Estimated path information gain-based robot exploration under perceptual uncertainty

Robotica ◽  
2022 ◽  
pp. 1-17
Author(s):  
Jie Liu ◽  
Chaoqun Wang ◽  
Wenzheng Chi ◽  
Guodong Chen ◽  
Lining Sun
Keyword(s):  
Decision Making ◽  
Topological Structure ◽  
Information Gain ◽  
State Of The Art ◽  
Selection Process ◽  
Random Trees ◽  
Experimental Results ◽  
Restricted Area ◽  
Structure Information ◽  
Path Information

Abstract At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

Content Independent Writer Identification on Bangla Script: A Document Level Approach

2018 ◽  
Vol 32 (09) ◽  
pp. 1856011 ◽  
Author(s):  
Chayan Halder ◽  
Sk. Md. Obaidullah ◽  
K. C. Santosh ◽  
Kaushik Roy
Keyword(s):  
Decision Making ◽  
State Of The Art ◽  
Variable Number ◽  
The State ◽  
Experimental Results ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Fields Of Study ◽  
Simple Logistic ◽  
Document Level

Offline writer identification is one of the major fields of study in behavioral biometric. It is a process of matching a questioned document with other documents of known writers to find the appropriate writer. In this paper, local handwriting-based attributes are used as features, and multi-layer perceptron and simple logistic classifiers are used for decision making. The method is tested on an unconstrained handwritten Bangla database of 1383 documents with variable number of datasets from 190 writers. Experimental results show the effectiveness of our system, since it outperforms the state-of-the-art methods by approximately 3% (top-3 and top-4 choices). Further, our method is approximately 27 times faster than conventional segmentation-based methods.

scholarly journals RDF-to-Text Generation with Graph-augmented Structural Neural Encoders

2020 ◽  
Author(s):  
Hanning Gao ◽  
Lingfei Wu ◽  
Po Hu ◽  
Fangli Xu
Keyword(s):  
State Of The Art ◽  
Global Structure ◽  
The State ◽  
Experimental Results ◽  
Text Generation ◽  
Structure Information ◽  
Text Quality ◽  
Human Evaluation ◽  
Proposed Model

The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

scholarly journals The Impact of Social Media Use on Consumers’ Restaurant Consumption Experiences: A Qualitative Study

2021 ◽  
Vol 13 (12) ◽  
pp. 6581
Author(s):  
Jooyoung Hwang ◽  
Anita Eves ◽  
Jason L. Stienmetz
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Selection Process ◽  
Decision Making Process ◽  
Structured Interviews ◽  
High Standards ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality ◽  
Search For Information ◽  
The Impact

Travellers have high standards and regard restaurants as important travel attributes. In the tourism and hospitality industry, the use of developed tools (e.g., smartphones and location-based tablets) has been popularised as a way for travellers to easily search for information and to book venues. Qualitative research using semi-structured interviews based on the face-to-face approach was adopted for this study to examine how consumers’ restaurant selection processes are performed with the utilisation of social media on smartphones. Then, thematic analysis was adopted. The findings of this research show that the adoption of social media on smartphones is positively related with consumers’ gratification. More specifically, when consumers regard that process, content and social gratification are satisfied, their intention to adopt social media is fulfilled. It is suggested by this study that consumers’ restaurant decision-making process needs to be understood, as each stage of the decision-making process is not independent; all the stages of the restaurant selection process are organically connected and influence one another.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 359
Author(s):  
Kai Ye ◽  
Yangheran Piao ◽  
Kun Zhao ◽  
Xiaohui Cui
Keyword(s):  
Decision Making ◽  
Essential Role ◽  
Online Discussion ◽  
Professional Community ◽  
Experimental Results ◽  
Agricultural Products ◽  
Price Prediction ◽  
Price Data ◽  
Rich Information ◽  
Lstm Network

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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