scholarly journals DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

2019 ◽  
Vol 33 ◽  
pp. 1020-1027 ◽  
Author(s):  
Ziqian Lin ◽  
Jie Feng ◽  
Ziyang Lu ◽  
Yong Li ◽  
Depeng Jin
Keyword(s):  
Traffic Control ◽  
State Of The Art ◽  
Real Life ◽  
Time Interval ◽  
Context Aware ◽  
Training Process ◽  
Flow Prediction ◽  
Wide Range ◽  
The City ◽  
Historical Flow

Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.

scholarly journals Training Temporal Word Embeddings with a Compass

2019 ◽  
Vol 33 ◽  
pp. 6326-6334 ◽  
Author(s):  
Valerio Di Carlo ◽  
Federico Bianchi ◽  
Matteo Palmonari
Keyword(s):  
State Of The Art ◽  
Word Meaning ◽  
Time Interval ◽  
Word Embeddings ◽  
Specific Time ◽  
Training Process ◽  
Text Corpora ◽  
Corpus Size ◽  
Generate Vector ◽  
Vector Representations

Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been proposed to generate vector representations of words that embed their meaning during a specific time interval. However, the training process used in these approaches is complex, may be inefficient or it may require large text corpora. As a consequence, these approaches may be difficult to apply in resource-scarce domains or by scientists with limited in-depth knowledge of embedding models. In this paper, we propose a new heuristic to train temporal word embeddings based on the Word2vec model. The heuristic consists in using atemporal vectors as a reference, i.e., as a compass, when training the representations specific to a given time interval. The use of the compass simplifies the training process and makes it more efficient. Experiments conducted using state-of-the-art datasets and methodologies suggest that our approach outperforms or equals comparable approaches while being more robust in terms of the required corpus size.

scholarly journals Methods and Algorithms for Detecting Objects in Video Files

2018 ◽  
Vol 155 ◽  
pp. 01016 ◽  
Author(s):  
Cuong Nguyen The ◽  
Dmitry Shashev
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Traffic Control ◽  
Motion Pictures ◽  
Real Life ◽  
Video File ◽  
Detection And Tracking ◽  
Object Movement ◽  
Wide Range ◽  
Automated Processing

Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.

scholarly journals Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

2021 ◽  
Vol 11 (17) ◽  
pp. 8172
Author(s):  
Jebran Khan ◽  
Sungchang Lee
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
State Of The Art ◽  
Context Aware ◽  
Intended Meaning ◽  
Text Data ◽  
Recall Accuracy ◽  
Wide Range ◽  
Textual Data ◽  
Noisy Text

We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.

scholarly journals Rock Segmentation in the Navigation Vision of the Planetary Rovers

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3048
Author(s):  
Boyu Kuang ◽  
Mariusz Wisniewski ◽  
Zeeshan A. Rana ◽  
Yifan Zhao
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Real Life ◽  
Visual Navigation ◽  
The State ◽  
Training Process ◽  
Planetary Rover ◽  
Transfer Training ◽  
Segmentation Framework

Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Tong Xia ◽  
Junjie Lin ◽  
Yong Li ◽  
Jie Feng ◽  
Pan Hui ◽  
...  
Keyword(s):  
Time Interval ◽  
Urban Structure ◽  
Dynamic Graph ◽  
Temporal Dependence ◽  
Convolutional Network ◽  
Urban Structures ◽  
Flow Prediction ◽  
Wide Range ◽  
Spatio Temporal ◽  
Real World Datasets

Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.

scholarly journals Emotional agents - state of the art and applications

2015 ◽  
Vol 12 (4) ◽  
pp. 1121-1148 ◽  
Author(s):  
Mirjana Ivanovic ◽  
Zoran Budimac ◽  
Milos Radovanovic ◽  
Vladimir Kurbalija ◽  
Weihui Dai ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
State Of The Art ◽  
Real Life ◽  
Data Sets ◽  
Emotional Information ◽  
Intelligent Software ◽  
Research Activities ◽  
Intelligent Software Agents ◽  
Wide Range ◽  
Emotional Agents

last decade, intensive research on emotional intelligence has advanced significantly from its theoretical basis, analytical studies and processing technology to exploratory applications in a wide range of real-life domains. This paper brings new insights in the field of emotional, intelligent software agents. The first part is devoted to an overview of the state-of-the-art in emotional intelligence research with emphasis on emotional agents. A wide range of applications in different areas like modeling emotional agents, aspects of learning in emotional environments, interactive emotional systems and so on are presented. After that we suggest a systematic order of research steps with the idea of proposing an adequate framework for several possible real-life applications of emotional agents. We recognize that it is necessary to apply specific methods for dynamic data analysis in order to identify and discover new knowledge from available emotional information and data sets. The last part of the paper discusses research activities for designing an agent-based architecture, in which agents are capable of reasoning about and displaying some kind of emotions based on emotions detected in human speech, as well as online documents.

scholarly journals Book Proposal on Biomimetic Tissue Simulants

2020 ◽  
Author(s):  
Arnab Chanda
Keyword(s):  
Soft Tissue ◽  
Ethical Issues ◽  
State Of The Art ◽  
Academic Research ◽  
Real Life ◽  
Test Results ◽  
Animal Tissues ◽  
Female Pelvis ◽  
Wide Range ◽  
Computational Research

Soft tissue surrogate based test dummies are used across industries to simulate real life accidents. To date, there are a wide range of surrogates available in the market, including gels, elastomers, and animal tissues, which are backdated and have mechanical properties very different from actual human tissues. However, in academic research, biofidelic soft tissue surrogates have evolved in the last two decades, but have lacked technology transfer. This book aims to bridge the gap between the industry and academia with the state of the art in soft tissue surrogate research. Surrogates are presented with respect to skin, muscles, brain tissue, arteries, and female pelvis. Fabrication techniques, mechanical testing, and test results required for reproducing these surrogates are discussed. Also, characterization methodologies and limitations of each type of surrogate are presented, for their use in both experimental and computational research. Some major industries which can use these biofidelic surrogates are car manufacturers, prosthetics and orthotics designers, ballistic testing facilities, military and sports equipment manufacturers. Also, hospitals and medical centres can take advantage of these synthetic surrogates over actual tissues for surgical training with minimal biosafety approvals and ethical issues.

scholarly journals Toward an Evaluation of Cultural Ecosystem Services Delivered by Urban Micro-Farms

2021 ◽  
Vol 13 (4) ◽  
pp. 1716 ◽  
Author(s):  
Giulia Giacchè ◽  
Jean-Noël Consalès ◽  
Baptiste J-P. Grard ◽  
Anne-Cécile Daniel ◽  
Claire Chenu
Keyword(s):  
Ecosystem Services ◽  
Urban Agriculture ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Cultural Ecosystem Services ◽  
Allotment Gardens ◽  
Wide Range ◽  
Rooftop Farming ◽  
The City ◽  
Soil Level

Since two decades, urban agriculture has been booming and a wide range of forms, from urban allotment gardens to rooftop farming under greenhouse, is developing. Various benefits are recognized for urban agriculture integration within the city and a specific consideration is dedicated to ecosystem services. In this article, we have focused on cultural ecosystem services provided by urban micro-farms. The state of the art reveals that urban agriculture delivers cultural ecosystem services that are well perceived and evaluated by users, but there are still few studies on this topic. Based on the analysis of specific literature on cultural ecosystems and micro-farms in parallel to a period of observation and documentary research of five urban micro-farms either on rooftop or at soil level, located in Paris and its surroundings, we proposed a specific methodology. This methodology aimed at quantitative and qualitative evaluation of the cultural ecosystem services provided by urban micro-farms and is based on a framework, which distinguishes exogenous and endogenous cultural ecosystem services.

scholarly journals Efficient Algorithm for the Computation of the Solution to a Sparse Matrix Equation in Distributed Control Theory

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1497
Author(s):  
Leonardo Pedroso ◽  
Pedro Batista
Keyword(s):  
Distributed Control ◽  
Matrix Equation ◽  
Efficient Algorithm ◽  
State Of The Art ◽  
Sparse Matrix ◽  
Linear Equations ◽  
Real Life ◽  
The State ◽  
Wide Range ◽  
Estimation And Control

In this short communication, an algorithm for efficiently solving a sparse matrix equation, which arises frequently in the field of distributed control and estimation theory, is proposed. The efficient algorithm stems from the fact that the sparse equation at hand can be reduced to a system of linear equations. The proposed algorithm is shown to require significantly fewer floating point operations than the state-of-the-art solution. The proposed solution is applied to a real-life example, which models a wide range of industrial processes. The experimental results show that the solution put forward allows for a significant increase in efficiency in relation to the state-of-the-art solution. The significant increase in efficiency of the presented algorithm allows for a valuable widening of the applications of distributed estimation and control.

Death in the City

2017 ◽  
Author(s):  
Kathryn A. Sloan
Keyword(s):  
Scientific Inquiry ◽  
The United States ◽  
Violent Death ◽  
Special Relationship ◽  
Intimate Friend ◽  
Counter Narrative ◽  
Wide Range ◽  
European Heritage ◽  
The City ◽  
The Temple

Popular culture has long conflated Mexico with the macabre. Some persuasive intellectuals argue that Mexicans have a special relationship with death, formed in the crucible of their hybrid Aztec-European heritage. Death is their intimate friend; death is mocked and accepted with irony and fatalistic abandon. The commonplace nature of death desensitizes Mexicans to suffering. Death, simply put, defines Mexico. There must have been historical actors who looked away from human misery, but to essentialize a diverse group of people as possessing a unique death cult delights those who want to see the exotic in Mexico or distinguish that society from its peers. Examining tragic and untimely death—namely self-annihilation—reveals a counter narrative. What could be more chilling than suicide, especially the violent death of the young? What desperation or madness pushed the victim to raise the gun to the temple or slip the noose around the neck? A close examination of a wide range of twentieth-century historical documents proves that Mexicans did not accept death with a cavalier chuckle nor develop a unique death cult, for that matter. Quite the reverse, Mexicans behaved just as their contemporaries did in Austria, France, England, and the United States. They devoted scientific inquiry to the malady and mourned the loss of each life to suicide.

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