Reviewerly/exHarmony huggingface dataset:
Upaya Peningkatan Literasi-Numerasi Peserta Didik dengan Pembuatan Pojok Baca dan Majalah Dinding Berhitung Kelas 5 di SD Negeri Pilang 1

Literacy and Numeracy are things that are not common in the world of education. Literacy and numeracy are needed when implementing the learning process in the classroom to increase students' insight in receiving learning. Therefore, as teachers must have innovation so that students can improve literacy and numeracy, for example by using interesting learning media as student insights. This article was created with the aim of improving students' literacy and numeracy by creating reading corners and counting mading in the classroom using qualitative research methods of classroom actions and case studies with data collection carried out through class observation and class AKM tests. The results obtained from the writing and implementation of this article achieved results with an average of 78 literacy competencies and 80 for numeracy competencies.
Research on NAS-Oriented Blockchain Aggregation Consensus Algorithm

Blockchain technology can be used to solve the problems of single point of failure and lack of trust in network-affiliated storage systems due to the centralized characteristics, but the existing schemes suffer from poor consensus performance and low storage efficiency, etc. To this end, a blockchain aggregation consensus algorithm for NAS is designed. The algorithm ensures fairness by introducing a storage volume probability mechanism, so that the probability of a node obtaining the bookkeeping right is proportional to the size of the storage volume it has, and guarantees uniqueness by designing a position mapping mechanism to ensure that one and only one node obtains the bookkeeping right at each block out round. The bookkeeping node combines the responsibilities of a management node, while using the storage proof mechanism to ensure data security. The experiments show that the system has significantly improved in scalability, openness and security compared with the scheme using traditional consensus algorithm.
A Decentralized Based Approach Using Hybrid Filtered Beam Search Algorithm for Monitoring Patrols

This paper addresses the problem of task allocation for monitoring patrols in industrial areas. The objective is to decentralize the process of task allocation so that each robot can make decisions based on its configuration and environmental information. In this context, we introduce a distributed heuristic approach based on the Hybrid Filtered Beam Search (HFBS) algorithm to determine the optimal trajectory of each robot. In order to validate our approach, a case study in the industrial port area of Le Havre city is presented. The results are therefore promising since each robot calculates its optimal trajectory using only measurements from sites it can process rather than using all environmental data.
Green Post Development Strategy in the Context of Ecological Civilization

“Green” has become a fundamental requirement for all development initiatives in China. The idea of harmonious coexistence between man and nature has gained significant prominence. General Secretary Xi Jinping places great importance on ecological and environmental protection, offering clear directives for ecological preservation efforts within the delivery industry [1]. He emphasizes the necessity to address packaging waste in online shopping and express delivery, eliminate excessive packaging, and reduce environmental waste and pollution. In this process, China Post will actively respond to the central government’s imperatives and formulate a series of strategies and plans, including green post ideas, transportation, packaging, and finance.
Forensics: Collection of Sound Digital Evidence

This summary paper will discuss the concept of forensic evidence and evidence collection methods. Emphasis will be placed on the techniques used to collect forensically sound digital evidence for the purpose of introduction to digital forensics. This discussion will thereafter result in identifying and categorizing the different types of digital forensics evidence and a clear procedure for how to collect forensically sound digital evidence. This paper will further discuss the creation of awareness and promote the idea that competent practice of computer forensics collection is important for admissibility in court.
What Do We Mean When We Say “Insight”? A Formal Synthesis of Existing Theory

Researchers have derived many theoretical models for specifying users' insights as they interact with a visualization system. These representations are essential for understanding the insight discovery process, such as when inferring user interaction patterns that lead to insight or assessing the rigor of reported insights. However, theoretical models can be difficult to apply to existing tools and user studies, often due to discrepancies in how insight and its constituent parts are defined. This paper calls attention to the consistent structures that recur across the visualization literature and describes how they connect multiple theoretical representations of insight. We synthesize a unified formalism for insights using these structures, enabling a wider audience of researchers and developers to adopt the corresponding models. Through a series of theoretical case studies, we use our formalism to compare and contrast existing theories, revealing interesting research challenges in reasoning about a user's domain knowledge and leveraging synergistic approaches in data mining and data management research.
Video Frame Interpolation With Many-to-Many Splatting and Spatial Selective Refinement

In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirectional flows to directly forward warp the pixels to the desired time step before fusing any overlapping pixels. In doing so, each source pixel renders multiple target pixels and each target pixel can be synthesized from a larger area of visual context, establishing a many-to-many splatting scheme with robustness to undesirable artifacts. For each input frame pair, M2M has a minuscule computational overhead when interpolating an arbitrary number of in-between frames, hence achieving fast multi-frame interpolation. However, directly warping and fusing pixels in the intensity domain is sensitive to the quality of motion estimation and may suffer from less effective representation capacity. To improve interpolation accuracy, we further extend an M2M++ framework by introducing a flexible Spatial Selective Refinement (SSR) component, which allows for trading computational efficiency for interpolation quality and vice versa. Instead of refining the entire interpolated frame, SSR only processes difficult regions selected under the guidance of an estimated error map, thereby avoiding redundant computation. Evaluation on multiple benchmark datasets shows that our method is able to improve the efficiency while maintaining competitive video interpolation quality, and it can be adjusted to use more or less compute as needed.
GPU for Monte Carlo Search

Monte Carlo Search algorithms can give excellent results for some combinatorial optimization problems and for some games. They can be parallelized efficiently on high-end CPU servers. Nested Monte Carlo Search is an algorithm that parallelizes well. We take advantage of this property to obtain large speedups running it on low cost GPUs. The combinatorial optimization problem we use for the experiments is the Snake-in-the-Box. It is a graph theory problem for which Nested Monte Carlo Search previously improved lower bounds. It has applications in electrical engineering, coding theory, and computer network topologies. Using a low cost GPU, we obtain speedups as high as 420 compared to a single CPU.
An Empirical Study on Indigenous Innovation Path of Chinese Enterprises

Ms. Chen Zhili pointed out that indigenous innovation consists of three aspects: first, to strengthen original innovation for more scientific discoveries and technological inventions; second, to strengthen integrated innovation for the organic integration of various related technologies to form products and industries with market competence; and third, to actively promote digestion, absorption and re-innovation based on the import of advanced foreign technologies.
De Hidden Deventer app en Broeders 3D

Amsterdam University Press is a leading publisher of academic books, journals and textbooks in the Humanities and Social Sciences. Our aim is to make current research available to scholars, students, innovators, and the general public. AUP stands for scholarly excellence, global presence, and engagement with the international academic community.
Meta-Learning for Neural Machine Translation

Abstract: Neural Machine Translation (NMT) has significantly advanced the field of automated language translation, yet challenges persist in adapting to diverse language pairs, handling low-resource languages, and ensuring domain-specific translation accuracy. To address these challenges, this study explores the integration of meta-learning methodologies in NMT, aiming to enhance the adaptability and generalization capabilities of translation models. Through a comprehensive analysis of various meta-learning approaches, including Model-Agnostic Meta-Learning (MAML), metric-based meta-learning, and optimization-based meta-learning, we demonstrate the potential for improved translation accuracy and fluency across diverse language pairs and domains. Drawing upon a diverse set of bilingual corpora and employing the Transformer model as the base architecture, our experimental evaluation highlights the substantial performance improvements achieved through the integration of metalearning techniques. The case studies and use cases presented in this study underscore the practical applications of the integrated meta-learning methodologies in facilitating cross-lingual information retrieval, low-resource language localization, specialized domain translation, and multimodal translation. While acknowledging the computational complexity and ethical implications, this study emphasizes the importance of collaborative and interdisciplinary research efforts to advance the development of more adaptive and contextually aware translation systems. The findings and insights presented in this study offer valuable implications for the advancement of NMT and automated language translation practices
Monument Tracker: Deep Learning Approach for Indian Heritage

Abstract: Monuments are physical structures built to commemorate a person or event. Their importance to the region necessitates their documentation and upkeep. Due to the many variations in how various monuments are built, monument recognition is a challenging task in the field of picture classification. The various angles of the building are critical in identifying the monuments in photographs. As more international landmarks and monuments are covered, there is a greater need to connect a structure's physical presence to its digital presence. As a result, the monument's automated recognition is enabled. Monuments represent the culturally rich legacy of people of all ethnicities, castes, and faiths. It reflects tremendous achievements in art and architecture, and it also serves as the backbone of the surrounding region's socioeconomic progress through tourism. As an important historical and cultural heritage asset, the monument must be digitally recognized and archived. The monument photographs should be identified and described to aid in the preservation of people's cultures from various locations. The goal of this project is to present a method for classifying different monuments based on the characteristics of the monument photographs. Machine Learning and Deep Learning are advancing, speeding up advances in image recognition and allowing computer vision to reach new heights. The results with Baseline Model had an overall accuracy of 73.2%. After using Transfer Learning, we achieved an overall accuracy of 94.5% with VGG16 Architecture, Inception with an accuracy of 91.2% and Resnet50 with an accuracy of 85.5%.
Modern Backbone for Efficient Geo-localization

With the development of autonomous driving technology, vision geo-localization has obtained a consistently growing following. How to match correct image pair from different perspectives is the key technology. Existing geo-localization methods focus on designing complex attention mechanism based on traditional backbone, e.g., VGG, ResNet, but neglect the importance of backbone network. In this article, we propose a modern backbone based geo-localization method (MBEG). MBEG introduces the latest vision fundamental network EVA-02 as backbone, which has been fully trained in large datasets. In addition, the feature rotate encoding strategy is presented to eliminate the effects of image rotation. We also apply the knowledge distillation to squeeze network's parameters for actual application. Our work exhibited excellent performance on the University-1652 dataset, and our solution attained the top-1 ranking in the UAVs in Multimedia Challenge for the University-160k dataset.
Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data

Abstract The accurate prediction of patients with complex diseases, such as Alzheimer’s disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropriately represented samples during training when applied to the gene expression data of AD and to cancer stage prediction tasks. Furthermore, we demonstrate that using an embedding vector from JTSC as an input to the classifiers for AD and cancer stage prediction significantly improves classification performance by extracting more accurate features. In conclusion, we show that feature embedding through JTSC can aid in classification when there are a small number of samples compared to a larger number of features.
Combined colour deconvolution and artificial intelligence approach for region‐selective immunohistochemical labelling quantification: The example of alpha smooth muscle actin in mouse kidney

Immunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi-quantitative and exhibits substantial intra- and inter-observer variability. Digital approaches based on stain quantification applied to IHC are precise but still operator-dependent and time-consuming when regions of interest (ROIs) must be defined to quantify protein expression in a specific tissue area. This study aimed at developing an IHC quantification workflow that benefits from colour deconvolution for stain quantification and artificial intelligence for automatic ROI definition. The method was tested on 10 whole slide images (WSI) of alpha-smooth muscle actin (aSMA) stained mouse kidney sections. The task was to identify aSMA-positive areas within the glomeruli automatically. Total aSMA detection was performed using two channels (DAB, haematoxylin) colour deconvolution. Glomeruli segmentation within the same IHC WSI was performed by training a convolutional neural network with annotated examples of glomeruli. For both aSMA and glomeruli, binary masks were created. Co-localisation was performed by overlaying the masks and assigning red/green colours, with yellow indicative of a co-localised signal. The workflow described and exemplified using the case of aSMA expression in glomeruli can be applied to quantify the expression of IHC markers within different structures of immunohistochemically stained slides. The technique is objective, has a fully automated threshold approach (colour deconvolution phase) and uses AI to eliminate operator-dependent steps.
Research on the Teaching Behavior of Physical Education and the Reform of Students' Learning Styles Based on the Background of Core Literacy

With the trend of the development of education globalization and the establishment and completion of the core literacy system, countries have joined the new era of core literacy education. Based on the reform of physical education teaching behavior and students ' learning style under the background of core literacy, this paper studies and analyzes the five aspects of teachers ' language information literacy, students ' self-understanding and practical literacy, teachers and students ' cooperation and reflection literacy, innovation and development literacy, social responsibility and international understanding literacy in the teaching process. It is concluded that under the influence of core literacy background, teachers ' teaching behavior and students ' learning style in the process of physical education teaching can realize the transformation and change, emphasis and inclination of teaching behavior and learning behavior, and strive to construct the socialist core literacy value system with Chinese characteristics in the process of physical education teaching.
Learning Hierarchical Modular Networks for Video Captioning

Video captioning aims to generate natural language descriptions for a given video clip. Existing methods mainly focus on end-to-end representation learning via word-by-word comparison between predicted captions and ground-truth texts. Although significant progress has been made, such supervised approaches neglect semantic alignment between visual and linguistic entities, which may negatively affect the generated captions. In this work, we propose a hierarchical modular network to bridge video representations and linguistic semantics at four granularities before generating captions: entity, verb, predicate, and sentence. Each level is implemented by one module to embed corresponding semantics into video representations. Additionally, we present a reinforcement learning module based on the scene graph of captions to better measure sentence similarity. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art models on three widely-used benchmark datasets, including MSVD, MSR-VTT, and VATEX. The source code and trained models will be made available to the public.
Real-Time Photo Captioning for Assisting Blind and Visually Impaired People Using LSTM Framework

This letter aims to tackle the problem of the visually impaired community by introducing an innovative framework that integrates computer vision with voice-based photo captioning via deep learning methodologies. In this research, a performance of three models was conducted, namely VGGNet-16, ResNet-152, and MobileNet-V3, to identify the best-performing model for hardware deployment. The models are trained using the Flickr30k, Flickr8k, and 2k custom datasets. Subsequently, the best-performed VGGNet-16 model is deployed on a Raspberry Pi 4B single-board computer with a graphics processing unit. To facilitate communication with visually impaired people, the generated captions are transformed into audible speech via text-to-speech synthesis technology. The VGGNet-16 model has superior performance compared to the other two models. The quality of generated captions is assessed using quantitative assessment criteria such as BLEU, BLEURT, and ROUGE scores. Also, input from the visually impaired to assess the usefulness, accessibility, and influence of the framework.
Designing User Interface and User Experience by Using User Centered Design on Gamified Platform "Ezedu"

Online learning activities using games or gamified methods make students motivated, capable, and involved to participate in the learning process. There are several gamified platforms that are already available, but there are several obstacles, for example the user takes a long time to compile quizzes and some features in paid gamified so that feature utilization is not optimal. The purpose of this research is to design the user interface and user experience of the gamified platform "Ezedu". Ezedu is a learning media created lecturers and teachers to design educational games easier. The design method uses the User Centered Design (UCD) method. UCD is a term that describes the design process where the end-user will influence how the design is formed. The results of this study are user interface designs that have been adapted to user needs.
Impact of Use of Prezi-based Media on Learning Outcomes Indonesian Language Class Student XI at Muhammadiyah Rappang Vocational High School

This study aims to determine the effect of using of Prezi-based Media on Learning Outcomes Indonesian Language Class Student XI at Muhammadiyah Rappang Vocational High School.The study uses a quantitative methodology with a true experimental design with a posttest-only control group design. The population of this study was class XI students totaling 30 people with a sample taken from the entire class XI as many as 30 students of class XI Muhammadiyah Rappang and using saturated sampling technique. There are 2 variables in this study, namely the use of Prezi-based media (independent variable) and Indonesian language learning outcomes of grade XI students (dependent variable). This research was conducted in class XI of Muhammadiyah Rappang Vocational High School. Data collection techniques used observation, documentation, and test techniques. The test data that has been collected is then analyzed using the mean formula and t-test. Based on the results of data analysis, the mean value of the experimental group = 77,333 and the mean value of the control group = 52,444. And in the t-test obtained t-count 4,383&gt; from t-table 5% (2,046) and 1% (2,763) with db 28.Based on the results of this study, it can be concluded that the hypothesis (H1) which states the Effect of Prezi-Based Media Use on Indonesian Language Learning Outcomes of Class XI Studentsof Muhammadiyah Rappang Vocational High School is "accepted".
Shifting Perspectives: A proposed framework for analyzing head-mounted eye-tracking data with dynamic areas of interest and dynamic scenes

Eye-tracking is a valuable research method for understanding human cognition and is readily employed in human factors research, including human factors in healthcare. While wearable mobile eye trackers have become more readily available, there are no existing analysis methods for accurately and efficiently mapping dynamic gaze data on dynamic areas of interest (AOIs), which limits their utility in human factors research. The purpose of this paper was to outline a proposed framework for automating the analysis of dynamic areas of interest by integrating computer vision and machine learning (CVML). The framework is then tested using a use-case of a Central Venous Catheterization trainer with six dynamic AOIs. While the results of the validity trial indicate there is room for improvement in the CVML method proposed, the framework provides direction and guidance for human factors researchers using dynamic AOIs.