在证券行业,用户数据处理和分析是核心技术,对业务决策和风险控制具有重要的影响。然而,证券公司庞大的用户数据规模和复杂的数据关系导致大数据计算面临Shuffle操作和数据倾斜问题。现有的Shuffle和数据倾斜优化方法或依赖于硬件升级,...在证券行业,用户数据处理和分析是核心技术,对业务决策和风险控制具有重要的影响。然而,证券公司庞大的用户数据规模和复杂的数据关系导致大数据计算面临Shuffle操作和数据倾斜问题。现有的Shuffle和数据倾斜优化方法或依赖于硬件升级,或存在领域局限性,难以针对性解决该问题。为此,基于证券行业用户数据的特点,提出了一种基于用户关系的多分组归并算法(multi group merging algorithm,MGMA)。该算法通过有效分组和优化处理策略,显著提升计算效率,并降低计算资源消耗。实验表明,相较于无优化对照组,MGMA算法的数据倾斜率为20%,内存占用为72%,计算用时为61%,且上述3项指标均优于其他4种对比优化方法。展开更多
For traditional JPEG image encryption,block position shuffling can achieve a better encryption effect and is resistant to non-zero counting attack.However,the numbers of non-zero coefficients in the 8×8 sub-block...For traditional JPEG image encryption,block position shuffling can achieve a better encryption effect and is resistant to non-zero counting attack.However,the numbers of non-zero coefficients in the 8×8 sub-blocks are unchanged using block position shuffle.For this defect,this paper proposes a fast attack algorithm for JPEG image encryption based on inter-block shuffle and non-zero quantization discrete cosine transformation coefficient attack.The algorithm analyzes the position mapping relationship before and after encryption of image blocks by detecting the pixel values of an image by the designed plaintext image.Then the preliminary attack result of the image blocks can be obtained from the inverse mapping relationship.Finally,the final attack result of the algorithm is generated according to the numbers of non-zero coefficients in each 8×8 block of the preliminary attack result.Every 8×8 block position is related with its number of non-zero discrete cosine transform coefficients in the designed plaintext.It is verified that the main content of the original image could be obtained without knowledge of the encryption algorithm and keys in a relatively short time.展开更多
Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing ...Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.展开更多
DNA shuffling技术是一项全新的体外人工进化模式,它通过基因在分子水平上的重组,再定向筛选具有预期性状的突变体,获得同时具有多个亲本基因的特征的突变基因。该文介绍DNA shuffling技术的基本原理,并列举了由该技术发展而来的新技术...DNA shuffling技术是一项全新的体外人工进化模式,它通过基因在分子水平上的重组,再定向筛选具有预期性状的突变体,获得同时具有多个亲本基因的特征的突变基因。该文介绍DNA shuffling技术的基本原理,并列举了由该技术发展而来的新技术及其在基因工程疫苗领域的应用,展望了DNAshuffling技术的发展方向。展开更多
文摘在证券行业,用户数据处理和分析是核心技术,对业务决策和风险控制具有重要的影响。然而,证券公司庞大的用户数据规模和复杂的数据关系导致大数据计算面临Shuffle操作和数据倾斜问题。现有的Shuffle和数据倾斜优化方法或依赖于硬件升级,或存在领域局限性,难以针对性解决该问题。为此,基于证券行业用户数据的特点,提出了一种基于用户关系的多分组归并算法(multi group merging algorithm,MGMA)。该算法通过有效分组和优化处理策略,显著提升计算效率,并降低计算资源消耗。实验表明,相较于无优化对照组,MGMA算法的数据倾斜率为20%,内存占用为72%,计算用时为61%,且上述3项指标均优于其他4种对比优化方法。
文摘For traditional JPEG image encryption,block position shuffling can achieve a better encryption effect and is resistant to non-zero counting attack.However,the numbers of non-zero coefficients in the 8×8 sub-blocks are unchanged using block position shuffle.For this defect,this paper proposes a fast attack algorithm for JPEG image encryption based on inter-block shuffle and non-zero quantization discrete cosine transformation coefficient attack.The algorithm analyzes the position mapping relationship before and after encryption of image blocks by detecting the pixel values of an image by the designed plaintext image.Then the preliminary attack result of the image blocks can be obtained from the inverse mapping relationship.Finally,the final attack result of the algorithm is generated according to the numbers of non-zero coefficients in each 8×8 block of the preliminary attack result.Every 8×8 block position is related with its number of non-zero discrete cosine transform coefficients in the designed plaintext.It is verified that the main content of the original image could be obtained without knowledge of the encryption algorithm and keys in a relatively short time.
文摘Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.
