Reviewerly/exHarmony huggingface dataset:
Wave-optics simulation of dynamic speckle: II. In an image plane

This two-part paper demonstrates the use of wave-optics simulations to model the effects of dynamic speckle. In Part II, we formulate closed-form expressions for the analytical irradiance correlation coefficient, specifically in the image plane of an optical system. These expressions are for square, circular, and Gaussian limiting apertures and four different modes of extended-object motion, including in-plane and out-of-plane translation and rotation. Using a phase-screen approach, we then simulate the equivalent scattering from an optically rough extended object, where we assume that the surface heights are uniformly distributed and delta correlated from grid point to grid point. For comparison to the analytical irradiance correlation coefficient, we also calculate the numerical irradiance correlation coefficient from the dynamic speckle after propagation from the simulated object plane to the simulated image plane. Overall, the analytical and numerical results definitely demonstrate that, relative to theory, the dynamic speckle in the simulated image plane is properly correlated from one frame to the next. Such validated wave-optics simulations provide the framework needed to model more sophisticated setups and obtain accurate results for system-level studies.
Russian Higher School: Forced Distance Learning and Planned Switch to Distance Learning During Pandemic (Experience of Sociological Analysis)

Challenges related to the COVID-19 pandemic require structural analysis and sociocultural comprehension, which has and will for quite a while have multi-aspect and systemic nature. All elements of the social system have been forced to function in the stress regime in the absence of any basic patterns and tried-and-true methods to solve the arising problem. The overall uncertainty in the organization of the social life and the enforced and, at times, very contentious steps taken at various levels of management have caused polarization of opinions as to the functioning of all social institutions. Special pressure has been put on education, since it is this institution that is mainly responsible for the social system’s stability. In this regard, it has been important and significant to comprehensively study the problems and limitations associated with the functioning of the higher education system in Russia during the pandemic, as well as to analyze the change in tone and attitude both on the part of the public and the teaching corps, as a response to the transition to the new forms of work at higher education institutions. The article was prepared using the materials of two stages of the sociological research “The opinion of teachers of Russia’s higher education institutions on distance learning during the pandemic” conducted in May, June and December 2020. The volume of sample sets made up 3431 people at the first stage, and 6006, at the second. An analysis of research data covers the following areas: organizational problems of transition to distance learning; resources used, organization of lectures and seminars, assessment of the positive and negative aspects of distance learning; perspectives, limitations and trajectories for the implementation of distance learning in Russia’s higher education institutions, as well as a set of social problems aimed at studying social well-being and assessing possible changes in the life and career of teachers. The research data are compared with the data of similar works by Russian and foreign analysts. On the basis of the analysis done and the conclusions obtained, the authors propose practical steps to optimize the work of the higher education system in stressful situations, to use distance technologies in the learning organization. Also, possible forms are considered that combine distance and full-time education, and recommendations are proposed on how to change the tone in perceiving inevitable informational and communication transformations on the part of teachers. This research was performed according to the Development program of the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University «Mathematical methods of complex systems analysis».
Layout-Guided Novel View Synthesis from a Single Indoor Panorama

Existing view synthesis methods mainly focus on the perspective images and have shown promising results. How-ever, due to the limited field-of-view of the pinhole cam-era, the performance quickly degrades when large cam-era movements are adopted. In this paper, we make the first attempt to generate novel views from a single indoor panorama and take the large camera translations into consideration. To tackle this challenging problem, we first use Convolutional Neural Networks (CNNs) to extract the deep features and estimate the depth map from the source-view image. Then, we leverage the room layout prior, a strong structural constraint of the indoor scene, to guide the generation of target views. More concretely, we estimate the room layout in the source view and transform it into the target viewpoint as guidance. Meanwhile, we also con-strain the room layout of the generated target-view images to enforce geometric consistency. To validate the effectiveness of our method, we further build a large-scale photo-realistic dataset containing both small and large camera translations. The experimental results on our challenging dataset demonstrate that our method achieves state-of-the-art performance. The project page is at https://github.com/bluestyle97/PNVS.
WITHDRAWN: A survey on machine-learning approaches: Theory and their concepts

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Unsupervised-learning-based method for chest MRI–CT transformation using structure constrained unsupervised generative attention networks

The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupervised generative attentional networks with adaptive layer-instance normalisation for image-to-image translation (U-GAT-IT), which specialised in unpaired image transformation based on attention maps for image transformation. We added the modality-independent neighbourhood descriptor (MIND) to the loss of U-GAT-IT to guarantee anatomical consistency in the image transformation between different domains. Our proposed method obtained a synthesised computed tomography of the chest. Experimental results showed that our method outperforms current approaches. The study findings suggest the possibility of synthesising clinically acceptable computed tomography images from chest MRI with minimal changes in anatomical structures without human annotation.
Privacy-preserving Density-based Clustering

Clustering is an unsupervised machine learning technique that outputs clusters containing similar data items. In this work, we investigate privacy-preserving density-based clustering which is, for example, used in financial analytics and medical diagnosis. When (multiple) data owners collaborate or outsource the computation, privacy concerns arise. To address this problem, we design, implement, and evaluate the first practical and fully private density-based clustering scheme based on secure two-party computation. Our protocol privately executes the DBSCAN algorithm without disclosing any information (including the number and size of clusters). It can be used for private clustering between two parties as well as for private outsourcing of an arbitrary number of data owners to two non-colluding servers. Our implementation of the DBSCAN algorithm privately clusters data sets with 400 elements in 7 minutes on commodity hardware. Thereby, it flexibly determines the number of required clusters and is insensitive to outliers, while being only factor 19x slower than today's fastest private K-means protocol (Mohassel et al., PETS'20) which can only be used for specific data sets. We then show how to transfer our newly designed protocol to related clustering algorithms by introducing a private approximation of the TRACLUS algorithm for trajectory clustering which has interesting real-world applications like financial time series forecasts and the investigation of the spread of a disease like COVID-19.
A Watermarking Scheme for Color Image Using Quaternion Discrete Fourier Transform and Tensor Decomposition

To protect the copyright of the color image, a color image watermarking scheme based on quaternion discrete Fourier transform (QDFT) and tensor decomposition (TD) is presented. Specifically, the cover image is partitioned into non-overlapping blocks, and then QDFT is performed on each image block. Then, the three imaginary frequency components of QDFT are used to construct a third-order tensor. The third-order tensor is decomposed by Tucker decomposition and generates a core tensor. Finally, an improved odd–even quantization technique is employed to embed a watermark in the core tensor. Moreover, pseudo-Zernike moments and multiple output least squares support vector regression (MLS–SVR) network model are used for geometric distortion correction in the watermark extraction stage. The scheme utilizes the inherent correlations among the three RGB channels of a color image, and spreads the watermark into the three channels. The experimental results indicate that the proposed scheme has better fidelity and stronger robustness for common image-processing and geometric attacks, can effectively resist each color channel exchange attack. Compared with the existing schemes, the presented scheme achieves better performance.
Image Classification Method Based on Improved KNN Algorithm

Abstract As the development of machine vision technology, artificial intelligence algorithms are gradually popularized for identifying images. However, traditional KNN algorithm actually costs too much time when classifying images, which is not qualified to actual application scenes. An improved algorithm is proposed in the paper. The test time has been greatly shortened and the efficiency of KNN algorithm is improved by increasing the screening of data sets. By setting STM32F103 as master control and OV7670 as camera, actual detection of volleyball, football, and basketball was carried out after test environment was set up. And the test time is shorter compared with that of general KNN algorithm. At the same time, the identification accuracy is high, which indicates that the method has good practicability.
Field Test Validations of Vision-based Multi-camera Multi-drone Tracking and 3D Localizing with Concurrent Camera Pose Estimation

This paper reports the field test validations of the recently proposed vision-based real-time multi-camera setups for detecting, tracking and 3D localizing multiple aerial targets (mainly drones). We also propose the additional concurrent camera pose estimation when the camera poses are not known beforehand. This extra step can be used alongside (in parallel) with the drone tracking and localizing process. We conducted flight tests using 2 drones flying in 2 specific scenarios, and used 3 cameras to observe, detect, track and locate the positions of both drones in global frame. The efficacy of our technique is measured by the accuracy of the temporal and spatial positions of the observed drones, against the drones' own GPS recordings. Our initial results show reasonable accuracy, i.e. ±1m at 50m, as such, the proposed vision-based methods can be used for drone detection and tracking.
Side Information Fusion for Recommender Systems over Heterogeneous Information Network

Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users’ preferences (ratings) based on their past behaviors. Recently, various types of side information beyond the explicit ratings users give to items, such as social connections among users and metadata of items, have been introduced into CF and shown to be useful for improving recommendation performance. However, previous works process different types of information separately, thus failing to capture the correlations that might exist across them. To address this problem, in this work, we study the application of heterogeneous information network (HIN), which offers a unifying and flexible representation of different types of side information, to enhance CF-based recommendation methods. However, we face challenging issues in HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance. To address these issues, we apply metagraph to similarity computation and solve the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and, at the same time, identify the useful metagraphs. Besides FMG, a two-stage method, we further propose an end-to-end method, hierarchical attention fusing, to fuse metagraph-based similarities for the final recommendation. Experimental results on four large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art methods in terms of recommendation performance.
Optimization of Embedded Mobile Teaching Model Based on Network Streaming Media Technology

Integrating network technology fully into traditional teaching can realize resource sharing to the greatest extent, so the establishment of distance education and network teaching platform has become the inevitable development of the time. The purpose of this paper is to build an embedded mobile teaching model based on network streaming media technology. The technology application, system composition and structure, realization process, and teaching method of the system are introduced in detail. The system energy consumption, bit rate of video information, and buffer technology were optimized, respectively. In this system, the energy consumption optimization method of mobile streaming media is adopted, and Central Processing Unit (CPU) resources are allocated reasonably according to the principle of maximizing rewards, so as to achieve the purpose of reducing power consumption. The results show that the system can effectively ensure the normal transmission of large multimedia information stream data through the network by using streaming media playback technology, and users can control the teaching process through interactive operation, which makes the network multimedia distance teaching based on streaming media develop in an all-round way and bring advanced teaching mode for education.
Secure and verifiable outsourced data dimension reduction on dynamic data

Dimensionality reduction aims at reducing redundant information in big data and hence making data analysis more efficient. Resource-constrained enterprises or individuals often outsource this time-consuming job to the cloud for saving storage and computing resources. However, due to inadequate supervision, the privacy and security of outsourced data have been a serious concern to data owners. In this paper, we propose a privacy-preserving and verifiable outsourcing scheme for data dimension reduction, based on incremental Non-negative Matrix Factorization (NMF) method. We emphasize the importance of incremental data processing, exploiting the properties of NMF to enable data dynamics in consideration of data updating in reality. Besides, our scheme can also maintain data confidentiality and provide verifiability of the computation result. Experiment evaluation has shown that the proposed scheme achieves high efficiency, saving about more than 80% computation time for clients.
A Parallel Algorithm Template for Updating Single-Source Shortest Paths in Large-Scale Dynamic Networks

The Single Source Shortest Path (SSSP) problem is a classic graph theory problem that arises frequently in various practical scenarios; hence, many parallel algorithms have been developed to solve it. However, these algorithms operate on static graphs, whereas many real-world problems are best modeled as dynamic networks, where the structure of the network changes with time. This gap between the dynamic graph modeling and the assumed static graph model in the conventional SSSP algorithms motivates this work. We present a novel parallel algorithmic framework for updating the SSSP in large-scale dynamic networks and implement it on the shared-memory and GPU platforms. The basic idea is to identify the portion of the network affected by the changes and update the information in a rooted tree data structure that stores the edges of the network that are most relevant to the analysis. Extensive experimental evaluations on real-world and synthetic networks demonstrate that our proposed parallel updating algorithm is scalable and, in most cases, requires significantly less execution time than the state-of-the-art recomputing-from-scratch algorithms.
An adaptive coarse-fine semantic segmentation method for the attachment recognition on marine current turbines

Microorganisms attached to marine current turbine may induce imbalance faults that badly affect the power generation efficiency. Therefore, it is necessary to conduct attachment recognition for prompt device maintenance. This paper proposes a coarse-fine semantic segmentation network (CSSN) to adaptively recognize the attachment location and size from blurry underwater images. The CSSN contains a deep coarse branch to perform global segmentation and a shallow fine branch to obtain local contours. The two branches are adaptively fused with dynamic weights in the training process. The final segmentation maps are produced by a softmax layer, after which the precise attachment area percentage can be computed. Besides, dropout is applied to estimate the recognition uncertainty that provides intuitive guidance for the maintenance decision. Experimental results show that the proposed method is efficient to recognize the attachment under turbid submerged conditions.
Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people's actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector's values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers' performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.
Heavy-Vehicle Detection Based on YOLOv4 featuring Data Augmentation and Transfer-Learning Techniques

Abstract Real-time Vehicle detection is crucial in today’s era for our complex interconnected transportation ecosystem built on an advanced technological network of intelligent systems encompassing a wide range of applications such as autonomous vehicles, traffic Surveillance, advanced driver assistance systems, and etcetera. The significance of its application to digital transportation infrastructure embarks upon a distinct framework for heavy-vehicle detection integrated with the YOLOv4 algorithm for real-time detection. In this proposed work, two entities of heavy vehicles such as buses, trucks are considered. The crux of the model, an algorithmic computational mechanism incorporates Mosaic Data augmentation and Transfer-learning techniques that are applied to avoid over-fitting and to improve the optimal speed during training. Subsequently, a fine-tuning YOLOv4 algorithm is implemented for detecting the heavy vehicle. The algorithm is tested for real-time situations in various traffic densities through Computer Vision. Experimental results show that the proposed system achieves higher detection accuracy of 96.54% mAP. More specifically, the performance of the proposed algorithm with the COCO test set and PASCAL VOC 2007 test set demonstrates improvement when compared with other state-of-the-art approaches.
ParSQuAD: Machine Translated SQuAD dataset for Persian Question Answering

Recent advances in the field of Question Answering (QA) have improved state-of-the-art results. Due to the availability of rich English training datasets for this task, most results reported are for this language. However, due to the lack of Persian datasets, less research has been done for the latter language therefore the results are hard to compare. In the present work, we introduce the Persian Question Answering Dataset (ParSQuAD) translated from the well-known SQuAD 2.0 dataset. Our dataset comes in two versions depending on whether it has been manually or automatically corrected. The result is the first large-scale QA training resource for Persian. We train three baseline models, one of which, achieves an F1 score of 56.66% and an exact match ratio of 52.86% on the test set with the first version and an F1 score of 70.84 % and an exact match ratio of 67.73% with the second version.