Reviewerly/exHarmony huggingface dataset:
360x180 stereoscopic panoramas from static light field capturing

With the increasing demand in visual experience, visual effects with good immersion and stereoscopic sense have become one of the research hotpots in computer vision and computer graphics. This paper presents a stereoscopic panoramic image generation method based on static light field capture. The work can be divided into two parts: the acquisition of the static light field and the stereoscopic panoramic image generation. The light field data is recorded by two mutually perpendicular cameras rotated around a fixed axis. The two cameras capture images with every 9 rotating degrees and a total of 40x2 images were obtained after spin a circle. Both two cameras are calibrated and the recorded images are re-projected to a spherical surface. The optical flow information was calculated from the overlapping part of the spherical projection using a block matching method based on pyramid model. As a result, the light filed information which contains texture and disparity information is acquired based on the spherical expression. On the basis of the above work, the stereoscopic panoramic images under the new viewpoints were then resampled densely based on a pixel level from the obtained light field. A Poisson blending algorithm is used afterward to fuse the image strips from different perspectives and eliminate the brightness difference between those different strips. The high quality stereoscopic panoramic images with an 8k resolution were achieved in the end. Compared with the commonly used panoramic image generating method, our method has the advantages of low cost, simple operation, and high automation. More importantly, it can generate stereoscopic panoramic images containing parallax information with the vertical 180 degrees of view.
A Hybrid Approach for Recommender Systems in a Proximity Based Social Network

Being one of the latest trends in technology, big data is proving to be fundamental in various fields and domains. Analyzing the large volume of data leads to fruitful information and depicts new methods of achieving growth and innovation in this competitive world. Similarly, analyzing large data sets from social media can enhance recommendations provided by recommender systems in a proximity based social network. This research work presents a hybrid approach for performing recommendations in a proximity based social network by using three recommendation techniques namely Content-based filtering, Collaborative filtering and Link Analysis. Additionally, big data from social media is analyzed to enhance the recommendations. The Hadoop ecosystem is used to help for processing large datasets. A prototype has been implemented and evaluated.
Wukong: Heuristic-Based Framework for Generating Generic-API for JointCloud

JointCloud gives developers the freedom to create applications that are portable across multiple clouds (e.g., Amazon EC2, Aliyun ECS). However, any modification in a vendor-lock API will make JointCloud unusable due to the problem of API synchronization. We assume that adapter flows should be decoupled from the manners in which they are executed. To verify this idea, we built Wukong, a framework that can dynamically map front-end adapter flows' descriptions to a broad range of back-end clouds by the generic APIs. Generic APIs for VM services have been proposed to evaluate our Wukong framework. The experimental results have shown three contributions: (1) the overhead of reflection is acceptable when WuKong generates the generic APIs, (2) the feasibility of Wukong has been verified by the major cloud vendor-lock APIs, (3) the usability of Wukong heuristic rules can accelerate the generation of APIs.
Design of Information System Vulnerability Governance Platform Based on Distributed Asset Acquisition and Vulnerability Verification Radar

There are a large number of information vulnerabilities in enterprise information systems. Many hackers use vulnerabilities to obtain enterprise or personal information, which greatly jeopardizes enterprise information security. This paper proposes a design method of integrated information system vulnerability governance platform based on distributed asset acquisition and vulnerability verification radar, discusses the business flow and architecture of the vulnerability governance platform, the logical relationship between modules, business function modules, and the technical framework for building platforms.
Speech Recognition and Speech Synthesis Models for Micro Devices

With the advent and breakthrough of interaction between humans and electronic devices using speech in communication, we have seen a lot of applications using speech recognition and speech synthesis technology. There are some limitations we have identified to these applications. Availability of a lot of resources and internet connectivity have made it possible in making case but with limited resources it is quite difficult to achieve this feat. As a result, it limits the application of the technology into micro devices and deploying them into areas where there are no internet connectivity. In this article, we developed a smaller Deep Neural Network models for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) for communication on micro devices such as Raspberry Pi. We tested and evaluated the models of the system. The accuracy and the performance of the models to be implemented on micro devices shows that they are good for application development in micro devices.
Data augmentation via photo-to-sketch translation for sketch-based image retrieval

Sketch-based image retrieval (SBIR) technique has progressed by deep learning to learn cross-modal distance metrics that relate sketches and photos from a large number of sketch-photo pairs. However, datasets of sketch-photo pairs are small, as acquisition of a large number of such pairs is expensive. To alleviate the issue, data augmentation via image transformation such as scaling, flipping, rotation, and deformation has been widely adopted. Still, insufficiency in training set seems to have impeded deep learning from achieving its full potential for SBIR. In this paper, we propose a novel data augmentation approach dedicated for SBIR. A deep neural network called Photo2Sketch (P2S) converts photos into line drawings that are visually similar to those sketched by human. An artificially augmented training dataset of sketch-photo pairs is generated at low cost by feeding photos from a large image corpus into the P2S. Experiments evaluate quality of sketch-like images generated by the P2S as well as efficacy of the proposed data augmentation algorithm under SBIR scenario. In particular, retrieval accuracy is significantly improved when the proposed algorithm is combined with the data augmentation by image transformation
Detection of Common English Grammar Usage Errors

Our research aims to provide writers with automated tools to detect grammatical usage errors and thus improve their writing. Correct English usage is often lacking in scientific and industry papers. [16] has compiled 130 common English usage errors. We address the automated detection of these errors, and their variations, that writers often make. Grammar checkers, e.g., [9] and [11], also implement error detection. Other researchers have employed machine learning and neural networks to detect errors. We parse only the part of speech (POS) tags using different levels of generality of POS syntax and word-sense semantics. Our results provide accurate error detection and are feasible for a wide range of errors. Our algorithm specifies precisely the ability to increase or decrease the generality in order to prevent a large number of false positives. We derive this observation as a result of using The Brown corpus, which consists of 55, 889 untagged sentences, covering most genres of English usage, both fiction and non-fiction. This corpus was much larger than any corpus employed by related researchers. We implemented 80 of Swan's most common 130 rules; and detected 35 true positives distributed among 15 of Swan's rules. Such a low true positive rate, 35/55889, had been expected. No false positives were detected. We employed a separate, smaller, test suite of both true positive and true negative examples. Our system, as expected, correctly detected errors in all the true positive examples, and ignored all the true negative ones. The Language-Tool system had a detection rate of 28/130 = 22%; Grammarly had a detection rate of 60/130 = 46%. Our results show significant improvement in the detection of common English usage errors.
Age‐related craniofacial differences based on spatio‐temporal face image atlases

A number of studies have been developed recently in order to explore associations between craniofacial differences and genetics. Most of these works have been based on spatial face image models, adjusted for the counter effects of age. This approach provides a limited understanding of normal and abnormal craniofacial development owing to the lack of age progression information. Here, the authors propose and implement an imaging framework that combines facial landmark positioning, non-rigid registration, novel age-dependent face modelling and common distance metrics to disclose the most facial differences that vary across the time due to the subjects' age. All the experiments carried out and corresponding results presented here are based on a database comprising ordinary two-dimensional (2D) frontal face images of Down Syndrome (DS) and control sample groups. A number of craniofacial metrics have been successfully identified that highlight statistically significant and clinically relevant differences between the controls and the faces associated with DS within the age range from 1 to 18 years old, producing realistic unbiased face models with similar level of detail at all age-intervals, despite the small sample size available.
A multiple people tracking algorithm for large view scene

Multiple people tracking is a significant sub-problem of object tracking with high demand during recent years. In the large view scene, the main difficulties are that the objects are small and they may be occluded or have sudden appearance changes. So most existing methods have high ID switches (a evaluation metric for multiple people tracking) in large view scene. We propose a multiple people tracking method that focus on solving high ID switches in large view scene. Our method uses intersection over union (IOU) information that is not sensitive to appearance changes and Euclidean distance-based appearance similarity that is helpful in solving the problem of occlusions to associate data. In order to make our Euclidean distance-based appearance similarity metric work better, we employ a soft-margin loss function to train a convolutional neural network (CNN), it can make the features extracted by the CNN more suitable for our similarity metric, so our method can effectively solve high ID switches problem. IOU-based data association has low computational complexity and the CNN is a lightweight network, it makes our method have real-time speed. On the other hand, we propose a multiple people tracking dataset of large view scene for research. We design our dataset according to the standards of MOT Challenge benchmark and we select yolov3 detector that has relatively good performance for small objects as a public detector. Finally, our method is compared with several multiple people tracking methods on our dataset. The experimental results show that our method has a better performance in large view scene.
Deep face recognition with weighted center loss

ID face photos are widely used for identity verification in many real-world applications. In these cases, the face from ID photo is expected to compare with the face obtained from the daily life image. To improve the ID/life face verification performance, the datasets containing various ID face photos and daily life face images are always needed to train an available CNN model for feature extraction. However, it’s really hard to get uniform distributed data for both the ID photos and daily life images. In most face datasets, each subject only contains one ID photo (for example, obtained from the identity card) while contains varies of life images. This imbalanced distribution causes the difficulty of learning a general deep feature representation for both the ID and life faces, since the daily life images tend to have a higher impact on the feature learning than the ID photos. To address this challenge, we propose a weighted center loss which aims to learn a center of deep features for each class and penalizes the distance between the image features and their corresponding class centers with different weights. By emphasizing the ID photo features in the loss computation, our weighted center loss can train a general CNN which obviously narrows the distance between the ID and life faces. Final experiments demonstrate that with the joint supervision of softmax and weighted center loss, our proposed framework can minimize the intra-class variations caused by the imbalanced ID/life data distribution and significantly improve the ID face based verification accuracy.
Assessing Repetitive Work When Processing Chinese, Japanese, and Korean Materials

This article looks at the repetitive tasks faced by East Asian Studies Librarians and considers how to automate them. By confirming the most repetitive tasks in East Asian Librarianship, the author, who is a selector for Japanese language materials, identified checking donations and user requests against library holdings as a task that was highly repetitive and automatable. The article explains the factors for assessing how to automate checking for duplicates. The basics of creating a script with the automation tool AutoHotKey are introduced and demonstrate how to create a basic macro. Finally, the macro for duplicate checking is assessed for efficiency, accuracy, and unanticipated benefits/challenges for using macros.
Multi-class breast tumor region detection for gigapixel pathology images using deep neural network with rescale approach

Breast cancer has become a worldwide disease in recent years. However, despite its growing prominence, the number of pathologists equipped to handle these cases is insufficient. Computer-aided diagnosis (CAD) system contributes to reduce costs and improve efficiency of this process. A framework based on convolutional neural networks (CNNs) which could be used to automatically detect the multi-class cancer areas on gigapixel pathology slide images was proposed. Moreover, combining the slide image characters, rescale and careful data augmentation methods were used to train the patch-based model with a small dataset. To validate the developed framework, we conducted experiments with Breast Cancer Histology Challenge (BACH) dataset and obtained International Conference on Image Analysis and Recognition (ICIAR) score of 0.582, outperforming the second-place finisher in BACH2018, for the 4-class tissue segmentation task.
A Unified Theory of the Blockchain Economy

In a blockchain economy, the incentive of record keepers and activity of blockchain users interact with each other through general equilibrium effects. On the one hand, users (buyers and sellers of goods) make transactions by using the blockchain protocol to mitigate informational frictions, and this activity pins down the demand for cryptocurrency and affects its price. On the other hand, record keepers maintain the blockchain system and determine how efficiently it can mitigate informational frictions among users, thereby affecting the users' activity and price of cryptocurrency. However, the price of cryptocurrency also affects the record keepers' behavior because they are motivated by a reward that is paid in cryptocurrency. These channels generate a feedback (complementarity) effect that is boiled down to a fixed point problem. We show that multiple equilibria can arise in which collective deviation of miners (e.g., fork) can deteriorate the blockchain's efficiency and consumers' welfare.
FPGA Design and Implementation of Accelerated Stereo Matching for Obstacle Detection

This paper present hardware architecture of low-complexity stereo matching and its implementation. The AD-CENSUS stereo matching algorithm is modified for hardware design and implemented in Xilinx Kintex UltraScale KU040 board. Furthermore the calibration and rectification for stereo camera is included in design. It is accelerated with low hardware resources, and can be obtained the reliable quality of disparity map for obstacle detection.
An improved YOLOv2 model with depth-wise separable convolutional layers for object detection

Object detection is the basic research direction in the field of computer vision. It provides basic image information data for other advanced computer vision processing and analysis tasks. With the continuous breakthrough of deep machine learning technology, especially convolutional neural network model in the field of digital image processing shows a strong ability to extract image features. By choosing the depth separable convolution layer to replace the standard convolution layer used in the traditional model, the number of parameters of CNN network model is compressed. Depth Separable Convolution Layer (DSCL) decomposes the standard convolution layer factor into depth convolution layer and point convolution layer, and extracts and merges image features in two steps to reduce the number of parameters. By introducing a depth-separable convolution layer instead of a standard convolution layer, the number of parameters of the model convolution layer is reduced by 78.1%. We choose image feature pyramid network to fuse the image features extracted from each layer of CNN network, so that the target detection model can use matching image fusion features for different size and shape of the target to be detected. The average detection precision on the PASCAL VOC dataset increased to 77.5%.
DEVELOPMENT OF EDUCATOR'S ICT COMPETENCY FOR WORK IN E-LEARNING SYSTEM

In the article the features of development of ІC- competences of teacher are examined for work in the electronic departmental (e-learning) teaching. An author is consider a concept "electronic studies". The role of information technologies is considered in the modern world grows constantly. It is indicated that E - learning integrates the row of concepts in area of application of modern information and of communication technologies in education, such medias, studies on the basis of Web- of technologies, on-line-studies et cetera. It is marked an author, that Web- technologies on the basis of hypertexts, that allow substantially to economize material facilities, so as be not necessity to buy additional software for work with hypertext documents. The necessity of capture of educational establishments pedagogical workers is well-proven by the basic terms of electronic studies and understanding of their value, realization of role and necessity of introduction of electronic studies in educational educational establishments of country. It is well-proven that introduction of different forms of the controlled from distance education in educational establishments will assist the increase of level of competence of informatics of workers of establishments of education, will facilitate perception and mastering of educational material, will add additional possibilities wishing to get the special or additional education. Advantages over of the use of facilities and technologies of electronic studies (e - learning) are brought and argued. The different going is considered near determination and maintenance of ІC-competences of modern teacher. Maintenance of professional competence of teacher is presented in area of informatively-communication technologies. By an author certainly, that the organizationally-pedagogical providing will allow to attain the high level of preparation of teacher for work in the system of e - learning due to implementation of next terms : basic tasks that decide a teacher in the system of e - learning are certain, on their basis systematized him basic types of activity at the controlled from distance teaching; on the basis of analysis of types of activity the made requirements are to qualification of teacher; on the base of qualifying requirements the set forth competences of teacher, for measuring of that there are the worked out tasks; taking into account competences the worked out model of course and program of in-plant training; worked out and approved controlled from distance course of in-plant training. Described key constituents of competence of electronic studies as direction of professional self-perfection.
The Design of Image Depth Information Extraction Algorithm Based on Joint Bilateral Filtering

With the vigorous development of science and technology, specialists and scholars pay their attention to the 2D-to-3D media conversion more and more. As the next generational display, 3D-TV has lots of problems to solve, which must depend on depth estimation. Here it proposes a novel line tracing method and depth refinement filter as core of depth estimation framework. First, edge detection is performed with the downscaled input image. The line tracing algorithm traces strong edge positions to generate an initial staircase depth map. The initial depth map is further improved by a recursive depth refinement filter. It finally presents visual results from depth estimation and stereo image generation. The experimental results show that the algorithm is effective and feasible which leads a good result.
Multi-valued GRAPPA

Abstract dialectical frameworks (ADFs) are generalizations of Dung’s argumentation frameworks which allow arbitrary relationships among arguments to be expressed. In particular, arguments can not only attack each other, they also may provide support for other arguments and interact in various complex ways. The ADF approach has recently been extended in two different ways. On the one hand, GRAPPA is a framework that applies the key notions underlying ADFs – in particular their operator-based semantics – directly to arbitrary labelled graphs. This allows users to represent argumentation scenarios in their favourite graphical representations without giving up the firm ground of well-defined semantics. On the other hand, ADFs have been further generalized to the multi-valued case to enable fine-grained acceptance values. In this paper we unify these approaches and develop a multi-valued version of GRAPPA combining the advantages of both extensions.
Improvement of LDA Topic Mining Algorithm and Its Application in Short Text

In order to quickly provide a large number of short text themes, an improved linear discriminant analysis (LDA) topic mining algorithm is proposed in this paper. Firstly, the acquisition method of obtaining the traditional short text themes is first analyzed. Focusing on the shortcomings of the original algorithm, the improvement process of the improved topic mining algorithm based on LDA is introduced. Finally, through the mining of short text themes by college students as an example, the improved LDA algorithm is improved in the accuracy of short text processing.