Reviewerly/exHarmony huggingface dataset:
Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans Using Sparse Fidelity Loss and Adversarial Regularization

In clinical practice, magnetic resonance (MR) imaging is often scanned with large slice thickness due to many limiting factors such as scanning time. The acquired images are thus anisotropic, with much lower inter-slice resolution than the intra-slice resolution. For better coverage of the organs of interest, multiple anisotropic scans, each of which focus to a certain scan direction, are usually acquired per patient. In this work, we propose a 3D deep learning based super-resolution (SR) framework to reconstruct the isotropic high-resolution MR images from multiple anisotropic scans. In particular, we employ the spatially sparse fidelity loss to the locations acquired in anisotropic inputs, such that their intensities keep the same before and after the reconstruction. Meanwhile, the adversarial regularization is adopted to make sure that the entire reconstructed image owns consistent appearance perceptually. Different from other SR methods, our approach fuses inputs of multiple anisotropic images, instead of a single one. Moreover, our reconstruction is attained without using any supervision from the isotropic high-resolution images, making it unique among early works and highly applicable to many real clinical scenarios.
Graph2Bots, Unsupervised Assistance for Designing Chatbots

We present Graph2Bots, a tool for assisting conversational agent designers.It extracts a graph representation from human-human conversations by using unsupervised learning.The generated graph contains the main stages of the dialogue and their inner transitions.The graphical user interface (GUI) then allows graph editing.
Formation of Requirements to the Competency Model for a University Graduate in the Digital Economy

The article considers the issues of formation of requirements to a competency model for university graduates in the digital economy that allow ones to be competitive in the current economic environment.The urgent character of the problem stems from the requirements that are shaped by digitalization processes in the economy.The recent years have witnessed another wave of activity model transformation in education caused by the emergence of cutting-edge digital technologies.Implementation of these technologies could increase company productivity by 40%.In the near future, international competitiveness will be determined by the effective use of digital technologies along with the specialists who possess the required skills and competences.This is particularly relevant for both individual companies and entire countries which form the infrastructure and legal environment for digitalization.
Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies

This paper describes the use of Multi-Task Neural Networks (NNs) for system dialogue act selection.These models leverage the representations learned by the Natural Language Understanding (NLU) unit to enable robust initialization/bootstrapping of dialogue policies from medium sized initial data sets.We evaluate the models on two goal-oriented dialogue corpora in the travel booking domain.Results show the proposed models improve over models trained without knowledge of NLU tasks.
Hardware Acceleration of Sparse Oblique Decision Trees for Edge Computing

This paper presents a hardware accelerator for sparse decision trees intended for FPGA applications. To the best of authors’ knowledge, this is the first accelerator of this type. Beside the hardware accelerator itself, a novel algorithm for induction of sparse decision trees is also presented. Sparse decision trees can be attractive because they require less memory resources and can be more efficiently processed using specialized hardware compared to traditional oblique decision trees. This can be of significant interest, particularly, in the edge-based applications, where memory and compute resources as well as power consumption are severely constrained. The performance of the proposed sparse decision tree induction algorithm as well as developed hardware accelerator are studied using standard benchmark datasets obtained from the UCI Machine Learning Repository database. The results of the experimental study indicate that the proposed algorithm and hardware accelerator are very favourably compared with some of the existing solutions.
Correction: Controlling Social Stress in Virtual Reality Environments

[This corrects the article DOI: 10.1371/journal.pone.0092804.].
DEVELOPMENT OF WEB GAME LEARNING MATERIALS FOR PRIMARY SCHOOL STUDENTS

The purpose of this study is to develop a web-based learning game media to improve problem-solving skills in two-dimensional figure. Research method with R &amp; D use the ASSURE development model consisting of 6 steps, that is 1) analyze learners, 2) state objectives, 3) select method, media, and materials, 4) utilize media and materials, 5) require learner participation, 6) evaluated and revise. The validity of the product is assessed by the experts with the expert validation sheets. The practicality of the product was tested by a limited trial of 10 students followed by interviews to see student responses. The Product effectiveness was seen from one class trial with one group pretest-posttest design and the results were analyzed using the Paired-Samples T-Test. The results of this study are in the form of web-based game learning media to improve problem-solving skills in flat-build material. The Learning media is proved valid based on the expert media, material, and learning tests. Learning media was also proved practical based on the results of interviews by 10 students. Learning media is proved effective based on the Paired Samples T-Test which was taken from the results of the pretest and post test
Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation

Automatic dense parcellation of brain MR image, which labels hundreds of regions of interest (ROIs), plays an important role for neuroimage analysis. Specifically, the brain image parcellation using deep learning technology has been widely recognized for its effective performance, but it remains limited in actual application due to its high demand for sufficient training data and intensive memory allocation of GPU resources. Due to the high cost of manual segmentation, it is usually not feasible to provide large dataset for training the network. On the other hand, it is relatively easy to transfer labeling information to many new unlabeled datasets and thus augment the training data. However, the augmented data can only be considered as weakly labeled for training. Therefore, in this paper, we propose a cascaded weakly super- vised confidence integration network (CINet). The main contributions of our method are two-folds. First, we propose the image registration-based data argumentation method, and evaluate the confidence of the labeling information for each augmented image. The augmented data, as well as the original yet small training dataset, contribute to the modeling of the CINet jointly for segmentation. Second, we propose the random crop strategy to handle the large amount of feature channels in the network, which are needed to label hundreds of neural ROIs. The demanding requirement to GPU memory is thus relieved, while better accuracy can also be achieved. In experiments, we use 37 manually labeled subjects and augment 96 images with weak labels for training. The testing result in overall Dice score over 112 brain regions reaches 75%, which is higher than using the original training data only.
A Device-Independent Novel Statistical Modeling for Cerebral TOF-MRA Data Segmentation

Among the model-driven segmentation methods, the Maximum a Posterior (MAP) & Markov Random Field (MRF) is the popular statistical framework. However, there remains a dominating limitation in the existing statistical modeling, i.e., the data imaged by MR scanners with different types and parameters cannot be adaptively processed to lead accurate and robust vessel segmentation, as is well-known to the researchers in this field. Our methodology steps contribute as: (1) a region-histogram standardization strategy is explored to the time-of-flight magnetic resonance angiography data; (2) a Gaussian mixture models (GMM) is constructed with three Gaussian distributions and a knowledge-based expectation-maximization algorithm is explored to obtain the GMM parameters; (3) a probability feature map is captured according the estimated vascular distribution weight in GMM and then is embedded into the Markov high-level process to relieve the label field noise and rich the vascular structure. Our method wins out the other models with better segmentation accuracy and the sensibility to small-sized vessels or large arteriovenous malformation mass, which is validated on three different datasets and obtains satisfying results on visual and quantitative evaluation with Dice similarity coefficient and positive predictive value of 89.12% and 95.66%.
EUNICERT: Ethereum Based Digital Certificate Verification System

The fake certificate is a special global problem in today's digital age.Thousands of universities and educational institutions around the world do not exist but can release hundreds of millions of fake degrees.Verifying the integrity of qualifications is a real challenge for today's employers.Applying the anti-data modifying properties of blockchain technology, this study proposes a solution issuing and verifying digital certificates called EUniCert to solve this problem.By changing the design and integrating new consensus algorithm used in Ethereum platform into the Unicoin network that was used to verify and store the information related to the issued digital certificate, the EUniCert improves the latency to validate transactions as well as the number of verified blocks in the blockchain network compared to the previous solution that we have proposed.We implement a simple blockchain system to illustrate the management operation of the digital certificates on the ethereum platform.Besides, we conduct a simulation to evaluate the performance of our proposal compared with the previous system.The result is that the average latency decreases by 3.27 times as well as the number of verified blocks increases by 11% compared with the previous system.
Denoising of Seismic Signals Through Wavelet Transform Based on Entropy and Inter-scale Correlation Model

For effective removal of noises in seismic signals, this paper proposes an adaptive threshold denoising algorithm that integrates wavelet transform with entropy and inter-scale correlation (EIS) model.Firstly, noisy signals were decomposed by discrete wavelet transform, the highfrequency sub-bands on each scale were divided into equal subintervals, and the wavelet entropies of the subintervals were computed one by one.Secondly, the correlation coefficients of sampling points on each scale were calculated, and then compared with the high-frequency coefficients at corresponding positions.The comparison results, coupled with the wavelet entropies, were determine the noise variance of high-frequency sub-bands on each scale.Finally, the signals were reconstructed from the above results according to the new threshold function and the self-adaptive threshold rule.The experimental results show that our method outperformed several popular denoising approaches in terms of signal-to-distortion ratio (SDR), signal-to-noise ratio (SNR) and mean squared error (MSE).
An innovative citation recommendation model for draft papers with varying degrees of information completeness

Purpose As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation. Design/methodology/approach This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance. Findings The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy. Originality/value None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.
Prediction model of Leptospirosis Occurrence for Seremban (Malaysia) using Meteorological Data

Waterborne disease has a worldwide distribution and it was frequently happening in developing countries but rarely happen in developed countries [1].The waterborne disease belongs to the top five common diseases that cause of death.While leptospirosis is one of the top killers of water-borne diseases because more than 500 000 cases were recorded every year [2].Malaysia is one of the developing countries and is one of the countries that face this disaster [3].Generally, human will be infected with leptospirosis when they have direct contacts with the product of infected animals such as urine.Besides, they also can be infected by the indirect way by contact with the contaminated water or soil which consist of the leptospira species.This disease also can be infected through human to human transmission but based on the number of cases it is very rare [4].This pathogen can survive in tropical and subtropical environments Abstract: Leptospirosis is one of the waters borne diseases that widespread in Asia Pacific regions, especially developed countries.Over the past few years, the clinical data have shown Seremban experienced a significant number of leptospirosis patient.To minimize the impact of this disease, this study has set one objective which is to develop one prediction model to predict the leptospirosis diseases confirmed-cases by using Back-Propagation Neural Network (BPNN).A growing number of studies has shown the climate can be a predictor in outbreak incidence.Likewise, climate variable such as rainfall, temperature, and relative humidity affect in many ways especially for the transmission of vector and pathogens.Thus, these 3 parameters will be the main input for this model.Technically, this study will focus on the accuracy and the sensitivity of the model by finding the relationship between the meteorological data and clinical data.The clinical data was provided from the ministry of health Negeri Sembilan, while the meteorological data was provided from the Drainage and Irrigation Department and the Malaysian Meteorological Department.This study acknowledges that the amount of rainfall was correlated with the leptospirosis cases in all region of Seremban states such as Mantin, Seremban, Perentian, and Sikamat.In this study, preliminary exploration was performed by finding the best time for the meteorological data to correlate with clinical data (1 until 5-month lag).The model achieved 70% accuracy in prediction by combining the sum of rainfall, relative humidity, and temperature with 3-month lag as an input of the BPNN model.In conclusion, the authors believe this achievement of the model is an early stage for the prediction model.This model can achieve more than 70% accuracy by adapting some exploratory data analysis for every single variable or predictor.
A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

In recent times, artificial intelligence applications in the field of dynamic systems control have proved to be an efficient tool for the improvement of processes.Programmable Logic Controller-based control systems are found in the vast majority of industrial automation systems.In this paper, a fuzzy controller approach is proposed to enhance the design of industrial control systems using commercial PLCs.The fuzzy controller is tuned by artificial intelligent techniques, and the performances of different fuzzy controller-tuning methods are compared, including the metaheuristic ones.The results shown in this paper validate the proposal that metaheuristics tuning methods of fuzzy controller have better performance in intelligent automation.
Object-oriented Programming Course Reform Using Python Language in the Background of Artificial Intelligence

Using C++ or Java as the describing language is the traditional way of teaching of object-oriented programming (OOP).Currently with the rapid development of AI and Python programming language, teaching OOP using Python become more and more important and suitable.In this paper, firstly the disadvantages of C++ and Java languages as well as the advantages of Python for OOP teaching are discussed.Then the main content of OOP teaching using Python is put forward, which consists of two parts: the basic part and application part.The basic part should include the basic grammar of Python and basic knowledge and skills of OOP.For the application part, we suggest using an AI library Sklearn to practice some practical AI application.A specific instance of K-means clustering case teaching using Sklearn is also discussed in this paper.
Demo: Toward Continuous User Authentication Using PPG in Commodity Wrist-worn Wearables

We present a photoplethysmography (PPG)-based continuous user authentication (CA) system leveraging the pervasively equipped PPG sensor in commodity wrist-worn wearables such as the smartwatch. Compared to existing approaches, our system does not require any users' interactions (e.g., performing specific gestures) and is applicable to practical scenarios where the user's daily activities cause motion artifacts (MA). Notably, we design a robust MA removal method to mitigate the impact of MA. Furthermore, we explore the uniqueness of the human cardiac system and extract the fiducial features in the PPG measurements to train the gradient boosting tree (GBT) classifier, which can effectively differentiate users continuously using low training effort. In particular, we build the prototype of our system using a commodity smartwatch and a WebSocket server running on a laptop for CA. In order to demonstrate the practical use of our system, we will demo our prototype under different scenarios (i.e., static and moving) to show it can effectively detect MA caused by daily activities and achieve a high authentication success rate.
Modeling Agent-Oriented Methodologies for Landslide Management

One of the most fatal natural disasters in Batu City was a landslide. The percentage of casualties if directly affected by landslides is 47%. This number is quite large when compared to other natural disasters. Even though the potential of the tsunami is the biggest, when compared to the intensity of the occurrence, landslides are the most common and most often cause fatalities. One of the causes of many fatalities in natural disasters is the lack of preparedness management. For that reason the need to develop a technology that can support to reduce fatalities in landslides is needed. One of the technologies used to prevent the number of fatalities caused by natural disasters is to use multi-agent system. One of the advantages of an agent is use Belief-Desire-Intention (BDI) models for building the Muiti-Agent Systems (MAS). The specific objective of this study was to model a simulated natural disaster landslide using the Prometheus Methodology. Evaluation is done by generating a model using jackCode and implementing it using the Java Agent Development Framework. The results of this study indicate that a model made using the Prometheus Methodology can be used as a simulation of natural disaster preparedness for landslides.
An Improved APF-based Path Planning Algorithm for a Quadrotor Intercepting Autonomous Ground Robots

In this paper, a path planning problem is investigated for a quadrotor which is required to intercept a group of target robots in an environment with moving obstacle robots. First of all, the kinematics model of the quadrotor, target robot and obstacle robot are established and the path planning problem is formulated. Then, an IMM-based estimation is given to provide the precise velocity of the target robot to be used in path planning algorithm, which performs a periodic movement with a rough period. Then, an improved Artificial Potential Field(APE)-based path planning method is proposed in which the suffer of the local minima can be avoided. Finally, simulation experimenal results show the effectiveness of the proposed algorithm.
Photovoltaic power generation probabilistic prediction based on a new dynamic weighting method and quantile regression neural network

In order to allow efficient planning of electric power system, the reliable prediction of photovoltaic power generation is very important. This paper proposes a new solar power probabilistic forecasting method based on dynamic weighting method, K-Nearest Neighbor (KNN) algorithm and quantile regression neural network (QRNN). Firstly, a new dynamic weighting method is used to tune the optimal weights of meteorological factors dynamically. Then based on the optimal weighted Euclidean distance metric method, KNN algorithm is used to find the similar examples more accurately. Finally, QRNN model is established to obtain different quantiles and approximately estimate the probability distribution of solar power output. The data from IEEE Working Group on Energy Forecasting is used to valid ate the effectiveness of proposed method and the experimental results show that the proposed model has reliable and accurate prediction ability.
MULTIPLE VEHICLES AND PEOPLE TRACKING IN AERIAL IMAGERY USING STACK OF MICRO SINGLE-OBJECT-TRACKING CNNS

Abstract. Geo-referenced real-time vehicle and person tracking in aerial imagery has a variety of applications such as traffic and large-scale event monitoring, disaster management, and also for input into predictive traffic and crowd models. However, object tracking in aerial imagery is still an unsolved challenging problem due to the tiny size of the objects as well as different scales and the limited temporal resolution of geo-referenced datasets. In this work, we propose a new approach based on Convolutional Neural Networks (CNNs) to track multiple vehicles and people in aerial image sequences. As the large number of objects in aerial images can exponentially increase the processing demands in multiple object tracking scenarios, the proposed approach utilizes the stack of micro CNNs, where each micro CNN is responsible for a single-object tracking task. We call our approach Stack of Micro-Single- Object-Tracking CNNs (SMSOT-CNN). More precisely, using a two-stream CNN, we extract a set of features from two consecutive frames for each object, with the given location of the object in the previous frame. Then, we assign each MSOT-CNN the extracted features of each object to predict the object location in the current frame. We train and validate the proposed approach on the vehicle and person sets of the KIT AIS dataset of object tracking in aerial image sequences. Results indicate the accurate and time-efficient tracking of multiple vehicles and people by the proposed approach.