Reviewerly/exHarmony huggingface dataset:
Editorial Perspectives: achieving real-world impact

Michael Templeton discusses the translation of research into real-world impact in the water sector in this ‘Editorial Perspective’ for <italic>Environmental Science: Water Research &amp; Technology</italic>.
Mensch ARgere dich nicht

"Mensch ARgere Dich Nicht" is a Mixed Reality (MR) game based on a popular German board game "Mensch Ärgere Dich Nicht", from the early 1900s, similar to Pachisi. Developed to be a test bed for investigating mixed interactions, the application offers real and virtual variations of all core game elements (board, dice, pieces) for a fully modifiable experience. Using the Microsoft HoloLens, players can interact with the virtual game elements and receive additional information about the current game state. The game also recognizes interactions with real game elements and translates them into the virtual world.
Hybrid technique for analysis of the faults in an underground cable

The suggested fault classification and location technique uses single cycle post fault voltage and current signal from the sending end of the underground cable (UC). Then signal disintegration is done using different signal processing method to collect set of statistical features. Thereafter by using feature extraction method (forward feature selection (FFS)) is implemented to collect the optimum features and the redundant features are discarded from the total feature set. The chosen features are fed to artificial neural network (ANN)/extreme learning machine (ELM) for fault classification and location. From the simulation of the test system it is noticed that wavelet packet transform-ELM with FFS based hybrid method provides least fault locality error (0.2%) and highest classification accuracy (99.5%) as associated with other method implemented by other researcher
Poster Support for an Obeya-Like Risk Management Approach

Lean management is trendy. This trend is also reaching risk management. It has become very concrete in France following the EBIOS-Risk Manager method publication by the French National Agency for cybersecurity (ANSSI) in October 2018. However, if the new method fosters an agile approach of risk management, it does not provide the tools to support the mandated brainstorming workshops. In this paper we propose a set of A0 posters (and A5 cheat-sheets) to support the efficient and user-friendly organisation of the EBIOS-Risk Manager brain-storming sessions. The workshop participants are given sticky notes and felt pens to actively contribute to the data collection work. A facilitator helps organise the emergence of contributions. This approach is inspired from the Japanese Obeya form of project management, with the goal of making risk management simple, dynamic and attractive, or in one word, fun!
Development of specialized algorithm and hardware implementation for calculation of value of truncated convolution of polynomials over Galois field

Abstract This scientific paper deals with an algorithm for calculation of the truncated convolution of polynomials over the Galois field GF( p m ). Calculation of the truncated convolution of polynomials is a key part of the information frame decoding algorithm on application of the Reed-Solomon codes in the noise protected data transmission and storage systems. Within the scientific paper, the definitions of polynomial and truncated convolution of the polynomials over the Galois field GF( p m ) are also overviewed. Finally, the author offers a specialized algorithm for calculation of the value of the truncated convolution of polynomials over the Galois Field GF( p m ) with a given argument. Its hardware implementation for the binary Galois field GF(2 m ) is also presented.
Multi-agent modeling of the control system of space-time states of man-made objects

Abstract The article discusses the development of a multi-agent system for solving problems of determining, controlling, and predicting the space-time states (STS) of objects. A generalized scheme of a multi-agent approach to solving the problem of determining the STS of an object is given. The article describes an algorithm “decision trees” on the example of the decomposition of the object in order to monitor and predict the STS of the structural parts of the object.
Accurate Traffic Flow Estimation for Highway Surveillance Systems with Scenes Tampered by Raindrops

A traffic flow estimation mechanism is proposed for highway surveillance systems with scenes tampered by raindrops. To detect rain-drop tampered scenes, features are extracted via salient region detection and block segmentation. Feature selection is performed to select more discriminative features. For traffic flow estimation, daytime and night time models are performed separately to adapt to the characteristics of the surveillance scenes. Finally, an effective graph-based mapping method is designed to map the vehicle count sequences to per minute traffic flow. The system is tested with a highly challenging dataset. The accuracy of the traffic flow analysis is satisfying with low mean absolute errors even when the cameras are seriously tampered by rain.
Region of Interest (ROI) Determination System on SO-40F Pias Card’s Type for Measurement of Sunshine Duration (Case Study: St. Barongan)

The burn marks on the Pias Card are located on a scale of 6 -18 data is the sunshine duration.Determination of Region of Interest (RoI) is done with the aim of reducing noise in the Pias Card image segmentation process.solar irradiation system developed to determine areas that are considered important according to scale using the Cropping method.The system testing was carried out using 107 data of Pias Cards.Type Pias Card is SO-40F.Cropping principle is still manual, namely by setting the height of the image (Heigth), the left border (Left), and the right border (Right) in pixels.Based on testing, the best value settings are Heigth (H) = 5, Left (L) = 125, and Right (R) = 250 with a percentage of cropping success is 91.67%.
Acquisition Terminal Status Evaluation Based on Fuzzy Analytic Hierarchy

In view of the potential fault risk in the field operation of the acquisition terminal, there is still no effective method to make a reasonable evaluation on its running state. In this paper, we pick up the important influence factors of terminal running state and divide them into four aspects by using the data record of MDS system, electricity information collection system and SG186 information system. Then the fuzzy theory and analytic hierarchy process are used to construct judgement matrix. At last, we establish the running state analysis model which can be accurate to evaluate the running state of the acquisition terminal. At the end of this paper, we use a concrete example to illustrate our assessment process and get good results. Using this method to analyze the operation state of the acquisition terminal can effectively monitor the operation state of the acquisition terminal, which is of great significance to the field operation and maintenance work.
(+, $$\wedge $$∧) Fuzzy Relational Inequality and Its Network Optimization

Fuzzy relational equations with $$(\vee ,\wedge )$$ composition were introduced [1, 1] and investigated by lots of fuzzy mathematics researchers. As an extension, fuzzy relational equations or inequalities with other compositions were studied. In fact, the composition could be replaced by the general max-t-norm composition, although $$(\vee ,\wedge )$$ and $$(\vee ,\cdot )$$ compositions were the most frequently and commonly used t-norms.
Culture and Art Education to Promote Cultural Welfare in Civil Society

Today, intellectual education based on the various experiences and emotions of citizens through culture and art education draws attention in terms of obtaining improvements in social welfare. Citizens can become further aware of culture and art and get a wider range of benefits in cultural welfare. Culture and art education are important activities of cultural welfare. This study examines some of the successes of the Seoul Arts Center academy, which represents and hosts diverse cultural and art activities in South Korea. These activities have led to successful promotion of culture and art education for Seoul citizens. Based on Lewin’s change management process model, the cases that were analyzed focus on the change in culture and art education in the Seoul Arts Center academy. Findings from this study indicate the contribution to the improvement in cultural welfare for citizens from their engagement in activities within art education and culture. The discussion also highlights that good management of the changes in educational activity with the citizens’ needs and improvements in the maturity of civil society were critical factors for educational sustainability and success.
Shape Estimation and Gesture Recognition for Origami-Based Deformable Surfaces

An origami tessellation display is an origami-based input/output device that has a flexible and unique deformable surface. This study achieves shape estimation and user gesture recognition for an origami-based surface without the use of external devices. Six tri-axis accelerometers are employed and embedded inside the surface, and shape estimation and gesture recognition models are constructed by means of neural networks and a random forest. Despite the small number of sensors and simple machine learning approaches, the models demonstrate sufficient estimation and recognition ability. In this paper, a framework in which shape estimation and gesture recognition are performed in real time is also presented.
Research on Rotation Angle of Faucet Detection Device Based on Image

In order to improve the accuracy of faucet sensitivity test and poor repeatability in the faucet detection equipment, image processing technology is applied to the faucet detection equipment for faucet auxiliary installation and its stroke angle calculation in this paper. The working principle of the rotation control device is first explained in this paper, then this paper proposes a correction scheme of the motor motion based on the image processing result, and then explains the advantages of the camera in the auxiliary installation of the faucet, and also explains the process on the calculation of the stroke angle of the faucet based on the machine vision. The actual tests show that the repeatability of the faucet sensitivity test is significantly improved.
Detection of splicing forgery using differential evolution and wavelet decomposition

Abstract In this paper, we have proposed a computationally efficient algorithm to detect splicing (copy-create) forgery, our proposed method is developed by using differential evolution and wavelet decomposition, the differential evolution algorithm automatically generates customized parameter values of tampered images, and wavelet decomposition is used to process large-size images under block-based framework. Our proposed method is resilient to distortions, such as the addition of Gaussian noise, scaling and compression of the forged images.
Emotional expression of students and the pedagogical process

This article discusses the need for adequate perception and interpretation of emotional states and expressions of students in the process of pedagogical interaction. The connection has been found between the emotional expression of students and their ethnic, social and cultural identities. Based on the research findings of the emotional space of the population in the Russian Far East (Kamchatka territory and Magadan region), the authors demonstrate the specifics of emotional states of students, conditioned by the harsh climate and extreme living conditions, including seismic activity, permafrost and other. The article suggests the ways of forming teachers’ professional skills and knowledge, necessary for a successful professional career in ethnically diverse regions with a high level of natural and technological risks. These include such as: special courses as part of the general curriculum, attending national celebrations, local history museums, familiarity with folklore, decorative and applied arts, which objectify ethnic mentality. This will increase the level of competences of teachers in interpreting emotional states and expressions of teachers.
The peculiarities of professionalization of students-teachers with different levels of psychological stability

The article is devoted to the study of the peculiarities of formation of professionalism of pedagogical high school students with different level of personal stability. The role of professional ideas as the characteristics of the professionalization of the subject is revealed. Quantitative and qualitative analysis of the peculiarities of development of a professional was carried out on the basis of the author’s methods of assessing of professional ideas of students distributed in four groups, corresponding to the type of their professional orientation: “organizers”, “subject teachers”, “communicators” and “intellectuals”. Analysis of the ideas about the object of future activity of students with different levels of stability demonstrated the existence of differences in the evaluation of the image parameters. It turned out that psychological stability is inversely interconnected with such parameters of ideas about the object as the activity and clarity. Indicators of ideas about the object of activity are also associated with the type of pedagogical orientation. The obtained data allow us to conclude that the personal stability of the subject in combination with the type of his professional orientation form an individual way of formation of the subject of pedagogical activity.
An Improved Approach for Sarcasm Detection Avoiding Null Tweets

Among the plethora of social media, Twitter has emerged as the favorite destination for researchers in recent times. Many researchers are inclined to work on Twitter due to the availability of massive tweets and its unique features like hashtags and short messages. In recent times, various studies have preferred the hashtags (#sarcasm and #sarcastic) to collect Twitter dataset for sarcasm detection. However, hashtag-based distant supervision suffers from the problem of the inclusion of null tweets in the datasets which can be considered as a critical one for sarcasm detection. In this article, an algorithm is proposed for automatic detection and filtration of null tweets in the Twitter data. Additionally, an algorithm to identify sarcastic tweets using context within a tweet is also proposed. This approach use dictionaries of handpicked hashtag words, emoticons as the context within a tweet. Finally, we deployed a rule-based algorithm to analyse the performance of the proposed approach. The proposed approach attains the accuracy of 97.3% (after filtering null tweets) and 83.13% (without filtering null tweets) using a rule-based approach. The attained results conclude that after elimination of null tweets, the performance of the proposed system improved significantly.
Applying Softmax Classifiers to Open Set

Many artificial neural network classifiers assume a closed-world, where the composition of classes is fixed and known, however problems where this assumption does not hold are found in nearly every case where multi-class classification is applied. For example, in network traffic classification the actual number of classes often dramatically exceeds the number of classes known or labelled at training time. Various treatments have been proposed to adapt closed-set classifiers for application in open-set scenarios, both formal and informal. To demonstrate the effectiveness of these treatments, we conducted an empirical study using a simple example based on the MNIST digit classification problem. Our results show the various treatments make trade-offs between classification recall and precision. We demonstrate that softmax output equalisation during training can significantly improve performance in open set classification.
Semantic Inference Network for Human-Object Interaction Detection

Recently many efforts have been made to understand the scenes in images. The interactions between human and objects are usually of great significance to scene understanding. In this paper, we focus on the task of detecting human-object interactions (HOI), which is to detect triplets $$<\!human, verb, object\!>$$ in challenging daily images. We propose a novel model which introduces a semantic stream and a new form of loss function. Our intuition is that the semantic information of object classes is beneficial to HOI detection. Semantic information is extracted by embedding the category information of objects with pre-trained BERT model. On the other hand, we find that the HOI task suffers severely from extreme imbalance between positive and negative samples. We propose a weighted focal loss (WFL) to tackle this problem. The results show that our method achieves a gain of 5% compared with our baseline.
Comparative Analysis of Methods for Segmentation of FMRI Images Based on Markov Random Fields

The problem of segmentation of three-dimensional fMRI images based on the Bayesian approach is considered, where Markov Random Field is used as the prior distribution, and von Mises-Fisher distribution is used as the observation model. The main problem when applying this approach in practice is an estimation of the model parameters. In this paper, we review algorithms HMRF-MCEM, HMRF-EM and GrabCut, which implement this statistical model and estimate parameters without the usage of the labeled training data. The methods HMRF-EM and GrabCut were introduced in conjunction with other statistical models, but after a small modification, they can be used with the von Mises-Fisher distribution. A comparative study was carried out by performing experiments on both synthetic, generated from the statistical model, and real fMRI data.
3D Hand Tracking by Employing Probabilistic Principal Component Analysis to Model Action Priors

This paper addresses the problem of 3D hand pose estimation by modeling specific hand actions using probabilistic Principal Component Analysis. For each of the considered actions, a parametric subspace is learned based on a dataset of sample action executions. The developed method tracks the 3D hand pose either in the case of unconstrained hand motion or in the case that the hand is engaged in some of the modelled actions. The tracker uses gradient descent optimization to fit a 3D hand model to the available observations. An online criterion is used to automatically switch between tracking the hand in the unconstrained case and tracking it in the case of learned action sub-spaces. To train and evaluate the proposed method, we captured a new dataset that contains sample executions of 5 different grasp-like hand actions and hand/object interactions. We tested the proposed method both quantitatively and qualitatively. For the quantitative evaluation we relied on our dataset to create synthetic sequences from which we artificially removed observations to simulate occlusions. The obtained results show that the proposed method improves 3D hand pose estimation over existing approaches, especially in the presence of occlusions, where the employed action models assist the accurate recovery of the 3D hand pose despite the missing observations.
Deep Learning for Designing an AI Player of the Puzzle Game Geometry Friends

Nowadays artificial intelligence (AI) has attracted much attention in many fields. To evaluate its performance, games have been often used as testbeds. Geometry Friends is a puzzle game whose objective is to collect targets by controlling one or two characters, the circle character and the rectangle character. In order to achieve the objective, an AI player must determine the collecting order of the targets and move a character to each target. Although methods to design AI players for the game have been proposed, the AI players cannot necessarily collect targets rapidly. For this difficult puzzle game, the application of deep learning is promising because the deep learning gives high performance in many fields such as image and speech recognition. In this paper, we propose a deep learning method for designing an AI player which moves the character to a target. In order to pply the deep learning, training data must be prepared. This preparation, however, is a hard task. We propose also a method to automatically generate the training data used for our deep learning.
Link Analysis Between Objects in Object-Associated Electronic Document Management Systems

The organization of links between documents in electronic document management systems is considered. It is shown that during the modification to the systems of objectassociated document management systems, mutual connectivity in the system increases and implicit relationships between objects are manifested, which allow for an in-depth analysis of the process of combining objects.
Multi-Agent Automated Reasoning Toward Machine Self-Awareness: A Case Study

In this paper, we present a study on building a special SAARA (Self-Aware Automated Reasoning Agent) system for solving Freudenthal's Sum and Product puzzle, aimed to train the "self-reflection" and "subjective experience" abilities as in the Three Wise Men test performed by the Nao robots in Rensselaer Polytechnic Institute in July 2015. We show the dynamic evolution of corresponding knowledge sets in the automated reasoning process for the Sum and Product puzzle.
Designing Indonesian Geographic Application

Abstract Assesment of geogaphic concept and skills confirm the failure of our educational system to provide students with an adequate understanding of geography. Information regarding Indonesian geographic is highly need by many people especially the users, thus they are able to understand the accurate placement of a subject which is related to geography. This aplication requires regional data to be search by the user. This research used a descriptive method by collecting data in detail, in-depth and actual. The method is adapted from the knowledge and facts found in the field. Therefore, the aim of this research can be achieved; furthermore, this research is able to describe the position of an object systematically. The designed application is expected to help the users understand the location of an object or a place that they want to find and help the user to find detailed information about the area which include the name of the province, name of city and other information.
Efficiently Handling Scale Variation for Pedestrian Detection

Pedestrian detection is a popular yet challenging research topic in the computer vision community. Although it has achieved great progress in recent years, it still remains an open question how to handle scale variation, which commonly exists in real world applications. To address this problem, this paper presents a novel pedestrian detector to better classify and regress proposals of different scales given by a region proposal network (RPN). Specifically, we have made the following major modifications to the Adapted FasterRCNN baseline. First, we divide all proposals into small and large pools according to their scales, and deal with each pool in a separate classification network. Also, we employ two auxiliary supervisions to balance the effect of two parts of proposals on the back propagation. It is worth noting that the proposed new detector does not bring extra computational overhead and only introduces very few additional parameters. We have conducted experiments on the CityPersons, Caltech and ETH datasets and achieved significant improvements to the baseline method, especially on the small scale subset. In particular, on the CityPersons and ETH datasets, our method surpasses previous state-of-the-art methods with lower computational costs at test time.