Reviewerly/exHarmony huggingface dataset:
Efficient Downscaling of Satellite Oceanographic Data With Convolutional Neural Networks

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.
Emotional Landscape Image Generation Using Generative Adversarial Networks

We design a deep learning framework that generates landscape images that match a given emotion. We are working on a more challenging approach to generate landscape scenes that do not have main objects making it easier to recognize the emotion. To solve this problem, deep networks based on generative adversarial networks are proposed. A new residual unit called emotional residual unit (ERU) is proposed to better reflect the emotion on training. An affective feature matching loss (AFM-loss) optimized for the emotional image generation is also proposed. This approach produced better images according to the given emotions. To demonstrate performance of the proposed model, a set of experiments including user studies was conducted. The results reveal a higher preference in the new model than the previous ones, demonstrating the production of images suitable for the given emotions. Ablation studies demonstrate that the ERU and AFM-loss enhanced the performance of the model.
An Algorithm for the Hole Filling of Motion Foreground Based on Superpixel Segmentation

For mainstream sports foreground extraction algorithm in complex scenarios to extract the prospect of movement target image problems is empty, this chapter to the extraction of the prospect areas and the prospects of a nearby empty area between nature has made the thorough analysis, put forward a kind of based on feature fusion of pixel segmentation combined with a significant test of hole filling algorithm. Since the SLIC superpixel segmentation has little difference in the color features of pixels, the moving foreground pixels will be divided into unified superpixel, resulting in the uneven interior of the region. This algorithm cleverly introduces the LBP texture feature, which can be better segmented according to the contour information of the moving foreground image. Finally, with the combination of saliency moving foreground target image filling, a more ideal moving foreground image is obtained.
Simplified process of dissipation-based Greenberger–Horne–Zeilinger state generation with Lyapunov control

In this paper, we propose a protocol to generate steady Greenberger–Horne–Zeilinger (GHZ) states of three Rydberg atoms with dissipation dynamics and simplified Lyapunov control. Firstly, combining with the dissipation dynamics and Rydberg antiblockade, a unique and steady solution of the master equation of the system is obtained. Furthermore, to polish the convergence rate, we add well-designed additional coherent control (ACC) fields with Lyapunov control into the traditional dissipation process and simplify the control fields with bang–bang design proposed in the Ref. (Hou et al., 2012) for better experimental feasibility. Numerical simulation reveals that the GHZ states generation is prominently accelerated and the simplified control fields have the similar efficiency with the traditional Lyapunov control fields but are easier to realize in realistic situations. We hope this work could provide an alternative way to polish the generation of entanglement.
Comparing dimensionality reductions for eye movement data

Eye movement data is high-dimensional, and therefore hard to visualize. In this paper we focus on a dataset of scanpaths: Eye movements performed by subjects and tracked during a task which is based on path-finding. We describe comparisons of different approaches of dimensionality reduction applied to eye movement data, including t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), principal component analysis (PCA), and metric multidimensional scaling (MDS). We describe a tool created to analyze and compare these different methods, and perform a case study in which we explore an eye movement dataset.
Collision Free Path Planning for Unmanned Aerial Vehicles in Environments with Dynamic Obstacles

A relevant problem in unmanned aerial vehicles flight is planning paths in 3D environments. The problem can be solved using different behaviour constraints and performance criteria. The goal of this work is to design a collision-free path that allows an unmanned aerial vehicle to fly from a starting pose to a destination one in an 3D environment with dynamic obstacles. A very important phase in completing the proposed goal is environment modelling. Based on an original contribution made by the authors, the free space in the environment is modeled by using a cuboid representation. Accounting information about the velocity magnitude and direction of the moving objects, a novel 3D cell decomposition planning algorithm is proposed. Testing and evaluation through numerical simulation show the modification in path design when comparing static parts of the environment with dynamic obstacle events.
ORGANIZATION OF SEMANTIC READING OF AN INFORMATICS TEXTBOOK USING A COMPUTER PRESENTATION

The purpose of the study considered in the article was to identify problems in the organization of semantic reading in future informatics teachers. The following research methodology was used: each of 20 students chose a paragraph of the 7th grade informatics textbook and developed a computer presentation for organizing semantic reading using the technique "Make up questions to which there are answers in the text". Difficulties and methodological errors of students were analyzed, their causes were eliminated. As a result, seven components of the educational text were identified: a definition; stages of topic development or comparison of different situations; characteristics or properties of objects; actions; a system of elements; the solution of the problem; an image or a table accompanying the text. Examples of questions when working with each of the text objects are summarized. The problem of systematizing the results of semantic reading is highlighted, it is suggested to compose a semantic map of the topic with students in a dialogue with the teacher. Using a computer presentation, when a fragment of text from a textbook is presented on the slide, leaves the problem of splitting the text into parts unsolved. It was suggested to use pre-defined tasks: 1) to divide the text into semantic parts; 2) to give each part a title. The problem of organizing students' notes when reading a textbook has been identified and it was proposed to use handouts with spaces that are filled in when analyzing the text. The research results in the form of computer presentations can be used by an informatics teacher. Theoretical conclusions and the technology of their application can be used for further research of the organization of semantic reading (on other topics of informatics, in other academic subjects), as well as in the search for new methodological solutions related to the organization of semantic reading.
Poirot: private contact summary aggregation

Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensitive and can be used to track individuals. In this work, we design Poirot, a system to collect aggregate statistics about physical interactions in a privacy-preserving manner. We show a preliminary evaluation of our system that demonstrates the scalability of our approach even while maintaining strong privacy guarantees.
Comparation of K-Nearest Neighboor (K-NN) and Naive Bayes Algorithm for the Classification of the Poor in Recipients of Social Assistance

Abstract Poverty is a problems faced by developing countries, as well as Indonesia. According to data from the Central Statistics Agency in 2018, more than half the distribution of the poor population in Indonesia is in Java, which is 13,340.15 million people. Somokerto village is one of the villages in the district of Magelang, Central Java, which receives government assistance in an effort to reduce poverty. But in the process of classifying citizens who are entitled to receive assistance is still done manually. Manual classification is considered inaccurate in obtaining the results of social assistance recipients. In overcoming this problem, we need a systematic calculation to get accurate results. In this case, the researcher uses data mining classification calculation by comparing 2 calculation methods, namely K-NN and Naïve Bayes. The reseachers use Rapidminer tools. The research stages are identification of problems, data collection, implementation K-NN, Implementation Naive bayes, data testing process to produce accuracy and compare the result. The results obtained are the accuracy of Naïve Bayes higher than K-NN, namely Naïve Bayes 89.04% and K-NN 87.67%. This figure is classified in the category of good classification. From the results of the study it can be concluded the Naïve Bayes algorithm is suitable to be applied in the calculation of recipients of social assistance.
A Novel Fusion Framework for Salient Object Detection Based on Support Vector Regression

Singh, Vivek Kumar Kumar, NitinSalient object detection is highly influenced with a variety of salient features extracted from an image. It is a challenging task in saliency detection to find the suitable contribution of each salient feature for salient object detection. In this paper, we propose a novel fusion framework for salient object detection based on support vector regression (SVR) that effectively combines salient features for computing saliency score. Firstly, we extract salient features using a state-of-the-art saliency method. Secondly, training samples are generated that include positive and negative samples. These samples are produced by applying two different thresholds on salient features and these thresholds are also obtained from salient features. Thirdly, the training samples are fed into support vector regression to learnt SVR model for obtaining a set of learnable parameters. SVR saliency score of the input image is computed by using learnt SVR model with salient features of the input image. Finally, SVR saliency score is refined by post processing operations to obtained enhanced saliency map. Experimental results are shown efficacy of the proposed framework against compared 10 state-of-the-art saliency methods on two publicly available salient object detection datasets viz. ASD and ECSSD. The proposed framework achieves 1.7% and 19.4% improvement of Recall and MAE score on ASD dataset and 11.1% and 3.2% improvement of Recall and F-measure score on ECSSD dataset from the compared highest performing method.
Image Quality Measurement by Probabilistic Principal Component Analysis

In order to evaluate the perceptual quality of images, a full-reference quality index, which is called principal component deviation (PCD), is presented. This research is motivated by the discovery that the filters learned by the probabilistic principal component analysis are closely resemble the neurons of the human visual system. These filters are used as a model of the visual system, which are trained on 100,000 color image patches of size 4×4 by probabilistic principal component analysis. The PCD relates the image quality with the deviation between two sets of features that extracted by the learned filters. Experimental results show that PCD has relatively low computational complexity and high correlation with subjective quality evaluations.
The Threshold Optimization of Track Image Under Strong Sunlight

The threshold optimization of track image plays an important role in intelligent car competition. The traditional binary imaging method is not ideal under strong light. After using mean filter to convolve specific image, we explore first-order difference to extract the trend of histogram, and obtain all peaks and troughs that satisfy corresponding condition in the histogram. As a result, the position of rightmost can be selected as the optimal threshold. The threshold value obtained by our proposed method can effectively extract the track part of the image, thus providing an accurate guarantee for the direction control of intelligent car.
Research into A Control Schema for Camera Robot Capable of Artistic Creation

Through the realization of complex trajectory motions and multi-layer material shooting, camera robot can be an important tool for film and television shooting. At present, the trajectory planning of camera robot needs to be plotted in advance by professions, and it has not been easy to adjust on site. In addition, on the actual shooting scene, it's required to adjust running speed of the robot to meet the performance and rhythm of the actors. This paper presents a robot control schema using real-time tracking for unknown trajectories, and real-time stepless speed regulation for existing trajectories. It mainly includes data reading strategies and motion parameter protection algorithms. The response time is within 25 ms. When the real-time target position exceeds the limit of the joint axis servo capability, the robot approaches the target at the maximum speed that can be realized on its own. Furthermore, the schema for tracking functions and speed regulated reproduction have been already realized on the camera robot physical prototype.
DeepVoxels++: Enhancing the Fidelity of Novel View Synthesis from 3D Voxel Embeddings

We present a novel view synthesis method based upon latent voxel embeddings of an object, which encode both shape and appearance information and are learned without explicit 3D occupancy supervision. Our method uses an encoder-decoder architecture to learn such deep volumetric representations from a set of images taken at multiple viewpoints. Compared with DeepVoxels, our DeepVoxels++ applies a series of enhancements: a) a patch-based image feature extraction and neural rendering scheme that learns local shape and texture patterns, and enables neural rendering at high resolution; b) learned view-dependent feature transformation kernels to explicitly model perspective transformations induced by viewpoint changes; c) a recurrent-concurrent aggregation technique to alleviate single-view update bias of the voxel embeddings recurrent learning process. Combined with d) a simple yet effective implementation trick of frustum representation sufficient sampling, we achieve improved visual quality over the prior deep voxel-based methods (33\(\%\) SSIM error reduction and 22\(\%\) PSNR improvement) on \(360^\circ \) novel-view synthesis benchmarks.
Imperceptible Visible Watermarking with Watermark Readability Improved

In recent years, most digital capture devices have been designed with several post capture processing functions such as histogram modulation, based on this an Imperceptible-Visible Watermarking (IVW) scheme has been proposed to deliver auxiliary information and claim the ownership of digital images. In this paper an improved versin of imperceptible-visible watermarking is introduced, where the just noticeable distortion (JND) is used in the embedding stage to improve the watermark readability through a novel histogram binarization function in the exhibition stage. The experimental results show that the proposed strategies can improve the performance of several IVW schemes in the context of watermark imperceptibility and readability, which are similar with the most recent IVW schemes reported until now. The benchmarking performance of the proposed strategies is done through of the Peak Signal to Noise Ratio, Structural Similarity Index Measure, and the Bit Error Rate.
Active Contour Without Edge and Watershed for Follicle Detection in Ultrasound Image of Ovary

The follicle produces the estrogen hormone, is needed in the development of the ovum; therefore, monitoring the follicle in the ovary is very important for women. This study proposes a computational model for the detection of follicles of various sizes. The proposed model consists of pre-processing, speckle noise reduction, follicle segmentation using active contour without edge and watershed. Watershed has the advantage of being able to detect joined objects, so it can recognize if there are follicles attached without having to separate follicles. The model's performance was tested on the ovary dataset ultrasound image in patients at Sakina Idaman Yogyakarta Hospital.
A Visually Impaired Assistant Using Neural Network and Image Recognition with Physical Navigation

In Hong Kong, over 2.4% of the total population suffered from visual impairment. They are facing many difficulties in their daily lives, such as shopping and travelling from places to places within the city. For outdoor activities, they usually need to have an assistant to guide their ways to reach the destinations. In this paper, a mobile application assisting visually impaired people for outdoor navigation is proposed. The application consists of navigation, obstacle detection and scene description functions. The navigation function assists the user to travel to the destination with the Global Positioning System (GPS) and sound guidance. The obstacle detection function alerts the visually impaired people for any obstacles ahead that may be avoided for collision. The scene description function describes the scene in front of the users with voice. In general, the mobile application can assist the people with low vision to walk on the streets safely, reliably and efficiently.
Feature Fusion based Cross-modal Retrieval for Traditional Chinese Painting

Recently, large-scale digital preservation of traditional Chinese paintings has been launched. Effective retrieving them is still a significant challenge. There are two main reasons: (1) compared with modern images, their color contrast is not sharp enough, and the realism of objects is different. (2) the lack of labeled datasets. In order to address these problems, in this paper, we build a new labeled dataset and employ cross-modal retrieval for images of traditional Chinese painting. Our method utility data in two modals, text and image. We use the Bert model to extract the semantic text feature, and use CNN to extract the image feature. The two types of features are fused to realize cross-modal retrieval task. We evaluate our approach on three datasets. Experimental results prove the effectiveness of our approach.
Design of IT Governance Evaluation Using COBIT Framework through Capability Maturity in Department of Transportation Cirebon

Abstract IT Governance Evaluation has not been carried out optimally because supervision and evaluation of IT is only done if there are complaints from work units regarding IT services. These problems relate to the services that need to be provided to users of the information system, starting from the operations that need to be carried out on data security, sustainability aspects, training of human resources that support the processes of the information system and related to the support processes that should first be determined to be able to give service. This research method used qualitative research by using COBIT 5 framework dealing with the system analysis and design using the Usecase Diagram, Activity Diagram, Sequence Diagram, and Class Diagram tools. The programming language used is PHP Hypertext Prepocessor (PHP) while the database uses MySQL. This application was created to provide information about managing the IT Governance Evaluation in Department of Transportation Cirebon. The results of this study is IT Governance Evaluation using Cobit 5 Framework. Regarding to the IT Governance using COBIT 5, the Capability Maturity Level is at 3.28 that means defined-process. It represents that the IT Governance has been implemented to reach the goal of this organization. This assessment was aimed to determine the capabilities of IT performance and make recommendations for the proper management of IT Performance Management so that it can be used as a guide that can be used by the user and can increase the use of facilities optimally for better improvement of Department of Transportation Cirebon.
Review on privacy-preserving data comparison protocols in cloud computing

Data comparison is a crucial step in many operations, including data mining, data classification, image feature extraction, and image retrieval. With the existing data encryption technology, encrypted data computation and encrypted data comparison can be executed; however, there is no single data encryption method can meet the challenge of tackle the two tasks at the same time. Therefore, in many data operations, two major steps are involved: one is to encrypt the data with a computation supporting encryption method; one is to construct a comparison protocol to conduct privacy-preserving data comparison. While the first step has become a fast-developing and systematic research spot, research in the second step area remains scattered. In this chapter, we focus on the latter, by conducting an in-depth review of the development of the privacy-preserving data comparison. We introduce and analyze several most commonly used privacy-preserving data comparison protocols and their application scenarios; then we examine their functionality, security, computation complexity and communication overhead. By a systematic investigation of these protocols, we reveal and define the security challenge underneath each protocol. Our findings provide systematic and in-depth reference for improvement of the existing data comparison protocols in cloud environment.
P.140 Automated video tracking and behavior recognition in rodents: deep learning improves tracking robustness and classification accuracy

The ability to hold an endoscope is an important skill for surgeons and assistants performing endoscopic neurosurgery. Motion tracking can provide an objective assessment for surgical skills and may aid in endoscopic neurosurgery. We developed a cost-effective system to study the feasibility of objectively distinguishing performance levels for operating an endoscope.The study was divided into 2 parts. First, a video motion tracking analysis tool was built based on a printed mark and free software (Kinovea 0.8.15). Second, the tool was used to distinguish the holding endoscope performance of the robotic arm by experts (surgeon, n = 3) and novice users (residents, n = 6) under both 0′ and 30′ endoscopes.Through the printed mark and free software, we successfully built a system for video motion tracking. The data analysis showed that for both 0′ and 30′ endoscopes, the experts had a shorter total distance and depth, smaller average speed and maximum acceleration, and a fewer number of extreme accelerations than their novice counterparts. Compared with experts and residents, the fixed arm had better results.The simple low-cost video motion tracking system can provide an objective assessment of an endoscope holding skill, which allows for discrimination between an expert and a novice, and can be used by clinical neurosurgeons to select a qualified assistant.