Chasing shared understanding in drilling operations

Lack of shared understanding is frequently found to be the main cause when accidents are investigated. Still, few studies explicitly explore and document the causal effects of shared understanding in successful work. Thus, the attribution of insufficient shared understanding as an accident cause lacks the substantiation of shared understanding as a contributor to successful work. In this article a case of measurement discrepancies in an offshore drilling operation is studied, and in the elaboration of the case shared understanding is found not to qualify as a condition with significant impact on the collaborative work. One important reason for this is the epistemological inadequacy of the different concepts of shared understanding. Although more critical research on shared understanding is needed before one can conclude more generic on this topic, the findings are important to the current development of Integrated Operations where shared understanding is pointed out as an important target area.     

Socially shared regulation of learning in CSCL: understanding and prompting individual- and group-level shared regulatory activities

The field of computer supported collaborative learning (CSCL) is progressing instrumentally and theoretically. Nevertheless, few studies examine the effectiveness and efficiency of CSCL with respect to cognitive, motivational, emotional, and social issues, despite the fact that the role of regulatory processes is critical for the quality of students’ engagement in collaborative learning settings. We review the four earlier lines in developing support in CSCL and show how there has been a lack of work to support individuals in groups to engage in, sustain, and productively regulate their own and the group’s collaborative processes. Our aim is to discuss how our conceptual work in socially shared regulation of learning (SSRL) contributes to effective and efficient CSCL, what tools are presently available, and what the implications of research on these tools are for future tool development.     

Analyzing collaborative interactions: divergence,shared understanding and construction of knowledge

One of the most important facets of collaborative learning is the interaction between individual and collaborative learning activities – between divergent perspectives and shared knowledge building. Individuals bring divergent ideas into a collaborative environment. While individuals bring their own unique knowledge and perspectives, the second important aspect of collaborative learning is how they move from seemingly divergent perspectives to collaborative knowledge building. This is clearly a social process among group members who could adopt various strategies for resolving differences including asserting dominance, acquiescing, or some form of reciprocal sense making. An important aspect of collaborative learning is the move from assimilation to construction, i.e., creating new understandings based on the discussions that they have had. Documenting this change from divergence to collaborative knowledge building to possible construction is therefore important in understanding the nature the collaborative interactions. In this paper we discuss our analysis of the process of collaborative interactions based on three dimensions – divergence of ideas, collaborative knowledge building and construction. Our aim was to document as well as to understand how collaborative interactions develop over time: whether students raise new issues (ideas) more frequently as they become more familiar with the discussion and discussants, and whether shared knowledge building becomes richer over time, and subsequent evidence that students were able to construct their own understanding based on their interactions with others. Our analyses were conducted in the context of an online graduate course conducted using the learning environment that we designed, CoDE, (Constructivist, Distributed learning Environment). In this paper, we will first describe the design of CoDE. We will then describe a study in which CoDE was used to offer an online graduate course in learning theories. We then discuss our analyses of both individual and collaborative learning as it progressed through the duration of the course.     

In-depth behavior understanding and use: The behavior informatics approach

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (BI), in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of BI in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific means by finding deeper and more informative patterns leading to greater in-depth behavior understanding. BI creates new directions and means to enhance the quantitative, formal and systematic modeling and analysis of behaviors in both physical and virtual organizations.     

Facilitating shared understanding of work situations using a tangible tabletop interface

As work is an inherently cooperative phenomenon, it requires a common understanding of the nature of collaboration for all involved parties. In this way, explicit articulation work becomes an integral and essential part of collaboration. Implicit aspects of collaboration have impact on the quality of work results, mainly through social norms and observations of working together. Eliciting those aspects interactively helps in avoiding (mutual) misrepresentations and lack of understanding. Tangible articulation support systems allow aligning mental models of how work should be carried out. Stakeholders can develop a common understanding of collaboration in a semantically open and non-intrusive way. They are not burdened by explication features and diagrammatic notations. We have utilised experiences with model-centred learning theory to support explicit articulation work. According to our field studies, the resulting models can be fed back to current work practices and help in preventing problematic work situations.     

Action recognition and understanding through motor primitives

In robotics, recognition of human activity has been used extensively for robot task learning through imitation and demonstration. However, there has not been much work performed on modeling and recognition of activities that involve object manipulation and grasping. In this work, we deal with single arm/hand actions which are very similar to each other in terms of arm/hand motions. The approach is based on the hypothesis that actions can be represented as sequences of motion primitives. Given this, a set of five different manipulation actions of different levels of complexity are investigated. To model the process, we use a combination of discriminative support vector machines and generative hidden Markov models. The experimental evaluation, performed with 10 people, investigates both the definition and structure of primitive motions, as well as the validity of the modeling approach taken.     

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 daily behavior enabled hidden Markov model for human behavior understanding

This paper presents a Hierarchical Context Hidden Markov Model (HC-HMM) for behavior understanding from video streams in a nursing center. The proposed HC-HMM infers elderly behaviors through three contexts which are spatial, activities, and temporal context. By considering the hierarchical architecture, HC-HMM builds three modules composing the three components, reasoning in the primary and the secondary relationship. The spatial contexts are defined from the spatial structure, so that it is placed as the primary inference contexts. The temporal duration is associated to elderly activities, so activities are placed in the following of spatial contexts and the temporal duration is placed after activities. Between the spatial context reasoning and behavior reasoning of activities, a modified duration HMM is applied to extract activities. According to this design, human behaviors different in spatial contexts would be distinguished in first module. The behaviors different in activities would be determined in second module. The third module is to recognize behaviors involving different temporal duration. By this design, an abnormal signaling process corresponding to different situations is also placed for application. The developed approach has been applied for understanding of elder behaviors in a nursing center. Results have indicated the promise of the approach which can accurately interpret 85% of the elderly behaviors. For abnormal detection, the approach was found to have 90% accuracy, with 0% false alarm.     

Coevolution of multiple species through niche construction modifying shared environments

Niche construction is a process whereby organisms, through their metabolism, activities, and choices, modify their own and/or each other’s niches. Our purpose is to clarify the interactions between evolution and niche construction by focusing on non-linear interactions between genetic and environmental factors shared by interacting species. We constructed a new fitness landscape model termed the NKES model by introducing environmental factors and their interactions with genetic factors into Kauffman’s NKCS model. The evolutionary experiments were conducted using hill-climbing and niche-constructing processes on this landscape. The results have shown that the average fitness among species strongly depends on the ruggedness of the fitness landscape (K) and the degree of the effect of niche construction on genetic factors (E). Especially, we observed two different roles of niche construction: moderate perturbations on hill-climbing processes on the rugged landscapes, and the strong constraint which yields the convergence to a stable state. Also, we show that the difference in the structures of (direct or indirect) interactions among species drastically changes the coevolutionary process of the whole ecosystem by comparing the evolutionary dynamics of the NKES model with that of the NKCS model.     

Providing understanding of the behavior of feedforward neuralnetworks

The advent of artificial neural networks has stirred the imagination of many in the field of knowledge acquisition. There is an expectation that neural networks will play an important role in automating knowledge acquisition and encoding, however, the problem solving knowledge of a neural network is represented at a subsymbolic level and hence is very difficult for a human user to comprehend. One way to provide an understanding of the behavior of neural networks is to extract their problem solving knowledge in terms of rules that can be provided to users. Several papers which propose extracting rules from feedforward neural networks can be found in the literature, however, these approaches can only deal with networks with binary inputs. Furthermore, certain approaches lack theoretical support and their usefulness and effectiveness are debatable. Upon carefully analyzing these approaches, we propose a method to extract fuzzy rules from networks with continuous-valued inputs. The method was tested using a real-life problem (decision-making by pilots involving combat situations) and found to be effective.     

Diagnosis of plant-wide oscillation through data-driven analysis and process understanding

Disturbances that propagate throughout a plant due to recycle streams, heat integration or other means can have an especially large impact on product quality and running costs. There is thus a motivation for automated detection of a plant-wide disturbance and for determination of the root cause so that the disturbance may be removed. In this article, data-driven techniques are used to diagnose a plant-wide oscillation in an Eastman Chemical Company plant. A numerical non-linearity index derived from routine measurements was able to suggest the root cause. Process understanding possessed by the plant control engineers then enhanced the data-driven analysis, for instance by identifying a proxy measurement for an unmeasured flow through the valve suspected of being the root cause. In situ tests of just one valve confirmed the suspected root cause and the plant-wide oscillation disappeared after repairing the valve. The diagnosis was right first time and the maintenance effort was thus minimized. The success of the study suggests there exists a fruitful direction for future research in the automated linkage of data-driven analysis with information about the structure and connectivity of the process.     

Collaborative eye tracking based code review through real-time shared gaze visualization

Code review is intended to find bugs in early development phases, improving code quality for later integration and testing. However, due to the lack of experience with algorithm design, or software development, individual novice programmers face challenges while reviewing code. In this paper, we utilize collaborative eye tracking to record the gaze data from multiple reviewers, and share the gaze visualization among them during the code review process. The visualizations, such as borders highlighting current reviewed code lines, transition lines connecting related reviewed code lines, reveal the visual attention about program functions that can facilitate understanding and bug tracing. This can help novice reviewers to make sense to confirm the potential bugs or avoid repeated reviewing of code, and potentially even help to improve reviewing skills. We built a prototype system, and conducted a user study with paired reviewers. The results showed that the shared real-time visualization allowed the reviewers to find bugs more efficiently.     

Calculation of the paired Haar transform through shared binary decision diagrams

A concept of paired Haar transform (PHT) for representation and efficient optimization of systems of incompletely Boolean functions has recently been introduced. In this article, a method to calculate PHT for incompletely specified switching functions through shared binary decision diagrams (SBDDs) is presented. The algorithm converts switching functions in the form of SBDDs into their paired Haar spectra and can operate on functions with many variables.     

Personal privacy through understanding and action: five pitfalls for designers

To participate in meaningful privacy practice in the context of technical systems, people require opportunities to understand the extent of the systems alignment with relevant practice and to conduct discernible social action through intuitive or sensible engagement with the system. It is a significant challenge to design for such understanding and action through the feedback and control mechanisms of todays devices. To help designers meet this challenge, we describe five pitfalls to beware when designing interactive systems—on or off the desktop—with personal privacy implications. These pitfalls are: (1) obscuring potential information flow, (2) obscuring actual information flow, (3) emphasizing configuration over action, (4) lacking coarse-grained control, and (5) inhibiting existing practice. They are based on a review of the literature, on analyses of existing privacy-affecting systems, and on our own experiences in designing a prototypical user interface for managing privacy in ubiquitous computing. We illustrate how some existing research and commercial systems—our prototype included—fall into these pitfalls and how some avoid them. We suggest that privacy-affecting systems that heed these pitfalls can help users appropriate and engage them in alignment with relevant privacy practice.