A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.     

矿用履带式运输车在复杂开采条件下的应用

为了方便煤炭的运输，某矿使用了矿用履带式搬运车。履带运输车解决了轮式运输车在恶劣道路条件下不能行走的问题，这种履带车是一种新型的运输工具，其特点是体积小、转弯灵活、动力强大，可在不受场地限制的情况下轻松通过狭窄的地方，能有效解决山区、土壤、陡坡等恶劣条件下物料的运输问题，适合普通运输车辆无法通行的地方。介绍了矿用履带式搬运车的主要结构以及工作原理，并给出了实际应用中应注意的事项。     

煤峪口矿81010工作面煤层底板裂隙发育特征及防治

针对煤炭开采过程中出现的突水事故,采用RFPA数值模拟软件建立采动模型,对底板裂隙破断过程和声发射进行模拟,研究煤层底板采动裂隙扩展突水通道,结果表明:离断层越近,断层内水压导升高度越高,断层出现活化,裂隙扩展发育,最终贯通形成导水通道,在进行注浆改造后,单个钻孔的最大涌水量为8 m³/h,说明注浆加固防治水效果较好,能确保工作面的安全回采。     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

Main trends on the simulation of the drying of refractory castables - Review

Refractory castables develop microstructures after curing that behave as partially saturated porous media. Upon heating (during its drying stage), the steam generated by the physical and chemically bond water can result in pore pressurization and explosive spalling. Numerical modeling can provide guidelines for designing safer heat-up profiles and also a better understanding of the mechanisms that lead to catastrophic damage. This work aims to review the fundamentals and models available, providing insightful thoughts on the current trends of the drying phenomena of ceramic compositions. The review also highlights that there are models better oriented to result in reasonable predictions of pore pressure values and others focused on a more accurate representation of the main physical phenomena that take place during heating. According to the findings, there are still various challenges to attain accurate models with high applicability capable of yielding safer and more efficient drying of refractory castables.     

Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.     

A novel multi-objective optimization method: Input values effect on responses (IVER)

In this study, a novel multi-objective optimization method based on the best effect of unique input (independent variable) values on responses (dependent variables) was proposed. The proposed method was compared with optimization using Derringer & Suich function that is still the most used. The comparison was made using the response values measured in real experiments and available in the literature. The advantages of the proposed method such as not needing the polynomial model aiming to predict the response values, no parameter selection problem, being able to offer optimum range instead of single optimum value, being suitable for use with existing experimental designs and being simple and interpretable were demonstrated as a result of comparison. It was also suggested how the proposed method will be effective according to experimental designs, and application for the users' application was presented.     

15209综采工作面端面破碎机理及控制

15209工作面回采期间端面处出现破碎、冒落现象,造成液压支架初撑力、工作阻力降低,威胁着工作面安全高效回采。通过技术研究,分析了15209工作面端面破碎机理,提出了"施工梯形锚索吊棚+注浆加固+走向掏槽搭梁"联合控制措施。实际应用表明端面破碎、煤壁片帮现象得到控制,工作面回采速度提高至7.5m/d,取得了显著应用成效。     

粮仓环境监测系统设计

随着科学技术的发展,粮食行业信息化发展得到了强有力的科技支撑。为满足粮库整体信息化建设的要求,打破"信息孤岛"现状,实现资源整合、数据共享,为大数据分析和数据应用提供数据基础,设计一款能够实时采集粮仓内温湿度、氧气、二氧化碳气体浓度、粮堆高度的数据集成系统。系统采用分层分布式结构,由数据集中器和多种传感器模块组成,数据集中器与各传感器模块间通过RS485总线进行数据通讯,并由数据集中器通过网络专线传输汇总的环境监测数据,通过专用软件进行数据处理和挖掘,从而实现粮仓环境监测和预警等功能。