Reviewerly/exHarmony huggingface dataset:
Hierarchical Attention Networks for Medical Image Segmentation

The medical image is characterized by the inter-class indistinction, high variability, and noise, where the recognition of pixels is challenging. Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation. Concretely, an HA module embedded in the HANet captures context information from neighbors of multiple levels, where these neighbors are extracted from the high-order graph. In the high-order graph, there will be an edge between two nodes only if the correlation between them is high enough, which naturally reduces the noisy attention information caused by the inter-class indistinction. The proposed HA module is robust to the variance of input and can be flexibly inserted into the existing convolution neural networks. We conduct experiments on three medical image segmentation tasks including optic disc/cup segmentation, blood vessel segmentation, and lung segmentation. Extensive results show our method is more effective and robust than the existing state-of-the-art methods.
The global development of cryptocurrencies

The global crypto marketplace has an impact on the functioning of financial markets and has huge implications for entire economies.The article discusses the functioning of the global cryptocurrency market, and based on the analysis of the history of the most popular cryptocurrency, bitcoin, an attempt was made to determine possible development prospects.The aim of the article is to assess the development of the current crypto marketplace in the world and to present possible development prospects.The foresight method was used to implement the research objective, in particular trend analysis (quantitative data) and discrete event system (qualitative and expert data).This allows us to learn about the regularity of the cryptocurrencies in the sphere of investment management from the investors' point of view and the creation of an appropriate legal framework by the supervisory authorities and ensuring the social security of the financial system.
Research on Rosenbrock Function Optimization Problem Based on Improved Differential Evolution Algorithm

The Rosenbrock function optimization belongs to unconstrained optimization problems, and its global minimum value is located at the bottom of a smooth and narrow valley of the parabolic shape.It is very difficult to find the global minimum value of the function because of the little information provided for the optimization algorithm.According to the characteristics of the Rosenbrock function, this paper specifically proposed an improved differential evolution algorithm that adopts the self-adaptive scaling factor F and crossover rate CR with elimination mechanism, which can effectively avoid premature convergence of the algorithm and local optimum.This algorithm can also expand the search range at an early stage to find the global minimum of the Rosenbrock function.Many experimental results show that the algorithm has good performance of function optimization and provides a new idea for optimization problems similar to the Rosenbrock function for some problems of special fields.
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin

We propose a system to develop a basic automatic speech recognizer(ASR) for Cantonese, a low-resource language, through transfer learning of Mandarin, a high-resource language. We take a time-delayed neural network trained on Mandarin, and perform weight transfer of several layers to a newly initialized model for Cantonese. We experiment with the number of layers transferred, their learning rates, and pretraining i-vectors. Key findings are that this approach allows for quicker training time with less data. We find that for every epoch, log-probability is smaller for transfer learning models compared to a Cantonese-only model. The transfer learning models show slight improvement in CER.
Normal Assisted Stereo Depth Estimation

Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with a limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to leverage a normal estimation model and the predicted normal maps to improve the depth quality. We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module. In addition, we propose a novel consistency loss to train an independent consistency module that refines the depths from depth/normal pairs. We find that the joint learning can improve both the prediction of normal and depth, and the accuracy & smoothness can be further improved by enforcing the consistency. Experiments on MVS, SUN3D, RGBD, and Scenes11 demonstrate the effectiveness of our method and state-of-the-art performance.
The detection of conflicts in the requirements specification based on an ontological model and a production rule system

The paper presents an approach to organizing the detection of conflicts between requirements based on an ontological model and a system of production rules. Requirements in the form of plain text are converted to instances of OWL ontologies for analysis. There are three basic elements “subject”, “action” and “object” in the requirements. These elements become classes of the ontology. The object properties between instances of the same classes are defined in the ontology. In the system of rules it is determined that one of four types of relations can be established between a pair of the requirements: isConflict, isDuplicate, isInterconnect, isNotInterconnect. We develop the software product in the Python language for building and applying production rules system for classes and property objects of the ontology. Protégé 5.2 is used to work with the ontology. Also Python library PySwip and development environment SWI-Prolog 7.6.4 are used in the work. The paper also considers the issues of extracting requirements ontology instances from the automated processing results of textual requirements. The UDPipe with Russian language model is used for text processing.
The Character Relationship Mining Based on Knowledge Graph and Deep Learning

Character relationship information is important for precision poverty alleviation. The main challenge is that the character relation data is heterogeneous and relation words are implicit in text short sentences. Therefore, we propose a method of character relationship mining based on the combination of knowledge graph and deep learning. The DNN and BiGRU neural network joint method are proposed to recognize person named entities and extract character relations. The character relation triples data are stored in the Neo4j graph database, and the deeper information is mined by using the multi-depth relation query method. The experimental results on the poverty alleviation data show that the best accuracy of relation extraction can reach 84.9%, and the query efficiency of character relationship is improved. Users could quickly view the implied character relationships through the graph.
Relation Network for Person Re-identification

Person re-identification (reID) aims at retrieving an image of the person of interest from a set of images typically captured by multiple cameras. Recent reID methods have shown that exploiting local features describing body parts, together with a global feature of a person image itself, gives robust feature representations, even in the case of missing body parts. However, using the individual part-level features directly, without considering relations between body parts, confuses differentiating identities of different persons having similar attributes in corresponding parts. To address this issue, we propose a new relation network for person reID that considers relations between individual body parts and the rest of them. Our model makes a single part-level feature incorporate partial information of other body parts as well, supporting it to be more discriminative. We also introduce a global contrastive pooling (GCP) method to obtain a global feature of a person image. We propose to use contrastive features for GCP to complement conventional max and averaging pooling techniques. We show that our model outperforms the state of the art on the Market1501, DukeMTMC-reID and CUHK03 datasets, demonstrating the effectiveness of our approach on discriminative person representations.
Monitoring Interfaces Fastethernet on Cisco Catalyst 3750 to Ensure Use of The Security Computer Network In STTA Computing Laboratories

Cisco is a company that concentrates on hardware and software related to computer networks.One of the hardware produced by Cisco is a switch device that can be used for management of a computer network.Many types of switches that have been produced by Cisco, one of which is the Cisco Catalyst 3750.In the configuration and monitoring of the Cisco Catalyst 3750 user / administrator must do management with command line based configuration.This is because Cisco has not facilitated its users with user interface based configurations.So the user is required to know the code syntax to execute a command on the switch.In this study, researchers will discuss how to create a user interface for monitoring web-based Fastethernet Interfaces on the Cisco Catalist 3750 and use notifications E-mail by utilizing an API to determine the up or down status of a network device.The results of the test show that the application can be monitored by the administrator remotely in real time, the user interface can run well on the personal computer browser and smartphone responsively.
The 16th Top Chess Engine Championship, TCEC16

TCEC16 was the 16th season of the Top Chess Engine Championship and ran from July 14th to October 13th, 2019. TCEC has become the largest Open Computer Chess Championship. It attracts the best engines in the field and provides an opportunity for a comparative analysis of the Shannon-AB and the new Neural-Network engines’ styles of play. STOCKFISH regained the title of Grand Champion by beating ALLIESTEIN in the Superfinal. The defending Grand Champion, LEELA CHESS ZERO was third. The attached files at http://centaur.reading.ac.uk/86830 provide the 950 games with engine PVs, the detail on them and some summary statistics.
Supporting collaborative modelling in UML class diagrams

Background. Modeling architectural aspects of the system is an essential activity in software development. Inthis context, developers work in parallel, and collaborate to define application software models, such as classdiagrams. Problem. Although many software modeling tools have been proposed, there is a lack of distributed collaboration features. Solution. This study proposes C-SAMT, a web tool for collaborative modeling of UML class diagrams. Developers can benefit from using C-SAMT when performing modeling tasks, such as creating domain models in parallel, and collaboratively. Evaluation. We recruited 20 industry professionals to perform a qualitative evaluation of the tool through a questionnaire. Results. Majority of Industry professionals (85%, 17/20) reported that totally agree that the communication channel of the tool worked properly, they also (80%, 16/20) totally agreed the models generated collaboratively with C-SAMT had small numbers of conflicts, and they also (90%, 18/20) perceived improved productivity gains using the proposed tool. Conclusion. C-SAMT provided a collaborative environment, which were positively evaluated by developers, who reported that C-SAMT had a good communication channel between team members.
Design and Implementation of Real Time Robot Controlling System Using Upper Human Body Motion Detection On FPGA

Humans body motion detection is widely used in variety of applications such as video processing, sport analysis and biometrics, telerobotic Controlling. Therefore, the precise upper body movement of people detected has essential theoretical significance and economic value. This paper describe our project to detect and track an upper body motion by implementing a real-time robot controlling system which is known as a FPGA based hardware platform for video and image processing with speed, integration and reliability requirements. The system useful upper body motion information extracted from real time image processing will be used to control the prototype humanoid robot. One of the most challenge of this work is to reduce the complexity of algorithm in low level for having small processing duration to meet requirement of a real-time system. The system includes camera for video capturing, FPGA for implement image processing and extracting useful information to send to humanoid robot controller, and monitor for displaying results, . Image processing algorithms and digital system design technique using Verilog Language will be describe in this paper. DE2 Altera FPGA kit and Humanoid robot are used to implement this research.
Text classification using convolutional neural network

In this article, the method of text classification using a convolutional neural network is presented. The problem of text classification is formulated, the architecture and the parameters of a convolutional neural network for solving the problem are described, the steps of the solution and the results of classification are given. The convolutional network which was used was trained to classify the texts of the news messages of Internet information portals. The semantic preprocessing of the text and the translation of words into attribute vectors are generated using the open word2vec model. The analysis of the dependence of the classification quality on the parameters of the neural network is presented. The using of the network allowed obtaining a classification accuracy of about 84%. In the estimation of the accuracy of the classification, the texts were checked to belong to the group of semantically similar classes. This approach allowed analyzing news messages in cases where the text themes and the number of classification classes in the training and control samples do not equal.
Knowledge Graph Construction for Intelligent Maintenance of Power Plants

The intelligent maintenance of power plants greatly relies upon previous experiences and historical information to make informed decisions. Previous research is predominantly focused on collecting data from the maintenance process while little work has been done on the automatic capture and mining of knowledge resources from the data accumulated. Focusing on the experience feedback issue in power plants maintenance, this work proposes a novel process of automatic construction and reasoning of knowledge graphs to support the intelligent maintenance of complex power equipment. In this process, the Bi-LSTM-CRF model and the attention-based Bi-LSTM are specifically used to identify and extract entities and relations from unstructured status reports. On this basis, the knowledge graph construction method based on the neo4j graph database is developed. This paper details the preliminary work towards implementing the proposed process.
Non-negative Representation Based Discriminative Dictionary Learning for Face Recognition

In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification. In contrast to traditional dictionary learning methods, NRDL investigates the use of non-negative representation (NR), which contributes to learning discriminative dictionary atoms. In order to make the learned dictionary more suitable for classification, NRDL seamlessly incorporates non-negative representation constraint, discriminative dictionary learning and linear classifier training into a unified model. Specifically, NRDL introduces a positive constraint on representation matrix to find distinct atoms from heterogeneous training samples, which results in sparse and discriminative representation. Moreover, a discriminative dictionary encouraging function is proposed to enhance the uniqueness of class-specific sub-dictionaries. Meanwhile, an inter-class incoherence constraint and a compact graph based regularization term are constructed to respectively improve the discriminability of learned classifier. Experimental results on several benchmark face data sets verify the advantages of our NRDL algorithm over the state-of-the-art dictionary learning methods.
Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque CONCURRENT system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable MODULAR algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.
Evaluating a Clinical Decision Support System for Drug-Drug Interactions.

Janusmed is a clinical decision support system, developed by the Stockholm County Council that supports physicians in identifying drug-drug interactions. To determine how Janusmed is used in and affects the clinical practice, an evaluation study is currently being carried out that analyzes multiple data sources through descriptive statistics. The study focuses on how Janusmed affects the behavior of the physicians, in particular, to what extent physicians reconsider their prescription decisions based on warnings from Janusmed.
Information Service of Web Resource “Books in Russian Published Abroad, 1927—1991”

The article considers the main prerequisites for the creation of the online bibliographic resource based on the unique collection of foreign publications in Russian collected by the experts of the Russian State Library (RSL). Specialists of the Research Scientific Department of Bibliography have been working since 2001 to create the local database (DB) of books published abroad in Russian. In 2017, on the basis of DB, there was developed the new web resource, from 2018 placed on the website of Rosinformkultura of RSL in open access. The purpose of the research work is to study the distribution of new web resource service and the completeness of information-library services to users. The article for the first time covers the technological capabilities of the resource and its service component. The author analyses the statistical data of access and use of the resource and reveals the key regions on access among the eight regions of the Russian Federation. The article presents classification of queries among the main search fields most actively used when working with the DB. The author considers the specific features of information search in various data areas, including — personalia, title, keywords, geographical names, publishers, and presents distribution of materials in DB by 16 thematic areas, the largest of which are publications of fiction works. There are given the examples of search queries. Data collection is carried out in automated way using the log files of resource accesses. The article emphasizes that for the first time the users are provided with online access to previously inaccessible information about sources devoted to the Russian emigration, life and creative heritage of scientific intelligentsia reflected in printed publications. The author concludes that information service of web resource has several advantages compared to traditional library-information services: the ability to remotely access the resource, no time limit work with it, not necessarily a personal visit to the library, cover of a wider range of users, raise of social status and image of the library. The results of study confirm the demand for new web resource of RSL among users.
DIGITAL PROCESSING OF INTERFEROGRAMS OBTAINED BY THE PHOTOELASTICITY METHOD

The article is devoted to the digital processing interferograms (isochromatic fringe patterns) obtained by the photoelasticity method. An application to interpretate the isochromatic fringe patterns is developed. It allows us to automate this procedure, gets to avoid the routine and time-consuming work. The features of the developed software package are described in detail by means of the classic problem of a diametrically compressed disk. The applications algorithm includes following main steps: image pre-processing, localization interference fringe, fringe tracing. The application creates a text ﬁle containing all the necessary data to further determine the stress-strain state (isochromatic fringes number and locus of fringes).
Designing a Code Vulnerability Meta-scanner

The concept of “secure by design” is based on preventive software security and aims at avoiding vulnerabilities as soon as possible. However, finding vulnerabilities manually is a time-consuming and error-prone process. Thus, the use of code scanner tools becomes a good practice for developers. Unfortunately, existing code scanner tools produce too many false positives, which complicates the cycle development task. In this paper, we present an approach to construct a code vulnerability scanner upon existing scanner tools. The aim of such a scanner, called code vulnerability meta-scanner (CVMS), is to be more efficient and reduce the number of false positives. Our experimental results show that none of the scanners strictly subsumes another, and none of them is better than all the others for all the vulnerabilities. So, we propose a method that combines their results with respect to their performances. We experimented our approach using three existing scanner tools (Fortify, Yag Suite and SpotBug). Then, we used the resulted CVMS to annotate a well-known Java application corpus, namely Qualitas Corpus. These experiment results demonstrated that the CVMS performs better than the scanners on which it is constructed.