Reviewerly/exHarmony huggingface dataset:
Psychological Readiness of Students for Digitalization of Educational Process

The purpose of this article, analyzing the core of digitalizing the education process, is a review of the extent of students’ readiness for starting a new platform and for taking part in the education process that is based on the use of information and communication technologies and digital technology. As the subject of analysis, the authors chose the sphere of studying a foreign language as an academic subject in the framework of a higher education program, in which digitalization causes an ambiguous attitude, both from students and the teaching community. Overcoming the negative attitude towards the digitalization of teaching foreign languages is one of the prerequisites for considering this issue. Recalling the work of IT specialists, the authors describe the basic directions for digitalizing. The first is the development of information sources (content). The next is the formation of telecommunication system (interaction). And the third direction is an organization of education process (management). In considering the term «psychological readiness», the authors rely on psychological research, in which scientists explored the use of this term in different kinds of activity. When the authors explored the nature of psychological readiness, they conducted a survey and analyzed the students’ written works. As a result they got some information that shows the level of students’ readiness for digitalizing education. That has led to a series of findings which confirmed that in general students treat the digital revolution in education system in a positive way. But they should be more active, autonomous and well-organized for full and effective functioning in the new environment. The article includes specific recommendations for the organization of education process aimed at language acquisition in the new environment: learning and realizing what digital revolution is, gradual entry into digital education platform, psychological preparation for the new learning conditions on a transitional stage and the support of educational technology referred to as «Blended Learning».
An FDA-Based Approach for Clustering Elicited Expert Knowledge

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.
Predication of Quality of Service (QoS) in Cloud Services: A Survey

Abstract Quality of Service (QoS) in cloud computing and web service has been commonly used as an essential measure for evaluating services of nonfunctional attributes. However, QoS prediction has become one of the key factors in services recommendation and selection to build high-quality software systems, so it becomes an urgent and crucial research area in both academia and industry. Therefore, several methods and techniques of service recommendation based on collaborative filtering (CF) have been exist for optimizing service selection. We investigate the different exist methods to solve challenging problems. Thence, we discuss and compare these approaches to provide an overview of the latest researches in the field.
Cyber Crises and Disaster Preparation in Austria: A Survey of Research Projects

In this paper, we survey some recent applied research and development projects dealing with threat analysis and disaster scenario generation, preparation, management, and training funded by the security-focused funding scheme KIRAS by the Austrian government, which include efforts for the development and execution of serious games in the respective domains. In our analysis, we found multiple lines of multiyear, multi-project activities, which consistently improve and advance the technologies and capabilities available to the affected stakeholders. Based on this review of the state of the art, we identify areas of high-potential interest to direct future applied research and development efforts.
Towards a Learning Architecture to Support Social Scaffolding for an Artificially Intelligent Disability Assistant

Humanoid workers that can improve the joy and achievement of workers with intellectual and developmental disabilities (IDD) hold promise in light manufacturing settings. In this paper, we provide details of an architecture to support social scaffolding for workers with IDD and efforts to adapt it to learn. This architecture is developed for human-robot interaction using the Pepper robot and will support future improvements using machine learning. Additional recommendations based on past experimentation are given for future work.
Personal and professional development of additional education teacher in the course of methodological work

The contemporary development strategy of Russian education puts forward several requirements for the system of additional education for children and teachers engaged in this field. The teacher must have the ability to self-development and self-improvement, be engaged in creative and innovative activities. The authors note that in the current conditions, the teacher must choose the only correct trajectory of professional advancement that will lead to the achievement of the desired results. The individual trajectory of personal and professional development of a teacher can be represented as a trajectory of professional self-development and self-improvement of a teacher of additional education in the course of teacher’s methodological work. The purpose of the current research is to develop, theoretically substantiate, and experimentally test the program for implementing an individual trajectory of personal and professional development of additional education teacher in the course of teacher’s methodological work. Implementation of individual trajectories of personal and professional development of additional education teachers in the educational organization involves the development of a specific action plan of the heads of departments, educational and methodological service of the institution, as well as very additional education teachers. The tasks of the educational and methodical service of an educational organization in this process are aimed at encouraging teachers, attracting them to innovative activities, various types of methodological work, and providing pedagogical and methodological support. The use of individual trajectory of personal and professional development in the course of organizing methodological work in the educational organization of additional education contributes to an increase in the professional competence of teachers as well as the development of their interest in this process.
Emotional Tweets Analysis on Social Media with Short Text Classification Using Various Machine Learning Techniques

Online Social media are a huge source of regular communication since most people in the world today use these services to stay communicating with each other in their modern lives. Today’s research has been implemented on emotion recognition by message. The majority of the research uses a method of machine learning. In order to extract information from the textual text written by human beings, natural language processing (NLP) techniques were used. The emotion of humans may be expressed when reading or writing a message. Human beings are willing, since human life is filled with a variety of emotions, to feel various emotions. This analysis helps us to realize the use of text processing and text mining methods by social media researchers in order to classify key data themes. Our experiments presented that the two main social networks in the world are conducting text-based mining on Facebook and Twitter. In this proposed study, we categorized the human feelings such as joy, fear, love, anger, surprise, sadness and thankfulness and compared our results using various methods of machine learning.
Aggregator based coordinated Transactive Energy trading between Microgrids

Plummeting costs of Renewable Energy Sources (RES), Energy Storage Systems (ESS) and technological advancements in ICT has resulted in the mushroom growth of Microgrids (MG) around the world. Due to the high composition of variable, inflexible and uncertain RESs in MGs, at times of low RES output, MGs have to either unwillingly reduce consumption, turn on polluting conventional generators or purchase expensive electricity from the main grid. Conversely, at times of high RES output, there are instances where RESs have to be forcefully curtailed to maintain the stability of the network. In this paper, we introduce a novel Transactive Energy (TE) trading approach to reduce the effect of the inconvenient characteristics of RES generation in MGs. This is achieved by optimized sharing of resources across MGs through an aggregator agent, thus, further reducing the dependency on the main grid. Since resources are shared across MGs, blockchain as a technology tool is considered to accurately record transactions and remunerate participating assets.
Auto-VirtualNet: Cost-adaptive dynamic architecture search for multi-task learning

Multi-task learning (MTL) improves learning efficiency by solving multiple tasks simultaneously compared to multiple instances of individual learning. However, despite its benefits, there still remain several major challenges: first, negative interference can reduce the learning efficiency when the number of tasks is high or the tasks are of limited relevance. Second, exploring an optimal model structure manually is quite restricted. Last but not least, offering cost-adaptive solutions has not been addressed in the MTL regime. In spite of its notable merits, the combined problem has not been well discussed. In this work, we propose a novel MTL approach to address the combinatorial problem while minimizing memory consumption. The proposed method discovers multiple network models dynamically from a pool of candidate models, and produces a set of widely distributed solutions with respect to different computational costs for each task. For the diversity of candidate models, we modularize the given backbone architecture that generates basic building blocks and then construct a hierarchical structure based on the building blocks. The proposed method is trained to optimize both task performance and computational costs of selected models. The proposed method dynamically generates optimal networks for each task and offers significant performance improvements over existing MTL approaches in a range of experiments.
A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.
Innovation Feedback Kalman Filter

Abstract The conventional Kalman filter demands complete knowledge of the actual filter model, which is usually inaccessible in practical problems. When dealing with the filtering problem with uncertainty, any improper prediction of the uncertainty may degrade the filtering performance and even lead to divergence phenomenon. In this paper, the innovation feedback Kalman filter, which introduces the innovation feedback controller to the Kalman filter equations, is newly proposed on the basis of automatic control theory to address the filtering problem with uncertainty and is different from other filtering methods. By studying the estimate error equations, the estimate bias is extracted and its propagation mechanism is formulated. The estimate bias propagation equations reveal that eliminating the estimate bias essentially equivalents to an output regulation problem with uncertain exosystem. And a concise yet effective innovation feedback controller is subsequently given as an example. The proposed method is applied to one-dimension target tracking scenario and its high estimation accuracy performance and good stability are simulated through a comparative analysis with the ideal filtering results.
A Framework for Jointly Training GAN with Person Re-Identification Model

To cope with the problem caused by inadequate training data, many person re-identification (re-id) methods exploited generative adversarial networks (GAN) for data augmentation, where the training of GAN is typically independent of that of the re-id model. The coupling relation between them which probably brings in a performance gain of re-id is thus ignored. In this work, we propose a general framework to jointly train GAN and the re-id model. It can simultaneously achieve the optima of both the generator and the re-id model, where the training is guided by each other through a discriminator. The re-id model is boosted for two reasons: 1) The adversarial training that encourages it to fool the discriminator; 2) The generated samples that augment the training data. Extensive results on benchmark datasets show that for the re-id model trained with the identification loss as well as the triplet loss, the proposed joint training framework outperforms existing methods with separated training and achieves state-of-the-art re-id performance.
Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes

Modern Internet of Things (IoT) end nodes must support computational intensive workloads at a limited power budget. Parallel ultra-low-power (PULP) architectures are a promising target for this scenario, and the availability of highly optimized software libraries is crucial to exploit parallelism and reduce software development costs. This letter proposes an efficient parallel design of the widely used short-time Fourier transform (STFT) and discrete wavelet transform (DWT) transforms targeting ultra-low-power IoT devices. We address key performance challenges related to fine-grained synchronization and banking conflicts in shared memory. We achieve high throughput (50.95 samples/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> , on average), good parallel speedup (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.79\times $ </tex-math></inline-formula> ), and high energy efficiency (up to 172.55 GOp/s/W) on a cluster of eight RISC-V cores optimized for PULP operation.
A vision-based fall detection framework for the elderly in a room environment using motion features and DAG-SVM

Falls are one of the major health risks for the elderly in public health care issues, thus fall detection is particularly necessary for the seniors. In this paper, we propose a novel computer vision-based fall activity detection framework which is based on a new feature extraction method of human motion. We detect falls by analyzing the motion process of human body rather than conventional human shape analysis. Approximate ellipse is first adopted to fit human silhouette, and the areas outside the ellipse are removed to confirm human body silhouette (HBS). Then HBS is divided into three regions, and the motion features are extracted from the centroid coordinate changes of each region. A directed acyclic graph support vector machine (DAG-SVM) classifier is finally applied to detect falls from normal daily activities. The advantage of the proposed method is that it can not only effectively distinguish falls from fall-like activities but also detect the falls parallel to camera optical axis. Experimental results show that our method achieves a state-of-the-art accuracy in fall detection. This method is lightweight and thus can be integrated into embedded devices for real-time health surveillance.
Real Time Detection of Object Blob Localization Application using 1-D Connected Pixel and Windowing Method on FPGA

Abstract Blob detection and localization is a common process used in the machine vision. Current existing blob detection method is using 2-dimensional kernel matrix which is higher in time consumption and also memory space. This study has proposed a dedicated digital architecture consist of two modules to detect binary image blob using only 1 -dimensional image pixel. First module is used to detect connected pixel in a row of pixel, and second module is used to perform windowing to justify blob location. This study has been successfully implemented and tested on Altera DE2 FPGA board. The proposed architecture only takes 24 clock cycles to deliver blob location and related features. The tested architecture only utilizes 1597 logic element, or 4.81% of the FPGA total resources.
WarningsFIX: a Recommendation System for Prioritizing Warnings Generated by Automated Static Analyzers

Recommendation systems try to guide the users in carrying out a task providing them with useful information about it. Considering the context of software development, programs are ever-increasing, making it difficult to conduct a detailed verification and validation. Automated static analyzers help to detect possible faults on software products earlier and quickly but, in general, the issue maybe a false-positive warning. In this sense, this work presents and evaluates a recommendation system, called WarningsFIX (WFX), which combines several static analyzers aim at: i) Expand the possible fault domain approached by each static analysis tool increasing the range of warnings types covered, allowing the concentration of a higher number of true-positive warnings. ii) Establish different prioritization strategies of warnings aiming at suggesting for reviewers first analyze the ones with a higher chance of being true-positive. WFX organizes the warnings information via treemaps considering four levels of abstraction: program, package, class, and line. The nodes of the treemap on each level may be classified by three different prioritization strategies based on the number of warnings, the number of tools, and the suspicions rate. The use of these strategies enables the reviewer to handle the set of warnings in a coordinated way depending on the cost and time constraint available. We perform a feasibility study to evaluate the WFX effectiveness whose results shown that: i) WFX was able to improve the results obtained from combined static analyzers to 44% of the analyzed programs, concentrating for them a greater number of true-positives. ii) WFX, depending on the adopted prioritization strategy, improved from 67.5% to 55% the ranking of lines with real bugs when compared with the list of warnings provided by the automated static analyzers without the WFX support.
Improving optimization using adaptive algorithms

Abstract In the research projects and industrial projects severe optimization problems can be met, where the number of variables is high, there are a lot of constraints, and they are highly nonlinear and mostly discrete issues, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In most cases in the logistics industry, the most robust constraint is the time. The optimizations are running on a typical office configuration, and the company accepts the suboptimal solution what the optimization method gives within the appropriate time limit. That is, why adaptivity is needed. The adaptivity of the optimization technique includes parameters of fine-tuning. On this way, the most sensitive setting can be found. In this article, some additional adaptive methods for logistic problems have been investigated to increase the effectivity, improve the solution in a strict time condition.
Operationalizing a consortial e-resources assessment and reporting plan

The authors detail the operationalization of a library consortium’s electronic resources assessment and reporting plan, including the implementation of procedures to collect and archive usage data, and the development of a sustainable annual reporting process. Despite finding that implementing a practical e-resources assessment plan for a consortium is possible even with limited available human resources, significant challenges related to data collection continue to limit the ability of library consortia to undertake more holistic assessments of the overall value of their procured e-resources.
Correction of the error induced by obscurations of Ritchey-Chretien collimators for high-resolution space camera MTF measured with the ISO 12233 slanted-edge method

We propose a method to correct the error induced by the obscurations of Ritchey-Chretien (R-C) collimators, when measuring modulation transfer function (MTF) of high-resolution space cameras by the ISO 12,233 slanted-edge method. The obscurations of an R-C collimator include the secondary mirror (SM) and its supporting bars. Mathematical models are derived to simulate possible obscurations by virtual surfaces of Zemax, with which we get the correction function (CF) of the obscuration induced error (OIE). Simulated results manifest that the proposed method can correct the OIE. Furthermore, an experiment setup is established with the same system parameters of the previous Zemax modeled system. The experiment results verify the effectiveness of the proposed method again. Our method can guarantee the precision of the ISO 12,233 slanted-edge method when an R-C collimator is chosen.
Development of Automatic Obscene Images Filtering Using Deep Learning

Because of Internet availability in most societies, access to pornography has become a severe issue. On the other side, the pornography industry has grown steadily, and its websites are becoming increasingly popular by offering potential users free passes. Filtering obscene images and video frames is essential in the big data era, where all kinds of information are available for everyone. This paper proposes a fully automated method to filter any storage device from obscene videos and images using deep learning algorithms. The whole recognition process can be divided into two stages, including fine detection and focus detection. The fine detection includes skin color detection with YCbCr and HSV color spaces and accurate face detection using the Adaboost algorithm with Haar-like features. Moreover, focus detection uses AlexNet transfer learning to identify the obscene images which passed stage one. Results showed the effectiveness of our proposed algorithm in filtering obscene images or videos. The testing accuracy achieved is 95.26% when tested with 3969 testing images.