Reviewerly/exHarmony huggingface dataset:
An API for Wearable Environments Development and Its Application to mHealth Field †

Wearable technologies are transforming research in traditional paradigms of software and knowledge engineering. Among them, expert systems have the opportunity to deal with knowledge bases dynamically varying according to real-time data collected by position sensors, movement sensors, etc. However, it is necessary to design and implement opportune architectural solutions to avoid expert systems are responsible for data acquisition and representation. These solutions should be able to collect and store data according to expert systems desiderata, building a homogeneous framework where data reliability and interoperability among data acquisition, data representation and data use levels are guaranteed. To this aim, the wearable environment notion has been introduced to treat all those information sources as components of a larger platform; a middleware has been designed and implemented, namely WEAR-IT, which allows considering each sensor as a source of information that can be dynamically tied to an expert system application running on a smartphone. As an application example, the mHealth domain is considered.
Veritaa - The Graph of Trust

Today the integrity of digital documents and the authenticity of their origin is often hard to verify. Existing Public Key Infrastructures (PKIs) are capable of certifying digital identities but do not provide solutions to immutably store signatures, and the process of certification is often not transparent. In this work we propose Veritaa, a Distributed Public Key Infrastructure and Signature Store (DPKISS). The major innovation of Veritaa is the Graph of Trust, a directed graph that uses relations between identity claims to certify the identities and stores signed relations to digital document identifiers. The distributed architecture of Veritaa and the Graph of Trust enables a transparent certification process. To ensure non-repudiation and immutability of all actions that have been signed on the Graph of Trust, an application specific Distributed Ledger Technology (DLT) is used as secure storage. In this work a reference implementation of the proposed architecture was designed and implemented. Furthermore, a testbed was created and used for the evaluation of Veritaa. The evaluation of Veritaa shows the benefits and the high performance of the proposed architecture.
University as a subject of career guidance work with school students

The study is devoted to the problem of transforming the content of career guidance of school students in the current conditions in connection with the existing contradictions. The career guidance work of universities is associated both with solving the problem of recruiting applicants and with fulfilling the mission of the key subject of career guidance for students along with the school and employers. Conscious professional self-identification of school students at the present stage is possible due to the concerned position of these actors. Schools should be interested in their graduates choosing the optimal path for further education, the university is interested in obtaining a highly motivated applicant, employers interested in a specialist who is ready to adapt to work. The main components of career guidance at the school and university levels are presented. Career guidance should be systemic, including at the level of the educational organization. Derzhavin Tambov State University implements a “level” model of career guidance, which covers the levels of the educational program, department, institute/faculty, university. The characteristics of the activities implemented at the designated levels are given.
Geocaching-Inspired Navigation for Micro Aerial Vehicles with Fallible Place Recognition

This paper extends an existing decisional framework for the navigation of Micro Aerial Vehicle (MAV) swarms. The work finds inspiration in the geocaching outdoor game. It leverages place recognition methods, information sharing and collaborative work between MAVs. It is unique in that a priori none of the MAVs knows the trajectory, waypoints and destination. The MAVs collectively solve a series of problems that involve the recognition of physical places and determination of their GPS coordinates. Our algorithm builds upon various methods that had been created for place recognition. The need for a decisional framework comes from the fact that all methods are fallible and make place recognition errors. In this paper, we augment the navigation algorithm with a decisional framework resolving conflicts resulting from errors made by place recognition methods. The errors divide the members of a swarm with respect to the location of waypoints (i.e., some members continue the trip following the proper itinary; others follow a wrong one). We propose four decisional algorithms to resolve conflicts among members of a swarm due to place recognition errors. The performance of the decisional algorithms is modeled and analyzed.
ANALISIS PENYEBAB BANJIR DI DKI JAKARTA

The aim in this article is to find out various factors that caused floods occur in DKI Jakarta. The Research method in this article is used a descriptive research design using a literature study approach. Based on the type of research and data source is used, the data collection technique in this study is to used secondary data. Then the results of this research that have been carried out are that the causes of flooding in DKI Jakarta in general are due to dynamics and urban development, in which there are various aspects that can be seen, such as the demographic aspects of the city that lead to rapid population growth of an urban area and it causes various problems that occur in urban areas, the next aspects is land use, where the development of an urban area causes needed for land to be used as a residence in urban areas continues to experience enhancement. And the last aspect is land use change, which is where changes in land used patterns occur in urban areas from non-developed areas to built up areas that are related to the expansion of urban areas as physical manifestation and pressure due to urbanization.
Impact of COVID-19 on the system of higher education

Analyzed is the impact of restrictions on higher educational system introduced by the Government of the Russian Federation in connection with spread of coronavirus infection COVID-19. Sociological study (online survey) was conducted among students, enrolled in budgetary and extra-budgetary forms of education, as well as among graduates of secondary schools (11th classes), located in small, medium and large cities of Yaroslavl region. In addition, video interviews were conducted, using Zoom service among number of applicants, parents of applicants, teachers, positional experts. It was established, that emergence and spread of COVID-19 and limitations, associated with it, made certain adjustments to strategies of applicants and students. Despite all changes that have arisen due to coronavirus infection, only a small part of applicants and students studying on extrabudgetary basis, decided to change their plans. It is concluded, that for significant part of applicants, obtaining higher education is an attempt to continue to remain in a familiar comfortable environment (continuation of “childhood”). Despite constant stresses at school, expectation from prospect of losing a measured, familiar, predictable life is even more stressful. Parents from families, belonging to medium-resource and high-resource groups, as a rule, support decision of their children to keep on studying. In this case, decision to enter university is made long before graduation. Parents from low-resource groups, as a rule, try to give profession, not a higher education. At the same time, it was revealed, that children from families, belonging to low-resource groups, for the most part, anyway note that if they had necessary resources, they would try to get higher education. In addition, the authors conclude, that decision on admission to university is greatly influenced by not at all economic capital of family, how much family’s value, as well as influence of the reference person.
Now You See Me (CME): Concept-based Model Extraction.

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In this work we present CME: a concept-based model extraction framework, used for analysing DNN models via concept-based extracted models. Using two case studies (dSprites, and Caltech UCSD Birds), we demonstrate how CME can be used to (i) analyse the concept information learned by a DNN model (ii) analyse how a DNN uses this concept information when predicting output labels (iii) identify key concept information that can further improve DNN predictive performance (for one of the case studies, we showed how model accuracy can be improved by over 14%, using only 30% of the available concepts).
GOOGLE CLASSROOM AS AN EFFECTIVE TOOL OF SMART LEARNING AND MONITORING OF STUDENTS' KNOWLEDGE IN VOCATIONAL SCHOOLS

The paper highlights the importance of monitoring students’ knowledge in the educational process organized on the basis of IT. The monitoring procedure in training enhances students’ knowledge. The results of this study show that the modernization of the monitoring in the educational process requires the use of modern computer equipment and means of communication. Electronic monitoring of students’ knowledge is the basis for obtaining an objective independent assessment of the level of students’ academic achievements (knowledge, intellectual and practical skills). The paper also presents the list of online services for creating broad-based tests, which include Google Forms, LEARNINGAPPS.ORG, PROPROFS, ClassMarker. Careful analysis of the learning forms allows us to present a way of organizing students’ training with the help of the Google platform, including the Google Classroom service (Learning Management System), which is essential for online learning and effective control of students’ knowledge of the subjects of their major. Unlike its predecessors, the Google Classroom as a separate learning management system allows teachers to use the integrated tools of the Google Apps package, such as Google Drive and Gmail. The main possibilities of using Google Classroom for the organization of Smart learning and monitoring of students’ knowledge are identified. The paper also presents detailed information on the possibilities for Google Classroom users (teachers, students, mentors, parents, administrators). The use of the Smart course, developed by means of the interactive services combined with the traditional methods of teaching information technologies, has made it possible to move the communication of students and teachers up to a new level – it is not limited only to a classroom. In the Google Classroom the educators can post tasks, send announcements, start discussions, and students can share materials, add comments on the course, communicate via e-mail. The information on the work done is constantly updated, which allows the educators to check the tasks quickly and monitor students’ knowledge. The article proves the effectiveness of using the Smart course on the basis of Google Classroom for the formation of information skills of students majoring in “Information and Software Processing Operator”.
Visual Pursuit Control with Target Motion Learning via Gaussian Process

In this paper, we propose an observer-based visual pursuit control law which integrates target motion learning via Gaussian Process (GP). We consider two rigid bodies: a controlled rigid body with a visual sensor, and a target rigid body whose velocity is unknown. Furthermore, a vision-based motion observer which estimates the target motion is introduced. Then, we propose an enhanced vision-based nonlinear observer and visual pursuit control which employ target motion learning by GP, where the GP prediction is based on estimated relative rigid body motion. Then, we quantify the performance and prove stability by the notion of uniformly ultimately boundedness. Finally, we demonstrate the effectiveness of the proposed control law through simulations.
Enhancing text pre-processing for Swahili language: Datasets for common Swahili stop-words, slangs and typos with equivalent proper words

Natural Language Processing requires data to be pre-processed to guarantee quality models in different machine learning tasks. However, Swahili language have been disadvantaged and is classified as low resource language because of inadequate data for NLP especially basic textual datasets that are useful during pre-processing stage. In this article we develop and contribute common Swahili Stop-words, common Swahili Slangs and common Swahili Typos datasets. The main source for these datasets were short Swahili messages collected from Tanzanian platform that is used by young people to convey their opinions on things that matters to them. Therefore, we derive list of common Swahili stop-words by reviewing most frequent words that are generated with Python script from our corpus, review common slang with help of Swahili experts with their corresponding proper words, and generate common Swahili typos by analysing least frequent words generated by a Python script from corpus. The datasets were exported into files for easy access and reuse. These datasets can be reused in natural language processing as resources in pre-processing phase for Swahili textual data.
Adaptive Adversarial Latent Space for Novelty Detection

Novelty detection is a challenging task of identifying whether a new sample obeys to a known class. Note that the boundary between normal and novel is not clear enough in existing works, resulting from adequately reconstructing novel samples or crudely reconstructing normal samples. To tackle the above issues, we propose a general framework named Adaptive Adversarial Latent Space (AALS), which mainly consists of two components, Adaptive Latent Space Generator (ALSG) and Constrained-based Adversarial AutoEncoder (CAAE). ALSG is established to obtain the real latent space distribution by adaptive mapper and the manner of adversarial learning. Moreover, CAAE is presented to obtain a better boundary between normal and novel by Global and Local Channel-wise Attention (GLCA), which is proposed to reweight the global and local information in different channels. Given a sample, the latent space representation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> of the sample is firstly obtained by encoder with GLCA. Next, the output of ALSG is applied to constrain the latent space by latent adversarial loss. Further, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> is transformed into the decoder to reconstruct the given sample with GLCA. Experiments on three available datasets demonstrates the effectiveness of the proposed method, which achieves state-of-the-art performance compared with other methods.
Learning Structured Distributions From Untrusted Batches: Faster and Simpler

We revisit the problem of learning from untrusted batches introduced by Qiao and Valiant [QV17]. Recently, Jain and Orlitsky [JO19] gave a simple semidefinite programming approach based on the cut-norm that achieves essentially information-theoretically optimal error in polynomial time. Concurrently, Chen et al. [CLM19] considered a variant of the problem where $\mu$ is assumed to be structured, e.g. log-concave, monotone hazard rate, $t$-modal, etc. In this case, it is possible to achieve the same error with sample complexity sublinear in $n$, and they exhibited a quasi-polynomial time algorithm for doing so using Haar wavelets. In this paper, we find an appealing way to synthesize the techniques of [JO19] and [CLM19] to give the best of both worlds: an algorithm which runs in polynomial time and can exploit structure in the underlying distribution to achieve sublinear sample complexity. Along the way, we simplify the approach of [JO19] by avoiding the need for SDP rounding and giving a more direct interpretation of it through the lens of soft filtering, a powerful recent technique in high-dimensional robust estimation. We validate the usefulness of our algorithms in preliminary experimental evaluations.
Robot Pathplanning By Image Segmentation Using The Fuzzy C-Means Algoritm And KNN Algorithm

Mobile robotics is a valuable tool for exploring environments inaccessible to humans due to their remoteness, cost or danger, and for performing unpleasant or laborious tasks. It is a relatively new field, until recently experimental, but it is already being applied to real problems with satisfactory results, 3D simulation plays a relevant role in both the design and control of mobile robots. It allows to reflect the situation of the real robot or simulate hypothetical scenarios. At the same time, it provides a graphical interface for robot control, in this paper, we present a scheme for guiding a robot into an uncharted area by using the fuzzy c-means segmentation algorithm and k-NN classification to classify the safe and unsafe areas, our method proves to be fast with satisfactory results in terms of both segmentation, classification, and correct path planning.
CoSaTa: A Constraint Satisfaction Solver and Interpreted Language for Semi-Structured Tables of Sentences

This work presents COSATA, an intuitive constraint satisfaction solver and interpreted language for knowledge bases of semi-structured tables expressed as text.The stand-alone COSATA solver allows easily expressing complex compositional "inference patterns" for how knowledge from different tables tends to connect to support inference and explanation construction in question answering and other downstream tasks, while including advanced declarative features and the ability to operate over multiple representations of text (words, lemmas, or part-of-speech tags).COSATA also includes a hybrid imperative/declarative interpreted language for expressing simple models through minimally-specified simulations grounded in constraint patterns, helping bridge the gap between question answering, question explanation, and model simulation.The solver and interpreter are released as open source.
Submodular Meta-Learning

In this paper, we introduce a discrete variant of the meta-learning framework. Meta-learning aims at exploiting prior experience and data to improve performance on future tasks. By now, there exist numerous formulations for meta-learning in the continuous domain. Notably, the Model-Agnostic Meta-Learning (MAML) formulation views each task as a continuous optimization problem and based on prior data learns a suitable initialization that can be adapted to new, unseen tasks after a few simple gradient updates. Motivated by this terminology, we propose a novel meta-learning framework in the discrete domain where each task is equivalent to maximizing a set function under a cardinality constraint. Our approach aims at using prior data, i.e., previously visited tasks, to train a proper initial solution set that can be quickly adapted to a new task at a relatively low computational cost. This approach leads to (i) a personalized solution for each individual task, and (ii) significantly reduced computational cost at test time compared to the case where the solution is fully optimized once the new task is revealed. The training procedure is performed by solving a challenging discrete optimization problem for which we present deterministic and randomized algorithms. In the case where the tasks are monotone and submodular, we show strong theoretical guarantees for our proposed methods even though the training objective may not be submodular. We also demonstrate the effectiveness of our framework on two real-world problem instances where we observe that our methods lead to a significant reduction in computational complexity in solving the new tasks while incurring a small performance loss compared to when the tasks are fully optimized.
The Effect of Ethical Leadership and Leadership Effectiveness on Employee’s Turnover Intention in SMEs: The Mediating Role of Work Engagement

Purpose: The purpose of the present study is to examine the impact of ethical leadership and leadership effectiveness on employee’s turnover intention in SMEs, with an emphasis on the mediating role of work engagement. Design/Methodology/Approach: A quantitative approach was used. In particular, a research model has been empirically tested, using primary data collected from SMEs employees in two different countries. Findings: Ethical leadership and leadership effectiveness play an important role in predicting turnover intention. Moreover, work engagement mediates the relationship between ethical leadership, leadership effectiveness and turnover intention. Practical Implications: Α leadership approach which is both ethical and effective at the same time is more likely to create a productive, ethical and trusting work climate, conducive to the work engagement of employees and, therefore, reduce turnover. Originality/value: The role of ethical leadership and leadership effectiveness as well as work engagement on employee turnover in SMEs is currently under-researched.
Sustainable Educational Ecosystems: Bridging the Gap between Educational Programs and in-Demand Market Skills

With the technological advancements, academics and educational programs’ developers need to provide students with competences and skills that match the market’s demand. The article considers the problems of updating educational programs and content, taking into account changing requirements of standards and employers for qualifications and competencies of specialists. It is shown that the updating process in transition to an innovative and digital economy is associated with the transition to the convergence model of education. The authors have developed a basic model for updating educational programs based on the convergence approach. The presented framework benefits both academia and industry by enhancing the programs and therefore bridging the skills gap.
Research On Operation And Maintenance Management Risk Assessment Of Power Communication Network Based On Dematel

In order to overcome the problem of low accuracy of operation and maintenance risk assessment of power grid communication network, this paper proposes a method of operation and maintenance management risk assessment of power communication network based on DEMATEL. Based on the analysis of DEMATEL's power communication network pedigree, the assessment index system is divided into three first-class indexes: common business support, normal operation of communication network and operation and maintenance quality. The analytic hierarchy process (AHP) is introduced to calculate the risk value of operation and maintenance management of power communication network. The experimental results show that the accuracy of risk assessment method is close to 100%, and the failure rate is less than 3%, which improves the early warning rate. The accuracy of risk assessment, risk assessment rate and early warning rate of operation and maintenance management of power communication network have been improved.
Towards a Generic Framework for Black-box Explanations of Algorithmic Decision Systems

The main goal of this paper is to define a generic framework for black-box explanationmethods in order to make it easier to compare and classify different approaches. We focus ontwo components of this framework, called respectively “Sampling” and “Generation”, which arecharacterized formally and used to build a taxonomy of explanation methods. This document givesdetails on how this framework can be used to describe black-box explanation methods found inthe literature.