scholarly journals Ego-zones: non-symmetric dependencies reveal network groups with large and dense overlaps

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Milos Kudelka ◽  
Eliska Ochodkova ◽  
Sarka Zehnalova ◽  
Jakub Plesnik
Keyword(s):  
Network Structure ◽  
Real World ◽  
Ground Truth ◽  
Structural Similarity ◽  
Global Network ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
First Case ◽  
New Perspective ◽  
The Individual

Abstract The existence of groups of nodes with common characteristics and the relationships between these groups are important factors influencing the structures of social, technological, biological, and other networks. Uncovering such groups and the relationships between them is, therefore, necessary for understanding these structures. Groups can either be found by detection algorithms based solely on structural analysis or identified on the basis of more in-depth knowledge of the processes taking place in networks. In the first case, these are mainly algorithms detecting non-overlapping communities or communities with small overlaps. The latter case is about identifying ground-truth communities, also on the basis of characteristics other than only network structure. Recent research into ground-truth communities shows that in real-world networks, there are nested communities or communities with large and dense overlaps which we are not yet able to detect satisfactorily only on the basis of structural network properties.In our approach, we present a new perspective on the problem of group detection using only the structural properties of networks. Its main contribution is pointing out the existence of large and dense overlaps of detected groups. We use the non-symmetric structural similarity between pairs of nodes, which we refer to as dependency, to detect groups that we call zones. Unlike other approaches, we are able, thanks to non-symmetry, accurately to describe the prominent nodes in the zones which are responsible for large zone overlaps and the reasons why overlaps occur. The individual zones that are detected provide new information associated in particular with the non-symmetric relationships within the group and the roles that individual nodes play in the zone. From the perspective of global network structure, because of the non-symmetric node-to-node relationships, we explore new properties of real-world networks that describe the differences between various types of networks.

Gravitational community detection by predicting diameter

2021 ◽  
pp. 2150145
Author(s):  
Himansu Sekhar Pattanayak ◽  
Harsh K. Verma ◽  
Amrit Lal Sangal
Keyword(s):  
Community Detection ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Local Information ◽  
Ground Truth Data ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Np Hard Problem ◽  
Community Detection Algorithm ◽  
The Individual

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

scholarly journals Multiple Noisy Label Distribution Propagation for Crowdsourcing

2019 ◽  
Author(s):  
Hao Zhang ◽  
Liangxiao Jiang ◽  
Wenqiang Xu
Keyword(s):  
Supervised Learning ◽  
Real World ◽  
Effective Means ◽  
Ground Truth ◽  
Cost Effective ◽  
Nearest Neighbors ◽  
True Label ◽  
Real World Datasets ◽  
The Individual ◽  
Label Distribution

Crowdsourcing services provide a fast, efficient, and cost-effective means of obtaining large labeled data for supervised learning. Ground truth inference, also called label integration, designs proper aggregation strategies to infer the unknown true label of each instance from the multiple noisy label set provided by ordinary crowd workers. However, to the best of our knowledge, nearly all existing label integration methods focus solely on the multiple noisy label set itself of the individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this study. MNLDP first transforms the multiple noisy label set of each instance into its multiple noisy label distribution and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Promising experimental results on simulated and real-world datasets validate the effectiveness of our proposed method.

scholarly journals Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luca Pappalardo ◽  
Leo Ferres ◽  
Manuel Sacasa ◽  
Ciro Cattuto ◽  
Loreto Bravo
Keyword(s):  
Mobile Phone ◽  
Ground Truth ◽  
Mobile Phone Data ◽  
Data Generation ◽  
Individual Level ◽  
Detection Algorithms ◽  
Home Location ◽  
Residence Location ◽  
The Individual ◽  
And Control

AbstractInferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.

Complex network structure extraction based on community relevance

2020 ◽  
Vol 31 (04) ◽  
pp. 2050062
Author(s):  
Jingyi Ding ◽  
Licheng Jiao ◽  
Jianshe Wu ◽  
Fang Liu
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Network Structure ◽  
Link Prediction ◽  
Detection Algorithm ◽  
Node Degree ◽  
Network Function ◽  
Detection Algorithms ◽  
New Perspective ◽  
Community Detection Algorithm

One way to understand the network function and analyze the network structure is to find the communities of the network accurately. Now, there are many works about designing algorithms for community detection. Most community detection algorithms are based on modularity optimization. However, these methods not only have disadvantages in computational complexity, but also have the problem of resolution restriction. Designing a community detection algorithm that is fast and effective remains a challenge in the field. We attempt to solve the community detection problem in a new perspective in this paper, believing that the assumption used to solve the link prediction problem is useful for the problem of community detection. By using the similarity between modules of the network, we propose a new method to extract the community structure in this paper. Our algorithm consists of three steps. First, we initialize a community partition based on the distribution of the node degree; second, we calculate the similarity between different communities, where the similarity is the index to describe the closeness of the different communities. We assume that the much closer the two different communities are, the greater the likelihood of being divided together; finally, merge the pairs of communities which has the highest similarity value as possible as we can and stop when the condition is not satisfied. Because the convergence of our algorithm is very fast in the process of merging, we find that our method has advantages both in the computational complexity and in the accuracy when compared with other six classical algorithms. Moreover, we design a new measure to describe how difficulty the network division is.

Impairment Tutorial: Functionally Limiting Upper Extremity Pain: Controversies in Impairment Rating

2003 ◽  
Vol 8 (5) ◽  
pp. 4-12
Author(s):  
Lorne Direnfeld ◽  
James Talmage ◽  
Christopher Brigham
Keyword(s):  
Upper Extremity ◽  
Radicular Pain ◽  
Illness Behavior ◽  
Orthopedic Surgeon ◽  
First Case ◽  
Clinical Presentations ◽  
Whole Person ◽  
Physical Examinations ◽  
Upper Extremity Impairment ◽  
The Individual

Abstract This article was prompted by the submission of two challenging cases that exemplify the decision processes involved in using the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides). In both cases, the physical examinations were normal with no evidence of illness behavior, but, based on their histories and clinical presentations, the patients reported credible symptoms attributable to specific significant injuries. The dilemma for evaluators was whether to adhere to the AMA Guides, as written, or to attempt to rate impairment in these rare cases. In the first case, the evaluating neurologist used alternative approaches to define impairment based on the presence of thoracic outlet syndrome and upper extremity pain, as if there were a nerve injury. An orthopedic surgeon who evaluated the case did not base impairment on pain and used the upper extremity chapters in the AMA Guides. The impairment ratings determined using either the nervous system or upper extremity chapters of the AMA Guides resulted in almost the same rating (9% vs 8% upper extremity impairment), and either value converted to 5% whole person permanent impairment. In the second case, the neurologist evaluated the individual for neuropathic pain (9% WPI), and the orthopedic surgeon rated the patient as Diagnosis-related estimates Cervical Category II for nonverifiable radicular pain (5% to 8% WPI).

scholarly journals A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system

2021 ◽  
Author(s):  
Volker A. Coenen ◽  
Bastian E. Sajonz ◽  
Peter C. Reinacher ◽  
Christoph P. Kaller ◽  
Horst Urbach ◽  
...  
Keyword(s):  
Surgical Planning ◽  
Ground Truth ◽  
Planning System ◽  
Single Subject ◽  
Fiber Structure ◽  
Native Space ◽  
Volume Of Interest ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

Abstract Background An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed. Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures. Results In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms. Conclusion Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.

scholarly journals CIRO: The Effects of Visually Diminished Real Objects on Human Perception in Handheld Augmented Reality

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 900
Author(s):  
Hanseob Kim ◽  
Taehyung Kim ◽  
Myungho Lee ◽  
Gerard Jounghyun Kim ◽  
Jae-In Hwang
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Human Perception ◽  
Ground Truth ◽  
Prior Work ◽  
Comparative Experiment ◽  
User Perception ◽  
Depth Distortion ◽  
Real Objects ◽  
Visual Artifacts

Augmented reality (AR) scenes often inadvertently contain real world objects that are not relevant to the main AR content, such as arbitrary passersby on the street. We refer to these real-world objects as content-irrelevant real objects (CIROs). CIROs may distract users from focusing on the AR content and bring about perceptual issues (e.g., depth distortion or physicality conflict). In a prior work, we carried out a comparative experiment investigating the effects on user perception of the AR content by the degree of the visual diminishment of such a CIRO. Our findings revealed that the diminished representation had positive impacts on human perception, such as reducing the distraction and increasing the presence of the AR objects in the real environment. However, in that work, the ground truth test was staged with perfect and artifact-free diminishment. In this work, we applied an actual real-time object diminishment algorithm on the handheld AR platform, which cannot be completely artifact-free in practice, and evaluated its performance both objectively and subjectively. We found that the imperfect diminishment and visual artifacts can negatively affect the subjective user experience.

scholarly journals Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Bingsheng Wei ◽  
Martin Barczyk
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Tracking System ◽  
Ground Truth ◽  
White Background ◽  
Cascade Classifier ◽  
Detection Algorithms ◽  
Squared Error ◽  
Test Conditions ◽  
Video Feed

We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accuracy (measured as the root mean squared error between ground truth and detected location), and consistency (measured as mean average precision) in a variety of flight patterns, backgrounds, and test conditions. Assigning relative weights of 20%, 40% and 40% to these three criteria, we find that when flying over a white background, the top three performers are YOLO v2 (76.73 out of 100), Faster RCNN v2 (63.65 out of 100), and Tiny YOLO (59.50 out of 100), while over a realistic background, the top three performers are Faster RCNN v2 (54.35 out of 100, SSD MobileNet v1 (51.68 out of 100) and SSD Inception v2 (50.72 out of 100), leading us to recommend Faster RCNN v2 as the recommended solution. We then provide a roadmap for further work in integrating the object detector into our vision-based UAV tracking system.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
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.

scholarly journals Fully Data-Driven Pseudohealthy Synthesis for Planning Valve-Sparing Aortic Root Reconstruction using Conditional Variational Autoencoders

2020 ◽  
Vol 6 (3) ◽  
pp. 284-287
Author(s):  
Jannis Hagenah ◽  
Mohamad Mehdi ◽  
Floris Ernst
Keyword(s):  
Aortic Root ◽  
Similarity Index ◽  
Ground Truth ◽  
Representation Learning ◽  
Patient Specific ◽  
Ultrasound Images ◽  
Specific Geometry ◽  
The Individual ◽  
Native Root ◽  
Original Information

AbstractAortic root aneurysm is treated by replacing the dilated root by a grafted prosthesis which mimics the native root morphology of the individual patient. The challenge in predicting the optimal prosthesis size rises from the highly patient-specific geometry as well as the absence of the original information on the healthy root. Therefore, the estimation is only possible based on the available pathological data. In this paper, we show that representation learning with Conditional Variational Autoencoders is capable of turning the distorted geometry of the aortic root into smoother shapes while the information on the individual anatomy is preserved. We evaluated this method using ultrasound images of the porcine aortic root alongside their labels. The observed results show highly realistic resemblance in shape and size to the ground truth images. Furthermore, the similarity index has noticeably improved compared to the pathological images. This provides a promising technique in planning individual aortic root replacement.

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