Motion estimation in vehicular environments based on Bayesian dynamic networks

2021 ◽  
pp. 1-12
Author(s):  
Lauro Reyes-Cocoletzi ◽  
Ivan Olmos-Pineda ◽  
J. Arturo Olvera-Lopez
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Ground Truth ◽  
Change Of Direction ◽  
Prediction Rate ◽  
Different Types ◽  
Novel Method ◽  
Comparison Of The Results ◽  
Multiple Obstacles ◽  
Real Traffic

The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work.

Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks

2022 ◽  
Vol 40 (3) ◽  
pp. 1-21
Author(s):  
Lili Wang ◽  
Chenghan Huang ◽  
Ying Lu ◽  
Weicheng Ma ◽  
Ruibo Liu ◽  
...  
Keyword(s):  
Real World ◽  
User Behavior ◽  
Dynamic Networks ◽  
General Purpose ◽  
Local Context ◽  
Structural Role ◽  
Time Dynamic ◽  
Evolving Networks ◽  
Structural Identity ◽  
Novel Method

Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynamic embeddings. Though these methods can learn node representations over dynamic networks, these representations capture the local context of nodes and do not learn the structural roles of nodes. In this article, we propose a novel method for learning structural node embeddings in discrete-time dynamic networks. Our method, called HR2vec , tracks historical topology information in dynamic networks to learn dynamic structural role embeddings. Through experiments on synthetic and real-world temporal datasets, we show that our method outperforms other well-known methods in tasks where structural equivalence and historical information both play important roles. HR2vec can be used to model dynamic user behavior in any networked setting where users can be represented as nodes. Additionally, we propose a novel method (called network fingerprinting) that uses HR2vec embeddings for modeling whole (or partial) time-evolving networks. We showcase our network fingerprinting method on synthetic and real-world networks. Specifically, we demonstrate how our method can be used for detecting foreign-backed information operations on Twitter.

Foundations of Blockchain Technology for Industrial and Societal Applications

2018 ◽  
Author(s):  
Shivika Narang ◽  
Praphul Chandra ◽  
Shweta Jain ◽  
Narahari Y
Keyword(s):  
Supply Chain ◽  
Data Structure ◽  
Real World ◽  
New Wave ◽  
Distributed Consensus ◽  
Wave Technology ◽  
Blockchain Technology ◽  
Different Types ◽  
Original Application

The blockchain concept forms the backbone of a new wave technology that promises to be deployed extensively in a wide variety of industrial and societal applications. In this article, we present the scientific foundations and technical strengths of this technology. Our emphasis is on blockchains that go beyond the original application to digital currencies such as bitcoin. We focus on the blockchain data structure and its characteristics; distributed consensus and mining; and different types of blockchain architectures. We conclude with a section on applications in industrial and societal settings, elaborating upon a few applications such as land registry ledger, tamper-proof academic transcripts, crowdfunding, and a supply chain B2B platform. We discuss what we believe are the important challenges in deploying the blockchain technology successfully in real-world settings.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

scholarly journals CIRO: The Effects of Visually Diminished Real Objects on Human Perception in Handheld Augmented Reality

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 900
Author(s):  
Hanseob Kim ◽  
Taehyung Kim ◽  
Myungho Lee ◽  
Gerard Jounghyun Kim ◽  
Jae-In Hwang
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Human Perception ◽  
Ground Truth ◽  
Prior Work ◽  
Comparative Experiment ◽  
User Perception ◽  
Depth Distortion ◽  
Real Objects ◽  
Visual Artifacts

Augmented reality (AR) scenes often inadvertently contain real world objects that are not relevant to the main AR content, such as arbitrary passersby on the street. We refer to these real-world objects as content-irrelevant real objects (CIROs). CIROs may distract users from focusing on the AR content and bring about perceptual issues (e.g., depth distortion or physicality conflict). In a prior work, we carried out a comparative experiment investigating the effects on user perception of the AR content by the degree of the visual diminishment of such a CIRO. Our findings revealed that the diminished representation had positive impacts on human perception, such as reducing the distraction and increasing the presence of the AR objects in the real environment. However, in that work, the ground truth test was staged with perfect and artifact-free diminishment. In this work, we applied an actual real-time object diminishment algorithm on the handheld AR platform, which cannot be completely artifact-free in practice, and evaluated its performance both objectively and subjectively. We found that the imperfect diminishment and visual artifacts can negatively affect the subjective user experience.

An algebraic approach to N-soft sets with application in decision-making using TOPSIS

2021 ◽  
pp. 1-21
Author(s):  
Muhammad Shabir ◽  
Rimsha Mushtaq ◽  
Munazza Naz
Keyword(s):  
Decision Making ◽  
Mathematical Models ◽  
Real World ◽  
Algebraic Approach ◽  
Multi Criteria Decision Making ◽  
Commutative Monoids ◽  
Soft Sets ◽  
Algebraic Properties ◽  
Different Types ◽  
Selection Of

In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

Deceleration of sliding motorcycles (mainly scooters) in accident reconstructions

2017 ◽  
Vol 232 (7) ◽  
pp. 866-876 ◽  
Author(s):  
Carlo Cialdai ◽  
Dario Vangi ◽  
Antonio Virga
Keyword(s):  
Urban Areas ◽  
Test Session ◽  
Road Accidents ◽  
Factors Affecting ◽  
Different Types ◽  
Energy Dissipated ◽  
Comparison Of The Results ◽  
Two Wheeler ◽  
The Impact ◽  
Good Agreement

This paper presents an analysis of the situation in which a two-wheeler (i.e. a motorcycle, where the term motorcycles includes scooters) falls over to the side and then successively slides; this typically occurs in road accidents involving this type of vehicle. Knowing the deceleration rate of the sliding phase allows the kinetic energy dissipated and the speed of the motorcycle just before the fall to the ground to be calculated. These parameters are very important in the analysis and reconstruction of accidents. The work presented in this paper was developed in two experimental test sessions on fully faired motorcycles which are mainly of the scooter type and widely used in urban areas. In the first session, sliding tests were carried out, with the speed in the range 10–50 km/h, on three different types of road surface. Analysis of the evidence allowed the dissipative main phases of motion of the motorcycle (the impact with the ground, the rebounds and the stabilized swiping) to be identified and some factors affecting the phenomenon to be studied. The coefficient of average deceleration was calculated using two typical equations. The second test session consisted of drag tests. In these tests, the motorcycle, which had previously laid on its side, was dragged for a few metres at a constant speed of about 20 km/h, while the drag force was measured. A comparison of the results obtained in these tests with those obtained in the sliding tests yielded very good agreement in the coefficients of deceleration.

Bose-Einstein-type statistics, order statistics and planar random motions with three directions

2004 ◽  
Vol 36 (3) ◽  
pp. 937-970 ◽  
Author(s):  
S. Leorato ◽  
E. Orsingher
Keyword(s):  
Partial Differential Equations ◽  
Order Statistics ◽  
Constant Velocity ◽  
Uniform Motion ◽  
Change Of Direction ◽  
Telegraph Process ◽  
Cyclic Motion ◽  
Different Types ◽  
Random Motions ◽  
Bose Einstein

In this paper we study different types of planar random motions (performed with constant velocity) with three directions, defined by the vectors dj = (cos(2πj/3), sin(2πj/3)) for j = 0, 1, 2, changing at Poisson-paced times. We examine the cyclic motion (where the change of direction is deterministic), the completely uniform motion (where at each Poisson event each direction can be taken with probability ) and the symmetrically deviating case (where the particle can choose all directions except that taken before the Poisson event). For each of the above random motions we derive the explicit distribution of the position of the particle, by using an approach based on order statistics. We prove that the densities obtained are solutions of the partial differential equations governing the processes. We are also able to give the explicit distributions on the boundary and, for the case of the symmetrically deviating motion, we can write it as the distribution of a telegraph process. For the symmetrically deviating motion we use a generalization of the Bose-Einstein statistics in order to determine the distribution of the triple (N0, N1, N2) (conditional on N(t) = k, with N0 + N1 + N2 = N(t) + 1, where N(t) is the number of Poisson events in [0, t]), where Nj denotes the number of times the direction dj (j = 0, 1, 2) is taken. Possible extensions to four directions or more are briefly considered.

scholarly journals Multiple Noisy Label Distribution Propagation for Crowdsourcing

2019 ◽  
Author(s):  
Hao Zhang ◽  
Liangxiao Jiang ◽  
Wenqiang Xu
Keyword(s):  
Supervised Learning ◽  
Real World ◽  
Effective Means ◽  
Ground Truth ◽  
Cost Effective ◽  
Nearest Neighbors ◽  
True Label ◽  
Real World Datasets ◽  
The Individual ◽  
Label Distribution

Crowdsourcing services provide a fast, efficient, and cost-effective means of obtaining large labeled data for supervised learning. Ground truth inference, also called label integration, designs proper aggregation strategies to infer the unknown true label of each instance from the multiple noisy label set provided by ordinary crowd workers. However, to the best of our knowledge, nearly all existing label integration methods focus solely on the multiple noisy label set itself of the individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this study. MNLDP first transforms the multiple noisy label set of each instance into its multiple noisy label distribution and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Promising experimental results on simulated and real-world datasets validate the effectiveness of our proposed method.

scholarly journals UKRAINIAN REALIA OF THE XIX CENTURY AND ITS TRANSLATION (BASED ON THE POEM “KATERINA” BY TARAS SHEVCHENKO)

2019 ◽  
Vol 1 (16) ◽  
pp. 124-130
Author(s):  
E.I. Panchenko
Keyword(s):  
General Theory ◽  
Cultural Differences ◽  
Real World ◽  
Species Replacement ◽  
Different Types ◽  
General Meaning ◽  
Definition Of ◽  
Xix Century ◽  
Entire Array

The article is written in line with current research, since the problem of studying Ukrainian realities is of unquestionable interest for several reasons. First, understanding the realities will promote bettermutual understanding of different peoples; and secondly, the definition of optimal means of translating the realities is a definite contribution to the general theory of translation. Different types of real-world classifications are proposed, the difficulties associated with the adequate transfer into the translated text of an entire array of cultural information encoded in the realities contained in the origina text are investigated. Basing on the analysis of numerous translations of literary works, Ukrainian researchers (R. Zorivchak, V. Koptilov, O. Kundzich, O. Cherednichenko, etc.) show ways to overcome linguistic obstacles caused by cultural differences. But, as far as we know, the problem of the translation of Ukrainian realities in the works of T. Shevchenko is not yet exhaustively highlighted. The purpose of this article is to analyze the peculiarities of the use of realities in the work of Taras Shevchenko "Katerina" and their translation into English. We have given an ideographic classification of lexical units - Ukrainian realities in fiction and analyzed such means of their translation as calque, renomination, transcription with explanation, the introduction of neologism, the principle of generic-species replacement, which allows  conveying (approximately) the content of the realities by a broader, general meaning, that is, the reception of generalization. The results of our analysis allow us to make an ideographic classification of Ukrainian realities that are used in fiction, as well as to summarize the prevalence of their means of translation. Prospects for further research are seen in the analysis of certain translation failures in the translation of realities and to offer the best options for their translation.

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