Data mining technique for medical informatics: detecting gastric cancer using case-based reasoning and single nucleotide polymorphisms

Abstract: Although data mining and knowledge discovery techniques have recently been used to diagnose human disease, little research has been conducted on disease diagnostic modelling using human gene information. Furthermore, to our knowledge, no study has reported on diagnosis models using single nucleotide polymorphism (SNP) information. A disease diagnosis model using data mining techniques and SNP information should prove promising from a practical perspective as more information on human genes becomes available. Data mining and knowledge discovery techniques can be put to practical use detecting human disease, since a haplotype analysis using high-density SNP markers has gained great attention for evaluating human genes related to various human diseases. This paper explores how data mining and knowledge discovery can be applied to medical informatics using human gene information. As an example, we applied case-based reasoning to a cancer detection problem using human gene information and SNP analysis because case-based reasoning has been applied in medicine relatively less often than other data mining techniques. We propose a modified case-based reasoning method that is appropriate for associated categorical variables to use in detecting gastric cancer.     

多特征融合的入侵检测

从人认识事物的特点出发，对认识过程进行抽象，把它用到入侵检测系统中，提出了多特征融合的入侵检测方法。在此方法的实现中，以特征元素为基本单元，形成特征规则，采用基于可信度的不精确推理，判断系统被入侵的可能性。还讨论了模式的形成，给出了实现模型。此方法能有效提高计算机系统抵御入侵及自身免疫的能力。     

面向语义服务结构中的服务接口问题研究

以往的服务接口模型没有规定业务伙伴任务的内部行为,这使业务处理难以准确地进行任务指派,也难以实现服务的按需组合以及业务处理模式的高度重用。为了克服上述问题并有效构建沟通服务提供者和需求者的桥梁,提出了面向语义服务结构的接口模型,该模型用抽象状态机抽象化描述和Web服务调用实例相关的服务行为,将服务交互的抽象和实现区分开,这既有效解决了服务双方的通讯和协作问题,便于交互模型的重用,而且为Web服务的自动发现、按需组合和智能推理提供了坚实的技术基础。     

行为理解的认知推理方法

目的 人类行为理解是机器智能研究中最富有挑战性的领域。其根本问题是语义获取,即从动作推理得到人的行为,需要跨越两者之间的语义鸿沟,为此提出一种人关于日常行为知识与人体动作行为、环境信息之间的建模方法,以及可扩展的开放式结构环境—行为关系模型,基于该模型提出一种新的行为理解的渐进式认知推理方法。方法 首先根据知识,建立多种特征、复合特征和行为之间的关系模型。系统根据当前的输入流,处理得到当前的特征与复合特征集,推理得到当前的可能行为集。该行为集指导处理模块,更新特征集,得到新的行为集。结果 应用本文渐进式连续推理方法,系统可以把人关于日常行为的知识与人体运动、环境变化等传感器数据处理获取到的信息动态绑定,实现知识辅助的行为理解。结论 提出的推理方法能连续处理长时间、同时发生的行为。     

A rule-based support system for dissonance discovery and control applied to car driving

This paper is based on the concept of dissonance, that is, gaps or conflicts existing in a specific knowledge base or among different knowledge bases. It presents a rule-based system that assists human operators in dissonance discovery and control by taking into account two kinds of dissonance, i.e., affordance to study conflicts of use, and inconsistencies to study conflicts of intention and action, through the analysis of cognitive behavior implemented in knowledge bases. This system elaborates the knowledge base composed of rules, and analyzes the knowledge content to discover new knowledge by creating additional rules, or to identify inconsistencies when conflicts between rules occur. The affordance discovery control process uses a deductive and an inductive reasoning algorithm of which the aim is to establish new rules using existing ones. The inconsistency discovery control process applies an abductive reasoning algorithm in order to determine contradictory rules when existing rules may result in opposite intentions being accomplished. Two groups of inconsistencies are addressed: interferences involving several decision makers, and contradictions involving the same decision maker. A knowledge acquisition control process facilitates the creation of the initial rules that contain parameters such as intentions relating to the goals to be achieved, actions to be performed to achieve these intentions, objects used to carry out these actions and the decision makers who execute these actions using the corresponding objects. A feasibility study taking into account five rule bases relating to the manual use of an Automated Speed Control System (ASCS), the automated control of the car speed by the ASCS, the manual control of aquaplaning, the manual control of the car speed, and the manual control of car fuel consumption is proposed to validate the rule-based support system.     

Discovering and modeling meta-structures in human behavior from city-scale cellular data

For a long time, researchers explore spatio-temporal properties in mobility to understand human behavior. They have discovered many statistical laws about human dynamics. Unfortunately, we still have limited knowledge about the spatio-temporal structure of individuals’ movement at a large scale. In this paper, we studied the unified spatio-temporal structures (i.e., meta-structures) in human mobility. We hereby propose a meta-structure discovery algorithm by coupling both topology and spatio-temporal attributes of mobility graphs. With the construction of individual profiles from meta-structure analyses, we provided a novel mobility model from a process-driven perspective, which reduced the dependence of many existing models on the consistency between local and global mobility statistics. We gained some insights on the dominating meta-structures in human mobility by leveraging mobile data in a large city. The statistical distribution of meta-structures is found to be determined by the intrinsic heterogeneity of spatio-temporal properties in human behavior. Our model evaluation showed that a process with basic rules could demonstrate the key statistical properties in mobility meta-structures. We believe that these approaches and observations would be a good reference for management of human mobility in mobile networks and transportation systems.     

Symbol system for symmetry of physical systems

Based on the view that symmetry recognition plays an essential role in human reasoning about the laws of physical phenomena, we propose a reasoning paradigm in which symmetry assist in the discovery of physical laws. Within this paradigm, symmetries are used as constraints which enable us to specify, derive, and generalize these equations. The symmetry-based reasoning is extracted and formalized from Einstein's work on relativity. We claim that the reasoning procedure thus formalized provides a general reasoning architecture that is common to dimensional analysis in engineering, mathematical proofs, and common sense reasoning. This symmetry-based reasoning system has been implemented as a symbol-processing system with a production system and a formula-processing system. Using the symmetry-based reasoning system, the equation of Black's Law of specific heat is demonstrated to be specifie, derived, and generalized.     

REDRAW — a diagrammatic reasoning system for qualitative structural analysis

For preliminary analysis of structures, human engineers often employ diagrams as a visual language to study and to gain intuitive understanding about the behavior of structures. This paper reports a preliminary study and the development of a prototype system for diagrammatic reasoning to better emulate the intuitive visual problem solving techniques of human engineers. Diagrammatic reasoning is a type of reasoning in which the primary means of inference is the direct manipulation and inspection of a diagram. Diagrammatic reasoning is prevalent in human problem-solving behavior, especially for problems involving spatial relationships among physical objects. Our research examines the relationship between diagrammatic reasoning and symbolic reasoning in a computational framework. We have built a system called REDRAW, that emulates the human capability for reasoning with pictures for qualitative analysis of simple frame structures. Diagrammatic representations provide an environment where inferences about the physical results of proposed structural configurations can take place in a more intuitive manner than that possible through purely symbolic representations.     

Effective attention allocation behavior and its measurement: a preliminary study

In general, evaluation of human–machine interface design remains a challenging task. Specifically, there remains a lack of method for tracking effective human operator's attention. This paper presents a study aimed at devising such a method. This method is based on a combination of operators' eye movement and hand movement behaviors. The eye movement reflects the operators' cognitive process and attention allocation, while the hand movement reflects the operators' physical action, which is the result of a cognitive process. Effectiveness of that piece of cognition (eye movement) can therefore be evaluated based on the result of an action (hand movement). The said measure, which may be called the hand–eye measure, is examined for its sensitivity to a good or poor operation behavior and patterns that are further correlated to the operator's behavior and performance. At present, the patterns across the whole operation period are explored. A reference system is employed to validate the hand–eye measure.     

Stereo Video Surveillance Multi-agent System: New Solutions for Human Motion Analysis

This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and identification within dependent environments that merges with other types of technologies. In this kind of environments, vision algorithms are very costly and require a lot of time to produce a response, which is highly inconvenient since many applications can require action to be taken in real time. A multi-agent system (MAS) can automate the process of analyzing images obtained by cameras, and optimize the procedure. This study presents a MAS that can process stereoscopic images to detect and classify people by combining a series of novel techniques. The article shows in detail the combination of techniques used to perform the detection process. The process can be subdivided into human detection, human tracking, and human behavior understanding. With the addition of a case-based reasoning (CBR) model, the system can also incorporate reasoning capabilities. The system was tested under different conditions and environments.     

一种软件演化过程模型的代数语义

随着大量的软件演化过程模型被软件演化过程元模型建模产生,如何验证过程模型的正确性,是摆在人们面前的一个重要任务.针对软件演化过程元模型,引入进程代数ACP(algebra of communicating processes)对其扩展,提出软件演化过程元模型代数,使用进程项指定软件演化过程模型的代数语义,在进程代数的统一框架下,基于等式推理验证软件演化过程模型的行为,使行为验证方式从模型推导变为代数推导这种方法充分结合了Petri网和ACP的长处,可以有效地支持软件演化过程的形式验证.     

可解释的知识图谱推理方法综述

近年来,以深度学习模型为基础的人工智能研究不断取得突破性进展,但其大多具有黑盒性,不利于人类认知推理过程,导致高性能的复杂算法、模型及系统普遍缺乏决策的透明度和可解释性。在国防、医疗、网络与信息安全等对可解释性要求严格的关键领域,推理方法的不可解释性对推理结果及相关回溯造成较大影响,因此,需要将可解释性融入这些算法和系统中,通过显式的可解释知识推理辅助相关预测任务,形成一个可靠的行为解释机制。知识图谱作为最新的知识表达方式之一,通过对语义网络进行建模,以结构化的形式描述客观世界中实体及关系,被广泛应用于知识推理。基于知识图谱的知识推理在离散符号表示的基础上,通过推理路径、逻辑规则等辅助手段,对推理过程进行解释,为实现可解释人工智能提供重要途径。针对可解释知识图谱推理这一领域进行了全面的综述。阐述了可解释人工智能和知识推理相关概念。详细介绍近年来可解释知识图谱推理方法的最新研究进展,从人工智能的3个研究范式角度出发,总结了不同的知识图谱推理方法。提出对可解释的知识图谱推理研究前景和未来研究方向。     

环境信息建模与推理方法

人类行为理解是机器智能研究中最富有挑战性的领域。其根本问题是语义获取,即从动作推理得到人的行为,需要跨越两者之间的语义鸿沟。一般认为这样的推理需要上下文感知和知识库的支持,文献中也存在大量的上下文感知、建模、推理方法,但是,缺乏一个普适的、切实可行的语义获取理论和途径,目前的这些工作都只能针对特定的场景提出特定的解决方案,不可推广。本文提出了一种人关于日常行为知识与人体动作、环境信息之间的建模方法,以及可扩展的开放式结构的环境-行为关系模型,基于该模型提出一种新的行为理解的渐进式认知推理方法。应用这种渐进式推理方法,系统可以把人关于日常行为的知识与人体运动、环境变化等传感器数据处理可以获取到的信息动态绑定,实现了知识辅助的行为理解。提出的推理方法能处理长时间、同时发生的行为。     

网络告警知识发现研究与实现

文章研究企业网络告警数据中的知识发现问题,设计并实现了以Apriori算法为核心的网络告警关联规则发现系统。系统试运行结果表明,该系统能够有效发掘隐藏在海量告警数据背后、不易为网络管理人员所知的告警及故障模式知识。将发现的新知识应用到告警关联/故障诊断专家系统,有效突破了专家系统“知识获取”瓶颈,显著增强了专家系统推理和诊断网络故障的能力。     

Idea discovery: A scenario-based systematic approach for decision making in market innovation

A new trend of researches on knowledge discovery and chance discovery is to identify human insights through data synthesis rather than to discover facts through data analysis. In this paper, we propose a systematic approach named idea discovery which is committed to turning data into effective human insights. Idea discovery focuses on dynamic and sustainable process for high-quality ideas cultivation, construction, integration and evaluation through human–computer and human–human interaction. It mainly relies on latent information and its dynamic changes to drive ideas creation, integration and evaluation during sustainable creativity process. The process of idea discovery is in accordance with a dynamic model which contains two key components: (1) mining algorithms to turn data into scenario maps for eliciting human insights; (2) scenario-based creativity support activities towards actionable ideas generation. An intelligence system called Galaxy integrated with IdeaGraph algorithm has been developed to support the dynamic process of idea discovery. A case study in an automobile company has validated the effectiveness of proposed method and system.     

Specifying concurrent systems with TSL

The Task Sequencing Language (TSL), which lets programmers specify constraints on the behavior of concurrent programs, is described. The constraints, specified by high-level annotations to Ada programs, are monitored at runtime; when a violation is detected, control is returned to the user, along with information about the nature of the violation. It is argued that such a tool is a necessary crutch for human reasoning capabilities when dealing with the complexities of concurrent process interaction. The first large-scale application of TSL, the specification and validation of a distributed tasking supervisor for Ada, is described     

A family of syntactic logical relations for the semantics of Haskell-like languages

Logical relations are a fundamental and powerful tool for reasoning about programs in languages with parametric polymorphism. Logical relations suitable for reasoning about observational behavior in polymorphic calculi supporting various programming language features have been introduced in recent years. Unfortunately, the calculi studied are typically idealized, and the results obtained for them offer only partial insight into the impact of such features on observational behavior in implemented languages. In this paper we show how to bring reasoning via logical relations closer to bear on real languages by deriving results that are more pertinent to an intermediate language for the (mostly) lazy functional language Haskell like GHC Core. To provide a more fine-grained analysis of program behavior than is possible by reasoning about program equivalence alone, we work with an abstract notion of relating observational behavior of computations which has among its specializations both observational equivalence and observational approximation. We take selective strictness into account, and we consider the impact of different kinds of computational failure, e.g., divergence versus failed pattern matching, because such distinctions are significant in practice. Once distinguished, the relative definedness of different failure causes needs to be considered, because different orders here induce different observational relations on programs (including the choice between equivalence and approximation). Our main contribution is the construction of an entire family of logical relations, parameterized over a definedness order on failure causes, each member of which characterizes the corresponding observational relation. Although we deal with properties very much tied to types, we base our results on a type-erasing semantics since this is more faithful to actual implementations.     

Extracting causation knowledge from natural language texts

SEKE is a semantic expectation‐based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 327–358, 2005.     

一种Agent的偏好系统及其建立

受到Agent偏好模型的启发,提出了一个新的描述人们行为的偏好系统称为AF系统,它将Michael Freund的理性偏好推理与Adams提出的经典的归纳逻辑和概率逻辑相结合,即借助经典的逻辑系统,把人们的逻辑推理和以人们的偏好取向为基础的常识性推理以及以主观愿望为基础的意向推理结合在一起成为一个有机系统,并且在这个系统下提出Agent的理性偏好的建立方法以及推理步骤.最后给出一个实例,说明它能根据人们的部分愿望,全面估计人们的偏好走向,而且一旦人们的基本态度有所转向,偏好结构也可做相应调整,无需做根本改动,因此它具有鲁棒性和实用价值.