Secure Dynamic Memory Scheduling Against Timing Channel Attacks

2017 ◽  
Author(s):  
Yao Wang ◽  
Benjamin Wu ◽  
G. Edward Suh
Keyword(s):  
Dynamic Memory ◽  
Memory Scheduling ◽  
Timing Channel

Reliable and double-blind IP covert timing channel

2013 ◽  
Vol 32 (6) ◽  
pp. 1636-1639
Author(s):  
Xing-xing GUAN ◽  
Chang-da WANG ◽  
Zhi-guo LI ◽  
Zhao-jun BO
Keyword(s):  
Double Blind ◽  
Timing Channel ◽  
Covert Timing Channel

How to write a class

2018 ◽  
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson
Keyword(s):  
Computer Science ◽  
Software Design ◽  
Memory Allocation ◽  
Quantum Mechanical ◽  
Sufficient Information ◽  
Trial And Error ◽  
Dynamic Memory ◽  
Dynamic Memory Allocation

While there is no such thing as a “typical” C++ class, several common syntactical constructs lend themselves to extremely widespread use and must be mastered by C++ programmers. To motivate the discussion of software design at the level of the C++ class, examples from computer science and optics are introduced. Important syntactical elements such as constructors, destructors, copy constructors, assignment operators, cast operators, and const qualifiers, together with function overloading, operator overloading, and dynamic memory allocation are discussed. These concepts, illustrated with examples from physics, are presented and explained. Further examples from optical and quantum mechanical problems are left to the exercises. This chapter and its exercises gives the reader sufficient information to begin developing his or her own classes and to experiment with class design through trial and error.

Improving memory scheduling via processor-side load criticality information

2013 ◽  
Vol 41 (3) ◽  
pp. 84-95 ◽  
Author(s):  
Saugata Ghose ◽  
Hyodong Lee ◽  
José F. Martínez
Keyword(s):  
Side Load ◽  
Memory Scheduling

scholarly journals Extractive Summarization Based on Dynamic Memory Network

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 600
Author(s):  
Ping Li ◽  
Jiong Yu
Keyword(s):  
Dynamic Memory ◽  
Extractive Summarization ◽  
Model Based ◽  
Network Method ◽  
Comparable Performance ◽  
Benchmark Datasets ◽  
Memory Network ◽  
Text Features

We present an extractive summarization model based on the Bert and dynamic memory network. The model based on Bert uses the transformer to extract text features and uses the pre-trained model to construct the sentence embeddings. The model based on Bert labels the sentences automatically without using any hand-crafted features and the datasets are symmetry labeled. We also present a dynamic memory network method for extractive summarization. Experiments are conducted on several summarization benchmark datasets. Our model shows comparable performance compared with other extractive summarization methods.

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