A Superlinerly Convergent ODE-type Trust Region Algorithm for LC^1 Optimization Problems

In this paper, a new trust region algorithm for unconstrained LC1 optimization problems is given. Compare with those existing trust regiion methods, this algorithm has a different feature: it obtains a stepsize at each iteration not by soloving a quadratic subproblem with a trust region bound, but by solving a system of linear equations. Thus it reduces computational complexity and improves computation efficiency. It is proven that this algorithm is globally convergent and locally superlinear under some conditions.

An Improved Line Search and Trust Region Algorithm

In this paper, we present a new line search and trust region algorithm for unconstrained optimization problems. The trust region center locates at somewhere in the negative gradient direction with the current best iterative point being on the boundary. By doing these, the trust region subproblems are constructed at a new way different with the traditional ones. Then, we test the efficiency of the new line search and trust region algorithm on some standard benchmarking. The computational results reveal that, for most test problems, the number of function and gradient calculations are reduced significantly.

A Novel Multi-Level Trust Algorithm for Mobile Ad Hoc Networks

Firstly, a multilevel trust algorithm for MANET （mobile ad hoe networks） is presented in this paper and the trust level is defined as a three-tuple type in this multilevel trust algorithm. The paper introduces the multilevel trust into MANET, thereby controlling restricted classified information flows among nodes that have different trust levels. Secondly, the infrastructure of MANET that suit to our multi-level trust is presented, Some conclusions are given at lastly.

Time-Based Dynamic Trust Model Using Ant Colony Algorithm

The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.

Collusion detector based on G-N algorithm for trust model

In the open network environment, malicious attacks to the trust model have become increasingly serious. Compared with single node attacks, collusion attacks do more harm to the trust model. To solve this problem, a collusion detector based on the GN algorithm for the trust evaluation model is proposed in the open Internet environment. By analyzing the behavioral characteristics of collusion groups, the concept of flatting is defined and the G-N community mining algorithm is used to divide suspicious communities. On this basis, a collusion community detector method is proposed based on the breaking strength of suspicious communities. Simulation results show that the model has high recognition accuracy in identifying collusion nodes, so as to effectively defend against malicious attacks of collusion nodes.

GLOBAL CONVERGENCE OF NONMONOTONIC TRUST REGION ALGORITHM FOR NONLINEAR OPTIMIZATION

A trust region algorithm for equality constrained optimization is given in this paper.The algorithm does not enforce strict monotonicity of the merit function for every iteration.Global convergence of the algorithm is proved under the same conditions of usual trust region method.

A FILTER-TRUST-REGION METHOD FOR LC^1 UNCONSTRAINED OPTIMIZATION AND ITS GLOBAL CONVERGENCE

In this paper we present a filter-trust-region algorithm for solving LC1 unconstrained optimization problems which uses the second Dini upper directional derivative. We establish the global convergence of the algorithm under reasonable assumptions.

Design and Implementation of Trusted-Router Based on Trust Management System

This paper presents a scheme to perform QoS management and assure network security by using the trusted-router based on the Trust Management System.In this trusted-router,every IP packet is forwarded and queued by its trust value,which is the quantification of the network's expectation for this packet's and its owner's behavior in the network.We outline the algorithms to calculate the trust value of the trusted-router and the IP packet.We also introduce the trust-based QoS management algorithm and the deployment of the trusted-routers which carry out this algorithm.The simulation results show that the least trusted IP packets will be dropped to save resources for those highly trusted IP packets.This will ecourage all the elements in the network to keep a good trust record.

大数据中一种基于Trust-Walker的信任推荐方法

随着互联网的快速发展,大量数据也随之产生,并且数据量级甚至达到了PB,ZB 级。那么如何在大数据环境中改进传统推荐算法已成为当务之急。为了一定程度上缓解传统推荐算法所遇到的稀疏的数据等问题。首先构建一种传递信任的模型,在此基础之上,提出一种基于信任的协同过滤推荐模型。最后,在大数据的背景下,针对海量数据时的可扩展性和计算效率问题,模型在Map Reduce处理平台上进行并行化处理,并进行了一系列的对比实验。实验结果证明,并行化处理后,模型在解决冷启动问题,减轻数据稀疏性和提高推荐精度的同时,可扩展性和计算效率也表现出良好的效果。

A Simple Game for the Study of Trust in Distributed Systems

Trust is an important aspect of the design and analysis of secure distributed systems. It is often used informally to designate those portions of a system that must function correctly in order to achieve the desired outcome. But it is a notoriously diffcult notion to formalize. What are the properties of trust? How is it learned, propagated, and utilized successfully? How can it be modeled? How can a trust model be used to derive protocols that are effcient and reliable when employed in today's expansive networks? Past work has been concerned with only a few of these issues, without concentrating on the need for a comprehensive approach to trust modeling. In this paper, we take a first step in that direction by studying an artificial community of agents that uses a notion of trust to succeed in a game against nature. The model is simple enough to analyze and simulate, but also rich enough to exhibit phenomena of real life interactive communities. The model requires agents to make decisions. To do well, the agents are informed by knowledge gained from their own past experience as well as from the experience of other agents. Communication among agents allows knowledge to propagate faster through the network, which in turn can allow for a more successful community. We analyze the model from both a theoretical and an experimental point of view.

算法透明何以提升公众的政府信任?

随着算法辅助决策逐渐嵌入公共部门,算法黑箱极易冲击公众对于政府公权力的信任。算法透明成为政府治理的应有之义。然而,算法透明何以实现,以及政府信任与算法透明的关联仍存在争议。论文基于“金税工程”税务算法现实案例,设计了一个情景调查实验,实证检验了算法透明的实现机制及其对政府信任的影响路径。研究发现:(1)算法过程透明、算法结果透明均对公众感知算法透明具有显著正向影响;(2)感知算法透明能够提高公众对政府的信任;(3)算法信任在感知算法透明、政府信任中发挥了部分中介效应。研究明晰了算法透明的概念,回应了算法透明与信任的迷思,并阐明了其中的强化机制,为推进算法善治提供了实践参考。

人工智能时代的算法黑箱与信任重建

信任是人机互动和人际互动的基石。然而,由于算法黑箱的不透明性和不可解释性,人工智能系统及其服务在赋权和便利一部分人的同时,算法滥用、算法误用和算法垄断也在侵蚀、排斥和边缘化另外一部分人的利益。就此而言,算法即重构、程序即政治,在智能化环境中,信任被破坏和侵蚀不仅存在于人机互动之间,人与人之间的传统信任关系也因在线匿名和算法驱动而陷入衰退。正是考虑到人工智能带来的种种社会信任挑战,以算法规制为核心的人工智能治理首先应着眼于以人为本构建可信赖人工智能生态体系,并基于技术秉性和道德法律框架重建人机信任关系和人际信任关系。

基于侧链和信任管理模型的数据共享方案

为解决在不信任的区块链环境中由于数据转售而造成的用户不愿共享数据等问题,提出一种基于侧链和信任管理模型的数据共享方案。使用信任管理模型量化节点信任值,将节点信任值集成到数据共享智能合约中,为用户提供可信的共享环境和动态的访问控制接口。对联盟链进行侧链扩容,使用信任管理模型优化共识算法。规定主链使用具有拜占庭容错能力的T-PBFT算法,侧链使用仅适用于高信任场景的T-Raft算法。用户根据预先设置的阈值动态决定在主链或侧链共享数据。实验就方案的安全性和性能进行分析,表明了该方案的可行性和有效性。

考虑数据稀疏性的图书推荐协同过滤算法仿真

图书推荐算法易忽略数据稀疏性问题,导致推荐结果与用户感兴趣内容之间存在较大的偏差。在考虑数据稀疏性的基础上提出一种图书推荐协同过滤算法,对数据预处理,通过对用户和用户之间综合信任度分析,利用分布估计算法对用户兴趣建模;构建用户兴趣簇类集,划分用户兴趣,从中选择出与检索对象最接近的邻居;计算邻近项目得分,按照从大到小的顺序排列,排名靠前的资源项即为图书推荐结果。实验结果表明,所提方法在推荐500本图书时,用时在12s内,且降低了平均绝对误差和均方根误差,实现了最精准的图书推荐。

基于RTM信任模型的DPoS共识机制改进研究

为了解决传统DPoS共识算法中存在的恶意行为“摇摆节点”问题和因权益分配不均所造成的“财富集中”问题,提出了一种基于RTM信任模型改进的R-DPoS共识算法。首先,该算法根据节点间的信息传播方向与交易关系对网络节点进行集合分类,在考虑了时间衰减因子等影响因素的条件下分别计算了各节点的直接信任值、推荐信任值以及全局信任值;其次,引入了节点摇摆度值概念,根据计算得到摇摆值的大小,将相邻共识轮次前后该值的变化情况进行比较,再对摇摆节点做出不同程度下的共识轮次惩罚限制措施;最后,在见证人节点投票过程中,增加了平衡反对票环节,再利用前期得到的综合信誉值,构建了一套新的记账节点选举评估准则,根据每轮计算后得到的节点评估值大小来动态地选择见证人节点,并完成一轮共识过程。仿真实验结果表明:改进后的共识算法相较原始DPoS共识算法、基于奖励机制和信用机制的改进共识算法而言,节点选举成为见证人节点的积极性提高了24%、18%,公平性增加了15%、10%,节点作恶的概率降低了9%、15%。

生成式人工智能算法的法律风险及其规范化防控

生成式人工智能为代表的新型科技的发展,迅速席卷了数字社会的每个角落,在数字营销新业态中数据和算法的深度融合深刻改变了传统营销模式。但生成式人工智能算法权力的逐渐失控却导致了消费者法律主体地位异化、个性化推荐算法歧视异化、算法黑箱形成信息茧房异化等一系列法律风险。这些算法法律风险使得在数字营销新业态中算法信任逐步丧失。只有重构算法信任和建构算法问责机制才能化解算法危机和算法风险,从外部的责任归属上建构算法规范的治理信任,从内部的契约诚信建构算法规范的合约信任,从底层技术设计上建构算法规范的技术信任。在重构算法信任和算法问责的基础之上规制算法,重塑法律层面的主体—行为—责任链条,赋予消费者数据访问权平衡数据流通和保护,通过区块链技术从技术层面治理算法,以此实现数字营销新业态中生成式人工智能算法风险的法治化防控。

志愿服务中算法透明度对感知可信度的影响——服务动机的调节效应

从党的十九大提出“推进志愿服务制度化”,到二十大强调“完善志愿服务制度”,志愿服务已经成为我国社会服务体系的重要构成。对志愿服务的重视和支持既是人民自治的体现,也是社会文明进步的标志。尽管经过三十余年的发展,我国在志愿者数量、服务领域等方面取得了显著成就,但公众参与率低下这一问题仍然是制约我国志愿服务可持续发展的主要因素。针对这一问题,学术界尝试应用算法推荐促进公众对志愿服务的了解和参与。然而,算法的使用过程中存在着广泛的用户信任问题,在志愿服务领域这一问题又格外突出。因此,论文聚焦于志愿服务活动的算法推荐,采用调查实验的方法,分析志愿服务动机如何影响算法透明度对于算法信任的作用。实验结果表明,(1)相对于不透明的算法,志愿者对透明算法的信任度更高;(2)志愿服务动机调节了算法透明度与算法可信度之间的关系,志愿者对与其志愿服务动机一致的算法信任度更高;(3)志愿服务动机与算法决策逻辑一致对算法透明的积极作用的加强在利己动机志愿者中更大。论文与算法信任及志愿服务领域的文献进行对话,通过评估志愿服务动机在算法透明度对算法信任的影响中的调节作用,扩展了已有研究在算法治理领域的发现,同时为相关管理人员在如何提高志愿者对于算法建议信任的方面提供了实践建议。

全密态数据库密态计算关键技术综述

随着近些年云服务的流行,越来越多的企业和个人将数据存储在云数据库上.但在享受云服务便利的同时,也带来数据安全的问题.其中一个比较关键的问题是敏感数据的机密性保护,即保护用户的敏感数据不被窥探和泄漏.在这样的背景下,全密态数据库应运而生.相对于传统数据库,全密态数据库能够在数据的传输、存储和计算整个生命周期中对数据进行加密,保护数据的机密性.目前,在对数据加密的同时,支持所有SQL功能并保持高性能等方面还存在很多挑战.全面调研全密态数据库密态计算的关键技术,根据技术类型进行归纳分类,并在功能性、安全性和性能等方面进行对比与总结.首先介绍全密态数据库架构,包括基于加密算法的纯软件架构、基于可信执行环境(TEE)的可信硬件架构和软硬融合式架构.然后,总结归纳各个架构的关键技术.最后,讨论当前研究的挑战和机会,并提供一些未来研究的开放性问题.

算法规制如何场景化

将算法纳入法治化的轨道,需要根据不同的场景设置差异化的规范路径。以高风险场景的算法产品使用效果实验启示,通过算法的解释可以提升用户的理解程度,并且可视化解释的理解效果较公式法更好;算法信任的机制构建,则更多取决于场景的信任基础、人机判断的一致性等因素。实证研究对算法规制如何场景化提供了诸多的启示:一是需要考虑不同场景用户特征的差异,根据用户特点设置权利义务;二是应强调场景中算法以及解释的用途差异,结合考虑相关解释技术的可行性;三是应当考虑不同场景的算法信任基础,算法引入不同场景有不同难度,不同场景应当设置不同的人机分工关系和算法的任务范围。

人工智能教育应用的算法隐忧及应对进路

算法是人工智能教育应用的核心技术支撑。算法在对教育活动进行算度化过程中,能够释放可观的技术效能,但同时也存在诸如衍生技术的控制困境、裂解教育的复杂特质、诱发学生的认知失衡以及钝化教师的教育智慧等教育隐忧,给教育带来不确定性风险。对此,教育实践者需要打破算法思维规限,构建适度算法信任,并进一步修正算法运行机制,寻求突破算法桎梏的技术性方案。此外,还需关注教师的教育智慧生长问题,以根植算法风险抗衡的智慧性基因,最终实现算法运行的实践增能。