arXiv subjects

Pengcheng Zhang

Publications and source records attributed to Pengcheng Zhang.

44 records · Page 3Linked to original sources

arXiv2021

Confining photonic nanojet in a microwell on microsphere lens for highly efficient light focusing, signal amplification and quantitative detection

Dielectric microspheres or microbeads can squeeze light into the subwavelength scale via photonic nanojet (PNJ) focusing. This enables strong light-matter interactions within its focus and induces extraordinary effects such as enhancements in light emission, signal collection and various other applications. However, critical challenges exist on how to efficiently and precisely loading the studied objects into the desired location of the PNJ focusing, and subsequently measure the field and signal of interests precisely and reliably. Such technique is currently missing. We present for the first time a unique microwell-decorated microsphere lens (u-well lens), with a semi-open microwell sample compartment directly fabricated on top of the microsphere lens. The u-well lens confines PNJ in a semi-open microwell and allows passive trapping of individual micro-object into the PNJ focusing area with high efficiency and spatial accuracy. We demonstrate that individual fluorescent microsphere of different sizes can be readily introduced to the designated location with loading efficiency >70% and generates reproducible enhanced fluorescence signals with standard deviation better than that can be quantitatively measured. A comprehensive analysis on the optical properties of the u-well lens reveals the synergistic effect of field enhancement and collection efficiency on the optical enhancement. We finally employ this special microsphere lens for fluorescent-bead-based biotin concentration analysis. The results suggest a greatly enhanced sensitivity and highly improved detection limit, opening the door for its application in highly sensitive and quantitative detection.

physics.optics↗

arXiv2020

Inflection Point: A New Perspective on Photonic Nanojets

When light propagates through the edge or middle part of microparticle's incoming interface, there is a basic rule that light converges and diverges rapidly or slowly at the output port. These two parts are referred to as region of rapid change (RRC) and region of slow change (RSC), respectively. Finding the boundary point between RRC and RSC is the key to reveal and expound this rule scientifically. Based on the correlation between light convergence-divergence and the slope of emergent light, combined with the relationship between natural logarithm and growth in physical reality and the second derivative of a function in practical significance, we determine the boundary point between RRC and RSC, namely the inflection point. From such perspective, photonic nanojet (PNJ) and near-field focusing by light irradiation on RSC and RRC, as well as the position of the inflection point under different refractive index contrast and the field distribution of light-focusing, are studied with finite-element-method-based numerical simulation and ray-optics-based theoretical analysis. By illuminating light of different field intensity ratios to the regions divided by the inflection point, we demonstrate the generation of photonic hook (PH) and the modulation of PNJ/PH in a new manner.

physics.optics↗

arXiv2020

A Framework and DataSet for Bugs in Ethereum Smart Contracts

Ethereum is the largest blockchain platform that supports smart contracts. Users deploy smart contracts by publishing the smart contract's bytecode to the blockchain. Since the data in the blockchain cannot be modified, even if these contracts contain bugs, it is not possible to patch deployed smart contracts with code updates. Moreover, there is currently neither a comprehensive classification framework for Ethereum smart contract bugs, nor detailed criteria for detecting bugs in smart contracts, making it difficult for developers to fully understand the negative effects of bugs and design new approaches to detect bugs. In this paper, to fill the gap, we first collect as many smart contract bugs as possible from multiple sources and divide these bugs into 9 categories by extending the IEEE Standard Classification for Software Anomalies. Then, we design the criteria for detecting each kind of bugs, and construct a dataset of smart contracts covering all kinds of bugs. With our framework and dataset, developers can learn smart contract bugs and develop new tools to detect and locate bugs in smart contracts. Moreover, we evaluate the state-of-the-art tools for smart contract analysis with our dataset and obtain some interesting findings: 1) Mythril, Slither and Remix are the most worthwhile combination of analysis tools. 2) There are still 10 kinds of bugs that cannot be detected by any analysis tool.

cs.SE↗

arXiv2020

CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems

Deep Learning systems (DL) based on Deep Neural Networks (DNNs) are more and more used in various aspects of our life, including unmanned vehicles, speech processing, and robotics. However, due to the limited dataset and the dependence on manual labeling data, DNNs often fail to detect their erroneous behaviors, which may lead to serious problems. Several approaches have been proposed to enhance the input examples for testing DL systems. However, they have the following limitations. First, they design and generate adversarial examples from the perspective of model, which may cause low generalization ability when they are applied to other models. Second, they only use surface feature constraints to judge the difference between the adversarial example generated and the original example. The deep feature constraints, which contain high-level semantic information, such as image object category and scene semantics are completely neglected. To address these two problems, in this paper, we propose CAGFuzz, a Coverage-guided Adversarial Generative Fuzzing testing approach, which generates adversarial examples for a targeted DNN to discover its potential defects. First, we train an adversarial case generator (AEG) from the perspective of general data set. Second, we extract the depth features of the original and adversarial examples, and constrain the adversarial examples by cosine similarity to ensure that the semantic information of adversarial examples remains unchanged. Finally, we retrain effective adversarial examples to improve neuron testing coverage rate. Based on several popular data sets, we design a set of dedicated experiments to evaluate CAGFuzz. The experimental results show that CAGFuzz can improve the neuron coverage rate, detect hidden errors, and also improve the accuracy of the target DNN.

cs.CV↗

arXiv2020

Twisting moduli for GL(2)

We prove various converse theorems for automorphic forms on Γ_0(N), each assuming fewer twisted functional equations than the last. We show that no twisting at all is needed for holomorphic modular forms in the case that N is 18, 20, or 24 - these integers are the smallest multiples of 4 or 9 not covered by earlier work of Conrey-Farmer. This development is a consequence of finding generating sets for Γ_0(N) such that each generator can be written as a product of special matrices. As for real-analytic Maass forms of even (resp. odd) weight we prove the analogous statement for N=1,...12,16,18 (resp. N=1,...,12,14,15,16,17,18,20,23,24).

math.NT↗

arXiv2020

ADF-GA: Data Flow Criterion Based Test Case Generation for Ethereum Smart Contracts

Testing is an important technique to improve the quality of Ethereum smart contract programs. However, current work on testing smart contract only focus on static problems of smart contract programs. A data flow oriented test case generation approach for dynamic testing of smart contract programs is still missing. To address this problem, this paper proposes a novel test case generation approach, called ADF-GA (All-uses Data Flow criterion based test case generation using Genetic Algorithm), for Solidity based Ethereum smart contract programs. ADF-GA aims to efficiently generate a valid set of test cases via three stages. First, the corresponding program control flow graph is constructed from the source codes. Second, the generated control flow graph is analyzed to obtain the variable information in the Solidity programs, locate the require statements, and also get the definition-use pairs to be tested. Finally, a genetic algorithm is used to generate test cases, in which an improved fitness function is proposed to calculate the definition-use pairs coverage of each test case with program instrumentation. Experimental studies are performed on several representative Solidity programs. The results show that ADF-GA can effectively generate test cases, achieve better coverage, and reduce the number of iterations in genetic algorithm.

cs.SE↗

arXiv2020

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, leading to a large amount of issues and problems. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: 1) the impact of the big data attributes of volume, velocity, and variety on the quality of big data applications; 2) the quality attributes that determine the quality for big data applications include correctness, performance, availability, scalability, reliability and so on; 3) the existing QA technologies, including analysis, specification, model-driven architecture (MDA), verification, fault tolerance, testing, monitoring and fault & failure prediction; 4) existing strengths and limitations of each kind of QA technology; 5) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications. However, many challenges of big data applications regarding quality still remain.

cs.SE↗

arXiv2019

SolidityCheck : Quickly Detecting Smart Contract Problems Through Regular Expressions

As a blockchain platform that has developed vigorously in recent years, Ethereum is different from Bitcoin in that it introduces smart contracts into blockchain.Solidity is one of the most mature and widely used smart contract programming language,which is used to write smart contracts and deploy them on blockchain. However, once the data in the blockchain is written, it cannot be modified. Ethereum smart contract is stored in the block chain, which makes the smart contract can no longer repair the code problems such as re-entrancy vulnerabilities or integer overflow problems. Currently, there still lacks of an efficient and effective approach for detecting these problems in Solidity. In this paper, we first classify all the possible problems in Solidity, then propose a smart contract problem detection approach for Solidity, namely SolidityCheck. The approach uses regular expressions to define the characteristics of problematic statements and uses regular matching and program instrumentation to prevent or detect problems. Finally, a large number of experiments is performed to show that SolidityCheck is superior to existing approaches.

cs.SE↗