Process mining online assessment data

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for generating recommendations and advice to students, for improving management of learning objects, etc. However, most of the traditional data mining techniques focus on data dependencies or simple patterns and do not provide a visual representation of the complete educational (assessment) process ready to be analyzed. To allow for these types of analysis (in which the process plays the central role), a new line of data-mining research, called process mining, has been initiated. Process mining focuses on the development of a set of intelligent tools and techniques aimed at extracting process-related knowledge from event logs recorded by an information system. In this paper we demonstrate the applicability of process mining, and the ProM framework in particular, to educational data mining context. We analyze assessment data from recently organized online multiple choice tests and demonstrate the use of process discovery, conformance checking and performance analysis techniques

Possibilistic WorkFlow nets to deal with non-conformance in Process Execution

In this paper, an approach based on WorkFlow nets and on possibilistic Petri nets is proposed to deal with non- conformance in Business Processes. Routing patterns existing in Business Process are modeled by WorkFlow nets. To express in a more realistic way the uncertainty attached to human activities, possibilistic Petri nets with uncertainty on the marking and on the transition firing are considered. Combining both formalisms, a kind of possibilistic WorkFlow net is obtained. An example of deviation at a process monitoring level due to human behavior in a “Handle Complaint Process” is presented

Inconsistency recovery in Business Processes using a possibilistic WorkFlow net

In this paper, an approach based on WorkFlow nets and on possibilistic Petri nets is proposed to deal with non- conformance in Business Processes. Routing patterns existing in Business Processes are modeled by WorkFlow nets. To express in a more realistic way the uncertainty attached to human activities, possibilistic Petri nets with uncertainty on the marking and on the transition firing are considered. Combining both formalisms, a kind of possibilistic WorkFlow net is obtained. An example of inconsistency recovery at a process monitoring level due to human behavior in a “Handle Complaint Process” is presented

Improving Human-Machine Interaction

This thesis studies human and machine interaction. For better interaction between humans and machines, this thesis aims to address three issues that remain unanswered in literature. Three objectives are proposed in this thesis to address the three issues, and the objectives are: (i) identification of the core capabilities of a Human Assistance System (HAS) and study of implementation strategy of the core capabilities; (ii) development of a framework for improving the accuracy of human mind state inference; (iii) study of the effect of representation of the machine’s state (which is represented in a “natural” way) on the user’s actions. By a natural way, it is meant a way that contains emotions known to be always present in humans (or human emotions in short). The study includes theoretical development, experimentation, and prototype implementation. This thesis has concluded: (1) the core capabilities to be addressed in designing a HAS are transparency, communication, rationale, cognition and task-sharing and they can be implemented with the existing technologies including fuzzy logics, Petri Net and ACT-R (Adaptive Control of Thought-Rational); (2) expert opinion elicitation technique is a promising method to construct a more general framework for integrating various algorithms on human state inference; (3) there is a significant effect of the representation of the machine’s state on the user’s actions. The main contributions of this thesis are: (1) provision of a case study for the proof-of-concept of HAS in the area of Computer Aided Design (CAD); (2) provision of an integrated framework for fatigue inference for improved accuracy, being readily generalized to inference of other mind states; (3) generation of a new knowledge regarding the effect of the natural representation of a machine’s states on the user’s actions. These contributions are significant in human-machine science and technology. The first contribution may lead to the development of a new generation CAD system in the near future. The second contribution provides a much powerful technology for human mind inference, which is a key capability in HAS, and the third contribution enriches the science of human-machine interaction and will give impact to the field of Artificial Intelligence (AI) as well. The application of the result of this thesis is rehabilitation, machine learning, etc

Fuzzy reasoning spiking neural P systems revisited: A formalization

Research interest within membrane computing is becoming increasingly interdisciplinary.In particular, one of the latest applications is fault diagnosis. The underlying mechanismwas conceived by bridging spiking neural P systems with fuzzy rule-based reasoning systems. Despite having a number of publications associated with it, this research line stilllacks a proper formalization of the foundations.National Natural Science Foundation of China No 61320106005National Natural Science Foundation of China No 6147232

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer