Deep Learning-based Side Channel Attack on HMAC SM3

SM3 is a Chinese hash standard. HMAC SM3 uses a secret key to encrypt the input text and gives an output as the HMAC of the input text. If the key is recovered, adversaries can easily forge a valid HMAC. We can choose different methods, such as traditional side channel analysis, template attack-based side channel analysis to recover the secret key. Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. In this paper, we try to recover the secret key with deep learning-based side channel analysis. We should train the network recursively for different parameters by using the same dataset and attack the target dataset with the trained network to recover different parameters. The experiment results show that the secret key can be recovered with deep learning-based side channel analysis. This work demonstrates the interests of this new method and show that this attack can be performed in practice

Efficient Approximation Algorithms for Spanning Centrality

Given a graph $\mathcal{G}$, the spanning centrality (SC) of an edge $e$ measures the importance of $e$ for $\mathcal{G}$ to be connected. In practice, SC has seen extensive applications in computational biology, electrical networks, and combinatorial optimization. However, it is highly challenging to compute the SC of all edges (AESC) on large graphs. Existing techniques fail to deal with such graphs, as they either suffer from expensive matrix operations or require sampling numerous long random walks. To circumvent these issues, this paper proposes TGT and its enhanced version TGT+, two algorithms for AESC computation that offers rigorous theoretical approximation guarantees. In particular, TGT remedies the deficiencies of previous solutions by conducting deterministic graph traversals with carefully-crafted truncated lengths. TGT+ further advances TGT in terms of both empirical efficiency and asymptotic performance while retaining result quality, based on the combination of TGT with random walks and several additional heuristic optimizations. We experimentally evaluate TGT+ against recent competitors for AESC using a variety of real datasets. The experimental outcomes authenticate that TGT+ outperforms the state of the arts often by over one order of magnitude speedup without degrading the accuracy.Comment: The technical report of the paper entitled 'Efficient Approximation Algorithms for Spanning Centrality' in SIGKDD'2

Capacity Constrained Influence Maximization in Social Networks

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consume content poses challenges for implementing IM in real-world scenarios. For example, players on online gaming platforms can only interact with a limited number of friends. In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. To solve CIM effectively, we design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the $1/2$-approximation ratio. To improve the efficiency, we devise the scalable implementation named RR-OPIM+ with $(1/2-\epsilon)$-approximation and near-linear running time. We extensively evaluate the performance of 9 approaches on 6 real-world networks, and our solutions outperform all competitors in terms of result quality and running time. Additionally, we deploy RR-OPIM+ to online game scenarios, which improves the baseline considerably.Comment: The technical report of the paper entitled 'Capacity Constrained Influence Maximization in Social Networks' in SIGKDD'2

DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

A benchmark and an algorithm for detecting germline transposon insertions and measuring de novo transposon insertion frequencies

Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo \u27singleton\u27 insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2

An Empirical Study on the Comprehensive Optimization Method of a Train Diagram of the China High Speed Railway Express

The rapid and stable development of China’s economy has driven the increasing demand for express transportation. Based on network operation, China Railway Corporation of High-speed Railway launched high-speed rail products, which have attracted wide attention from all walks of life. With the application of high-speed express trains, the market structure of express transportation in China will change dramatically, from highways as the main mode of transportation to high-speed railway transportation relying on a high-speed railway network, which will effectively reduce the environmental pollution caused by express transportation and further improve the sustainable development of the economy and the logistics industry. At present, the freight Electric Multiple Units (EMU) has been successfully developed and has entered the final test stage. In the last paper, we have introduced the theory and method of the high-speed rail express train operation plan. In addition, a train diagram is an important foundation of railway transportation organization. In order to ensure the sustainable development of high-speed rail express trains after they are put into use, based on the operation plan of high-speed rail express trains, this paper establishes a comprehensive compilation model of a high-speed rail express train diagram, considering train running time, freight flow distribution scheme, and the operation plan of freight multiple units, and an exact solution algorithm based on the Lagrange relaxation algorithm is designed. The computational results are encouraging and demonstrate the effectiveness of the model and solution method. Document type: Articl

Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019

Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky–Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P < 0.001 and P < 0.05) compared to TVDIEVI, and the average Pearson’R correlation coefficient was slightly higher than that of TVDIEVI, indicating that TVDINDVI responded better to drought in Guangdong Province. Our conclusion reveals that drought-prone regions in Guangdong Province are concentrated in the Leizhou Peninsula in southern Guangdong and the Pearl River Delta in central Guangdong. We also analyzed the phenomenon of winter-spring drought in Guangdong Province over the past 20 years. The area coverage of different drought levels was as follows: mild drought accounted for 42% to 64.6%, moderate drought accounted for 6.96% to 27.92%, and severe drought accounted for 0.002% to 1.84%. In 2003, the winter-spring drought in the entire province was the most severe, with a drought coverage rate of up to 84.2%, while in 2009, the drought area coverage was the lowest, at 49.02%. This study offers valuable insights the applicability of TVDI, and presents a viable methodology for drought monitoring in Guangdong Province, underlining its significance to agriculture, environmental conservation, and socio-economic facets in the region

Influence of a groove-structured vortex generator on the drag reduction characteristics of a multiphase pump

The oil–gas mixture pump significantly contributes to marginal oil field extraction and remote transportation of deep-sea oil. Nevertheless, during the operation of the mixture pump, it is inevitable to encounter problems like the separation of the mixed media from the hydraulic components as well as the gas phase from the liquid phase, which leads to enhancing the flow resistance of the mixed media. Therefore, this study investigates the influence of a groove-structure vortex generator on the drag reduction characteristics of a helical axial-flow gas–liquid multiphase pump under the design flow rate condition and various inlet gas content rates. The findings show that the vortex generator with diverse groove depths can prevent the separation of the mixed media from the blade suction surface effectively and minimize the flow resistance of the media in the 1/10 of the blade inlet. In particular, excellent drag reduction results were gained with a maximum drag reduction rate of 36.7% when the relative depth was 3/40. In addition, the efficiency of the mixture pump increased by a maximum of 2.1%, and the head increased by a maximum of 4.3%. The significance of this study lies in its potential to further optimize the design and performance of gas–liquid multiphase pumps. It provides new insights into the design and application of vortex generators. It offers robust support for the optimization and enhancement of gas–liquid multiphase pumps

Recombination Monophosphoryl Lipid A-Derived Vacosome for the Development of Preventive Cancer Vaccines

Recently, there has been an increasing interest for utilizing the host immune system to fight against cancer. Moreover, cancer vaccines, which can stimulate the host immune system to respond to cancer in the long term, are being investigated as a promising approach to induce tumor-specific immunity. In this work, we prepared an effective cancer vaccine (denoted as vacosome) by reconstructing the cancer cell membrane, monophosphoryl lipid A as a toll-like receptor 4 agonist, and egg phosphatidylcholine. The vacosome triggered and enhanced bone marrow dendritic cell maturation as well as stimulated the antitumor response against breast cancer 4T1 cells in vitro. Furthermore, an immune memory was established in BALB/c mice after three-time preimmunization with the vacosome. After that, the immunized mice showed inhibited tumor growth and prolonged survival period (longer than 50 days). Overall, our results demonstrate that the vacosome can be a potential candidate for clinical translation as a cancer vaccine.Peer reviewe