scholarly journals A Novel Method for Assessing the Spatial Presence of Omnidirectional Videos: Comparing Virtual and Real World

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 183405-183413 ◽  
Author(s):  
Wenjie Zou ◽  
Wei Zhang ◽  
Jiarun Song ◽  
Fuzheng Yang ◽  
Patrick Le Callet
Keyword(s):  
Real World ◽  
Spatial Presence ◽  
Novel Method

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.

scholarly journals A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms

2017 ◽  
Vol 7 (4) ◽  
pp. 1906 ◽  
Author(s):  
Abouzid Houda ◽  
Chakkor Otman
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real Life ◽  
Signal Separation ◽  
Mixing Process ◽  
The Real ◽  
Input Signals ◽  
Novel Method

Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the  nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation.  But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used.

Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring

2020 ◽  
pp. 1237-1247
Author(s):  
Xiangdong Wang ◽  
Yang Yang ◽  
Hong Liu ◽  
Yueliang Qian ◽  
Duan Jia
Keyword(s):  
Speech Recognition ◽  
Real World ◽  
Computational Cost ◽  
User Feedback ◽  
Recognition Result ◽  
Novel Method ◽  
Mandarin Speech Recognition ◽  
Recognition Errors ◽  
Dynamic Updating ◽  
Manual Correction

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.

Community detection via closure extension

2018 ◽  
Vol 29 (12) ◽  
pp. 1850119
Author(s):  
Jingming Zhang ◽  
Jianjun Cheng ◽  
Xiaosu Feng ◽  
Xiaoyun Chen
Keyword(s):  
Community Structure ◽  
Computational Complexity ◽  
Community Detection ◽  
Network Structure ◽  
Real World ◽  
Prior Information ◽  
State Of The Art ◽  
Local Information ◽  
Second Step ◽  
Novel Method

Identifying community structure in networks plays an important role in understanding the network structure and analyzing the network features. Many state-of-the-art algorithms have been proposed to identify the community structure in networks. In this paper, we propose a novel method based on closure extension; it performs in two steps. The first step uses the similarity closure or correlation closure to find the initial community structure. In the second step, we merge the initial communities using Modularity [Formula: see text]. The proposed method does not need any prior information such as the number or sizes of communities, and it is able to obtain the same resulting communities in multiple runs. Moreover, it is noteworthy that our method has low computational complexity because of considering only local information of network. Some real-world and synthetic graphs are used to test the performance of the proposed method. The results demonstrate that our method can detect deterministic and informative community structure in most cases.

SEMANTIC FIELD: A THEORETICAL PERSPECTIVE OF MODELING INFORMATION RETRIEVAL

2009 ◽  
Vol 18 (06) ◽  
pp. 825-851
Author(s):  
KUN YUE ◽  
WEI-YI LIU
Keyword(s):  
Information Retrieval ◽  
Theoretical Model ◽  
Electrostatic Field ◽  
Real World ◽  
Field Model ◽  
Theoretical Perspective ◽  
Keyword Extraction ◽  
Semantic Field ◽  
Novel Method ◽  
Interpretable Model

Information retrieval has been paid much attention and it is widely studied and applied in real world paradigms. For various aspects of information retrieval, various approaches have been proposed from various perspectives. It is necessary to provide a formally-unified and physically-interpretable model for classical problems in information retrieval (e.g., document classification, authority-page selection, and keyword extraction, etc.). In this paper we propose a theoretical model, called semantic field, inspired by the theories of lexical semantics and electrostatic field. Based on this physical model, information retrieval can be viewed from a theoretical perspective and interpreted by people's physical intuitions and natural heuristics. Centered on the concept of semantic field, we give some relevant properties, including semantic affinity, semantic coacervation degree and radiation of a semantic source. As the representative application of the proposed semantic field model, a novel method for automatic keyword extraction is discussed, and the feasibility is verified by corresponding experiments.

scholarly journals Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings

2014 ◽  
Vol 2 ◽  
Author(s):  
Jasper Schipperijn ◽  
Jacqueline Kerr ◽  
Scott Duncan ◽  
Thomas Madsen ◽  
Charlotte Demant Klinker ◽  
...  
Keyword(s):  
Real World ◽  
Health Research ◽  
Gps Receivers ◽  
Dynamic Accuracy ◽  
Novel Method ◽  
Gps Accuracy

scholarly journals Coarsening optimization for differentiable programming

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-27
Author(s):  
Xipeng Shen ◽  
Guoqiang Zhang ◽  
Irene Dea ◽  
Samantha Andow ◽  
Emilio Arroyo-Fang ◽  
...  
Keyword(s):  
Real World ◽  
Control Flow ◽  
Algorithmic Differentiation ◽  
Symbolic Reasoning ◽  
Single Operation ◽  
Real World Applications ◽  
Novel Method ◽  
Expression Swell

This paper presents a novel optimization for differentiable programming named coarsening optimization. It offers a systematic way to synergize symbolic differentiation and algorithmic differentiation (AD). Through it, the granularity of the computations differentiated by each step in AD can become much larger than a single operation, and hence lead to much reduced runtime computations and data allocations in AD. To circumvent the difficulties that control flow creates to symbolic differentiation in coarsening, this work introduces phi-calculus, a novel method to allow symbolic reasoning and differentiation of computations that involve branches and loops. It further avoids "expression swell" in symbolic differentiation and balance reuse and coarsening through the design of reuse-centric segment of interest identification. Experiments on a collection of real-world applications show that coarsening optimization is effective in speeding up AD, producing several times to two orders of magnitude speedups.

scholarly journals Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

2021 ◽  
Author(s):  
Gaode Chen ◽  
Xinghua Zhang ◽  
Yanyan Zhao ◽  
Cong Xue ◽  
Ji Xiang
Keyword(s):  
Real World ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Time Interval ◽  
Interest Representation ◽  
Novel Method ◽  
Real World Datasets ◽  
Item Representation ◽  
Global And Local

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.

scholarly journals Modeling Trajectories with Neural Ordinary Differential Equations

2021 ◽  
Author(s):  
Yuxuan Liang ◽  
Kun Ouyang ◽  
Hanshu Yan ◽  
Yiwei Wang ◽  
Zekun Tong ◽  
...  
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Real World ◽  
Continuous Time ◽  
Trajectory Data ◽  
Time Dynamics ◽  
Trajectory Classification ◽  
Novel Method ◽  
Spatial Trajectory ◽  
Hidden States

Recent advances in location-acquisition techniques have generated massive spatial trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such trajectory data. After revisiting RNN-based methods for trajectory modeling, we expose two common critical drawbacks in the existing uses. First, RNNs are discrete-time models that only update the hidden states upon the arrival of new observations, which makes them an awkward fit for learning real-world trajectories with continuous-time dynamics. Second, real-world trajectories are never perfectly accurate due to unexpected sensor noise. Most RNN-based approaches are deterministic and thereby vulnerable to such noise. To tackle these challenges, we devise a novel method entitled TrajODE for more natural modeling of trajectories. It combines the continuous-time characteristic of Neural Ordinary Differential Equations (ODE) with the robustness of stochastic latent spaces. Extensive experiments on the task of trajectory classification demonstrate the superiority of our framework against the RNN counterparts.

scholarly journals Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Longjie Li ◽  
Shenshen Bai ◽  
Mingwei Leng ◽  
Lu Wang ◽  
Xiaoyun Chen
Keyword(s):  
Decision Making ◽  
Complex Networks ◽  
Similarity Measure ◽  
Real World ◽  
Link Prediction ◽  
State Of The Art ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Multiple Attribute ◽  
Novel Method

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.

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