Reviewerly/exHarmony huggingface dataset:
Challenges and interventions in developing instructional materials: Perspectives of public school teachers in basic education

This study aimed to explore the challenges faced by public school teachers in Bauko District II in developing instructional materials. The researchers used a quantitative method and distributed questionnaires to 150 teachers across the district as their primary means of data collection. This method allowed the researchers to gain insights from a substantial number of teachers, facilitating a comprehensive understanding of the challenges they faced. The results of the study highlighted two primary critical issues faced by teachers: the scarcity of time due to heavy workloads and the difficulty of keeping pace with technological advancements. Additionally, teachers reported significant challenges related to insufficient skills, knowledge and understanding of instructional materials, limited policy support and inadequate administrative assistance. In accordance with these findings, the study demonstrated some significant ways to improve the creation of instructional materials in rural communities with limited resources. The study emphasised the need for computer literacy training as a result of the expanding digitalization of instructional content. A functional instructional material development committee should be established to improve the process as well as organizing training workshops for instructional material development and encouraging partnerships with higher education institutions (HEIs) to gain access to specialized expertise. The study strongly advocates for the implementation of sustainable capacity-building mechanisms by the department of education such as regular workshops and follow-up sessions to provide continuous support to teachers in material development. Moreover, the study proposed fostering collaboration with other institutions and inviting experts as resource persons to seminars and workshops to enhance teachers' proficiency in crafting effective instructional materials.
Data Collection and Safety Use Cases in Smart Infrastructures

Smart infrastructures provide the opportunity to study the interaction between traffic participants in various situations like dense or little traffic, daytime or nighttime, sunny or rainy weather, etc. Such a smart infrastructure, a High-Definition-Testfield (HDT), has been set up in Ingolstadt, which allows studying the interaction between traffic participants and user interfaces in intersections. The HDT comprises three highly congested intersections with local sensor technology, i. e., LiDAR scanners and infrared cameras. A part of the HDT has a physical twin on the CARISSMA outdoor facility, allowing to collect data also in critical traffic scenarios, that are rarely observed in real traffic. This work presents the setting to collect data with a focus on critical scenarios and safety use cases. User interfaces for future mobility solutions have to take into account the information from smart infrastructures as well as use cases that can be implemented in such an environment.
Scrambling of definite object NPs in Dutch

The canonical view on Dutch scrambling in the syntactic literature is that it is subject to information-structural conditions: “new” information is located in a right-peripheral position in the middle field (Jan heeft waarschijnlijk het boek gelezen ‘Jan has probably read the book’) while given information is located in a more leftward position (Jan heeft het boek waarschijnlijk gelezen); see e.g. Verhagen (1986), Neeleman (1994a/b), and Broekhuis (2008). The generally accepted idea that scrambling of “given” definite NP-objects is obligatory was contradicted in Van der Does &amp; De Hoop (1998). Subsequent studies attempted to resolve this intuition conflict through corpus and experimental studies. This article critically reviews these studies and concludes that their results are unsuitable for evaluating the claim made in current generative grammar that object scrambling involves A-movement of the object into its case position (Broekhuis 2009/2020). The article ends with recommendations that can help overcome this shortcoming.
Improving the Effectiveness of Hybrid Learning Through the Computer-Supported Collaborative Learning Model Facing Pandemic Covid-19

Hybrid Learning using Computer Supported Collaboration Learning (CSCL) is an effort to maximize learning during the Covid-19 pandemic. This study aims to increase the effectiveness of implementing hybrid learning using CSCL in dealing with COVID-19. The research method uses qualitative case studies, and the data is processed using interactive data analysis from Miles and Huberman. The results showed: 1) CSCL learning is a very appropriate model applied to blended learning during the Covid-19 pandemic, this model is able to improve student learning outcomes, students study harder, active, focused, learn quickly, discuss and cooperate in groups, have confidence, manage learning time and help each other in their groups; 2) The CSCL model is more effective compared to conventional learning models because it can create a more active, creative and innovative learning atmosphere; 3) CSCL learning during face-to-face classes is better able to improve student performance and creativity and innovation in learning compared to implementing CSCL in online learning, because students can discuss and cooperate directly; 4) CSCL learning is able to improve student learning outcomes, this condition can be seen from the assignment scores and midle test and final test grades; 5) CSCL learning is able to improve students' ability to discuss and collaborate in groups; 6) CSCL learning is able to overcome the limited internet network in Higher Education.
Fast Dust Sand Image Enhancement Based on Color Correction and New Fuzzy Intensification Operators

Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and New Fuzzy Intensification Operators to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a fuzzy intensification operator to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.
Efficient Hyperspectral Data Processing using File Fragmentation

In this article, we present a method for processing hyperspectral data in an easy and quick manner. We explain how we split the hyperspectral cube in different sections to be processed using fewer resources. We describe the processing, which includes extraction of the raw data along with white and black calibration data, calibration of the data and application of desired light source, color space, and gamma transformation. We then present a built-in software, including an easy interactive Graphical User Interface (GUI) that will allow fellow researchers to process hyperspectral images in a simple fashion.
The Seven Standards of Textuality in News Texts: A Discourse Analysis

The textuality of a text is important to decide whether a text could be considered a textual text or not. This study examines the textuality of some news articles about a recent trending topic that has drawn the world’s attention as it took many people’s lives: the Kanjuruhan tragedy. The data of this study were words, phrases, and sentences taken from five news articles which were then analyzed using de Beaugrande and Dressler’s theory concerning the seven standards that make a text considered acceptable or unacceptable in terms of textuality. The study revealed that those five texts have fulfilled all standards of textuality, which cover Cohesion, Coherence, Intentionality, Acceptability, Informativity, Situationality, and Intertextuality. Thus, those five articles can be considered trustworthy texts.
Transparent Embedding Space for Interpretable Image Recognition

When humans explain their reasoning, such as their classification decisions, they often break down an image into parts and highlight the evidence from those parts to support the concepts they have in mind. Drawing inspiration from this cognitive process, several self-explaining models have been proposed to explain predictions by part-level concepts. However, these models can be limited by their structure and difficulty in determining the effect of individual parts on the output category. To address these challenges, we introduce a self-explaining architecture that uses a plug-in transparent embedding space (TesNet) to connect high-level input patches (e.g. feature maps or tokens) with output categories. The transparent embedding space is spanned by basis concepts and constructed on the Grassmann manifold. The basis concepts are enforced to be category-aware, and within-category concepts are orthogonal to each other, ensuring the embedding space is disentangled. To reduce concept redundancy and restore the concept space structure, we introduce two concept pruning methods and a new re-training strategy to build a slimming transparent embedding space. We verify the scalability of TesNet through experiments on deep networks such as VGG, ResNet, DenseNet, and Vision Transformer. Additionally, we design several metrics for self-explaining models to quantify interpretability and compare them with state-of-the-art self-explaining methods. Our experiments demonstrate that TesNet is much more effective for classification tasks, providing better interpretability on predictions and improving final accuracy.
UFinAKA: Fingerprint-Based Authentication and Key Agreement With Updatable Blind Credentials

Authentication and key agreement are two basic functionalities to guarantee secure network communications, which are naturally integrated as an Authentication and Key Agreement (AKA) protocol. AKAs usually either need a dedicated device to store a cryptographic key or require the user to remember a password. In recent years, AKAs built on biometrics, e.g., human fingerprints, have gained research attention since they avoid these issues. Unlike keys or passwords that can be updated, biometrics are at greater risk that cannot be reused once disclosed. However, existing mechanisms either explicitly expose the biometrics to the server or consume a massive amount of resources. This paper proposes UFinAKA, a privacy-preserving fingerprint-based authentication and key agreement system with updatable blind credentials. UFinAKA explores a fingerprint-based blind credential authentication scheme as a building block such that the server has no access to the fingerprint data hidden within the credential. Furthermore, UFinAKA provides an updatable fingerprint-based credentials AKA protocol, which allows the server to update the blind credentials and guarantees anonymous fingerprint authentication to mitigate further leakage when the server is corrupted. We perform security analysis and experimental evaluation on UFinAKA. The evaluation results show that UFinAKA requires only linear computation overhead for the client, a single round of interaction, and roughly linear computation and storage cost for the server. The running time of UFinAKA is at least 4 times faster than the state-of-the-art solutions, and the storage cost of these solutions is at least 100 times more than UFinAKA.
RemovalNet: DNN Fingerprint Removal Attacks

With the performance of deep neural networks (DNNs) remarkably improving, DNNs have been widely used in many areas. Consequently, the DNN model has become a valuable asset, and its intellectual property is safeguarded by ownership verification techniques (e.g., DNN fingerprinting). However, the feasibility of the DNN fingerprint removal attack and its potential influence remains an open problem. In this paper, we perform the first comprehensive investigation of DNN fingerprint removal attacks. Generally, the knowledge contained in a DNN model can be categorized into general semantic and fingerprint-specific knowledge. To this end, we propose a min-max bilevel optimization-based DNN fingerprint removal attack named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RemovalNet</small> , to evade model ownership verification. The lower-level optimization is designed to remove fingerprint-specific knowledge. While in the upper-level optimization, we distill the victim model's general semantic knowledge to maintain the surrogate model's performance. We conduct extensive experiments to evaluate the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fidelity</b> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effectiveness</b> , and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficiency</b> of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RemovalNet</small> against four advanced defense methods on six metrics. The empirical results demonstrate that (1) the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RemovalNet</small> is <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective</b> . After our DNN fingerprint removal attack, the model distance between the target and surrogate models is ×100 times higher than that of the baseline attacks, (2) the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RemovalNet</small> is <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient</b> . It uses only 0.2% (400 samples) of the substitute dataset and 1,000 iterations to conduct our attack. Besides, compared with advanced model stealing attacks, the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RemovalNet</small> saves nearly 85% of computational resources at most, (3) the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RemovalNet</small> achieves high <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fidelity</b> that the created surrogate model maintains high accuracy after the DNN fingerprint removal process.
KEPRAKTISAN ENSIKLOPEDIA FAMILI RUTACEAE DI KEBUN RAYA BANUA UNTUK MELATIHKAN KETERAMPILAN BERPIKIR KRITIS MAHASISWA

Education is an effort to develop individual abilities and character through various processes such as guidance, teaching, training, and interaction with the environment. 21st century learning emphasizes critical thinking skills and environmental approaches related to the phrases think globally and act locally through the development of encyclopedias at Kebun Raya Banua. An encyclopedia is a type of learning resource that contains descriptions containing knowledge and is arranged (alphabetically) or according to the scientific environment by collaborating on text and images. This study aims to describe the practicality of the Rutaceae family encyclopedia at Kebun Raya Banua. The research method used is the EDR (Educational Design Research) method with Tessmer's formative evaluation design. The one-to-one test subjects were three students and the small group test subjects were four students. The object of this research is the encyclopedia of the Rutaceae family. The results of the study show that the practicality of the content is categorized as good and the practicality of expectations is categorized as very good. This shows that the encyclopedia of the rutaceae family at Kebun Raya Banua is as expected interesting and easy to use to train students' critical thinking skills (practical).Â Kata kunci: ensiklopedia, famili rutaceae, kepraktisan, penelitian pengembangan
Exploiting the Illumination Knowledge in Real World for Low-Light Image Enhancement

Recently, deep learning-based Low-light image enhancement (LLIE) methods achieved promising results with large collections of data. However, low-/normal-light image pairs are difficult to obtain in practice. Despite that the indoor training pairs may be synthesized by manually adjusting light exposure, there is currently no outdoor LLIE benchmark captured in the real world. To bridge the gap and benchmark outdoor LLIE tasks, we propose the first outdoor REal-world two-dimensional Low-light dataset, dubbed RE2L. Based on RE2L, we propose a semi-supervised LLIE framework to further exploit the illumination knowledge from both the signal fidelity constraint and characteristics of normal-light natural images. As the images in our RE2L dataset have various illumination conditions, we propose to integrate the illumination degree information into the generator as guidance for controllable LLIE. Experimental results demonstrate that using RE2L for training deep LLIE schemes can improve their model effectiveness, both quantitatively and visually, especially on SS-LLIE.
Rectify ViT Shortcut Learning by Visual Saliency

Shortcut learning in deep learning models occurs when unintended features are prioritized, resulting in degenerated feature representations and reduced generalizability and interpretability. However, shortcut learning in the widely used vision transformer (ViT) framework is largely unknown. Meanwhile, introducing domain-specific knowledge is a major approach to rectifying the shortcuts that are predominated by background-related factors. For example, eye-gaze data from radiologists are effective human visual prior knowledge that has the great potential to guide the deep learning models to focus on meaningful foreground regions. However, obtaining eye-gaze data can still sometimes be time-consuming, labor-intensive, and even impractical. In this work, we propose a novel and effective saliency-guided ViT (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data. Specifically, a computational visual saliency model (either pretrained or fine-tuned) is adopted to predict saliency maps for input image samples. Then, the saliency maps are used to filter the most informative image patches. Considering that this filter operation may lead to global information loss, we further introduce a residual connection that calculates the self-attention across all the image patches. The experiment results on natural and medical image datasets show that our SGT framework can effectively learn and leverage human prior knowledge without eye-gaze data and achieves much better performance than baselines. Meanwhile, it successfully rectifies the harmful shortcut learning and significantly improves the interpretability of the ViT model, demonstrating the promise of transferring human prior knowledge derived visual saliency in rectifying shortcut learning.
DW-GAN: Toward High-Fidelity Color-Tones of GAN-Generated Images With Dynamic Weights

Color-tone represents the prominent color of an image, and training generative adversarial nets (GAN) to change color-tones of generated images is desirable in many applications. Advances such as HistoGAN can manipulate color-tones of generated images with a target image. Yet, there are challenges. Kullback-Leibler (KL) divergence adopted by HistoGAN might bring the color-tone mismatching, because it is possible to provide infinite score to a generator. Moreover, only relying on distribution estimation also produces images with lower fidelity in HistoGAN. To address these issues, we propose a new approach, named dynamic weights GAN (DW-GAN). We use two discriminators to estimate the distribution matching degree and details' similarity, with Laplacian operator and Hinge loss. Laplacian operator can help capture more image details, while Hinge loss is deduced from mean difference (MD) that could avoid the case of infinite score. To synthesize desired images, we combine the loss of the two discriminators with generator loss and set the weights of the two estimated scores to be dynamic through the previous discriminators' outputs, given that the training signal of a generator is from a discriminator. Besides, we innovatively integrate the dynamic weights into other GAN variants (e.g., HistoGAN and StyleGAN) to show the improved performance. Finally, we conduct extensive experiments on one industrial Fabric and seven public datasets to demonstrate the significant performance of DW-GAN in producing higher fidelity images and achieving the lowest Frechet inception distance (FID) scores over SOTA baselines.
Improving brain tumor segmentation with anatomical prior-informed pre-training

Introduction Precise delineation of glioblastoma in multi-parameter magnetic resonance images is pivotal for neurosurgery and subsequent treatment monitoring. Transformer models have shown promise in brain tumor segmentation, but their efficacy heavily depends on a substantial amount of annotated data. To address the scarcity of annotated data and improve model robustness, self-supervised learning methods using masked autoencoders have been devised. Nevertheless, these methods have not incorporated the anatomical priors of brain structures. Methods This study proposed an anatomical prior-informed masking strategy to enhance the pre-training of masked autoencoders, which combines data-driven reconstruction with anatomical knowledge. We investigate the likelihood of tumor presence in various brain structures, and this information is then utilized to guide the masking procedure. Results Compared with random masking, our method enables the pre-training to concentrate on regions that are more pertinent to downstream segmentation. Experiments conducted on the BraTS21 dataset demonstrate that our proposed method surpasses the performance of state-of-the-art self-supervised learning techniques. It enhances brain tumor segmentation in terms of both accuracy and data efficiency. Discussion Tailored mechanisms designed to extract valuable information from extensive data could enhance computational efficiency and performance, resulting in increased precision. It's still promising to integrate anatomical priors and vision approaches.
Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach

Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts. Methods: In this paper, we propose a machine learning approach for evaluating the difficulty level of handwriting tasks. We propose two convolutional neural network (CNN) models for single- and multilabel classification where single-label classification is based on the mean of expert evaluation while the multilabel classification predicts the distribution of experts’ assessment. The models are trained with a dataset containing 117 spatio-temporal features from the stylus and hand kinematics, which are recorded for all letters of the Arabic alphabet. Results: While single- and multilabel classification models achieve decent accuracy (96% and 88% respectively) using all features, the hand kinematics features do not significantly influence the performance of the models. Discussion: The proposed models are capable of extracting meaningful features from the handwriting samples and predicting their difficulty levels accurately. The proposed approach has the potential to be used to personalize handwriting learning tools and provide automatic evaluation of the quality of handwriting.