Reviewerly/exHarmony huggingface dataset:
Multi-oriented Scene Text Detector with Atrous Convolution

Recently, semantic segmentation has been widely used in text detection tasks and many excellent text detection methods have been proposed. They usually adopt deep convolutional neural network with consecutive striding or pooling operations to obtain a larger receptive field. However, that would lead to the lack of context which is crucial for text detection. In this paper, we propose an end-to-end trainable neural network to directly detect text regions without redundant stage other than a locality-aware non-maximum suppression is involved. We introduce atrous convolution in the backbone network to enlarge the receptive field, retaining more context information while controlling the spatial resolution of feature maps. The Atrous Spatial Pyramid Pooling (ASPP) module is attached on top of the feature maps to effectively detect texts of multiple scales. We have benchmarked our algorithm on three public datasets. It achieves highly competitive results in terms of text localization precision. More specifically, on the MSRA-TD500 datasets, the proposed algorithm achieves an F-score of 0.813, outperforming the previous best by a large margin.
Cardiovascular Disorder Severity Detection Using Myocardial Anatomic Features Based Optimized Extreme Learning Machine Approach

This study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF. This assessment is carried out on cardiovascular magnetic resonance (CMR) images of 104 subjects available in Kaggle Second Annual Data Science Bowl. The Segment CMR framework is used to segment myocardium from cardiac MR images, and it is subdivided into 16 sectors. 86 clinically significant anatomical features are extracted and subjected to ELM framework. Regularization coefficient and hidden neurons influence the prediction accuracy of ELM. The optimal value for these parameters is achieved with the butterfly optimizer (BO). A comparative study of BOELM framework with different activation functions and feature set has been conducted. Among the individual feature set, myocardial volume at ED gives a better classification accuracy of 83.3% compared to others. Further, the given BOELM framework is able to provide higher multi-class accuracy of 95.2% with the entire feature set than ELM. Better discrimination of healthy and moderate abnormal subjects is achieved than other sub groups. The combined anatomical sector wise myocardial features assisted BOELM is able to predict the severity levels of CVDs. Thus, this study supports the radiologists in the mass diagnosis of cardiac disorder.
Perceptions of Primary Education Teachers on Mobile Learning

The objective of this article is to investigate perceptions of primary education teachers towards mobile learning (m-learning) in the classroom. This is a qualitative research carried out with a sample of 16 participants, 8 in Greece and 8 in Latvia. Semi-structured interviews were conducted to teachers who express their opinions on whether they could enhance m-learning environment and whether mobile devices could be an effective tool for students' learning, interaction and communication. From the analysis of the data, the majority of the teachers in both countries had a positive attitude about m-learning and they had the intention to use these devices in the future. According to their view, such technology could increase students' motivation on learning and as more new applications appear, the educational process can become more attractive and productive. Only few were those teachers who doubt about its efficiency for some reasons. These differences in teachers' opinions were related to the age factor.
Image skeletonization based on combination of one- and two-sub-iterations models

This paper is focused on the field of the skeletonization of the binary image. Skeletonization makes it possible to represent a binary image in the form of many thin lines, the relative position, sizes and shape of which adequately describe the size, shape and orientation in space of the corresponding image areas. Skeletonization has many variety methods. Iterative parallel algorithms provide high quality skeletons. They can be implemented using one or more sub-iterations. In each iteration, redundant pixels, the neighborhoods of which meet certain conditions, are removed layer by layer along the contour and finally they leave only the skeleton. Many one-sub-iterations algorithms are characterized by a breakdown in connectivity and the formation of excess skeleton fragments. The highest-quality skeletons are formed by the well-known single-iteration OPTA algorithm, which based on 18 binary masks, but it is sensitive to contour noise and has a high computational complexity. The Zhang and Suen two-iteration algorithm (ZS), which is based on 6 logical conditions, is widely used due to its relative simplicity. But it suffers from the problem of the blurs of the diagonal lines with a thickness of 2 pixels and the lost of the square which size is 2×2 pixels. Besides, both algorithms mentioned above do not achieve the unit pixel thickness of the skeleton lines (many non-node pixels have more than two neighbors). Mathematical model and OPCA (One-Pass Combination Algorithm) algorithm which is based on a combination and simplification of single-iterative OPTA and two-iterative ZS are proposed for constructing extremely thin bound skeletons of binary images with low computational complexity. These model and algorithm also made it possible to accelerate the speed of skeletonization, to enhance recoverability of the original image on the skeleton and to reduce the redundancy of the bonds of the skeleton elements.
Improving visual question answering with pre-trained language modeling

Visual question answering is a task of significant importance for research in artificial intelligence. However, most studies often use simple gated recurrent units (GRU) to extract question or image high-level features, and it is not enough for achieving a better performance. In this paper, two improvements are proposed to a general VQA model based on the dynamic memory network (DMN). We initialize the question module of our model using the pre-trained language model. On the other hand, we utilize a new module to replace GRU in the input fusion layer of the input module. Experimental results demonstrate the effectiveness of our method with the improvement of 1.52% on the Visual Question Answering V2 dataset over baseline.
A Brief View on Medical Diagnosis Applications with Deep Learning

It is thought that deep learning will be more effective in the near future and will be used more in medical applications. Because it does not require too many input parameters and users do not need to have expert knowledge. In addition, it is not affected by the increases in the amount of calculation and data, and responds faster than traditional methods. Continuous improvement and development in the field of deep learning will also contribute to this process.
Blind Image Quality Assessment with Visual Sensitivity Enhanced Dual-Channel Deep Convolutional Neural Network

Recent years, various blind image quality assessment (BIQA) methods based on deep neural network have been proposed and achieved excellent performance. Most existing deep BIQA methods learn a regression model from distorted images with corresponding human subjective scores with end-to-end neural networks. However, such schemes ignore the characteristics of human visual system (HVS) since human beings are the ultimate receivers of the images. This paper proposed a dual-channel deep neural architecture for BIQA, which incorporated the visual sensitivity with taken the psychophysical characteristics of human visual system (HVS) into consideration. Furthermore, a new loss function is employed, which penalizes the deep network when the order of prediction scores is different from the ground truth order. The experimental results on two benchmark IQA databases show that the proposed method outperforms the state-of-the-arts.
Why is Information Retrieval a Scientific Discipline?

It is relatively easy to state that information retrieval (IR) is a scientific discipline but it is rather difficult to understand why it is science because what is science is still under debate in the philosophy of science. To be able to convince others that IR is science, our ability to explain why is crucial. To explain why IR is a scientific discipline, we use a theory and a model of scientific study, which were proposed recently. The explanation involves mapping the knowledge structure of IR to that of well-known scientific disciplines like physics. In addition, the explanation involves identifying the common aim, principles and assumptions in IR and in well-known scientific disciplines like physics, so that they constrain the scientific investigation in IR in a similar way as in physics. Therefore, there are strong similarities in terms of the knowledge structure and the constraints of the scientific investigations between IR and scientific disciplines like physics. Based on such similarities, IR is considered a scientific discipline.
Design of small-scale electrocoagulation reactor for non-sugar removal of sugarcane juice

Abstract The most common sugarcane juice purification method practiced in Java is liming and sulphitation. In this research, electrocoagulation method was used to purify the sugarcane juice. This research purpose is to design an electrocoagulation reactor for the non-sugar removal of sugarcane juice. The design method of the reactor is based on IR-Drop and surface area per volume ratio (S/V) optimization. Electrode type and configuration was chosen considering the characteristic of sugarcane juice. A circulated vertical flow reactor type was built to conduct the electrocoagulation. The dimension of the reactor was 10 × 10 × 24 cm and had 8 electrodes installed with 1 cm inter electrode distance. The electrode was aluminium plate and arranged in bipolar configuration. The experiment used 35, 45, and 55 volt, flow rate of 40 L/min, and 30 minutes reaction time with 5 minutes interval. The result showed that an increase in voltage and reaction time increases the non-sugar removal efficiency. The mixed juice treated by electrocoagulation with 55 volt for 5 minutes could remove the non-sugar up to 21.47% and 33.49% for 30 minutes reaction time. The average of non-sugar removal by sulphitation in Java is 11.50% and 29.00% by carbonation. This study shows that electrocoagulation is more effective than the conventional method in removing the non- sugar. With the right scaling up method, electrocoagulation is possible to be practiced in a large scale sugar industry. Future studies are needed to evaluate the performance of electrocoagulation in terms of the clear juice quality and process efficiency.
Deep Reinforcement Learning based Dynamic Object Detection and Tracking from a Moving Platform

This article proposes Deep Reinforcement learning inspired image based visual servoing (DRL-IBVS) controller for aerial robots. The proposed DRL-IBVS controller uses monocular images to map target's bounding box based errors to linear-velocity command to the robot for following the target. Deep learning based object detection algorithm YOLOv2 architecture is used to identify the target in the image. The noise in detection is filtered using multi-object Bayes filter, where the prediction model utilizes a correlation tracker for target prior estimation. Filtered bounding box output is fed to the Deep Deterministic Policy Gradient (DDPG) for performing the visual servoing. DDPG agent is trained separately using the pinhole camera model and the kinematic relation between two drones (follower and target). Gazebo-based simulation environment is used to successfully validate the controller.
Digital Platforms and Banks

The article presents some issues of innovative modernization of banks and banking. It investigates debatable issues of theoretical understanding and practical use of digital platforms and banking ecosystems. The article shows how technologies change the financial market and the perception of bank customers, stimulate entrepreneurial activity and change the appearance of traditional financial markets in the process of implementing innovative business models (so-called ecosystems of modern banks). The author assesses the role of banks in the process of technological transformation of financial markets in Russia. The main research objectives are: to identify and quantify customer needs, as well as obstacles to the technological transformation of banks in Russia; to analyze market conditions and regulatory support for banking activities and determine its further development direction; to evaluate policy options for banks in terms of ecosystem development. The main purpose of the article is to identify the current problem of the bank transformation and show directions of its further development. In general, the research was aimed at bridging the knowledge gap between technology developers, bank employees and other financial market participants, as well as at developing the optimal policy of the Russian financial regulator in modern conditions.
Communication-Efficient (Proactive) Secure Computation for Dynamic General Adversary Structures and Dynamic Groups

In modern distributed systems, an adversary's limitations when corrupting subsets of a system's components (e.g., servers) may not necessarily be based on threshold constraints, but rather based on other technical or organizational characteristics. This means that the corruption patterns (and thus protection guarantees) are based on the adversary being limited by what can be captured by a General Adversary Structure (GAS). We consider efficient secure multiparty computation (MPC) under such dynamically-changing GAS settings. In such settings, one desires to protect against and during corruption profile changes; such adaptivity also renders some (secret sharing-based) encoding schemes underlying MPC protocols more efficient than others when operating with the (currently) considered GAS. One of our contributions is a set of new protocols to efficiently and securely convert back and forth between different MPC schemes for GAS; this process is often called share conversion. We consider two MPC schemes, one based on additive secret sharing and the other based on Monotone Span Programs (MSP). The ability to convert between the secret sharing representations of these MPC schemes enables us to construct the first communication-efficient structure-adaptive proactive MPC protocol for dynamic GAS settings. By structure-adaptive, we mean that the choice of the MPC protocol to execute in future rounds after the GAS is changed (as specified by an administrative entity) is chosen to ensure communication-efficiency (the typical bottleneck in MPC). Furthermore, since such secure "collaborative" computing may be long-lived, we consider the mobile adversary setting, often called the proactive security setting. As our second contribution, we construct communication-efficient MPC protocols that can adapt to the proactive security setting. Proactive security assumes that at each (well defined) period of time the adversary corrupts different parties and may visit the entire system overtime and corrupt all parties, provided that in each period it controls groups obeying the GAS constraints. In our protocol, the shares can be refreshed, meaning that parties receive new shares reconstructing the same secret, and some parties who lost their shares because of the reboot/reset can recover their shares. As our third contribution, we consider another aspect of global long-term computations, namely, that of the dynamic groups. Settings with dynamic groups and GAS were not dealt with in existing literature on (proactive) MPC. In dynamic group settings, parties can be added and eliminated from the computation, under different GAS restrictions. We extend our protocols to this additional dynamic group settings defined by different GAS (see the full version of the paper [18] for formal details of protocols and proofs).
Comparative Study of Relevant Methods for MRI/X Brain Image Registration

Several methods of brain image registration have been proposed in order to overcome the requirement of clinicians. In this paper, we assess the performance of a hybrid method for brain image registration against the most used standard registration tools. Most traditional registration tools use different methods for mono- and multi-modal registration, whereas the hybrid registration method is providing both mono and multi-modal brain registration of PET, MRI and CT images. To determine the appropriate registration method, we used two challenging brain image datasets as well as two evaluation metrics. Results show that the hybrid method outperforms all other standard registration tools and has achieved promising accuracy for MRI/X brain image registration.
A Method of Recognizing Flake Surfaces from Noisy Point Cloud for Measuring Stone Tools

Point-cloud-based techniques play a very significant role in the archaeological application for stone tools. Measured point data involve small noises, which are overlaps obtained through measurement by laser devices. Such noisy data make it difficult to extract highly accurate segmented flakes, which will be used for the refitted flake matching process, because potential feature points lying on the boundary edges are hardly extracted. To overcome this issue, this paper describes a method of recognizing flake surfaces with noisy point clouds. First, the resampling method is applied to remove the noise in the input data. Then, the surface variation is calculated with a various number of neighbors and the potential feature points are detected by analyzing its surface variation. After that, feature lines are extracted from the potential feature points. The feature lines represent boundary edges of the flake surfaces. Finally, flake surfaces are extracted by the feature-line-based segmentation method. The implementation of this work can recognize flake surfaces from noisy data.
Vertex Feature Classification (VFC)

In this paper a new algorithm for multi-class classification is presented. The algorithm, that is named Vertex Feature Classification (VFC), maps input data sets into an ad-hoc built space, called "simplex space" in order to perform geometric classification. More precisely, each class is first associated to a specific vertex of the polytope computed in the feature space. Successively, pattern classification is performed according to the geometric arrangement of patterns in a higher dimensional feature space. The experimental results, carried out on datasets of the UCI Machine Learning Repository, demonstrate the accuracy of the new algorithm is comparable with KNN, VDA and SVM, without or with a little training phase. An important aspect of this algorithm is its training time, which takes often a few milliseconds. Furthermore, the algorithm is robust and computationally efficient.
Placing Query Term Proximity in Search Context

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e. term frequency (tf), inverse document frequency (idf), etc.. Query Term Proximity (QTP) within matched documents is mostly under-explored for the relevance estimation in the information retrieval. In this paper, we systematically review the lineage of the notion of QTP in IR and proposed a novel framework for relevance estimation. The proposed framework is referred as Adaptive QTP based User Information Retrieval (AQtpUIR), is intended to promote the document’s relevance among all relevant retrieved ones. Here, the relevance estimation is a weighted combination of document-term (DT) statistics and query-term (QT) statistics. The notions ‘term-term query proximity’ is a simple aggregation of contextual aspects of user search in relevance estimates and query formation. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The design analysis asserts the usability of QTP measures to balance several seeking tradeoffs, e.g. relevance, novelty, result diversity (Coverage and Topicality), and highlight various inherent challenges and issue of the proposed work.
Haze Removal by Modeling the Scattering Properties of the Medium

This letter proposes a simple and effective method to remove haze from a single input foggy or underwater image. The algorithm is originated from the physically valid description of the transmission of the scattering light during the volumetric scattering medium, interpreting the formation of the haze with the layered transmission model. We establish the explicit expression of the transmittance with respect to statistics of the back scattered light in front of the scene. The parameters for the evaluation of the transmittance can be estimated by the second order statistics of the back scattered light both with and without the scene. Since the transmittance map is evaluated for each RGB channel respectively, the color recovery by the proposed method is wavelength dependent and physically valid. Experimental results on both terrestrial and underwater images are given, which show the effectiveness and advantage of the proposed method.
Short Remark on 3-Dimensional Game Method for Modelling

The Game Method for Modelling (GMM) was introduced, developed and applied in some areas in the recent years, but in all cases it used a 2-dimensional grid of squares. Here, for the first time, we discuss the GMM over 3-dimensional grid of cubes.
Complexity in the use of artificial intelligence in anatomic pathology

Over and above the characterization of an image in terms of classification labels, artificial intelligence can detect specific features within that image that contribute to an understanding of its clinical behavior. The analysis of an image can be more robust and provide more information than that which can be obtained by unaided morphological interpretation by the human eye. However, the capability is hampered by the huge annotated datasets that are required for supervised deep learning training as currently implemented. The kinds of unsupervised experiential training that occurs in humans are not currently helpful. However, there are a variety of weakly supervised strategies that allow for substantial reduction in the amount of specific annotation necessary for training. In any training regimen, care must be taken to preserve and detect information about the relationships among patterns in the data. New approaches toward this goal will also be discussed.
Research on Autonomous Navigation Algorithm of Aircraft

In order to make up for the deficiency of the SINS/GNS integrated navigation system in attitude correction, based on the geomagnetic matching algorithm, a new scheme is proposed. First, the principle of this SINS/GNS autonomous integrated navigation system is explored, and the corresponding mathematical model is established. Second, the Extended Kalman Filter (EKF) is designed for this autonomous integrated navigation system, considering the nonlinear characteristics of the model, the Marginalized Particle Filter (MPF) is also designed. The simulation experiments are conducted, and comparison analysis demonstrate that the proposed approach can improve the accuracy of attitude calculation, and the MPF filter has better robustness than EKF Filter.