Mining transportation logs for understanding the after-assembly block manufacturing process in the shipbuilding industry

In the after-assembly block manufacturing process in the shipbuilding industry, domain experts or industrial managers have the following questions regarding the first step in terms of reducing the overhead transportation cost due to irregularities not defined in a process design: “What tasks are bottlenecks?” and “How long do the blocks remain waiting in stockyards?” We provide the answers to these two questions. In the process mining framework, we propose a method automatically extracting the most frequent task flows from transport usage histories. Considering characteristics of our application, we use a clustering technique to identify heterogeneous groups of process instances, and then derive a process model independently by group. Process models extracted from real-world transportation logs, are verified by domain experts and labelled based on their interpretations. Consequently, we conceptualize the “standard process” from one global process model. Moreover, local models derived from groups of process instances reflect unknown context regarding characteristics of blocks. Our proposed method can provide conceptualized process models and process (or waiting in stockyards) times as a performance indicator. Providing reasonable answers to their questions, it helps domain experts better understand and manage the actual process. With the extension of the conventional methodology for our application problem, the main contributions of this research are that our proposed approach provides insight into the after-assembly block manufacturing process, and describes the first step for reducing transportation costs.     

基于交易网络的公有链用户识别方法

分析与研究公有链交易数据及系统用户行为对于保证公有链应用安全至关重要。比特币作为公有链的代表性应用,是一种基于P2P网络的电子现金系统。比特币交易地址具有匿名特征,无法关联到用户真实信息,这使得比特币溯源非常困难。为识别比特币中交易地址间的关联关系,推断出用户真实信息,提出一种基于交易网络的用户识别方法。对比特币区块数据进行预处理,通过解析比特币区块数据中的脚本信息,将比特币原始交易数据处理为更加直观的数据格式。衡量交易输入与输出地址间的相似程度,根据交易地址关联信息识别出比特币匿名地址对应的所有用户。在实验中应用真实的比特币区块数据,利用可视化方式对用户识别结果进行分析,结果表明该方法不受交易规则的限制,能对比特币匿名地址进行有效识别,且随着比特币区块数量的增加,识别准确率基本稳定于80%。     

关于比特币中BWH攻击的一个注记

比特币是当前信息安全应用研究领域的热点问题之一.在比特币所采用的PoW共识协议中,挖矿具有重要作用.在现实生活中,矿工为获得更多的奖励,往往聚集成矿池,以达到在挖矿中获取更高算力进而获取更多区块奖励的目的.针对比特币矿池,Meni Rosenfeld首次提出了一种称为BWH攻击的攻击方式,Loi Luu等人进一步从理论上证明了相对于诚实挖矿,攻击者通过实施BWH攻击可以获得更高的收益.在本文中,我们分析了BWH攻击的理论基础,发现Loi Luu等人关于BWH攻击的理论分析中存在的一个错误,即Loi Luu等人忽略了整体算力改变对系统产生区块所需时间的影响,从而导致其所对比的关于攻击者实施BWH攻击所获得的收益与不实施攻击所获得的收益,实际上是在不同时间长度下的收益对比.显然这种对比缺乏合理性.在相同时间长度下,我们进一步讨论了攻击者实施BWH攻击与不实施攻击所获得的收益对比,得到了与Loi Luu等人完全相反的结论,即相对诚实挖矿来说,攻击者实施BWH攻击反而获得了相对较少的收益.因此攻击者缺乏实施BWH攻击的动机,除非其纯粹出于破坏矿池的目的而采用BWH攻击.     

采用工作量证明共识机制的区块链中挖矿攻击者间的“鲶鱼效应”

近年来,采用工作量证明共识机制(Proof of Work,PoW)的区块链被广泛地应用于以比特币为代表的数字加密货币中.自私挖矿攻击(Selfish mining)等挖矿攻击(Mining attack)策略威胁了采用工作量证明共识机制的区块链的安全性.在自私挖矿攻击策略被提出之后,研究者们进一步优化了单个攻击者的挖矿攻击策略.在前人工作的基础上,本文提出了新颖的两阶段挖矿攻击模型,该模型包含拥有单攻击者的传统自私挖矿系统与拥有两个攻击者的多攻击者系统.本文的模型同时提供了理论分析与仿真量化分析,并将两个攻击者区分为内部攻击者与外部攻击者.通过引入内部攻击者与外部攻击者的概念,本文指出传统自私挖矿系统转化为多攻击者系统的条件.本文进一步揭示了在多攻击者系统中两个攻击者将产生竞争并面临着“矿工困境”问题.攻击者间的竞争可被总结为“鲶鱼效应”:外部攻击者的出现导致内部攻击者的相对收益下降至多67.4%,因此内部攻击者需要优化攻击策略.本文提出了名为部分主动发布策略的全新挖矿攻击策略,相较于自私挖矿策略,该策略是半诚实的攻击策略.在特定场景下,部分主动发布策略可以提高攻击者的相对收益并破解攻击者面临的“矿工困境”问题.     

Implementing a Semantic Service Provision Platform

Seamless integration of information systems plays a key role in the development and maintenance of products. In today’s dynamic market environments, change is the rule rather than the exception. Consequently, the ability to change products is an effective way to adapt to a changing information technology landscape which is an important competitive advantage of a successful company. The vision of service-oriented computing is to capture business relevant functionalities of existing software systems as services and use service composition to form composite applications. Unfortunately, this vision has yet to be achieved. We present here a high-level overview of a semantic service discovery, composition, and enactment system that realizes this vision. Rather than addressing a fully fledged industrial strength system, we present a research prototype that realizes this vision in a narrow application domain to show the general feasibility of automatic semantic discovery, composition and flexible enactment of services. The lessons we learned from implementing this prototype are: a) The requirements regarding the features of logical reasoners for the implementation of “real” scenarios are high. b) A formal and exact specification of the semantics of “real world” services is a laborious task. c) It is hard to find adequate scenarios, because people don’t trust this technology and they don’t like to give the control on business processes to a machine, because questions like “Who is responsible?” arise. Furthermore, the application of automated and flexible service discovery and composition at run-time is only cost-effective if changes and volatilities are frequent in the service landscape.     

User-driven page layout analysis of historical printed books

In this paper, based on the study of the specificity of historical printed books, we first explain the main error sources in classical methods used for page layout analysis. We show that each method (bottom-up and top-down) provides different types of useful information that should not be ignored, if we want to obtain both a generic method and good segmentation results. Next, we propose to use a hybrid segmentation algorithm that builds two maps: a shape map that focuses on connected components and a background map, which provides information about white areas corresponding to block separations in the page. Using this first segmentation, a classification of the extracted blocks can be achieved according to scenarios produced by the user. These scenarios are defined very simply during an interactive stage. The user is able to make processing sequences adapted to the different kinds of images he is likely to meet and according to the user needs. The proposed “user-driven approach” is capable of doing segmentation and labelling of the required user high level concepts efficiently and has achieved above 93% accurate results over different data sets tested. User feedbacks and experimental results demonstrate the effectiveness and usability of our framework mainly because the extraction rules can be defined without difficulty and parameters are not sensitive to page layout variation.     

不确定环境下多机器人编队控制研究

针对多机器人在不确定环境下难以保持某种队形到达预定目标等问题,提出了一种无须全局坐标的多机器人编队控制算法。该算法通过Reynolds类鸟群模拟方法设计了“分离”“对齐”“队形”三规则的控制器来实现多机器人任意队形保持控制,并增加切换规则来实现多机器人队形变换。在不同场景及复杂环境下进行仿真实验,验证了提出方法的优越性。该算法能够使多机器人在不确定环境下保持队形到达目标,对于救援、救灾下多机器人的运用具有重要意义。     

TopPI: An efficient algorithm for item-centric mining

In this paper, we introduce item-centric mining, a new semantics for mining long-tailed datasets. Our algorithm, TopPI, finds for each item its top-k most frequent closed itemsets. While most mining algorithms focus on the globally most frequent itemsets, TopPI guarantees that each item is represented in the results, regardless of its frequency in the database.TopPI allows users to efficiently explore Web data, answering questions such as “what are the k most common sets of songs downloaded together with the ones of my favorite artist?”. When processing retail data consisting of 55 million supermarket receipts, TopPI finds the itemset “milk, puff pastry” that appears 10,315 times, but also “frangipane, puff pastry” and “nori seaweed, wasabi, sushi rice” that occur only 1120 and 163 times, respectively. Our experiments with analysts from the marketing department of our retail partner demonstrate that item-centric mining discover valuable itemsets. We also show that TopPI can serve as a building-block to approximate complex itemset ranking measures such as the p-value.Thanks to efficient enumeration and pruning strategies, TopPI avoids the search space explosion induced by mining low support itemsets. We show how TopPI can be parallelized on multi-cores and distributed on Hadoop clusters. Our experiments on datasets with different characteristics show the superiority of TopPI when compared to standard top-k solutions, and to Parallel FP-Growth, its closest competitor.     

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。     

Prediction of Manipulation Actions

By looking at a person’s hands, one can often tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor’s intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded videos of subjects performing different manipulation actions on the same object, such as “squeezing”, “flipping”, “washing”, “wiping” and “scratching” with a sponge. In psychophysical experiments, we evaluated human observers’ skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input image patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets show that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithms to predict in real time what and how a dexterous action is performed.     

Formal verification of SysML diagram using case studies of real-time system

System and software engineers use SysML models for the graphical modeling of the embedded systems. The SysML models are inadequate to express the discrete controllers with continuously evolving variables. The real-time constraints such as discrete and continuous dynamics are considered to be an important aspect in embedded systems. The lack of support of real-time aspect in SysML model can lead to inexplicit modeling of the embedded systems. The imprecise modeling could cause catastrophic results when an embedded system gets operational. In this paper, we propose hybrid automata-based semantics that supports the discrete and continuous behavior in upgraded SysML block diagram. The upgraded SysML block diagram is used for the modeling of the embedded system. Furthermore, we use model checker PRISM for the early design verification of upgraded SysML block diagram. Finally, we demonstrate the effectiveness of our proposed approach with the help of two case studies “temperature control system” and “water level control system”.

     

KIND‐DAMA: A modular middleware for Kinect‐like device data management

In the last decades, we have witnessed a growing interest toward touchless gestural user interfaces. Among other reasons, this is due to the large availability of different low‐cost gesture acquisition hardware (the so‐called “Kinect‐like devices”). As a consequence, there is a growing need for solutions that allow to easily integrate such devices within actual systems. In this paper, we present KIND‐DAMA, an open and modular middleware that helps in the development of interactive applications based on gestural input. We first review the existing middlewares for gestural data management. Then, we describe the proposed architecture and compare its features against the existing similar solutions we found in the literature. Finally, we present a set of studies and use cases that show the effectiveness of our proposal in some possible real‐world scenarios.     

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment.     

Practical Constant-Size Ring Signature

Bitcoin has gained its popularity for almost 10 years as a “secure and anonymous digital currency”. However, according to several recent researches, we know that it can only provide pseudonymity rather than real anonymity, and privacy has been one of the main concerns in the system similar to Bitcoin. Ring signature is a good method for those users who need better anonymity in cryptocurrency. It was first proposed by Rivest et al. based upon the discrete logarithm problem (DLP) assumption in 2006, which allows a user to sign a message anonymously on behalf of a group of users even without their coordination. The size of ring signature is one of the dominating parameters, and constant-size ring signature (where signature size is independent of the ring size) is much desirable. Otherwise, when the ring size is large, the resultant ring signature becomes unbearable for power limited devices or leads to heavy burden over the communication network. Though being extensively studied, currently there are only two approaches for constant-size ring signature. Achieving practical constant-size ring signature is a long-standing open problem since its introduction. In this work, we solve this open question. We present a new constant-size ring signature scheme based on bilinear pairing and accumulator, which is provably secure under the random oracle (RO) model. To the best of our knowledge, it stands for the most practical ring signature up to now.     

区块链原理及其核心技术

随着第一个去中心化加密货币系统——比特币系统自2009年上线成功运行至今,其背后的区块链技术也受到广泛关注.区块链技术独有的去中心化、去信任的特性,为构建价值互联平台提供了可能.在比特币白皮书中,区块链的概念十分模糊,而现有的一些介绍区块链的文章中,也多从抽象层次进行介绍,对于更深入的后续研究提供的帮助十分有限.本文首先将区块链技术从具体应用场景中抽象出来,提取出其五层核心架构,并就其中数据、网络、共识三层基础架构作详细说明.这三层架构包含了区块链系统中的三大核心技术:密码学、共识算法、网络.文中介绍这三种技术的研究现状,能够使读者迅速了解区块链技术的发展状况,并能根据自己的需要进行深入阅读.最后,介绍了区块链目前的应用现状和技术展望.     

Modeling systems by means of artificial intelligence

This paper describes the methods of research to reveal the relationship between human mental disorders and the functional itch disorder. A hypothesis is suggested about the formation of a pathological system in a patient’s body (and the disease; we conventionally call the system “Psikhozud”). To test the hypothesis, it is suggested that a model of this system be built using artificial-intelligence methods. Some information from systems theory is presented. The problems of the simulation of multi-agent systems are considered. The potential for using intelligent data mining based on the JSM method for the automatic generation of the behavioral patterns of the Psikhozud system is discussed.     

New gauss–seidel like block iterative methods to solve discrete boundary value problems

The concept of new Gauss–Seidel like iterative methods, which was introduced in [3], will be extended so as to obtain a class of convergent Gauss–Seidel like block iterative methods to solve linear matrix equations Ax=b with an M-Matrix A. New block iterative methods will be applied to finite difference approximations of the Laplace's equation on a square (“model problem” [8]) which surpass even the block successive overrelaxation iterative method with optimum relaxation factor in this example.     

Global regulation of almost feedforward nonlinear systems by an adaptive event-triggered controller with multi-triggering conditions

We consider a global regulation problem for almost feedforward nonlinear systems with uncertain time-varying parameters using an adaptive event-triggered controller. The systems we consider are called “almost” feedforward systems because, in addition to the feedforward nonlinearity with the unknown linear growth rate, there is a non-zero input-matching term. Notably, this input-matching term is a non-feedforward term and it cannot be directly canceled due to the presence of input uncertainty and the use of event-triggered control. To solve our considered problem, we propose an adaptive event-triggered controller with multi-triggering conditions and dynamic gain. We show that the closed-loop system is globally regulated and the times between executions are positively lower bounded. Moreover, we show that the positive lower bounds of interexecution times can be enlarged by a control parameter. For a clear illustration, a practical example is given.