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Graph processing on GPUs: Where are the bottlenecks?

Qiumin Xu, Hyeran Jeon, Murali Annavaram
2014 2014 IEEE International Symposium on Workload Characterization (IISWC)  
Graph processing is used from social networking web sites that provide context-aware services from user connectivity data to medical informatics that diagnose a disease from a given set of symptoms.  ...  Large graph processing is now a critical component of many data analytics.  ...  [16] recently showed a preliminary characterization of graph applications on a real GPGPU machine.  ... 
doi:10.1109/iiswc.2014.6983053 dblp:conf/iiswc/XuJA14 fatcat:fw4utwvtrzcdndtqaas5rbs6he

Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

Jaiteg Singh, Tanya Gera, Farman Ali, Deepak Thakur, Karamjeet Singh, Kyung-sup Kwak
2021 Computers Materials & Continua  
Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them.  ...  Research trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation, financial attacks and stealing user information.  ...  Researchers were in need of a robust solution to analyze and mitigate the impact of malware.  ... 
doi:10.32604/cmc.2021.014504 fatcat:rkzjksdfejhhvoeswilyfcbeoy

Graph Neural Networks in IoT: A Survey [article]

Guimin Dong, Mingyue Tang, Zhiyuan Wang, Jiechao Gao, Sikun Guo, Lihua Cai, Robert Gutierrez, Bradford Campbell, Laura E. Barnes, Mehdi Boukhechba
2022 arXiv   pre-print
In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching  ...  Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art  ...  The first type of GNNs for ISI is spatio-temporal GNNs, which are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying  ... 
arXiv:2203.15935v2 fatcat:jkqg5ukg5fezbewu5mr5hqsp4e

Persistence homology of networks: methods and applications

Mehmet E. Aktas, Esra Akbas, Ahmed El Fatmaoui
2019 Applied Network Science  
In this paper, we provide a conceptual review of key advancements in this area of using PH on complex network science.  ...  We give a brief mathematical background on PH, review different methods (i.e. filtrations) to define PH on networks and highlight different algorithms and applications where PH is used in solving network  ...  MEA studied the filtrations defined on networks. MEA, EA and AEF studied the algorithm and applications of the persistent homology in network settings. MEA, EA and AEF wrote the manuscript.  ... 
doi:10.1007/s41109-019-0179-3 fatcat:qgnch4zb2jdy3igvjdtzgrfvd4

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning [article]

Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu
2021 arXiv   pre-print
a single graph, or anomalous graphs in a database/set of graphs.  ...  In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.  ...  A brain disorder can be diagnosed by analyzing the dynamics of brain graphs at different stages of aging in sequence and finding an inconsistent snapshot at a specific time stamp.  ... 
arXiv:2106.07178v4 fatcat:efargsqnxndqbfqat2q5iz54u4

Cloud Security Auditing Based on Behavioral Modeling

Zachary Birnbaum, Bingwei Liu, Andrey Dolgikh, Yu Chen, Victor Skormin
2013 2013 IEEE Ninth World Congress on Services  
The timely prevention of intrusive behavior and malicious processes using signature based intrusion detection technologies, or system call level anomaly analysis is a very challenging task due to a high  ...  Our preliminary results have validated the effectiveness and efficiency of this novel approach.  ...  During the training phase we intercept and analyze a stream of system calls for a sufficient time period to cover the majority of normal system operations.  ... 
doi:10.1109/services.2013.81 dblp:conf/services/BirnbaumLDCS13 fatcat:755ory3d75dbpg6twdpkncz4de

Tips from TIPS: The Informational Content of Treasury Inflation-Protected Security Prices

Stefania D'Amico, Don H. Kim, Min Wei
2010 Social Science Research Network  
Ignoring this spread also significantly distorts the informational content of TIPS breakeven inflation, a widely-used proxy for expected inflation.  ...  financial crisis.  ...  A regression of the monthly inflation onto estimates of x t obtained in the second step gives a preliminary set of estimates of the parameters governing the inflation dynamics. 4.  ... 
doi:10.2139/ssrn.1783640 fatcat:5wizffqg2bfadj7h73snjgpv3y

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 [article]

Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu
2019 arXiv   pre-print
As such, a significant amount of surveys exist covering ML for financial time series forecasting studies.  ...  Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas  ...  Graphs can be used to represent portfolios, social networks of financial communities, fundamental analysis data, etc.  ... 
arXiv:1911.13288v1 fatcat:npvyhewuvvcvri4e43jwj3c45y

A Study on Scalar Multiplication Parallel Processing for X25519 Decryption of 5G Core Network SIDF Function for mMTC IoT Environment

Changuk Jang, Juhong Han, Akshita Maradapu Vera Venkata Sai, Yingshu Li, Okyeon Yi, Qiang Ye
2022 Wireless Communications and Mobile Computing  
a short time.  ...  A key method of the proposed 5G SIDF configuration is the use of GPUs.  ...  Figure 11 : 11 Figure 11: Operation time: each graph means the time required for each number of operations. The graph on the left means one decryption time consumed in each experiment.  ... 
doi:10.1155/2022/4087816 fatcat:uzx4zftz25gkjnbh742kpovtwy

Modeling and Pricing Cyber Insurance – A Survey [article]

Kerstin Awiszus, Thomas Knispel, Irina Penner, Gregor Svindland, Alexander Voß, Stefan Weber
2022 arXiv   pre-print
network and strategic interactions.  ...  The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts.  ...  The tail of their distributions follows a GPD, and the distribution body is modeled using a non-parametric kernel distribution.  ... 
arXiv:2209.07415v1 fatcat:tqrlvwmuujacxao2llg3q7rd3i

Challenging Traditional Risk Models by a Non-Stationary Approach with Nonparametric Heteroscedasticity

Marc Gürtler, Ronald Rauh
2012 Social Science Research Network  
Volatility dynamics are modelled by nonparametric regression; consistency and asymptotic normality of a symmetric and of a one-sided kernel estimator are outlined with remarks on the bandwidth decision  ...  For simulated price processes and a multitude of financial time series we observe a satisfying model approximation and good short-term forecasting abilities of the univariate approach.  ...  Hence, next to providing a solid statistical background we survey the practicability and potentials of automatization of the implementation for real financial time series.  ... 
doi:10.2139/ssrn.2175663 fatcat:454f7pl63bdztn6sy4nasi5tzu

Reconstruction of Time-varying Graph Signals via Sobolev Smoothness

Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, Thierry Bouwmans
2022 IEEE Transactions on Signal and Information Processing over Networks  
However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption.  ...  of time-varying graph signals from discrete samples.  ...  Social, financial, and sensor networks are examples of data that can be modeled on graphs.  ... 
doi:10.1109/tsipn.2022.3156886 fatcat:6jkc77mln5e2flvvjhawvfzqey

On packet marking and Markov modeling for IP Traceback: A deep probabilistic and stochastic analysis

Peppino Fazio, Mauro Tropea, Miroslav Voznak, Floriano De Rango
2020 Computer Networks  
packets in order to build a meaningful attack graph and analyze how marking routers must behave to minimize the overall overhead.  ...  A B S T R A C T From many years, the methods to defend against Denial of Service attacks have been very attractive from different point of views, although network security is a large and very complex topic  ...  National Supercomputing Center -LM2015070'' and partially received a financial support also from a grant No.  ... 
doi:10.1016/j.comnet.2020.107464 fatcat:7wx5kviy5vcbtmnsigqvfkxmtq

Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach

Ayush Sinha, Raghav Tayal, Aamod Vyas, Pankaj Pandey, O. P. Vyas
2021 Frontiers in Energy Research  
Neural Network (CNN).  ...  Performance metrics such as Mean Square Error, Root Mean Square Error, and Mean Absolute Error have been used to evaluate the performance of the discussed approaches.  ...  A time series that is having one variable changing over time is univariate time series. If greater than one variable varying with time, then that time series is multivariate.  ... 
doi:10.3389/fenrg.2021.720406 fatcat:axvxzi7wgzgu3fmneshdwehfwm

Exploring Spatial Features of Population Activities and Functional Facilities in Rail Transit Station Realm Based on Real-Time Positioning Data: A Case of Xi'an Metro Line 2

Di Wang, Bart Dewancker, Yaqiong Duan, Meng Zhao
2022 ISPRS International Journal of Geo-Information  
between them, this paper takes four different types of stations of Xi'an Metro Line 2 as the research object, using real-time positioning data to represent population activities and points of interest  ...  In order to accurately identify the spatial and temporal distribution of population activities and functional facilities in the rail transit station realm and understand the dynamic influence relationship  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi11090485 fatcat:zu5qhocerzaopoh3fonax6eqb4
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