Reviewerly/exHarmony huggingface dataset:
Peningkatan Hasil Belajar Penjumlahan dan Pengurangan Bilangan Siswa Kelas I dengan Media Konkret

Mathematics learning is still a problem for many students. This can be seen from the average mathematics scores being lower than other subjects. Of the 23 first grade students at SDN Haurpugur 02 Elementary School, only 8 students or 34.78% have completed mathematics learning, while 15 students or 65.21% have not, especially on addition and subtraction materials. To teach this material, a suitable learning medium is concrete objects so that students get real and concrete learning experiences according to their developmental stage. This classroom action research was conducted by applying concrete object media in learning mathematics on addition and subtraction materials. The results showed an increase in student activity and learning outcomes. In Cycle I using concrete object media, 20 students (62.5%) have completed. In Cycle II the number of students completed increased to 28 students (90.63%). Thus it can be concluded that the use of concrete object media can improve student learning outcomes in addition and subtraction materials.
Techlash, Platformization and the Struggle to Govern Online Content

This chapter adopts a comparative approach to examine the techlash and its aftermath. It begins by identifying major themes in the hearings between global governments and Facebook in 2018 and in the regulatory and policy responses that followed. It then turns to competing efforts to regulate social media through commercial actors and coalitions such as the World Federation of Advertisers. This comparative thematic analysis of the techlash demonstrates pressure on platforms from both governments and commercial partners. The chapter argues that the scale of social media has led private and public regulators to novel compromise positions, including partnerships between civil rights organizations and advertisers, in their attempts to find the pain points of global tech giants. We argue that policymakers’ attempts to apply financial and public relations definitions of good and responsible content in conflicts with the social media companies indicate a failure to challenge the “common sense” of global platform function (Cammaerts and Mansell. International Journal of Communication 14:20, 2020) in the face of the worst that social media has to offer. Looking towards the future, we note the investment in content moderation, rather than questions of transparency, user-targeting or user empowerment, as a point of particular concern.
A Frangi Filter Aided Deep Learning Approach for Paleochannel Recognition

SUMMARY In view of the problem of identifying palaeochannels with high concealment and complex geological structures, this paper proposes an intelligent recognition method for palaeochannels based on Frangi filtering and deep learning. The methodology makes use of Maximum Entropy Wigner–Ville Distribution (MEWVD) method to process the original instantaneous amplitude data, which enhance the distinct features of micro palaeochannels in different sensitive frequency bands. The sample 2-D stratigraphic images generated from these data is labelled for the pre-training process of Attention R2U-Net neural network model. Subsequently, Frangi filter is used to identify and enhance the linear structures of river channels in target stratigraphic images, improving the identification effect of the neural network. Finally, RGB data fusion and 3-D visualization carving are performed on the identification data. This method not only eliminates redundant information using the Frangi filter but also proves that the Attention R2U-Net network model structure with attention mechanism and residual convolution structure can effectively improve the segmentation effect for river channels at different scales. Experimental examples show that this method achieves pixel-level feature segmentation of 3-D seismic data for palaeochannels, accurately depicting their shape, width, thickness, flow direction and other features, thus providing support for subsequent well deployment and horizontal well fracturing selection.
Comparison of Pixel Size and Anatomy Information of Radiograph Digital Image using DICOM viewer and Whatsapp

Technological advancement provides the biggest impact on the administration and information delivery system in hospitals. The adoption of a Paperless policy by hospitals demands that all activities involving information delivery to be done digitally. The availability of relevant media such as smartphones serve to support the adoption of such a policy. Patients who required the services of the Radiology Facility used to be given printed rontgen images to assist their doctors in their diagnosis, but with the emergence of digitalization, the patients are now provided with digital images that are distributed via WhatsApp. Such a method can also be applied to Radiograph interpretation, where information can be delivered remotely through the usage of PACs and/or WhatsApp. The interpretation standard for radiographs utilizes the DICOM Viewer, which means that recent social media developments offer an increased ease of access to the interpretation process by way of WhatsApp. The image that is sent via WhatsApp typically gets compressed, which consequently causes a loss of information in the image. This study aims to compare doctorâ€™s opinions and image quality viewed between WhatsApp and DICOM viewer. This study is a quanitative comparative study through documentation, observation, and data collecting questionnaire. The study found that the image that was sent using WhatsApp suffered a decrease in size and pixel quantity. Therefore, the anatomical information provided by the two radiographs were different.
FLAME-Based Multi-view 3D Face Reconstruction

At present, face 3D reconstruction has broad application prospects in various fields, but the research on it is still in the development stage. In this paper, we hope to achieve better face 3D reconstruction quality by combining a multi-view training framework with face parametric model FLAME, and propose a multi-view training and testing model MFNet (Multi-view FLAME Network). We build a self-supervised training framework and implement constraints such as multi-view optical flow loss function and face landmark loss, and finally obtain a complete MFNet. We propose innovative implementations of multi-view optical flow loss and the covisible mask. We test our model on AFLW and facescape datasets and also take pictures of our faces to reconstruct 3D faces while simulating actual scenarios as much as possible, which achieves good results. Our work mainly addresses the problem of combining parametric models of faces with multi-view face 3D reconstruction and explores the implementation of a FLAME-based multi-view training and testing framework for contributing to the field of face 3D reconstruction.
Knowledge-integrated Multi-modal Movie Turning Point identification

The rapid development of artificial intelligence provides rich technologies and tools for the automated understanding of literary works. As a comprehensive carrier of storylines, movies are natural multimodal data sources that provide sufficient data foundations, and how to fully leverage the benefits of data remains a sustainable research hotspot. In addition, the efficient representation of multi-source data also poses new challenges for information fusion technology. Therefore, we propose a knowledge-enhanced turning points identification (KTPi) method for multimodal scene recognition. First, the BiLSTM method is used to encode scene text and integrate contextual information into scene representations to complete text sequence modeling. Then, the graph structure is used to model all scenes, which strengthens long-range semantic dependencies between scenes and enhances scene representations using graph convolution network. After, the self-supervised method is used to obtain the optimal number of neighboring nodes in sparse graph. Next, actor and verb knowledge involved in the scene text are added to the multimodal data to enhance the diversity of scene feature expressions. Finally, the teacher-student network strategy is used to train the KTPi model. Experimental results show that KTPi outperforms baseline methods in scene role recognition tasks, and ablation experiments show that incorporating knowledge into multimodal model can improve its performance.
A ReSTIR GI Method Using the Sample-Space Filtering

In real-time ray tracing applications, only a small number of samples per pixel can be traced, limited by the computational power of the hardware. Maximizing the rendering quality with a low sampling rate is an important problem for real-time ray tracing. Reservoir-based spatiotemporal importance resampling with multi-bounce global illumination (ReSTIR GI) improves the rendering quality of a low sampling rate. However, the noise introduced by Monte Carlo sampling still exists. We propose a lightweight and efficient sample-space filtering method applied to ReSTIR GI that filters the sample distribution before resampling, thus reducing the noise in the final rendering result. Compared to the original ReSTIR GI, our method achieves a smaller mean squared error (MSE) of 1.1 $$\times $$ to 5.6 $$\times $$ and a higher peak signal-to-noise ratio (PSNR) of 1.1 $$\times $$ at the cost of an average increase in rendering time of 12%.
Graph Time-series Modeling in Deep Learning: A Survey

Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains. Both topics have been separately explored in the field of deep learning. For time-series modeling, recurrent neural networks or convolutional neural networks model the relations between values across time steps, while for graph modeling, graph neural networks model the inter-relations between nodes. Recent research in deep learning requires simultaneous modeling for time-series and graphs when both representations are present. For example, both types of modeling are necessary for time-series classification, regression, and anomaly detection in graphs. This paper aims to provide a comprehensive summary of these models, which we call graph time-series models. To the best of our knowledge, this is the first survey paper that provides a picture of related models from the perspective of deep graph time-series modeling to address a range of time-series tasks, including regression, classification, and anomaly detection. Graph time-series models are split into two categories, a) graph recurrent/convolutional neural networks and b) graph attention neural networks. Under each category, we further categorize models based on their properties. Additionally, we compare representative models and discuss how distinctive model characteristics are utilized with respect to various model components and data challenges. Pointers to commonly used datasets and code are included to facilitate access for further research. In the end, we discuss potential directions for future research.
MSINET: Multi-scale Interconnection Network for Medical Image Segmentation

In this work, an improved end-to-end U-Net structure, a hierarchical multi-scale interconnection network (HMINet), is proposed to make full use of the information contained in different feature maps in encoders and decoders to improve the accuracy of medical image segmentation. The network consists of two main components: a multi-scale fusion unit (MSF) and a multi-head feature enhancement unit (MFE). In the encoder part, the multi-scale fusion unit is used to fuse the information between the feature maps of different scales. By using convolution at different levels, a wider range of context information can be captured and fused into a more comprehensive representation of features. In the decoder part, multiple feature enhancement units can fully pay attention to the coordinates and channel information between feature maps, and then splice the encoded feature maps step by step to maximize the use of information from different feature maps. These feature maps are joined by a well-designed skip connection mechanism to retain more feature information and minimize information loss. The proposed method is tested on four public medical datasets and compared with other classical image segmentation models. The results show that HMINet can significantly improve the accuracy of medical image segmentation tasks and exceed the performance of other models in most cases.
A Multi-dimensional Unified Concavity and Convexity Detection Method Based on Geometric Algebra

The detection of concavity and convexity of vertices and edges of three-dimensional (3D) geometric objects is a classic problem in the field of computer graphics. As the foundation of other related graphics algorithms and operations, scholars have proposed many algorithms for determining the concavity and convexity of vertices and edges. However, existing concavity and convexity detection algorithms mainly focus on vertices and not on concavity and convexity detection methods for edges of 3D geometric objects. Furthermore, existing algorithms often require different detection methods when dealing with two-dimensional (2D) planar geometric objects and 3D spatial geometric objects. This means that the algorithm structure of those algorithms becomes very complex when dealing with concavity and convexity judgments involving both planar polygon vertices and 3D geometric object edges. To solve the above problems, this paper proposes a multi-dimensional unified concave convex detection algorithm framework for geometric objects taking advantage of geometric algebra in multi-dimensional unified expression and calculation. The method proposed in this article can not only achieve concavity and convexity detection of planar polygon vertices and 3D geometric object vertices based on unified rules, but also further achieve concavity and convexity detection of 3D geometric object edges on this basis. By unifying the framework and detection rules of different dimensional geometric object concavity detection algorithms, the complexity of synchronous detection algorithms for planar polygon vertices and 3D geometric object vertices and edges concavity can be effectively simplified.
Cross-Modal Information Aggregation and Distribution Method for Crowd Counting

Crowd counting is a fundamental and challenging task in computer vision. However, existing methods are relatively limited in dealing with scale and illumination changes simultaneously. To improve the accuracy of crowd counting and address the challenges of illumination and scale changes, we adopt the concept of crowding degree information. Due to the fact that a count map can accurately obtain the population in an image and solve the occlusion problem, we use the count map as a specific form of crowding degree information and propose a new cross-modal information aggregation and distribution model for crowd counting. We first input the crowding degree information into LibraNet and modify it with Information Aggregation Transfer (IAT) and Information Distribution Transfer (IDT) modules to obtain a count map. Then, light information, thermal information and crowding degree information are respectively input into the network through RGB image, themal image, and count map. A more accurate density map can be obtained through multiple convolution operations and IADM processing to improve counting accuracy. Finally, the density map is integrated to obtain the number of people. Experiments demonstrate that our methods provide superior quality and higher parallelism. Therefore, we can obtain higher-accuracy density maps by using light information, thermal information, and crowding degree information.
Research on cloud robot security strategy based on chaos encryption

Summary This article proposes a security strategy based on chaos encryption for the transmission of robot data on cloud platforms. Based on chaos encryption technology, the data information collected by the robot is encrypted before transmission. After receiving the request, the cloud platform server uses chaos decryption technology to analyze the ciphertext information and obtain the original relevant data information. The security strategy proposed in this article is simple and feasible, and can resist all kinds of security attacks.
Continuous Exploration via Multiple Perspectives in Sparse Reward Environment

Exploration is a major challenge in deep reinforcement learning, especially in cases where reward is sparse. Simple random exploration strategies, such as $$\epsilon $$ -greedy, struggle to solve the hard exploration problem in the sparse reward environment. A more effective approach to solve the hard exploration problem in the sparse reward environment is to use an exploration strategy based on intrinsic motivation, where the key point is to design reasonable and effective intrinsic reward to drive the agent to explore. This paper proposes a method called CEMP, which drives the agent to explore more effectively and continuously in the sparse reward environment. CEMP contributes a new framework for designing intrinsic reward from multiple perspectives, and can be easily integrated into various existing reinforcement learning algorithms. In addition, experimental results in a series of complex and sparse reward environments in MiniGrid demonstrate that our proposed CEMP method achieves better final performance and faster learning efficiency than ICM, RIDE, and TRPO-AE-Hash, which only calculate intrinsic reward from a single perspective.
Loss Filtering Factor for Crowd Counting

In crowd counting datasets, each person is annotated by a point, typically representing the center of the head. However, due to the dense crowd, variety of scenarios, significant obscuration and low resolution, label noise in the dataset is inevitable and such label noise has a negative impact on the performance of the model. To alleviate the negative effects of label noise, in this paper we propose the Loss Filtering Factor, which can filter out the losses assumed to be caused by label noise during the training process. By doing so, the model can prioritize non-noise data and focus on it during training and predicting. Extensive experimental evaluations have demonstrated that the proposed Loss Filtering Factor consistently improves the performance of all models across all datasets used in the experiments. On average, it leads to a 5.48% improvement in MAE and 6.43% in MSE. Moreover, the proposed approach is universal and it can be easily implemented into any neural network model architecture to improve performance.
Convex Hull Collaborative Representation Learning on Grassmann Manifold with $$L_1$$ Norm Regularization

Collaborative representation learning mechanism has recently attracted great interest in computer vision and pattern recognition. Previous image set classification methods mainly focus on deriving collaborative representation models on Euclidean space. However, the underlying manifold geometry structure of the image set is not well considered. In this paper, we propose a novel manifold convex hull collaborative representation framework with $$L_1$$ norm regularization from geometry-aware perspective. Our model achieves the goal of inheriting the highly expressive representation capability of the Grassmann manifold, while also maintaining the flexible nature of the convex hull model. Notably, the collaborative representation mechanism emphasizes the exploration of connections between different convex hulls on Grassmann manifold. Besides, we regularize the collaborative representation coefficients by using the $$L_1$$ norm, which exhibits superior noise robustness and satisfies the data reconstruction requirements. Extensive experiments and comprehensive comparisons demonstrate the effectiveness of our method over other image set classification methods.
Causal Discovery via the Subsample Based Reward and Punishment Mechanism

The discovery of causal relationships between variables from a large number of nonlinear observations is an important research direction in the field of data mining. Extensive studies have highlighted the challenge of constructing accurate causal networks using existing algorithms using large-scale, nonlinear, and high-dimensional data. To address the challenge of this, we propose a general method for causal discovery algorithms applicable to handling large sample data, namely the subsample based reward and punishment mechanism (SRPM) method, which can handle nonlinear large sample data more effectively. We mainly made three contributions. First, we determine the size of the sample split based on experiments to obtain better learning results. Secondly, we developed a reward and punishment mechanism where each sub-sample dynamically changes its own weight in the process of learning the network skeleton, and proposed the SRPM, a subsample method based on the reward and punishment mechanism. Finally, we combine SRPM with three different additive noise model structure learning algorithms applicable to non-Gaussian nonlinear data respectively, and demonstrate the effectiveness of the method through experiments on data generated by a variety of nonlinear function dependencies. Compared with the existing algorithms, the causal network construction algorithm based on SRPM method has a great improvement in accuracy and time performance, and the effectiveness of the method is also verified in the real power plant data.
Explore Across-Dimensional Feature Correlations for Few-Shot Learning

Few-shot learning (FSL) aims to learn new concepts with only few examples, which is a challenging problem in deep learning. Attention-oriented methods have shown great potential in addressing the FSL problem. However, many of these methods tend to separately focus on different dimensions of the samples, like channel dimension or spatial dimension only, which may lead to limitations in extracting discriminative features. To address this problem, we propose an across-dimensional attention network (ADANet) to explore the feature correlations across channel and spatial dimensions of input samples. The ADANet can capture across-dimensional feature dependencies and produce reliable representations for the similarity metric. In addition, we also design a three-dimensional offset position encoding (TOPE) method that embeds the 3D position information into the across-dimensional attention, enhancing the robustness and generalization ability of the model. Extensive experiments on standard FSL benchmarks have demonstrated that our method can achieve better performances compared to the state-of-the-art approaches.
Network Transplanting for the Functionally Modular Architecture

This paper focuses on the problem of transplanting category-and-task-specific neural networks to a generic, modular network without strong supervision. Unlike traditional deep neural networks (DNNs) with black-box representations, we design a functionally modular network architecture, which divides the entire DNN into several functionally meaningful modules. Like building LEGO blocks, we can teach the proposed DNN a new object category by directly transplanting the module corresponding to the object category from another DNN, with a few or even without sample annotations. Our method incrementally adds new categories to the DNN, which do not affect representations of existing categories. Such a strategy of incremental network transplanting can avoid the typical catastrophic-forgetting problem in continual learning. We further develop a back distillation method to overcome challenges of model optimization in network transplanting. In experiments, our method with much fewer training samples outperformed baselines.