Reviewerly/exHarmony huggingface dataset:
Collaboration Spotting Cite: An Exploration System for the Bibliographic Information of Publications and Patents

Collaboration Spotting is a knowledge discovery web platform that visualizes linked data as graphs. This platform enables users to perform operations to manipulate the graph to see and explore different facets of complex networks with multiple node and edge types. It combines information retrieval and graph analysis to effectively explore arbitrary data-sets. The platform is designed in a way that non-expert users without data science knowledge can explore it. For this, the data has to be specifically crafted in a form of a schema. The paper explores the platform in a bibliometrics context and demonstrates its search and relevance feedback mechanisms which can be applied through the navigation of an underlying knowledge graph based on publication and patent metadata. This demonstrates a novel way to interactively explore linked datasets through the combination of visual analytics for graphs with the combination of relevance feedback.
Development of Ontologies for Reasoning and Communication in Multi-Agent Systems

In future cyber-physical systems, such as smart factories and energy grids, ontologies can serve as the enabler for semantically precise communication as well as for knowledge representation and reasoning. Multi-agent systems have shown to be a suitable software development paradigm for cyber-physical systems and may well profit from harnessing ontologies in terms of reduced engineering effort and better interoperability. This contribution presents a development methodology for ontologies that enable communication and reasoning in Multi-Agent Systems for cyber-physical systems. The methodology is unique in addressing a set of requirements specific to this application domain.
A License-Based Search Engine

The reuse of licensed resources to produce new ones is very common and encouraged on the Web. But producing resources whose licenses are compliant with all reused resource licenses is not easy. It is necessary to know (1) the set of licenses with which the license of the produced resource is compliant and (2) what are the available resources whose licenses are part of this set. With CaLi, we provide an answer to the first concern. CaLi is a lattice-based model that partially orders licenses in terms of compatibility and compliance. In this demonstration, we illustrate the usability of CaLi through a prototype for the second concern. That is, based on a CaLi ordering of licenses we implement a license-based search engine which can answer questions such as “find licensed resources that can be reused under a given license” or “find licensed resources that can reuse a resource that has a particular license”.
Heat diffusion embedded level set evolution for infrared image segmentation

In this study, the authors present a novel level set method for infrared image segmentation. Local region-based models can fit intensity inhomogeneity partly but they are sensitive to local window scale. To deal with it, they embed an heat diffusion process in conventional level set evolution and convert heat to a part of data term in level set energy function. Besides, bias field model can extract the local intensity clustering property of the image. Therefore, the proposed method can deal with the interference of intensity inhomogeneity and complex background if appropriate seeded pixels are selected. Finally, the energy functional is minimised by a combinatorial optimal algorithm in a graph model to get a global optimal solution and accelerate the level set evolution implementation. The experiments show that the proposed method is robust to parameter setting, noise, and initial contour position. The comparisons on a large quantity of infrared image datasets with standard level set methods also demonstrate the efficiency of the proposed method.
Robust Diversity in Cognitive Science

The target article on robust modeling (Lee et al. in review) generated a lot of commentary. In this reply, we discuss some of the common themes in the commentaries; some are simple points of agreement while others are extensions of a practical or abstract nature. We also address a small number of disagreements or confusions.
Fast median filter with various kernel sizes

As a well known filter in the category of order statistic filters, median filter has been widely employed in the fields of image processing, computer graphics and computer vision etc. This paper proposes an approach for implementing the median filter with varying kernel sizes at each different pixels. The proposed algorithm consists of two kinds of histograms. One is column histograms for all columns, the other is a kernel histogram for kernel window. The column histograms are updated according to the scales when moving to the next row, while the kernel histogram is updated using column histograms when moving to the next pixel. Since the kernel sizes are varying at each pixel, the updating of column and kernel histograms uses operations of addition and subtraction depending on the scales. Compared with the brute-force implementation, the experiments show that the proposed algorithm is very fast and effective.
Missing the Forest for the Trees: Why Cognitive Science Circa 2019 Is Alive and Well

Abstract Núñez and colleagues (2019) chronicle in extraordinary detail the “demise” of cognitive science, as it was first defined in the late 1970s. The problem is that their account, however accurate, misses the forest for the trees. Cognitive science circa 2019 is alive and well; it just has not followed the path anticipated by its founders over 40 years ago.
A Mesh-Aware Ball-Pivoting Algorithm for Generating the Virtual Arachnoid Mater

We introduce the Mesh-Aware Ball-Pivoting Algorithm (MABPA), which generates a concave hull triangle mesh, taking one or more oriented, manifold triangle surfaces as input. All vertices of the concave hull mesh output consist of some of the vertices of the input meshes, and the output includes outmost triangle polygons from the input meshes. The MABPA was developed for synthesizing the virtual arachnoid mater around the brain, but is also useful for computationally generating a virtual membrane mesh around any internal organ in general. The arachnoid mater by the MABPA is useful for deformable brain simulation such as a virtual cerebral aneurysm clipping surgery, because collision detection between the membrane and the brain is basically unnecessary. They seldom intersect with each other, because each vertex of the membrane is associated with a vertex from the input.
iSMORE: An Iterative Self Super-Resolution Algorithm

In 3D medical imaging, images with isotropic high resolution (HR) are almost always preferred. In practice, however, many acquired images, including magnetic resonance imaging (MRI) and fluorescence microscopy, have HR in the in-plane directions and low resolution (LR) in the through-plane direction. The blurriness and aliasing artifacts that result cannot be solved by simple interpolation. Instead, many researchers have proposed super-resolution algorithms including state-of-art convolutional neural network (CNN)-based methods that require matched training data that have paired LR/HR examples. Since these data are often unavailable in practice, self super-resolution algorithms that do not need external training data have also been proposed. These self super-resolution methods assume that the in-plane slices are HR, and can therefore be used as HR training data. By degrading them into LR images, 2D CNNs can be trained and then used to restore the images in the through-plane. However, there are two issues with these approaches. The first one is that the assumption of HR in-plane slices is actually not solid since these thick in-plane slices are averaged true HR thin slices. Training on thick slices is equivalent to training on averaged true HR images, which is suboptimal. The second one relates to the 2D CNNs used on 3D volume, which cannot guarantee slice consistency. Regarding both issues as well as the generalizability of algorithm, we made four contributions. We show in this paper that one of the existing 2D CNN-based self super-resolution methods, SMORE, can be further improved by iteratively applying it using 2D or 3D networks, yielding 2D and 3D iSMORE. This iterative framework improves training data from thick slices to thinner slices after each iteration, thus improves super-resolution accuracy after each iteration, and solves the first issue. The second contribution is that it uses a 3D network to preserve slice consistency. The third contribution is the use of an edge-based loss function and noise reduction to enhance the performance. Finally, we perform iSMORE on both MRI and two-photon fluorescence microscopy, which demonstrates its generalizability.
Orderings of Extensional Fuzzy Numbers

This article follows the arithmetics of extensional fuzzy numbers that are based on the transitive closure of the used similarity relations and that form particular MI-prefields. The direction of the contribution goes towards defining the ordering of extensional fuzzy numbers in order to construct further notions from the mathematical analysis in the environment of extensional fuzzy numbers, especially the metric spaces.
Geostatistical Approach for Groundwater Quality Evaluation in Zarin Abad Plain, Iran

Background and Purpose: This study was undertaken, first, to investigate the hydrogeological setting of the study area and geophysical data, second to examine the general nature of the groundwater quality. In this regard, ordinary Kriging, Co-Kriging, and Inverse Weighted Distance (IWD) strategies were applied to develop spatial variability maps, and study the fluctuations in groundwater quality parameters in Zarin Abad plain, Zanjan Province, Iran in 2017-2018.&#x0D; Materials and methods: To inquire the groundwater quality parameters, samples were provided from 61 shallow and deeply drilled observed wells in Zarin Abad Goltapeh plain. The studies were carried out by using geostatistical methods to find out the most applicable method, which can be used to develop spatial variability maps in order to study the changes in groundwater quality parameters (Na+, K+, Ca2+, Mg2+, SO42-, HCO3-, Cl- and EC). The local geophysical, geological, and hydrogeological surveys were precisely accomplished to specify the architecture of various subsurface geological horizons. In addition, a geophysical investigation with a Schlumberger configuration was performed in the study region for the purpose of field data generation.&#x0D; Results: Based on key results, the values of electrical conductivity (EC) were recorded within the range of 480 and 6580 μS/cm. The order of major cations and anions were Na+&gt;Ca2+&gt;Mg2+ and SO42-&gt;Cl-&gt;HCO3-, respectively. It is worthwhile mentioning that groundwater salinity was found to be dependent upon factors, such as water long residence time and minerals dissolution.&#x0D; Conclusion: To assess the spatial distribution in groundwater parameters, the variable mode was used. The results obtained from Kriging, Co-Kriging, and IDW methods were then evaluated by the error indices of RMSE and MAE. Co-Kriging Model was the most optimal approach in studying the spatial variation of groundwater quality parameters.
Distances between Interval-valued Fuzzy Sets Taking into Account the Width of the Intervals

In this work we propose a new axiomatic definition of distance between interval-valued fuzzy sets which takes into account the width of the membership intervals and linear orders. We discuss some construction methods using aggregation functions which are defined in terms of admissible orders.
An Application of Convolutional Neural Networks on Human Intention Prediction

Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in demand.However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility.A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions.This study aims at improving the robotic intelligence by training it to understand human intentions.Different from previous studies that recognizing human intentions from distinctive actions, this paper introduces a method to predict human intentions before a single action is completed.The experiment of throwing a ball towards designated targets are conducted to verify the effectiveness of the method.The proposed deep learning based method proves the feasibility of applying convolutional neural networks (CNN) under a novel circumstance.Experiment results show that the proposed CNN-vote method out competes three traditional machine learning techniques.In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.
IOT based Biometric Finger Print Electronic Voting System for Rigging Free Governance

Biometric Fingerprint systems are used in the Voting machine for voter confirmation. fingerprint voting machine is designed where there is no need for the user to carry his ID which contains his essential details, so that the controller fetches the data from the fingerprint module and then compares it with already registered fingerprints of the voters. If this data matches then the person is allowed to cast is vote if not then the person is barred from polling his vote. By using push buttons voting will be done, the message regarding polling booth location and serial number information send to mobile numbers before a day of voting by using GSM module. MIT application is used for outside voters this is easier application to build electronic projects. Results are displayed in LCD as well as in thinkspeak IOT platform as a graph according to the applications thinkspeak is opensource IOT platform Is used to save and store data.To connect this server esp2866 Wi-Fi module is used.
Facial Based Human Age Estimation Using Deep Belief Network

Facial based human age estimation has attracted lot of attention nowadays. Age estimation has become quite challenging task due to variation in lighting conditions, poses, and facial expression. Despite so much research in facial based human age estimation still there is room to improve performance. To improve accuracy we present age estimation using deep belief network. Deep belief network have shown superior performance as compared to other classification models. Success of deep belief network lies in contrastive divergence algorithm. Facial images passes though viola johns facial detection algorithm, once face is detected facial featured are extracted using active appearance and scattering transform feature method. These feature extraction model not only extracts geometric features but also extracts texture features. Subsequently deep belief network classification model is built on partitioned training images and evaluated on testing images. We performed experimentation on training images. Dataset and results are obtained by varying training percentages. Compared to other age estimation models we achieved low mean absolute error of 4.95 for 70% training images dataset. This study shows that due to inclusion of deep belief network performance is excelled.
INTEGRATING SOCIAL, SOCIETAL, CULTURAL, AND SITUATIONAL CONTEXTS TO DEVELOP PRAGMATICS COURSE LEARNING MATERIALS: PRELIMINARY STUDY (INTEGRASI SOSIAL, SOSIAL, BUDAYA, DAN KONTEKS SITUASIONAL UNTUK MENGEMBANGKAN MATERI PEMBELAJARAN PRAGMATIK: STUDI AWAL)

The industrial revolution 4.0 clearly demands language learners to keep evolving to adapt to the environmental development. The learning of Pragmatics in the higher education cannot be based on the pragmatic concepts as what has happened recently, but it must be integrated with the fundamental research findings in the relevant fields of Pragmatics. This R & D raises the issue of: How the research findings on the situational and socio-cultural contexts are integrated in the material development of the Pragmatics course. The development of the Pragmatics course learning materials which integrates research findings in the pragmatic contexts is based on R & D model. The development output is then validated by experts, and piloted restrictively to several students. In the next step, the product output of the material development is revised to improve, which then will be published and disseminated. Due to the time constraint, this paper limits the scope of discussion only on the result of preliminary study. The other steps belong to the scope of different papers.
Creativity Support and Multimodal Pen-based Interaction

Creativity as a skill is associated with a potential to drive both productivity and psychological wellbeing. Since multimodality can foster cognitive ability, multimodal digital tools should also be ideal to support creativity as an essentially cognitive skill. In this paper, we explore this notion by presenting a multimodal pen-based interaction technique and studying how it supports creativity. The multimodal solution uses micro-controller-technology to augment a digital pen with RGB LEDs and a Leap Motion sensor to enable bimanual input. We report on a user study with 26 participants demonstrating that the multimodal technique is indeed perceived as supporting creativity significantly more than a baseline condition.
Path planning of 3D solid objects using approximate solutions

Path planning of 3D solid objects has many practical applications. Sampling-based planners like Rapidly-exploring Random Tree (RRT) tackle the problem by the randomized sampling of the configuration space. The well-known issue of the sampling-based planners is the narrow passage problem. Narrow passages are small collision-free regions in the configuration space that are, due to their low volume, difficult to cover by the random samples, which prevents the sampling-based planners from finding a path leading through the passages. To increase the success rate of the planners, the search in the configuration space can be guided using an approximate solution. An approximate solution is found, for example, considering a smaller object (robot). The performance of this planning concept depends on the way of reducing the size of the object and the ability to find an initial solution. In this paper, we propose a novel technique to reduce the geometry of the object by a combination of triangulation and iterative removal of surface triangles. This technique is suitable for both convex and non-convex objects. In many real-world applications, narrow passages can also contain the initial or goal configurations. We thus propose the extension of the goal region to increase the probability of finding the initial approximate solution. Experiments have shown that the proposed modification outperforms the simple guiding using approximate solutions, as well as other related state-of-the-art planners.
Australia's 'Abhorrent Violent Material' Law: Shouting 'Nerd Harder' and Drowning Out Speech

In the wake of the Christchurch Massacre, the Australian government passed the Criminal Code Amendment (Sharing of Abhorrent Violent Material) Act 2019 (“the AVM Act”) with essentially no debate or consultation. Creating new criminal offences for social media and hosting companies that fail to move certain violent content “expeditiously”, the legislation treats the matter of the viral spread of violent content online as a simple problem that could be solved by technology companies if they were only sufficiently motivated. But the problem is not simple, and the AVM Act instead creates incentives for tech companies to over-censor in order to avoid the threat of liability while failing to address the many deficits that currently exist in online content moderation and associated regulation.
Quasi-Entropies and Non-Markovianity

We address an informational puzzle that appears with a non-Markovian open qubit dynamics: namely the fact that, while, according to the existing witnesses of information flows, a single qubit affected by that dissipative dynamics does not show information returning to it from its environment, instead two qubits do show such information when evolving independently under the same dynamics. We solve the puzzle by adding the so-called quasi-entropies to the possible witnesses of information flows.
Notion and boundaries of objective form of expression of a work: analyze of European and Ukrainian approaches

In article the concept of an objective form of expression of a work in copyright law is analyzed in connection with the approaches of the European Union and Ukraine. On the basis of the analysis of judicial practice, the topical issues of the possibility of legal protection of works that are perceived not only by human sight and hearing, but also taste and olfactory receptors are considered. Perception and reproducibility are defined as the key elements of the objective form of the expression of a work in the copyright law of Ukraine. The inability of legal protection of the taste of a food product by copyright is conductedbased on the practice of the European Court of Justice. The preciseness and objectivity of perception are determined as the criteria of the form of expression of the work in the sense of the EU Court. The legal protection of the smell is carried out on the basis of the positive practice of the Danish Supreme Court in protecting the smell. A comparison is made with the position of the European Court of Justice regarding the protection of olfactory trademarks. It is concluded that the objective form of expression is revealed through precise and clear perception of a particular work.
Pedagogic conditions of moral and psychological training of stydents in the educational process of a military higher education institution

The article reveals the pedagogic conditions of moral and psychological training of students who are able to improve the efficiency of the educational process in a military higher education institution, to develop students ' military-professional, moral and psychological qualities, stable moral and psychological state necessary for further military service in the ministries and departments of the Russian Federation. These include: the creation of a clear understanding of students about the essence of moral and psychological support; carrying out a number of measures to improve the quality and effectiveness of moral and psychological training; reliance on the historical experience of hostilities; the formation of motivation of future military specialists to active training and service activities in the educational process of military high school; the formation of military students solid, deep and versatile knowledge on the basis of interdisciplinary connections; the activities of commanders (heads of departments) and teaching staff, contributing to the development of the student's positive attitude to self-education and self-upbringing; increasing attention to the upbringing process of young people.
Open Data at CMS: Status and Plans

The CMS Experiment at the LHC has released many large datasets of proton-proton collision data as well as simulation to the public as part of its commitment to data preservation and open access.The collision data released totals around 2 fb -1 at 7 TeV and nearly 12 fb -1 at 8 TeV.Data preservation describes the efforts to not only preserve the data itself but the conditions in which it can be analyzed.This requires archiving and documenting information such as run environment and conditions and tools such as analysis software and workflows.Open access describes the efforts to share these tools and information with the public, including educators and researchers.These efforts present many challenges, partly due to the complexity and size of the CMS datasets and also to the knowledge required to make meaningful use of them.We describe here how we dealt with these challenges and the usage of CMS open data not only in public education and engagement but also in fundamental research.Furthermore we describe plans for future releases of data.