A survey of memory deduplication approaches for intelligent urban computing

Huang, Hailiang; Yan, Chenggang; Liu, Bingtao; Chen, Licheng

doi:10.1007/s00138-017-0834-6

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Seven other papers are selected in response to an open call for papers. These papers cover a wide range of subtopics of intelligent urban computing with big data, including intelligent video surveillance [4,5,11], intelligent urban sensing technologies [8,10], machine vision algorithms in urban computing [2,3,9], intelligent traffic system [7], 3D vision in urban computing [1,6].…”

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Special issue on intelligent urban computing with big data

Liu

Cui

Nurminen

et al. 2017

Machine Vision and Applications

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The rapid proliferation of urbanization has modernized many people's lives and also engendered critical issues, such as traffic congestion, energy consumption, and environmental pollution. These urbanization challenges seriously deteriorate people's life quality in big cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, which provide rich knowledge about a city to help tackle these challenges. Consequently, the intelligent urban computing, which holistically exploits the big data in big cities to improve the urban environment, human life quality, and city operation systems, has obtained massive attention in research and industrial fields. Many efforts have been dedicated to connecting unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods to structure intelligent urban computing systems for smart cities. Furthermore, the intelligent systems and applications are emerging and becoming pervasive in the field of urban planning, transportation systems, environmental conservation, energy consumption, social applications, economy, public security, and presenting representative scenarios.The goal of this special issue is to call for a coordinated effort to understand the opportunities and challenges emerging in intelligent urban computing with big data, identify key tasks, and evaluate the state-of-the-art methods, showcase innovative methodologies and ideas, introduce interesting real-world intelligent urban computing systems or applications, propose new real-world datasets and discuss future directions. We solicit original contributions in all fields of intelligent urban computing that explore the big data in big cities to help us understand the nature of urban phenomena and even predict the future of cities.With the assistance of professional referees, 11 papers out from 33 submissions are accepted after at least two rounds of rigorous reviews. Four of them were developed from conference contributions at IEEE CCIS 2016 [4,5,7,10]. Seven other papers are selected in response to an open call for papers. These papers cover a wide range of subtopics of intelligent urban computing with big data, including intelligent video surveillance [4,5,11], intelligent urban sensing technologies [8,10], machine vision algorithms in urban computing [2,3,9], intelligent traffic system [7], 3D vision in urban computing [1,6].In the first paper, Huang et al. [4] propose a novel human body segmentation framework based on shape constraint. The shape constraint is the combination of human star convexity and body parts' locations with high precision. The experimental results demonstrate that the proposed method outperforms many state-of-the-art methods on public challenging datasets. Li et al. [5] propose a new person re-identification framework. They firstly design a Siamese Inception network to 123

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“…Huang et al [3] survey several memory deduplication techniques for intelligent urban computing. First of all, limited memory size is considered as a major bottleneck in data centers for intelligent urban computing.…”

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confidence: 99%

Special issue on intelligent urban computing with big data

Liu

Cui

Nurminen

et al. 2017

Machine Vision and Applications

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“…Intelligent computing has become a part of our daily life, and advanced computing methods and technologies have become complex [1][2][3]. The explosive growth of intelligent computing data has brought some security problems.…”

Section: Introductionmentioning

confidence: 99%

Bytecode Similarity Detection of Smart Contract across Optimization Options and Compiler Versions Based on Triplet Network

Zhu¹,

Feng²,

Pang³

et al. 2022

Electronics

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In recent years, the number of smart contracts running in the blockchain has increased rapidly, accompanied by many security problems, such as vulnerability propagation caused by code reuse or vicious transaction caused by malicious contract deployment, for example. Most smart contracts do not publish the source code, but only the bytecode. Based on the research of bytecode similarity of smart contract, smart contract upgrade, vulnerability search and malicious contract analysis can be carried out. The difficulty of bytecode similarity research is that different compilation versions and optimization options lead to the diversification of bytecode of the same source code. This paper presents a solution, including a series of methods to measure the similarity of smart contract bytecode. Starting from the opcode of smart contract, a method of pre-training the basic block sequence of smart contract is proposed, which can embed the basic block vector. Positive samples were obtained by basic block marking, and the negative sampling method is improved. After these works, we put the obtained positive samples, negative samples and basic blocks themselves into the triplet network composed of transformers. Our solution can obtain evaluation results with an accuracy of 97.8%, so that the basic block sequence of optimized and unoptimized options can be transformed into each other. At the same time, the instructions are normalized, and the order of compiled version instructions is normalized. Experiments show that our solution can effectively reduce the bytecode difference caused by optimization options and compiler version, and improve the accuracy by 1.4% compared with the existing work. We provide a data set covering 64 currently used Solidity compilers, including one million basic block pairs extracted from them.

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An Examination Method of Mergeable Memory Page Distribution for Memory Deduplication

Shiba

2019

2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)

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A survey of memory deduplication approaches for intelligent urban computing

Cited by 3 publications

References 29 publications

Special issue on intelligent urban computing with big data

Special issue on intelligent urban computing with big data

Bytecode Similarity Detection of Smart Contract across Optimization Options and Compiler Versions Based on Triplet Network

An Examination Method of Mergeable Memory Page Distribution for Memory Deduplication

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