Reviewerly/exHarmony huggingface dataset:
Development of Marine Engineering Equipment Manufacturing Industry: A Forecasting Study through Online Data

Xu, X.; Cui, Y., and Jiang, Y., 2020. Development of marine engineering equipment manufacturing industry: A forecasting study through online data. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 677–681. Coconut Creek (Florida), ISSN 0749-0208.Online data are widely used today, and whether and how to use them in industry forecasting has become an important field of economic research. In economic analysis, two types of data can be applied: structured data and unstructured information. Government statistics are well structured, whereas Internet online information belongs to the nonstructured information category. This study uses government statistics and online data, applies the OxMetrics software packages, and forecasts the development of the marine engineering equipment manufacturing industry. The research shows that the application of online data can help improve the forecast results, while making full use of both structured data and unstructured information. From the structured data and unstructured information of 2012–2018, development of the 2019 marine engineering equipment manufacturing industry is forecast.
Reformation of Teaching “Introduction to Computer Science” Course at Non-English-Speaking Country

In recent years, to cultivate international computer professionals and improve the education quality for foreign students, some Chinese universities have established computer science majors where all the courses are taught in English.As the introductory course to computer science majors, "introduction to computer science" plays an important role in helping students lay a professional foundation and cultivate their interest in the major.As one of the pioneering universities in China to set up computer science majors taught in English, Jinan University has ten years' teaching experience in delivering such a course.The reform and practice of teaching "introduction to computer science" in English at Jinan University is expounded in this paper from the following three aspects: (1) Developing a teaching-content system that conforms to professional training direction, adapts to the characteristics of freshmen and the teaching characteristics of a course delivered in English; (2) Comprehensively using various advanced teaching methods; (3) Creating an evaluation system with multiple assessment modes that focuses on examining students' comprehension and practical skills.The reforms and practices at Jinan University provide a reference for other universities, especially those in non-English-speaking countries.
Application of Chaos-Based Fragile Watermarking to Authenticate Digital Video

Fragile watermarking algorithm is a technique used to authenticate of digital data multimedia such as video. A watermarking algorithm consists of two processes: embedding and extraction of watermark. In this paper, a secure video fragile watermarking algorithm in spatial domain based on chaos is proposed. The watermark is a binary image which has the same size with frame size of the video. Before embedding, in order to increase security, the watermark is encrypted using XOR operation with a random image. The random image is generated by using Cross-Coupled Chaotic random Bit Generator (CCCBG). The encrypted watermark is embedded to each frame. In the extraction process, the encrypted watermark is extracted from the watermarked video, decrypted it, and then compared to the original watermark. This algorithm has capability to localize the area being tampered in the video frames. We have performed some typical attacks to the watermarked video and then authenticated it. Based on the experiment results, the algorithm can detect and localize the modified region of video frames very well. Sensitivity to the slightest change on initial conditions of the chaos map provided security of the algorithm.
Simple, Efficient and Convenient Decentralized Multi-task Learning for Neural Networks

Machine learning, and in particular neural networks, require large amounts of data, which is increasingly highly distributed (e.g. over user devices, or independent storage systems). Aggregating this data at one site for learning can be unpractical due to network costs, legal constraints, or privacy concerns. Decentralized machine learning holds the potential to address these concerns, but unfortunately, most of the approaches proposed so far for distributed learning with neural networks are mono-task, and do not transfer easily to multi-task problems. In this paper, we propose a novel learning method for neural networks that is decentralized, multi-task, and that keeps users’ data local. Our approach works with different learning algorithms, on various types of neural networks. We formally analyze the convergence of our method, and we evaluate its efficiency in a range of neural networks and learning algorithms, demonstrating its benefits in terms of learning quality and convergence.
Multiview Detection with Feature Perspective Transformation

Incorporating multiple camera views for detection alleviates the impact of occlusions in crowded scenes. In a multiview system, we need to answer two important questions when dealing with ambiguities that arise from occlusions. First, how should we aggregate cues from the multiple views? Second, how should we aggregate unreliable 2D and 3D spatial information that has been tainted by occlusions? To address these questions, we propose a novel multiview detection system, MVDet. For multiview aggregation, existing methods combine anchor box features from the image plane, which potentially limits performance due to inaccurate anchor box shapes and sizes. In contrast, we take an anchor-free approach to aggregate multiview information by projecting feature maps onto the ground plane (bird's eye view). To resolve any remaining spatial ambiguity, we apply large kernel convolutions on the ground plane feature map and infer locations from detection peaks. Our entire model is end-to-end learnable and achieves 88.2% MODA on the standard Wildtrack dataset, outperforming the state-of-the-art by 14.1%. We also provide detailed analysis of MVDet on a newly introduced synthetic dataset, MultiviewX, which allows us to control the level of occlusion. Code and MultiviewX dataset are available at https://github.com/hou-yz/MVDet.
An Input-Perceptual Reconstruction Adversarial Network for Paired Image-to-Image Conversion

Image-to-image conversion based on deep learning techniques is a topic of interest in the fields of robotics and computer vision. A series of typical tasks, such as applying semantic labels to building photos, edges to photos, and raining to de-raining, can be seen as paired image-to-image conversion problems. In such problems, the image generation network learns from the information in the form of input images. The input images and the corresponding targeted images must share the same basic structure to perfectly generate target-oriented output images. However, the shared basic structure between paired images is not as ideal as assumed, which can significantly affect the output of the generating model. Therefore, we propose a novel Input-Perceptual and Reconstruction Adversarial Network (IP-RAN) as an all-purpose framework for imperfect paired image-to-image conversion problems. We demonstrate, through the experimental results, that our IP-RAN method significantly outperforms the current state-of-the-art techniques.
Analysing Academic Texts with Computational Linguistics Tools

Writing academic texts in English introduces certain difficulties associated with translating Russian sentences with pronounced stylistic peculiarities, especially for young researchers who are just starting their publication activity. It seems impossible to study any genre without analysing examples of the discourse, which highlights the use of computational linguistics as it allows automating a lot of language and text processing mechanisms and generates relatively accurate quantitative results. The present study considers the application of AntConc and Coh-Metrix toolkits for analyzing master students’ abstracts to research papers written for international English-language journals or conference proceedings (Learner Corpus) in comparison with international researchers’ abstracts published in high-impact journals (Reference Corpus). The analysis conducted in the above-mentioned software tools revealed the drawbacks and strengths of master students’ texts, allowed characterizing them on the words, sentence and discourse levels, as well as outlined the potentials of their use in teaching academic writing skills.
Study of the Interception Scheme Based on A* Path Finding Algorithm in Computer Game

In the game, we generally use A* algorithm for pathfinding according to the target location and map information to realize the tracking and interception task.This approach is simple to implement, but is often less than ideal in its presentation, because it does not allow game units to predictably choose paths based on dynamic targets or obstacles, and may lead to blocked channels or difficulty in choosing the actual optimal path.This paper focuses on analyzing the need of a unit when intercepting another unit in the game, and make an interception scheme which can adapt itself to dynamic obstacles and moving target thus presents artificial intelligence to some extent.
Animated Character Style Investigation with Decision Tree Classification

Although animated characters are based on human features, these features are exaggerated. These exaggerations greatly differ by country, gender, and the character’s role in the story. This study investigated the characteristics of US and Japanese character designs and the similarities and differences or even the differences in exaggerations between them. In particular, these similarities and differences can be used to formulate a shared set of principles for US and Japanese animated character designs; 90 Japanese and 90 US cartoon characters were analyzed. Lengths for 20 parts of the body were obtained for prototypical real human bodies and animated characters from Japan and the United States. The distributions of lengths were determined, for all characters and for characters as segmented by country, gender, and the character’s role in the story. We also compared the body part lengths of animated characters and prototypical real human bodies, noting whether exaggerations were towards augmentation or diminishment. In addition, a decision tree classification method was used to determine the required body length parameters for identifying the classification conditions of animated characters by country, gender, and character’s role in the story. The results indicated that both US and Japanese male animated characters tend to feature exaggerations in head and body sizes, with exaggerations for US characters being more obvious. The decision tree only required five length parameters of the head and chest to distinguish between US and Japanese animated characters (accuracy = 94.48% and 67.46% for the training and testing groups, respectively). Through a decision tree method, this study quantitatively revealed the exaggeration patterns in animated characters and their differences by country, gender, and character’s role in the story. The results serve as a reference for designers and researchers of animated character model designs with regards to quantifying and classifying character exaggerations.
Insight to Model Clone’s Differentiation, Classification, and Visualization

Model clones are model fragments in terms of model elements clustered together in form of containment relationship that is highly similar. Due to their property of defect propagation, these are harmful from maintenance point of view. Work on model cloning is less mature as compared to code cloning. In order to fill this gap, the authors identified the key areas regarding model clones with important concepts along with their benefits, limitations, and findings on basis of existing literature that is helpful for further research. It creates awareness about the attributes in which code and model clones differentiates. Then the classification of model clones is refined and proposed on basis of similarity and clustering strategy followed by the techniques for detection of model clones where importance of hybrid clone detection is studied on basis of pros and cons of other existing techniques for clone detection. Recommendations are given regarding the techniques for the visualization and reporting of model clones detected.
Concealing the Confidential Information Using LSB Steganography Techniques in Image Processing

Steganography allows us to conceal any form of confidential data inside any cover file without getting noticed by an unknown person and not make significant changes to the cover object. Steganography is a process to hide your confidential data into a cover medium (text/image/audio/video file). In research, it is a very good area in the current years having numerous applications. This paper presents text data hiding inside a file (video) taking as a concealment vehicle by using LSB steganography with the XOR methodology. This paper presents a comparison between the basic LSB technique and the XOR methodology in a different form. The comparative analysis is done on the performance of the quality parameters MSE, PSNR, and SNR of steganography.
Design of Ensemble Learning Model to Diagnose Malaria Disease Using Convolutional Neural Network

Malaria is one of the most fatal infectious diseases caused by the Plasmodium parasite. It often goes undiagnosed due to human error. Machine learning and many other computational algorithms of soft computing can be used as a novelty approach which can be effectively applied on diagnosing malaria. In this paper, a novel deep neural network model is identified as optical microscopy. An ensemble model emerged the concept of convolutional neural network as LeNet, AlexNet and ResNet to obtain the accurate result. The accuracy of input modified ResNet comes 90.50% after training for four epochs; accuracy of modified AlexNet with input size of 64 * 64 * 3 is 93.50% with just three epochs, and finally, we applied modified LeNet which gives accuracy of 95.50% with 10 epochs from UCI dataset. The obtained result shows that deep neural network models have potential effect in healthcare domain especially in malaria diagnosis.
Automated verification of reactive and concurrent programs by calculation

Reactive programs combine traditional sequential programming constructs with primitives to allow communication with other concurrent agents. They are ubiquitous in modern applications, ranging from components systems and web services, to cyber-physical systems and autonomous robots. In this paper, we present an algebraic verification strategy for concurrent reactive programs, with a large or infinite state space. We define novel operators to characterise interactions and state updates, and an associated equational theory. With this we can calculate a reactive program's denotational semantics, and thereby facilitate automated proof. Of note is our reasoning support for iterative programs with reactive invariants, based on Kleene algebra, and for parallel composition. We illustrate our strategy by verifying a reactive buffer. Our laws and strategy are mechanised in Isabelle/UTP, our implementation of Hoare and He's Unifying Theories of Programming (UTP) framework, to provide soundness guarantees and practical verification support.
An accurate 3D edge measurement method for guided precise modification

Abstract This paper describes an accurate and adaptive edge measurement method for deformed complex components (DCCs). The edges of these components are irregular, spatially gradual curves. Furthermore, measured edges are used to guide precise modification, which requires the measurement to have high accuracy and integrity. The existing methods for edge detection cannot meet the above two requirements for DCCs. The proposed method is based on optimizing the template edge to reduce the errors caused by deformation, which mainly includes two steps: (1) extraction and preprocessing of the edge template; (2) optimizing the sub-edge poses (pose and position). In the first step, an edge template is extracted using a 3D design model and divided based on structural discontinuities. In the second step, the edge is divided into sub-edges, then support regions are adaptively searched for sub-edges and the sub-edge poses are optimized. The sub-edge transition area is interpolated to achieve smoothness of the entire edge. This approach was experimentally validated using DCCs and achieves a higher accuracy than conventional approaches. The experimental results demonstrate that our proposed method performs well in terms of accuracy (0.06 mm) and efficiency (within 85 s), and there is no local missing at the edge.
Framework for role discovery using transfer learning

Abstract Discovering the node roles in a network helps to solve diverse social problems. Role discovery attempts to predict the node roles from a network structure, and this method has been extensively studied in various fields. Role discovery using transfer learning has many advantages, but methods using this approach face two kinds of problems: domain-shift problems and model selection. To address these problems, we propose a general framework that includes network representation learning, domain adversarial learning for suppressing domain-shift problems, and model selection without using target labels. As a result of computational experiments, we show on publicly available datasets that the proposed model outperforms conventional methods, the proposed model selection method performs well without using target labels, and the proposed method can be used in real-world datasets. Furthermore, we found that our framework suppressed domain-shift problems, worked well even with differences between networks, and could handle imbalanced classes.
A Survey on Hybrid Machine Translation

Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).
Subjective Study of Perceptual Quality for Micro-Video Applications

Past few years have witnessed the surprising popularization of micro-videos. Various micro-video applications have occupied a dominant portion in the mobile application market. To enhance user experience, it is crucial to explore the perceptual quality of micro-videos. In this paper, we establish a new subjective quality assessment database for micro-videos. The database consists of 121 mobile user-captured videos and mean opinion scores (MOS) generated by 2541 subjective rating scores from 21 naive subjects. The videos are chosen to be representative for micro-videos, including different capture devices, composition styles, orientations, content categories, and a wide range of authentic and complex distortions. The subjective test is conducted on mobile phones, simulating the realistic viewing condition of micro-video applications. With this database, we also tested the performance of some existing no-reference (NR) video quality assessment models to verify their ability of handling the realistic distortions in micro-videos. The proposed database will be made available to the researchers so that it can help the future work on the study of perceptual quality of micro-video applications.
LibMI: An Open Source Library for Efficient Histopathological Image Processing

Background: Whole-slide images (WSIs) as a kind of image data are rapidly growing in the digital pathology domain. With unusual high resolution, these images make them hard to be supported by conventional tools or file formats. Thus, it obstructs data sharing and automated analysis. Here, we propose a library, LibMI, along with its open and standardized image file format. They can be used together to efficiently read, write, modify, and annotate large images. Materials and Methods: LibMI utilizes the concept of pyramid image structure and lazy propagation from a segment tree algorithm to support reading and modifying and to guarantee that both operations have linear time complexity. Further, a cache mechanism was introduced to speed up the program. Results: LibMI is an open and efficient library for histopathological image processing. To demonstrate its functions, we applied it to several tasks including image thresholding, microscopic color correction, and storing pixel-wise information on WSIs. The result shows that libMI is particularly suitable for modifying large images. Furthermore, compared with congeneric libraries and file formats, libMI and modifiable multiscale image (MMSI) run 18.237 times faster on read-only tasks. Conclusions: The combination of libMI library and MMSI file format enables developers to efficiently read and modify WSIs, thus can assist in pixel-wise image processing on extremely large images to promote building image processing pipeline. The library together with the data schema is freely available on GitLab: https://gitlab.com/BioAI/libMI.
The Notary in the Haystack – Countering Class Imbalance in Document Processing with CNNs

Notarial instruments are a category of documents. A notarial instrument can be distinguished from other documents by its notary sign, a prominent symbol in the certificate, which also allows to identify the document’s issuer. Naturally, notarial instruments are underrepresented in regard to other documents. This makes a classification difficult because class imbalance in training data worsens the performance of Convolutional Neural Networks. In this work, we evaluate different countermeasures for this problem. They are applied to a binary classification and a segmentation task on a collection of medieval documents. In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task. We evaluate different techniques, such as data augmentation, under- and oversampling, as well as regularizing with focal loss. The combination of random minority oversampling and data augmentation leads to the best performance. In segmentation, we evaluate three loss-functions and their combinations, where only class-weighted dice loss was able to segment the notary sign sufficiently.
Effect of Various Image Information in Polyp Classification by Deeping Learning with Small Dataset

Clinical colonoscopy is the gold standard for polyp detection and resection in clinic. A main challenge in the polyp identification by deep learning is the limited patients number. In this paper, we discussed the usage of various image layers which provide and emphasize different image information in the polyp recognition and polyp classification. The results showed that comparing with the intensity image in RGB color space, the gradient images and images in chromaticity color space may provide more critical character to the deep learning model, and achieve a better performance with small training dataset.