Reviewerly/exHarmony huggingface dataset:
Increasing the Accessibility of Static Content using CDN Networks as PaaS

In this paper we consider the problem of access to content that is located at geographically remote locations and the access delay from the perspective of the end user. We have come to conclusion that the use of CDN networks is especially effective in terms of providing the platform as a cloud service. We investigate the processes of access to static content and loading duration under the conditions of the provisioning of PaaS using the Azure Cloud service. We aim at minimizing the content delivery duration in the operation stage of CDN PaaS solution considering the set of customer and content mutual locations. As a result, we experimentally calculated the content delivery duration.
A cane-based low cost sensor to implement attention mechanisms in telecare robots

Telepresence robots have been recently used for Comprehensive Geriatric Assessment (CGA). Since the robot can not track a person continuously, there are several strategies to decide when to check them, from cyclic checks to simple requests from users and/or caregivers. In order to adapt to the user needs and condition, it is preferable to perform CGA as soon as regularities appear. However, this requires detection of potential issues in users to offer immediate service. In this work we propose a new low cost force sensor system to detect user's condition and attract attention of CGA robots, so they can perform a full examination on a need basis. The main advantages of this system are: i) it can be attached to any standard commercial cane; ii) its power consumption is very reduced; and iii) it provides continuous information as long as the user walks. It has been tested with several elderly volunteers in care facilities. Results have proven that the sensor readings are indeed correlated with the users' condition.
Impact of Team Size, Project Scale and Level of Education on Software Build Event Status in Enriched Event Streams

Creating a successful team based on number of developers, previous experience of programmer, and their level of education has been a topic of research for decades. Some studies suggest small size teams are better, while others find larger teams more successful. In addition, large projects are more complicated and more error prone. Discovery of the optimal values for these factors in software engineering is always considered as a major challenge. This paper examined the size of the team and scale of project and the level of education of developers on the status of Build events of software based on 11 million actual interactions performed by 83 programmers in a dataset called Enriched Event Streams dataset. The programmers had various programming skills and used variety of programming languages. The results based on 15,000 hours of development activity log indicate that the optimal size of the team is medium size, with developers engaged in large scaled projects without graduate degrees from universities. In other words, experience in large scale projects provides more advantages in contrast to graduate education that has negative impact on programming skills.
Dense Facial Landmark Localization: database and annotation tool

Databases are of great significance to researchers to achieve a satisfactory model. The lack of data is always a bottleneck to facial landmark localization, especially for the dense facial landmark detection. In this article, we provide a new dataset, called Dense Landmark Localization (DLL) database, which contains 39,198 images and is annotated in high quality. Annotating dense landmarks is a very tedious work due to two challenges. (a) Not every facial point has clear definition. Some of them distribute uniformly along the contour. Their labelled positions are determined by subjective judgement of the annotators, so that the quality of the annotation is poor. (b) Adjusting facial points one by one is time-consuming. The workload will increase dramatically when there are more points. To overcome the aforementioned problems, we propose a semiautomatic annotation tool to annotate dense points with much less clicks.
Design and Implementation of a Reactive Framework for the Development of 3D Real-Time Applications

In this paper we present Bestbrau, a reactive programming framework for the development of 3d real-time applications. We show its most relevant features, explaining how it differs from the systems used nowadays in commercial game engines and underlining which new functionalities it would provide to a 3D engine integrating it. Our framework is entirely written in C++17 and has been tested on the SAM Engine, a work-in-progress 3D game engine that we have designed and developed in parallel with the reactive framework itself. Bestbrau features are also compared with those offered by Unreal Engine 4 and Unity 2019, two of the most important commercial game engines today available. After a brief introduction, in the second section we briefly introduce the generalities of reactive programming, in the third section we focus, instead, on the FRP (Functional Reactive Programming). In the latest sections we make an accurate and exhaustive description of the peculiarities of such a framework and of the opportunities it provides to 3D and VR programming.
An Improved Social Learning Particle Swarm Optimization Algorithm with Selected Learning

Particle Learning Optimization (PSO) is a novel heuristic algorithm that has undergone decades of evolution. Social learning particle swarm optimization (SL-PSO) proposed by Cheng, Jin et al. in 2016 [1] remarkably improves the PSO algorithm by applying multi-swarm learning strategy. Nevertheless, randomness on setting inertia and choosing learning objects gives rise to an unbalanced emphasis on global search, and thus impairs convergence rate and exploitation ability. The proposed ISL-PSO algorithm strengthens global search capability through modelling selected learning mechanism, in which learning objects are selected through generated learning possibility subjected to Gauss distribution. Furthermore, ISL-PSO algorithm models condition-based attraction process, in which particles are attracted to the center by calculating transformed distance between particles and the center. By applying the strategies, ISL-PSO improves convergence speed and accuracy of the original algorithm.
Objective necessity to update Russian education system

The author considers the problem of the urgent need to upgrade the education system in terms of improving the quality of services, material and technical base and graduates' competitiveness.
Bidirectional type class instances

GADTs were introduced in Haskell’s eco-system more than a decade ago, but their interaction with several mainstream features such as type classes and functional dependencies has a lot of room for improvement. More specifically, for some GADTs it can be surprisingly difficult to provide an instance for even the simplest of type classes.
Research on the Extraction Method of Requirement Modeling Data Based on XML

Material requirement Modeling is the basis of the weapon requirements analysis. According to the problem of lack of data storage management in weapon equipment requirements modeling, the method of modeling data extraction based on XML is presented. The paper takes good advantage of XSLT in solving the problem of document conversion, and combines it with XPath to generate extraction rules. Good-structure modeling data document is generated in the end.
Speech Signal Embedding into Digital Images Using Encryption and Watermarking Techniques

In the present article, is proposed a novel encryption-watermarking approach. It is applied for embedding speech signals into digital images. It consists at first step in encrypting the watermark. After that, the obtained encrypted watermark is embedded into the host image. This watermark encryption is performed employing Arnold cat map and the embedding is performed employing a secure image watermarking technique based on $$ {\text{LWT}} - {\text{SVD}} $$ . We evaluate the performance of this proposed technique by comparing it to an existing encryption watermarking approach called $$ {\text{DSAWM}} $$ . The latter is also a secure watermarking scheme based on $$ {\text{DWT}} - {\text{SVD}} $$ . It also applies Arnold Cat Map for encrypting the watermark at first step and then inserting the obtained encrypted watermark into the cover image. Evaluation and comparison of the two techniques are performed by computing the $$ SegSNR, PESQ, PSNR, RMSE $$ , $$ {\text{pcc}} $$ , $$ MAE $$ and $$ SSIM $$ . The obtained results from this evaluation show the performance of this proposed encryption-watermarking approach. In fact, the PSNR, $$ {\text{RMSE}} $$ , $$ {\text{pcc}} $$ , $$ {\text{MAE}} $$ and $$ {\text{SSIM}} $$ values show a good perceptual quality of the watermarked images. Moreover, the $$ {\text{SegSNR}} $$ and $$ {\text{PESQ}} $$ values show a very good perceptual quality of the speech signals reconstructed after watermark extraction and decryption. We also tested the robustness of the proposed technique by applying three sorts of attacks on the watermarked images. Those attacks are Median, $$ {\text{JPEG}} $$ Compression and additive White Noise attacks. The results obtained from calculation of the $$ {\text{SNR}} $$ , $$ {\text{PESQ}}, {\text{PSNR}} $$ and $$ {\text{SSIM}} $$ , show the robustness of this proposed approach against those attacks.
Vehicle Detection in ITS Based on Improved Background Modeling Method As An Advanced Pattern Recognition Strategy for Surveillance Purposes

Moving object detection from municipal surveillance cameras is an important issue for Intelligent Transportation Systems (ITS) purposes. This paper presents a moving object detection algorithm that is more robust than adaptive Gaussian mixture (GMM) model, and provides a novel and practical choice for intelligent video surveillance systems using static cameras. The proposed method comprises of three steps. In the first step, statistical bit maps (BMs) are constructed randomly in a block-wise manner. The second step is assigned to background image construction based on two types of comparisons named as intra and inter block comparative approaches. In the third step, based on the smooth or unsmooth states detection for each block of the original video frames, an update or unmatched process will be applied. The results of proposed method's implementation show its better and more efficient performance than other competitive methods like GMM for live and real time moving object detection from video images acquired by the means of municipal ITS surveillance cameras.
The Vulnerability of Climate Change in Indonesia to Renewable Energy

The need for electrical energy is growing along with economic and population growth. The position of Indonesia is located on the equator line and is a tropical country that has the potential to get maximum solar energy throughout the year. Solar energy is a potential source of energy for Renewable Energy (RE) which is an alternative energy of low-carbon and clean power plants to solve energy problems. Based on the 14-km CCAM model, we have calculated solar radiation indicators at current baseline conditions (1981 – 2010) and 100 years future projections in order to analyze vulnerability and adaptation of the renewable energy sector to climate change in Indonesia. Although the climate change leads to a projected significant increase in mean air temperature 1,72 K across the whole of Indonesia that affect solar radiation indicator value, especially in Kalimantan with an average decrease of 6,5 W/m2, but the results of the analysis indicate that the solar radiation indicator value of Indonesia region has excellent area and strength for the use of solar energy both now and in the next 100 years.
먹거리를 활용한 유아 수학교육 프로그램이 만 4세 유아의 수학능력에 미치는 효과

Objective: The purpose of the study was to develop a mathematics education program utilizing food to improve the mathematical abilities of 4-year-olds and to analyze the effects of this program on 4-years-olds’ mathematical concepts (number and operation, algebra, geometry, measurement, data analysis, and probability).Methods: The study selected 30 4-year-olds from two daycare centers located in K city. The experimental group (N=15) participated in the mathematics education program utilizing food, 10 times for five weeks, while the comparative group (N=15) participated in the seasonal mathematics education program based on the Nuri Curriculum. The activities of this intervention program were designed to cover all domains of Mathematical Exploratory areas in the Nuri Curriculum. For data processing and analysis, pre-test and post-test score differences between the two groups were analyzed through MANCOVA.Results: The experimental group had significantly higher scores on five mathematical concepts compared with the control group. A mathematics education program utilizing food had the positive effect of improving 4-year-olds’ mathematical ability.Conclusion/Implications: Mathematic education programs utilizing food are recommended as necessary pedagogical data to develop the mathematical abilities of children in education centers, families, or relating to parenting education.
Research on the Optimized Clustering Method Based on CFSFDP

Aiming at deficiencies of the clustering by fast search and find of density peaks algorithm (CFSFDP) in which cluster centers cannot be selected automatically and the segmentation of cluster points is unreasonable, this paper proposes an optimized CFSFDP algorithm (O-CFSFDP) based on kernel density estimation and the max-min clustering algorithm. First, kernel density estimation is used to adaptively determine the optimal value of the cutoff distance by iteration. Then the max-min clustering algorithm is adopted to obtain the number of categories of a given data set, and by calculating the product of local density and distance for each point, corresponding cluster centers can be selected. Finally, O-CFSFDP introduces the idea of local density within the cluster. By computing it, points at the edge of each cluster will be aggregated more reasonable. Contrast experiments show that O-CFSFDP can not only realize cluster centers automatic selection and reasonable partition of cluster points, but also achieve better performances in clustering robustness and accuracy.
Convolutional Capsule-Based Network for Person Re-identification

Person re-identification is yet a critical challenging task in video surveillance domain. It aims to match the same person across different cameras. Practically, pedestrian’s appearances may vary greatly due to the complex background. Most deep learning methods rely on convolutional neural network to extract the feature of the pedestrian. But most of them lose the crucial details of the pedestrian and are sensitive to the viewpoints of the camera. To remedy this problem, we propose using capsule network as the feature extractor and introduce an improved loss function for the network. The experiment results on the Market-1501 dataset show the effectiveness of the proposed method.
Effects of Positivization on the Paragraph Vector Model

Natural language processing (NLP) is an important field of Artificial Intelligence. One of the fundamental problems in NLP is to create vector (distributed) representations of words so that vectors of words that have similar meaning lie closer in space. One of the most popular algorithms for creating these representations are word embedding models such as word2vec and fastText. Similarly the paragraph vector model (doc2vec) is used to create distributed representations of documents while simultaneously creating distributed representations for the words in these documents. These models create a dense, and low dimensional (usually in the low hundreds) vector representations which may include negative values. In this study we focus on these negative values and introduce a family of regularization methods in which document, word and/or context vectors of the paragraph vector model are forced to have only positive components. We measure its effects on several tasks; text classification, semantic similarity, and analogy tasks. Although positivization greatly increases the sparsity of the word embeddings, and should be expected to result in a loss of information, our results show that there is almost no reduction in the performance of the regularized embeddings in these tasks. We also observe an increase in the classification accuracy in one case. We foresee that these approaches can be beneficial in machine learning systems which require non-negative vectors.
SWIFT ATTACK VIA PHISHING AGAINST MIS OF MOBILE BANKING SECURITY

As technology is developed on the world, crime types continue to develop with it; recent research shows that even corporate banks can face weaknesses in the face of cyber attacks. We have investigated the most severe attacks that the banking systems have been facing and tried to sketch out major measurements against hackers who are using phishing attacks to the swift system. Web based managing accounts includes numerous sorts of dangers. Phishing attacks can be particularly harming to banks and clients who do not play it safe against this sort of security hazard. Since phishing programmers utilize a few refined strategies, going from tricky attacks to DNS attacks, banks must refresh their safety efforts consistently.
Intergenerational Education Development Trends

There is a transition to the third generation of education today in Russia. The article has given consideration to three generations of the Internet: Web 1.0, Web 2.0 and Web 3.0 as well as the main education development trends related to these processes. Relation between the generations of the Internet and the problems of development of modern education has been found. The topicality of the problem which was chosen for research is related to the existence of inconsistencies, development barriers, the need for overcoming them and solving them, the apprehension of failure to grasp the processes occurring in the education system. An analysis of theoretical research has been made proving that development of modern education, particularly higher education, wends the way of creation of innovative and profit oriented business. A new educational model is of interest to us; it is based on the actual project activities and leading-edge information technology which was developed in the higher education system. The content of the article proves that informatization of education is a multifaceted and multidimensional problem.
E-RPID PEARC 2019

The expansion of the research community's ability to collect and store data has grown much more rapidly than its ability to catalog, make accessible, and make use of data. Recent initiatives in Open Science and Open Data have attempted to address the problems of making data discoverable, accessible and reusable at internet scales. The Enhanced Robust Persistent Identification of Data (E-RPID) project's goal is to address these deficiencies and enable options for data interoperability and reusability in the current research data landscape by utilizing Persistent Identifiers (PIDs) and a kernel of state information available with PID resolution. To do this requires integrating a set of preexisting software systems along with a small set of newly developed software solutions. The combination of these software components and the core principles of making data FAIR (findable, accessible, interoperable and reusable) will allow us to use Persistent Identifiers to create an end-to-end fabric capable of realizing the Digital Object Architecture for researchers.
SDP: An Improved Baseline Estimation Model Based On Standard Deviation Proportion

This paper analyzes the limitation of baseline estimation by defining four kinds of rating personalization corresponding to four kinds of users' rating criterions, including Normal, Strict, Lenient, and Middle. We find a standard deviation proportion pattern from ratings' normal distribution to enhance the handling capability of users' personalized rating behavior, and propose a novel baseline estimation model based on Standard Deviation Proportion, named SDP model, to improve the accuracy of existing recommendation algorithms which used traditional baseline estimation. We also propose two application instances of SDP, including SDPSVD++ and SDPTrustSVD, to show how to apply the proposed SDP. Experiments show that the SDP can not only improve the baseline estimation performance, but also can effectively improve predictive accuracies of existing recommendation algorithms.
Approaches of Behavioural Biometric Traits

This paper shows a user authentication framework for computational conditions in view of behavioral biometrics specially gait study and signature analysis. Biometrics acknowledges the utilization of physiological and behavioral qualities which recognizes a person. Numerous biometric characteristics have been created and are being utilized to confirm the individual's personality. In order to ensure the holiness of the starting frameworks we require username and passwords. Username are effectively known and one can be effortlessly speculate passwords. Since the passwords are basic, they are powerless against assaults like phishing assaults and animal compel assaults and so on. Along these lines a superior approach to reinforce the secret word is to consolidate the passwords with biometrics contains behavioral or mental biometrics. Gait analysis enables individuals to comprehend on the off chance that they have any anomalies in strolling or enables clinicians to survey fall-hazard and decide how individual's gait is not quite the same as past evaluations to comprehend the impact of medication or malady movement. Gait analysis is being used to simplify the password technique used as this analyses the walking dynamics and recognizes a person individually.
Machine Perception—Machine Perception MU

In the previous chapter the short survey of the philosophical inquires and psychological research in the human visual perception was outlined.
Compositionality in Privative Adjectives: Extending Dual Content Semantics

Privative adjectives such as fake have long posed problems to theories of adjectives in a compositional semantics. In this paper, I argue that a theory like Del Pinal’s recent Dual Content semantics, which encodes lexical entries with both an extension-determining component and a conceptual component, is best equipped to account for privativity while maintaining compositionality, but requires some revisions to do so. I provide some novel evidence for this system regarding recursive privativity to justify a new denotation for fake, use this to derive the patterns in some recent experimental data, and introduce an extension to the system to account for privative behaviors of intersective adjectives.
Computer Thinking and Basic Computer Education in Colleges and Universities

The computer thinking is closely related to the basic education of the university computer, and the university should pay enough attention to it so as to improve the basic education quality of the computer. This paper first introduces the meaning and function of computer thinking, analyzes the relation between computer thinking and computer teaching, then researches on the current situation of computer basic education in university, and finally explores the computer education path of university based on computer thinking in order to ensure the smooth development of the basic computer education in the university.