bluuebunny/crossref_metadata_2025_split huggingface dataset:
ARL-IL CNN for Automatic Facial Expression Recognition of Infants under 24 Months of Age
Authors: Yan Simeng , Zheng Wenming , Tang Chuangao , Zong Yuan , Qiu Nana , Ke Xiaoyan 
Published: May 2020

Abstract
Automatic facial expression coding of infants plays an important role in infants-related applications, including computer-aided ASD diagnosis, automatic intervention for ASD children and diagnosis of ADHD, etc. However, most of existing facial expression researches focused on adult facial expression analysis, the infant facial expression recognition has been less investigated. Due to an age gap between the facial expression datasets of adults and infants, a facial expression recognition model trained on adult datasets usually shows poor generalization to infants datasets. A labeled infant facial expression dataset can mitigate this problem, and hence we first collect a facial expression dataset of 30 infants under 24 months of age by recording videos of infants’ facial expression during a face-to-face mother-infant interaction. Due to infants spontaneous facial behaviors, the dataset covers multiple challenges, such as large head-poses, occlusion, facial expression intensities, etc. To develop an automatic facial expression coding system, we propose a framework consisted of adaptive region learning and island loss, i.e., ARL-IL, to self-adaptively discover facial regions with higher discriminability between different emotion classes. The framework was verified on our collected dataset, and attained a classification accuracy of 86.86%, which has shown better performance than conventional method based on hand-crafted features and some basic CNN architectures. To interpret the effectiveness of ARL-IL, we also visualize the learned features and find that the proposed framework focuses on facial regions with more emotion information compared with other hand-crafted features or learned features from basic CNN architectures. The experimental results show that our proposed framework has robustness to the large head-poses and occlusion.
Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator
Authors: Ning Xin , Xu Shaohui , Zong Yixin , Tian Weijuan , Sun Linjun , Dong Xiaoli 
Published: May 2020

Abstract
Since the Generative Adversarial Networks (GANs) was proposed, researches on image generation attract many scholars’ general attention and good graces. Traditional GANs generate a sample by playing a minimax game between generator and discriminator. In this paper, we propose a new method called EmotionGAN for generating facial expression. Specifically, the inverse of the generator is firstly utilized to establish the mapping between the input and feature vector. Then the Generalized Linear Model (GLM) is used to fit the changing direction of different expressions in the feature space, which provide a linear guidance to the feature vector along the expression axis, and thus spatial distribution consistence with the target feature vector is assured. Finally the generator is applied to reconstruct the facial image of the expression. By controlling the intensity of the feature vector, the generated image can be smoothly changed on a specific expression. Experiments have shown that EmotionGAN can quickly generate face images with arbitrary expressions while ensuring identity information is not changed, and the image attributes are more accurate and the resolution is higher.
Social VR : A New Form of Social Communication in the Future or a Beautiful Illusion?
Authors: Wang Minhan 
Published: May 2020

Abstract
The further development of 5G technology provides new development possibilities for the application of VR technology in various fields. The application of VR in the social field has derived the concept of “social VR”. This paper focuses on where social VR is new and where is the future of social VR going. By means of comparing with traditional ways of socializing, this paper proposes three new features of social VR: high immersion, diverse interactive modes, and contextualized social content. We classify social VR applications and take two popular social VR games: VRChat and Facebook Horizon, as examples to illustrate the current development of social VR applications and the possible existence of social VR. Moreover, a series of problems and countermeasures are explained and further considered. This paper is of great significance in the development of social VR. It indicates that there is a good prospect for the future of social VR.
RAFM: A Real-time Auto Detecting and Fingerprinting Method for IoT devices
Authors: Cheng Weihua , Ding Zhengyang , Xu Chao , Wu Xiaohu , Xia Yuanyi , Mao Jiaming 
Published: May 2020

Abstract
In recent years, with the rapid development of Internet of Things (IoT) technology, a large number of Internet of things devices such as network printers, webcams and routers have emerged in the cyberspace. However, the situation of network security is increasingly serious. Large-scale network attacks launched by terminal devices connected to the Internet occur frequently, causing a series of adverse effects such as information leakage and property loss to people. The establishment of a set of fingerprint generation system for Internet of things devices to accurately identify the device type is of great significance for the unified security control of the Internet of things. We proposed a RAFM which is a detection and identification system of IoT. RAFM consists two major module including auto detection and fingerprinting. RAFM collects messages sent by different Internet of things devices by means of passive listening. Based on the differences in the header fields of different devices, it USES a series of multi-class classification algorithms to identify device types. Simulation experiments show that RAFM can achieve an average prediction accuracy of 93.75%.
Detection of Late Blight in Potato Leaves Based on Multi-Feature and SVM Classifier
Authors: Liu Wenjie , Zhang Yongjun , Fan Haisheng , Zou Yongjie , Qin Yongbin 
Published: May 2020

Abstract
Due to the influence of germs and viruses, plants often show various symptoms of diseases and insect pests during the growth process, which leads to a large economic loss of fruit farmers. It also brings a certain economic loss to our society, so prevent earlier and advise growers about plant diseases and insect pests have important value and significance. In this case, this paper proposes a detection method which is based on the combination of HOG, LBP and CSS features with Support Vector Machine (SVM) classifier. This method extracts the histogram of oriented gradients, texture, and color self-similar features of potato leaves, and then training samples with SVM classifier to detect late blight as early as possible in the early stages of potato growth. In addition, this paper proposes a method to increase virtual samples, that is, generating symmetrical samples according to the original samples. Due to the limitation of the number of collected samples, increasing symmetrical samples can expand the diversity of samples. The results show that this method can obtain a detection rate of 92.7%, and has better detection and recognition performance in practical application.
Infrared Image Enhancement by Multi-Modal Sensor Fusion in Enhanced Synthetic Vision System
Authors: Cheng Yue , Li Yahui , Han Wei , Liu Zuolong , Yu Guanfeng 
Published: May 2020

Abstract
ESVS (Enhanced Synthetic Vision System) employs multi-modal sensor fusion to provide equivalent out-of-cabin view for pilots. By using such device, pilots can identify the runway and obstacles even in low visibility weather condition during approach and landing. Previously, short-wave infrared video was used as real-time image sensor in our developed ESVS. While, compared to visible image, infrared image is not easy to understand. Moreover, the image quality of infrared degrades to a considerable extent in haze weather condition. To improve the infrared image visibility in ESVS, we proposed a multi-modal sensor fusion strategy by combining real-time infrared video and previous recorded visible video in the clear weather condition. During image fusion, considerable color detail of the visible image is injected to infrared image to ameliorate the infrared image quality. Furthermore, the proposed fusion method has two extra advantages. First, the important tagged information including runway and obstacles in visible image are transferred to infrared video, and second, the missing information in infrared vision can be complemented. To evaluate our proposed method, we use the Y-12F aircraft equipped with both visible and infrared video camera for flight test data collection. By collecting visible and infrared video in clear and haze weather conditions, as well as necessary navigation data, we carry out the final image fusion processing. Experimental results show that the fused video frame has enhanced image quality and improved readability for the pilots, which will significantly promote the pilots’ situation awareness during approach and landing. Thus, the proposed idea for fusion of multi-sensor image has the great potential to be applied to other cockpit display systems.
A Survey on Monocular 3D Object Detection Algorithms Based on Deep Learning
Authors: Wu Junhui , Yin Dong , Chen Jie , Wu Yusheng , Si Huiping , Lin Kaiyan 
Published: May 2020

Abstract
An accurate and effective perception of environment is important for autonomous vehicle and robot. The perception system needs to obtain the 3D information of objects, which includes objects’ space location and pose. Camera is widely equipped on autonomous vehicle because of its price advantage. However, the monocular camera cannot provide depth information which is necessary for 3D object detection. Many algorithms based on monocular 3D object detection have been developed in recent years. Deep learning is popular for perception system which transforms image data from camera into semantic information. This paper presents an overview of monocular 3D object detection algorithms based on deep Learning and summarize the contributions and limitations of these algorithms. We also compare the performance of different algorithms on different datasets.
Ccfcnet: Channel-Communication Factorization Convnet for Real-Time Semantic Segmentation
Authors: Lin J , Hu J 
Published: May 2020

Abstract
In this paper, we address a challenging real-time semantic segmentation task by proposing a lightweight encoder-decoder architecture. The architecture called channel-communication factorization ConvNet (CCFCNet), in which both efficiency and accuracy are taken into account. The core of our work is channel-communication factorization convolution module (CCFC) and dense dialation-rate block (DDB). CCFC module, where split, channel communication and fuse operations are utilized to greatly save reasoning time and improve the quality of information refinement with a few additional calculations. Meanwhile, four CCFC modules with different dialation rates form a dense dialation-rate block (DDB), which can obtain denser feature refinement and enlarge receptive field to improve the segmentation accuracy of objects with different size in images. The proposed architecture, which contains only 1.25M parameters, achieves a mean IOU of 71.4% on the Cityscapes dataset and can run over to 42FPS on a single GTX 1080Ti GPU. It makes a better trade-off between accuracy and efficiency than state-of-the-art methods with comparable performance.
A Method of Text Detection and Recognition from Receipt Images Based on CRAFT and CRNN
Authors: Wang Xiaohui , Zhang Xi , Lei Shuya , Deng Hongmei 
Published: May 2020

Abstract
Text detection and recognition from paper bank receipts image has a lot of applications in the business field, such as in power system marketing, which is applied in verification and cancellation of electricity charges and automatic archiving. In this paper, we use a text detection method to effectively detect text area in receipt image by exploring each character and affinity between characters. Then put the text sequence into text recognizition model which is combined by DCNN and RNN and integrates feature extraction, sequence modeling and transcription. The experiments demonstrate the superiority of the proposed algorithm over the prior arts, which performs well in the task of blurred image-based bank receipt chinese text recognition.
Research on Consistency Enhancement of Wireless Channel Key Against RF Fingerprint
Authors: Zhang Mingming , Xu Chao , Li Meng , Xia Fei , Zhao Ran 
Published: May 2020

Abstract
The development of the physical layer key extraction technology provides a new type of encryption mechanism that uses the reciprocity and time varying of wireless channels to generate keys. This encryption mechanism can achieve informational security. However, the wireless channels in the real environment are not completely reciprocal, which results in low consistency in generating the keys. In this paper, we study the influence of RF fingerprint on channel reciprocity, and propose a calibration matrix algorithm resist the RF fingerprint effect. Based on the RF fingerprinting model established previously, we propose two calibration methods from the perspective of RF gain and I/Q gain. The superiority of the two calibration methods is proved by the average system capacity and the linear quantification error rate. Next, we extend the calibration matrix algorithm of narrowband systems to wideband OFDM systems. The applicability of the calibration matrix algorithm in OFDM systems is proved by the linear quantification error rate of the uplink and downlink channels. In order to prove the reliability of the algorithm in the real environment, we use a USRP to collect multiple sets of measured data, and analyse the data through the calibration algorithm. The final result shows that this algorithm makes the uplink and downlink channel reciprocity compensated.
Video Flame Detection Method Based on Improved Fast Robust Feature
Authors: Lingkai Zhang , Li Lu 
Published: May 2020

Abstract
Flame detection has important practical significance. Based on the uniqueness of the color information of the flame, using the flame’s color model to initially extract the suspected flame area to improve the accuracy of flame detection in complex environments. In order to reduce the amount of unnecessary algorithm calculation, an improved feature extraction method combing color information with acceleration robust combining features (SURF) is proposed. On this basis, the morphological features of flame, such as roundness and rectangularity, are added as auxiliary classification features, and the flame region features extracted from the image are input to support vector machine (SVM) for training and learning. The experimental results show that the flame detection method proposed in this paper is applicable to a wider range of scenes, with the advantages of high accuracy and robustness, high reliability, small calculation amount, short detection time, and still has good robustness in complex scenes with more interferences.
Factor Analysis and Optimization Strategy of Differences in Dual Channel Service Experience
Authors: Zhang Deran , Yuan Yunyun 
Published: May 2020

AbstractDual channel sales provide consumers with different service experiences. Studies have shown that “cheap” and “safety” are the most important factors for consumers in the online direct selling experience, however, with the higher pursuit of quality of life, The traditional entity sales experience is unique, and a series of real shopping experiences such as “situation consumption” and “physical experience” can bring joy to the consumers, and stimulate the consumer’s desire to purchase to a certain extent. Therefore, scientifically integrating the dual-channel supply chain, carrying out orderly competition, and complementing each other is an effective strategy to further optimize the dual-channel supply chain and better serve consumers.
Hamiltonian path problem: the performance comparison deoxyribonucleic acid computing and the branch-and-bound method
Authors: Sergeenko A.N. , Granichin O.N. , Yakunina M.V. 
Published: May 2020

Abstract
In this article different approaches to one of the most popular combinatorial problem — the Hamiltonian path problem – are illustrated and compared between each other. It is shown that it becomes inefficient to use branch-and-bound method, the most popular method which is realized on a computer, from the counted number of vertices because of its exponentially growing complexity, one more algorithm which is based on working with deoxyribonucleic acid (DNA) molecules in a laboratory is analysed. That method works parallel and has linearly growing time consumption. Due to the improvements in the biophysics methods, which are needed for DNA computing, that algorithm became much faster than it was several years ago and it is now possible to add some new stages in DNA computing, which are shown in this paper.
Underwater radiated noise model of marine object
Authors: Prokopovich V.V. , Shafranyuk A.V. 
Published: May 2020

Abstract
The paper is devoted to the developing a comprehensive model for underwater radiated noise of marine object in order to model and simulate sonar systems. Many works present underwater radiated noise models, but as a rule, they consider either one of the noise components in order to develop algorithms for the corresponding sonar operation mode, or one of the noise sources in order to design ship parts and assemblies. This paper combines several noise models of marine objects considering additional factors that were not taken into account in these models. The obtained comprehensive model is under verification phase, but its application will significantly expand the possibilities for developing and synthesizing classification and tracking algorithms of sonar system
Research and development of a method for determining the dimensional characteristics of sea waves with measurements made by a wave buoy
Authors: Gryazin D.G. , Gleb K.A. 
Published: May 2020

Abstract
The problem of determining the spatial characteristics of sea waves with incomplete measurement information obtained by modern wave buoys is discussed. Information about wave ordinates, slopes and curvature of the waves in two perpendicular directions is needed to evaluate the full directional spectrum. Since the direct measurement of wave curvature by buoys is difficult due to technical reasons, their manufacturers decided to correct incomplete spectra using mathematical methods, such as maximum entropy, maximum likelihood methods, etc. The current study shows that the curvature of the waves can be estimated by using additional information on the parameters of the wave propagation, which can be obtained using ordinary wave buoys. Such an approach will make it possible to obtain more reliable spatial spectra.
Influence of structural parameters on instrumental error of electromagnetic log sensor
Authors: Voronov A.S. , Evstifeev M.I. 
Published: May 2020

Abstract
Instrumental error of an electromagnetic log sensor at depths of more than one kilometer is studied. Operating principle of the electromagnetic sensor is shown and a mathematical model is developed. Analysis of sensor-registered speed dependence on external hydrostatic pressure is presented. It is demonstrated that an additional instrumental error occurs in the electromagnetic sensor due to the structure deformation during deep-sea submersion. It has been found that the value of relative error depends on design parameters and operational conditions of the sensor. It is shown that even when deep-sea vehicles travel at a low speed, the error caused by external hydrostatic pressure can make up a significant portion of the instrumental error and should be taken into account during operation. The design parameters of the sensor, affecting its instrumental error are determined. The influence of temperature on this error is studied. The ratio of design parameters at which the instrumental error does not exceed the permissible value is identified.
Clinical effect of uterine artery embolization in the treatment of refractory postpartum hemorrhage
Authors: Wang H Y , Li M Z , Sun J H , Li K Z , Li X 
Published: May 2020

Abstracts
This study is to investigate the clinical effect of uterine artery embolism in the treatment of refractory postpartum hemorrhage. Retrospective analysis of 50 patients with refractory postpartum hemorrhage admitted to PLA Group 83 Military Hospital from July 2014 to July 2018, using uterine artery embolization (observation group) and hysterectomy (control group), compared the effective rate of hemostasis, total bleeding volume, operation time and postoperative complications (fever, pain, infection) between the two groups. The effective rate of hemostasis was 96% in the observation group and 92% in the control group. There was no significant difference between the two groups (P > 0.05). The total blood loss was 2237.34 ±548.33 ml and 39.02 ±12.14 min in the observation group and 3427.55 ±667.42 ml in the control group, and 74.21 ±16.16 min in the control group. There was significant difference between the two groups (P < 0.05).The postoperative complications of the observation group were lower than that of the control group (P <0.05). The two methods are effective in the treatment of refractory postpartum hemorrhage, but uterine artery embolism has the advantages of less bleeding, short operation time and rapid recovery after operation, which is worthy of clinical application.
Competition and management of wood-cement compositions among light concretes in the market of construction materials
Authors: Dolmatov S N , Nikonchuk A V , Gorbunova S V 
Published: May 2020

Abstract
The paper describes the stages and motivation of a reasonable choice when purchasing construction materials for low-rise housing in B2C and B2B segments. Due to the optimal combination of indicators of strength, thermal conductivity, low cost in the market of construction materials for low-rise housing, light concretes took a stable position as a thermal insulation construction material. Light concretes are divided into aerated concretes and foam concretes, as well as concretes based on wood-cement compositions. The volumes of aerated and foam concrete production and consumption are hundreds times as high as the production of wood-cement compositions. The negative attitude of the consumers to the processed products and the high price of such materials are deterrence to the wide distribution of wood-cement compositions in low-rise housing. The policy of integrated use of renewable wood resources can be the basis for increasing the production and consumption of wood-cement compositions.
Fruiting indicators of the apple tree growing in the creeping form in the Botanical garden named after Vs. M. Krutovsky
Authors: Moksina N V , Gerasimova O A , Gorbunova S V 
Published: May 2020

Abstract
Study of the fruiting indicators of the apple tree growing in the creeping form in the Botanical garden named after Vs. M. Krutovsky resulted in the distinguishing of the cultivars characterized by the high annual yield, apples mass and sizes. The distinguished cultivars can be recommended for growing in the given and similar climate conditions.