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"title": "Application-specific graph sampling for frequent subgraph mining and community detection",
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"abstract": "Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). We explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function. Original Datasets, implementation of sampling algorithms, and results are available online.1",
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"title": "2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)",
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"abstract": "Random walk-based graph sampling methods can effectively estimate feature values in large-scale social networks wherein the node IDs are unknown. Real social networks are sampled by repeatedly querying their APIs to acquire the lists of adjacent nodes. These queries can then become a bottleneck in the sampling process because nearly all social network services restrict the rate at which queries can be issued. However, most existing graph sampling studies have not focused on the number of queries but have instead compared methods based on sample size. Therefore, such graph sampling methods cannot be recommended for estimating feature values in actual social networks. This study presents an approach to assess graph sampling methods that focus on the number of queries. This describes the time taken by algorithms for typical random walk-based graph sampling methods, such as a simple random walk with re-weighting (SRW-rw), a non-backtracking random walk with re-weighting (NBRW-rw), and a Metropolis-Hastings random walk (MHRW), which require queries. The graph sampling precision was then experimentally evaluated based on sample size and query number standards using actual social networks, and the types of changes that occur were observed.",
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"content": "Random walk-based graph sampling methods can effectively estimate feature values in large-scale social networks wherein the node IDs are unknown. Real social networks are sampled by repeatedly querying their APIs to acquire the lists of adjacent nodes. These queries can then become a bottleneck in the sampling process because nearly all social network services restrict the rate at which queries can be issued. However, most existing graph sampling studies have not focused on the number of queries but have instead compared methods based on sample size. Therefore, such graph sampling methods cannot be recommended for estimating feature values in actual social networks. This study presents an approach to assess graph sampling methods that focus on the number of queries. This describes the time taken by algorithms for typical random walk-based graph sampling methods, such as a simple random walk with re-weighting (SRW-rw), a non-backtracking random walk with re-weighting (NBRW-rw), and a Metropolis-Hastings random walk (MHRW), which require queries. The graph sampling precision was then experimentally evaluated based on sample size and query number standards using actual social networks, and the types of changes that occur were observed.",
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"abstract": "We formulate the graph signal sampling problem as a reinforcement learning process in discrete action space. A deep Q-network (DQN) is used for the agent to learn the sampling strategy. To adapt to the training environment, we redesigned the step and episode. In each episode, the agent learns how to select a node for the current state, and selects the best one at the final step of this episode. Besides, we redefined the action and reward. Based on the sampling sets, we use the same reconstruction method to test the performance. Through comparative experiments, the reconstruction accuracy of our method outperforms that of uniform random sampling and random walk sampling methods. Our method can quickly select the key nodes in the initial sampling stage. According to our investigation, it is the first time that deep Q-learning is successfully applied to the graph signal sampling field.",
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"content": "We formulate the graph signal sampling problem as a reinforcement learning process in discrete action space. A deep Q-network (DQN) is used for the agent to learn the sampling strategy. To adapt to the training environment, we redesigned the step and episode. In each episode, the agent learns how to select a node for the current state, and selects the best one at the final step of this episode. Besides, we redefined the action and reward. Based on the sampling sets, we use the same reconstruction method to test the performance. Through comparative experiments, the reconstruction accuracy of our method outperforms that of uniform random sampling and random walk sampling methods. Our method can quickly select the key nodes in the initial sampling stage. According to our investigation, it is the first time that deep Q-learning is successfully applied to the graph signal sampling field.",
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"Learning Artificial Intelligence",
"Random Processes",
"Sampling Methods",
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"affiliation": "Harbin Engineering University",
"fullName": "Menghao Wu",
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"affiliation": "Harbin Engineering University",
"fullName": "Qiang Zhang",
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"affiliation": "Harbin Engineering University",
"fullName": "Yanbin Gao",
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"abstract": "Medical Diagnosis is the utmost need of an hour. Gestational Diabetics in women represents the second leading cause of yielding children born with birth defects. The ultrasound images are usually low in resolution making diagnosis difficult. Specialized tools are required to assist the medical experts to categorize and diagnose diseases to accuracy. If the anomalies in the ultrasound images are detected in the preliminary screening of placenta, fetal loss could be minimized. This pilot study was carried out to find the feasibility for detecting anomalies in placental growth due to the implications of gestational diabetics by considering the stereo image mapping based on wavelet analysis for 2D reconstruction. The research uses wavelet based methods to extract features from the ultrasonic images of placenta. The shape of the placenta is generated using the Back Propagation Network. Euclidean Distance Classifier is used for classifying the ultrasonic images of placenta.",
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"content": "Maternal and Neonatal health has been greatly constrained by the in-access to essential maternal health care services due to the preventive measures implemented against the spread of covid-19 hence making maternal and fetal monitoring so hard for physicians. Besides maternal toxic stress caused by fear of catching covid-19, affordable mobility of pregnant mothers to skilled health practitioners in limited resource settings is another contributor to maternal and neonatal mortality and morbidity. In this work, we leveraged existing health data to build interpretable Machine Learning (ML) models that allow physicians to offer precision maternal and fetal medicine based on biomedical signal classification results of fetal cardiotocograms (CTGs).We obtained 99%, 100% and 97% accuracy, precision and recall respectively for the LightGBM classification model without any GPU Learning resources. Then we explainably evaluated all built models with ELI5 and comprehensive feature extraction.",
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"abstract": "The purpose of fetal monitoring during childbirth is the early recognition of any pathological conditions to guide a clinician in early intervention to avoid any complication in the health of the fetus. Non-Invasive Fetal Electrocardiography (NIFECG) represents an alternative fetal monitoring technique. The fetal ECG (fECG) derived from maternal thoracic and abdominal ECG recordings, provides an alternative to typical embryo monitoring means. In addition, it allows for long-term and ambulatory registrations that broaden the diagnostic capabilities for assessing the fetal health. However, in real situations, clear fECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander and high-frequency interference. In this paper, a novel integrated adaptive methodology based on the combination of blind source separation, empirical mode decomposition, wavelet shrinkage denoising and correlation analysis, for the non-invasive extraction and processing of the FECG, is proposed. The methodology has been evaluated using both real and simulated recordings, and the obtained results indicate it efficiently.",
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"content": "The purpose of fetal monitoring during childbirth is the early recognition of any pathological conditions to guide a clinician in early intervention to avoid any complication in the health of the fetus. Non-Invasive Fetal Electrocardiography (NIFECG) represents an alternative fetal monitoring technique. The fetal ECG (fECG) derived from maternal thoracic and abdominal ECG recordings, provides an alternative to typical embryo monitoring means. In addition, it allows for long-term and ambulatory registrations that broaden the diagnostic capabilities for assessing the fetal health. However, in real situations, clear fECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander and high-frequency interference. In this paper, a novel integrated adaptive methodology based on the combination of blind source separation, empirical mode decomposition, wavelet shrinkage denoising and correlation analysis, for the non-invasive extraction and processing of the FECG, is proposed. The methodology has been evaluated using both real and simulated recordings, and the obtained results indicate it efficiently.",
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"title": "Detection of Fetal Single Gene Mutations through Targeted Sequencing of Maternal cell-free DNA",
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"abstract": "Non-invasive prenatal tests are available for chromosomal aneuploidy but not for monogenic diseases yet. This study aimed to develop analysis method for detecting fetal mutation status by using only maternal blood sample. We introduce two different approaches to detect a fetal mutation of the Duchene Muscular Dystrophy (DMD) gene. The algorithms are consisted of two steps: detection of mutation types and regions and determination of fetal mutation status. For the second step, one approach uses allele frequencies of heterozygous sites and the other approach uses relative read depth that is the read depths of cfDNA divided by gDNA of the mother.",
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"abstract": "The recent advances and price reduction of technologies for collecting spatial and spatio-temporal data like Satellite Images, Cellular Phones, Sensor Networks, and GPS devices has facilitated the collection of data referenced in space and time. These huge collections of data often hide interesting information which conventional systems and classical data mining techniques are unable to discover. Spatial and spatio-temporal data are embedded in continuous space, whereas classical datasets (e.g. transactions) are often discrete. Spatial and spatio-temporal data require complex data preprocessing, transformation, data mining, and post-processing techniques to extract novel, useful, and understandable patterns. The importance of spatial and spatio-temporal data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, repositories of remote-sensing images, trajectories of moving objects generated by mobile devices, etc. Applications include Mobile-commerce industry (location-based services), climatologically effects of El Nino, land-use classification and global change using satellite imagery, finding crime hot spots, local instability in traffic, migration of birds, fishing control, pedestrian behavior analysis, and so on. Thus, new methods are needed to analyze spatial and spatio-temporal data to extract interesting, useful, and non-trivial patterns. The main goal of this tutorial is to disseminate this research field, giving an overview of the current state of the art and the main methodologies and algorithms for spatial and spatio-temporal data mining. This tutorial is directed to researches and practitioners, experts in data mining, analysts of spatial and spatio-temporal data, as well as knowledge engineers and domain experts from different application areas.",
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"abstract": "The knowledge of the anatomical shape of both gross and microscopic structures is the key to understanding the effects of disease processes on cellular structure. Geometric morphometric methods, such as Procrustes superimposition, and Spherical Harmonics (SPHARM), have been used to capture the biological shape variation and group differences in morphology. Previous SPHARM-MAT techniques use the CALD algorithm to parameterize the mesh surface. It starts from initial mapping and performs local and global smoothing methods alternately to control the area and length distortions simultaneously. However, this parameterization may not be sufficient in complex morphological cases. To bridge this gap, we propose SPHARM-OT, an enhanced SPHARM surface modeling method using optimal transport (OT) for spherical parameterization. First, the genus 0 3D objects are conformally mapped onto a sphere. Then the optimal transport theory via spherical power diagram is introduced to minimize the area distortion. This new algorithm can effectively reduce the area distortion and lead to a better reconstruction result. We demonstrate the effectiveness of the method by applying it to the human sphenoidal paranasal sinuses.",
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"fullName": "Katharine Ryan",
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"abstract": "We designed and evaluated SplitVector, a new vector field display approach to help scientists perform new discrimination tasks on scientific data shown in virtual environments (VEs). Our empirical study compared the SplitVector approach with three other approaches of direct linear representation, log, and text display common in information-rich VEs or IRVEs. Our results suggest the following: (1) SplitVectors improve the accuracy by about 10 times compared to the linear mapping and by 4 times to log in discrimination tasks; (2) SplitVectors lead to no significant differences from the IRVE text display approach, yet reduce the clutter; and (3) SplitVector improved task performance in both mono and stereoscopy conditions.",
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"abstract": "Studies of inter-domain routing in the Internet have highlighted the complex and dynamic nature of connectivity changes that take place daily on a global scale. The ability to assess and identify normal, malicious, irregular and unexpected behaviors in routing update streams is important in daily network and security operations. In this paper we describe Bigfoot, a Border Gateway Protocol (BGP) update visualization system that has been designed to highlight and assess a wide variety of behaviors in update streams. At the core of Bigfoot is the notion of visualizing the announcements of network prefixes via IP geolocation. We investigate different representations of polygons for network footprints and show how straightforward application of IP geolocation can lead to representations that are difficult to interpret. Bigfoot includes techniques to filter, organize, analyze and visualize BGP updates that enable characteristics and behaviors of interest to be identified effectively. To demonstrate Bigfoot's capabilities, we consider 1.79B BGP updates collected over a period of one year and identify 139 candidate events in this data. We investigate a subset of these events in detail, along with ground truth from existing literature to show how network footprint visualizations can be used in operational deployments.",
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"abstract": "Commercial service systems should allow customers to control their service configurations. The challenge is to make this feature feasible in such a way that the system is easy to administer while ensuring the security of both customer's service and the control system itself. Simple security mechanisms, such as access control lists used in general-purpose computer systems, are insufficient for administration control and authority delegation in commercial service systems. The author proposes a security policy called an 'authorization policy' suitable for administration and authorization controls. This security policy is enforced through a unified protection mechanism called an 'access control hierarchy'. The application of this access control hierarchy is demonstrated by examples.<>",
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"abstract": "Research into how virtual reality (VR) can be a beneficial technology for new and emerging large, complex data visualizations for data scientists is ongoing. In this paper, we evaluate three-dimensional VR navigation technique for data visualizations and test their effectiveness with a large graph visualization. We evaluate two prominent navigation techniques employed in VR (Teleportation and One-Handed Flying) against two less common methods (Two-Handed Flying and Worlds In Miniature) and evaluate their performance and effectiveness through a series of tasks. We found Steering Patterns (One-Handed Flying and Two-Handed Flying) to be faster and preferred by participants for completing searching tasks in comparision to Teleportation. Worlds-In-Miniature was the least physically demanding of the navigations, and was preferred by participants for tasks that required an overview of the graph such as triangle counting.",
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"abstract": "Risk scoring provides a simple and quantifiable metric for decision support in cyber security operations, including prioritizing how to address discovered software vulnerabilities. However, scoring systems are often opaque to operators, which makes scores difficult to interpret in the context of their own networks, each other, or in a broader threat landscape. This interpretability challenge is exacerbated by recent applications of artificial intelligence (AI) and machine learning (ML) for vulnerability assessment, where opaque machine reasoning can hinder domain experts' trust in the decision-support toolkit or the actionability of its outputs. In this paper, we address this challenge through a combination of visualizations and analytics that complement existing techniques for vulnerability assessment. We present a study toward designing more interpretable visual encodings for decision support for vulnerability assessment. In particular, we consider the problem of making datasets of known vulnerabilities more interpretable at multiple scales, inspired by focus and context principles from the information visualization design community. The first scale considers individually scored vulnerabilities by using an explainable AI (XAI) toolkit for an ML risk-scoring model and by developing new visualizations of CVSS score features. The second scale uses an embedding for vulnerability descriptions to cluster potentially similar vulnerabilities. We outline use cases for these tools and discuss opportunities for applying XAI concepts to cyber risk and vulnerability management.",
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"affiliation": "Massachusetts Institute of Technology",
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"affiliation": "Massachusetts Institute of Technology",
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"abstract": "Voice over IP (VoIP) gains more and more attractiveness by large companies as well as private users. Therefore, the risk increases that VoIP systems get attacked by hackers. In order to effectively protect VoIP users from misuse, researchers use, e.g., honeynets to capture and analyze VoIP attacks occurring in the Internet. Global VoIP security threats are analyzed by studying several millions of real-world attacks collected in independent VoIP honeynet solutions with different capture mechanisms over a long period of time. Due to the validation of results from several honeynet designs we have achieved a unique, much broader view on large scale attacks. The results show similar attacker behavior, confirm previous assumptions about attacks and present new insights in large scale VoIP attacks, e.g., for toll fraud.",
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"abstract": "New threats are constantly appearing on the Internet.System administrators have developed many tools to try tomitigate those threats, however, currently available coun-termeasures are still limited. Moreover, it is difficult forsystem administrators to fully understand what happens intheir networks in (near) real time. We focus on the moni-toring of network traffic sent to unused IP addresses withhoneypot devices to capture information about network ac-tivity. More precisely, we consider ways of handling suchunused addresses on network segments managed via DHCP(Dynamic Host Configuration Protocol). In this paper, wepropose to exploit that DHCP service to dynamically assignunused IP addresses to honeypot devices, and, discuss thedesign of such monitoring system.",
"abstracts": [
{
"abstractType": "Regular",
"content": "New threats are constantly appearing on the Internet.System administrators have developed many tools to try tomitigate those threats, however, currently available coun-termeasures are still limited. Moreover, it is difficult forsystem administrators to fully understand what happens intheir networks in (near) real time. We focus on the moni-toring of network traffic sent to unused IP addresses withhoneypot devices to capture information about network ac-tivity. More precisely, we consider ways of handling suchunused addresses on network segments managed via DHCP(Dynamic Host Configuration Protocol). In this paper, wepropose to exploit that DHCP service to dynamically assignunused IP addresses to honeypot devices, and, discuss thedesign of such monitoring system.",
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"abstract": "Cloud computing related insider threats are often listed as a serious concern by security researchers, but to date this threat has not been thoroughly explored. We believe the fundamental nature of current insider threats will remain relatively unchanged in a cloud environment, but the paradigm does reveal new exploit possibilities. The common notion of a cloud insider as a rogue administrator of a service provider is discussed, but we also present two additional cloudrelated insider risks: the insider who exploits a cloud-related vulnerability to steal information from a cloud system, and the insider who uses cloud systems to carry out an attack on an employer's local resources. We also characterize a hierarchy of administrators within cloud service providers, give examples of attacks from real insider threat cases, and show how the nature of cloud systems architectures enables attacks to succeed. Finally, we discuss our position on future cloud research.",
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"abstract": "Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs, but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates.",
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