Reviewerly/exHarmony huggingface dataset:
Image and Video Dehazing based on Gamma Correction Method Contrast Enhancement

As the revolution in computational photography and computer vision applications facilitates fast and reliable information, quality of the scene and visual perception and is being increasingly used in various fields like public safety, traffic accident analysis, crime forensics, remote sensing area and military surveillance. We make an investigation of the dehazing effect of scenes affected by weather phenomena. ‘Dehazing’ has emerged as a promising technology to recover the clear image and video from an input hazy scene, such that the quality can be significantly enhanced. A scene captured in the outdoor environment affected by haze like fog mist and dust particles in the atmosphere. We are utilizing a ‘Dehazing algorithm, to remove this unwanted haze from videos and Real-time video. Also, remove haze from images, for this, we use a novel method of video dehazing based on contrast enhancement. From our observation, it is concluded that hazy image and video has low contrast, so we estimate transmission map to maximize the contrast of output scene. And we use the depth estimation process to detect or identify hidden parameters from the scene, and also creates a corresponding haze scene with high fidelity. Finally, we reconstruct or restore the seen without changing its originality. Hence dehazing performance with fewer artifacts and better coding efficiency and demonstrate that the proposed algorithm can remove haze efficiently and recover the parameters of the original scene
Progressive digital and information technologies in natural and technical education

Abstract In the context of a pandemic in 2020, a serious transformation of natural and technical education took place, in this connection; the authors consider modern approaches to the introduction of educational activities in conditions of restrictions. The paper proposes a concept that allows for a high-quality transition in natural and technical education, which allows students to assimilate material in a virtual learning environment. The work formulates a step-by-step system that allows transforming natural and technical education.
Visualization analysis method based on multidimensional scale transformation for indicators on simulation data of weapon system

Abstract Visual analytic methods are a growing area of research that targets the effective description of weapon system, which capitalizing on displaying of various indicators information of weapon system simulation. We provide a method which can display all kinds of indicators and architectural features of simulation results in the same screen. First of all, the simulation data of multidimensional information index system and the corresponding dimension is mapped into the two-dimensional matrix. Secondly, the index system of two-dimensional graphs and planar map simulation data are fused by computation. Finally, distinguish the architectural features by drawing the influence range of the indicators to the systems. The method can analyze the correlation between indicators, similarity comparisons system features, and describe some kind of index and the system characteristics of the correlation degree. Because of the above characteristics of the method, we can adjust the index system, using visualization method to display the comprehensive characteristic of the weapon equipment system, and select the weapon equipment system in accordance with the specific conditions of the system.
Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding

Multi-label semantic decoding is a challenging task with great scientific significance and application value. The existing methods mainly focus on label learning and ignore the amount of information contained in the sample itself, especially non-image sample, which may limit their performance. To address these issues, we propose a novel semi-supervised modality assistance co-training network, which utilizes image modality to assist non-image modality for multi-label learning. In real application, there are two thorny issues: (i) non-image modality tends to be missing owing to the difficulty in obtaining them; (ii) although the image modality is easy to obtain from the Internet, image label annotation is still time-consuming and expensive. Therefore, the proposed method utilizes a small number of paired &amp; labeled images and non-image modalities, and a large number of unpaired &amp; unlabeled images from web sources to improve results. It consists of the modality-specific feature generators, the feature translators and the label relationship network. Specifically, the modality-specific feature generators are used to generate different features (views) for each modality. Semantic translators are employed to capture the relationship between the paired modalities and impute the missing modality feature by using unpaired &amp; unlabeled images. Label relation network is a graph convolution network (GCN) aiming to capture the correlation between labels. To mine the information in unlabeled features, the co-training mechanism is considered. With this mechanism, we introduce a multi-view orthogonality constraint and a multi-label co-regularization constraint. Extensive experiments on three computer vision and neuroscience datasets demonstrate the effectiveness of the proposed method.
Research on the Strategy of Automatic Needle Knitting Based on Machine Vision and Cross-stitch Embroidery

With the development of cross stitch market demand, machine cross stitch instead of manual cross stitch has become an inevitable trend in the development of cross stitch industry. This paper studies a fully automatic stitch knitting method based on machine vision technology and cross stitch spectrum extraction, in order to improve the automation and intelligence level of cross stitch industry and improve the quality of embroidery products.
Test-Time Adaptation for Out-Of-Distributed Image Inpainting

Deep-learning-based image inpainting algorithms have shown great performance via powerful learned priors from numerous external natural images. However, they show unpleasant results for test images whose distributions are far from those of the training images because their models are biased toward the training images. In this paper, we propose a simple image inpainting algorithm with test-time adaptation named AdaFill. Given a single out-of-distributed test image, our goal is to complete hole region more naturally than the pre-trained inpainting models. To achieve this goal, we treat the remaining valid regions of the test image as an another training cue because natural images have strong internal similarities. From this test-time adaptation, our network can exploit externally learned image priors from the pre-trained features as well as the internal priors of the test image explicitly. The experimental results show that AdaFill outperforms other models on various out-of-distribution test images. Furthermore, the model named ZeroFill, which is not pre-trained also outperforms the pre-trained models sometimes.
ECLAIR: An artificial intelligence research and experimentation workstation

Keeping a rigorous focus on the lifecycle and evolution of data mining can prove difficult, especially when dealing with devising a new machine learning mechanism or process. Access to the appropriate tooling and experimentation resources is instrumental for researchers operating in this domain. As data scientists, we all probably spent hours customizing a working environment at least once. We recognized that the tools, utilities, and techniques are more or so the same. Therefore, maintaining a custom environment while keeping all the software up-to-date can be a monotonous chore. This paper presents our take on developing a community workstation with artificial intelligence in mind. Dubbed ECLAIR, the framework includes a full portable laboratory for all kinds of data science processes, from data collection to analysis and insights delivery. With this distribution, end-users can apply statistical modeling to investigate and exploit various datasets. It also embeds everything needed by a developer to write his software for experimentation.
Deteksi Dini Masalah dalam Proses Belajar Mengajar Secara Daring Menggunakan Sistem AT-OLP

The pandemic period covid-19 pandemic period forced many sectors in various fields to do work from home, as well as in education. Various schools ranging from elementary to tertiary level, are asked to carry out the teaching and learning process from home online or what is commonly referred to as online learning. The use of e-learning does help in the implementation of online learning, thus making students learn independently and motivation increases but there are difficulties that occur in controlling the implementation of online learning so that students are not supervised effectively. The research method used in this study is the FAST method because the framework is flexible enough for various types of projects and strategies. To control, the AT-OTP system is used is a system used to monitor the teaching and learning process activities online, using the concept of openness to all activities carried out. So that with the open mutual, the problems faced can be immediately known and sought solutions to these problems.&#x0D; Keywords: Online learning, e-learning, AT-OTP system
Contextual Label Transformation For Scene Graph Generation

For scene graph generation, it is crucial to properly understand the relationships of objects within the context of the image. We design a label transformation method using a Transformer-VAE (Variational Autoencoder) structure, which converts bounding box labels into auxiliary labels that contain each object’s context in an unsupervised manner. The auxiliary labels are then trained jointly with bounding box labels and relation labels in a multi-task way. Our approach does not require any external datasets or language prior and is applicable to any graph generation models that infer the relationship between pairs of objects. We validate our method’s effectiveness and scalability with state-of-the-art scene graph generation models on VRD and VG datasets.
Filtering of Gaussian Systems

The filter problem is to derive an expression for the conditional distribution of the state of a stochastic system conditioned on the past outputs of the considered system and a recursion of the parameters of that conditional distribution. In this chapter the filter problem for a Gaussian stochastic system is solved. The Kalman filter is derived, including the time-varying, the time-invariant Kalman filter, and the conditional Kalman filter. The relation of the Kalman filter with stochastic realization is discussed. The Kalman filter is used in a very large number of research areas including signal processing, information theory, communication theory, weather prediction, hydrology, etc.
Metu Loss: Metric Learning With Entangled Triplet Unified Loss

Metric learning aims to define a distance that measures the semantic difference between the instances in a dataset. In this paper, we analyze several well-known triplet loss functions and argue that the gradients of these triplet loss functions do not move the instances in each triplet in the desired direction with the right magnitude. Hence, in order to determine precise interacting forces, we establish a weak analogy with the phenomena of the electromagnetic forces affecting a charged body in free space. Since gradients of the loss function (matching up to the potential energy) with respect to the anchor, positive and negative instances (corresponding to the point charges) of any valid triplet give forces, a loss function can be obtained in a reverse manner, i.e., starting from the desired interacting forces. Based on this idea, we propose a novel triplet loss function, namely, metric learning with entangled triplet unified (METU) loss, that considers the distances between instances in a triplet as well. In order to present only the effect of the loss function, no mining (including the effect of hinge function) is utilized during the experiments. Based on the results on the used fine-grained dataset, CUB-200-2011, it can be concluded that the proposed loss function outperforms the scores of the state-of-the-art methods in the same category.
A Spherical Mixture Model Approach for 360 Video Virtual Cinematography

360 video virtual cinematography attempts to direct a virtual camera and capture the most salient regions of 360 videos. In this paper, we propose a data-drive solution to achieve high-quality and diversified 360 cinematography based on crowd-sourced viewing histories. Specifically, we try to address two problems: 1) how to locate the semantically important regions of interest (RoI) from raw data, 2) how to generate virtual camera paths that follow chronological narratives. We first design a dynamic spherical mixture model based algorithm to locate variable number of RoIs on each video frame. We then model the camera transition and chronological orders with a Bayesian network and conditional probabilities. With the above two designs, we can generate “optimal” cinematography paths based on a dynamic programming algorithm. By modeling the RoIs as spherical mixture model, we are also able to provide diversified cinematography results. We show its effectiveness through extensive experiments.
Interpretable Representation Learning on Natural Image Datasets via Reconstruction in Visual-Semantic Embedding Space

Unsupervised learning of disentangled representations is a core task for discovering interpretable factors of variation in an image dataset. We propose a novel method that can learn disentangled representations with semantic explanations on natural image datasets. In our method, we guide the representation learning of a variational autoencoder (VAE) via reconstruction in a visual-semantic embedding (VSE) space to leverage the semantic information of image data and explain the learned latent representations in an unsupervised manner. We introduce a semantic sub-encoder and a linear semantic sub-decoder to learn word vectors corresponding to the latent variables to explain factors of variation in the language form. Each basis vector (column) of the linear semantic sub-decoder corresponds to each latent variable, and we can interpret the basis vectors as word vectors indicating the meanings of the latent representations. By introducing the sub-encoder and the sub-decoder, our model can learn latent representations that are not just disentangled but interpretable. Comparing with other state-of-the-art unsupervised disentangled representation learning methods, we observe significant improvements in the disentanglement and the transferability of latent representations.
Automatic White Blood Cell Classification Using Deep Learning Network

Changes in white blood cell, a.k.a. leukocyte, subtypes count are often indicator of medical conditions. Counting each of white blood cell subtypes is thus essential in blood tests. To do this, it is necessary to classify white blood cell images into different subtypes, namely, Eosinophils, Lymphocytes, Monocytes, Neutrophilic, etc. Traditional image processing techniques have been utilized to classify white blood cell images. However, in general, accuracy and stability of these techniques are sensitive to variations in image size, staining technique, and image quality. Recently, application of deep learning networks in white blood cell classification has drawn a lot of interest. In this work, we present two deep learning networks based on VGG16 and SqueezeNet convolutional neural networks (CNNs) that can automatically classify white blood cell images into different subtypes. The two networks were trained on a public dataset of leukocytes named Blood Cell Count and Detection (BCCD) and a self-built dataset named the Extended BCCD dataset. Although having relatively light-size, 16,005,884 and 35,858 parameters for VGG16 and SqueezeNet respectively, both models achieved ~ 97% accuracy in the BCCD dataset. In the extended BCCD dataset, the VGG16-based model achieved 97% accuracy, and the SqueezeNet-based model achieved 95% accuracy. These two models are thus potential candidates for deployment on computers and IoT devices with limited computational power for white blood cell classification task.
Detection of Insulators by Point Feature Matching Using Speeded Up Robust Features

Online monitoring of electrical substation through computer vision technology is based on image processing algorithm to perform visual analysis. Object identification is a significant part in image processing field. For identification object from the outset, we take a photograph called test picture and afterward identify some district or point on the picture and compare it and reference picture. For this errand, a calculation that is utilized in numerous vision applications furthermore, is additionally viewed as fast by contrasted with others. This calculation can distinguish and portray nearby highlights for any interest article and concentrate highlights or descriptor focuses from it and contrast it and the highlights that separated from unique picture. Coordinating cycle is done among highlights and choice is made dependent on comparable highlights found. This Algorithm is called Speeded up Robust Features (SURF) calculation. In this paper, the SURF calculation which can identify the situation of the interest object in unique picture by utilizing mathematical change. This strategy turns out best for objects that show in a clutter textured pattern.
Optimized navigation using deep learning technique for automatic guided vehicle

Autonomous driving has passed the point of being called the biggest step, as the smart car revolution is already taking shape around the world. Self-driving cars are relevant if not prevalent and the biggest obstacles to reach the mass adoption are customer acceptance, cost, infrastructure, and the reliance on several onerous algorithms that include perception, lane marking detection, path planning, and variation in pathways. This study tackled the mentioned problems with a straightforward and cost-effective solution, using end-to-end learning and replacing the numerous sensors with a camera and commandeering just the forward, backward, left, and right controls. In this research, authors have used the most popular method of deep learning that is convolutional neural network to train the collected data on the VGG16 model. Later these have optimized directly by the proposed system with cropping each unnecessary image and mapping pixels from a single front-facing camera to direct steering instructions. It has been observed from the experimental work that the proposed model has given a better result than the existing work, that is, increase in the accuracy from 88% (Udacity training dataset) to 98% (proposed). This model is suitable for industrial use and robust in real time scenarios, therefore can be applied in modern industrialized systems.
IDECF: Improved Deep Embedding Clustering With Deep Fuzzy Supervision

Deep clustering algorithms utilize a deep neural network to map data points in a lower-dimensional space which is more suitable for clustering task. Recent algorithms employ autoencoder to jointly learn a lower-dimensional space (aka latent space) and perform data clustering through minimizing a clustering loss. These algorithms suffer from the fact that the true cluster assignments are unknown because of the unsupervised nature of the task. Thus, they adopt a self-training strategy and estimate the true cluster labels using the algorithm parameters; while the true parameters’ value is unknown at the problem outset. To address this difficulty, we propose a deep clustering technique, called IDECF, whereby the true cluster assignments are estimated using an individual deep fully connected network (FCM-Net) which takes its input from the latent space of an autoencoder. The proposed IDECF is trained in an end-to-end manner by minimizing a linear combination of reconstruction loss and clustering loss. Experimental results on benchmark datasets demonstrate the viability and effectiveness of the proposed algorithm.
Convolution Neural Networks for Traffic Sign Classification

Recognizing traffic signs is very important in applications such as traffic surveillance and self-driving cars. Deep learning models are found to be effective to classify the images with higher accuracy. Extracting features for classification task has always been difficult. This paper proposes traffic sign classification system based on Convolution Neural Networks (CNN).
How to improve students' learning initiative in online teaching of College Mathematics

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Development and Implementation of Java Game Engine Based on Network Information Technology

Abstract With the development of computer technology and network technology, various digital learning methods have seriously impacted the traditional education and teaching mode, among which gameplay learning represented by teaching games has attracted wide attention of educators. It is considered to be the most effective way to stimulate learners’ learning motivation. Mobile game is an important part of mobile phone value-added service. The research of mobile game engine has extremely important application value for the development of mobile phone game. This paper mainly studies how to realize a high-performance mobile game engine. Through the analysis of mobile phone operating system, mobile game market, mobile phone game user needs, the technology selection and main function modules of the engine are determined. Through the research of java technology, the technical architecture of engine server and client is determined. Performance and algorithm optimization are the focus of this paper, because performance issues are the biggest bottleneck in developing games on java phones.
Research on Position and Recognition Algorithms for Insulators Based on EfficientDet

Insulators, as one of the most used power equipment on transmission lines, are essential for maintaining the safety and stability of the power system. The positioning and recognition of insulators is the prerequisite for fault diagnosis. Although deep learning methods can improve the inspection efficiency, there remain problems such as low accuracy and poor real-time performance for the existing insulator positioning and recognition methods. To solve this problem, this paper proposes an insulator positioning and recognition method based on EfficientDet. The EfficientDet algorithm uses EfficientNet as the backbone and BiFPN as a feature network layer, repeatedly stacking BiFPN to obtain a more advanced feature fusion method. The EfficientDet algorithm performs feature extraction on the collected insulator image data to realize insulator positioning and recognition. This article chooses the open source Pytorch as a tool, combined with tensorboard to visualize the loss curve during the training process. The results show that using the EfficientDet algorithm can achieve rapid model convergence. The average detection time is 0.033s and the average processing speed reaches 29.35 FPS. The method proposed in this paper achieves a balance between model size and detection speed, which verifies that the model has excellent performance in insulator detection, and provides a new idea for intelligent positioning and recognition of power equipment.