Creating a Test Model Library for GUI Testing of Smartphone Applications (Short Paper)

2008 ◽  
Author(s):  
Antti Jääskeläinen ◽  
Antti Kervinen ◽  
Mika Katara
Keyword(s):  
Short Paper ◽  
Test Model ◽  
Smartphone Applications ◽  
Gui Testing ◽  
Model Library

scholarly journals Convolutional Neural Network Copy Detection with Neural Network Perceptual Hashing

2021 ◽  
Author(s):  
Chen Haozhe
Keyword(s):  
Neural Network ◽  
Test Model ◽  
Copy Detection ◽  
Neural Network Models ◽  
Ip Protection ◽  
Model Library ◽  
Perceptual Hashing ◽  
Hashing Algorithm ◽  
Hash Codes ◽  
Query Model

In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, as an important part of the model IP protection system, the model copy detection task has not received enough attention. With the increasing number of neural network models transmitted and deployed on the Internet, the search for similar models is in great demand, which concurrently triggers the security problem of copy detection of models for IP protection. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we propose a perceptual hashing algorithm for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, the similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for CNNs. The experiment performed on a model library containing 3,565 models indicates that our proposed perceptual hashing scheme has a superior copy detection performance.

scholarly journals Convolutional Neural Network Copy Detection with Neural Network Perceptual Hashing

2021 ◽  
Author(s):  
Chen Haozhe
Keyword(s):  
Neural Network ◽  
Test Model ◽  
Copy Detection ◽  
Neural Network Models ◽  
Ip Protection ◽  
Model Library ◽  
Perceptual Hashing ◽  
Hashing Algorithm ◽  
Hash Codes ◽  
Query Model

In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, as an important part of the model IP protection system, the model copy detection task has not received enough attention. With the increasing number of neural network models transmitted and deployed on the Internet, the search for similar models is in great demand, which concurrently triggers the security problem of copy detection of models for IP protection. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we propose a perceptual hashing algorithm for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, the similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for CNNs. The experiment performed on a model library containing 3,565 models indicates that our proposed perceptual hashing scheme has a superior copy detection performance.

The promises and perfidy of cryomicroscopy and analysis

1994 ◽  
Vol 52 ◽  
pp. 382-383
Author(s):  
Patrick Echlin
Keyword(s):  
Low Temperature ◽  
High Energy ◽  
Short Paper ◽  
List Type ◽  
Time Resolved ◽  
Energy Beam ◽  
Cellular Analysis ◽  
Dissolved Ions ◽  
Embedding Medium ◽  
Low Temperature Microscopy

The unusual title of this short paper and its accompanying tutorial is deliberate, because the intent is to investigate the effectiveness of low temperature microscopy and analysis as one of the more significant elements of the less interventionist procedures we can use to prepare, examine and analyse hydrated and organic materials in high energy beam instruments. The promises offered by all these procedures are well rehearsed and the litany of petitions and responses may be enunciated in the following mantra.Vitrified water can form the perfect embedding medium for bio-organic samples.Frozen samples provide an important, but not exclusive, milieu for the in situ sub-cellular analysis of the dissolved ions and electrolytes whose activities are central to living processes.The rapid conversion of liquids to solids provides a means of arresting dynamic processes and permits resolution of the time resolved interactions between water and suspended and dissolved materials.The low temperature environment necessary for cryomicroscopy and analysis, diminish, but alas do not prevent, the deleterious side effects of ionizing radiation.Sample contamination is virtually eliminated.

New crash test model

Nature ◽  
2001 ◽  
Author(s):  
Philip Ball
Keyword(s):  
Crash Test ◽  
Test Model

Frontiers in Complex Dynamics

2014 ◽  
Author(s):  
Araceli Bonifant ◽  
Misha Lyubich ◽  
Scott Sutherland
Keyword(s):  
Historical Perspective ◽  
Complex Dynamics ◽  
Short Paper ◽  
Combinatorial Group Theory ◽  
Entropy Theory ◽  
Holomorphic Dynamics ◽  
Eightieth Birthday ◽  
Several Variables ◽  
Fields Medal ◽  
Combinatorial Group

John Milnor, best known for his work in differential topology, K-theory, and dynamical systems, is one of only three mathematicians to have won the Fields medal, the Abel prize, and the Wolf prize, and is the only one to have received all three of the Leroy P. Steele prizes. In honor of his eightieth birthday, this book gathers together surveys and papers inspired by Milnor's work, from distinguished experts examining not only holomorphic dynamics in one and several variables, but also differential geometry, entropy theory, and combinatorial group theory. The book contains the last paper written by William Thurston, as well as a short paper by John Milnor himself. Introductory sections put the papers in mathematical and historical perspective, color figures are included, and an index facilitates browsing.

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