Reviewerly/exHarmony huggingface dataset:
ACTUALITY OF IMPLEMENTATION OF EDUCATIONAL ROBOTICS IN UKRAINIAN SCHOOL

The paper considers the questions of substantiation of relevance of educational robotics implementation in the learning process of Ukrainian schools. The current state of development of robotics as an applied field and as an educational trend is analyzed to solve the research problems. The analysis of the global trends in the development of robotics as an applied field has shown that robotics is one of the most intensively developing fields of science and technology. This leads to the conclusion that there is a need to train relevant specialists and to update the curriculum in school and university education according to the daily requirements. Therefore, the issue of robotics implementation into the learning process of secondary and higher education institutions as an obligatory component of training of future specialists in robotics and future robotics teachers is of particular importance. The analysis of the robotics development as an educational trend has shown that educational robotics is one of the most perspective and popular trends in STEM education. In addition, it is determined that learning via robotics provides students with the opportunity to solve real life problems that require knowledge of STEM subjects. Based on previous research, the author also clarifies the concept of educational robotics, substantiates the aim and main tasks of its implementation into the educational process of educational institutions, which, as a consequence, leads to the importance of solving the issue of training future robotics teachers. The paper also presents and analyzes the results of the conducted research among educators and scientists on determining the state of teaching of robotics in Ukrainian schools. Based on the obtained results, we can conclude that there is a need to implement of the educational robotics into the learning process of secondary education institutions as a compulsory component of the school curriculum. In particular, possible ways of implementation of the educational robotics in Ukrainian schools are defined.
Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions

Incomplete or inconsistent temporal neuroimaging records of patients over time pose a major challenge to accurately predict clinical scores for diagnosing Alzheimer’s Disease (AD). In this paper, we present an unsupervised method to learn enriched imaging biomarker representations that can simultaneously capture the information conveyed by all the baseline neuroimaging measures and the progressive variations of the available follow-up measurements of every participant. Our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show improved performance in predicting cognitive outcomes thereby demonstrating the effectiveness of our proposed method.
Lower-body posture estimation with a wireless smart insole

Typical optical marker and camera based systems for motion capture suffer from several limitations. They are restricted to indoor environments, have difficulties tracking multiple people simultaneously and require expensive camera setups. In this work, we present a new method for lower-body posture estimation with a wireless smart insole using end-to-end training of a deep neural network. Our model is able to predict the movement of the entire lower body (including the hip, knee, ankle and toe) accurately in a wide range of activities. Inference only takes 1.62ms and hence can be used in real-time. The proposed method can potentially provide a very efficient and portable solution for applications like sports analysis, rehabilitation and virtual reality.
Hybrid Intelligent Systems Based on Fuzzy Logic and Deep Learning

The purpose of this lecture is to establish the fundamental links between two important areas of artificial intelligence - fuzzy logic and deep learning. This approach will allow researchers in the field of fuzzy logic to develop application systems in the field of strong artificial intelligence, which are also of interest to specialists in the field of machine learning. The lecture also examines how neuro-fuzzy networks make it possible to establish a link between symbolic and connectionist schools of artificial intelligence. A lot of methods of rule extraction from neural networks are also investigated.
Parallel Lattice Structure for Dual Windows Computation in Multiwindow Gabor Transform

The multiwindow discrete Gabor transform (M-DGT) is a useful time-frequency analysis tool for non-stationary signal processing. Given an arbitrary Gabor frame, a parallel lattice structure of block time-recursive algorithm for fast and efficient computation of dual/analysis Gabor windows for M-DGT is presented. By using a multiple window dual Gabor frame, the dual Gabor windows can be expressed by synthesis and analysis windows with a block-circulant matrix. Then, a parallel lattice structure of block time-recursive can be derived to solve the dual Gabor windows by the block-circulant matrix computed by the fast discrete Fourier transform (FFT). When compared to three existing methods, the proposed algorithm can reduce computational complexity and save computational time. Experimental results indicate that the proposed algorithm is valid to compute the dual windows of the M-DGT, which make the algorithm attractive for fast time-frequency signal analysis and processing.
Integer Version of Ring-LWE and Its Applications

In this work, we introduce an integer version of ring-LWE (I-RLWE) over the polynomial rings and present a public key encryption based on I-RLWE. The security of our scheme relies on the computational hardness assumption of the I-RLWE problem.
Unpaired Pose Guided Human Image Generation

This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance or type of outfit worn by the user. This is a difficult problem because input and output domain are very different and direct image-to-image translation becomes infeasible. We propose an end-to-end trainable network under the generative adversarial framework, that provides detailed control over the final appearance while not requiring paired training data and hence allows us to forgo the challenging problem of fitting 3D poses to 2D images. The model allows to generate novel samples conditioned on either an image taken from the target domain or a class label indicating the style of clothing (e.g., t-shirt). We thoroughly evaluate the architecture and the contributions of the individual components experimentally. Finally, we show in a large scale perceptual study that our approach can generate realistic looking images and that participants struggle in detecting fake images versus real samples, especially if faces are blurred.
What are Academic Subreddits Talking About?

Graduate school and academia can often be challenging and hard to navigate. This work explores how people are using Reddit to reach out to others in academic subreddits to talk about issues one might face in their academic journey. We also explore how such discussion differs between subreddits by comparing two popularly used academic subreddits: r/gradschool and r/academia. For each subreddit, we investigated 300 posts and 500 comments. Using topic modelling, we identify and distinguish the main emergent types of posts and comments we find in these two subreddits. We find that posts in r/academia center more on the challenging aspects of academia such as plagiarism, working in academia, and mental health, whereas r/gradschool posts deal with more generic issues on graduate school life. However, we find that the way the community reacts and provides support via comments is similar in both subreddits, mostly by providing moral support and solidarity.
Research on a method for predicting the underflow concentration of a thickener based on the hybrid model

As a method for extracting metals and their compounds, hydrometallurgy has the advantages of high comprehensive metal recovery rate, low environmental pollution, and easier production process. The intensive washing process is a key process in the hydrometallurgical process, and the underflow concentration is a key indicator for measuring the quality of the concentrated washing process. In this paper, after analyzing the characteristics of the thick washing process, the hybrid model combining mechanism modeling and error compensation model based on EDO-TELM (three hidden layers Extreme Learning Machine optimized with Entire Distribution Optimization algorithm) is used to achieve accurate measurement of the underflow concentration in the dense washing process. The hybrid model uses the improved EDO-TELM algorithm as an error compensation model to compensate the error of the un-modeled part of the mechanism model, and gives a reasonable estimate of the uncertain part of the model, which theoretically reduce the prediction error of the model. The Matlab simulation results show that the prediction error of the hybrid model is significantly lower than that of the mechanism model and the data model, and can be adapted to the measurement needs of the industrial site.
Detection of Ship from Satellite Images Using Deep Convolutional Neural Networks with Improved Median Filter

Detection of maritime object is of greater attention in the field of satellite image processing applications in order to ensure the security and traffic control. Even though several approaches were built in the past few years, still it requires proper revamp in the architecture to focus toward the reduction of barriers to improve the performance of ship identification or appropriate vessel detection. The inference due to cluttered scenes, clouds, and islands in between the ocean is the greater challenge during the classification of ship or vessel. In this paper, we proposed a novel ship detection method called deep neural method which works very faster and based on the concept on deep learning methodology. Experimental results provide the better accuracy, and time complexity also reduces little further when compared to the traditional method.
Made for Each Other: Broad-Coverage Semantic Structures Meet Preposition Supersenses

Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles.We argue that lexicon-free annotation of the semantic roles marked by prepositions, as formulated by Schneider et al. ( 2018), is complementary and suitable for integration within UCCA.We show empirically for English that the schemes, though annotated independently, are compatible and can be combined in a single semantic graph.A comparison of several approaches to parsing the integrated representation lays the groundwork for future research on this task.
Poster

Private Function Evaluation (PFE) allows two parties to jointly compute a private function provided by one party on the secret input of the other party. However, in many applications it is not required to hide the whole function, which is called Semi-Private Function Evaluation (SPFE). In this work, we develop a framework for SPFE which allows to split a function into public and private parts. We show the practicability of using SPFE in a real world scenario by developing a car insurance application for computing user-specific tariffs. We evaluate the performance of our SPFE framework on this concrete example which results in a circuit consisting of 377032 AND gates which improves over PFE by a factor of 9x.
A Blockchain Technology Solution to Enhance Operational Efficiency of Rice Supply Chain for Food Corporation of India

Rice and wheat are the staple food grains among the major commodities that are procured and securely stored by Food Corporation of India (FCI), which is the responsible body to implement the decisions made by the Ministry of Consumer Affairs, Food and Public Distribution (MCAFPD). The Government of India (GOI), alongside other state offices, attempts procurement of wheat and rice under price support scheme. Food grains are procured from farmers from mandis, and then they are moved to various FCI depots through rail or road and finally distributed to state agencies for public distribution. FCI has been able to make sure the availability of sufficient foodgrains in depots of all states by proper planning and efficient supply chain operations. For effective SCM, FCI has implemented IIFSM (Integrated information system for food grain management) and FAP (Financial accounting package) to make the increasing demand of the population and food security. We have studied the existing FCI’s supply chain management for rice, and the extensive literature review was also done to understand the supply chain management in the food sector. To escalate and track the productivity of paddy and increase the security and efficiency of rice supply chain operations of FCI, we have proposed a Blockchain technology model of RSCM (Rice Supply Chain Management) in this paper.
Project Management Metamodel Construction Regarding IT Departments

Given the fast technological progress, the need for project management continues to grow in terms of methodology and new concepts.In this article, we will build a framework of generating a metamodel that we will apply on the project management to generate a generic metamodel of project management, in this approach we will be based on two methodologies of project management; predictive method (ex: PRINCE 2), Agile method (ex: SCRUM).The goal of this research is to validate and apply this methodology on all the components of IT Governance then to aggregate the metamodels to restore a global metamodel for all IT Governance domains.
Hand Gesture Recognition Using Movidius Neural Compute Stick

Image classification aims to recognize the object detected to differ between different classes such as human being, vehicles, buildings and et cetera. In classifying any object, it is important to measure the accuracy to acknowledge its effectiveness on certain tasks specifications. Hence, this work is carried out to develop an application which can recognize the hand gestures on embedded system. There are four sorts of hand gestures used in this work which are for zooming in, zooming out, scrolling up and scrolling down. We proceed to measure the accuracy and performance on embedded system using Movidius Neural Compute Stick (NCS). In this work, AlexNet and GoogleNet models are trained in Caffe as it is compatible with OpenVino. The result obtained showed that GoogleNet delivers the best classification accuracy at 99.6%, while the performance of GoogleNet on NCS is acceptable for real-time implementation where it took around 110ms to infer the image. This is slower than CPU inference time which is at 68ms. Nevertheless, the result showed that the trained model can be used in embedded system with good performance.
Level of satisfaction and utilization of EBSCOhost among UPHSL students and faculty

Purpose Electronic resources are becoming an integral part of the academe. It has revolutionized the idea of research, making information more accessible. This study aims to determine the level of utilization and satisfaction on EBSCOhost database among UPHSL students and faculty members. It analyzes the effect of the respondents’ user type, gender and education to the usability and satisfaction of users to EBSCOhost. Design/methodology/approach The study, which used a survey questionnaire as a primary tool to gather data, had 300 respondents who willingly participated from the faculty and student body of the University of Perpetual Help System – Laguna. Findings Results revealed that majority of the respondents were found to be female, aging 20-25 with a college degree or taking their degree. The results of the study showed that the students and faculty members of UPHSL are moderately satisfied of the services of EBSCOhost and users are only moderately using it. In addition, the study revealed that the respondents have the same level of satisfaction and utilization of EBSCOhost regardless of their user type, age, gender and educational attainment. Furthermore, there is a significant relationship between the respondents’ level of satisfaction and level of utilization of EBSCOhost. The higher is the respondents’ level of satisfaction, the higher is their level of utilization of EBSCOhost. Originality/value Subscription to online databases is an integral part of the academic library. However, in developing countries, subscription can take a lot from the overall library budget. EBSCOhost, as one of the popular research databases, is among the plethora of databases that libraries subscribe to. This paper analyzes the level utilization and satisfaction of users to EBSCOhost. The analysis presented in this research can provide insight on the usability of among developing countries.
Architecture and Business Logic Specification for Dynamic Cloud Federations

Cloud federations have been seen as a possible solution for the volatility in the number of user requests and for the anti-competitive externalities of the economies of scale in the cloud service sector. In order for a federation to exist in the commercial market, an efficient mechanism for resource and revenue sharing is of paramount importance. In this paper, we design the architecture and specify the business logic for the dynamic operation of such federation platforms. The architecture and federation business logic specification include components, a federation SLA management framework, and revenue sharing mechanisms. It can also offer appropriate incentives to cloud providers for joining a federation. With such dynamism in the platform, cloud providers have the ability to automatically form and dissolve federations, to maintain resource compatibility, and to self-adapt to policies for managing contractual and economic relationships between federation members. This helps in streamlining the overall business process without being dependent on existing business relationships between service providers, between service providers of a federation, and between service providers and customers. This can encourage cloud providers to join in and be benefitted from the federation, thereby contributing to moving cloud computing to the next level.
Perceptually Motivated Method for Image Inpainting Comparison

The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the problem’s challenging nature: image-­inpainting algorithms strive for realism in the resulting images, but realism is a subjective concept intrinsic to human perception. Existing objective image-­quality metrics provide a poor approximation of what humans consider more or less realistic. To improve the situation and to better organize both prior and future research in this field, we conducted a subjective comparison of nine state-­of­-the­-art inpainting algorithms and propose objective quality metrics that exhibit high correlation with the results of our comparison.
Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction

Multivariate compositional count data arise in many applications including ecology, microbiology, genetics and paleoclimate.A frequent question in the analysis of multivariate compositional count data is what underlying values of a covariate(s) give rise to the observed composition.Learning the relationship between covariates and the compositional count allows for inverse prediction of unobserved covariates given compositional count observations.Gaussian processes provide a flexible framework for modeling functional responses with respect to a covariate without assuming a functional form.Many scientific disciplines use Gaussian process approximations to improve prediction and make inference on latent processes and parameters.When prediction is desired on unobserved covariates given realizations of the response variable, this is called inverse prediction.Because inverse prediction is often mathematically and computationally challenging, predicting unobserved covariates often requires fitting models that are different from the hypothesized generative model.We present a novel computational framework that allows for efficient inverse prediction using a Gaussian process approximation to generative models.Our framework enables scientific learning about how the latent processes co-vary with respect to covariates while simultaneously providing predictions of missing covariates.The proposed framework is capable of efficiently exploring the high dimensional, multi-modal latent spaces that arise in the inverse problem.To demonstrate flexibility, we apply our method in a generalized linear model framework to predict latent climate states given multivariate count data.Based on cross-validation, our model has predictive skill competitive with current methods while simultaneously providing formal, statistical inference on the underlying community dynamics of the biological system previously not available.
Two‐Weight Codes and Second Order Recurrences

Cyclic codes of dimension 2 over a finite field are shown to have at most two nonzero weights. We compute their weight distribution, and give a condition on the roots of their check polynomials for them to be maximum distance separable code.
Network Analysis of the Integration Level of the Universities Participating in Project 5-100 in the International Higher Education Area

The paper analyzes measures adopted by the Russian Federation on internationalization and globalization of the higher education system, its integration into the international scientific and education area. One of the initiatives of the authorities of the Russian Federation in this direction is Project 5-100, designed to increase the competitiveness of both a selected group of universities and the Russian higher education system as a whole. Among the successful practices of Project 5-100, one can identify the presentation of a single stand of participating universities at the international education exhibitions APAIE, EAIE, NAFSA. Within this study, we explore the cooperation of the universities participating in Project 5-100 with potential international partners at global educational exhibitions by means of network analysis with graphs. The effectiveness of such cooperation from the point of view of integration of the universities from this group into the international higher education area is determined through estimations of the usefulness of participation in such events made by the universities and through scientometric analysis. As a result of this study, it was revealed that active participation in international educational exhibitions including negotiating, establishing contacts with international partners, contributes significantly to the promotion of the universities participating in Project 5-100 in the international arena.