Reviewerly/exHarmony huggingface dataset:
Sensor Fusion with Censoring Limits

Sensor fusion enables higher precision and wider sensing ranges when only multiple low quality sensors with limited dynamic range are available or afforded. Here, the sensor fusion approach is used for the radar tracking problem to extend the range of tracking capability. The proposed approach is performed with specific consideration to censoring limitations of a radar system. In this paper a Cram\'er Rao lower bound for a censored measurement model is derived. Motivated by the results of this bound the Tobit Kalman filter is used as a mechanism for data fusion amongst sensors. An event based system is proposed, where the probability of censoring will dictate whether specific measurements are used. This approach can be used for stacked sensors over large measurement regions, and, for applications with measurement redundancy. The method is demonstrated with a radar tracking problem, and shows great tracking ability of the state, even when the latent measurement is not being observed for some time.
Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation

Cross-domain image-to-image translation should satisfy two requirements: (1) preserve the information that is common to both domains, and (2) generate convincing images covering variations that appear in the target domain. This is challenging, especially when there are no example translations available as supervision. Adversarial cycle consistency was recently proposed as a solution [29], with beautiful and creative results, yielding much follow-up work. However, augmented reality applications cannot readily use such techniques to provide users with compelling translations of real scenes, because the translations do not have high-fidelity constraints. In other words, current models are liable to change details that should be preserved: while re-texturing a face, they may alter the face's expression in an unpredictable way. In this paper, we introduce the problem of high-fidelity image-to-image translation, and present a method for solving it. Our main insight is that low-fidelity translations typically escape a cycle-consistency penalty, because the back-translator learns to compensate for the forward-translator's errors. We therefore introduce an optimization technique that prevents the networks from cooperating: simply train each network only when its input data is real. Prior works, in comparison, train each network with a mix of real and generated data. Experimental results show that our method accurately disentangles the factors that separate the domains, and converges to semantics-preserving translations that prior methods miss.
Realization of Artificial Neural Networks on FPGA

Artificial Neural Networks (ANNs) are generally modeled and used as software based. Software models are insufficient in real time applications where ANN output needs to be calculated. ANN has an architecture that can operate in parallel to calculate hidden layers. The fact that ANN has such an architecture makes it potentially fast in calculating certain transactions. However, the speed of these operations in real-time systems depends on the specification of the hardware. Therefore, ANN design has been realized on FPGA which is capable of parallel processing. In this way, the ANN structure was realized in a hardware structure and it was provided to be used on real-time structures.
ROI Localization and Initialization Method for Left Ventricle Segmentation

This paper proposed a ROI localization method from cardiac short axis MR images and an initialization strategy for LV segmentation methods. To localize the ROI, we firstly apply cumulative value of the difference between adjacent temporalsequences during the whole cardiac cycle to estimate the motion region of the LV, namely the ROI we tend to locate. After localizing the ROI of the LV, we use Circle Hough Transform (CHT) to detect circle region in the ROI, basing on the shape characteristic of the LV. Then, the detected circle is used for initial contour of LV segmentation methods. Experimental results demonstrate that our proposed method is effective for ROI localizationand initialization ofLV segmentation methods.
Real-Time Multi-Person Smoking Event Detection

Smoking is a widespread problem around the globe and having an algorithm that can automatically detect smoking events is crucial in helping to public areas free of dangerous cigarette smoke. This paper presents a method of detecting smoking events through multiple human tracking, action recognition and smoke detection. In the proposed method, human objects are first detected using Histogram of Oriented Gradients (HOG). Then, the detected human objects are tracked using centroid tracking. The smoking event detection requires two factors. The first one is whether the detected human object is displaying actions that would suggest the act of smoking using CNN from Keras library and the other factor is whether there is cigarette smoke in the frame using SVM to identify the candidate areas and color features and pixel values as thresholds. The model obtained an f-score of 72.55% having one person in the frame and 64.37% having 2 or more.
"Rebooting memories"

In this paper, we explain the creation of "flow" in social media and real spaces using AI technology to colorize black-and-white stock photographs from digital archives and other locations. When visualizing the colors that the photographs should have had, the impressions of "freezing" in black-and-white photographs are "rebooted," and viewers can more easily imagine the events depicted. This bridges the psychological gap between past events and modern daily life, sparking conversations. The "flow" generated here causes the emergence of lively communication and increases the value of information. This method can help to pass precious materials and memories of past events into the future.
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models

Abstract Kernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.
Natural Language Processing-Based Information Extraction and Abstraction for Lease Documents

Lease abstraction is the method of compartmentalization of key data from a lease document. Lease document for a property contains key business, money, and legal data about a property. A lease abstract report contains details concerning the property location and basic lease details, price schedules, key events, terms and conditions, automobile parking arrangements, and landowner and tenant obligations. Abstracting a true estate contract into electronic type facilitates easy access to key data, exchanging the tedious method of reading the whole contents of the contract every time. Language process may be used for data extraction and abstraction of knowledge from lease documents.
Some highlights from the PKP 2019 International Scholarly Publishing Conference

This an overview of the PKP 2019 International Scholarly Publishing Conference that took place at the Autonomous University of Barcelona (Spain) on November 20–22, 2019.
Application of Chance-Constrained and Sample-Based Path Search for High Level Behavioural Planning: A Case Study of Autonomous Highway Lane Change Scenario

In this paper, we address the problem of high-level path-planning, for the application of an autonomous vehicle on a highway, using a chance-constraint based rapidly exploring random tree (CC-RRT*) methodology. Random exploration tree search quickly explores the free space available to the vehicle to generate feasible and/or optimal paths from one point to another. The inclusion of chance constraints allows us to effectively handle the uncertainty that arises from the behavior of other agents on the highway, as well as the noise in the measurements and process noise associated with our vehicle model. The novel contribution of this work is the adaptation of the constraints of high way driving (such as kinodynamic model, collision avoidance, and routing) to the framework of chance constraints and point-mass sample-based tree search. A succinct description of our problem formulation and solution method are presented along with some numerical simulations to illustrate the efficacy and performance of the proposed algorithm for the application of highway driving.
Correlation to redict Global Solar Radiation and Accuracy Evaluation of that Correlation for Korea during 10 years to 2014

Several studies have been performed to show how well the horizontal global solar radiation in Korea can be predicted using the Zhang-Huang model proposed in 2002 for China. A previous study proposed a new correlation with a new term for the duration of sunshine, which was proven to be closely related to the hourly solar radiation in Korea, into the Zhang-Huang model. Therefore, this study performed a validation and accuracy estimation of the model for all regions for 2005-2014. Another modified correlation for the regions without measuring the cloud cover was proposed and the accuracy and validity were evaluated. Finally, this study proposed a new correlation that could well predict the hourly and daily total solar radiation for all regions, seasons, and weather conditions, including overcast and clear, with greater accuracy and smaller error than other models proposed previously in Korea.
学校複合化と地域コミュニティの運営参画

Education decentralization in Nepal was initiated by naming all government schools as ‘community school’ which encouraged community participation in management and funding. However, integration of school facilities with communities for informal learning has been less practiced. Therefore, this study on 45-community schools analyses decentralization patterns of education system constructed from the history of transformation seen in each school. Furthermore, school facility types were analysed based on their assignment of functions within the integration approach. Four transformation patterns and three integration approaches were identified and compared to clarify the prospects of integrated approach.
Multi-Sensor-Fusion System for People Counting Applications

For economical as well as environmental reasons, logistical planning and efficient assignment of transport vehicles in public transportation need a precise knowledge of passenger utilization. To provide reliable figures sophisticated counting methods are demanded. Previously developed systems, mostly using a single 2D image sensor or a 3D depth sensor, can not fully achieve the required accuracy. In this paper, we present a robust people counting algorithm, based on multi sensor data fusion. Our solution runs on embedded systems with reasonable requirements with respect to computational power. 3D distance information, obtained from the ToF system, is used to perform the basic detection of objects. Once detected, the objects get additional classification features exploiting the data provided by a RGB camera and an IR thermal sensor. We describe the current state of fusing and processing of the collected data including the detection, classification (vital, non-vital), as well as sequential tracking and counting. We provide current counting results along with insights to future development concepts to improve the stated algorithms especially in terms of vital, non-vital classification and object recognition.
Performative education: is sociology required outside the humanities?

This article analyzes the importance of sociological education in the professional training of specialists outside the humanities from the point of view of “performative education”. The “performativity” of education is understood as the production of knowledge and educational activity and it becomes meaningful only in the situation of their demand and efficiency (J.-F.&amp;nbsp;Liotard). The сurrent trends in the formation of the curricula of higher educational institutions by academic disciplines of a “performative” nature have been expressed in reducing the hours of general humanities, among which the sociology teaching has been minimized or completely eliminated. The material for the empirical stage of the research was the organizational and methodological documentation accompanying the educational process in a number of Russian universities. The authors performed a content analysis of the curriculum of higher education. The following methods of collecting information were used: analytical-synthetic, induction and deduction, content and comparative analyzes. The performativity of sociological knowledge and the study of sociology at the individual level is expressed in several aspects: 1)&amp;nbsp;in the formation of the self-consciousness of the individual and the development of a professional integrated into social relations; 2)&amp;nbsp;in the development of graduates’ ability to analyze and forecast social transformations; 3)&amp;nbsp;in maintaining the value basis and civil law culture in society. The practical significance of the findings is expressed in strengthening the position of sociology as a science and academic discipline in the simulation of educational programs for professional training of university students.
Need-analysis of Interactive Games of Reading Literacy for Slow Learners

In the 21st century, the way of learning has changed based on sophisticated technology.The use of technology tools is a basic necessity in today's education.In the meantime, interactive games are one of the most famous and effective technology tools in teaching and learning.Interactive games have been used to facilitate learning of reading skills.There is a need to pay attention to reading skills to improve and encourage Literacy Numeracy Screening (LINUS) students to engage and be responsible for their own learning.Many studies have shown that interactive games are capable of helping students improve their reading skills.However, researchers found that the existing interactive games do not emphasize on reading skills.In addition, the games have failed to sustain students' attention in learning because they are not empowered with gaming elements for learning purpose.These issues indirectly have limited active participation of students.The purpose of this paper is to identify slow learners' difficulties in reading literacy and develop Reading Literacy Interactive Games by incorporating the relevant pedagogical elements namely 5 Gaming Elements for Effective e-Learning by Kapp which listed 5 principles which engage learners into subject learning.The research method focuses on following stages: requirement analysis, design and development including alpha testing by validation from expert and beta testing which is user acceptances test to support efficiency and effectiveness of the courseware for further improvement.The result from the alpha and beta testing indicates a positive feedback on Reading Literacy Interactive Games (RLIG).
Effects of Activation Function and Input Function of ANN for Solar Power Forecasting

Artificial Neural Networks are being used in many applications and forecasting is one of such application where it solves the purpose like stock market predictions, sales forecasting, etc., over the past. In this paper, ANN models are used for forecasting solar power. Multilayer perceptron (MLP) neural network models have been tested for different combinations of transfer functions and net input function on different number of neurons and layers for forecasting solar power. The evaluation and implementation of models are being measured by mean square error.
Scientific approaches and pedagogical principles providing the formation of Primary School students’ solo singing skills

The article presents the issues related to the formation of primary school schoolchildren’s solo singing skills. The purpose of the article is to substantiate scientific approaches and principles, the implementation of which contributes to successful formation of primary school schoolchildren’s solo singing skills. These approaches ae bound to the ideas of humanistic, structural-systemic, anthropological approaches. The principles of their implementation have been grounded ‒ the creation of a psychologically comfortable environment; individual-personal planning of the vocal-forming process; monitoring of the process and results of the classes aimed at mastering junior schoolchildren’s solo vocal skills; artistic and communicative orientation of the vocal and educational process; stimulation of primary schoolchildren’s creative activity in musical and playing forms alongside with the solution of their artistic and performing tasks. Some typical mistakes demonstrated by starting teachers have been analysed, which inhibit the process aimed at the formation of primary schoolchildren’s solo singing skills and their manifestation at three levels: interpretative-communicative, performing-artistic, forming-technological. The importance of mastering propaedeutic competence by university teachers has been substantiated as a way to anticipate professional mistakes typical for teachers-beginners. Keywords: singing skills; primary school pupils; scientific approaches; pedagogical principles; propaedeutics of typical pedagogical mistakes.
How to Obtain and Run Light and Efficient Deep Learning Networks

As the model size of deep neural networks (DNNs) grows for better performance, the increase in computational cost associated with training and testing makes it extremely difficulty to deploy DNNs on end/edge devices with limited resources while also satisfying the response time requirement. To address this challenge, model compression which compresses model size and thus reduces computation cost is widely adopted in deep learning society. However, the practical impacts of hardware design are often ignored in these algorithm-level solutions, such as the increase of the random accesses to memory hierarchy and the constraints of memory capacity. On the other side, limited understanding about the computational needs at algorithm level may lead to unrealistic assumptions during the hardware designs. In this work, we will discuss this mismatch and provide how our approach addresses it through an interactive design practice across both software and hardware levels.
Constructing Knowledge Graphs from Data Catalogues

We have witnessed about a decade’s effort in opening up government institutions around the world by making data about their services, performance and programmes publicly available on open data portals. While these efforts have yielded some economic and social value particularly in the context of city data ecosystems, there is a general acknowledgment that the promises of open data are far from being realised. A major barrier to better exploitation of open data is the difficulty in finding datasets of interests and those of high value on data portals. This article describes how the implicit relatedness and value of datasets can be revealed by generating a knowledge graph over data catalogues. Specifically, we generate a knowledge graph based on a self-organizing map (SOM) constructed from an open data catalogue. Following this, we show how the generated knowledge graph enables value characterisation based on sociometric profiles of the datasets as well as dataset recommendation.
Detection of Train Bottom Parts Based on XIoU

Due to the complexity, diversity, or even small size of train bottom parts, the current object detection algorithm cannot identify it accurately. We propose a new method to solve the above problem. This method changes the computational way of loss based on Darknet-yolov3 for specific train bottom parts and improves the situation that the object detection network has low accuracy in detecting. IoU(Intersection over Union) can be directly used as a regression loss. However, IoU has a plateau making it infeasible to optimize in the case of nonoverlapping bounding boxes. We put forward XIoU to calculate the loss function. XIoU calculates the case when IoU is equal to zero and increases the amount of prediction sample for regression loss. The test set used the No.2 camera pictures provided by railway administration. Compared with Mobile-Net, Yolov3 and Yolov3-giou, the experimental results showed that the training results of XIoU were 10% higher than Mobile-Net and Yolov3 on mAP, and 0.2% higher than Yolov3-giou.