scholarly journals The Finding and Dynamic Detection of Opinion Leaders in Social Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Baocheng Huang ◽  
Guang Yu ◽  
Hamid Reza Karimi
Keyword(s):  
Social Network ◽  
Real World ◽  
Structural Characteristics ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Opinion Leaders ◽  
Data Sources ◽  
The Real ◽  
Data Source ◽  
Different Characteristics

It is valuable for the real world to find the opinion leaders. Because different data sources usually have different characteristics, there does not exist a standard algorithm to find and detect the opinion leaders in different data sources. Every data source has its own structural characteristics, and also has its own detection algorithm to find the opinion leaders. Experimental results show the opinion leaders and theirs characteristics can be found among the comments from the Weibo social network of China, which is like Facebook or Twitter in USA.

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 GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

SARCP - Exploiting Cyber-Attack Prediction Through Socially-Aware Recommendation

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Social Network ◽  
Real World ◽  
Cyber Security ◽  
Betweenness Centrality ◽  
Cyber Attacks ◽  
Experimental Results ◽  
Cyber Attack ◽  
Defence Mechanisms ◽  
Network Measure ◽  
Recommender Algorithm

In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.

Semantic Analysis of Bloggers Experiences as a Knowledge Source of Average Human Morality

2015 ◽  
pp. 73-95 ◽  
Author(s):  
Rafal Rzepka ◽  
Kenji Araki
Keyword(s):  
Text Mining ◽  
Knowledge Base ◽  
Real World ◽  
Semantic Analysis ◽  
Experimental Results ◽  
Artificial Agents ◽  
Knowledge Source ◽  
The Real ◽  
Long Run

This chapter introduces an approach and methods for creating a system that refers to human experiences and thoughts about these experiences in order to ethically evaluate other parties', and in a long run, its own actions. It is shown how applying text mining techniques can enrich machine's knowledge about the real world and how this knowledge could be helpful in the difficult realm of moral relativity. Possibilities of simulating empathy and applying proposed methods to various approaches are introduced together with discussion on the possibility of applying growing knowledge base to artificial agents for particular purposes, from simple housework robots to moral advisors, which could refer to millions of different experiences had by people in various cultures. The experimental results show efficiency improvements when compared to previous research and also discuss the problems with fair evaluation of moral and immoral acts.

Semantic-Based Geospatial Data Integration With Unique Features

2019 ◽  
pp. 254-277 ◽  
Author(s):  
Ying Zhang ◽  
Chaopeng Li ◽  
Na Chen ◽  
Shaowen Liu ◽  
Liming Du ◽  
...  
Keyword(s):  
Data Integration ◽  
High Performance ◽  
Data Access ◽  
Heterogeneous Data ◽  
Geospatial Data ◽  
Experimental Results ◽  
Data Sources ◽  
Data Format ◽  
Access Protocols ◽  
Data Source

Since large amount of geospatial data are produced by various sources, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. We mainly adopt four kinds of geospatial data sources to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

Semantic-Based Geospatial Data Integration With Unique Features

2018 ◽  
pp. 393-416
Author(s):  
Ying Zhang ◽  
Chaopeng Li ◽  
Na Chen ◽  
Shaowen Liu ◽  
Liming Du ◽  
...  
Keyword(s):  
Data Integration ◽  
High Performance ◽  
Data Access ◽  
Heterogeneous Data ◽  
Geospatial Data ◽  
Experimental Results ◽  
Data Sources ◽  
Data Format ◽  
Access Protocols ◽  
Data Source

Since large amount of geospatial data are produced by various sources, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. We mainly adopt four kinds of geospatial data sources to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

scholarly journals Novel User Level Data Leakage Detection Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2378-2381
Keyword(s):  
Performance Metrics ◽  
Detection Algorithm ◽  
Point Of View ◽  
Experimental Results ◽  
Detection Technique ◽  
Data Sources ◽  
Research Issue ◽  
Leakage Detection ◽  
Important Research ◽  
Level Data

Data leakage detection (DLD) is the most widely used detection technique in many applications such as etc. detecting data leakage by various data sources is an important research issue. Several researchers contributed to detect the data leakage by proposing various techniques. In the existing DLD techniques the performance metrics such as accuracy and time have been neglected. In this paper, we have proposed a new DLD algorithm and named it as novel user level data leakage detection algorithm (NULDLDA). In the proposed NULDLDA we have considered the user point of view to know the leakage of data by which agent among several existing agents. We have implemented and compared the NULDLDA with existing DLD. The experimental results indicate that proposed NULDLDA improved the performance over DLD with respect to time and accuracy.

scholarly journals From Reality to Perception: Genre-Based Neural Image Style Transfer

2018 ◽  
Author(s):  
Zhuoqi Ma ◽  
Nannan Wang ◽  
Xinbo Gao ◽  
Jie Li
Keyword(s):  
Transfer Process ◽  
Real World ◽  
Experimental Results ◽  
Style Transfer ◽  
The Real ◽  
Design Aspect ◽  
Semantic Correspondence

We introduce a novel thought for integrating artists’ perceptions on the real world into neural image style transfer process. Conventional approaches commonly migrate color or texture patterns from style image to content image, but the underlying design aspect of the artist always get overlooked. We want to address the in-depth genre style, that how artists perceive the real world and express their perceptions in the artwork. We collect a set of Van Gogh’s paintings and cubist artworks, and their semantically corresponding real world photos. We present a novel genre style transfer framework modeled after the mechanism of actual artwork production. The target style representation is reconstructed based on the semantic correspondence between real world photo and painting, which enable the perception guidance in style transfer. The experimental results demonstrate that our method can capture the overall style of a genre or an artist. We hope that this work provides new insight for including artists’ perceptions into neural style transfer process, and helps people to understand the underlying characters of the artist or the genre.

On Handling the Evolution of External Data Sources in a Data Warehouse Architecture

2011 ◽  
pp. 106-147 ◽  
Author(s):  
Robert Wrembel
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Practical Importance ◽  
Data Sources ◽  
External Data ◽  
External Data Source ◽  
On Line ◽  
Advanced Analysis ◽  
Data Source ◽  
Analytical Processing

A data warehouse architecture (DWA) has been developed for the purpose of integrating data from multiple heterogeneous, distributed, and autonomous external data sources (EDSs) as well as for providing means for advanced analysis of integrated data. The major components of this architecture include: an external data source (EDS) layer, and extraction-transformation-loading (ETL) layer, a data warehouse (DW) layer, and an on-line analytical processing (OLAP) layer. Methods of designing a DWA, research developments, and most of the commercially available DW technologies tacitly assumed that a DWA is static. In practice, however, a DWA requires changes among others as the result of the evolution of EDSs, changes of the real world represented in a DW, and new user requirements. Changes in the structures of EDSs impact the ETL, DW, and OLAP layers. Since such changes are frequent, developing a technology for handling them automatically or semi-automatically in a DWA is of high practical importance. This chapter discusses challenges in designing, building, and managing a DWA that supports the evolution of structures of EDSs, evolution of an ETL layer, and evolution of a DW. The challenges and their solutions presented here are based on an experience of building a prototype Evolving-ETL and a prototype Multiversion Data Warehouse (MVDW). In details, this chapter presents the following issues: the concept of the MVDW, an approach to querying the MVDW, an approach to handling the evolution of an ETL layer, a technique for sharing data between multiple DW versions, and two index structures for the MVDW.

Big-Data-Based Architectures and Techniques

2019 ◽  
pp. 19-50
Author(s):  
Gopala Krishna Behara
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Reference Architecture ◽  
The Real ◽  
Key Concepts ◽  
Insight Into

This chapter covers the essentials of big data analytics ecosystems primarily from the business and technology context. It delivers insight into key concepts and terminology that define the essence of big data and the promise it holds to deliver sophisticated business insights. The various characteristics that distinguish big data datasets are articulated. It also describes the conceptual and logical reference architecture to manage a huge volume of data generated by various data sources of an enterprise. It also covers drivers, opportunities, and benefits of big data analytics implementation applicable to the real world.

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