基于模糊和证据理论的行为建模

针对人类行为模型系统中存在大量的复杂任务关系和过程,系统输出的概率不确定性等问题,提出了基于Mamdani-Zadeh推理范式和D-S证据理论的建模方法.这种方法把输入空间分割成人们比较容易理解和表达问题的小区域,使用Mamdani-Zadeh和模糊D-S规则控制信度函数的输出值,从而得到了用户预期焦化元的清晰值,实现了模型系统关系、过程简单化;并通过具体例子证实了此方法的精确性和有效性。     

战略网络形成动因之制造模式篇

本研究目的在于探究战略网络生成中制造模式的推动作用。文章首先分析了制造模式在技术经济范式变迁中扮演的角色,总结出制造模式与技术经济范式之间的关系,接着运用数学抽象和哲学思辨的方法,提炼出人类历史上三次制造模式对技术经济范式的价值实现函数。基于该函数研究发现:精益生产中目的在于充分调动人的主观能动性,以实现人的解放的复合机制——现场管理、劳工关系管理和知识管理等体系是制造模式未来的发展方向;战略网络的演进服务于制造模式的价值实现功能,它的生成是为了弥补制造模式柔性不足的缺陷。     

Linear-space algorithms that build local alignments from fragments

This paper presents practical algorithms for building an alignment of two long sequences from a collection of alignment fragments, such as all occurrences of identical 5-tuples in each of two DNA sequences. We first combine a time-efficient algorithm developed by Galil and coworkers with a space-saving approach of Hirschberg to obtain a local alignment algorithm that uses0((M+N+F logN) logM) time and0(M+N) space to align sequences of lengthsM andN from a pool of F alignment fragments. Ideas of Huang and Miller are then employed to develop a time- and space-efficient algorithm that computesn best nonintersecting alignments for anyn>1. An example illustrates the utility of these methods.This work was supported in part by Grant RO1 LM05110 from the National Library of Medicine.     

隐蔽图像后门攻击

目的 图像后门攻击是一种经典的对抗性攻击形式,后门攻击使被攻击的深度模型在正常情况下表现良好,而当隐藏的后门被预设的触发器激活时就会出现恶性结果。现有的后门攻击开始转向为有毒样本分配干净标签或在有毒数据中隐蔽触发器以对抗人类检查,但这些方法在视觉监督下很难同时具备这两种安全特性,并且它们的触发器可以很容易地通过统计分析检测出来。因此,提出了一种隐蔽有效的图像后门攻击方法。方法 首先通过信息隐藏技术隐蔽图像后门触发,使标签正确的中毒图像样本(标签不可感知性)和相应的干净图像样本看起来几乎相同(图像不可感知性)。其次,设计了一种全新的后门攻击范式,其中毒的源图像类别同时也是目标类。提出的后门攻击方法不仅视觉上是隐蔽的,同时能抵御经典的后门防御方法(统计不可感知性)。结果 为了验证方法的有效性与隐蔽性,在ImageN et、MNIST、CIFAR-10数据集上与其他3种方法进行了对比实验。实验结果表明,在3个数据集上,原始干净样本分类准确率下降均不到1%,中毒样本分类准确率都超过94%,并具备最好的图像视觉效果。另外,验证了所提出的触发器临时注入的任意图像样本都可以发起有效的后门攻击。结论 ...     

广告视觉隐喻的符号意义生成机制

目的解析广告视觉隐喻的符号运作机制与组合方式。方法以符号意义的生成理论为原点,以索绪尔符号的价值理论为方法,分析广告聚合段上的符号关系与视觉隐喻修辞的内在联系,提出意义二元值的同向替代机制,以及广告视觉隐喻符号的组合方式。结论广告视觉隐喻是通过聚合段上的符号互相替代而形成的,其符号组合有本体融入喻体、喻体融入本体、本喻体互融3种模式。     

统计分布的多方保密计算

多方保密的概念是姚期智教授首先提出的,是计算网络计算环境中隐私保护的关键技术,在密码学中占有重要的地位,是构造许多密码学协议的基本模块,是国际密码学界近年来研究的热点问题.这方面国内外的学者进行了大量的研究,已经取得了许多理论成果与实用成果,但还有许多应用问题需要研究.介绍了多方保密计算方面的研究现状和一些需要研究的问题,研究了统计工作中所遇到的保密问题,主要解决在统计工作中经常遇到的统计分布的保密计算问题,基于计算离散对数困难性假设,运用严格的逻辑推理方法,提出了该问题的3个多方保密计算方案,并用模拟范例证明了方案的保密性.这样的问题尚没有见到研究报道,解决方案对于实际工作中的保密统计计算有重要的意义,它们可以用于保护统计过程中被统计对象的各种数据的保密,从而使被统计者不用担心隐私的泄漏,使所获得的数据更为可靠,更具有参考价值.     

浅谈云计算

云计算已经对IT界产生了十分重大的影响,是当前重要的研究领域。该文综述了当前云计算所采用的技术,剖析其背后的技术含义以及当前云计算参与企业所采用的云计算实现方案。通过此文可以了解云计算的当前发展状况以及未来的研究趋势。     

发布／订阅范型中信息可靠投递的研究

以发布／订阅范型在时间维度上解耦导致的不确定性为出发点,采用时序分析方法,研究范型中信息投递的可靠性保障。在定义投递信息可用性的基础上,给出保证信息向订阅者可靠投递系统应满足的基本计算条件,探讨底层通信设施对可靠性的影响。针对发布／订阅范型中信息可靠投递的问题,提出必要的计算条件。分析结果表明,信息的可靠投递与订阅活跃期、订阅延迟、发布传播延迟密切相关。     

Wald's mighty maximin: a tutorial

Ever since its appearance on the scene in the 1940s, in the then nascent field of statistical decision theory, Wald's maximin paradigm has played a vital role in many disciplines in the treatment of nonprobabilistic uncertainty, both as a tool of thought and as a practical instrument. In fact, in some fields, such as modern robust optimization, Wald's paradigm dominates the scene. It is important to note that this paradigm's preeminence in many fields continues unabated, despite its obvious limitations and the criticism that had been and continues to be leveled at it. So, in this tutorial we examine the methodological aspects of this stalwart of decision theory from the viewpoint of robust decision‐making, paying special attention to its obvious and not so obvious limitations and to its relation to other maximin paradigms.     

Uncertainties of facial emotion recognition technologies and the automation of emotional labour

ABSTRACT

Automated detection and recognition of faces have been implemented in a broad range of media environments. Following that development, what concerns us in this article is the analysis of emotions from facial expressions using computer-based systems, in relation to which we critically investigate the use of theories of basic emotions. We explore in depth the company Affectiva’s attempts to translate, represent and schematize human emotions, as they raise a variety of problems and issues of uncertainty. We analyse the uncertainties concerning the processing of the human face ‘as image’ due to issues concerning temporality and static images as well as polyphony and modulations of the spectrum of expressions. One of our key arguments concerns the temporal character of human emotions, and we address how algorithmically regulated protocols of discretization may be said to prompt specific patterns of emotional responses and expressions based on an ideal of eliminating uncertainty. Through discussions via art pieces by Lauren McCarthy and Kyle McDonald, we question what happens when the protocols of computer systems start to perform aspects of emotional labour for us, making judgments by predicting adequate emotional responses based on the use of the strict metrics criticized in the article.