A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.

Improving the accuracy of mechanistic models for dynamic batch distillation enabled by neural network:An industrial plant case

Neural networks are often viewed as pure‘black box’models,lacking interpretability and extrapolation capabilities of pure mechanistic models.This work proposes a new approach that,with the help of neural networks,improves the conformity of the first-principal model to the actual plant.The final result is still a first-principal model rather than a hybrid model,which maintains the advantage of the high interpretability of first-principal model.This work better simulates industrial batch distillation which separates four components:water,ethylene glycol,diethylene glycol,and triethylene glycol.GRU(gated recurrent neural network)and LSTM(long short-term memory)were used to obtain empirical parameters of mechanistic model that are difficult to measure directly.These were used to improve the empirical processes in mechanistic model,thus correcting unreasonable model assumptions and achieving better predictability for batch distillation.The proposed method was verified using a case study from one industrial plant case,and the results show its advancement in improving model predictions and the potential to extend to other similar systems.

Exact Tail Asymptotics for a Queueing System with a Retrial Orbit and Batch Service

This paper discusses a queueing system with a retrial orbit and batch service, in which the quantity of customers’ rooms in the queue is finite and the space of retrial orbit is infinite. When the server starts serving, it serves all customers in the queue in a single batch, which is the so-called batch service. If a new customer or a retrial customer finds all the customers’ rooms are occupied, he will decide whether or not to join the retrial orbit. By using the censoring technique and the matrix analysis method, we first obtain the decay function of the stationary distribution for the quantity of customers in the retrial orbit and the quantity of customers in the queue. Then based on the form of decay rate function and the Karamata Tauberian theorem, we finally get the exact tail asymptotics of the stationary distribution.

A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

一种有效的Batch RSA改进算法

Batch RSA算法的解密性能与指数计算阶段的大数模幂运算的实现效率有着直接的关系.针对提升Batch RSA算法的解密性能,提出一种Batch RSA算法的改进方案.提升通过将Batch RSA算法指数计算阶段的一些运算量转移到加密方,并且运用多素数技术使得解密时大数模幂运算的模数位数和指数位数减小.理论分析和实验结果表明该方案不仅提升了批处理RSA算法的解密性能,且该方案易于并行实现,可使得基于多核平台的RSA密码算法的性能得到进一步提升.

一种有效的Batch RSA算法的研究

提出了一种改进的Batch RSA算法来提升Batch RSA算法的解密性能。该改进算法结合了负载转移技术和Multi-Power RSA技术,在Batch RSA算法的指数计算阶段提升Batch RSA算法的解密性能。实验结果和理论分析表明,该改进算法使得Batch RSA算法的解密性能得到显著提升,且易于并行实现,可使基于多核平台的BatchRSA算法的整体性能得到进一步提升。

改进的Batch RSA算法的设计与实现

Batch RSA算法的解密性能与其指数计算阶段的大数模幂运算的实现效率有着直接的关系。提出了一种Batch RSA算法的改进方案,通过将Batch RSA算法解密时指数计算阶段的一些运算量转移到加密方,运用多素数技术使大数模幂运算的模位数和指数位数减小来加速Batch RSA的解密过程。理论分析和实验数据表明该改进算法使得Batch RSA算法的解密性能得到明显提升。

视频流点播Dynamic Batched Patching算法研究

本文提出了一个新的视频流点播传输策略 ,用以解决现有传输策略中存在的系统资源利用率低 ,QoS较差等问题 .该策略的思想是服务器根据用户请求到达时刻 ,按动态批处理的方式来接纳并服务请求用户 ,每组用户必须同时从一个或两个信道接收视频内容 .文中对本策略的性能进行了理论推导与定量分析 ,并与现有传输策略作了性能比较 ,最后采用仿真实验对前面的理论分析与比较进行了验证 .理论分析及实验结果表明该策略是一个简单高效的传输策略 。

Aspen Batch Process Developer在间歇化工流程模拟与优化中的应用

间歇生产过程具有弹性大、灵活等特点,其市场适应性较强。化工生产中间歇生产过程占相当比例,文章就间歇生产工艺的模拟与优化进行介绍,利用Aspen Batch Process Developer模拟间歇工艺过程,可以快速地得到工艺流程的物料衡算、热量衡算、操作时间、公用工程和成本估算等结果。同时,还可以对模拟结果进行分析,找出制约生产工艺的瓶颈,并对生产周期、生产规模、生产设备等进行优化,提高工艺设计效率,降低生产成本。

应用Aspen Batch对年产25吨的美罗培南原料药生产工艺模拟设计

美罗培南是一种新型碳青霉烯类抗生素,具有广阔的市场前景,其生产过程为间歇生产。文章利用Aspen Batch Process Developer 7.2对年产25吨的美罗培南原料药生产工艺流程进行模拟,得到生产过程中的物料衡算结果误差为0.8%,生产时间甘德图表明,生产周期为48小时,并得到该生产过程的公用工程消耗量,对实际的工艺设计具有一定的参考价值。

基于Spring Batch数据迁移处理系统的设计与实现

本省自动气象观测系统根据观测要素的不同分为单雨量系统、两要素系统和六要素系统。三类观测系统分别接收各自站点采集的报文进行解码入库,同时通过FTP将数据报文发送到综合处理系统再次进行解码入库。这样的处理流程不仅增加网络负担,还进行了重复的解码工作,资源利用率低。为此本文采用基于Spring Batch的框架,定时对三个不同观测系统的后台数据库的实时数据向综合处理系统的数据库进行迁移。该流程采用数据分区技术,多线程并行处理,在减少了对网络带宽的占用量的同时,降低了各个系统CPU使用量,提高了工作效率。

两跳通信中一种含有编码密度信息的Batch方案

两跳通信中现有的Batch方案解码成功率较低,解码开销较高。为此,提出了一种基于Batch的改进编解码方案。在编码方案中,源节点将编码密度和系数放置在分组包头,中继节点根据这些编码信息对分组进行筛选,优先发送稀疏性大的分组;在解码方案中,目的节点通过包头中的编码密度对分组进行分类,将分组按照编码密度由低到高的顺序依次进行解码,以提高解码成功率。最后,采用MATLAB对改进方案和现有的Batch方案进行仿真,结果表明:改进的方案相比于现有的Batch方案提高15%的解码成功率,并且解码开销较低。

视频点播系统中Batching调度方案的一种改进实现

节目调度是视频点播系统设计与实现的一个重要问题。本文首先对视频点播系统中的节目调度方案作了探讨 ,然后提出一种Batching调度方案的改进实现方法。理论与实验证明 。

应用Aspen Batch对年产25吨鲁拉西酮原料药工艺设计优化

采取专利CN 102863437A的工艺,设计间歇工艺过程,将Aspen Batch Process Developer软件应用于盐酸鲁拉西酮原料药车间设计的全流程模拟和优化。间歇操作具有显著的优势:生产灵活,同一设备可生产不同产品,可根据市场需要调节生产能力及变更产品。适合于小批量,高收益的精细化学品。过去的四十年里,使用计算机对化工连续化生产进行模拟和设计已经十分普及。制药工业与传统化工最大的区别是生产过程多采用间歇法操作。目前世界上应用于化工间歇生产的计算机软件有BATCHES、gPROMS和Aspen Batch Process Developer。本文所用版本为Aspen Tech V8.6,以年产25t盐酸鲁拉西酮原料药车间为例,对车间进行全流程模拟及优化。整个设计贯彻质量源于设计理念,运用元葱模型,将盐酸鲁拉西酮的生产工艺分为磺化、氨解、氢化、缩合、成盐、精烘包等6个模块。

基于Mini-batch神经网络的船舶柴油机风险等级预测

文章针对现有的船舶柴油机健康管理智能化程度不高、管理技术落后等问题,引入神经网络风险预测方法。提出Mini-batch梯度下降方法,通过在网络训练不同阶段使用不同权重的训练样本,提升原有全量梯度下降算法预测模型不准确和较难更新等问题。并从船舶柴油机在一次机务风险所承担的风险着手,建立设备健康风险状态的综合评价指标体系,通过Minibatch梯度下降优化了风险等级分类器。结果表明,该方法能够对柴油机风险等级进行有效预测。

基于TFIDF+LDA和Mini Batch K⁃means算法的在线课程推荐方法研究

在线教育资源急剧增长让学习者难以抉择,研究在线课程分类推荐,能帮助学习者快速获取所需资源。首先将潜在狄利克雷分配算法融入词频-逆向文件频率算法对数据进行预处理,生成词向量矩阵;之后采用Mini Batch K-means算法训练聚类模型,并采用T分布随机邻域嵌入降维算法对训练结果进行可视化分析。实验采用从Pluralsight在线课程API获取8016条数据进行实验,实验结果表明融入潜在狄利克雷分配算法的词频-逆向文件频率算法效果更好。

基于Spring Batch构建企业级大数据批处理应用研究

传统的企业联机分析处理(OLAP)主要基于关系型数据库,存在实时分析瓶颈。随着企业数据的爆炸性增长,需要采用大数据分析实现从"业务驱动"向"数据驱动"的转变,充分挖掘海量数据中有价值的信息。而要实现"大数据"分析,企业迫切需要构建高效的大数据管理平台。文章引入Spring Batch核心框架,通过剖析其对文件和数据库的读写操作,重点研究基于Spring Batch开发批处理程序的方法和技巧,从而高效构建高内聚、低耦合的企业大数据批处理应用。