A state-driven modeling approach to human interactions for knowledge intensive services

Knowledge intensive service activities have become to play a fundamentally important role in various industrial fields. Human workers generally undertake complex operations relying heavily on professional knowledge in service processes to develop and deliver the knowledge intensive services. That means the ability of humans to create, disseminate, or utilize the knowledge is the dominant factor in the processes. Therefore, the processes should be managed in a human-oriented way. In order to help humans work together, a strong representation of processes should be provided to facilitate them to clearly understand who they should interact with, which resources are exchanged, and what activities need to be performed. Human Interaction Management (HIM) has been suggested to comprehensively support the human-oriented processes, but it cannot provide a way to structure and visualize the interaction works although the interaction is the most basic nature of human works. Therefore, this paper presents a state-driven approach to modeling human interactions which clearly visualizes the interactions so that human workers can be guided through it. However, it cannot be expected for human workers to follow the guidelines completely. They continuously and dynamically redefine their processes towards the way that they want throughout the life of the processes. To support the dynamic human work behavior, this paper also presents a hybrid modeling methodology that consists of the top-down specification of interaction models for guideline modeling and the bottom-up evolution of the models for flexible enactment. The suggested methodology for human interactions based on the state-driven modeling approach provides a way to effectively manage the complex interactions in a human-oriented way.     

An ontology-based approach to role-human assignment in human processes for knowledge intensive services

Knowledge intensive service processes should be managed in a human-oriented way since human workers who naturally undertake complex operations of an intellectual nature in the processes are the most valuable resources for service-providing companies. For the human-oriented management, a role-based approach has been widely applied. The foremost step in the role-based human process management is to assign relevant roles to appropriate humans. The role assignment task is also highly dependent on the ability of humans to consider information about roles and humans. In order to manage the role assignment in a human-oriented way, this paper presents two fundamental principles upon which the assignment task is carried out. First, roles should not be assigned until they need to be activated; second, the authority for the role assignment should be distributed to participants in the processes. In order to support the principles, the information should be explicitly and formally described to enable all the participants to easily understand and utilize it. Consequently, this paper proposes a methodology for managing the role assignment in a human-oriented way by adopting an ontological approach which can precisely describe the information. From the proposed methodology, human workers can be notified which roles are triggered to be activated and wait to be assigned. Moreover, they can be provided appropriate candidate humans when carrying out the assignment task. The contribution of this paper is to suggest a role assignment methodology that not only lightens the assignment workload by providing the capability of identifying a small set of appropriate candidates but also enables the efficient assignment by streamlining the assignment task.     

Modeling and mining of dynamic trust in complex service-oriented systems

The global scale and distribution of companies have changed the economy and dynamics of businesses. Web-based collaborations and cross-organizational processes typically require dynamic and context-based interactions between people and services. However, finding the right partner to work on joint tasks or to solve emerging problems in such scenarios is challenging due to scale and temporary nature of collaborations. Furthermore, actor competencies evolve over time, thus requiring dynamic approaches for their management. Web services and SOA are the ideal technical framework to automate interactions spanning people and services. To support such complex interaction scenarios, we discuss mixed service-oriented systems that are composed of both humans and software services, interacting to perform certain activities. As an example, consider a professional online support community consisting of interactions between human participants and software-based services. We argue that trust between members is essential for successful collaborations. Unlike a security perspective, we focus on the notion of social trust in collaborative networks. We show an interpretative rule-based approach to enable humans and services to establish trust based on interactions and experiences, considering their context and subjective perceptions.     

A scenario-based verification technique to assess the compatibility of collaborative business processes

Successful E-Business is based on seamless collaborative business processes. Each partner in the collaboration specifies its own rules and interaction preconditions. The verification of the compatibility of collaborative business processes, based on local and global views, is a complex task, which is critical for the success of the cooperation. The verification of process compatibility should be a key element in the design of new business alliances, which makes this verification essential in inter-organizational business process design. The advent of Web Services and Service Oriented Architectures stresses the need to solve complex business cooperations under the pressure of reduced costs and risks, combined with shorter time to market. At the same time, the message-based paradigm results in large and complex processes which makes the verification process quite hard.First the use of an atomic business activity based interaction paradigm is proposed to reduce the complexity of collaborative process models. Next, formal compatibility criteria for business processes are developed. The key construction is the notion of semantic compatibility, which can be used to assess the compatibility of the publicly exposed behavior (a.k.a. business interfaces) between business partners in joint business processes. The formal nature of this compatibility allows to give a precise indication of the degree of compatibility between cooperating business partners. It enables exhaustive lists of incompatible (i.e. unsupported scenarios) business cooperations, which is more comprehensive and complete than the typical compatible or incompatible answer to this question. Finally, complex multiple party interactions can be examined in terms of the safety of the interaction protocols.     

Using Dialogue Features to Predict Trouble During Collaborative Learning

A web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning has the potential for improving on-line learning. The agent would follow the discussion and interact with the participants when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. These elements can serve as the basis for student and group models. In this paper, we discuss group interaction during collaborative learning, our representation of participant dialogue, and the statistical models we are using to determine the role being played by a participant at any point in the dialogue and the effectiveness of the group. We also describe student and group models that can be built using conversational elements and discuss one set that we built to illustrate their potential value in collaborative learning. An erratum to this article is available at .     

Investigating face‐to‐face peer interaction patterns in a collaborative Web discovery task: the benefits of a shared display

Mobile computers are now increasingly applied to facilitate face‐to‐face collaborative learning. However, the factors affecting face‐to‐face peer interactions are complex as they involve rich communication media. In particular, non‐verbal interactions are necessary to convey critical communication messages in face‐to‐face communication. Through gathering discourse and non‐verbal interaction records, this study explores the peer interactions supported by two collaborative applications: one with mobile computers and the other with shared‐display groupware (SDG). The results show that the students tended to interact with each other according to a distributed and an unsocial interaction pattern when using the application with mobile computers. In contrast, the students who learned with the SDG demonstrated a shared interaction pattern, whereby they often jointly focused on and referred to the shared work. The analysis of the students' work further found that a higher level of discussion was generally associated with the shared interaction pattern. The results seem to support SDG as being useful in augmenting face‐to‐face peer interaction supported by mobile computers. The implications derived from the findings also support the argument that non‐verbal interaction records are useful for quantitatively and qualitatively analysing face‐to‐face peer interactions.     

Using interaction signatures to find and label chairs and floors

Our research takes an action-centered approach to automatically learning and classifying functional objects. Our premise is that interpreting human motion is much easier than recognizing arbitrary objects because the human body has constraints on its motion. Moreover, humans tend to interact differently with different objects, so you should be able to identify an object by analyzing how people move when they manipulate it. We call these motions the human-object interaction signature. An interaction signature is a method to find and classify objects on the basis of how humans interact with those objects. The method addresses many key problems encountered in smart-home monitoring systems.     

Dynamic interactions in physically realistic collaborative virtual environments

This work describes our efforts in creating a general object interaction framework for dynamic collaborative virtual environments. Furthermore, we increase the realism of the interactive world by using a rigid body simulator to calculate all actor and object movements. The main idea behind our interactive platform is to construct a virtual world using only objects that contain their own interaction information. As a result, the object interactions are application independent and only a single scheme is required to handle all interactions in the virtual world. In order to have more dynamic interactions, we also created a new and efficient way for human users to dynamically interact within virtual worlds through their avatar. In particular, we show how inverse kinematics can be used to increase the interaction possibilities and realism in collaborative virtual environments. This results in a higher feeling of presence for connected users and allows for easy, on-the-fly creation of new interactions. For the distribution of both the interactive objects and the dynamic avatar interactions, we keep the network load as low as possible. To demonstrate the effectiveness of our techniques, we incorporate them into an existing CVE framework.     

FlowWiki: A wiki based platform for ad hoc collaborative workflows

Traditional workflow management systems provide rich capabilities for designing, executing, and monitoring well-defined collaborative processes. Yet, for many occasions of collaboration, we do not often have sufficient information about who will participate, what activities people will carry out, and how the entire workflow will change. Accordingly, the problem of managing flexible workflows has been receiving increasing attention during the last decade. This paper presents a novel approach by which collaborative workflows can be configured independently as needed by participants and managed in an ad hoc way. Motivated by the emerging paradigm of collective intelligence, the proposed platform, named FlowWiki, provides a set of useful mechanisms to enable dynamic collaborations without requiring prescribed collaboration model. FlowWiki is an extension of conventional wiki system, and it aims for flexibly managing collaborative workflows by allowing on-demand workflow configuration and event-driven interactions.     

Online group work patterns: How to promote a successful collaboration

Participation is a complex process, engaging the whole person, implying cognitive, emotional and relational aspects. In online open and distant learning, group work is a commonly used strategy, given its collaborative nature and constructivist framework (2, 4 and 7). In this context, collaborative learning processes are highly dependent on the shared written information and the interactions that are established among the participants. The types of interactions that occur within such groups can influence their knowledge convergence processes, and are often decisive for its success.     

Fragmentary shape recognition: A BCI study

Recently, Brain–Computer Interface (BCI) has emerged as a potential modality that utilizes natural and intuitive human mechanisms of thinking process to enable interactions in CAD interfaces. Before BCI could become a mainstream mode of HCI for CAD interfaces; fundamental studies directed towards understanding how humans mentally represent and process the geometry are needed. The outlined work in this paper presents an objective user study to understand shape recognition process in the humans. Specifically, we focus on the fundamental task of fragmentary shape identification. The problem of fragmentary shape recognition can be defined as follows: given a partial and incomplete minimalistic representation of a given shape, can one recognize the actual complete shape or object? In user studies, each subject was progressively (in stages) shown more informative fragmented images of an object to be recognized. During each stage of the experiment, the brain activity of users in the form of electroencephalogram (EEG) signals was recorded with a BCI headset. The recorded signals are then processed to objectively study the fragmentary shape recognition process. The results of user studies conclusively show that the measured brain activities of subjects can serve as a very accurate proxy to estimate subjects fragmentary shape recognition process.     

Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017

ABSTRACT

Currently, a large number of industrial robots have been deployed to replace or assist humans to perform various repetitive and dangerous manufacturing tasks. However, based on current technological capabilities, such robotics field is rapidly evolving so that humans are not only sharing the same workspace with robots, but also are using robots as useful assistants. Consequently, due to this new type of emerging robotic systems, industrial collaborative robots or cobots, human and robot co-workers have been able to work side-by-side as collaborators to accomplish tasks in industrial environments. Therefore, new human–robot interaction systems have been developed for such systems to be able to utilize the capabilities of both humans and robots. Accordingly, this article presents a literature review of major recent works on human–robot interactions in industrial collaborative robots, conducted during the last decade (between 2008 and 2017). Additionally, the article proposes a tentative classification of the content of these works into several categories and sub-categories. Finally, this paper addresses some challenges of industrial collaborative robotics and explores future research issues.     

A context-based model for Knowledge Management embodied in work processes

Knowledge Management has become a prominent subject for organizations, but often the information that flows in a well-defined design work process is not characterized and treated in such a way as to promote its reuse. We argue that context is a fundamental information resource for improving how activities and interactions are understood and carried on. Our premise is that it is important for organizational learning that decisions, solutions, discussions and actions executed in work processes should be retrievable. We describe an environment that supports the cycle of creating and dealing with information about activities and interactions, focusing on their context. A formal ontology-based representation of context is presented to support the use of this environment. Two case studies are described and their results analyzed. The goal of this paper is to discuss and specify mechanisms that can be used to collect contextual information within such an environment.     

Managing collaboration performance to govern virtual organizations

The ability to collaborate with partners will become an essential core competence that is required from companies when they are going to take up future challenges. Growing complexity of products and services, increasing global competition and accelerated business processes will exceed in many cases the capabilities and capacities of single companies. The involvement of other companies can help to overcome these limitations. However, only what is measured can be managed. Consequently it is necessary to assess the effectiveness and efficiency of how partners work together in joint processes for a common goal. In other word: the collaboration performance has to be measured. But traditional Performance Measurement (PM) methodologies and indicators are designed to assess the performance of single companies or static cooperation like in supply chains. Evaluation and management of collaboration performance as a particular performance perspective in cooperation is not covered by existing approaches so far. Therefore there is still a need for an approach that provides an information basis for the management of collaboration when companies work together in in cooperation. In this paper, which is initially based on a paper presented on the ProVE conference in 2007, different perspectives of collaboration performance are identified and structured. The considerations are based on Virtual Organisations VOs, a particular type of cooperation that requires usually intensive collaborative interactions between the partners.     

A Flexible Architecture for Virtual Humans in Networked Collaborative Virtual Environments

Complex virtual human representation provides more natural interaction and communication among participants in networked virtual environments, hence it is expected to increase the sense of being together within the same virtual world. We present a flexible framework for the integration of virtual humans in networked collaborative virtual environments. A modular architecture allows flexible representation and control of the virtual humans, whether they are controlled by a physical user using all sorts of tracking and other devices, or by an intelligent control program turning them into autonomous actors. The modularity of the system allows for fairly easy extensions and integration with new techniques making it interesting also as a testbed for various domains from “classic” VR to psychological experiments. We present results in terms of functionalities, example applications and measurements of performance and network traffic with an increasing number of participants in the simulation.     

Models, methods and middleware for grid-enabled multiphysics oil reservoir management

Meeting the demands for energy entails a better understanding and characterization of the fundamental processes of reservoirs and of how human made objects affect these systems. The need to perform extensive reservoir studies for either uncertainty assessment or optimal exploitation plans brings up demands of computing power and data management in a more pervasive way. This work focuses on high performance numerical methods, tools and grid-enabled middleware systems for scalable and data-driven computations for multiphysics simulation and decision-making processes in integrated multiphase flow applications. The proposed suite of tools and systems consists of (1) a scalable reservoir simulator, (2) novel stochastic optimization algorithms, (3) decentralized autonomic grid middleware tools, and (4) middleware systems for large-scale data storage, querying, and retrieval. The aforementioned components offer enormous potential for performing data-driven studies and efficient execution of complex, large-scale reservoir models in a collaborative environment.     

Human knowledge acquisition from 3D interaction in virtual environments

Recently,virtual environment (VE) based design and planning,which is a kind of interaction intensive computing,has shown great potential.Most users of such systems are specialists or technicians and their 3D interactions with the system embed a great deal of knowledge and skills.Thus,how to integrate human knowledge and skills into VE is a great challenge to researchers in the human computer interaction field.This paper proposes a method for acquiring user knowledge from VE.The main ideas and work include,abstracting the interactive process and formalizing the interactive semantics,as well as providing a semantic model of an interactive information repository,from which user interactive processes can be retrieved and all kinds of application logics can be established.A virtual assembly planning system is introduced as an example of a practical application of this method.Experiments show that the related models can well capture user knowledge and retrieve the interaction process.     

A real-time architecture for time-aware agents

This paper describes the specification and implementation of a new three-layer time-aware agent architecture. This architecture is designed for applications and environments where societies of humans and agents play equally active roles, but interact and operate in completely different time frames. The architecture consists of three layers: the April real-time run-time (ART) layer, the time aware layer (TAL), and the application agents layer (AAL). The ART layer forms the underlying real-time agent platform. An original online, real-time, dynamic priority-based scheduling algorithm is described for scheduling the computation time of agent processes, and it is shown that the algorithm's O(n) complexity and scalable performance are sufficient for application in real-time domains. The TAL layer forms an abstraction layer through which human and agent interactions are temporally unified, that is, handled in a common way irrespective of their temporal representation and scale. A novel O(n2) interaction scheduling algorithm is described for predicting and guaranteeing interactions' initiation and completion times. The time-aware predicting component of a workflow management system is also presented as an instance of the AAL layer. The described time-aware architecture addresses two key challenges in enabling agents to be effectively configured and applied in environments where humans and agents play equally active roles. It provides flexibility and adaptability in its real-time mechanisms while placing them under direct agent control, and it temporally unifies human and agent interactions.