scholarly journals A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Maria Grazia Puxeddu ◽  
Manuela Petti ◽  
Laura Astolfi
Keyword(s):  
Community Detection ◽  
Simulation Study ◽  
State Of The Art ◽  
Brain Networks ◽  
Comprehensive Analysis ◽  
The State ◽  
Modular Organization ◽  
High Temporal Resolution ◽  
Eeg Signals ◽  
Modular Structures

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.

Requirements Engineering for Courseware Development

2011 ◽  
pp. 170-188 ◽  
Author(s):  
Ines Grützner ◽  
Barbara Paech
Keyword(s):  
Requirements Engineering ◽  
State Of The Art ◽  
Comprehensive Analysis ◽  
Further Education ◽  
The State ◽  
Requirements Specification ◽  
Development Projects ◽  
Learning Context ◽  
Active Involvement ◽  
The Web

Technology-enabled learning using the Web and the computer and courseware, in particular, is becoming more and more important as an addition, extension, or replacement of traditional further education measures. This chapter introduces the challenges and possible solutions for requirements engineering (RE) in courseware development projects. First the state-of-the-art in courseware requirements engineering is analyzed and confronted with the most important challenges. Then the IntView methodology is described as one solution for these challenges. The main features of IntView RE are: support of all roles from all views on courseware RE; focus on the audience supported by active involvement of audience representatives in all activities; comprehensive analysis of the sociotechnical environment of the audience and the courseware as well as of the courseware learning context; coverage of all software RE activities; and development of an explicit requirements specification documentation.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

scholarly journals A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450018 ◽  
Author(s):  
XIN LIU ◽  
WEICHU LIU ◽  
TSUYOSHI MURATA ◽  
KEN WAKITA
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
General Structure ◽  
The State ◽  
New Method ◽  
World Systems ◽  
Relational Networks ◽  
Relational Network ◽  
Art Techniques

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.

Revealing False Positive Features in Epileptic EEG Identification

2020 ◽  
Vol 30 (11) ◽  
pp. 2050017 ◽  
Author(s):  
Jian Lian ◽  
Yunfeng Shi ◽  
Yan Zhang ◽  
Weikuan Jia ◽  
Xiaojun Fan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Eeg Classification

Feature selection plays a vital role in the detection and discrimination of epileptic seizures in electroencephalogram (EEG) signals. The state-of-the-art EEG classification techniques commonly entail the extraction of the multiple features that would be fed into classifiers. For some techniques, the feature selection strategies have been used to reduce the dimensionality of the entire feature space. However, most of these approaches focus on the performance of classifiers while neglecting the association between the feature and the EEG activity itself. To enhance the inner relationship between the feature subset and the epileptic EEG task with a promising classification accuracy, we propose a machine learning-based pipeline using a novel feature selection algorithm built upon a knockoff filter. First, a number of temporal, spectral, and spatial features are extracted from the raw EEG signals. Second, the proposed feature selection algorithm is exploited to obtain the optimal subgroup of features. Afterwards, three classifiers including [Formula: see text]-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) are used. The experimental results on the Bonn dataset demonstrate that the proposed approach outperforms the state-of-the-art techniques, with accuracy as high as 99.93% for normal and interictal EEG discrimination and 98.95% for interictal and ictal EEG classification. Meanwhile, it has achieved satisfactory sensitivity (95.67% in average), specificity (98.83% in average), and accuracy (98.89% in average) over the Freiburg dataset.

Leachate Generation and Control at Landfill Disposal Sites

1985 ◽  
Vol 20 (3) ◽  
pp. 10-24 ◽  
Author(s):  
Frederick G. Pohland ◽  
Stephen R. Harper ◽  
Ker-Chi Chang ◽  
Joseph T. Dertien ◽  
Edward S. K. Chian
Keyword(s):  
Simulation Study ◽  
Landfill Leachate ◽  
State Of The Art ◽  
Treatment Options ◽  
The State ◽  
Leachate Treatment ◽  
Landfill Management ◽  
And Control ◽  
Landfill Disposal

Abstract A two-part overview of the state-of-the-art in landfill management is presented with emphasis on the production and characteristics of landfill leachate and gas and methods for control and treatment. The patterns of production of landfill leachate and gas are illustrated with results from a landfill simulation study with and without leachate recycle. Leachate treatment options are summarized and fortified by information derived from selected literature reports.

A comprehensive analysis on the state‐of‐the‐art developments in reflectarray, transmitarray, and transmit‐reflectarray antennas

2020 ◽  
Vol 30 (9) ◽  
Author(s):  
Kavitha Narayanasamy ◽  
Gulam Nabi Alsath Mohammed ◽  
Kirubaveni Savarimuthu ◽  
Ramprabhu Sivasamy ◽  
Malathi Kanagasabai
Keyword(s):  
State Of The Art ◽  
Comprehensive Analysis ◽  
The State

scholarly journals Enumerating Maximal k-Plexes with Worst-Case Time Guarantee

2020 ◽  
Vol 34 (03) ◽  
pp. 2442-2449
Author(s):  
Yi Zhou ◽  
Jingwei Xu ◽  
Zhenyu Guo ◽  
Mingyu Xiao ◽  
Yan Jin
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Enumeration Algorithm ◽  
Worst Case ◽  
Running Time ◽  
Real World Applications ◽  
First Time

The problem of enumerating all maximal cliques in a graph is a key primitive in a variety of real-world applications such as community detection and so on. However, in practice, communities are rarely formed as cliques due to data noise. Hence, k-plex, a subgraph in which any vertex is adjacent to all but at most k vertices, is introduced as a relaxation of clique. In this paper, we investigate the problem of enumerating all maximal k-plexes and present FaPlexen, an enumeration algorithm which integrates the “pivot” heuristic and new branching schemes. To our best knowledge, for the first time, FaPlexen lists all maximal k-plexes with provably worst-case running time O(n2γn) in a graph with n vertices, where γ < 2. Then, we propose another algorithm CommuPlex which non-trivially extends FaPlexen to find all maximal k-plexes of prescribed size for community detection in massive real-life networks. We finally carry out experiments on both real and synthetic graphs and demonstrate that our algorithms run much faster than the state-of-the-art algorithms.

scholarly journals Future Open Mobile Services

2005 ◽  
Vol 1 (1) ◽  
pp. 44
Author(s):  
Do Van Thanh
Keyword(s):  
Business Models ◽  
State Of The Art ◽  
Mobile Services ◽  
Comprehensive Analysis ◽  
The State ◽  
Mobile Service ◽  
Mobile System ◽  
End User ◽  
Mobile Data ◽  
Data Services

The major barriers for the success of mobile data services are the lack of comprehensible mobile service architectures, their confusing business models and the complexity combined with the inconsistency of the technology enablers. This paper attempts to present a more structured and comprehensive analysis of the current mobile service architectures and their technology enablers. The paper starts with a thorough study of the evolution of mobile services and their business models, and a collection of expectations of the different actors, including the end-user. Next, starting from the original mobile services architecture and environment, an attempt to place the different technology enablers in relation to each other and in relation to their position in the mobile system, will be carried out. Each technology enabler together with their contribution in the enhancement of mobile services are then summarised in a complete and comprehensive way. The paper concludes with a recapitulation of the achievement of the state-of-the-art technology enablers and an identification of future improvements.

scholarly journals THE TEACHING OF EFL VOCABULARY IN THE INDONESIAN CONTEXT: THE STATE OF THE ART

2015 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Bambang Yudi Cahyono ◽  
Utami Widiati
Keyword(s):  
Foreign Language ◽  
Teaching And Learning ◽  
State Of The Art ◽  
Comprehensive Analysis ◽  
The State ◽  
Future Research ◽  
Extensive Literature ◽  
Current Practices

There has been extensive literature on the teaching of vocabulary of English as a foreign language (EFL vocabulary) in the Indonesian context. However, a comprehensive analysis on the teaching of EFL vocabulary in this country has been a rare endeavour. This article aims to underpin various issues of the teaching of EFL vocabulary and relate them to a wider context of second/foreign language vocabulary teaching and review results of research as well as current practices of EFL vocabulary teaching and learning in the Indonesian context. It is expected that this article could provide an outline of the teaching of EFL vocabulary and some recommendations for future research and practices.

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