Reviewerly/exHarmony huggingface dataset:
Problems and prospects of financing innovative development in sub-Saharan African countries

The article discusses the place and role of the financial system in the development &#x0D; of national economic systems in developing countries. There have been identified the main func-tions of the financial system: mobilizing financial resources, creating a database of investment projects and investors, monitoring and corporate deposit management, forming a block of information on diversification, transformation and risk management, facilitating the sale of goods through &#x0D; a payment system. The existing approaches to determining the concept of accessibility of financial resources are considered. The role of the financial sector in advancing technological innovations and industrialization in sub-Saharan African countries has been identified. The analysis of reasons for low financial integration, low access to financial services in the shadow economy has been carried out. The main reasons for the lack of access to banking services include the language barrier, lack of simple identification documents or data due to a poorly organized local infrastructure. It is noted that about 52% of the population of sub-Saharan Africa have access to formal financial services; 65% of small, medium and micro-enterprises (90% of the economic sector in Africa) do not have access to bank loans. Research and development expenditures in the economic regions have been systematized. The conclusions have been made about the underdevelopment and lag of infrastructure in sub-Saharan African countries, low availability of financial resources retarding the economic growth and innovative development of small and medium-sized businesses. Possibilities for improving the systems of financial innovative development in the investigated countries have been given.
Ensemble Learning Sentiment Classification for Un-labeled Arabic Text

Sentiment classification has become one of the most trending research topics, due to the rapid growth of social media platforms and applications. It is the process of determining the opinion or the feeling of a piece of text and assigning a label to it (positive, negative or neutral). One of the issues in sentiment classification is the need for labeled data – that is often carried out manually - in order to train the classifiers which is a time consuming task. In this paper we consider the lexicon-based classification as labeling technique instead of the manual labeling. In addition, for an effective sentiment classification we investigate the using of multiple ensemble learning methods - where multiple classifiers are combined - in order to improve the performance of the classification. Experiments have been run on datasets of reviews written in Modern Standard Arabic. Results show that the labeling technique is effective and promising and the use of ensemble learning has clearly improved the accuracy for the sentiment classification compared to the traditional methods.
Performance Comparison Between Different Control Algorithms in Regulating DC-Voltage

This paper compares three different techniques that control the output voltage of a DC-DC converter working in continuous conduction mode, the first technique is the proportional-Integral-derivative controller, the second one is the fuzzy logic controller, and finally the digital sliding mode controller. The sensed output voltage of the converter is considered as the variable value that should be controlled. The designed control techniques are simulated in MATLAB/Simulink software and the dynamical response of each technique is derived in order to choose the adequate control technique.
A Case Study of Japanese Textbook Linked Open Data: Publishing a Small Bibliographic Collection from a Special Library

Japanese Textbook Linked Open Data (LOD) is an LOD dataset of bibliographic and educational information that has been organized over the years by the Library of Education at the National Institute for Educational Policy Research. The dataset consists of bibliographic information for 7,548 volumes of Japanese textbooks authorized from 1992 to 2017, and provides 219,018 Resource Description Framework (RDF) triples as of April 2019. This paper reports a case study of the development and publication of Japanese Textbook LOD.
Identification of Adverse Event Patterns in Loperamide Using FP-Growth Algorithm

Abstract Loperamide is a drug to treat diarrhea, and should not be used for self-medication. There are cases of cardiac disorders due to the use of this drug, this is called an adverse event. Based on this problem, this research will identify the adverse event pattern in Loperamide using the FP-Growth algorithm. The data used is FAERS data from 2015-2017 with the attributes is the age group, sex, drug name, and adverse event. The amount of data after the preprocessing process is 2,840 records. The results is we found the pattern with the highest support values is a combination of adult age groups, male sex, the drug name Loperamide and adverse event cardiac disorders that have a support value of 3.8%, confidence 28.35% and lift ratio 1.483. The implemented using Matlab software and tested using SPMF tools.
Multi-criteria rating conversion without relation loss for recommender systems

Traditional recommender systems let users provide a single rating indicating their overall preferences toward items. Beside overall rating, multi-criteria recommender systems let users rate on multiple aspects of items with multi-criteria ratings. Most methods in recommender systems are based on collaborative filtering, which makes recommendations by exploiting ratings from neighbor users. However, most of them ignore the fact that rating behaviors of users vary due to their personal preference biases. Thus, exploiting ratings from neighbors directly might result in a poor recommendation. To solve this, the rating conversion techniques have been applied in some single criterion recommendations. For multi-criteria ratings, converting each criterion rating independently might result in loss of relation among criteria ratings. We propose a novel method that simultaneously converts all criteria ratings between users to maintain their implicit relations. The multi-criteria ratings are first normalized by variances of users and principle component analysis is applied to extract user preference patterns. Such patterns are then used for multi-criteria rating conversion. The experiment results show that our method outperforms both current single and multi-criteria rating conversion techniques with RMSEs of 1.1082 and 3.5574 on TripAdvisor and Yahoo datasets respectively, while maintaining considerably high level of prediction coverages.
Accurate Single Image Super-Resolution Using Deep Aggregation Network

Recent studies have shown that effectively combining rich representations of convolution neural network can significantly boost the performance of single image super resolution. Although dense skip connections can aggressively reduce depth and parameter count by feature reuse, it is a memory-intensive fusion operation. In this paper, we proposed a tree-structured deep aggregation block that spans the spectrum of layers to achieve more accuracy with less parameters and memory in super-resolution. Most of methods fuse the all features of blocks by a simple one-step aggregation. But it don’t robust enough for train data with discrepancy. So we propose a recursive aggregation structure to get rich semantic information and perform better on propagation features and gradient. We performed our method on three benchmark datasets and get a comparable result in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity) compared with state-of-the-art methods.
A Region-based Fusion Scheme for Human Detection in Autonomous Navigation Applications

Human and object detection is a continuously explored subject in tracking and navigation applications as well as within the machine vision community. More precisely, in navigation applications that are designed for robotics purposes or in order to support car drivers, the real-time detection of presence of humans and other objects is an important and challenging task. Specifically, human and object detection is a significant part of a human-computer collaboration in the sense of allowing automatic navigation and control systems to obtain a better model of the real world. Thus, new concepts are needed that will overcome difficulties faced by existing human detection approaches and will allow navigation systems to perform accurately. This paper offers a general classification scheme combining different detector systems towards navigation tasks. To evaluate the performance of the proposed methodology we used INRIA and PASCAL VOC 2007 datasets. Experimental results show that the combination of different image feature descriptors, classifier models and deep learning techniques are advantageous for human detection.
Verbal and Non-verbal Markers for the Analysis of Political Discourse

The paper describes a linguistic analysis performed over a corpus of Italian political speeches annotated through nonverbal tags. With the purpose to locate lexical features correlated with non-verbal phenomena, we exploited a set of electronic dictionaries of simple and compounds words and a network of Local Grammars. A Distributional Analysis of the texts has, then, been carried out in order to automatically explore the corpus.
A Probability-Based Close Domain Metric in Lifelong Learning for Multi-label Classification

Lifelong machine learning has recently become a hot topic attracting the researchers all over the world by its effectiveness in dealing with current problem by exploiting the past knowledge. The combination of topic modeling on previous domain knowledge (such as topic modeling with Automatically generated Must-links and Cannot-links, which exploits must-link and cannot-link of two terms), and lifelong topic modeling (which employs the modeling of previous tasks) is widely used to produce better topics. This paper proposes a close domain metric based on probability to choose valuable knowledge learnt from the past to produce more associated topics on the current domain. This knowledge is, then, used to enrich features for multi-label classifier. Several experiments performed on review dataset of hotel show that the proposed approach leads to an improvement in performance over the baseline.
Staining of Unstained Histology using Style Transfer with Colour-Based Segmentation

Staining of unstained histology using digital methods has been explored before through pure image processing methods. However, these methods are not sufficiently general as they considered only a few types of issues within a particular type of staining. This work showcases a potential learning-based solution utilising style transfer algorithms with actual unstained images. A colour-based segmentation masking method provides reliable style transfer.
Linked Open Data for Subject Discovery: Assessing the Alignment between Library of Congress Vocabularies and Wikidata

Linked open data (LOD) has long been touted as a means to enhancing discovery of library resources through the use of robust links between related items and concepts. Recently, libraries have begun to experiment with LOD sources such as Wikidata and DBpedia to harness usercontributed resources and enhance information displayed in library discovery systems. The Michigan State University Libraries (MSUL) Digital Repository Team has embarked on a project to display contextual information from Wikidata and DBpedia in (informational pop-up windows) alongside subject headings with the goal of providing users with more information on items in the digital repository. This paper will briefly describe this project and outline a quality analysis initiative meant to evaluate linkages between Library of Congress Subject Heading (LCSH) and Wikidata as well as the results of this analysis. It will also address a number of challenges encountered in terms of mapping between different controlled vocabularies. Finally, it will conclude with possible next steps for improving the accuracy of knowledge cards and the LOD that supports them.
Research on the Influence Law of Driving Experience on Drivers’ Gaze Behavior

Abstract To collect the information about driver’s visual features and the manipulation to vehicles, an experiment platform is built based on eye movement tracking device-Smart Eye, GPS, accident recorder equipment etc. We select a typical urban road to test. By analyzing the changes on different experience driver’s visual features at peak and peaceful peak period, find out the change laws of different experience drivers in their driving process. Some theoretical bases are provided for studying driver features.
Deep Residual Networks with Pyramid Depthwise Separable Convolution

Convolutional neural networks (CNNs) have shown remarkable performance in various computer vision tasks in recent years. However, the increasing model size has raised challenges in adopting them in real-time applications as well as mobile or embedded vision applications. Many works try to build networks as small as possible while still have acceptable performance by using Depthwise Separable Convolution (DWConvolution) in place of standard Convolution. This paper proposes a network which uses an extended version of DWCon-volution, called Pyramid DW Convolution. Instead of using just a 3 × 3 kernel size for DWConvolution, the proposed network uses a pyramid kernel size to capture more spatial information. The proposed architecture is evaluated on the highly competitive object recognition benchmark datasets ImageNet and the two CIFAR (CIFAR-10, CIFAR-100). The experiments demonstrate that the proposed network achieves better performance compared with other state-of-the-art networks.
Videl

Leukemia is one of TOP 10 cancers in China, especially to young children and elderly. Treating Leukemia at the early stage can significantly increase the cure rate. However, detecting early stage Leukemia, i.e, finding a small amount of blood cells among massive normal cells, is both technically challenge and labor intensive. Therefore, we designed an AI-powered diagnoser for precise identification of white blood cells. The precise identification has two steps, blood image collecting and cell classification. First, with the help of doctors in Ruijing Hospital, we have been collecting about 1,000 high-quality labeled blood cell samples each week and accumulated more than 200,000 samples in total. This is the largest blood cell image dataset for leukemia in China. Second, based on these blood cell samples, we adopted the ResNet-variant classification method and achieved above 95% accuracy in 17 subtypes of white blood cells. The classification accuracy is comparable to experienced doctors in Ruijing Hospital. Third, models are trained on the latest Intel Knights Landing 7210 Platform with Intel Omni-Path interconnections. Applying MKL-optimized matrix operatiors and RDMA-enable comunication library (PSM) shortens the training time from more than 6 hours to less than an hour. Our AI-powered diagnoser can reduce the time and the cost of early Leukemia diagnosis from one week to one day, and 1,000 USD to 100 USD, respectively.
Towards the Use of Model-Driven Technologies in an Integral Software Development Process

This work presents a model-driven engineering approach to the software development process for space applications following the standards ECSS-E-ST-40C and ECSS-Q-ST-80C. This approach is based on the use of models for the definition of the different constituent elements of the process, i.e. specification, requirements and validation procedures. The proposed approach provides development teams with a set of features that deliver an integrated solution to the software development process. These features include the use of model checking techniques to avoid inconsistencies between the different items and the ability to obtain input data from different sources, like telemetry and telecommand databases. Furthermore it enables the definition of transformations to generate different products such as component code, formatted output files, or configuration files for a ground support equipment software that allows the automatic execution of the defined functional tests. This approach has been validated through a case study in which a subset of the functionality of the software of the Instrument Control Unit of the Energy Particle Detector instrument on-board Solar Orbiter has been modelled.
Guided-Gated Recurrent Unit for Deep Learning-Based Recommendation System

Discovering and drawing out the relationship between users and items in a service-based companies or organizations are the essence of a recommendation system. It attracts many researches trying to solve such problems. Here we address a novel approach for the recommendation system, incorporating the means of collaborative aspect between the users internal hidden patterns and the items or goods to be recommended. Unlike the existing methods, our algorithm introduces a guiding factor between the user hidden state and the choice over the item set, such that it gives additional degree of freedom for the recommendation system to opt on which factor is more prominent. Experimental results suggest the advantages of the proposed algorithm over the existing state-of-the-art algorithms for the recommendation system.
The Formation of the Motivation and Value Attitude of Future Teachers to the Individual Style of Professional Activity by means of Case-technologies

The article presents the essence of the concept of "the individual style of professional activity of a teacher", the main functions of the individual style of professional activity of a modern teacher, and the peculiarities of the formation of the individual style of professional activity of future teachers during the process of education in higher education institutes. Taking into account the points of personally oriented and axiological methodological approaches, the motivation and value component of the individual style of professional activity of future teachers has been defined, as well as the role of goal-setting and motivation for success in the process of its development. The content and functional aspects of case technologies in training future teachers have been disclosed. The types of pedagogical cases and requirements for selection of the cases aimed at the realisation of tasks of forming the motivation and value attitude of future teachers to the individual style of professional activity have been disclosed as well. Examples of pedagogical cases of different kinds (an author-narrative case, the opened, problematic, and diagnostic cases) have been presented.
Research on Image Super-resolution Based on Improved Convolutional Neural Network

In this paper, an ACT-R (Adaptive Control of Thought-Rational) research model applied to the research of super-resolution reconstruction is constructed. In the matching stage, we add recurrent layer to the underlying convolutional neural network to train an improved end-to-end deep threshold recurrent neural network; In the selection stage, the neighborhood embedding algorithm is used to compensate the residual part of the image. The high resolution residual image with higher pixel density and higher quality detail information is obtained. In the decision-making stage, the reconstructed residual image and the high resolution image obtained by the recurrent convolution layer are integrated to complete the reconstruction of the image super-resolution. The experimental results show that the model has good reconstruction ability to the image and achieved good results in both PSNR and SSIM evaluation criteria.
Multi-channel Classification Resonance Network

A fusion ARTMAP is an online incremental supervised learning algorithm with multiple input channels. Whenever the ARTMAP receives labeled data, it can learn the data instantly. The fusion ARTMAP, however, is not robust to noise, which means the network predicts the wrong classes from noisy inputs. To solve this problem, we propose a multi-channel classification resonance network (MCRN). MCRN consists of two phases. In the first phase, the network maintains multiple channels without concatenating the inputs. In the second phase, the network identifies the inputs near the decision boundaries and reclassifies them by employing multi-layer perceptron (MLP) networks of which weights are trained by a back-propagation algorithm. A parallel match tracking process in MCRN finds the inputs near the decision boundaries. Two-channel classification simulations are carried out to demonstrate the effectiveness of MCRN for multi-channel cases. The simulation results show that the performance of MCRN is better than that of the fusion ARTMAP for artificial data sets.
Design of a biomedical kit for bedridden patients: a conceptual approach

Considering an Ambient Assisted Living context, it is proposed to design a system/equipment that allows the support of bedridden patients who require continuous or palliative medical care through a Medical Care Module (MCM) operated by a single person. The system should allow the autonomy, comfort, and safety of bedridden patients, in the scope of home care. During the development of the MCM, different biomedical and environmental sensors are considered, allowing the collection of different measurement data regarding quality of life-related parameters. The graphical interface between the user and the biomedical sensor kit will establish the order and priority of the different tasks of collecting the readings, indicating how and when should they be performed, according to: previous diagnosis, most recent data from previous readings and the remote indications from medical assistance personnel. The collected data will be stored in the patient's online monitoring profile database, which will have different levels of remote web access regarding the quality of the stored information, allowing its management, analysis, and screening by specialized medical personnel and relatives/caregivers. It is envisaged that the system will enable the primary caregiver to request assistance from a real-time specialist with sound and image to obtain information about the patient's condition and receive advice on the procedures to be followed. One of the inherent aspects of the project is the pursuit of greater integration of intangible solutions, leveraging current wireless and miniaturization technologies.
Audience measurement using a top-view camera and oriented trajectories

A crucial aspect for selecting optimal areas for commercial advertising is the probability with which that publicity will be seen. This paper presents a method based on top-view camera measurement, where the probability of viewing is estimated based on the trajectories and movements of the heads of the passerby individuals in the area of interest. Using a camera with a depth sensor, the heads of the people in the range of view can be detected and modeled. That allows to determine the orientation of the head which is used to estimate the direction of vision. A tracking by detection algorithm allows to compute the trajectory of each user. The attention given at each advertising spot is estimated based on the trajectories and head orientations of the individuals in the area of interest.