Reviewerly/exHarmony huggingface dataset:
Performance-aware programming for intraoperative intensity-based image registration on graphics processing units

Abstract Purpose Intensity-based image registration has been proven essential in many applications accredited to its unparalleled ability to resolve image misalignments. However, long registration time for image realignment prohibits its use in intra-operative navigation systems. There has been much work on accelerating the registration process by improving the algorithm’s robustness, but the innate computation required by the registration algorithm has been unresolved. Methods Intensity-based registration methods involve operations with high arithmetic load and memory access demand, which supposes to be reduced by graphics processing units (GPUs). Although GPUs are widespread and affordable, there is a lack of open-source GPU implementations optimized for non-rigid image registration. This paper demonstrates performance-aware programming techniques, which involves systematic exploitation of GPU features, by implementing the diffeomorphic log - demons algorithm. Results By resolving the pinpointed computation bottlenecks on GPU, our implementation of diffeomorphic log - demons on Nvidia GTX Titan X GPU has achieved ~ 95 times speed-up compared to the CPU and registered a 1.3-M voxel image in 286 ms. Even for large 37-M voxel images, our implementation is able to register in 8.56 s, which attained ~ 258 times speed-up. Our solution involves effective employment of GPU computation units, memory, and data bandwidth to resolve computation bottlenecks. Conclusion The computation bottlenecks in diffeomorphic log - demons are pinpointed, analyzed, and resolved using various GPU performance-aware programming techniques. The proposed fast computation on basic image operations not only enhances the computation of diffeomorphic log - demons , but is also potentially extended to speed up many other intensity-based approaches. Our implementation is open-source on GitHub at https://bit.ly/2PYZxQz .
Rare Publications of the Library for Natural Sciences of the Russian Academy of Sciences in the Service of Culture and Historical Science

The article aims to demonstrate the results of research and bringing to the technological implementation, in the Library for Natural Scien­ces of the Russian Academy of Sciences (LNS RAS), of a full cycle of works, from identification of rare books in the collections, their description and scanning, to their registration in the Union Catalog and Electronic Library. The author gives a brief description of the software package developed at LNS RAS for creating the Electronic Catalog and full-text Electronic Library of Rare Publications, as well as the scheme of automated interaction of all technological departments involved in the process. The information on practical implementation and directions of the technology development is given. The article presents a brief review of the research, with the identified part of the collection of rare publications of the LNS RAS. For the first time, the Central Library of the LNS RAS implements the Union Catalog and Electronic Library of Rare Publications, reflecting not only the bibliographic information on the publications, but also the data on their specific features (author’s inscriptions, bookplates, stamps, etc.). Rare publications previously inaccessible to researchers, though already used by scientists of the RAS institutes, are introduced into scientific circulation. The article identifies and describes materials about geographical expeditions of the 18th—19th centuries and samples of Russian and foreign book printing of the 18th century. For the first time, the LNS RAS conducts research related to the history of Russian science and printing, and identifies bookplates and proprietary marks of Russian scientists, which have not previously been reflected in ca­talogs. This opens a new cultural layer of historical data, now available to researchers.
The Dots and Boxes Records Storing Standard Format for Machine Learning and The Design and Implementation of Its Generation Tool

Dots and Boxes is a very popular game for two players. There are many teams in the world who are studying smart game programs about this game. Unfortunately, different from the popular game Go, there are no uniform Dots and Boxes game storing records standards format. This results in a large number of classical match records that cannot be preserved, and it also affects the study of this game algorithms. In particular, machine learning algorithms rely on a large number of records. The AlphaGo developed by Google defeated human top players for the first time in Go with the help of a large number of human chess games. In order to facilitate the recording and saving of Dots and Boxes game records. Based on the JSON standard, this paper designs a Dots and Boxes game records standard format and its storage standard, which has excellent expansion and strong compatibility. It also supports cross-platform applications, facilitates secondary development, and is easy to read and write. In order to facilitate the use and promotion of this standard format, simplify the development process of the game AI algorithm. This article uses Python3 language, based on PyQt5 framework, to implement a Dots and Boxes game software DotsAndBoxes, which can generate and read the above-mentioned game records standard file. It has the characteristics of strong versatility, high portability, good scalability, and low maintenance cost.
Novel RNS-to-binary converters for the three-moduli set {2m − 1, 2m, 2m + 1}

In this paper, Mixed Radix Conversion (MRC)-based Residue Number System (RNS)-to-binary converters for the three-moduli set {2m − 1, 2m, 2m + 1} are presented. The proposed reverse converters are evaluated and compared to reverse converters proposed earlier in literature using Chinese Remainder Theorem (CRT) and New CRT for this moduli set as well as two four-moduli sets {2n − 1, 2n, 2n + 1, 2n+1 − 1} and {2n − 1, 2n, 2n + 1, 2n+1 + 1} regarding hardware requirement and conversion time.
A new night traffic light recognition method

This paper proposed an effective recognition algorithm for night traffic lights. The algorithm first carries out the pre-processing of collected night traffic light images, and then makes the RGB color space of images transfer into the HSI, further removing noise through morphological operations to extract candidate regions, and last realizes night traffic light identification based on template matching and regional pixel information. The experimental results show that this algorithm has a higher identification accuracy than other ones.
A Study on the Needs for Enhancement of Personal Information Protection in Cloud Computing Security Certification System

Recently, the development of cloud computing technology has brought not only an easier access on information, but also security issues on personal information in the cloud industry. In this response, Cloud Security Certification System is implemented to solve the various cloud security problems in Korea. However, it is difficult to see that the level of technical protection measures of the cloud security certification system is satisfied with all the technical protection measures required by the personal information protection laws and regulations. As a result, it was proposed that security products that are highly related to the contents of the personal information protection laws and regulations should be in line with the CC(common criteria) certified product group. In this paper, we propose items that the cloud security certification system should supplement from the viewpoint of security requirements that are compliant with the CC certified product line
Research on fault analysis and positioning technology of distribution network based on big data

With the rapid development of big data technology, more and more data are gradually applied to power distribution systems. Based on the fault analysis and positioning technology of distribution network based on big data, the fault is analyzed through the data collection analysis and state detection of distribution network, and the relevant positioning technology is optimized, so as to shorten the fault location time of distribution network, improve the fault analysis efficiency of distribution network and the accuracy of fault location.
When the Future Is the Past. Digital Databases for the Virtualization of Museum Collection

The paper through an excursus on the technological development of virtual fruition systems, intends to reflect on the methods of communication and dissemination of cultural heritage, taking as a reference part of the results of research recently conducted on some case studies. The museum, configured in its new immaterial form and no longer as an exclusively physical place, amplifies the availability of space open to visitors, revolutionizing its role as cultural mediator in the extension of a potential connective that increases the dynamics of social development. The present research was aimed at programming a methodological protocol for the documentation and the virtualization of museum assets, collections and architectural heritage of tourist interest, based on a new approach to fruition of the space and the work of art in it contained.
An Intelligent Voice Interaction Model Based on Mobile Teaching Environment

To improve the effect of voice interactive learning in mobile teaching, this paper analyses the system characteristics of Android architecture and the characteristics of voice interactive mode in embedded environment. Firstly, the specific speech recognition algorithm based on practical application background is studied, and the complexity and real-time performance of the algorithm module are analyzed. Then, a speech recognition algorithm, Dynamic Time Planning(DTW) is adopted in the off-line mode of the network, and the adaptability of the related algorithms is optimized to enable it to be more suitable for the application in Android embedded environment. At the same time, the speech recognition technology and speech synthesis technology are integrated, and the database technology in Android is used to synthesize several modules into a complete and comprehensive intelligent voice interaction system. Finally, the function of the system is tested as a whole and the test data show that the voice interaction model designed in this paper can achieve speech recognition and intelligent voice control more completely. The recognition speed is faster, and it can meet people's needs for efficient, simple and fast interaction.
Development and popularization of QR code

Due to advances in information technology, we predicted the widespread decline of barcodes as an information tool. Instead, we developed QR codes, to replace barcodes, using image recognition techniques. We have made innovation to QR codes to meet market needs. To popularize QR codes, we made QR codes available to the public and have formed a market in cooperation with many companies. As a result, QR codes have been used to improve work efficiency and convenience. QR codes are currently available as a communication tool for people all over the world.
Machine Learning Support for EU Funding Project Categorization

Abstract European Union reallocates its money to their member states using different kinds of funding. EU member states categorize EU funding projects using their own categorization system. While EU prepared an integrated European categorization system, many EU members do not use it in their reports. This hinders a straightforward fiscal analysis. The article aims at an automatic support for categorization of EU funding projects by Machine Learning. The experiments showed that Support Vector Machines (SVM) is the top performance Machine Learning algorithm for this task. We experimented with the SVM classifier and the results disclosed that by employing this approach we can classify EU funding projects using a lexical description better than a baseline (i.e. the classification to a major class). Further, we experienced that the approach using the natural language translator outperforms the approach using the word sense disambiguation. Finally, we investigated the influence of the length of project description on the performance of the classifier. The results showed that while there was a positive correlation between the length of project description and the classifier performance for project descriptions in English, in the case of project description in Non-English languages the classifier performed better for shorter project descriptions. In future, we plan to build a new online application which would use the classifier on the back-end and a user would get a category recommendation on the front-end using a visualization of the EU categorization system.
Optimization of DBN Network Structure Based on Information Entropy

To determine the appropriate depth of DBN network and hidden layer neurons at the same time, the information entropy of the input layer is analyzed on the basis of information entropy and the traditional reconstruction error calculation and the decision of network depth. Thus, according to the relationship between information entropy and hidden layers, an optimization method based on information entropy to determine the number of hidden neurons is proposed, which makes the structure of the DBN network model tend to be better. Experimental results on the handwritten numeral recognition demonstrated that the proposed method is capable of self-organizing the depth of the network and hide the number of neurons in the hidden layer, effectively optimize the DBN network structure, reduce the training time of the network, as well as improve the network accuracy and recognition accuracy.
Improving Learning time in Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation can map local textures between two domains, but typically fails when the domain requires big shape changes. It is difficult to learn how to make such big change using the basic convolution layer, and furthermore it takes much time to learn. For faster learning and high-quality image generation, we propose to use Cycle GAN that is combined with Resnet in a network that is connected with the residual block for upsampling to make big shape change and construct faster image-to-image translation.
Double fidelity terms unidirectional variation model for single-image rain removal

Videos and images captured in poor weather conditions not only have seriously degraded visual quality, but they can also restrict the performance of many computer vision algorithms. One such severe weather condition is rain, which causes complex local intensity fluctuations and fuzzy background images. We study the single-image rain removal to improve the visual quality of images captured in rain. We propose a rain streak removal method with a double fidelity terms unidirectional variational model based on wavelet transform, which is utilized to separate the rain streaks. The rain streaks component is only included in the approximate component and the corresponding high-frequency components. The double fidelity terms unidirectional variational model is then used for removing the rain streaks. The regularization term mainly introduces image-prior information by unidirectional variation. The numerical algorithm is realized by the augmented Lagrange algorithm. Experimental results on a variety of simulated and real rain images show that the proposed method can efficiently remove rain streaks. Experimental results demonstrate the good performance of the proposed model, removing rain streaks while reducing information loss from the background. The proposed method also requires much less computation.
Two-tier classifier for identifying small objects in histological tissue classification: experiments with colon cancer tissue mapping

Tissue classification on histological images is a useful alternative to manual histology analysis, and has been well-studied in a variety of machine learning approaches. However, classification of whole slide images at high resolution is a difficult and computationally-intensive task. In addition, many tissue analysis tasks are targeted at identifying rare or small regions of tissue. In colon cancer, small groups of tumor cells (tumor buds) exist on the front edge of the invasive tumor region and are an important indicator of cancer aggressiveness. These small objects are difficult or impossible to detect when examining an image at lower resolution, while running the classifier at an appropriate high resolution can be time consuming. In this work, a two-tier convolutional neural network classification approach is explored to identify small but important tissue regions on whole-slide tissue scans. The first tier is a coarse-level classifier trained with patches extracted from the image at a low power field (4x optical magnification), designed to identify two main tissue types: tumor and nontumor areas. Regions that are likely to contain tumor buds (non-tumor regions) are passed to a fine-level classifier that classifies the patches into 9 additional tissue types at a high-power field (40x). The system achieves a 43% reduction in processing time (3 hours to 1.7 hours for a 19,200-by-19,200 pixel image). The two-tier classifier provides an efficient whole-slide tissue classification by narrowing down the regions of interest, increasing the chances of tumor buds being identified.
Filtering of Mammograms Based on Convolution with Directional Fractal Masks to Enhance Microcalcifications

The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature.
Research on Configuration Arrangement of Spatial Interface in Mobile Phone Augmented Reality Environment

Recently, due to the release of ARKit and ARCore, the mobile phone can achieve stable motion track via COM (concurrent odometry and mapping). By this way, mobile phones can display the interface in the real space like the multimodal interface in VR (virtual reality) glass, users can browse and interact with interface not only by touching screen but also by motion modal (moving and rotating mobile phone). This paper studied the optimal arrangement of spatial interface and the influence of various layout parameters on browsing efficiency. Through the test platform program, users can adjust the layout parameters (shape, distance, and size) of the spatial interface, and do some browsing tasks in each layout type. The result of interview feedback data analysis showed that the main factors affected by layout parameter on the browsing experience are screen size, movement rate and mapping deformation. And calculated their alternative relationship with layout parameters. Finally, through the recorded data of browsing speed and satisfaction coming from test platform, it figured out the best value and critical point for each layout parameters.
Human interaction with shopping assistant robot in natural language

In spite of having been investigated for over fifty years, developing a robust spoken dialog management system remains an open research issue in robotics and natural language processing. In this paper, we present a language-independent spoken dialog management module integrated into a human-robot i nteraction system. We adopt an algorithmic approach to dialog modeling. A mobile robot functioning as a shopping assistant exemplifies the proposed approach. The dialog module is composed of a state transition network, in which state switches are conditioned by both visual and communicative factors. We use the formalism of a finite state automaton, where the robot changes its state by performing a speech act or a non-verbal action from the set of specified act/action types.
A Stable Collaborative Filtering Algorithm for Long Tail Recommendation

As a relatively successful recommendation system, the collaborative filtering recommendation system (CFRS) has been widely used in e-commerce. However, the current CFRS is mainly based on mainstream or popular products to recommending similar items for users and is less efficiency in recommend the so called "Long Tail" products to meet the individual needs of users. Based on the Item-based system filtering recommendation algorithm, this paper proposes a collaborative filtering recommendation algorithm that implements long tail recommendation by using the item rating probability matrix and item rating reliability. Compared with the traditional collaborative filtering algorithm, the experimental result based on MovieLens 1M dataset shows that the proposed algorithm can deal with the data sparsity problem better, and is better for producing recommendation for the long tail products effect, and furthermore, it shows stability to a certain extent in producing recommendations under different situations of data sparseness.
Video Anomaly Detection Based on Hierarchical Clustering

Recently the video surveillance market has developed rapidly, but judging whether there is abnormal behavior in the video relying on manpower is too expensive. Therefore, a method is needed to identify the abnormal behavior automatically. Scholars at home and abroad have done in-depth research on video abnormal events detection in different scenarios. However, the current detection technology still needs improvement in the speed of the algorithm. From this point of view, this paper proposes a video abnormal event detection method based on hierarchical clustering. In order to construct the sparse coefficient matrix more accurately and quickly, the hierarchical clustering is introduced into sparse coding in this paper. And the structure information of the sparse coefficient matrix is used as the clustering criteria, which improves the standard group sparse coding method. In addition, the BK-SVD algorithm is used to train the dictionary so that we can further improve the speed of the algorithm through dictionary division. In the experimental part, we prove that the proposed algorithm has great performance in frame level and pixel level in MATLAB environment.
The Application of Clustering Techniques to Group Archaeological Artifacts

Modern methods of data analysis are rarely used in archaeology. Meanwhile, it is archaeology that opens up impressive opportunities for various interdisciplinary studies at the junction of archaeology, chemistry, physics and mathematics. XRF analysis, which has long been used to determine the qualitative and quantitative composition of discovered archaeological artifacts, among other things, provides arrays of digital information that can be used by machine learning methods for more accurate clustering or classification of artifacts. This is especially true for artifacts that are presented in the form of fragments of ancient ceramic amphorae or glass vessels. Such fragments, as a rule, represent the mass of the fragments mixed among themselves. There is a need to divide them into groups and then restore them as a single artifact from the detected fragments of one group. This paper presents a comparative analysis of the application of different clustering methods to combine artifacts into groups with similar properties.