulated in FPGA), a pairing operation between two elliptic curve points takes around 3.68s to complete. Under the BLS12-381 digital signature scheme, the module for signing messages takes 0.76s, while the module for verifying signatures takes 7.35s. Finally, we identified that the parallel point-scalar multiplication technique was the most efficient, and the module for generating a session key on an IoT node takes around 4.03s. To summarize, this paper highlights the importance of addressing the security risks associated with IoT devices and presents a low-cost implementation of hardware-based cryptography for achieving robust security.
Targeted Background Removal Creates Interpretable Feature Visualizations

Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of the visualizations. We argue that by using background removal techniques as a form of robust training, a network is forced to learn more human recognizable features, namely, by focusing on the main object of interest without any distractions from the background. Four different training methods were used to verify this hypothesis. The first used unmodified pictures. The second used a black background. The third utilized Gaussian noise as the background. The fourth approach employed a mix of background removed images and unmodified images. The feature visualization results show that the background removed images reveal a significant improvement over the baseline model. These new results displayed easily recognizable features from their respective classes, unlike the model trained on unmodified data.
PENINGKATAN KETERAMPILAN MENULIS KARANGAN DESKRIPSTIF MELALUI PEMBELAJARAN DENGAN MENGGUNAKAN MEDIA POP UP PADA SISWA SEKOLAH DASAR

The research is motivated by the low writing skills of the fifth grade studens of SDN 024 Limau Manis. One solution to overcome this problem is to use Pop Up media. This study aims to describe the application of Pop Up media to improve studens’ descriptive essay writing skills in thematic learning of indonesian languange content. This reseach method is classroom action reseach (PTK) which is carried out in two cycles. Each cycle consists of two mwwtings and four stages, namely planning, implementation, observation, reflection. The subject of this research of SDN 024 Limau Manis for the academic year 2021/2022, totaling 9 students. Data collection teachniques used in this study were observation, test, and documentation techniques. Based on the results of the researchers, it can be concluded that Pop Up media can improve students’ descriptive essay writing skills in thematic learning content in the thematic learning content of Indonesian languange students at SDN 024 Limau Manis, improve students’ descriptive essay writing skills, that is, from an average of 70,5% in cycle I, and increased to 80,5% with good criteria in cycle II. Thus it can be concluded that using Pop Up media can improve students’ descriptive essay writing skills in thematic learning of Indonesian languange content in fifth grade students of SDN 024 Limau Manis.
PROJECT METHOD OF LEARNING IN ORGANIZATIONS OF ADDITIONAL EDUCATION

This article reveals the use of project methodology in teaching, the importance of this learning technology for students in organizations of additional education. As well as the effectiveness of their application in teaching a foreign language in an out-of-school organization. The popularity of the project method is due to the fact that, due to its didactic nature, it allows solving the problems of developing the creative abilities of students, the ability to independently design their knowledge and apply it to solve cognitive and practical problems, navigate the information space, analyze the information received, since at different moments of cognitive, experimental or applied, creative activities, students use the totality of all the listed intellectual skills and abilities. The project method is always focused on the independent activity of students - individual, pair, group, which students perform for a certain period of time. This method is organically combined with group methods. Through this method, the social competencies of students are formed. It should be noted that project activities develop communication and public presentation skills in students, the ability to work in a team, research competencies, and increase motivation for learning.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAMS GAMES TOURNAMENT (TGT) BERBANTU MEDIA POP-UP BOOK UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR PESERTA DIDIK KELAS IV SDN 3 KRIAN

The quality of learning is determined by the way the teacher provides services in learning and provides facilities to students in all student activities in learning activities, so that it influences student learning outcomes. However, the learning outcomes of fourth grade students at SDN 3 Krian-Sidoarjo on the great ideals sub-theme are many students who have not finished studying. From the results of observations with these problems, this study aims to increase student activity and learning outcomes. This type of research uses classroom action research (CAR) using four stages in each cycle including planning, action, observation and reflection. As for several techniques in data collection, namely observation data of student activities and test data. This study uses observational data analysis techniques and data on student test results. The results of research on student activity in cycle I reached 66%, increasing in cycle II to 80%. Student completeness in cycle I reached 60% and increased in cycle II to 90%. It can be concluded that the application of the cooperative learning model of the Teams Games Tournament (TGT) type assisted by pop-up book media can increase the activity of class IV students at SDN 3 Krian.
PENINGKATAN KETERAMPILAN MENULIS KARANGAN PERSUASI DALAM PEMBELAJARAN TEMATIK MENGGUNAKAN MEDIA POP UP BOOK PADA SISWA SEKOLAH DASAR

This study aims to describe the skills of writing persuasive essays in thematic learning using pop up book for elementary school students. The method used is a classroom action research method, the research was conducted at Elementary School 005 Langgini, Bangkinang District, Kampar Regency, Riau Province. Subjects used in this study were class fifth with a total of 25 students consisting of 10 boys and 15 girls. Results of this study can be seen from the completeness of the cycle, it is found that in the pre-action activities, the class average score is 57.8 with classical completeness of 52%, increasing in cycle 1 meeting 1 to 60.2 with classical completeness of 56%, increasing in cycle 1 meeting 2 to 66.4 with classical completeness of 64%. The average value of the class in cycle 2 meeting 1 is 74.4 with classical completeness of 76%, and increased in cycle 2 meeting 2 which is 76.6 with classical completeness of 84%. Conclusion of this research is that through for media Pop Up Book in thematic learning for fifth grade students at Elementary School 005 Langgini can improve student learning outcomes in improving persuasive essay writing skills in thematic learning using Pop Up Book media elementary school students.
Kajian Pengembangan Pengisian Data Hujan yang Hilang dengan Data CHIRPS di Wilayah Bali

Research has been carried out related to the study of filling in missing rain data with CHIRPS data in the Bali Region. This study aims to determine the validation of daily and monthly CHIRPS rain data with observational data in the Bali region and to know the results of filling in observation rain data with corrected CHIRPS data. There are also differences in patterns between CHIRPS data and observational data that require data correction that was carried out using simple regression. This study shows that corrected CHIRPS shows RMSE and MBE valueshave lower error rates than CHIRPS without correction. As an example, the results of the calculation of data accuracy are shown by the RMSE and MBE values after being corrected which are better than CHIRPS before being corrected. For Sukasada rain post, the RMSE values before and after correction are 15.355 mm and 13.527 mm for daily data and 77.586 mm and 65.083 mm for monthly data, respectively. Meanwhile, the MBE values for the Sukasada rain post before and after correction were -0.875 mm and -0.017 mm for daily data and -27.142 mm and 0.005 mm for monthly data, respectively
DIGITAL MINDSET in Education. Security Education

Safety and the protection of freedoms constitute key challenges the education system is currently facing in the context of digitalization, challenges that require the promotion of a more elaborate definition of digital democracy correlated with concerns related to human rights, development inequality depending on access to education, responsibility and, last but not least, building consensus in highly diverse environments. Therefore, security as a fundamental value of democratic societies requires a reassessment in relation to the principles promoted by political institutions – inclusion, responsibility and transparency –, debatable against the background of political polarization and the emergence of new digital technologies. The present article is not intended to formulate a general theory on how the digitalization of education and democracy relate or are mutually exclusive. It deals with more or less contingent situations and raises additional – including empirical – questions about the role that digitalization in the education system can have for the state of democracy, regardless of the level of understanding. The intent is directed to the concrete mode in which digitalization can be beneficial or inadequate for democracy, contributing to a better understanding of the challenges. The reader can translate it into own environment, linking personal reading to specific democratic processes, including the level and type of digital activity.
Tehnologiile cloud și necesitatea implementării cloud-ului hibrid în mediul militar

Cloud computing has been widely adopted as the next-generation digitisation model for transforming the organisation into an entity that drives development and innovation. Nowadays, users of computing systems aim to get quick access to the virtual ecosystem and a new experience in cyberspace that seamlessly integrates with the existing services they use today. From the perspective of the military organisation/Military Enterprise, the cloud provides services for users and structures in a scalable, highly reliable and highly available manner, specifically with different security levels associated with the individual profile and functional roles in the organisation. From the end user’s perspective, the cloud provides a simple model for accessing information technology/IT services without the need for the human factor to fully understand the transport/transmission infrastructure and technology used. This article explores how modern military-classified and unclassified cloudnative infrastructures can be secured and managed in a national, military private, or mixed hybrid deployment cyber environment, along with various requirements and considerations for adapting cloud-native applications for military systems. In context, the article provides a simple but comprehensive introduction to the cloud native overview and the major technologies that developers use to build such reliable environments in cyberspace that could be the subject of a feasibility study to implement the concept of hybrid cloud in cyberspace with military use. The material is intended for IT experts, DevOps engineers, CIS (Communications and Information Technology) systems and infrastructure architects, cloud enthusiasts, cloud security experts, and any military professional involved in the development, migration, deployment, and management of current services and operations. of a cloud-native system.
PENGARUH ALAT PERAGA KINEMATIKA GLB DAN GLBB UNTUK PENGUATAN PEMAHAMAN TENTANG GERAK DI KELAS IV SDN KEBONAGUNG 02 JEMBER

The purpose of this research article is to determine the effect of GLB and GLBB kinematics props to strengthen students’ understanding of motion in class IV SDN Kebonagung 02 Jember. The method used is quantitative with the type of pre-experimental design and the form of one group pretest posttest design. This research technique uses field research techniques with test instruments and questionnaires. Data analysis using the N-Gain test, normalization test, T test on test results and elaboration on questionnaire results. The results showed that the N-Gain test was found to be 0.60 which means the medium category and normally distributed through the normality test. Likewise, the T-test results show that after the use of props, the t value is 8.816 with sig. (2-tailed) 0.000 which means that there is a significant difference in student understanding before and after being given exposure through kinematics props. So it can be concluded that the use of kinematics props has an effect on strengthening students’ understanding of motion for grade IV students at SDN Kebonagung 02 Jember.
Video Summarization with Encoder-Decoder Based Graph-Attention Networks

Video summarization aims to generate a brief summary containing key video information by eliminating redundant information in videos. Video summarization faces many challenges such as the high cost of video data annotation, the lack of professional datasets, and the inaccuracy of keyframes selection algorithms. To address the above issues, we propose a non-manual dataset annotation method based on the number of online video barrage, and construct a new video summarization dataset based on this method. In addition, we propose an encoder-decoder based graph-attention video summarization algorithm, which introduces a graph attention mechanism and only assigns different weights to relevant sampled frame features to improve the precision of keyframes selection. Our method achieved state-of-the-art results on both benchmark datasets (SumMe and TVsum) and our new dataset.
Multi-scale Generation and Discrimination for Sketch Extraction of Painted Cultural Relics

Sketches of painted cultural relics can accurately represent the main content and outline structure of painted images, providing convenience for researchers studying ancient culture and the painting styles of artists. Most existing sketch extraction methods rely on edge detection technology, which may result in incomplete details, blurring, and artifacts. Additionally, for severely damaged murals, these methods may not yield ideal extraction results. In recent years, generative adversarial networks have made remarkable progress in image processing. Cycle-consistent generative adversarial networks, in particular, provide a new approach for the extraction of painted cultural relics. Therefore, this paper proposes a multi-scale generation and discrimination method for the extraction of painted cultural relics based on cycle-consistent generative adversarial networks. The method utilizes multi-scale gradient technology, allowing the discriminator to view the multi-scale output of the generator. Furthermore, a feature fusion optimization module replaces the traditional skip connection to achieve multi-scale feature fusion. Additionally, an attention residual module is designed to retain the original features, strengthen the constraints on the features of the sketch, and reduce the influence of background noise on the generated results. Experimental results demonstrate that compared with existing methods, this approach yields higher-quality sketches and performs better in objective indicators.
Adversarial Attacks Based on Feature Integration

Deep learning models are vulnerable to adversarial attacks. The existence of adversarial examples poses a significant threat to the security of deep learning models. However, most existing adversarial attacks are supervised methods that rely on labels, limiting the transferability of adversarial examples between different task models. In contrast, SSP, a self-supervised method for generating adversarial examples, was introduced at the top computer vision conference, CVPR, and is independent of the task and label space. However, experimental analysis shows that different feature layers of different models exhibit varying degrees of adversarial transferability. NRP only uses a single middle layer feature of a single pre-trained model, making it challenging to achieve optimal attack transferability across all models simultaneously. Inspired by ensemble learning, this paper proposes a feature integration strategy based on NRP to enhance the middle layer feature information, maximize the distance between adversarial and clean samples in the feature space of multiple pre-trained models, and effectively improve the blackbox transferability of adversarial samples.
Fungsi Gelombang Pseudospin Simetri Untuk Potensial Rosen Morse Plus Coulomb Like Tensor Untuk Variasi q Dengan Menggunakan Metode Polymial Romanovski

This study aims to determine the wave function of the Dirac equation of the Rosen Morse potential plus the coulomb type tensor potential for the pseudospin symmetry case using the Polynomial Romanovski method.Solving Dirac's equation using Romanovski polynomials is done by reducing the second-order differential equation to a hypergeometric type equation through variable substitution and the appropriate wave function. By comparing the second order differential equation of the hypergeometric type with the standard differential equation for the Romanovski polynomial, the relativistic energy equation and wave function are obtained. The symmetrical pseudospin wave function for the Rosen Morse potential is obtained from the weight function and expressed in Romanovski polynomial form.
Pedestrian Detection Algorithm Using Local Feature Cascade Classifier in Traffic Scenes

Aiming at the low recognition rate of pedestrian detection algorithm in traffic scene, this paper designs a pedestrian recognition algorithm based on local feature cascade classifier. The pedestrian candidate area is extracted, and the imaging intensity of the scattered light in the camera sensor is calculated. A local feature cascade classifier is used to segment the image boundary, and the rectangular box whose height is smaller than the height of the HOG window is enlarged to design a pedestrian detection algorithm for traffic scenes. The experimental results show that the average recognition rate of the pedestrian detection algorithm proposed in this paper is 94.764%. The results show that the proposed method can achieve better pedestrian recognition in traffic scenes after introducing local feature cascade classifier.
Multi-Label Text Classification Based on BERT and Label Attention Mechanism

In natural language processing, multi-label text classification is a crucial task. Recently, many methods had introduced information related to labels, which had improved the classification effect of methods. This paper proposed a model that utilizes BERT and a label attention mechanism to effectively leverage the semantic information present in labels. Through fine-tuning of BERT, the textual and label data were transformed into vector representations, and then the label attention mechanism was employed to extract text features, which are more relevant to labels. Finally, a corresponding classifier was constructed to complete the classification task. Experiments show that compared with the baselines mentioned in the paper, the proposed method had an improvement on both AAPD and RCVl-v2 datasets, which proved the effectiveness of the proposed method in the multi-label text classification task.
TUDF-NeRF: Generalizable Neural Radiance Field via Truncated Unsigned Distance Field

Novel view synthesis is a classical research problem in computer vision and has been well studied in various ways. While recently proposed NeRF model achieves photo-realistic novel view synthesis result, traditional NeRF can represent only a single scene and is not able to generalize to novel scenes, which limits its application. This paper proposes TUDF-NeRF, a generalizable neural radiance field representation for high-fidelity novel view synthesis. This is achieved by modeling the volume density as the function of truncated unsigned distance field (TUDF) and predicting radiance field by feature matching. Compared with the existing methods that generalize NeRF to novel scenes, the TUDF based representation enforces the rendering results to be influenced only by samples near the surface instead of the whole space, which reduces the ambiguity during feature matching over low-texture areas and improves view synthesis quality. To further improve the quality of view synthesis over arbitrary views on novel scenes, this paper design a per-scene fine-tuning scheme that utilizes the gated recurrent unit (GRU) to fuse local information for global optimization. Extensive experiments demonstrate that TUDF-NeRF outperforms state-of-the-art view synthesis methods, and photo-realistic view synthesis over arbitrary view direction is achieved after per-scene fine-tuning.