Reviewerly/exHarmony huggingface dataset:
AUTOMATIC MUCOUS GLANDS SEGMENTATION IN HISTOLOGICAL IMAGES

Abstract. Mucous glands is an important diagnostic element in digestive pathology. The first step of differential diagnosis of colon polyps in order to assess their malignant potential is gland segmentation. The process of mucous glands segmentation is challenging as the glands not only needed to be separated from a background but also individually identified to obtain reliable morphometric criteria for quantitative diagnostic methods. We propose a new convolutional neural network for mucous gland segmentation that takes into account glands’ contours and can be used for gland instance segmentation. Training and evaluation of the network was performed on a standard Warwick-QU dataset as well as on the collected PATH-DT-MSU dataset of histological images obtained from hematoxylin and eosin staining of paraffin sections of colon biopsy material collected by our Pathology department. The collected PATH-DT-MSU dataset will be available at http://imaging.cs.msu.ru/en/research/histology.
Deep Learning Inference Service at Microsoft

This paper introduces the Deep Learning Inference Service, an online production service at Microsoft for ultra-low-latency deep neural network model inference. We present the system architecture and deep dive into core concepts such as intelligent model placement, heterogeneous resource management, resource isolation, and efficient routing. We also present production scale and performance numbers.
A comparison of back-end frameworks for web application development

Web applications have a complex structure, and for more efficient and faster writing of original program code, frameworks are often used. There are numerous different frameworks on the market that are used to build different parts of software architecture. Only back-end frameworks for web applications development are analyzed. Previous research on evaluation criteria for back-end frameworks, and various sources which by various reasons, represent the list of popularity of back-end frameworks are shown. The framework selection procedure is described, and the reasons of framework selection (Laravel, Rails, Django and Spring) for the analysis are given. The examination (verification) and evaluation of the degree of satisfaction of the selected basic and additional comparison criteria in selected frameworks are carried out. The analysis shows insignificant differences in the overall ratings of analyzed frameworks according to the basic comparison criteria. According to the additional comparison criteria, the analysis shows significant differences in the overall ratings of analyzed frameworks. It is shown that the Django framework, because it is suitable for development of larger applications, suitable for beginner developers, and achieves the highest overall rating of additional comparison criteria, is the best choice of analyzed back-end frameworks for web application development.
Wavelet Transform Analysis to Applications in Electric Power Systems

The wavelet transform has received great importance in the last years on the power system analysis because the multi-resolution analysis presents proprieties good for the transient signal analysis.This chapter presents a review on main application of wavelet transform in electric power systems.The study areas have been classified as power system protection, power quality disturbances, power system transient, partial discharge, load forecasting, faults detection, and power system measurement.The areas in which more works have been developed are the power quality and protections field, where both cover 51% of the articles analyzed.
China Seismic Experiment Site: scientific challenges

Acta Geologica Sinica - English EditionVolume 93, Issue S1 p. 273-273 Session 8: Seismicity, volcanism and deep processesFree Access China Seismic Experiment Site: scientific challenges Zhongliang Wu, Zhongliang Wu wuzhl@ucas.ac.cn Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036 ChinaSearch for more papers by this authorXiaodong Zhang, Xiaodong Zhang Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036 ChinaSearch for more papers by this authorKe Sun, Ke Sun Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036 ChinaSearch for more papers by this author Zhongliang Wu, Zhongliang Wu wuzhl@ucas.ac.cn Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036 ChinaSearch for more papers by this authorXiaodong Zhang, Xiaodong Zhang Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036 ChinaSearch for more papers by this authorKe Sun, Ke Sun Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036 ChinaSearch for more papers by this author First published: 31 May 2019 https://doi.org/10.1111/1755-6724.14084Citations: 5AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume93, IssueS1Special Issue: Abstracts of the International Symposium on Deep Earth Exploration and Practices, 24–26 Oct 2018, Beijing, ChinaMay 2019Pages 273-273 RelatedInformation
Front-View Vehicle Damage Detection using Roadway Surveillance Camera Images

Vehicle body damage detection from still images has received considerable interest in the computer vision community in recent years. Existing methods are typically developed towards the auto insurance industry to minimize the claim leakage problem. Earlier studies utilized images taken from short proximity (< 3 meters) to the vehicle or to the damaged region of vehicle. In this study, we investigate the vehicle frontal body damage detection using roadway surveillance camera images. The proposed method utilizes deep learning based object detection and image classification methods to determine damage status of a vehicle. The proposed method combines the symmetry property of vehicles’ frontal view and transfer learning concept in its inference process. Experimental results show that the proposed method achieves 91 % accuracy on a test dataset.
PPCD: Privacy-preserving clinical decision with cloud support

With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients. However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient. Each party learns nothing about the other’s private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure.
Unsupervised Learning for Depth, Ego-Motion, and Optical Flow Estimation Using Coupled Consistency Conditions

Herein, we propose an unsupervised learning architecture under coupled consistency conditions to estimate the depth, ego-motion, and optical flow. Previously invented learning techniques in computer vision adopted a large amount of the ground truth dataset for network training. A ground truth dataset, including depth and optical flow collected from the real world, requires tremendous effort in pre-processing due to the exposure to noise artifacts. In this paper, we propose a framework that trains networks while using a different type of data with combined losses that are derived from a coupled consistency structure. The core concept is composed of two parts. First, we compare the optical flows, which are estimated from both the depth plus ego-motion and flow estimation network. Subsequently, to prevent the effects of the artifacts of the occluded regions in the estimated optical flow, we compute flow local consistency along the forward-backward directions. Second, synthesis consistency enables the exploration of the geometric correlation between the spatial and temporal domains in a stereo video. We perform extensive experiments on the depth, ego-motion, and optical flow estimation on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset. We verify that the flow local consistency loss improves the optical flow accuracy in terms of the occluded regions. Furthermore, we also show that the view-synthesis-based photometric loss enhances the depth and ego-motion accuracy via scene projection. The experimental results exhibit the competitive performance of the estimated depth and the optical flow; moreover, the induced ego-motion is comparable to that obtained from other unsupervised methods.
MediBot: An Ontology based Chatbot for Portuguese Speakers Drug’s Users

Brazil is one of the countries with the highest level of drug consumption in the world.By 2012 about 66% claimed to practice self-medication.Such activity can lead to a wide range of risks, including death from drug intoxication.Studies indicate that a lack of knowledge about drugs and their dangers is one of the main aggravating factors in this scenario.This work aims to universalize access to information about medications and their risks for different user profiles, especially Brazilian and lay users.In this paper, we presented the construction process of a Linked Data Mashup (LDM) integrating the datasets: consumer drug prices, government drug prices and drug's risks in pregnant from ANVISA and SIDER from BIO2RDF.In addition, this work presents MediBot, an ontology-based chatbot capable of responding to requests in natural language in Portuguese through the instant messenger Telegram, smoothing the process to query the data.MediBot acts like a native language query interface on an LDM that works as an abstraction layer that provides an integrated view of multiple heterogeneous data sources.
Declarative Choreographies and Liveness

We provide the first formal model for declarative choreographies, which is able to express general omega-regular liveness properties. We use the Dynamic Condition Response (DCR) graphs notation for both choreographies and end-points. We define end-point projection as a restriction of DCR graphs and derive the condition for end-point projectability from the causal relationships of the graph. We illustrate the results with a running example of a Buyer-Seller-Shipper protocol. All the examples are available for simulation in the online DCR workbench at http://dcr.tools/forte19 .
Toward Automatic Generation of Column-Oriented NoSQL Databases in Big Data Context

&lt;span&gt;The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.&lt;/span&gt;
Stability Analysis of Pose-Based Visual Servoing Control of Cable-Driven Parallel Robots

Cable-driven parallel robots are robots with cables instead of rigid links. The use of cables introduces advantages such as high payload to weight ratio, large workspaces, high velocity capacity. Cables also bring drawbacks such as bad accuracy when the robot model is not accurate. In this paper, a visual servoing control is proposed in order to achieve high accuracy no matter the robot model precision. The stability of the solution is analyzed to determine the tolerable perturbation limits. Experimental validation is performed both in simulation and on a real robot to highlight the differences.
Head Pose Classification Based on Line Portrait

Head pose classification has great significance in the field of biometrics. But most of the head pose classification methods depend on facial landmark detection, which results in that the performance relies not only on the pose classification. To this end, this paper proposes a head pose classification method based on the algorithm of line portrait generation and convolutional neural network (CNN). Experiments on the Color FERET and BIWI database show that the proposed algorithm achieves better results than the algorithm of head pose classification depend on facial landmark detection and has strong robustness.
Exploring Image Reconstruction Attack in Deep Learning Computation Offloading

Deep learning (DL) computation offloading is commonly adopted to enable the use of computation-intensive DL techniques on resource-constrained devices. However, sending private user data to an external server raises a serious privacy concern. In this paper, we introduce a privacy-invading input reconstruction method which utilizes intermediate data of the DL computation pipeline. In doing so, we first define a Peak Signal-to-Noise Ratio (PSNR)-based metric for assessing input reconstruction quality. Then, we simulate a privacy attack on diverse DL models to find out the relationship between DL model structures and performance of privacy attacks. Finally, we provide several insights on DL model structure design to prevent reconstruction-based privacy attacks: using skip-connection, making model deeper, including various DL operations such as inception module.
LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)

This paper describes our contribution to the SemEval-2019 Task 5 on the detection of hate speech against immigrants and women in Twitter (hatEval).We considered a supervised classification-based approach to detect hate speech in English tweets, which combines a variety of standard lexical and syntactic features with specific features for capturing offensive language.Our experimental results show good classification performance on the training data, but a considerable drop in recall on the held-out test set.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erdős-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds.
The Modern Rector: Competencies Required in the Global Academic Marketplace

The article presents the results of studying the competencies of university leaders, including those that ensure university effectiveness in the global academic marketplace. Rector appointment and selection procedures are analyzed across countries. The study uses pre-collected data on careers of 93 university leaders, of which 52 are in charge of Russia’s leading universities (including 21 Project 5–100 institutions) and 41 govern foreign universities ranked in the top 100 of the QS World University Rankings® 2018. The following parameters are analyzed as constituent parts of university leaders’ rectorship experience: working abroad experience, business work experience as Head of Department or higher, academic work experience including top management positions, scientometric indicators, work experience in public service, age, years of teaching and research experience, years of university leadership experience, years of rectorship at another university. Significant differences have been revealed in the competencies possessed by university leaders in Russia and abroad. The rectors of top foreign universities have a more diverse experience in varied fields necessary to ensure university effectiveness, and they also demonstrate higher career mobility.
Fast Algebraic Immunity of $2^m+2$ &amp; $2^m+3$ Variables Majority Function

Boolean functions should possess high fast algebraic immunity (FAI) when it is used in stream ciphers in order to stand up to fast algebraic attacks.However, in previous research, the FAI of Boolean functions was usually calculated by computer.But, as everyone knows, it is very difficult to calculate the FAI of a given Boolean function with high algebraic degree when the variable number is greater than 18.In 2016, Tang et al. gave that the exact value of FAI of the majority function on 2 m and 2 m + 1 (m ≥ 2) variables is 2 m-1 + 2. This paper proves that the FAI of 2 m + 2 and 2 m + 3 (m ≥ 2) variables majority function equals 2 m-1 + 4.
Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection

Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods.
Unsupervised Joint Training of Bilingual Word Embeddings

State-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space.Despite its remarkable results, unsupervised mapping is also well-known to be limited by the dissimilarity between the original word embedding spaces to be mapped.In this work, we propose a new approach that trains unsupervised BWE jointly on synthetic parallel data generated through unsupervised machine translation.We demonstrate that existing algorithms that jointly train BWE are very robust to noisy training data and show that unsupervised BWE jointly trained significantly outperform unsupervised mapped BWE in several cross-lingual NLP tasks. 13We used the News Commentary corpora provided by WMT for en→de and en→fr to train SMT systems performing at 15.4 and 20.1 BLEU points on Newstest2016 en-de and Newstest2014 en-fr, respectively.
Unsupervised Rewriter for Multi-Sentence Compression

Multi-sentence compression (MSC) aims to generate a grammatical but reduced compression from multiple input sentences while retaining their key information.Previous dominating approach for MSC is the extractionbased word graph approach.A few variants further leveraged lexical substitution to yield more abstractive compression.However, two limitations exist.First, the word graph approach that simply concatenates fragments from multiple sentences may yield nonfluent or ungrammatical compression.Second, lexical substitution is often inappropriate without the consideration of context information.To tackle the above-mentioned issues, we present a neural rewriter for multisentence compression that does not need any parallel corpus.Empirical studies have shown that our approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation.A parallel corpus with more than 140,000 (sentence group, compression) pairs is also constructed as a by-product for future research.
Learning Low-Dimensional Latent Graph Structures: A Density Estimation Approach

We aim to automatically learn a latent graph structure in a low-dimensional space from high-dimensional, unsupervised data based on a unified density estimation framework for both feature extraction and feature selection, where the latent structure is considered as a compact and informative representation of the high-dimensional data. Based on this framework, two novel methods are proposed with very different but intuitive learning criteria from existing methods. The proposed feature extraction method can learn a set of embedded points in a low-dimensional space by naturally integrating the discriminative information of the input data with structure learning so that multiple disconnected embedding structures of data can be uncovered. The proposed feature selection method preserves the pairwise distances only on the optimal set of features and selects these features simultaneously. It not only obtains the optimal set of features but also learns both the structure and embeddings for visualization. Extensive experiments demonstrate that our proposed methods can achieve competitive quantitative (often better) results in terms of discriminant evaluation performance and are able to obtain the embeddings of smooth skeleton structures and select optimal features to unveil the correct graph structures of high-dimensional data sets.
Social Network Self-Protection Model:What Motivates Users to Self-Protect?

Social networks are an indispensable activity for billions of users making them an attractive target for cyberattacks. There is however only scarce research on self-protection of individuals outside the organizational context. This study aims to address this gap by explaining what motivates individuals to self-protect on social networks. A survey (N = 274) has been conducted among Slovenian Facebook users to test the proposed social network selfprotection model. The results show that privacy concerns and perceived threats significantly affect user’s intention to self-protect. Descriptive norm only affects intention indirectly through perceived threats appearing to contradict a large body of research on behavioral intentions. “If others protect themselves, there must be a serious threat.” On the other hand, it also helps to explain why the direct effect of descriptive norm on security-related behavior is relatively small in other studies. Surveillance concerns, regulation and information sensitivity all significantly affect privacy concerns.Although privacy concerns are currently high due to the recent high-profile privacy-related scandals (e.g., Cambridge Analytica, Facebook, Google+), it may not affect the motivation of users to self-protect as they dealt with issues far beyond their control. Nevertheless, users with higher levels of privacy concerns than their peers may be more motivated to self-protect.1