A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

Download Full-text

A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

Applied Sciences ◽

10.3390/app10051603 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1603

Author(s):

Jinli Zhang ◽

Tong Li ◽

Zongli Jiang ◽

Xiaohua Hu ◽

Ali Jazayeri

Keyword(s):

Real World ◽

Structural Features ◽

Information Networks ◽

Heterogeneous Information ◽

Heterogeneous Information Networks ◽

Real World Applications ◽

Different Types ◽

Multiple Challenges ◽

Real World Datasets

There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.

Download Full-text

Real-World Deployments of Participatory Sensing Applications: Current Trends and Future Directions

ISRN Sensor Networks ◽

10.1155/2013/583165 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 23

Author(s):

Sameer Tilak

Keyword(s):

Sensor Networks ◽

Real World ◽

Cell Phones ◽

Research Community ◽

Consumer Electronics ◽

Mobile Sensors ◽

Participatory Sensing ◽

Future Directions ◽

Sensing Applications ◽

Current Trends

With the advent of participatory sensing (sensors integrated with consumer electronics such as cell phones and carried by people), exciting new opportunities arise. Mobile sensors (e.g., those mounted on cars or carried by people) can provide spatial sampling diversity not possible with traditional static sensor networks. Recently, participatory sensing has attracted considerable attention of research community. In this paper, we survey existing participatory sensing deployments and discuss current trends and few possible future directions.

Download Full-text

Variational Mixtures of Gaussian Processes for Classification

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/642 ◽

2017 ◽

Cited By ~ 3

Author(s):

Chen Luo ◽

Shiliang Sun

Keyword(s):

Gaussian Processes ◽

Latent Variables ◽

Predictive Performance ◽

Logistic Function ◽

Classification Problems ◽

Em Method ◽

Regression Problems ◽

Real World Datasets ◽

Mixture Of Gaussian ◽

Better Than

Gaussian Processes (GPs) are powerful tools for machine learning which have been applied to both classification and regression. The mixture models of GPs were later proposed to further improve GPs for data modeling. However, these models are formulated for regression problems. In this work, we propose a new Mixture of Gaussian Processes for Classification (MGPC). Instead of the Gaussian likelihood for regression, MGPC employs the logistic function as likelihood to obtain the class probabilities, which is suitable for classification problems. The posterior distribution of latent variables is approximated through variational inference. The hyperparameters are optimized through the variational EM method and a greedy algorithm. Experiments are performed on multiple real-world datasets which show improvements over five widely used methods on predictive performance. The results also indicate that for classification MGPC is significantly better than the regression model with mixtures of GPs, different from the existing consensus that their single model counterparts are comparable.

Download Full-text

Foundations of Blockchain Technology for Industrial and Societal Applications

10.34048/2018.1.f2 ◽

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.

Download Full-text

Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

ACM Transactions on Internet Technology ◽

10.1145/3409265 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1-17

Author(s):

Wu Chen ◽

Yong Yu ◽

Keke Gai ◽

Jiamou Liu ◽

Kim-Kwang Raymond Choo

Keyword(s):

Ensemble Learning ◽

Real World ◽

Interaction Mechanism ◽

Training Model ◽

Edge Computing ◽

Learning Techniques ◽

Multi Agent ◽

Real World Datasets ◽

Entire Dataset ◽

Exchange Data

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

Download Full-text

OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽

10.3390/data6010001 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Ahmed Elmogy ◽

Hamada Rizk ◽

Amany M. Sarhan

Keyword(s):

Data Mining ◽

Image Processing ◽

Intrusion Detection ◽

Real Time ◽

Outlier Detection ◽

Real World ◽

Medical Data ◽

Experimental Results ◽

Real Time Applications ◽

Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Download Full-text

Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽

10.3390/e23060680 ◽

2021 ◽

Vol 23 (6) ◽

pp. 680

Author(s):

Hanyang Lin ◽

Yongzhao Zhan ◽

Zizheng Zhao ◽

Yuzhong Chen ◽

Chen Dong

Keyword(s):

Community Detection ◽

Real World ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Adjustment Strategy ◽

Topology Information ◽

Overlapping Community ◽

Real World Datasets ◽

Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Download Full-text

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

ACM Transactions on the Web ◽

10.1145/3448015 ◽

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.

Download Full-text

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202717 ◽

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.

Download Full-text

Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽

10.1155/2018/9653404 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Jibing Wu ◽

Lianfei Yu ◽

Qun Zhang ◽

Peiteng Shi ◽

Lihua Liu ◽

...

Keyword(s):

Real World ◽

Tensor Decomposition ◽

Information Networks ◽

Community Discovery ◽

Star Network ◽

Heterogeneous Information ◽

Heterogeneous Information Networks ◽

General Network ◽

Real World Datasets ◽

Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

Download Full-text