Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN LKPD BERBASIS PROBLEM BASED LEARNING PADA PELAJARAN BIOLOGI MATERI SISTEM PERNAPASAN DI MADRASAH ALIYAH

The aim of developing student worksheets based on Problem-Based Learning are appropriate, effective, and practical respiratory system material to help participants learn in the learning process. This research is a research and development (R&amp;D) with the ADDIE (Analysis, Design, Development, Implementation, Evaluation). The research instrument uses interview sheets, questionnaires, validation sheets, and tests. Data analysis techniques are carried out quantitatively and qualitativelyThe purpose of quantitative data analysis is to determine the degree of feasibility, practicality and effectiveness. Meanwhile, quantitative analysis consists of finding comments, criticisms, suggestions and observations. The result of the study shows that the worksheets of the students based on the problem-based study developed by 82% (media expert) are correct and feasible. 100% (Subject Matter Expert) and 100% (Language Expert), Student Worksheets are also graded on Teacher Answer Sheet achievement with 90.91% and Student Answer with 93%. Student worksheets are also considered effective if a total N-Gain of 0.76 is achieved in the high category. Based on the result above, it can be concluded that the student worksheet is Problem-Based Learning that has been developed is declared feasible, practical, and effectively used in the learning process. The developed student worksheets can improve the learning activities of students and teachers can use student worksheets as teaching materials in the learning process.Â Kata kunci: Lembar Kerja Peserta Didik, Problem Based Learning, Sistem Pernapasan
ANALISIS KESESUAIAN KONTEN BUKU TEKS BIOLOGI KELAS XI SMA TERHADAP KURIKULUM 2013

This study aims to determine the suitability of the content of biology textbooks for class XI SMA to the curriculum 2013. The type of research used is descriptive research with a qualitative approach and uses content/document analysis methods. The subject of this research is a biology textbook for class XI high school with cell material consisting of 3 books to be analyzed by researchers, namely the book publisher Bumi Aksara, the book publisher Erlangga, and the book publisher Grafindo Media Pratama. The research instrument used was an analysis sheet of student books based on the Curriculum 2013 Implementation Assistance Activity Guidelines. The results showed that the level of conformity of the content of biology textbooks based on: (1) SKL aspects in books A and B was 93% (very appropriate), and book C was 87% (very suitable). (2) Aspects of Core Competence for book A are 92% (very suitable), and books B and C are 83% (appropriate). (3) Aspects of Basic Competence for Book A 96% (very suitable), book B 85% (very suitable), book C 89% (very suitable). (4) Aspects of Sufficiency of Material for book A 67% (appropriate), books B and C for 50% (not appropriate). (5) Aspect of Material Depth of Book A 75% (appropriate), book B 68% (appropriate), and Book C 71% (appropriate). (6) Aspects of the Scientific Approach of books A, B, and C by 100% (very appropriate). (7) The authentic assessment aspect of Book A is 57% (quite appropriate), book B is 54% (not suitable) and C is 59% (quite appropriate). The average agreement coefficient of the three books is 0.94% (very good). The data illustrates that the three books can be recommended by the 2013 curriculum formulation. Kata kunci: Analisis Kesesuaian Konten, Buku Teks Biologi, Kurikulum 2013
Image Inpainting via Correlated Multi-resolution Feature Projection

With the advancement in image editing applications, image inpainting is gaining more attention due to its ability to recover corrupted images efficiently. Also, the existing methods for image inpainting either use two-stage coarse-to-fine architectures or single-stage architectures with a deeper network. On the other hand, shallow network architectures lack the quality of results and the methods with remarkable inpainting quality have high complexity in terms of number of parameters or average run time. Despite the improvement in the inpainting quality, these methods still lack the correlated local and global information. In this work, we propose a single-stage multi-resolution generator architecture for image inpainting with moderate complexity and superior outcomes. Here, a multi-kernel non-local (MKNL) attention block is proposed to merge the feature maps from all the resolutions. Further, a feature projection block is proposed to project features of MKNL to respective decoder for effective reconstruction of image. Also, a valid feature fusion block is proposed to merge encoder skip connection features at valid region and respective decoder features at hole region. This ensures that there will not be any redundant feature merging while reconstruction of image. Effectiveness of the proposed architecture is verified on CelebA-HQ [1], [2] and Places2 [3] datasets corrupted with publicly available NVIDIA mask dataset [4]. The detailed ablation study, extensive result analysis, and application of object removal prove the robustness of the proposed method over existing state-of-the-art methods for image inpainting.
In-Vehicle Digital Forensics for Connected and Automated Vehicles With Public Auditing

Connected and autonomous vehicles produce a substantial amount of data that is essential for implementing advanced and intelligent features. Given the importance and the volume of in-vehicle data, storing it in the cloud for later extraction as critical evidence for vehicle digital forensics is a logical choice. However, ensuring the security of forensic data against tampering and forgery attacks throughout the process is a significant challenge. Existing solutions typically assume that vehicles will generate and upload the in-vehicle data to the cloud honestly. In reality, it may be necessary to prove whether the vehicle has uploaded authentic driving-related data in case of disputes about data authenticity. To address this issue, we propose an in-vehicle digital forensic scheme with public auditing, enabling anyone to perform a public auditing algorithm to check whether the data has been modified. The proposal is based on a process-oriented data integrity proof method that enables a vehicle to generate public verifiable integrity proof. Furthermore, we evaluated the practicality of our scheme by assessing its computational and communication overhead. In terms of computational cost, our proposed scheme demonstrates a power consumption of 0.0385 kWh per 100 km at a speed of 60 km/h. Regarding communication delay, our method exhibits a 50.1% decrease compared to similar approaches.
Multilevel Self-Training Approach for Cross-Domain Semantic Segmentation in Intelligent Vehicles

The use of intelligent vehicle technology is increasing; however, this technology requires further improvement. Semantic segmentation enables intelligent vehicles to understand the environment. Although advances have been achieved in a deep learning model for semantic segmentation, large-scale real-world datasets with manual pixel-level annotations, which are expensive, are required for adequately training these models. Unsupervised domain adaptation (UDA) for semantic segmentation enables training on a synthetic source domain and allows a model to be adapted to the real-world target domain without target domain annotations. Self-training is a highly effective UDA method; however, current UDA methods struggle to shift domains effectively and rely heavily on aggressive image-based perturbations for consistency training, resulting in poor-quality pseudolabels during the early training phase and decreased training efficiency. Therefore, we propose a novel multilevel self-training UDA method for semantic segmentation. First, a feature guidance and resampling strategy is adopted to improve the learning of rare classes in the source domain, and a spatial-aware mixing method is then used for gradually and smoothly eliminating the domain gap through training in various cross-domain intermediate spaces. In addition, we developed an instance and prototype feature consistency technique that explores optimization in the feature space to improve the training process by increasing model consistency. We conducted large-scale experiments on two challenging UDA benchmarks, “GTA5-to-Cityscapes” and “SYNTHIA-to-Cityscapes.” Our method achieved similar or superior performance to that of previously developed UDA methods for semantic segmentation and has advantages in terms of training stability and convergence efficiency.
A Survey on Scenario Theory, Complexity, and Compression-Based Learning and Generalization

This work investigates formal generalization error bounds that apply to support vector machines (SVMs) in realizable and agnostic learning problems. We focus on recently observed parallels between probably approximately correct (PAC)-learning bounds, such as compression and complexity-based bounds, and novel error guarantees derived within scenario theory. Scenario theory provides nonasymptotic and distributional-free error bounds for models trained by solving data-driven decision-making problems. Relevant theorems and assumptions are reviewed and discussed. We propose a numerical comparison of the tightness and effectiveness of theoretical error bounds for support vector classifiers trained on several randomized experiments from 13 real-life problems. This analysis allows for a fair comparison of different approaches from both conceptual and experimental standpoints. Based on the numerical results, we argue that the error guarantees derived from scenario theory are often tighter for realizable problems and always yield informative results, i.e., probability bounds tighter than a vacuous [0,1] interval. This work promotes scenario theory as an alternative tool for model selection, structural-risk minimization, and generalization error analysis of SVMs. In this way, we hope to bring the communities of scenario and statistical learning theory closer, so that they can benefit from each other's insights.
Speed-Up DDPM for Real-time Underwater Image Enhancement

Underwater images often suffer from serious color bias and blurred features because of the effect of the water bodies on the light. To enhance underwater images, we present SU-DDPM, a method of real-time underwater image enhancement( UIE) based on a denoising diffusion probabilistic model (DDPM). SU-DDPM outperforms other baseline and generative adversarial network models in underwater image enhancement, thus establishing a new state-of-the-art baseline. SU-DDPM processes images more rapidly than the diffusion model, which makes it competitive with other deep learning-based methods. We demonstrate that if conditional DDPM is used directly for the UIE task, the processing speed is slow, and the enhanced images are of poor quality and show color bias. The quality of the enhanced image is improved by combining the degraded image with the reference image in the diffusion stage to create a fusion–DDPM model. The specificity of the UIE task allows us to accelerate the inference process by changing the initial sampling distribution and reducing the number of iterations in the denoising stage of the model. We evaluate SU-DDPM on the UIE task using challenging real underwater image datasets and a synthetic image dataset and compare it to state-of-the-art models. SU-DDPM ensures increased enhancement quality, and enhancement processing speed is comparable to the speed of real-time enhancement models.
TECO: A Unified Feature Map Compression Framework Based on Transform and Entropy

The massive memory accesses of feature maps (FMs) in deep neural network (DNN) processors lead to huge power consumption, which becomes a major energy bottleneck of DNN accelerators. In this article, we propose a unified framework named Transform and Entropy-based COmpression (TECO) scheme to efficiently compress FMs with various attributes in DNN inference. We explore, for the first time, the intrinsic unimodal distribution characteristic that widely exists in the frequency domain of various FMs. In addition, a well-optimized hardware-friendly coding scheme is designed, which fully utilizes this remarkable data distribution characteristic to encode and compress the frequency spectrum of different FMs. Furthermore, the information entropy theory is leveraged to develop a novel loss function for improving the compression ratio and to make a fast comparison among different compressors. Extensive experiments are performed on multiple tasks and demonstrate that the proposed TECO achieves compression ratios of 2.31 × in ResNet-50 on image classification, 3.47 × in UNet on dark image enhancement, and 3.18 × in Yolo-v4 on object detection while keeping the accuracy of these models. Compared with the upper limit of the compression ratio for original FMs, the proposed framework achieves the compression ratio improvement of 21%, 157%, and 152% on the above models.
Online Active Continual Learning for Robotic Lifelong Object Recognition

In real-world applications, robotic systems collect vast amounts of new data from ever-changing environments over time. They need to continually interact and learn new knowledge from the external world to adapt to the environment. Particularly, lifelong object recognition in an online and interactive manner is a crucial and fundamental capability for robotic systems. To meet this realistic demand, in this article, we propose an online active continual learning (OACL) framework for robotic lifelong object recognition, in the scenario of both classes and domains changing with dynamic environments. First, to reduce the labeling cost as much as possible while maximizing the performance, a new online active learning (OAL) strategy is designed by taking both the uncertainty and diversity of samples into account to protect the information volume and distribution of data. In addition, to prevent catastrophic forgetting and reduce memory costs, a novel online continual learning (OCL) algorithm is proposed based on the deep feature semantic augmentation and a new loss-based deep model and replay buffer update, which can mitigate the class imbalance between the old and new classes and alleviate confusion between two similar classes. Moreover, the mistake bound of the proposed method is analyzed in theory. OACL allows robots to select the most representative new samples to query labels and continually learn new objects and new variants of previously learned objects from a nonindependent and identically distributed (i.i.d.) data stream without catastrophic forgetting. Extensive experiments conducted on real lifelong robotic vision datasets demonstrate that our algorithm, even trained with fewer labeled samples and replay exemplars, can achieve state-of-the-art performance on OCL tasks.
Efficient Homomorphic Evaluation of Arbitrary Uni/Bivariate Integer Functions and Their Applications

We propose how to homomorphically evaluate arbitrary univariate and bivariate integer functions such as division. A prior work proposed by Okada et al. (WISTP'18) uses polynomial evaluations such that the scheme is still compatible with the SIMD operations in BFV and BGV schemes, and is implemented with the input domain 𝕫257. However, the scheme of Okada et al. requires the quadratic numbers of plaintext-ciphertext multiplications and ciphertext-ciphertext additions in the input domain size, and although these operations are more lightweight than the ciphertext-ciphertext multiplication, the quadratic complexity makes handling larger inputs quite inefficient. In this work, first we improve the prior work and also propose a new approach that exploits the packing method to handle the larger input domain size instead of enabling the SIMD operation, thus making it possible to work with the larger input domain size, e.g., 𝕫215 in a reasonably efficient way. In addition, we show how to slightly extend the input domain size to 𝕫216 with a relatively moderate overhead. Further we show another approach to handling the larger input domain size by using two ciphertexts to encrypt one integer plaintext and applying our techniques for uni/bivariate function evaluation. We implement the prior work of Okada et al., our improved version of Okada et al., and our new scheme in PALISADE with the input domain 𝕫215, and confirm that the estimated run-times of the prior work and our improved version of the prior work are still about 117 days and 59 days respectively while our new scheme can be computed in 307 seconds.
Spelling Techniques

Phonology is the ability to map letters to sounds which is required when spelling words. However, phonological processing and memory is usually impacted by dyslexia which means that dyslexic learners can have difficulty hearing the different small sounds in words (phonemes) and struggle to break words into smaller parts to spell them. Thus, this chapter provides strategies to assist with spelling difficulties. These techniques range from using mnemonics which involves using memorable phrases or rhymes to help with remembering the spelling of difficult words, using the process of sounding out words, not phonetically, but as the word is spelt, using repetition by using the Copy, Cover, Compare (CCC) technique combined with sounding it out, the Look, Say, Cover, Write, Check method, copying words down from academic papers, or dictionaries and thesauruses to become familiar with the visualisation of the word, and using technology, such as Grammarly, the spelling and grammar checker on word processing software, and / or Google dictionary.
Augmented Reality Based Brain Tumor 3D Visualization

&lt;p&gt;In this paper we present an augmented reality system for mobile devices that facilitates 3D brain tumor visualization in real time. The system uses facial features to track the subject in the scene. The system performs camera calibration based on the face size of the subject, instead of the common approach of using a number of chessboard images to calibrate the camera every time the application is installed on a new device. Camera 3D pose estimation is performed by finding its position and orientation based on a set of 3D points and their corresponding 2D projections. According to the estimated camera pose, a reconstructed brain tumor model is displayed at the same location as the subject’s real anatomy. The results of our experiment show the system was successful in performing the brain tumor augmentation in real time with a reprojection accuracy of 97%.&lt;/p&gt; &lt;p&gt; &lt;/p&gt;
Rallying Around Empty Signifiers: Understanding and Defiing Anit-Givernemnt Protest in the Netherlands

As in many other parts of Europe, anti-government protest has been a topic of political and academic discussion in the Netherlands for some years. The definition of what anti-government protest entails and which groups and individuals it refers to is not as straightforward as it may seem. Where other institutions have attempted to place anti-governmental protest movements in frameworks of traditional extremist movements, the Dutch authorities describe the anti-government protestors as a group that is characterised by its pluriformity and fluidity. In 2022, they have even changed the term ‘anti-governmental’ to ‘anti-institutional’ to more broadly refer to the set of targets that the movement opposes, which includes government, but also the media, the scientific community, experts and the judicial system. In this paper we try to understand the anti-government, or anti-institutional, protest in the Netherlands using a different lens. We use Ernesto Laclau’s discourse theory and its concept of empty signifiers to show how opposed interests and goals can be seen as a unified ideology to its proponents. Instead of trying to primarily define the group of people who are anti-government by their actions or characteristics, we look at how those actors define themselves and how they see their struggle against the authorities. We describe the rise and dynamics of anti-government protest in the Netherlands since the start of the COVID-19 pandemic, focusing on “genealogies” and the eclectic topics of the protest movement. We compare our observations with the definition of anti-governmental protest and the anti-institutional narrative as used by the authorities. We arrive at the conclusion that analysing the anti-government protest through the lens of their shared ideas and grievances gives us more insight into the nature and dynamics of this movement than solely looking through the lens of the potential threat to the
Toxic play: Examining the issue of hate within gaming

This article examines the problem of hate and toxic behavior in gaming. Videogames have risen to become a dominant cultural form, seeing significant increases in players, playtime, and revenue. More people are playing games than ever before, broadening “gamers” into a highly diverse demographic. Yet this rise has been accompanied by a growing recognition of the racism, sexism, xenophobia, and other forms of harassment taking place on these platforms. Hate within gaming creates toxic communities and takes a toll particularly on marginalized groups, raising both ethical and financial issues for the industry, who seek to address this problem in multiple ways. This paper surveys and synthesizes recent research on the topic from both inside and outside academia, laying out the problem, its manifestations, key drivers, and current responses. It concludes with a research agenda that offers a foundation for researchers, policy-makers, and companies to build from.
Analysis of Time Series Water Level Data Prediction Using Deep Learning Method at the Water Gate of DKI Jakarta Water Resources Office

Indonesia has 2 seasons, namely the dry season and the rainy season. During the rainy season, many points in the DKI Jakarta area experience flooding or inundation. The reason why Jakarta often experiences flooding is caused by several factors, including local rain floods, shipment floods and tidal floods. The DKI Jakarta Water Resources Agency currently does not have a system that can predict future water levels by referring to past and present water level data. Through this background, the author tries to conduct research in one of the floodgates in the northern area of DKI Jakarta in predicting water levels using deep learning methods , namely Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). The purpose of this research is to analyze the best deep learning models and predict water level time series data. From the results of the analysis carried out, the best deep learning model is Long Short Term Memory (LSTM) using several tests such as n-input, split data with a composition of 90.33% train data and 9.67% test data , as well as testing of different parameters including epoch, batch size, learning rate, dropout , so the results obtained are the lowest error values with RMSE (17.65), MAPE (0.29), MAE (3.37) and the time needed in the process (runtime) is 39 minutes