Reviewerly/exHarmony huggingface dataset:
Adaptive Based Frequency Domain Filter for Periodic Noise Reduction in Images Acquired by Projection Fringes

An application of interest of images 2D is to obtain the shape of 3D objects by fringe projection. However, such images contain periodic or quasi/periodic noise produced during the generation of the images and interference during the transmission of digital images. The application of an adaptive filter based in the frequency domain with a smoothing filter based in the spatial domain reduced the noise from corrupted digital images acquired by projection fringes. This paper presents an algorithm that determines the frequencies corrupted and replaces them with the mean of its neighbors in the frequency domain, then, a smooth filter is applied to the image in the spatial domain. With the implementation of the proposed algorithm, it was possible to reduce the mean square error (MSE) in the 3D reconstruction. Finally, a 3D reconstruction can be built based on phase-shifting techniques. The implementation and experimental results show high-precision 3D information.
Efficient Black-Box Search for Adversarial Examples using Relevance Masks

Machine learning classifiers for image recognition are prevalent in many applications. We study the problem of finding adversarial examples for such classifiers, i.e., to manipulate the images in such a way that they still look like the original images to a human but are misinterpreted by the classifier. Finding adversarial examples corresponds to a search problem in the image space. We focus on black-box attacks that can only use the original classifier to guide the search. The challenge is not to find adversarial examples, but rather to find them efficiently, ideally in real time. We show two novel methods that increase the efficiency of black-box search algorithms for adversarial examples: The first uses a relevance mask, i.e., a bitmask on the original image that restricts the search to those pixels that appear to be more relevant to the attacked classifier than others. The second exploits the discovery of merge drift, a phenomenon that negatively affects search algorithms that are based on the merging of image candidates. We evaluate both concepts on existing and new algorithms.
Keefektifan Teknik Mutual Stoytelling untuk Meningkatkan Motivasi Belajar Siswa SD

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&lt;strong&gt; &lt;/strong&gt;This research aimed to test the effectiveness of mutual storytelling technique to improve the motivation to learn of fifth grade students&lt;strong&gt;. &lt;/strong&gt;This research was a Time Series Design that consist of one group. The research subjects were six students selected using purposive sampling technique. The technique of collecting data using questionnaire. Data analysis using the Wicoxon Signed Rank test.&lt;strong&gt; &lt;/strong&gt;Based on the result of the analisis using Wilcoxon Signed Rank Test, it showed that the significance value is 0,027 (0,027&amp;lt;0,05). So there is a difference between before and after given treatment to students.&lt;strong&gt; &lt;/strong&gt;The conclusion of the research was that mutual storytelling technique was effective to improve the motivation to learn of fifth grade students. The result of this research could be used by classroom teachers as a reference to deepen the understanding of the implementation of mutual storytelling technique to improve motivation of learn for students. It could also be used as a reference for other researchers to innovate to conduct research using mutual storytelling techniques in other service fields.&lt;/p&gt;&lt;strong&gt;Abstrak : &lt;/strong&gt;Tujuan penelitian ini adalah untuk mengetahui keefektifan teknik &lt;em&gt;mutual storytelling &lt;/em&gt;untuk meningkatkan motivasi belajar peserta didik kelas lima SD. Penelitian ini menggunakan &lt;em&gt;Time Series Design &lt;/em&gt;yang terdiri dari satu kelompok. Subjek penelitian berjumlah 6 peserta didik yang dipilih menggunakan teknik &lt;em&gt;purposive sampling&lt;/em&gt;. Teknik pengumpulan data menggunakan angket. Analisis data menggunakan uji &lt;em&gt;Wilcoxon Signed Rank Test&lt;/em&gt;.&lt;strong&gt; &lt;/strong&gt;Berdasarkan hasil analisis uji &lt;em&gt;Wilcoxon Signed Rank Test&lt;/em&gt; menunjukkan nilai signifikansi 0,027 (0,027&amp;lt;0,05). Dengan demikian, dapat diketahui bahwa ada perbedaan antara sebelum dan sesudah diberikan &lt;em&gt;treatment&lt;/em&gt; pada peserta didik.&lt;strong&gt; &lt;/strong&gt;Simpulan dari hasil penelitian ini adalah teknik &lt;em&gt;mutual storytelling&lt;/em&gt; efektif untuk meningkatkan motivasi belajar peserta didik kelas lima SD. Hasil dari penelitian ini dapat digunakan sebagai referensi bagi guru kelas untuk memperdalam pemahaman pelaksanaan teknik &lt;em&gt;mutual storytelling &lt;/em&gt;untuk meningkatkan motivasi belajar bagi peserta didik serta dapat dijadikan referensi bagi peneliti lain untuk berinovasi melakukan penelitian menggunakan teknik &lt;em&gt;mutual storytelling&lt;/em&gt; pada bidang layanan yang lain.
Support Vector Guided Locally Aggregated Descriptors for Feature Encoding

Feature encoding, as a crucial component of image categorization, has been widely investigated in recent works. Among all kinds of encoding strategies, the vector of locally aggregated descriptors (VLAD) and its extensions have gained a lot of attentions and provide a brand-new framework, aggregating the deviations between a set of descriptors and their average distribution. However, the VLAD-like frameworks only utilize statistics to represent the deviations, which may not achieve the optimal discrimination for classification tasks. In this paper, we propose the support vector guided locally aggregated descriptors (SVGLAD) to leverage the residuals of support vectors between descriptors and clusters of the dataset, which can preserve more geometric information. We validate the effectiveness of our methods on two image datasets (15-Scenes and PASCAL VOC 2007). The experimental results demonstrate that the proposed method outperforms the existing coding algorithms.
Firefly Detection Based on Improved YOLOv4 under a Small Number of Samples

In order to solve the problem of firefly detection in complex scenes with a small number of samples, a data generation method and an improved YOLOv4 algorithm (Efficient-A-YOLOv4) are proposed. The data generation method is proposed to generate enough image data and annotation information with firefly objects for training the model. Then the more efficient and lightweight EfficientNet-B0 is introduced as the feature extraction backbone network of the YOLOv4 model, which improves the detection speed while maintaining the accuracy. Finally, the attention mechanism is introduced to further improve the model's attention to the target area, thereby reducing the false detection rate of the object in complex scenes. The experimental results show that the recall, precision, mAP and FPS of the proposed Efficient-A-YOLOv4 model are 85.26%, 78.64%, 86.84% and 33.5, respectively, which are better than the standard YOLOv4. The experimental results show that the algorithm proposed in this paper is suitable for real-time detection of fireflies in complex scenes.
GMDSS Operation Intelligent Evaluation Based on Tree Digraph

To solve the problems of subjectivity and low efficiency caused by human factors in GMDSS operation evaluation, an intelligent evaluation method based on tree digraph is proposed. After analyzing the general operation rules of GMDSS, a tree digraph is designed to transform the problem of operation process evaluation into graph theory, DFS recognition algorithm is combined with LCSS dynamic programming algorithm to obtain the indexes of proficiency, deviation and accuracy in operation process. Base on the operation process, the operation results and the operation time, GMDSS intelligent evaluation model is established. The experimental results show that the model can be applied to complex tasks with multiple evaluation items and high randomness of operation and it has certain universal adaptability.
EFDO: Solving Extensive-Form Games Based On Double Oracle

Although algorithm like counterfactual regret minimization (CFR) proved to be effective in small games, the demands on computing and storage resources limits its application among large EFGs. We propose a new algorithm called extensive-form double oracle (EFDO) based on double oracle to solve extensive-form games. EFDO solves a smaller game instead of the original one via CFR repeatedly and is guaranteed to converge to an approximation of Nash Equilibrium. We also introduce extensive-form deep double oracle (EFD2O) by replacing best response solver with deep reinforcement learning. In vanilla Leduc poker, we show that EFDO fits different CFR algorithms well and converges faster than policy space response oracle and extensive-form fictitious play. On a modified Leduc poker game, EFDO achieves an approximate Nash Equilibrium in a number of iterations 1–2 orders of magnitude smaller than different CFR algorithms. Besides, EFD2O also performs better than NFSP and PSRO on the modified Leduc Poker.
Allo: Optimizing Federated Learning via Guided Epoch Allocation

Federated learning has shown great potential for addressing the challenge of data islands while preserving data privacy. Unlike traditional distributed machine learning, federated learning faces the problem of system and statistical heterogeneity, which makes it difficult to deploy at the edge. However, current methods cannot optimize the federated learning by taking the clients' system heterogeneity and statistical heterogeneity together. This paper proposes Allo, a framework based on deep reinforcement learning that can automatically choose the appropriate number of local epochs for each client, which can mitigate the impact of stragglers and unbalanced data distribution. With extensive experiments performed in PyTorch, we show that the performance of federated training can be improved by over 15% on the MNIST dataset and over 12% on CIFAR-10 dataset, as compared to the baseline.
3D Reconstruction of Weakly Textured Objects Based On Denoising Polarized Images

Traditional 3D visual reconstruction technology based on feature extraction is difficult to estimate the surface depth information of a smooth object with a single texture, while the polarization 3D reconstruction technology can recover the surface normal information from the image pixel by pixel, which has the characteristics of high precision, low texture dependence and strong material applicability. However, the normals recovered from the polarization information have the problem of multi-value ambiguity, which needs to be corrected before the accurate normal information can be recovered. At the same time, the image collected by the polarization camera contains a lot of noise, which will greatly affect the accuracy of reconstruction results. In this paper, the characteristics of polarization information are used to denoise the polarization image, and the reference normal information is used as the prior information for correcting the polarization normal. Firstly, the degree of polarization and angle of polarization are calculated based on the polarized image, then the two are fused and denoised by the Block Matching three-dimensional (BM3D) method, and then the multi-valued ambiguity of polarization normal is corrected according to the reference normal map. Finally, the corrected polarization normal is integrated to obtain a high-precision 3D shape of the object.
Head Point Positioning and Spatial-Channel Self-Attention Network for Multi-Object Tracking

Multi-Object Tracking (MOT) aims to generate trajectories for multiple objects in the surveillance scene. This is a challenging task because the pedestrians in tracking video often gather together and occlude each other. Consequently, the two main problems in the popular tracking-by-detection framework are how to alleviate unreliable detection and extract robust object appearance features. In this paper, we propose a new tracking method that is composed of two novel types of modules - an object detection strategy based on pedestrian head point positioning and a Spatial-Channel Self-Attention feature extraction network (SCSAN). Specifically, the proposed detection strategy generates more accurate tracking object bounding boxes with Soft-Head-NMS, which combines the advantages of object detection and head point positioning. The head point location information is used as a guidance to screen unreliable detection. The SCSAN utilizes the Spatial-Channel Self-Attention mechanism to lead and determine the optimal attention value for each area and channel. Extensive experiments are carried out to demonstrate the proposed tracker achieves competitive results and is state-of-the-art in half metrics.
Repulsion-GNNs:Use Repulsion to Supplement Aggregation

Graph Neural Networks (GNNs) have achieved prominent performance in the node classification task, by constructing an aggregation process to integrate node features and graph topology. The aggregation process makes the features of the connected nodes similar, which helps to classify. However, this will also cause nodes that are connected but belong to different classes to be more confusing. In this paper, we propose Repulsion-GNNs, in which a repulsion process is introduced to supplement the aggregation process. First, the nodes are divided into hyper nodes based on a basic node classification model. Then, the repulsion for each node can be obtained according to these hyper nodes. Finally, the node embeddings are obtained by combining the aggregation and repulsion. Many existing GNNs can be combined with the repulsion without adding any learnable parameter. Extensive experiments on benchmark datasets for node classification demonstrate that the repulsion can boost the performance of many GNNs, such as GCN, GAT, SAGE, and GCNII.
Embedding Spatial Relations in Visual Question Answering for Remote Sensing

Remote sensing images carry a wealth of information that is not easily accessible to end-users as it requires strong technical skills and knowledge. Visual Question Answering (VQA), a task that aims at answering an open-ended question in natural language from an image, can provide an easier access to this information. Considering the geographical information contained in remote sensing images, questions often embed an important spatial aspect, for instance regarding the relative position of two objects. Our objective is to better model the spatial relations in the construction of a ground-truth database of image/question/answer triplets and to assess the capacity a VQA model has to answer these questions. In this article, we propose to use histograms of forces to model the directional spatial relations between geo-localized objects. This allows a finer modeling of ambiguous relationships between objects and to provide different levels of assessment of a relation (e.g. object A is slightly/strictly to the west of object B). Using this new dataset, we evaluate the performances of a classical VQA model and propose a curriculum learning strategy to better take into account the varying difficulty of questions embedding spatial relations. With this approach, we show an improvement in the performances of our model, highlighting the interest of embedding spatial relations in VQA for remote sensing applications.
Population Density Prediction Using Surveillance Cameras in Enclosed Public Spaces

People have always disliked offline shopping due to its tough and stressful nature. However, after the COVID epidemic, people now have no choice but to avoid it as much as possible. With the help of this project, users will be able to plan their visits to avoid rush hour and crowd and learn when there will be the least amount of consumer traffic. The store owner will also profit from knowing how customers are distributed throughout the store according to a heat map because this information will eventually allow him to ascertain which areas of the store customers are more drawn to and which areas receive the fewest customers, allowing him to more efficiently plan staffing and advertising. As the e-commerce industry grows, more people are choosing to purchase online in order to avoid crowds and noise. Thanks to this initiative, people may also enjoy offline shopping just as much, which will benefit offline business owners and enhance the entire shopping experience.
Pose-Unconstrainted 3D Lip Behaviometrics via Unsupervised Symmetry Correction

Lip behavior in human speaking is a remarkable bio-modality with individual uniqueness and repeatability, which can be used for secure identity recognition. The dynamic lip modality in 3D point cloud sequences has depth cues. However, due to self-occlusion of the 3D lip, the single-view point cloud of a pose-unconstrainted speaking lip suffers missing local structure, which affects the accuracy of 3D lip behaviometrics. This paper proposes a pose-unconstrainted 3D lip behaviometrics method based on symmetry correction of lip point clouds. We construct an unsupervised neural network to capture the latent symmetry cue of a 3D lip under any pose and correct the structure of the lip point cloud. The improved point cloud sequence is fed into a spatiotemporal lip feature learning network for lip behaviometrics. The experiments on the public dataset S3DFM demonstrate that lip symmetry correction can improve the accuracy of the 3D lip behaviometrics by 2.65%.
Privacy-Preserved Computation Offloading Scheme in 5G enabled IoT Based on Smart blockchain

To effectively utilize multiple 5G resources (such as computing, communication, storage and service resources), provide low delay offload decisions, and meet data privacy, we propose a framework to integrate blockchain, federated learning (FL), and mobile-edge computing (MEC) into the Internet of Things (IoT) system. However, the insufficient throughput of the blockchain may hinder the efficiency of the overall system. At the same time, due to the dynamic characteristics of IoT and MEC systems, the whole framework involves high-dimensional features and large-scale actions, and the optimization problem is very complex and challenging. Therefore, we design the whole system flow process as a Markov decision process (MDP) sequence by defining state space, action space, and reward function. The deep deterministic policy gradient (DDPG) algorithm is adopted to dynamically select and adjust action to ensure the maximum long-term return of the system. Finally, the simulation results show that our scheme can significantly improve the performance of system.
Focused Area of Movement as an Effective Rest Frame for Reducing VR Sickness

Sickness is one major obstacle in proliferating the use of virtual reality. The sensory mismatch between the visual and vestibular senses about user motion is attributed as the main cause. One effective method has been the use of rest frame such as the virtual nose, which is the reference object that remain fixed in relation to the real world and do not move as the user moves around. We propose the “Focused Area of Movement” as an alternative rest frame, which is a small moving area within the screen space, where there is little relative motion (or optical flow) with respect to the user - thereby causing less sensory conflict, but with naturally directed attention and less content intrusion. Our pilot experiment has shown its potential in significantly reducing the level of VR sickness without much degradation in the sense of presence/immersion.
The Influence of The Multimedia-Based Glenn Doman Method Beginning Reading Interest of Grade II Elementary School Students

The low interest in reading in students greatly affects the ability to read early. The effect of the multimedia based glenn doman method on the reading interest of elementary school students in grade II at SDN Ketegan. the method developed by glenn doman utilizes multimedia technology, by teaching students to read and recognize letters with flash card. So that students will have the ability to be confident, imaginative and creative. this method is designed or created with the aim of making teachers, students and parents more aware and understanding how to learn to read the beginning. The main purpose of this multimedia based method, is to increase the reading interest of elementary school students who are very low and experiencing a decline. It is also expected that after students understand this method. this study aims to apply research method using quantitative research methods. sampling and data collection techniques used research instruments, namely questionnaire sheets and pretest and posttest test sheets to determine the results of early reading interest in second grade students of SDN Ketegan. To find out how much influence the use of the multimedia based glann doman method on early reading interest in students.
Boosting and Stacking

The ensemble technique relies on an aggregate of models’ output to provide a better prediction. Other than voting and bagging, we can use boosting and stacking.
Design of Legal Intelligence Evaluation System Based on Encryption Algorithm

Under the background of China's implementation of the innovation-driven development strategy and the new economic normal, technological innovation, as an important driving force for the country's high-quality development and economic transformation, has increasingly received high attention and attention from the party and the state, as well as in the field of law. This paper sorts out the concepts and contents of encryption algorithm and intelligent judicial adjudication system, combines the current application status of encryption algorithm technology, analyzes the implementation path of legal intelligent adjudication system, and summarizes the complementary characteristics of judicial adjudication application and encryption algorithm, with a view to the future. Related research to pave the way.
Learning to Active Learn by Gradient Variation based on Instance Importance

A major challenge in active learning is to select the most informative instances to be labeled by an annotation oracle at each step. In this respect, one effective paradigm is to learn the active learning strategy that best suits the performance of a meta-learning model. This strategy first measures the quality of the instances selected in the previous steps and then trains a machine learning model that is used to predict the quality of instances to be labeled in the current step.In this paper, we propose a new approach of learning-to-active-learn that selects the instances to be labeled as the ones producing the maximum change to the current classifier. Our key idea is to select such instances according to their importance reflecting variations in the learning gradient of the classification model. Our approach can be instantiated with any classifier trainable via gradient descent optimization, and here we provide a formulation based on a deep neural network model, which has not deeply been investigated in existing learning-to-active-learn approaches. The experimental validation of our approach has shown promising results in scenarios characterized by relatively few initially labeled instances.
An Extended Scatterplot Selection Technique for Representing Three Numeric Variables

There have been automatic techniques that selects scatterplots from the ones generated from a multi- dimensional dataset as a powerful multi-dimensional data visualization method. One of the criteria used in this method supposes that plots are color-coded by a specific categorical variable in the multi-dimensional dataset. We extended this method and show the visualization results by color-coding the plots according to a specific numeric variable in the multidimensional dataset. This extended method represents a specific numeric variable by color, and generates a set of scatterplots in which all pairs of other two numeric variables are assigned to the two axes. Then, the method automatically selects interesting scatterplots that represent three numeric variables.