EASYFLOW: Keep Ethereum Away From Overflow

While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum , mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EASYFLOW , an overflow detector at Ethereum Virtual Machine level. The key insight behind EASYFLOW is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EASYFLOW can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EASYFLOW managed to find potentially vulnerable Ethereum contracts with little runtime overhead.Comment: Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings. IEEE Press, 201

Parameter-tuning Networks: Experiments and Active Walk Model

The tuning process of a large apparatus of many components could be represented and quantified by constructing parameter-tuning networks. The experimental tuning of the ion source of the neutral beam injector of HT-7 Tokamak is presented as an example. Stretched-exponential cumulative degree distributions are found in the parameter-tuning networks. An active walk model with eight walkers is constructed. Each active walker is a particle moving with friction in an energy landscape; the landscape is modified by the collective action of all the walkers. Numerical simulations show that the parameter-tuning networks generated by the model also give stretched exponential functions, in good agreement with experiments. Our methods provide a new way and a new insight to understand the action of humans in the parameter-tuning of experimental processes, is helpful for experimental research and other optimization problems.Comment: 4 pages, 5 figure

No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction

No-service rail surface defect (NRSD) segmentation is an essential way for perceiving the quality of no-service rails. However, due to the complex and diverse outlines and low-contrast textures of no-service rails, existing natural image segmentation methods cannot achieve promising performance in NRSD images, especially in some unique and challenging NRSD scenes. To this end, in this paper, we propose a novel segmentation network for NRSDs based on Normalized Attention and Dual-scale Interaction, named NaDiNet. Specifically, NaDiNet follows the enhancement-interaction paradigm. The Normalized Channel-wise Self-Attention Module (NAM) and the Dual-scale Interaction Block (DIB) are two key components of NaDiNet. NAM is a specific extension of the channel-wise self-attention mechanism (CAM) to enhance features extracted from low-contrast NRSD images. The softmax layer in CAM will produce very small correlation coefficients which are not conducive to low-contrast feature enhancement. Instead, in NAM, we directly calculate the normalized correlation coefficient between channels to enlarge the feature differentiation. DIB is specifically designed for the feature interaction of the enhanced features. It has two interaction branches with dual scales, one for fine-grained clues and the other for coarse-grained clues. With both branches working together, DIB can perceive defect regions of different granularities. With these modules working together, our NaDiNet can generate accurate segmentation map. Extensive experiments on the public NRSD-MN dataset with man-made and natural NRSDs demonstrate that our proposed NaDiNet with various backbones (i.e., VGG, ResNet, and DenseNet) consistently outperforms 10 state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/NaDiNet.Comment: 10 pages, 6 figures, Accepted by IEEE Transactions on Instrumentation and Measurement 202

A quantum secret sharing scheme with verifiable function

In the $\left( {t,n} \right)$ threshold quantum secret sharing scheme, it is difficult to ensure that internal participants are honest. In this paper, a verifiable $\left( {t,n} \right)$ threshold quantum secret sharing scheme is designed combined with classical secret sharing scheme. First of all, the distributor uses the asymmetric binary polynomials to generate the shares and sends them to each participant. Secondly, the distributor sends the initial quantum state with the secret to the first participant, and each participant performs unitary operation that using the mutually unbiased bases on the obtained $d$ dimension single bit quantum state ($d$ is a large odd prime number). In this process, distributor can randomly check the participants, and find out the internal fraudsters by unitary inverse operation gradually upward. Then the secret is reconstructed after all other participants simultaneously public transmission. Security analysis show that this scheme can resist both external and internal attacks

Gene cloning and characterization of a novel esterase from activated sludge metagenome

A metagenomic library was prepared using pCC2FOS vector containing about 3.0 Gbp of community DNA from the microbial assemblage of activated sludge. Screening of a part of the un-amplified library resulted in the finding of 1 unique lipolytic clone capable of hydrolyzing tributyrin, in which an esterase gene was identified. This esterase/lipase gene consists of 834 bp and encodes a polypeptide (designated EstAS) of 277 amino acid residuals with a molecular mass of 31 kDa. Sequence analysis indicated that it showed 33% and 31% amino acid identity to esterase/lipase from Gemmata obscuriglobus UQM 2246 (ZP_02733109) and Yarrowia lipolytica CLIB122 (XP_504639), respectively; and several conserved regions were identified, including the putative active site, HSMGG, a catalytic triad (Ser92, His125 and Asp216) and a LHYFRG conserved motif. The EstAS was overexpressed, purified and shown to hydrolyse p-nitrophenyl (NP) esters of fatty acids with short chain lengths (≤ C8). This EstAS had optimal temperature and pH at 35°C and 9.0, respectively, by hydrolysis of p-NP hexanoate. It also exhibited the same level of stability over wide temperature and pH ranges and in the presence of metal ions or detergents. The high level of stability of esterase EstAS with its unique substrate specificities make itself highly useful for biotechnological applications