Reviewerly/exHarmony huggingface dataset:
Maneuvering Target Tracking Based on Neural Network and Error Self-correction Technology

Neural network has strong nonlinear data characterization ability and solves many complex problems successfully. Trajectory estimation and prediction is time series forecasting but different from convention problems such as time video analysis. A method based on neural network and error self-correction technology achieving trajectory estimation and prediction is proposed in this paper. The method needs neural network without additional filtering algorithm, so the maneuver models and noise characteristics are not needed. According to the information of the previous moments before the investigated time, the information of the next moment or a specified time later can be obtained. For tracking a simple maneuvering target model with unknown parameters and noise characteristics, numerical simulation results show that FNN achieves filtering and it achieves a higher prediction accuracy than the Least Squares filtering. For tracking a complex maneuvering target model with strong nonlinearity, RNN combining with FNN is employed. For the measurement error with D standard deviation 2m, azimuth angle and the altitude angle measurement errors standard deviation with 2mil, the angle predicting error standard deviation is less than 1.3mil, which shows RNN combing with error self-correction technology has high accuracy. It meets the technical requirements for maneuvering target tracking as well as various similar applications.
Development of Web Application for Visualizing City Emergencies

To solve problems with emergencies, you need to have an idea in which regions more different emergencies occurred, in order to identify some kind of report and analysis. It is also not enough to have only the location of the emergency itself, you need to have different types of information about the emergency itself. This work describes a system that displays different types on a map with the necessary information about from sources of news sites. RandomForest and xgboost machine learning methods were used for forecasting. With the help of the geographic information system, types of emergencies are visualized on the maps of the city of Almaty. All visualization algorithms and technologies are implemented in Python.
Building Health State Recognition Method Based on Multi-channel Convolution Neural Network Fusion

The identification of the health status of buildings has been paid more and more attention by all sectors of the society. The early warning of catastrophes or the assessment of the damage degree and residual life of building structures after catastrophes has become a hot topic for scholars from all over the world. In order to improve the performance of building health state recognition, a novel framework based on multi-channel convolution neural network fusion is proposed in this paper. By combining the output results of different convolution neural networks, temporal information and spatial information are used to achieve the accurate classification of building health status. Eventually, with the data collected by the sensor during the earthquake, the proposed framework is proved to be effective and superior.
An Efficient Rash Driving Identification System Based on Neural Networks Using TensorFlow

The proposed research aims at developing an application which uses artificial neural network to identify rash driving by analyzing the data coming from the rider’s smartphone sensors. The neural network classifies the driving data between normal driving and aggressive driving behavior using real-time smartphone sensor values. This way, we will be able to identify the aggressive style of a particular rider and can provide alert signals so that appropriate steps can be taken. To develop the neural network, TensorFlow is used so that the network can be integrated in the Android application and other platforms. To integrate the neural network in an Android environment TensorFlow Lite is used. The sensors required for the application to work are found in every modern smartphone in today’s world; no extra hardware is required by the application. The dataset used in the proposed paper was specifically prepared to train the neural network. The accuracy obtained during the training procedure was 93.67% and the evaluation of the model using testing dataset gave the accuracy of 96.11%. The proposed research is implemented to develop an Android application that identifies aggressive driving style of the smartphone owner.
An Efficient Local Outlier Factor for Data Stream Processing: A Case Study

In the field of data mining and machine learning, outlier detection is considered a significant procedure for many applications, such as fraud detection in bank transactions and decision support systems. Data streams are a major player in the big data era. Currently, data streams are generated from various sources with huge amounts of data. This has led to difficulty when using older algorithms, which are designed for static data. The Local Outlier Factor (LOF) is one of these algorithms. The most challenging issue of the LOF is that it needs to preserve the whole dataset in computer memory. A new LOF that can deal with a data stream in limited memory is needed. This paper is a case study of several benchmark datasets for outlier detection that aim to increase the efficiency of the accuracy of local outlier detection in data streams.
Learning Robot Arm Controls Using Augmented Random Search in Simulated Environments

We investigate the learning of continuous action policy for controlling a six-axes robot arm. Traditional tabular Q-Learning can handle discrete actions well but less so for continuous actions since the tabular approach is constrained by the size of the state-value table. Recent advances in deep reinforcement learning and policy gradient learning abstract the look-up table using function approximators such as artificial neural networks. Artificial neural networks abstract loop-up policy tables as policy networks that can predict discrete actions as well as continuous actions. However, deep reinforcement learning and policy gradient learning were criticized for their complexity. It was reported in recent works that Augmented Random Search (ARS) has a better sample efficiency and a simpler hyper-parameter tuning. This motivates us to apply the technique to our robot-arm reaching tasks. We constructed a custom simulated robot arm environment using Unity Machine Learning Agents game engine, then designed three robot-arm reaching tasks. Twelve models were trained using ARS techniques. Another four models were trained using the state-of-the-art PG learning technique i.e., proximal policy optimization (PPO). Results from models trained using PPO provide a baseline from the policy gradient technique. Empirical results of models trained using ARS and PPO were analyzed and discussed.
Tri-Power Analysis: The Advanced Post Hoc Triostatistical Assurance Model That Consists of Multiple Advanced Triostatistics to Further Verify, Validate, and Make Viable Ideally Replicated Results of Innovative Investigative Inquiry

This chapter provides an in-depth, novel, and innovative advanced post hoc triostatistical methodology called "Tri-Power Analysis" (also represented as "Tri-", where z = v [x, y]).Tri-Power Analysis is a complex, comprehensive, and cumulative triostatistic that is designed to determine the level of assurance of ideally replicated statistically significant Tri-Squared Test results.Tri-Power Analysis as the final calculated "True Tri-Power Analysis" accurately and precisely determines Confirmatory Trichotomous Data Analysis by using four additional triostatistics: the Triostatistically significant initial Tri-Squared Test (as "Trichotomous Hindsight"); the advanced calculation validation metric G-TPA ["Geometric-Trichotomous Progression Analysis"]; the innovative TRIASIM ["Trichotomous Analysis of Similarity"] (as "Trichotomous Insight"), the universally applicable TRILOG ["Trichotomous Logit Model"]; and the novel TRIROC ["Trichotomous Rate of Change"] (as "Trichotomous Foresight").Tri-Power Analysis uses the three Hindsight, Insight, and Foresight triostatistics in a systemic sequential series of mathematical equations to verify, validate, and make viable the outcomes of future Tri-Squared Tests if replicated under the same ideal conditions.As such, this novel and innovative triostatistic thereby advances the science of trichotomous investigative analysis into previously untouched realms of original inquiry.The Tri-Power Analysis approach to "Trichotomous Extrapolative Data Analysis" is both meticulous and stringent detailing a unique approach to research in-How to cite this paper:
Construction of Wechat Mini Program for an Air Rescue Based on Decoupling Drupal

Decoupling Drupal not only provides a wide range of Content Management functions, but also provides APIs for applications as a backend Web service provider. Air rescue is an emerging industry that plays an important role in some special circumstances, similar to the fight against Corona Virus Disease 2019. This paper uses JSON API module to decouple Drupal, providing content nodes as API to application programs, and then using the WeChat mini program framework as the call usage interface to provide the functions of location, one-click call, search and so on. The experimental results show that the decoupling Drupal combined with WeChat mini program to realize the normal communication between the front and back. At the same time, a back-end management system is established. In the later stage, only the content needs to be maintained, which meets the expectations of many companies for zero-code maintenance. And these content nodes can also be applied to other types of applications, and they can be written once and published everywhere.
Implementation of Means of Art Pedagogy: Origami Classes with Long-Term Ill Children

The article, based on the results of the personal practical experience of the author working with seriously ill children who are in hospitals and&#x0D; deprived of the opportunity to study in ordinary schools, reveals the effectiveness of using such a means of art pedagogy as origami. Classes on the design of various paper figures have a positive effect on child patients: they reduce the state of fear and anxiety, distract from pain, increase self-confidence, and contribute to the establishment of friendships. In the process of folding origami, in addition to the pleasure of getting a new model, children improve their knowledge in the field of geometry, develop spatial abilities, learn new concepts and properties.
Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records

Medical knowledge graphs (KGs) constructed from Electronic Medical Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different medical tasks. However, existing models do not properly incorporate patient demographics and most of them ignore the probabilistic features of the medical KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical entities space by associating patient demographics with a corresponding hyperplane. Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance. We evaluate DARLING through link prediction for treatments and medicines, on a medical KG constructed from EMR data, and illustrate its superior performance compared to existing KG embedding models.
Marmoset brain segmentation from deconvolved magnetic resonance images and estimated label maps

The proposed method aims to create label maps that can be used for the segmentation of animal brain MR images without the need of a brain template. This is achieved by performing a joint deconvolution and segmentation of the brain MR images.It is based on modeling locally the image statistics using a generalized Gaussian distribution (GGD) and couples the deconvolved image and its corresponding labels map using the GGD-Potts model. Because of the complexity of the resulting Bayesian estimators of the unknown model parameters, a Gibbs sampler is used to generate samples following the desired posterior probability.The performance of the proposed algorithm is assessed on simulated and real MR images by the segmentation of enhanced marmoset brain images into its main compartments using the corresponding label maps created. Quantitative assessment showed that this method presents results that are comparable to those obtained with the classical method-registering the volumes to a brain template.The proposed method of using labels as prior information for brain segmentation provides a similar or a slightly better performance compared with the classical reference method based on a dedicated template.
Generative adversarial networks for histopathology staining

Histology and histopathology provide the gold standard for diagnosticians to understand the underlying relationship between the structures on a tissue sample and the affliction of the subject from whom the tissue was taken. A fundamental step toward achieving this is staining the tissue with chemicals to accentuate features within it. Over the past one-and-a-half centuries many stains have been developed, with each stain providing its own unique set of features. Very often, obtaining multiple slides with diverse stains is a cumbersome process that is both expensive and prone to human error. The objective of this chapter is to treat the issue of obtaining the image of a stained tissue from a differently stained one as an image-to-image translation problem. The solution is based on a suitably chosen conditional generative adversarial network, which transforms an image from one feature space to another. This chapter discusses the choice of objective functions and image-quality metrics. In addition, it highlights some of the issues with suggestions to address them.
Hybrid Explainable Smart House Control System

This paper describes hybrid method for smart house control system, where control is made by training neural network with user habit data and then applying fuzzy rule set and computing with words engine to provide user with explanation why control change to light, heating or ventilation was made. For neural networks two different approaches were tested: classical support vector machine and newly emerged ML.Net framework. Both methods could correctly predict user desired controls with ~99% accuracy, but ML.Net was recommended for further use because it was less selective for the data format, on the other hand SVM required data separation for binary classification thus increasing required number of SVM machines if control areas would expand. Given verbal explanations accurately described current house control situation to the users, thus increasing confidence level for Explainable Artificial Intelligence.
Investigating the Determinants of College Students Information Security Behavior Using a Validated Multiple Regression Models

The frequency, intensity and repercussions of information security breaches in higher education has prompted colleges and universities around the world to devote more resources to enhance technical and human controls capabilities. Research has repeatedly found that technical solutions to cybercrime are insufficient in preventing incidents. The present analysis utilizes the Health Belief Model (HBM) to explain users' computer security behavior by replicating an earlier research study. The study, however, applies the HBM model to a new context, higher education, and college students serve as the sample for this research. A validated questionnaire was employed to collect responses from 263 students attending a public state Midwestern university in the United States. Multiple Linear Regression mathematical analysis was conducted on the dataset collected to measure constructs of the information security of college students. Findings of this research suggest that perceived susceptibility, perceived benefits and self-efficacy are good determinants of information security behavior for college students at least on the sample observations. Further, the analysis supported the moderating logic of perceived severity on the effects of susceptibility, benefits, general security orientation, self-efficacy and cues to action. Findings of this research call upon higher education security administrators to enact more effective awareness and training programs based on real-work security incidents simulations and incorporating information security into the general education curricula.
Fuzzy Dissimilarity Measure Based K-Means Clustering

The k-means algorithm is a classic unsupervised learning approach for the clustering problem, which has a good effect on processing spherical data. Since its initial centroids are randomly assigned, the clustering effect is usually not stable. Moreover, the traditional k-means and related methods often only consider the similarity of the instances to each cluster’s centroid, therefore such methods are easily affected by noise points and hard to solve linearly indivisible data. In this paper, a novel clustering algorithm is proposed to improve the processing power of complex data distribution using the fuzzy dissimilarity of instances in different clusters. Experimental results confirm that the proposed approach works effectively and generally outperforms the popular representative algorithms for both artificial and benchmark datasets.
Improve the Robustness of Deep Learning Models against Adversarial Attacks in Judicial Field

With the rapid development of deep neural networks (DNNs), they have been applied in various domains including crowd counting, object detection and digital forensic. Consequently, DNN-based forensic process plays an essential role in judicial field, which will simplify the trial procedure in real world as well as cut down the workload of criminal case judgment. Apart from manipulating the visual forensic evidence (i.e., digital images) by existing editing softwares, malicious adversarial attacks can also pose a series threat for DNN-based trial system. In general, by adding some imperceptible perturbations to generate the adversarial forensic evidence, DNN-based trial system may output totally wrong results. To this end, we propose an adversarial fine-tuned training (AFT) method to improve the robustness of deep learning models for defensing against black-box adversarial attacks in judicial process (e.g. digital forensic). Concretely, we utilize both query-based attackers and transfer-based enemies to generate a set of adversarial examples at first, and then mix up both clean and perturbed data as training set to specific fine-tuned layers of models. Base on this training manner, the generated models not only can improve the robustness to resist adversarial examples, but also reduce the computational cost since only parts of parameters need to be retrained. Extensive experiments on both two pre-trained vanilla models and three defensive models demonstrate the efficacy of our proposed method to tackle the adversarial attacks and potential value in digital court system.
Factors Affecting Blockchain in Fruit Retail Market: An Unveiling Myth of Blockchain

The purpose of this study is to present and explore blockchain technology and its possible application in the retail market. It also, anticipate the possible applications of blockchain technology that benefit the retail business, as well as the industry in general. The study also identified the critical factor of fruit blockchain in the retail market and the relationship between the key factors blockchain in the retail industry. The unified theory of acceptance and use of technology (UTAUT2) by adding trust as an independent variable. The cross-sectional approach used in this study. Hence, one hundred seventy-six (176) self-administered questionnaires distributed through simple random sampling technology aimed at blockchain in retail market users in Malaysia from 25 to 44 years. Data analysis performed using PLS. This document provides discussions, the important functions of blockchain mechanisms that implemented in critical existing operations of the retail market. This study provide different angles of this Malaysian retail fruit blockchain environment, not only in business processes and competition but also in cooperation between retailer and customers.This study serves as a guide for future research, facilitating the accumulation of knowledge and creating new understanding and awareness. The Malaysian retail industry needs an urgent technological transformation, industrial innovations and new growth paths, such as the adoption of blockchain systems and systems to maintain its global market share in the future. Therefore, the application of blockchain is a paper type research paper.
On the statistical indicators of the effectiveness of teaching methodologies

The article is devoted to the problem of carrying out a proper educational experiment. A critical analysis of the conventional approaches to an educational experiment is given. The author argues that the majority of methods, assessing the results of educational experiments, were borrowed from sociology and psychology which, in turn, led to the misinterpretation of the results of these experiments. The criticism of the author is primarily aimed at the incorrect use of central tendency measures and the selection of tests for checking the probability of significance of samples. The qualitative approach, based on the percentile values, was proposed as one of the most relevant results of an experiment. The lack of universal measure with could allow comparing results of multiple educational experiments and meta-analyses was argued. The term «effective learning hours» was coined. The methodology of defining «effective learning hours» and corresponding them to the levels of the acquisition was proposed.
Introduction to Epistemic Logic and Epistemology

This chapter summarizes the most relevant preliminary concepts from Epistemic LogicEpistemic Logic (EL) and EpistemologyEpistemology. Regarding the former, the problem of logical omniscienceLogical omniscience and its different solutions are revised, with Awareness LogicAwareness Logic (AL) as the chosen solution for carrying out this research. With respect to EpistemologyEpistemology, a brief historical background is presented and the view of Awareness Justification InternalismAwareness Justification Internalism (AJI) is highlighted as the one that will serve as a theoretical background for the logical approach. To conclude, some aspects of the disconnection between both disciplines are pointed out and the concept of Epistemic AwarenessEpistemic awareness is defined as the bridge-notion through wich the theoretical re-connection will be developed.
Stabilization of the Modular Selective Neural Network Model Based on Inter-Class Correlation

We propose an optimization of Modular Selective Network or MS-Net by reducing the number of expert network evaluations. MS-Net is composed of a router and a set of expert networks. In our original proposal, MS-Net is constructed based on a Round-Robin dataset partition with controlled redundancy among the subsets of classes. In this paper, we propose a novel way for reducing the inference cost by performing Inter-Class-Correlation (ICC) analysis through calculating the joint-probability of appearance of top-2 classes in router's prediction. Next, we construct subset of classes on the most frequently occurring class pairs and train experts on those subsets. We do not enforce redundancy in these subsets, thus during inference, only one expert is leveraged per sample. Our empirical results show that, with the ResNet-20 backbone, the optimized MS-Net reduces parameter utilization by over 70% yet performs with neck and neck score with the original MS-Net for CIFAR-10 and CIFAR-100 dataset.