Reviewerly/exHarmony huggingface dataset:
Development of Training System for Remotely Operation using Robot Simulator

This paper describes development of a training system for remotely operation using robot simulator. Prior training and preparation are important for decommissioning of Fukushima Daiichi Nuclear Power Station. As one method, we are developing a training simulator system with an evaluation function for inexperienced operators in remotely operation. Proficiency level of operator is assessed by collecting interaction data of robot and environment. In this paper, we describe the overview of the training system. Moreover, we developed a function to measure the collision between the robot and environment as a trial for quantitative evaluation for spatial awareness of operator in a narrow space movement task. We implemented the function in robot simulator, also discuss the results obtained by subject experiment.
Learning to Program on KaiOS: a Hands-on Coding School for Developing Climate Service Apps

The demand for short and intense courses intended to teach new or improve existing pragmatic ICT skills, required for solving a given problem in a real context, has been raised especially by the industry. We provide an example where a pragmatic coding school model has been shown to be an effective way to teach new skills and enhance prior knowledge in a short timeframe, among undergraduate computer science students of the University of Namibia (UNAM). This paper presents a coding school for climate service app development, organized jointly by the University of Turku (UTU), Finland, and UNAM, at the UTU satellite campus within the UNAM main campus. We describe the coding school's design and implementation phases, as well as analyze lessons learned, based on which we suggest actions to improve similar schools in the future. Furthermore, we demonstrate a method by which the coding school is linked to the requirements of an international research project demanding local input in its co-design.
A Study On Accelerating Adversarial Imitation Learning By Behavioral Cloning

Imitation learning is a popular method to obtain policies on autonomous robots given expert demonstrations. Recently, adversarial imitation learning methods, such as generative adversarial imitation learning (GAIL), have achieved great successes even on complex continuous control tasks. However, GAIL as well as its variants require a huge amount of environment interactions that often take impractically long time for training the robot. An intuitive way to reduce the number of interactions is initializing a policy by behavioral cloning (BC) before performing GAIL as pointed out in [1]. However, Sasaki et al reports that the BC initialization does not lead to reduce the number of interactions at all, rather significantly harms the imitation results. In this paper, we further analyze the BC initialization to figure out why the results are opposed to the intuition. Experimental results show that one of the cause of failure due to the BC initialization is that BC vanishes gradients of objective functions for the adversarial imitation learning algorithms, even though the objective differs from that of BC.
Viewpoint planning for object search on the desk using a mobile robot

The goal of this research is to develop an efficient object search method by an autonomous mobile robot. There are two main steps in object search. One is the object detection step to detect an object using sensors, and the other is the viewpoint planning step to move to the next viewpoint when the object is not detected. This research focuses on the viewpoint planning step that decides where to move for the next observation. In object search tasks, since searching unknown areas for the target object is preferable, we plan the next viewpoint which maximuize the unknown area to be observed. We apply the viewpoint planning method to the task of searching for a specific object on the desk using a human support robot (HSR) and verify its effectiveness in simulation and real environments.
Retrieval of Multiple Spatiotemporally Correlated Images on Tourist Attractions Based on Image Processing

The thriving of information technology (IT) has elevated the demand for intelligent query and retrieval of information about the tourist attractions of interest, which are the bases for preparing convenient and personalized itineraries. To realize accurate and rapid query of tourist attraction information (not limited to text information), this paper proposes a spatiotemporal feature extraction method and a ranking and retrieval method for multiple spatiotemporally correlated images (MSCIs) on tourist attractions based on deeply recursive convolutional network (DRCN). Firstly, the authors introduced the acquisition process of candidate spatiotemporally correlated images on tourist attractions, including both coarse screening and fine screening. Next, the workflow of spatiotemporal feature extraction from tourist attraction images was explained, as well as he proposed convolutional long short-term memory (ConvLSTM) algorithm. After that, the ranking model of MSCIs was constructed and derived. Experimental results demonstrate that our strategy is effective in the retrieval of tourist attraction images. The research results shed light on the fast and accurate retrieval of other types of images.
Unification of Social Media Data When Building a Graph Knowledge Base

The work presents an ontological model for the unification of data profiles of different social networks. This model avoids data redundancy by including contextual information in annotations to ontology relations. In addition, an approach to information retrieval using syntagmatic patterns in the formation of a database tree of posts of social network users is proposed. The article also presents the results of experiments with data from the social network Facebook confirming the effectiveness of the proposed models and algorithms.
Resourcefully: Let’s End the Serials Crisis

In the first installment of a new column, Resourcefully, one more call for the end of the Serials Crisis that argues we have so many new crises to confront it’s time to let this one go.
A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

This paper presents a novel geometrical scale and rotation independent feature extraction (FE) technique for multilingual character recognition (CR). The performance of any CRtechniques mainly depends on the robustness of the proposed FE methods.Currently, there are very few scale and rotation independent FE techniques present in the literature which successfully extract the robust features from characters with noise such as distortion and breaks in the characters.Many FE methods from the literature failed to distinguish the characters which look similar in their appearance.So, in this paper, we have proposed a novel scale and rotation independent geometrical shape FE technique which successfully recognized distorted, broken, and similarly looking characters.Aside from the proposed FE technique, we've used crossing count (CC) features.Finally, we have combined the proposed features with CC features to make as Feature Vector (FV) of the character to be recognized.The proposed CR technique is evaluated using publicly available media-lab license plate (LP), ISI_Bengali, and Chars74K benchmark data sets and achieved encouraging results.To further assess the performance of the proposed FE method, we've used a proprietary data set containing nearly 168000 multi-lingual characters from English, Devanagari, and Marathi scripts and achieved encouraging results.We have observed better classification rates for the proposed FE method using publicly available benchmark data sets as compared to few of the CR FE methods from the literature.
Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data

In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer's disease study is provided.
Quaternion Simulated Annealing

Simulated annealing (SA) is a well-known stochastic local search algorithm for solving unconstrained optimization problems. It mimics the annealing process used in the metallurgy to approximate the global optimum of an optimization problem and uses the temperature to control the search. Unfortunately, the effectiveness of simulated annealing drops drastically when dealing with a large-scale optimization problem. This is due in general to a premature convergence or a stagnation. The both phenomenons can be avoided by a good balance between exploitation and exploration. This paper focuses on the same problem encountered by simulated annealing and try to heal it using the quaternion which are a number system that extends complex numbers. Quaternion representation helps the simulated annealing algorithm to smooth the fitness landscape and thus avoiding to get stuck in the local optima by expanding the original search space. Empirical analysis was conducted on many numerical benchmark functions. The experimental results show that the quaternion representation of neighborhood improves the solution quality compared with the classical simulated annealing. Our approach was also compared with other nature-inspired optimization algorithms. It was shown that the quaternion simulated annealing overcomes other heuristics in terms of solution quality for most of the benchmark functions.
Multi-Feature Fusion for Airport FOD Detection

The prevention of foreign object debris in domestic small and medium-sized airports mainly depends on manual detection. To reduce the cost of detection of foreign object debris in airport flight areas, a method that relies on image detection to identify foreign invasive objects at the airport is proposed. To improve the reliability of the saliency detection, the classical ITTI model is improved. The D-S evidence fusion theory is used to fuse features according to the degree of support, and saliency detection is performed on the processed images. The simulation was performed in 500 samples. The experiment proves that the accuracy of the foreign object recognition in the image is over 92%, which is about 11% higher than the highest color feature detection in the single feature. The unrecognized condition and the detected noise dropped below 10%, which greatly increased the efficiency of foreign object recognition.
Syllabus: structure, content and significance for the educational process in higher education institutions

The article is devoted to topical issues related to the elaboration of modular educational programs, syllabi being their necessary components. The purpose of the article is to draw the attention of teachers and students in higher education institutions (HEI) to the problem of educational and methodological support of the educational process focusing on the content and meaning of a syllabus as one of the important educational and methodological instructional documents. The phenomenon “syllabus” is defined, its essence and objectives within the system of methodological support for teaching an academic discipline are revealed. A syllabus to the section of General Physics "Optics" intended for the specialty 104 (Physics and Astronomy) has been developed and introduced into the educational process, its most important elements have been singled out. Since, according to the authors’ viewpoints, the development of a separate syllabus for the laboratory workshop is inexpedient, one joint syllabus covering the theoretical part of the course "Optics", involving laboratory workshops within the study of this discipline, has been elaborated. The syllabus of optics consists of 16 sections as follows: title page; the contents of the syllabus; general characteristics of the discipline; prerequisites; post-requisites; discipline abstract; the purpose and objectives of the discipline; the results of the study; the organisation of training; course topics; academic calendar of discipline studies; knowledge assessment system; course policy ("Rules of the game"); policy of academic behaviour and ethics; recommended literature (references) for mastering the course. The syllabus is supplemented with a glossary of terms in the form of an appendix, which will undoubtedly be of service to the syllabus applicants. The results of the study revealed the content and importance of the syllabus for the educational process; its structure for the section of General Physics "Optics" was proposed which, in the authors’ opinion, fully meets the requirements of the credit-modular system of educational process. The authors hope that the designated syllabus will serve as an example for other sections of General Physics and some other disciplines. One of the authors has applied the structure of the developed syllabus with corresponding changes in its content for developing a syllabus of the discipline "Profession-targeted Foreign Language (English)".
Digital Objects in Rhombic Dodecahedron Grid

Abstract Rhombic dodecahedron is a space filling polyhedron which represents the close packing of spheres in 3D space and the Voronoi structures of the face centered cubic (FCC) lattice. In this paper, we describe a new coordinate system where every 3-integer coordinates grid point corresponds to a rhombic dodecahedron centroid. In order to illustrate the interest of the new coordinate system, we propose the characterization of 3D digital plane with its topological features, such as the interrelation between the thickness of the digital plane and the separability constraint we aim to obtain. We also present the characterization of 3D digital lines and study it as the intersection of multiple digital planes. Characterization of 3D digital sphere with relevant topological features is proposed as well along with the 48-symmetry appearing in the new coordinate system.
Experiment Design for Complex Immersive Visualisation

Experimentation focused on assessing the value of complex visualisation approaches when compared with alternative methods for data analysis is challenging. The interaction between participant prior knowledge and experience, a diverse range of experimental or real-world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant experimentation results. This paper outlines a hybrid approach proposed for experimentation with complex interactive data analysis tools, specifically for computer network traffic analysis. The approach involves a structured survey completed after free engagement with the software platform by expert participants. The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of software performance which is supported by statistically significant experimental results. This work is particularly applicable to field of network analysis for cyber security and also military cyber operations and intelligence data analysis.
An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem

The explosion of information constrains the judgement of search terms associated with Knowledge-Based Web Ecosystem (KBWE), making the retrieval of relevant information and its knowledge management challenging. The existing information retrieval (IR) tools and their fusion in a framework need attention, in which search results can effectively be managed. In this article, we demonstrate the effective use of information retrieval services by a variety of users and agents in various KBWE scenarios. An innovative Integrated Search Framework (ISF) is proposed, which utilises crawling strategies, web search technologies and traditional database search methods. Besides, ISF offers comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, extranet, intranet, to personal desktop. In this empirical research, experiments are carried out demonstrating the improvements in the search process, as discerned in the conceptual ISF. The experimental results show improved precision compared with other popular search engines.
Evidentiality ‘In’ and ‘As’ Context Corpus-based insights about the Mandarin V-过 construction

In this paper we argue that evidentiality can be a category of a linguistic system that emerges from the intersection between form, usage and ‘contextual situatedness’. We provide a multivariate corpus-based case study about the usage of the V-过 guo construction in written Mandarin, and show how the text types in which the chunk appears significantly contribute to determine its pragmatic usage and its emergent meaning grounded in shared knowledge and collective recognition. This approach sheds new light on two critical issues. The first is that evidentiality is an important grammatical category of documentary, factual and academic prose in Mandarin Chinese. The second, much broader, claim of this paper is that generalisations about grammatical/semantic categories need to account for the usage of specific item in context. In this sense, ‘physical and socio-cultural situatedness’ is as important a dimension as form and meaning in order to define categorial membership.
GenNext—An Extractive Graph-Based Text Summarization Model for User Reviews

User opinions are considered as the core of any business as it helps in reviewing the product or the service. It is the voice of the customer and every business strives to get full benefit from it. Relatively, this research work is based on analysis of the user reviews to provide a better approach to effectively understand the core of the numerous customer reviews present over the Web. It is almost inhumane job to search and read all these user reviews manually, and hence, the concepts of text summarization are applied with the sole aim to derive the meaningful abstract of the entire review for quicker understanding of its content. This research article peculiarly focuses on preparing a genre independent review summary model that can be used on any existing systems. GenNext is a unique model which provides extractive text summary for multi-genre data and even for user review of as less as one sentence is developed with graphical method of extractive text summarization. This model has been proven significantly accurate in providing effective summaries as compared to the existing summarization models in the market, with the help of sentiment analysis and polarity classification through natural language processing techniques. The summaries are also assigned related emoticons based on their polarity scores for animated representation.
Estimation of the excess relative risk using the piecewise linear model with Gaussian process

As technology develops, many people are exposed to radiation in hospitals and living spaces. It is widely known that radiation-related cancer risk increases as the background rate increase over time. Accurately quantifying the risk of exposure to radiation is a major study in radiation epidemiology. The solid cancer incidence data among Life Span Study (1958-2009) was used to estimate the risk associated with radiation by using Bayesian analysis. First, we considered the piecewise linear model, which estimates the slope by dividing the dose range, as dose-response function. In the piecewise linear model, the slope tends to change rapidly at the cut points. In this paper, we consider the Gaussian process with a covariance matrix to allow a dose difference structure to the dose category slopes. Finally we compare the results with other models using Bayesian analysis. As a result, estimated risk appeared slightly smaller than the Piecewise linear model with general assumption.
A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model

Particle swarm optimization (PSO) is a population-based stochastic metaheuristic algorithm; it has been successful in dealing with a multitude of optimization problems. Many PSO variants have been created to boost its optimization capabilities, in particular, to cope with more complex problems. In this paper, we provide a new approach of multi-population particle swarm optimization with a cooperation strategy. The proposed algorithm splits the PSO population into four sub swarms and attributes a main role to each one. A machine learning technique is designed as an individual level to allow each particle to determine its suitable swarm membership at each iteration. In a collective level, cooperative rules are designed between swarms to ensure more diversity and realize the better solution using a Master/Slave cooperation scheme. Several simulations are performed on a set of benchmark functions to examine the performances of this approach compared to a multitude of state of the art of PSO variants. Experiments reveal a good computational efficiency of the presented method with distinguishable performances.
Encode the Unseen: Predictive Video Hashing for Scalable Mid-stream Retrieval

This paper tackles a new problem in computer vision: mid-stream video-to-video retrieval. This task, which consists in searching a database for content similar to a video right as it is playing, e.g. from a live stream, exhibits challenging characteristics. Only the beginning part of the video is available as query and new frames are constantly added as the video plays out. To perform retrieval in this demanding situation, we propose an approach based on a binary encoder that is both predictive and incremental in order to (1) account for the missing video content at query time and (2) keep up with repeated, continuously evolving queries throughout the streaming. In particular, we present the first hashing framework that infers the unseen future content of a currently playing video. Experiments on FCVID and ActivityNet demonstrate the feasibility of this task. Our approach also yields a significant mAP@20 performance increase compared to a baseline adapted from the literature for this task, for instance 7.4% (2.6%) increase at 20% (50%) of elapsed runtime on FCVID using bitcodes of size 192 bits.