Reviewerly/exHarmony huggingface dataset:
Towards an Extensible Architecture for Ideation

Ideation is an important activity of Design Thinking, a process that may benefit from different levels of automation in its activities. Preceded by Immersion and Analysis activities, Ideation can be enhanced by computational approaches like debate synthesis and mediation, sentiment analysis, and so on. In this paper an architecture for an extensible platform for ideation is addressed. Initially, it comprises a set of components to cope with those functionalities. The extensibility of this platform is in the sense that it shall allow the inclusion of new components like creation of domain ontologies for integration of different studies in the same domain. The general purpose of this platform is to support the creation of ideas by (i) constructing consensus among specialists and (ii) managing dissents in order to keep in track of marginal ideas that can become dominant ones as the discussion advances. It can be applied to many fields, like innovation, Studies of Future, definition of complex diagnoses, etc. Actually, this proposal came up from an experience with a study of future in which some experts had tried to envision trends for some years ahead in order to propose strategic actions for reaching a desired status for Brazil as a successful, fair, and inclusive country. The proposal includes an open and interactive computational environment to enable (i) structuring debates in threads of discussion; (ii) the gathering of ideas about topics of interest; (iii) the debate on the ideas put forward in order to identify the most relevant ones; (iv) synthesis of a debate (anytime summarization); (v) identification of the prevailing sentiments in a debate; and (vi) identification of variables relevant for the sake of the debate target.
SoundscapePillow; a Design for Creating Personalised Auditory Experiences for People with Dementia

This paper explores how we can design personalised auditory experiences for people with dementia (PwD). We introduce a new product design called the SoundscapePillow. The SoundscapePillow is a long tubular shape changing pillow that enables personalised auditory experiences for PwD by listening to (nature) soundscapes. The built-in sound system produces these soundscapes to empower the PwD to relax, to reduce agitation, and to remember experiences from the past. In combination with an accompanying App the experience can be personalised for the PwD. The concept was put in practice by conducting two small scale explorative user tests with a prototype of the concept. This paper discusses the insights gained from the design process of the SoundscapePillow, the concept itself, and what future work is needed.
Mathematical Model for the Assignment of Participants in Selection Processes

This work proposes two mathematical models for the assignment of selection processes candidates, such as the distance traveled by the participants and the organizing entity total cost are minimum. A trade-off curve is indicated, based on the two models.
Developing an Information Security Management System for Libraries Based on an Improved Risk Analysis Methodology Compatible with ISO/IEC 27001

This paper describes a new risk analysis methodology for libraries based on steps filtered from existing methodologies that are compatible with the ISO/IEC 27000: 2013 standard. After analyzing MAGERIT, OCTAVE and NIST 800-30 risk analysis methodologies, the most important steps were identified and those that do not fit in library type of organization were discarded. Once the methodology was created, it was tested through a real implementation in the Library system of a university to verify its benefits.
A Parallel and Improved Quadrivalent Quantum-Inspired Gravitational Search Algorithm in Optimal Design of WSNs

Wireless Sensor Networks (WSNs) are recently used in monitoring applications. One of the most important challenges in WSNs is determining the operational mode of sensors, decreasing the energy consumption while the connectivity requirements and the special properties are satisfied. This problem is an NP-hard one and is a time–consuming progress. In this study, an improved version of quadrivalent quantum-inspired gravitational search algorithm as a new metaheuristic, well suitable for quadrivalent problems is proposed using Not Q-Gate to optimize the performance of the WSN. Beside, to enhance the speed and the accuracy of the algorithm more, we used a parallelizing technique using Open-MP. Parallelizing this algorithm on mentioned problem is useful from four aspects; 1 - accelerate the speed of the algorithm, 2 - improving the quality of solutions by letting the increasing the population size, 3 - The possibility of using the algorithm, in larger-scale WSNs and 4 - power affectivity of the base station using multicore processors. To validate the performance of our proposed approach, a comparison between this approach and the previous methods is performed. Our experiments verified that the proposed method can effectively improve the performance more than 2.25 times and the speedup faster than 4 times on an 8-core CPU.
Innovative LQR Control Method for Vessel Rudder Control

In order to effectively control vessel rudder under the random interference, this paper presented an innovative method based on extended linear-quadratic regulator (LQR) control method and adaptive genetic algorithm (AGA). With respect to the actual vessel navigation environment, the extended LQR control method is explored by using a series of calculus transformation under the random interference. Besides, the AGA is utilized to optimize two kinds of weighted matrices in Riccati differential equation. What's more, the crossover and mutation probabilities are adaptively computed in the process of AGA. The numeric simulation indicated that the proposed method can effectively control vessel rudder with the satisfactory robustness.
Mongolian-Chinese Unsupervised Neural Machine Translation with Lexical Feature

Machine translation has achieved impressive performance with the advances in deep learning and rely on large scale parallel corpora. There have been a large number of attempts to extend these successes to low-resource language, yet requiring large parallel sentences. In this study, we build the Mongolian-Chinese neural machine translation model based on unsupervised methods. Cross-lingual word embedding training plays a crucial role in unsupervised machine translation which generative adversarial networks (GANs) training methods only perform well between two closely-related languages, yet the self-learning method can learn high-quality bilingual embedding mappings without any parallel corpora in low-source language. In this work, apply the self-learning method is better than using GANs to improve the BLEU score of 1.0. On this basis, we analyze the Mongolian word lexical features and use stem-affixes segmentation in Mongolian to replace the Bytes-Pair-Encoding (BPE) operation, so that the cross-lingual word embedding training is more accurate, and obtain higher quality bilingual words embedding to enhance translation performance. We reporting BLEU score of 15.2 on the CWMT2017 Mongolian-Chinese dataset, without using any parallel corpora during training.
Quantitative evaluation of the defocusing degree in fringe projection profilometry

Camera/projector defocusing is a method of generating sinusoidal images for binary images in fringe projection profilometry. While the most appropriate degree of defocus is often difficult to determine. Therefore, in this paper, it is proposed a defocusing degree evaluation algorithm. This algorithm is to calculate the image gray value error E of the fitted sinusoidal image and the actual image based on image difference and Levenberg-Marquardt (LM) iteration method. Firstly, differential operation is performed on the image captured by the camera. And then, the LM iteration method is executed on the difference image. The phase fluctuation error E<sub>M</sub> is quantitatively described as E ∙ S &alpha; by image gray range S, where &alpha; is constant which can be determined experimentally. For a system with no defocusing, the measured phase fluctuation error of &plusmn; 0.05 radian, this can be reduced to &plusmn; 0.01 radian under the optimal defocus degree obtained by this method. The algorithm can handle one image within 20 ms, which meets the requirements of real-time calculation.
Emergency Landing Aware Surveillance Planning for Fixed-wing Planes

In this paper, we introduce the Emergency Landing Aware Surveillance Planning (ELASP) problem that stands to find the shortest feasible trajectory to visit a given set of locations while considering a loss of thrust may happen to the vehicle at any time. Two main challenges can be identified in ELASP. First, the ELASP is a planning problem to determine a feasible close-loop trajectory visiting all given locations such that the total trajectory length is minimized, which is a variant of the traveling salesman problem. The second challenge arises from the safety constraints to determine the cost-efficient trajectory such that its altitude is sufficiently high to guarantee a gliding emergency landing to a nearby airport from any point of the trajectory. Methods to address these challenges individually already exist, but the proposed approach enables to combine the existing methods to address both challenges at the same time and returns a safe, feasible, and cost-efficient multi-goal trajectory for the curvature-constrained vehicle.
Entanglement Generation by Delocalized Single-Photon Addition

We report an experimental realization of entangled states obtained by applying a superposition of single-photon creation operations between two uncorrelated temporal modes populated by coherent states of the same amplitude. Such entangled states of light can be populated with a large number of photons while preserving their non classical correlations. Besides their importance from the fundamental point of view to study quantum effects in large-scale systems, these states are found to be surprisingly robust against losses [1] and thus well suited to travel over long distances and to be stored in atomic ensembles.
A Coffee Break for Bitcoin

For many, the appeal of bitcoin is in its detachment from government regulation. However, the Coffee bonding theory, which initially arose in the context of foreign stocks, suggests certain benefits of regulation for bitcoin, including increased legitimacy. By invoking the Coffee bonding theory, this Article offers another perspective on the regulation of bitcoin.
The Design &amp; Implementation of a Small Scale Crawler based Web Search Engine

This paper presents a framework of a small-scale crawler based web search engine, being implemented togather content over the web. Our Search Engine is efficiently designed to search and store the Web content, thereby producing equally effective search results as other similar structures. The process behind a search engine is complicated. Search engines indexesa large content crawled fromnumber of web pages havingheaps of unique words. They respondsa large number of queries every seconds. Although there is a greatsignificance of web search engines at large-scale, less analysis has been done regarding their working. With the recent advancement in technology and increment in the usage of web,the way a web search engine is created few years back is quite different as it created now. This project provides an in-depth explanation of our small-scale crawler based web search engine.
“I don't understand it so it can't be good”: Users' acg domain expertise and perceived quality of video tags

ABSTRACT Tag quality is an important factor to the success of social tagging systems and platforms. Users' domain expertise may influence they perceive tag quality. This study aims to explore how users of different domain experience (frequent user, occasional user, and non‐user) perceive the quality of the same tags. We examined an online video community, Bilibili, which specializes in Anime, Comic and Games (ACG) subculture. We asked 60 users to watch 15 videos and rate the 95 tags of these videos, and found that: 1) Users with more domain expertise give higher ratings for tags' relevance to the videos and their retrieval value; 2) Occasional users have the lowest understandability rating, followed by non‐users, and frequent users; 3) users think high‐frequency tags are less suitable for retrieval. These results may provide insights to high quality tag selection for personalized recommendation and retrieval.
Thermal Conductivity Simulation of High Temperature Overalls Materials

Abstract When workers work in high temperature environments, they need to wear high temperature work clothing to avoid burns. The garment is often composed of three layers of fabric material, which are referred to as layers I, II, and III from the outside to the inside, and there is a gap between the layer III and the skin. We refer to this void as the IV layer. In order to make the design of special clothing more efficient, reduce research and development costs, and shorten the development cycle, this paper uses mathematical models to determine the temperature changes outside the skin of the dummy. We assume that the body temperature of a dummy wearing a high temperature workwear can be controlled at 37 °C. We have established a set of thermal differential equations as a heat transfer model for the four layers of high temperature work clothing. First, we consider three heat transfer modes of heat conduction, heat radiation and heat convection, and analyze the heat conduction form of each layer to obtain a one-dimensional unsteady heat transfer model. Then, the numerical solution of all partial differential equations is obtained by the finite difference method of Crank-Nicolson format, and the temperature distribution outside the skin of the dummy is obtained. Finally, this paper gives a proposal for the design work of four-layer high temperature work clothing in combination with the actual situation.
Evaluation of super-resolution algorithms for mosaic hyperspectral imagery

Hyperspectral imaging sensors acquire images in a large number of spectral bands, unlike traditional electro-optical and infrared sensors which sample only one or few bands. Hyperspectral mosaic sensors acquire an image of all spectral bands in one shot. Using a patterned array of spectral filters they measure different wavelength bands at different pixel locations, but this comes at the cost of a lower spatial resolution, as the sampling per spectral band is lower. Software algorithms can compensate for this loss in spatial sampling in each spectral channel. Here we compare the image quality obtained with spatial bicubic interpolation and two categories of super resolution algorithms: two single frame super resolution algorithms which exploit spectral redundancies in the data and two multiframe super resolution algorithms which exploit spatio-temporal structure. We make a quantitative assessment of the spatial and spectral image reconstruction quality on synthetic data as well as on semi-synthetic mosaic sensor data for applications in security and medical domains. Our results show that multi frame super resolution provides the best spatial and signal-to-noise quality. The single frame super resolution approaches score lower on spatial sharpness but do provide a substantial improvement compared to mere spatial interpolation, while providing in some cases the best spectral quality.
Interaction 3D Human Anatomy of Ear, Nose, Throat Using Mix Reality

Abstract The development of information technology in the future will continue to grow and full of innovations, all shortcomings in an application or equipment will continue to be minimized, all ways to simplify and efficiently a technology are continuously looked for and developed by experts in their respective fields. The frequent use of 3D objects and animation in medical learning media is felt to be incomplete without the direct interaction of users with modern computers so that users are more interested in learning for Ear, Nose and Throat. Therefore, in this study, an interaction system was made on the Ear Nose Throat medical learning application using Microsoft HoloLens. In this study used interactions that use hand gesture and voice input. The results of this study, users will be able to interact with the Ear, Nose and Throat using Microsoft HoloLens. With this extensive application, it will facilitate users in Ear, Nose and Throat medical learning more interactively with the unique features of applications that use voice input and hand gesture.
Method for Assessing the Importance Level of Influencing Factors of Network Information System-of-System

The importance level evaluation of the influencing factors of network information system-of-system linkage is studied in this paper. Firstly, the concept of network information system-of-system and its linkage are proposed, and the influencing factors of network information system-of-system linkage are extracted from the four aspects of physical domain, information domain, cognitive domain and social domain. Then, a method that evaluating the importance coefficient of linkage influencing factors and mapping the importance rank is designed. And the method includes expert group opinion consistency test and selection steps of domain expert subgroup with high authority and consistent opinion which is based on the group decision theory. Lastly, the validity and applicability of the proposed method are illustrated by typical cases.
Improved UFHLSNN (IUFHLSNN) for Generalized Representation of Knowledge and Its CPU Parallel Implementation Using OpenMP

Fuzzy Hyper Line Segment Neural Network (FHLSNN) (Kullarni et al., International Joint conference on 4:2918–2933, 2001) is a hybrid system that combines fuzzy logic (Zadeeh, IEEE Trans. Fuzzy Syst. 4:103, 1996) and neural networks (Zurada, Fundamental Concepts and Models of Artificial Neural Systems, 1992, pp. 30–36). It is used extensively for real-world pattern classification (Zurada, Fundamental Concepts and Models of Artificial Neural Systems, 1992, pp. 30–36). It learns patterns in terms of n-dimensional Hyper Line Segment (HLS). Modified Fuzzy Hyper Line Segment Neural Network (MFHLSNN) (Patil et al., The 12 IEEE International Conference, vol. 2, 2003) is a modified version of FHLSNN (Kullarni et al., International Joint conference on 4:2918–2933, 2001) that improves the quality of reasoning and recall time per pattern using modified fuzzy membership function. Updated Fuzzy Hyper Line Segment Neural Network (UFHLSNN) (Dhabe, 2016 International Conference on Computing, Analytics and Security Trends, 2016) for larger pattern datasets is proposed using minimum computational efforts to compute membership. In this chapter, we proposed improved version of UFHLSNN (Dhabe, 2016 International Conference on Computing, Analytics and Security Trends, 2016), called IUFHLSNN, for generalized representation of knowledge for better recognition. IUFHLSNN uses midpoints of HLSs computed for the recall phase and thus expected to provide better recognition, as suggested in Occam’s razor principle (Blumer et al., Inform. Process. Lett. 24:377–380, 1987). We compared serial and parallel implementations using Intel’s Xeon E5-2620 and obtained average speedup of 16.96× and 77.22×, respectively, for classification and recognition, for all the used datasets (Poker Dataset, https://archive.ics.uci.edu/ml/datasets/Poker+Hand ; QtyT40I10D100K DataSet, https://archive.ics.uci.edu/ml/datasets/QtyT40I10D100K; Skin Segmentation Dataset, https://archive.ics.uci.edu/ml/datasets/skin+segmentation ). In the same experiment the obtained percentage gain in time are 92% and 94%, respectively, for classification and recognition. Thus, we strongly recommend IUFLSNN and its OpenMP parallel execution. We also compared parallel executions on two different computing CPU architectures, viz. IBM’s POWER8 and Intel’s Xeon E5-2620. We found that IBM’s POWER8 is two times faster than Intel’s Xeon E5-2620. Thus, we strongly recommend generalized representation of HLS knowledge and OpenMP parallelization.
A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

Abstract The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell‐location‐based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance‐based threshold, K‐Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell‐graph. Histopathological images of liver were classified by using features extracted from T‐Distance, K‐Nearest Neighbor, Voronoi, Delaunay, and the proposed cell‐graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell‐graph model.
A Fast Hybrid-kernel Correlation Filter Tracking Algorithm

With the development of computer vision and imaging technology, high frame rate cameras improve tracking performance by higher quality videos. However, recent tracking algorithms are mostly proposed for ordinary cameras, of which the processing speed is far from enough to keep along with the acquisition speed in high frame rate cameras. In this paper, a fast tracking algorithm for high frame rate cameras was proposed. The algorithm improves the kernel correlation filter method in the kernel function and feature description. For the kernel function, a hybrid-kernel function was employed by fusing local and global kernel. For feature description and dimensionality reduction, the deformable part model without image pyramid is adopted for image description due to the improvement of video quality. The proposed fast hybrid-kernel correlation filter tracking algorithm performs better for real-time target tracking in the video of high frame rate camera than other methods. The experimental results on 50-segment videos of the OTB2015 and Need for speed (240fps) database verify the effectiveness. In OTB2015, the accuracy is 74.3% with 320fps in CPU, and the accuracy and frame rate are 70.6% and 273fps, respectively in Need for Speed.
A Point-Track Correlation Algorithm Based On Pixel Gradient and Direction

The point-track correlation is an technique for the radar system to track two targets. The traditional Bayesian point-track correlation algorithm is associated with statistical distance based on target state parameters as a metric. However, with the increasing popularity of high-precision radars, the imaging performance of radars is gets better and better, and the use of image processing principles of image processing to solve radar signal processing to solve radar signal processing problems has become a new trend. This paper proposes an association algorithm based on pixel gradient and direction distribution features, and improves the Mahalanobis distance association algorithm by using the pixel gradient and directional characteristics of radar echoes. It can solve the technical problems that the target batch number disappears and the target track is chaotic because of the multi-target radar reflection signal slumps for a long time. The simulation results demonstrate the effectiveness of the algorithm.