Reviewerly/exHarmony huggingface dataset:
3D asymmetric expectation‐maximization attention network for brain tumor segmentation

Automatic brain tumor segmentation on MRI is a prerequisite to provide a quantitative and intuitive assistance for clinical diagnosis and treatment. Meanwhile, 3D deep neural network related brain tumor segmentation models have demonstrated considerable accuracy improvement over corresponding 2D methodologies. However, 3D brain tumor segmentation models generally suffer from high computation cost. Motivated by a recently proposed 3D dilated multi-fiber network (DMF-Net) architecture that pays more attention to reduction of computation cost, we present in this work a novel encoder-decoder neural network, ie a 3D asymmetric expectation-maximization attention network (AEMA-Net), to automatically segment brain tumors. We modify DMF-Net by introducing an asymmetric convolution block into a multi-fiber unit and a dilated multi-fiber unit to capture more powerful deep features for the brain tumor segmentation. In addition, AEMA-Net further incorporates an expectation-maximization attention (EMA) module into the DMF-Net by embedding the EMA block in the third stage of skip connection, which focuses on capturing the long-range dependence of context. We extensively evaluate AEMA-Net on three MRI brain tumor segmentation benchmarks of BraTS 2018, 2019 and 2020 datasets. Experimental results demonstrate that AEMA-Net outperforms both 3D U-Net and DMF-Net, and it achieves competitive performance compared with the state-of-the-art brain tumor segmentation methods.
Evaluating the performance of radiometers for solar overirradiance events

This paper describes a methodology used to evaluate pyranometers and reference cell performance in relation to solar overirradiance (OIR) events. By definition, these events are characterized when global horizontal irradiance (GHI) exceeds the extraterrestrial horizontal irradiance (ETH) due to cloud enhancement (CE) effects (e.g. scattering, reflection, cloud lense augmentation). Evaluation metrics are proposed to assess relevant features of the events such as uncertainty, event identification, and response time. The metrics were validated with high standard radiometers deployed at the Solar Energy Research Laboratory Fotovoltaica/UFSC in Florianópolis, Brazil. The results were checked against solar radiometers' specifications, and sensors were analyzed according to their performances. An ISO outdoor calibration, as well as a system uncertainty analysis and data response time evaluation, were performed, to assure results reliability. In addition, two extreme overirradiance (EOIR) events were evaluated separately, and seasonal occurrence of the energy fraction of overirradiance events was assessed and discussed.
CREATION OF 3D MODELS OF REAL OBJECTS IN THE TEACHING OF COMPUTER SCIENCE

Photogrammetry is one of the modern methods of mapping and can also be used as a tool for digitizing small and large objects, with good results, which currently occupies its place in practice. The use of photogrammetry is no longer limited to geodetic activities, but its wide range of uses also includes the protection of cultural heritage, modeling of the environment in the world of robotics, navigation and many others. Based on the above, it can be seen that it is important to implement photogrammetry procedures in the teaching of computer science. In addition, it allows the connection of knowledge from several subjects, such as mathematics, geometry, physics or optics, and thus supports between subject relationships, which is another important element, which we also follow in this paper. The result of the work is a realistic three-dimensional model of the mapped object. Acquisition of this model is conditioned by obtaining photographic images from which the model will be created. Of course, we can use expensive equipment and apparatus, but that is by no means our goal. To obtain photo shots, we will use only such equipment that students already have at their disposal today - mobile phones, tablets, digital cameras, etc. Creating a model does not necessarily end the work, on the contrary, with the help of a suitable software tool allows e.g. determination of its dimensions, placement in the coordinate system or for the already mentioned protection of cultural heritage. In our paper, we will focus on the description and exclusion of such key issues that will significantly affect the quality of the model, which can lead to disappointment in students, reduce their enthusiasm for this work or even loss of interest. We will point out the importance of the preparation and selection of photo shots and the correct process of creation by both the teacher and the students, which we will also substantiate with the results of the work. We will also point out the creation of a model in the pix4D software and implement the whole procedure into computer science lessons, focusing mainly on secondary schools, taking into account the specified range of lessons. Of course, we aim to teach students to create such models without the help of a teacher.
Social Network Analysis Text Mining on Networks Publication Citation

The Publication Citation Network helps you visualize the distribution of journal articles or reviews. This extension helps determine the exposure of local and international university press. The relationship between one paper and another. Once formed, it is difficult to see the distribution of articles referenced in published articles. Unpublished publications may be due to complex relationships between one publication and another. Publications are widely distributed. Difficulty in determining the relationships between the objects of a publication and the need to determine the level of publication of the publication. This study consists of several parts and is only a part. In this study, we will use a text mining and social analysis approach to create a model that creates a graph that can visualize the distribution of date posts within a date post network. SINTA Science and Technology Index and data relationships using text mining on the Google Scholar website. Identify nodes in building a network of important publications. The data used is data from the SINTA Science and Technology Index website which involves publication data from Universitas Sumatera Utara lecturers from 2015 to 2020 and consists of 1730 records. The variables used included the article title, author name, institution, citation, and country. Text Mining runs in Python and generates a graph on the board.
Expandable quantum deep width learning-based distributed voltage control for smart grids with high penetration of distributed energy resources

With numerous distributed energy resources accessed to smart grids, the randomness of distributed generators exacerbates the risk of voltage violations in the smart grid. To reduce the risk of voltage violations caused by distributed energy resources, this paper designs a real-time distributed voltage control method for smart grids to replace the centralized voltage control method of smart grids. In the designed real-time distributed voltage control method, the adjacent regional controllers communicate with each other to realize the optimal global control of the voltage of the entire smart grid without the need for a central controller. To study the reactive states of distributed energy resources access to smart grids on the voltage of smart grids, this paper proposes a three-state energy voltage model. To match the expandable distributed voltage control method for smart grids, this paper proposes the expandable quantum deep width learning algorithm. By combining the proposed algorithm with the designed real-time distributed voltage control method, the proposed algorithm has strong expandability and can match the distributed voltage control problem of smart grids. Finally, the effectiveness and expandability of the proposed algorithm and the designed real-time distributed voltage control method are demonstrated under four complex systems. The integral squared time-weighted absolute error index value of the expandable quantum deep width learning is less than or equal to 6.461.
Interactive Behavior-driven Development: a Low-code Perspective

Within behavior-driven development (BDD), different types of stakeholders collaborate in creating scenarios that specify application behavior.The current workflow for BDD expects non-technical stakeholders to use an integrated development environment (IDE) to write textual scenarios in the Gherkin language and verify application behavior using test passed/failed reports.Research to date shows that this approach leads non-technical stakeholders to perceive BDD as an overhead in addition to the testing.In this vision paper, we propose an alternative approach to specify and verify application behavior visually, interactively, and collaboratively within an IDE.Instead of writing textual scenarios, non-technical stakeholders compose, edit, and save scenarios by using tailored graphical interfaces that allow them to manipulate involved domain objects.Upon executing such interactively composed scenarios, all stakeholders verify the application behavior by inspecting domain-specific representations of run-time domain objects instead of a test run report.Such a low code approach to BDD has the potential to enable nontechnical stakeholders to engage more harmoniously in behavior specification and validation together with technical stakeholders within an IDE.There are two main contributions of this work: (i) we present an analysis of the features of 13 BDD tools, (ii) we describe a prototype implementation of our approach, and (iii) we outline our plan to conduct a large-scale developer survey to evaluate our approach to highlight the perceived benefits over the existing approach.
The impact of factors on information sharing: An application of meta-analysis

Information sharing plays a key role in supply chain performance. According to previous individual studies, technology, trust, commitment, and uncertainty are four potential factors affecting information sharing. However, most studies focus on testing a positive relationship between each factor and information sharing. Therefore, it is necessary to evaluate the effect of each factor on information sharing.To determine the accurate effect of factors on sharing information and propose key factors to support decision-makers in improving their information sharing.Correlation coefficients between factors are collected from 41 individual studies with a total of 8,983 samples on Google Scholar.Using the rank correlation test and Egger's regression test to test publication bias. The meta-analysis method is used to perform analysis models, including fixed-effect, random-effect, and Hunter and Schmidt methods.Commitment plays the most important role in information sharing when compared to technology, trust, and uncertainty. Commitment accounts for 19% in the Hunter and Schmidt method and 22% in both fixed-effect and random-effect models. In addition, the result indicates that there are around 50% of other factors that affect the efficiency of sharing information besides four factors in our studies.Technology, trust, and commitment significantly affect information sharing, of which the impact of commitment on information sharing is strongest and should be a priority in improving the effectiveness of information sharing. Our study contributes two findings to literature in the field of supply chain information sharing: 1) certain confirming the important role of commitment on sharing information, and 2) the necessity of considering other factors besides these four elements.
Global algorithmic capital markets: High frequency trading, dark pools, and regulatory challenges

"Global algorithmic capital markets: High frequency trading, dark pools, and regulatory challenges." The Social Science Journal, 59(2), pp. 334–335
OpenMPD: An Open Multimodal Perception Dataset for Autonomous Driving

Multi-modal sensor fusion techniques have promoted the development of autonomous driving, while perception in the complex environment remains a challenging problem. In order to tackle the problem, we propose the Open Multi-modal Perception dataset (OpenMPD), a multi-modal perception benchmark objected at difficult examples. Compared with existing datasets, OpenMPD focuses more on those complex traffic scenes in urban areas with overexposure or darkness, crowded environment, unstructured roads and intersections. It acquires the multi-modal data through a vehicle with six cameras and four LiDAR for a 360-degree field of view and collected 180 clips of 20-second synchronized images at 20 Hz and point clouds at 10 Hz. Particularly, we applied a 128-beam LiDAR to provide Hi-Res point clouds to better understand the 3D environment and sensor fusion. We sampled 15 K keyframes at equal intervals from clips for annotations, including 2D/3D object detections, 3D object tracking, and 2D semantic segmentation. Moreover, we provide four benchmarks for all tasks to evaluate algorithms and conduct extensive experiments of 2D/3D detection and segmentation on OpenMPD. Data and further information are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.openmpd.com/</uri> .
Image space trajectory tracking of 6-DOF robot manipulator in assisting visual servoing

As vision is a versatile sensor, vision-based control of robot is becoming more important in industrial applications. The control signal generated using the traditional control algorithms leads to undesirable movement of the end-effector during the positioning task. This movement may sometimes cause task failure due to visibility loss. In this paper, a sliding mode controller (SMC) is designed to track 2D image features in an image-based visual servoing task. The feature trajectory tracking helps to keep the image features always in the camera field of view and thereby ensures the shortest trajectory of the end-effector. SMC is the right choice to handle the depth uncertainties associated with translational motion. Stability of the closed-loop system with the proposed controller is proved by the Lyapunov method. Three feature trajectories are generated to test the efficacy of the proposed method. Simulation tests are conducted and the superiority of the proposed method over a Proportional Derivative – Sliding Mode Controller (PD-SMC) in terms of settling time and distance travelled by the end-effector is established in the presence and absence of depth uncertainties. The proposed controller is also tested in real-time by integrating the visual servoing system with a 6-DOF industrial robot manipulator, ABB IRB 1200.
Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Deep learning is a new direction of research for specific emitter identification (SEI). Radio frequency (RF) fingerprints of the emitter signal are small and sensitive to noise. It is difficult to assign labels containing category information in noncooperative communication scenarios. This makes network models obtained by conventional supervised learning methods perform unsatisfactorily, leading to poor identification performance. To address this limitation, this paper proposes a semisupervised SEI algorithm based on bispectrum analysis and virtual adversarial training (VAT). Bispectrum analysis is performed on RF signals to enhance individual discriminability. A convolutional neural network (CNN) is used for RF fingerprint extraction. We used a small amount of labelled data to train the CNN in an adversarial manner to improve the antinoise performance of the network in a supervised model. Virtual adversarial samples were calculated for VAT, which made full use of labelled and large unlabelled training data to further improve the generalization capability of the network. Results of numerical experiments on a set of six universal software radio peripheral (USRP; model B210) devices demonstrated the stable and fast convergence performance of the proposed method, which exhibited approximately 90% classification accuracy at 10 dB. Finally, the classification performance of our method was verified using other evaluation metrics including receiver operating characteristic and precision-recall.
Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.
Real time deep learning framework to monitor social distancing using improved single shot detector based on overhead position

The current COVID 19 halo infection has caused a severe catastrophe with its deadly spread. Despite the implementation of the vaccine, the severity of the infection has not diminished, and it has become stronger and more destructive. So, the only solution to protect ourselves from infection is social-distancing. Although social-distancing has been in practice for a long time, in most places it is not effectively followed, and it is very difficult to find out manually at all times whether people are following it or not. Therefore, we introduced a newly developed framework of deep-learning technique to automatically identify whether people maintain social-distancing or not using remote sensing top view images. Initially, we are detecting the context of image which includes information about the environment. Our detection model recognizes individuals using the boundary box. Then centroid is determined over every detected boundary box. By means of applying Euclidean distance, the pair range distances of the detected boundary box centroid are determined. To evaluate whether the distance measurement exceeds the minimum social distance limit, the violation threshold is established. We used Improved Single Shot Detector model for detecting a person over an image. Experiments are carried out on widely collected remote sensing images from various environments. Based on the object detection algorithm of deep learning, a variety of performance metrics are compared to evaluate the efficiency of the proposed model. Research outcome shows that, our proposed model outperforms well while recognize and detect a person in a well excellent way.
CNN-based Super Resolution for Video Coding Using Decoded Information

Down-sampling followed by an up-sampling is a well-known strategy to compress high-resolution pictures given a limited bandwidth in image as well as video coding. Recently, inspired by the latest advances of image super resolution (SR) technologies using convolutional neural network (CNN), CNN-based SR has been explored for resampling-based coding. However, the side information generated during the compression process is not utilized efficiently by the SR network in prior arts. In this paper, we propose a CNN-based SR method for video coding, where more side information is leveraged as a supplement to reconstruction samples. Specifically, we introduce prediction samples to be the auxiliary information, as it can provide the texture and directional information about the original picture. Considering the different characteristics, we design different networks for the luma and chroma components. When designing the chroma up-sampling CNN, the luma reconstruction is used as the auxiliary information of the chroma network, which exploits the cross-component correlation. Experimental results show that the proposed method achieves 11.07% BD-rate savings in all-intra configuration compared with VTM-11.0. Further experiments validate the effectiveness of using luma information to aid the chroma up-sampling process.
Normalisation of Indonesian-English Code-Mixed Text and its Effect on Emotion Classification

Usage of code-mixed text has increased in recent years among Indonesian internet users, who often mix Indonesian-language with English-language text.Normalisation of this code-mixed text into Indonesian needs to be performed to capture the meaning of English parts of the text and process them effectively.We improve a state-of-the-art code-mixed Indonesian-English normalisation system by modifying its pipeline modules.We further analyse the effect of code-mixed normalisation on emotion classification tasks.Our approach significantly improved on a state-of-the-art Indonesian-English code-mixed text normalisation system in both the individual pipeline modules and the overall system.The new feature set in the language identification module showed an improvement of 4.26% in terms of F 1 score.The combination of machine translation and ruleset in the lexical normalisation module improved BLEU score by 25.22% and lowered WER by 62.49%.The use of context in the translation module improved BLEU score by 2.5% and lowered WER by 8.84%.The effectiveness of the overall pipeline normalisation system increased by 32.11% and 33.82%, in terms of BLEU score and WER, respectively.Code-mixed normalisation also improved the accuracy of emotion classification by up to 37.74% in terms of F 1 score.
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation

Omnidirectional 3D information is essential for a wide range of applications such as Virtual Reality, Autonomous Driving, Robotics, etc. In this paper, we propose a novel, model-agnostic, two-stage pipeline for omnidirectional monocular depth estimation. Our proposed framework PanoDepth takes one 360 image as input, produces one or more synthesized views in the first stage, and feeds the original image and the synthesized images into the subsequent stereo matching stage. In the second stage, we propose a differentiable Spherical Warping Layer to handle omnidirectional stereo geometry efficiently and effectively. By utilizing the explicit stereo-based geometric constraints in the stereo matching stage, PanoDepth can generate dense high-quality depth. We conducted extensive experiments and ablation studies to evaluate PanoDepth with both the full pipeline as well as the individual modules in each stage. Our results show that PanoDepth outperforms the state-of-the-art approaches by a large margin for 360 monocular depth estimation. Our code is available at https:// github.com/yuyanli0831/PanoDepth_3dv.