Reviewerly/exHarmony huggingface dataset:
Multiresolution Mapping of Land Cover From Remote Sensing Images by Geometric Generalization

Land cover multiresolution mapping of remote sensing images contributes greatly to land-use management, environmental protection, and city planning. In traditional mapping of this type, the representation of different land-use types depends on the image resolution, and the geometric, topologic, and semantic characteristics are not considered. This approach can cause a loss of useful information and the redundancy of useless information. In this study, we propose a superpixel-based land cover (multiresolution representation SULR) method for remote sensing images that employs multifeature fusion. In this process, we first define three basic superpixel operations, collapse, connection, and cutting, as the basic operators of multiresolution land cover mapping. Then, the topological adjacent land parcels are combined through the amalgamation of polygons with heterogeneous properties and aggregation of polygons with homogeneous properties based on the three proposed superpixel operators. Finally, the geometric boundaries of parcels are simplified by combining the superpixel collapse operator and image thinning technologies. Compared with traditional image scale transformation methods, the proposed method can more effectively achieve multiresolution mapping of land cover from remote sensing images by considering the geometric, topologic, and semantic characteristics of land parcels.
Utilizing VR/AR for Interior Design Program

This research explores the utilization of a new media known as Virtual Reality (VR) and Augmented Reality (AR) to enhance interior design (ID) students’ presentation at the College of Fine Arts and Design (CFAD), University of Sharjah. Through a survey, we identify the most popular media used by the students for their presentations and investigates the potential barrier of using new media such as AR and VR technology from the students’ perspective. The study reveals that the use of VR and AR technology for project presentations is minimal, although the students did show interest in the technology. Among the identified reason for not using this technology is lack of exposure, obsolete syllabi, and lack of expertise. Finally, we propose a feasible approach to employ AR/VR for ID project presentation purposes. We believe that AR/VR is a potential tool that can open a new dimension of experiencing and engaging with visuals of design by using interactive and immersion features.
Three-dimensional Reconstruction of Working Environment in Remote Control Excavator

Remote control excavator solves the personal safety problem of operators in dangerous conditions, but the video surveillance of remote control excavators can't sense the stereo distance of the target. To solve the problem, the binocular vision method is used to reconstruct the 3D operating scene of the excavator, and help the drivers to effectively sense the spatial position of the target and bucket to improve the efficiency of remote control operation. Firstly, the noise generated by binocular camera during data collection was removed by guided filtering. And then, left and right images were matched by features based on Sum of Absolute Differences (SAD). Then, the generated disparity value was optimized according to the Winner Takes All(WTA) strategy. Finally, the disparity map of the working scene was obtained and the 3D reconstruction of the scene was completed. The experimental results show that the binocular 3D reconstruction system used in this paper can realize the 3D reconstruction of different scenes indoors and outdoors through the cooperation of software and hardware.
Functional Requirements Analysis of Militia Emergency Communication Equipment Based on SOA Architecture

Militia emergency communication involves a wide range of information fields, which requires real-time sharing of information in various fields, extraction of information services as required, and standardized and smooth command process. Based on the system architecture of service-oriented architecture (SOA), this paper makes a deep discussion and research from two aspects of theory and practice, and designs and implements a distributed, heterogeneous and open emergency management information system based on service-oriented architecture. The framework is based on SOA, and adopts message transmission and event-driven mechanism to support agile application development and system upgrading. After the militia emergency communication system based on SOA is put into use, the linkage and information sharing between different systems are realized in the process of ensuring emergency communication, and the efficiency of emergency communication processing is effectively improved.
Forms of presentation of content in educational media (on the example of the educational media project Arzamas)

The subject of the research in the article is the forms of presentation of content in educational media, which are a very voluminous developing typological group and constitute a significant segment of the Russian information market. The editorial offices of these resources use modern formats for presenting scientific and educational information from different fields of knowledge in accordance with the trends in the development of journalism and following the interests and needs of the audience. The hypothesis of the study is the idea that educational media mainly use formats with a greater engaging ability to solve creative tasks and realize their functional purpose. Using the example of publications of the Arzamas network media project, the author examines the possibilities of various forms of presentation of educational information. The article presents an intelligence study of the forms of presentation of content in educational media. The novelty of the research is due to the small knowledge of the formats of online publications of this type and consists in identifying and characterizing the main forms of content presentation in educational media in the context of the development of online journalism formats. The methodological basis of the study was a systematic approach. The forms of educational media content are considered by the author of the article in the context of the development of journalism and the characteristics of modern media text. The study uses descriptive, informative, comparative and structural-functional methods to solve a scientific problem, highlights the main forms of presentation of the content of the educational media project Arzamas, reveals their features and functional purpose. As a result of the research, the author comes to the conclusion that in order to realize their functional purpose, educational media use combined forms that allow solving educational and recreational tasks. The editorial board of the Arzamas educational media project creates a hypertextual and multimedia educational environment, addressing both traditional journalistic genres and new media formats.
Word sense induction using leader-follower clustering of automatically generated lexical substitutes

Word Sense Induction (WSI) concerns the automatic identification of the various senses of polysemous words. Any improvement in this process can directly affect the quality of the applications in which knowing the word’s senses is important. For example, word sense disambiguation, information retrieval, and clustering of web search result in lexically ambiguous queries. In this paper, we propose a novel WSI model that makes use of automatically generated lexical substitutes for a target word to construct a graph and data preparation for the next steps. Following the data preparation step, we make use of Leader–Follower graph clustering to find the basic senses of the target word. The senses of the target word inside the remaining or new upcoming instances will be decided according to their contextual embedding’s similarities with the basic sense. Besides, to make the number of found sense groups of a target word much closer to the reality, we apply post-processing at the end. The results of experiments on SemEval2010 dataset confirm that the proposed method outperforms all the state-of-the-art solutions in terms of both harmonic and geometric v-measure and f-score with a lower average number of sense groups.
Leveraging Stack Overflow to Detect Relevant Tutorial Fragments of APIs

Developers often use learning resources such as API tutorials and Stack Overflow (SO) to learn how to use an unfamiliar API. An API tutorial can be divided into a number of consecutive units that describe the same topic, denoted as tutorial fragments. We consider a tutorial fragment explaining the API usage knowledge as a relevant fragment of the API. Discovering relevant tutorial fragments of APIs can facilitate API understanding and learning. However, existing approaches, based on supervised or unsupervised approaches, often suffer from either high manual efforts or lack of consideration of the relevance information. In this paper, we propose a novel approach, called SO2RT, to detect relevant tutorial fragments of APIs based on SO posts. SO2RT first automatically extracts relevant and irrelevant 〈API,QA〉 pairs based on heuristic rules of SO, and constructs 〈API, FRA〉 pairs (FRA stands out fragment) by using tutorial fragments and APIs. SO2RT then trains a semi-supervised transfer learning based detection model, which can transfer the API usage knowledge in SO Q&A pairs to tutorial fragments by utilizing the easy-to-extract relevance of 〈API, QA〉 pairs. Finally, relevant fragments of APIs can be discovered by consulting the trained model. In this way, the effort for labeling the relevance between tutorial fragments and APIs can be reduced. We evaluate SO2RT on Java and Android datasets containing 21,008 〈API, QA〉 pairs. Experimental results show that SO2RT improves the state-of-the-art approaches in terms of F-Measure on both datasets. Our user study further confirms the effectiveness of SO2RT in practice.
Class-Incremental Learning with Topological Schemas of Memory Spaces

Class-incremental learning (CIL) aims to incrementally learn a unified classifier for new classes emerging, which suffers from the catastrophic forgetting problem. To alleviate forgetting and improve the recognition performance, we propose a novel CIL framework, named the topological schemas model (TSM). TSM consists of a Gaussian mixture model arranged on 2D grids (2D-GMM) as the memory of the learned knowledge. To train the 2D-GMM model, we develop a novel competitive expectation-maximization (CEM) method, which contains a global topology embedding step and a local expectation-maximization fine-tuning step. Meanwhile, we choose the image samples of old classes that have the maximum posterior probability with respect to each Gaussian distribution as the episodic points. When finetuning for new classes, we propose the memory preservation loss (MPL) term to ensure episodic points still have maximum probabilities with respect to the corresponding Gaussian distribution. MPL preserves the distribution of 2D-GMM for old knowledge during incremental learning and alleviates catastrophic forgetting. Comprehensive experimental evaluations on two popular CIL benchmarks CIFAR100 and subImageNet demonstrate the superiority of our TSM.
Approaches for SMS encryption and user accounts verification

People consider short message services (SMS) as secure way of messaging and many OTT and application provider use SMS to verify subscriber identity. SMS have a different layer of encryption and provide security on the provider network but there is no end-to-end encryption as the information contained in the SMS transmits as plain text from one mobile phone to other. The phone number (MSISDN) is used as identity just like user name and the ID numbers. The most common way to prove the MSISDN identity is by using SMS. Therefore, it is desirable to do end to end encryption for sort message services. This paper discusses SMS encryption for both regular SMS sending and receiving and MSISDN identity verification scenario.
LighterBody: RNN based Anticipated Virtual Body Makes You Feel Lighter

The virtual body representation had shown the potential of intervention into the sense of body. To investigate how the temporal shift of body representation affects the user's kinetic sensation, we developed a system to anticipate body movement with RNN. We then conducted a user study to assess the effect with the anticipated body movement and the system baseline. Results revealed that the transition from the baseline to the anticipated body induced a lighter bodyweight feeling, and the opposite transition induced a heavier feeling. Our work enlightens the potential of interactive manipulation of the full-body kinetic sensation using virtual body representation.
Project Esky: an Open Source Software Framework for High Fidelity Extended Reality

Recent years have seen an explosion in Extended Reality (XR) experiences. Until now, researchers and developers were limited to relatively expensive, closed-source, and consumer-level hardware when creating high-fidelity XR experiences, as these were the only devices that can create a high-fidelity XR experience out of the box with minimal setup times. In this paper, we present Project Esky, a complete open-source modular software framework that enables the rapid creation of high-fidelity XR experiences on any combination of display and tracker hardware. Our framework includes several components that handle 6DOF head tracking with head pose prediction to minimize latency, hand interactions with virtual content, and network components to facilitate between-user co-located experiences. Our framework also includes an asynchronous rendering pipeline that simplifies the representation of optical distortions, as well as a simplified planar-based temporal re-projection technique to minimize rendering latency. It is our hope that the techniques described in this paper, and the open source software implementations that support it, will be a stepping stone towards bringing high-fidelity XR content to researchers and hobbyist users alike.
Adversarial Facial Obfuscation against Unauthorized Face Recognition

Abstract To protect individual privacy from unauthorized face recognition based on DNN models, adversarial facial obfuscation tries to generate an adversarial image with a feature vector differing markedly from the original image in the embedding space and keep perceptually similar between the two images simultaneously. This paper makes a brief survey of adversarial facial obfuscation. The preliminary theory about facial obfuscation is introduced first. With regard to adversarial facial obfuscation, the most important implementation factors consisting of transferability, perceptibility and compression resistance are also presented.
Exploiting Relevant Hyperlinks in Knowledge Base for Entity Linking

In this study, we propose a new model aiming to enhance the quality of entity linking by exploiting highly relevant hyperlinks in knowledge base for entity disambiguation. We find that most existing studies do not filter the corresponding hyperlinks for each entity, where using the irrelevant ones may introduce noises and lower the linking accuracy. To address this issue, we design and combine the hyperlink extraction stage and the hyperlink attention stage to learn more suitable hyperlinks in the document-level disambiguation. In addition, we also enhance the context-level disambiguation by utilizing additional entity descriptions and work on retrieving high-quality candidate set for entities at the beginning of our model. Experimental results show that our proposed model outperforms the state-of-the-arts on various benchmark datasets, and even being competitive to the models that rely on additional information.
Research on Vehicle Detection Based on YOLOv3

In order to improve the recognition accuracy and detection speed of vehicle models, a vehicle model detection based on the improved YOLOv3 model is proposed. On the one hand, the SENet structure is introduced into the feature extraction network, and the weight optimization rules of features in the convolution process are added; On the other hand, the number of high-level repeated convolutional layers of the feature extraction network is improved to improve the time cost of the model in vehicle type detection. The test results on the BIT-Vehicle dataset show that the proposed method improves the recognition speed relative to YOLO v3 by about 24% on the premise of ensuring the vehicle model recognition accuracy, and verifies the effectiveness of the proposed target detection method.
A two-step approach to Lidar-Camera calibration

Autonomous vehicles and robots are typically equipped with Lidar and camera. Hence, calibrating the Lidar-camera system is of extreme importance for ego-motion estimation and scene understanding. In this paper, we propose a two-step approach (coarse + fine) for the external calibration between a camera and a multiple-line Lidar. First, a new closed-form solution is proposed to obtain the initial calibration parameters. We compare our solution with the state-of-the-art SVD-based algorithm, and show the benefits of both the efficiency and stability. With the initial calibration parameters, the ICP-based calibration framework is used to register the point clouds which extracted from the camera and Lidar coordinate frames, respectively. Our method has been applied to two Lidar-camera systems: an HDL-64E Lidar-camera system, and a VLP-16 Lidar-camera system. Experimental results demonstrate that our method achieves promising performance and higher accuracy than two open-source methods.
Optimization of Regularization and Early Stopping to Reduce Overfitting in Recognition of Handwritten Characters

Character recognition of handwritten texts is one of the most crucial aspects of any pattern recognition algorithm. The application of digit recognition is very vast, some examples but not limited to, include postal mail sorting, digitizing any hand filled form, bank check processing, signature verification, etc. For the purpose of this research, logistic regression was used to train a model to accurately identify the input digits/alphabets. Also, to improve accuracy of the model, parameter optimization was done on two major factors of logistic regression. The two parameters, the regularization parameter (lambda) and the number of iterations (during training), were closely studied and were optimized to eliminate any chance of overfitting. Experimental results show that an optimized set of parameters would provide maximum accuracy on the test set and on the training set if regularization and early stopping were to be applied in a joint manner. The simulation was done on MATLAB using a gradient descent-based algorithm to minimize the cost function. Gradient descent was chosen as it is guaranteed to find the global minimum of a convex surface; however, it can incur a high computational cost. I concluded from the simulation results that machine learning algorithms provide near to 100% accuracy in the training set, but face difficulties and significantly reduced accuracy on the test set.
Significantly improving human detection in low-resolution images by retraining YOLOv3

Human detection in images is a crucial task due to its usage in different areas including person detection and identification, abnormal surveillance and crowd counting. Low-resolution of image sequences taken by stationary outdoor surveillance cameras is very challenging. Detecting human with deep learning techniques, is more powerful than traditional methods due to its ability to learn high-level deeper features, high detection accuracy and speed. Therefore, this paper proposes a method for human detection in low-resolution images based on YOLOv3. This method will prepare a dataset of low-resolution images collected by outdoor surveillance cameras and annotate them manually. Next, we retrain YOLOv3 to make an improved model for low-resolution images. The model achieves F1-score of 0.804 human detecting for low-resolution test images.
An Information Retrieval Method Based on Data Key Rules Mining

There are rich and complex knowledge networks behind the network big data. The effective use of network big data is the de redundant classification and refinement of data. This paper analyzes and discusses the well-known open network knowledge base at home and abroad and its supporting applications. This paper summarizes the construction of open network knowledge base, the application methods and technologies of information retrieval and data mining based on open network knowledge base, and analyzes the relationship between people and knowledge. This paper solves the matching problem of information retrieval, data mining and system application based on open network knowledge base. Experiments show that the method proposed in this paper has certain reference value to improve the accuracy of information retrieval method.
Recent advances of quantum neural networks on the near term quantum processor

<p indent=0mm>In recent years, artificial neural networks have been widely used in various fields and have shown strong computational intelligence. At the same time, quantum computing hardware has been developed rapidly. In particular, near term quantum processors have relatively stable computing capabilities and robustness against decoherence. With the aid of this powerful quantum technique, nowadays many commercial cloud quantum computing companies can provide online quantum computing experimental platforms for scholars around the world. Various technical developments have promoted the emergence of many new research ideas and experimental progress in the cross-field of quantum neural networks. In this paper, the research ideas of early quantum neural networks are summarized first. The data encoding, algorithm flows, and quantum circuit design of quantum neural networks based on recent quantum processors are then reviewed and analyzed. Finally, several key scientific problems in the field of quantum neural networks are summarized, and prospective future research directions are presented.
Method for Viewing Real-World Scenes while Recording Video

Because smartphones support various functions, they are carried by users everywhere. Whenever a user believes that a moment is interesting, important, or meaningful to them, they can record a video to preserve such memories. The main problem with video recording an important moment is the fact that the user needs to look at the scene through the mobile phone screen rather than seeing the actual real-world event. This occurs owing to uncertainty the user might feel when recording the video. For example, the user might not be sure if the recording is of high-quality and might worry about missing the target object. To overcome this, we developed a new camera application that utilizes two main algorithms, the minimum output sum of squared error and the histograms of oriented gradient algorithms, to track the target object and recognize the direction of the user’s head. We assumed that the functions of the new camera application can solve the user’s anxiety while recording a video. To test the effectiveness of the proposed application, we conducted a case study and measured the emotional responses of users and the error rates based on a comparison with the use of a regular camera application. The results indicate that the new camera application induces greater feelings of pleasure, excitement, and independence than a regular camera application. Furthermore, it effectively reduces the error rates during video recording.