Reviewerly/exHarmony huggingface dataset:
Ontology for group of rescuing robots

Abstract Annotation. In general, almost all fields of activities in the High North and the Arctic Region can be classified as either of high or extreme risks, as any kind of emergency at industrial site/object facility is very likely to lead to a large number of casualties. Rescuing people in distress is a complicated and challenging task. Having experience of numerous rescuing activities, it has shown that application of conventional rescue equipment in northern environments is not so effective: part of rescue facilities (such as medical aircrafts and ships) are based far from location of emergency, whereas the other part (floats, boats) have very limited capabilities. However, if arrived 48 hours after the catastrophe, there is almost zero possibility to find anyone alive, and the situation often becomes even worse because of the fact that rescuers have absolutely no idea about the actual situation and conditions within the emergency area or zone. Use of robotic groups in order to provide searching of survivors, lifting them up from either sea or ice surface, considering also further transporting of the people suffered - to stationary point-of-care or station of medical aid. Should tasks of robot group operation planning and of challenging situations complex analysis be considered, the most important is to implement standardized language with very clearly defined terminology. To solve this task, it is necessary to have appropriate ontology, i.e. it is ontology that determines core constructs, properties and connections. The article indicates core robotic ontologies, standardized and currently applied by international engineering community, as well as proposes extended solution of already existing IEEE standards (CORA, etc.), formulated with application of core SUMO ontology.
A static VM placement and hybrid job scheduling model for green data centers

Reducing energy consumption has become a critical issue in today data centers. Reducing the number of required physical and Virtual Machines results in energy-efficiency. In this paper, to avoid the disadvantages of VM migration, a static VM placement algorithm is proposed which places VMs on hosts in a Worst-Fit-Decreasing (WFD) fashion. To reduce energy consumption further, the effect of job scheduling policy on the number of VMs needed for maintaining QoS requirements is studied. Each VM is modeled by an M/M/* queue in space-shared, time-shared, and hybrid job scheduling policies, and energy consumption of real-time as well as non-real-time applications is analyzed. Numerical results show that the hybrid policy outperforms space-shared and time-shared policies, in terms of energy consumption as well as Service Level Agreement (SLA) violations. Moreover, our non-migration method outperforms three different algorithms which use VM migration, in terms of reducing both energy consumption and SLA Violations.
FFT-based Human Detection using 1-D Laser Range Data

In general, a socially-aware mobile robot must have an ability to safely navigate among human environment. To address with this competency, the mobile robots must be able to detect the existence of humans around. This paper proposes the use of Fast Fourier Transform (FFT) to analyze shape-models of human legs that is obtained from Laser Range Finder (LRF) scanning results. A 240° of LRF was used to capture and visualize the environment in one dimensional plane. The plane is then converted into one dimensional signal that consists of 1,024 data points. These data points represented the distance of all measured points. A specific set of points formed the pattern of human legs only is then resized into 32 data. This resized-data is transformed into frequency by using FFT. The result of FFT is then fed into Support Vector Machine (SVM) to be classified into two classes, they are human or not human. Based on the experimental results, our proposed method shows a promising result in order to detect human based on one dimensional feature of his legs by achieving more than 80% of accuracy.
Data Jackets as Communicable Metadata for Potential Innovators – Toward Opening to Social Contexts

Data Jackets are human-made metadata for each dataset, reflecting peoples’ subjective or potential interests. By visualizing the relevance among DJs, participants in the market of data think and talk about why and how they should combine the corresponding datasets. Even if the owners of data may hesitate to open their data to the public, they can present the DJs in the Innovators Marketplace on Data Jackets that is a platform for innovations. Here, participants communicate to find ideas to combine/use/reuse data or future collaborators. Furthermore, explicitly or implicitly required data can be searched by the use of tools developed on DJs, which enabled, for example, analogical inventions of data analysis methods. Thus, we realized a data-mediated birthplace of seeds in business and science. In this paper, we show a new direction to collect and use DJs to fit social requirements externalized and collected in living labs. The effect of living labs here is to enhance participants’ sensitivity to the contexts in the open society according to the author’s practices, and the use of DJs to these contexts means to develop the process of evidence-based innovation, i.e., the loop of living humans’ interaction to create dimensions of performance in businesses.
Estimation of the Number of Communities in the Stochastic Block Model

In this article we introduce an estimator for the number of communities in the Stochastic Block Model (SBM), based on the maximization of a penalized version of the so-called Krichevsky-Trofimov mixture distribution. We prove its eventual almost sure convergence to the underlying number of communities, without assuming a known upper bound on that quantity. Our results apply to both the dense and the sparse regimes. To our knowledge this is the first consistency result for the estimation of the number of communities in the SBM in the unbounded case, that is when the number of communities is allowed to grow with the same size.
Two-Steam Fully Connected Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based human action recognition has recently drawn a lot of attentions with the increasing availability of large-scale skeleton datasets. Graph Convolutional Network (GCN) methods have achieved relatively good performances in action recognition. However, most GCN methods based on predefined graphs with fixed topology constraints always neglect the potential dependencies derived from the cooperative movement of all joints. Besides, the lengths and the directions of skeletons are rarely involved. These easily cause a larger deviation of the estimated action from the actual action. Here, a two-steam fully connected graph convolutional network (2s-FGCN) is proposed. The topology structure of the 2s-FGCN covers the local physical connections and the global potential cooperation of all joints and the joints, lengths and directions of skeletons are all input to the model. The experimental results on two datasets (NTU-RGB+D and Kinetics-Skeleton) demonstrate that the proposed model can obtain the state-of-the-art results.
An Overview on the Application of Machine Learning and Deep Learning for Photovoltaic Output Power Forecasting

By the end of 2019 the global cumulative installed photovoltaic (PV) capacity is more than 600 GWp corresponding to several millions of photovoltaic (PV) systems installed worldwide. Thus, the operation and maintenance activities of such plants are today important for a great number of professionals working in this solar sector. Forecasting of PV output power play very important role in power planning and dispatching, optimal management, grid quality and stability. Designing of an accurate PV output power forecasting models stay quite challenging issue and a crucial task, as the PV output power is extremely uncertain due mainly to solar irradiance variation. Broadly forecasting methods can be classified mainly into four groups: Physical model (e.g., numerical weather prediction models), statistical methods (e.g., AR, ARMA, ARIMA, etc.), method-based artificial intelligence techniques, including machine learning (ML) and deep learning (DL), and the group named hybrid methods (e.g., Combining two methods). Different timescales forecasting are important for PV plants, for example intra-hour forecasts (up to 1 h) are useful for grid quality and stability. Intra-day forecasts (up to 6 h) are essential and could be used for optimal integration. Forecasts up to one-day mainly used for unit commitment planning and dispatching power. Up to one-week forecasts could be used for trading, management and maintenance. The main aim of this talk is to give an overview on the available forecasting methods, special attention will be paid to methods recently developed, including ML and DL. Pros and cons of reviewed methods in terms of accuracy and complexity will be discussed in this presentation.
Algorithmic support of the television scanning optical microscope in the study of microobjects

The problems relating to algorithmic support of television scanning optical microscope during the study and determination of various parameters and characteristics of individual and group dynamic microobjects with different sizes and shape. The block diagrams of the corresponding algorithms of the microscope operation are given, which provide universality and high accuracy of measurements.
Real-time Map Construction and Emergency Obstacle Avoidance Algorithm for Mobile Robot

The decision-making frequency for traditional robotic autonomous systems is low and cannot fit the real-time problem of the underlying control system. Firstly, a method fusion of single line laser radar sensor data and ultrasonic data is proposed to construct a high-frequency real-time map of a mobile robot. Secondly, based on the real-time map, an obstacle avoidance algorithm combining artificial potential field (APF) method and nearness diagram navigation (ND) is used. Finally, the emergency obstacle avoidance effect of the mobile robot is simulated in the Gazebo dynamic simulator and the feasibility of real-time map construction and emergency obstacle avoidance of the mobile robot is verified through experiments. The experimental results show that the real-time map constructed can reflect the state of the robot's surroundings in real time and provide a reliable data basis for high-frequency obstacle avoidance planning. The obstacle avoidance algorithm used has a high frequency and smooth route planning, which can meet the real-time needs of the lower-level control system.
Analysis of IoT Implementation Using the Dematel Method and TOES Framework

Mobile network operators in Indonesia are experiencing stagnation and declining revenue growth, this is caused by a shift in service trends used. PT XL Axiata is one of the mobile network operators that experienced stagnant revenue growth. IoT is an opportunity for new sources of income. The Internet of Things (IoT) has many opportunities and challenges in its implementation. For successful IoT implementation, it is necessary to know the factors that influence this implementation and the relationship between these factors. The factors that influence IoT implementation are technology readiness, technology integration, IT expertise, expected benefits, top management support, organizational readiness, government policies, supporting industries, competitive pressures, data security, institutional security, and systems security. By using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method, the causal effect relationship between these factors can be obtained and a causal effect diagram can be produced showing the Critical Factors of Success (CFS) in the implementation of the IoT. The Technology, Organization, Environment, and Security (TOES) framework was used in connection with 12 factors to facilitate the distribution of the factors that influence the implementation of the IoT. The recommended strategy for PT XL Axiata is to improve the company's competitiveness, create business models that follow government regulations, and integrate new technologies with previous types of services.
The Importance of Colormaps

Colormaps control the way data are understood when plotted because they map data values to color. When the colors used do not match the way humans perceive color, there can be a mismatch between interpretation and the data. Here I present relevant background information and provide suggestions to improve colormap choices.
Towards simpler and more transparent quantitative research reports

Abstract The average quantitative research report in applied linguistics is needlessly complicated. Articles with over fifty hypothesis tests are no exception, but despite such an onslaught of numbers, the patterns in the data often remain opaque to readers well-versed in quantitative methods, not to mention to colleagues, students, and non-academics without years of experience in navigating results sections. I offer five suggestions for increasing both the transparency and the simplicity of quantitative research reports: (1) round numbers, (2) draw more graphs, (3) run and report fewer significance tests, (4) report simple rather than complex analyses when they yield essentially the same results, and (5) use online appendices liberally to document secondary analyses and share code and data.
Mathematical Model of the Process of Tabular’s Implementation of the Operation Algebraic Multiplication in the Residues Class

The article synthesizes a mathematical model of the process of tabular implementation of modular multiplication, for both positive and negative numerical ranges of means of processing integer information. This model is recommended for practical application when developing methods and algorithms for fast processing of cryptographic information.
Kullback-Leibler distance criterion consolidation in cloud

The dynamic virtual machine (VM) consolidation is a key resource management technique used for achieving a trade-off between performance and energy efficiency of cloud computing systems. Selection of the source and destination hosts is a key problem in the process of VM consolidation which faces different challenges in the effective management of cloud resources. One of these challenges is utilizing burstiness-aware algorithms for selecting the source and destination hosts to prevent frequent migrations, reduce Service Level Agreement (SLA) violations, and improve energy efficiency. In the current study, we propose a new approach based on the Kullback-Leibler Distance (KLD) criterion to tackle this problem. The proposed approach includes burstiness-aware algorithms for selecting the source and destination hosts to minimize SLA violations, improve energy efficiency, and decrease the number of migrations. We utilize both real-world and random workloads and CloudSim simulator to inspect the performance of proposed algorithms. The experimental results reveal that the proposed approach outperforms the previous algorithms in terms of performance.
A Military Chess Game Tree Algorithm Based on Refresh Probability Table

Computer game is divided into complete information game and incomplete information game. As a kind of incomplete information game, military chess game has many shortcomings in game-tree algorithm. In view of the fact that it is difficult to develop the game-tree because the specific information of the opponent's chess pieces can not be obtained accurately in military chess game, this paper proposes an algorithm to expand the game tree by refreshing the probability table formed by the rules of military chess, the result of the opponent's chess game and the result of the collision of the two opponents' chess pieces. The algorithm of the refresh probability table is used to provide the score support of the expanded game tree for the military chess game, thereby ensuring the expansion of the game tree. The initial values and weights in the refresh probability table algorithm are optimized by machine learning to improve the speed and accuracy of the refresh probability table, and the game tree is optimized by the minimax algorithm and the Alpha-beta pruning algorithm. Experiments show that the refresh probability table algorithm based on the game tree improves the winning rate in the game and has achieved good results.
Ensemble-based Synthetic Data Synthesis for Federated QoE Modeling

Quality of Experience (QoE) models need good generalization that necessitates sufficient amount of user-labeled datasets associated with measurements related to underlying QoE factors. However, obtaining QoE datasets is often costly, since they are preferably collected from many subjects with diverse background, and eventually dataset sizes and representations are limited. Models can be improved by sharing and merging those collected local datasets, however regulations such as GDPR make data sharing difficult, as those local user datasets might contain sensitive information about the subjects. A privacy-preserving machine learning approach such as Federated Learning (FL) is a potential candidate that enables sharing of QoE data models between collaborators without exposing ground truth, but only by means of sharing the securely aggregated form of extracted model parameters. While FL can enable a seamless QoE model management, if collaborators do not have the same level of data quality, more iterations of information sharing over a communication channel might be necessary for models to reach an acceptable accuracy. In this paper, we present an ensemble based Bayesian synthetic data generation method for FL, LOO (Leave-One-Out), which reduces the training time by 30% and the network footprint in the communication channel by 60%.
Image Retrieval via Gated Multiscale NetVLAD for Social Media Applications

Image retrieval on the social media platforms propels the tourism information sharing on the Internet. Existing image retrieval methods lack the capability of discovering global statistical distribution of feature representations at multiple scales. In this article, we propose a gated multiscale NetVLAD network, which constructs feature pyramid network based on ResNet backbone and computes NetVLAD features at each pyramid level to capture the multiscale information. In addition, we use gate mechanism for each level of the pyramid to adaptively represent the contribution of each level of features to the retrieval task. Experimental results on CIFAR-10, MNIST, and the Google street view datasets show that our image retrieval method has achieved better results than several existed methods.
Performance Knowledge Acceleration Optimization with MARS to Customer Behavior in Merchant Ecosystem

Merchant are sellers who develop business related to the provisions and procedures regulated by business entities that are established in accordance with PBI for through e-money. Merchants are divided into two, individual merchants and legal merchants. The impact of commercial digital business (commercial entrepreneurship and social entrepreneurship) is very large on users who are currently increasing in number. Uncertainty arising from sustainable business operators by considering aspects of Business Metrics related. MARS is a nonparametric method that determines statistical modelling in finding optimization of large-scale data reduction through secondary data. The results of learning statistics error from 0.11% to 0.36% get optimal results.
Knowledge Base Representation of Emails Using Ontology for Spam Filtering

With the development of the Internet, emails became the swiftest, cheapest as well as the simplest form of communication. The viability of it has been tempered by the noxious spreading of undesirable, unsolicited bulk mails called spam. Currently, to endeavor the issue of extending volumes of spam, researchers have contributed much work on spam filtering. One of the greatest challenges faced by the researchers is the novel techniques adopted by the spammers to bypass spam from the developed filters. Even the users’ interests also get changed consequently resulting in the need for an adaptive filter for classification. If the emails are semantically annotated, an effective search and reasoning process can be provided. As ontologies provide semantics of data understandable for machines, better decisions can be made, thereby providing better solutions. In this paper, we illustrate an innovative method to extract knowledge from emails and represent them in ontological pattern consisting of hierarchical structures. This knowledge-based framework that represents the information artifacts of the email domain familiar to the users aids the classifier to point out the actual spam or ham.
Testing of Python Models of Parallelized Genetic Algorithms

The paper describes the testing of three models (master slave, fine-grained, and coarse grained) of parallelized genetic algorithms and the comparison of their computational time with each other and with the basic serial model. The analysis of the number of iterations, the load of the main memory and the central processing unit is the subject of other contributions. Corresponding Python modules have been implemented for these models. A test scenario and a test environment were prepared. Testing was realized on a Linux server with the Ubuntu operating system. A RabbitMQ server creating processes by the SCOOP module on the selected workstation was used. Models have been tested by a single-workstation and multi-workstation scenarios. The tested models bring time savings and efficiency improvement compared to the serial model; the fastest was the fine-grained model.
Automatic Annotation of Speech Corpora using Approximate Transcripts

High-performance automatic speech recognition (ASR) systems are regularly trained on tens of thousands of hours annotated speech (i.e. speech paired with correct transcripts). Collecting such amount of data is prohibitively costly if done manually (i.e. humans listening and transcribing audio clips). However, raw speech data (without transcripts) is widely available and easily collectable. This paper proposes an automatic method that uses approximate transcripts of raw speech and an already existing ASR system to generate annotations. The method is evaluated in terms of annotation efficiency (i.e. the percentage of the initial raw speech corpus for which it provides annotations) and in terms of data usefulness for further training ASR systems. We show that, although the method is able to produce less data than other methods, the ASR system retrained using the newly created dataset performs significantly better than the baseline. Furthermore, we report ASR results that are better by 17% to 25% than what was reported up to now on Romanian read and spontaneous speech.
Designing an Adaptive Question Bank and Question Paper Generation Management System

This paper discusses the design and implementation of automatic question paper generation and retrieval system for the engineering domain. The system uses C#.NET for user interface design. SQL server 2017 is used for database storage. Question paper generation is accomplished using SAP crystal report 2016. The system consists of four modules, namely administrator and login module, question input module, question retrieval module, and evaluation module. The question paper is generated using a dynamic algorithm with minimal redundancy. Since the inputted question items are marked for their difficulty index, the question paper may be customized as per testing requirement, i.e., basic to advanced level of difficulty. Evaluation module generates password-protected expert validated answer keys for the objective type of questions and answer cues for the subjective type of questions. The system may duly cater to the need of an instant generation of confidential different sets of question papers having same difficulty index for the purpose of competitive engineering examinations or tests.