Reviewerly/exHarmony huggingface dataset:
Momentum Based on Adaptive Bold Driver

The momentum-based stacked attention networks (SANs) is one of the best models for image question answering. However, we find that it is easy to fall into the local optimal solution, which results in the higher question answering error rate. To solve the problem, we propose adaptive bold driver (ABD). The experimental results and analysis show that it outperforms the state-of-the-art global learning rate adaptive algorithm in the local learning rate adaptive stochastic gradient descent (SGD). It is deeply integrated with momentum, and we propose momentum based on ABD (MABD). The experimental results show that its accuracy is 2.33% higher than the baseline (momentum), 2.54% higher than momentum based on bold driver, and 1.80% higher than the annealing-based momentum. The experimental analysis proves that it is the state-of-the-art optimization algorithm in the SANs-based image question answering and it has effectiveness, significance, generalization performance, and promotional value.
Vision-Based Mobile People Counting System

People detection and counting systems are highly valuable in multiple situations including managing emergency situations and efficiently allocating resources. However, most people counting systems are based on fixed sensors or fixed cameras, which lack flexibility and convenience. In this paper, we have developed a vision-based mobile people counting system which uses Android smartphones to capture images, and state-of-the-art person detectors, based on artificial intelligence, to count the number of people in a designated area. The embedded devices in smartphones such as camera, clock, GPS, are utilized to provide additional information for data collection. Several person detection frameworks such as You Only Look Once v2 (YOLO2), Aggregate Channel Features (ACF) and Multi-Task cascade Convolutional Neural Network (MTCNN) were evaluated to determine the best performing algorithm capable of offering accurate counting results across different scenarios. The experiments results show that YOLO2 outperforms ACF and MTCNN detection algorithms in different scenarios. However, YOLO2 has its own limitations as it often outputs redundant detections, requiring an additional Non-Maxima Suppression (NMS) algorithm to output a single bounding box per detection. The NMS threshold has to be carefully pre-fixed to provide top detection and counting performance across different scenarios.
An Open Solution for a Low-Cost Educational Toy

In the summer of 2018 we organized two 5-day summer camps each for 20 children aged 11-15 in Fablab Bratislava. In addition to learning about 2D and 3D modelling and Arduino programming, every child has built and experimented with a humanoid toy robot. For this purpose, we have developed a flexible and extensible software solution that easily transitions and scales up to any other toy or even advanced robot controlled by Arduino. In this article, we describe our experiences from the camp as well as some other makers events and efforts and give details on the respective framework and discuss the role of makers movement in the educational process.
Cryptocurrency – Money of the Digital Economy

The study considers modern aspects of the digital economic development based on cryptocurrencies. The above problems are analyzed using qualitative and quantitative methods for assessing the use of cryptocurrencies in the national economy and in international relations. The study covers the period of 2013–2019. The author assesses the influence of positive and negative factors of international economic and financial cooperation. An analysis of the practice of applying digital currencies allowed justifying the need for urgent innovative solutions in the field of macro-prudential regulation of the cryptocurrency turnover. The gradual transition to a new model of economic growth implies a more intensive use of new digital technologies. The study presents debatable issues of the possibility and feasibility of using collective cryptocurrencies and illustrates the author’s position on the prospects and risks of using blockchain technologies and global digital currencies, which is a key issue in the international cooperation of countries. In the context of the global information space, the first steps to achieve a balance of currency relations and trade exchange are to develop national monetary systems based on national digital currencies and their gradual integration into the global currency system in the future.
On Computer-Aided Prognosis of Septic Shock from Vital Signs

Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.
A New Lightweight CSPRNG Implemented in a 0.18μm CMOS Technology

In this work, a new Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) has been proposed. This generator has been implemented in a 0.18 μm technology and has been capable of generating sequences at a high speed, using a small area and low power consumption. The output sequences generated by this system have been subjected to the NIST randomness tests proving that they are indistinguishable from a truly random sequence. Finally, other security-related aspects have been studied proving that the proposed generator is cryptographically secure.
Improving of Current CAPTCHA Systems

The internet these days plays an important role in our daily lives, as the number of web services are increasing such as search engines and email, they are threatened by different types of attacks from computer programs such as robots. In order to solve this problem, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) was implemented to be used as a base to distinguish between human users and computer bots. Although this technique limits the automatic registration to web services to provide security but, still some CAPTCHAs have several weaknesses which allow the computer bots to infiltrate the mechanism of the CAPTCHA. The aim of this paper is to search for the problems in the existing types of CAPTCHA systems and to provide solutions or alternative approaches to solve these problem.
The Fault Diagnosis Model for Railway System Based on an Improved Feature Selection Method

Finding reasons for the railway system faults is an important task for guaranteeing the safety of railway system. However, it usually takes a lot of time for people to do that task since the text data written by natural language to describe the faults are often very large. To overcome that problem by using computers, a diagnosis model can be built to process and classify those text data so as to find the causes of the railway system faults. But how to improve the accuracy of the diagnosis model is also an important issue. In this paper, we build a fault diagnosis model for railway system and improve the accuracy of the model by proposing a MI-RFE (Mutual Information and Recursive Feature Elimination) feature selection method. Compared with the traditional MI (Mutual Information) feature selection method, the MI-RFE feature selection method proposed in this paper improves the accuracy of diagnosis model of the railway system fault by 9%.
Educazione linguistica accessibile e inclusiva

This essay presents a reflection on the management modalities of a class, in which students are considered as different not only for their individual factors, but also for their characteristics generating specific linguistic learning needs. The essay aims at offering: firstly, an overview of the Italian regulations about the Italian non-native speakers students and SLD; secondly, some scientific references at the base of the concept of ‘Mixed Abilities Class’, and thirdly, a concise but meaningful presentation of the model of an inclusive Learning Unit in a mixed abilities class.
Improving Solution Quality for Experience-Based Framework Through Clustering Algorithms

This paper presents an extension for the current developed experience-based frameworks. The current experience-based scheme depends on executing two parallel threads; one tries to solve the problem using traditional approaches, while the other thread uses experience from past solutions for solving it. Once one of these threads solves the problem, its solution is promoted to be the problem's solution, yet disregarding the solution quality. However, our extension to experience-based frameworks uses clustering to decides which thread will solve the problem while maintaining solution quality. We have used experience-based motion planners for benchmarking our approach, where the presented results demonstrate that this approach works with different experience representations while maintaining better path quality, experience utilization, and reduced computational cost.
An Analysis of Savings Among Rural Poor Households in Rwanda

A household’s savingsSavings are considered a primary source of investments which drive economic growthEconomic growth and development. This study identifies the determinants of private savingsSavings among poor rural households in Rwanda. It uses data from the 5th round of the Integrated Household Living Conditions surveysIntegrated household living conditions surveys conducted in Rwanda every 4 years. It specifies a simultaneous equations modelSimultaneous equations model and does the estimations using the 2SLS method to account for missing variablesMissing variables and a possible correlation between some covariates and the error termError term, which is complemented with a T-testT-test and correlation analysis. The results of the T-testT-test show that savingsSavings among poor rural households are significantly positive. The econometric estimates show that in contrast with existing literature, income and all other forms of wealth are factors which do not influence the savingsSavings of poor rural households, while consumption is positively correlated with savingsSavings. We recommend that socioeconomic developmentEconomic development programs, projects, and policies that aim at improving income and consumption levels thus affecting savingsSavings among poor rural households should be enhanced.
Agile Data Mining Approach for Medical Image Mining

As Record keeping has turned into an unavoidable action in our everyday life, Medical aspect of this isn't immaculate medical data in digitized structure for example images is gathered from medical health centers. We need a successful quickly developing application to store, procedure and break down such a gigantic measure of information. Information mining approaches comprises of a lot more systems to break down future prediction and decision making. In our proposed procedure, Ensuring the storage of image dataset we perform hybrid technique of data mining approached such as CBIR technique to extract the features for region of interest, based on similarity of attributes like color, texture, shape, attribute, and text domain concepts and then deal with the classification of images using SVM. The performance statistics shown based on the accuracy of the classified brain images.
Research on Human Gesture Recognition Based on Wearable Technology

This subject adopts the acceleration inertial sensor to design and make a set of wearable devices, which are applied in the research field of human body posture recognition. This research system is mainly divided into two parts. The first part is the body posture data acquisition subsystem. The second part is the upper computer body posture recognition subsystem, including data processing, recognition and database. The data acquisition subsystem is mainly to place the inertial sensor of 9 points in nine parts of the human body for human posture data acquisition and send the data to the receiver. The upper computer human body posture recognition subsystem receives data from the data acquisition subsystem, processes the data and classifies the posture recognition. Through the overall test, the system can well identify the sitting, running and climbing the stairs, achieve the desired goal.
Deferring the details and deriving programs

A commonly-used technique in dependently-typed programming is to encode invariants about a data structure into its type, thus ensuring that the data structure is correct by construction. Unfortunately, this often necessitates the embedding of explicit proof terms within the data structure, which are not part of the structure conceptually, but merely supplied to ensure that the data invariants are maintained. As the complexity of the specifications in the types increases, these additional terms tend to clutter definitions, reducing readability. We introduce a technique where these proof terms can be supplied later, by constructing the data structure within a proof delay applicative functor. We apply this technique to Trip, our new language for Hoare-logic verification of imperative programs embedded in Agda, where our applicative functor is used as the basis for a verification condition generator, turning the typed holes of Agda into a method for stepwise derivation of a program from its specification in the form of a Hoare triple.
Graph neural network for website element detection

Websites are a mixture of structured HTML tags, unstructured natural language and styling, which gives a wide range of possibilities how a website can look like. The paper introduces a website node detector based on the so-called graph neural networks-a new kind of neural networks, which are not working just with tensors like traditional neural networks do, but operates with graphs (or tree structures-special variations of graphs). To assess the accuracy of the proposed methodology, a privately collected and labeled data set was created. Although the data set used for the experiment is relatively limited, results on this limited data set suggest, that this methodology may be a promising path for automatic content generation.
Subpixel real-time jitter detection algorithm and implementation for polarimetric and helioseismic imager

The polarimetric and helioseismic imager instrument for the Solar Orbiter mission from the European Space Agency requires a high stability while capturing images, specially for the polarimetric ones. For this reason, an image stabilization system has been included in the instrument. It uses global motion estimation techniques to estimate the jitter in real time with subpixel resolution. Due to instrument requirements, the algorithm has to be implemented in a Xilinx Virtex-4QV field programmable gate array. The algorithm includes a 2-D paraboloid interpolation algorithm based on 2-D bisection. We describe the algorithm implementation and the tests that have been made to verify its performance. The jitter estimation has a mean error of 125 pixel of the correlation tracking camera. The paraboloid interpolation algorithm provides also better results in terms of resources and time required for the calculation (at least a 20% improvement in both cases) than those based on direct calculation.
Promoting Reminiscences with Virtual Reality: Feasibility Study with People with Dementia

This study aimed to examine the feasibility of promoting reminiscences with people with dementia, using 360° videos presented with virtual reality headsets.Four individual sessions were conducted with nine people with dementia.Average duration of the exposure was approximately ten minutes.The experience appeared to be pleasant for most of the participants (with the exception of one of the participants who reported visual limitations).Most participants shared positive memories while viewing the recordings.None of the participants experienced any significant increase in symptoms associated with nausea and disorientation.
Initial Exploration on an Effective Social Media Analytics Method and Algorithm for Instagram Hashtags

Over the years, social media has brought many benefits to different fields, especially in the business sector. Most of the existing organizations have taken these benefits to actively engage with the public to increase their online business value. The use of hashtags on numerous social media platforms especially on Instagram is one of the highly used benefits. By tagging specific postings, business organizations are able to promote and communicate with their customers directly in a more interactive manner. In this article, the authors are exploring the following: (1) to determine the effectiveness of the existing analytics method (text identification and trend analysis) for analyzing Instagram hashtag data and; (2) to determine the effectiveness of existing analytic techniques such as Naïve Bayes and Support Vector Machines (SVM) suited for the selected analytics method. As a result, the authors have identified that the combination of Trend Analysis method and SVM are an effective social media analytics approach for analyzing Instagram hashtag data.
Historian, Archivist, Bibliographer: P.A. Zaionchkovskij and the Russian State Library

At present, when the majority of users in search for information excessively turn to the Internet as a primary data source, the problem of reliability of the obtained data is particularly acute. Libraries, whose main function is to store and provide access to information existing both in print and electronic forms, traditionally tend to maintain the exceptional level of completeness and validity of the data and sources provided. One of the ways to achieve such reliability is interaction of libraries with scientists in the field of data systematization, verification, storage and access providing. The article analyses the scientific work of the outstanding Russian historian P.A. Zaionchkovskij in the V.I. Lenin State Library of the USSR (now — the Russian State Library, RSL) and considers his scientific cooperaction with the Library for several decades as a successful example of library-scientists’ partnership. In 1944—1952, he served as the Head of the Department of manuscripts of the V.I. Lenin State Library. Under his supervision, the accounting of the Department’s stock was reorganized, preservation of the existing and newly acquired collections was ensured; the user access of researches to the information on the collections’ content and composition was significantly improved; the unique team of scientific personnel was formed. The article gives thorough overview of the scientific and publication activity of the Department during that period. The authors also review the contribution of P.A. Zaionchkovskij to the development of the problem of bibliographic providing for historical science. In the 1960s — 1980s, under his scientific supervision, the bibliographic staff of the V.I. Lenin State Library of the USSR and other major Russian libraries prepared the unique publications — “Reference Books on the History of Pre-revolutionary Russia” and multivolume annotated Index “The History of Pre-revolutionary Russia in Diaries and Memoirs”. P.A. Zaionchkovskij laid the foundation for the lasting many years and still developing project on creation of retrospective scientific auxiliary indices of memoir literature. The partnership of P.A. Zaionchkovskij and the V.I. Lenin State Library is an excellent example of the successful and fruitful interaction of scientists and library staff.
On the Computational Complexity of Head Movement and Affix Hopping

Head movement is a syntactic operation used in most generative syntactic analyses. However, its computational properties have not been extensively studied. [27] formalises head movement in the framework of Minimalist Grammars by extending the item representation to allow for easy extraction of the head. This work shows that Stabler’s representation is in fact suboptimal because it causes higher polynomial parsing complexity. A new algorithm is derived for parsing head movement and affix hopping by changing the kinds of representations that the parser deals with. This algorithm has much better asymptotic worst-case runtime of $$\mathcal {O}(n^{2k+5})$$ . This result makes parsing head movement and affix hopping computationally as efficient as parsing a single phrase movement.
Structured Processing Method of Medical Examination Text Reports Based on Tree Model

There are a lot of medical text reports available provided by hospital. These reports are written by doctors or generated by medical equipment specifying the clinical problems and diagnosis of patients. These reports contain very valuable information that can be useful for future patient diagnosis. But the problem occurred with these medical reports are most of them are not structured, so we will not be able to leverage full advantage of the information. This paper proposes a structured processing method based on tree model. First, data processing is performed on the text reports in order to make the data more accurate. Then establish part of speech dictionary to obtain part of speech from all words in the text reports. Semantic relationships between each sentence need to be obtained by performing dependency syntax analysis. By combining part of speech and semantic relationship we propose a rules to establish a tree model. The experiment has shown that this method can be used to achieve a good result for structuring thyroid ultrasound reports in Chinese language.
Dynamics load balancing in virtual machine for cloud computing

Recent drastically growth of latest technology such as smart phone, smart watch, Iot and big data analytic create a huge demand in cloud computing. Manager at Data Centre deeply concerns on the data consumption and it definitely create a big impact on environment as a whole. Thus researchers are struggling to come out with a systematic and efficient method or system to reduce this power consumption at the Data Centre. Load balancing in VM selection and VM placement is one of the potential method which can reduce the power consumption at the Data Centre.
Human Interaction Recognition Based on the Co-occurring Visual Matrix Sequence

The human interaction recognition methods based on motion co-occurrence have been an efficient solution for its reasonable expression and simple operation. However this kind of methods has relatively low recognition accuracy. An innovative and effective way based on the co-occurring visual matrix sequence was proposed to improve the accuracy in this paper, which sufficiently utilized the superiority of co-occurring visual matrix and probability graph model. In the individual segmentation framework, ROI was firstly extracted by frame difference and the distance analysis between two interacting persons, and segmented into two separate interacting persons with prior knowledge, such as color and body outline. Then the k-means algorithm was utilized to build the bag of visual words (BOVW) with HOG feature from all the training videos, and each frame in a video was described by co-occurring visual matrix with BOVW, and the video was represented by the co-occurring visual matrix sequence. Finally, HMM method was utilized to model and recognize the human interactions. Experimental results on the UT-Interaction dataset show that the method achieved better recognition performance with simple implementation.