Context-aware Decision Support Research Articles

Sensor-fused simulation-based decision support system for earthmoving projects

Discrete Event Simulation (DES) is an effective method to study cyclic and uncertain projects at the operation level. However, qualified DES results highly depend on the input data quality. This paper describes a simulation-enabled context-aware Decision Support System (DSS) based on sensor fusion to model earthwork operations realistically. The DSS backbone is a DES continually updated using real-time data. The information on earthmoving operations is exchanged between reality and DES using GPS and equipment-mounted cameras. The equipment spatiotemporal data and shovel-mounted cameras enhance the DES quality (i.e., locational and contextual awareness) by providing timely information about task durations, load types, and bucket teeth status. Sensor data is fused and fed into the DES model to replicate the earthmoving project. The proposed method is successfully implemented and validated in a large-scale earthmoving case study. The developed DES is proven promising as a reliable DSS for enhancing resource allocation and time-cost reduction.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition.

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

Forest Graph Convolutional Network for Surgical Action Triplet Recognition in Endoscopic Videos

Recognizing surgical activities in endoscopic videos is of vital importance for developing context-aware decision support in the operating room. In this work, we model each surgical activity as an action triplet, consisting of the surgical instrument, the action, and the target organ that the instrument is interacting with. The goal is to recognize these action triplets from endoscopic videos. However, correctly recognizing fine-grained activity triplets is challenging because of the long-tail distribution of the triplet classes and the complex associations between triplets as well as within each triplet. In addition, multiple triplets may appear in a given video frame. To address these challenges, we propose a new model for surgical action triplet recognition based on a classification forest and Graph Convolutional Network (GCN), which we call Forest GCN. The classification forest is employed to calibrate fine-grained triplet classifiers by the upstream parent classifiers to suppress noisy logits of the triplet classes in the long tail. And stacked GCNs are designed to model the dependencies between triplet classes while leveraging the language embedding. Experiments on the endoscopic video dataset, CholecT50, demonstrate that our proposed method outperforms current state-of-the-art methods on surgical action triplet recognition.

Context-aware system for cardiac condition monitoring and management: a survey

ABSTRACT Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also proposes ‘mCardiac’, an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation.

Adaptive Learning in Electricity Market Negotiations Based on Determinism Theory

This research proposes a novel methodology for adaptive learning in electricity markets negotiations, based on the principles of the determinism theory. The determinism theory states that all events are predetermined due to the cause-effect rule. At the same time, it is unmanageable to consider all causes to a certain effect, making it impossible to predict future events. However, in a controlled simulation environment, it is possible to access and analyze all involved variables; thus, making the application of this theory promising in such environments. This research applies the principles of the determinism theory to a new learning methodology, which optimizes players’ actions, considering the predicted behavior of all involved players, with the objective of maximizing market gains. A case-based reasoning approach is used, providing adaptive context-aware decision support. Results show that the proposed approach is able to achieve better market results than all reference market strategies.

Pervasive health monitoring through video-based activity information integrated with sensor-cloud oriented context-aware decision support system

Medical informatics comprises of huge amount of medical resources to enhance storage, retrieval, and employ these resources in healthcare. The advancement has been done to monitor the health of the patients and provide the details to the caretakers, who are near by the remote areas. This could be done in a real-time with the help of the internet access. Due to the condition of monitoring the patient at a real-time, the caretaker can provide the suggestions regarding their essential signs of their body situation through a video conference. In this paper, we have proposed a system to report the progress of the elderly in an appropriate manner with the help of the technology used in the healthcare system and integrate the report of progress to the remote caretakers employing smartphones and videos. Through this advanced method we could able to identify the abnormalities at early stages so that the doctors could cure it without any difficulty. This could increase the physical and mental health of the patients. The system incorporated in this method requires certain sensors which are of very low in cost, certain electronic devices and smart phone for the communication purpose and WSN.

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data

PurposeProbe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures.MethodsThe aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods.ResultsWe demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%.ConclusionsThis work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Consistent context aware behaviour in smart home environment

The home automation controls the electronic devices remotely and automatically with the help of context aware behaviour. Context awareness is the process where data from various sensors and input from user or user action must be interpreted and corresponding commands to actuators must be generated. This context aware automation and decision support process is done with the help of adaptive rules which is defined for the automatic behaviour of devices in the smart home environment. The consistent behaviour of the devices in the smart environment will capture the customer satisfaction. This work describes the process of checking the consistent behaviour of the context aware system in smart home environment using formal modelling and verification method. We have discussed a mechanism to check the consistent behaviour of the adaptive mechanism of smart home environment. The result ensures the consistent behaviour of the context aware system in the smart environment.

Modeling and Reasoning about Preference-Based Context-Aware Agents over Heterogeneous Knowledge Sources

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. A Protégé plug-in for rules extraction from distributed ontologies has been developed, which allows us to model context-aware agents using the notion of multi-context systems. Extracted rules can be annotated to match the users’ needs and to develop a preference model to support their preferences so as to provide a user with a more personalized services. The use of the proposed framework is illustrated using a simple fact-based preference model developed from ontologies considering two different smart environment domains.

МЕТОДОЛОГИЯ ПОСТРОЕНИЯ ПРОАКТИВНЫХ РЕКОМЕНДУЮЩИХ СИСТЕМ ДЛЯ ИНФОМОБИЛЬНЫХ ПРИЛОЖЕНИЙ

Introduction: Development of personal mobile devices makes it possible for the users to access various information anywhere and at any time. However, traditional methods do not provide efficient decision support when the situation is continuously changing and the intensive interaction of the user with the device is not possible (e.g., when driving or walking). Purpose: We develop theoretical and technological foundations of proactive recommender systems for infomobile applications capable of providing personalized context-aware intelligent decision support for the users, taking into account not only the current situation but also its possible development. Results: The state of the art in the area of proactive recommender systems and related areas (personalization, context-aware decision support, tourist information support) has been analyzed. On the base of this analysis and scenarios of infomobile application usage, the following requirements to such applications have been specified: distributed architecture, availability of technical and semantic interoperability support means, context awareness, personalization, usage of mobile devices to interact with the user, proactiveness, and the usage of the recommender system concept. The major principles of the methodology have been formulated: availability of a common ontology; context specification by means of formalism and terminology of the common ontology; usage of service-oriented architecture and Web-service standards, as well as usage of methods of context management, profiling and proactive generation of recommendations. A service-oriented architecture for the systems of the considered class has been proposed. Practical relevance: The proposed methodology and service-oriented architecture can be used for the creation of infomobile applications.

A Context-aware Decision Support Tool for Assessing and Mitigating Drivers of Civil Instability

Military planners and decision-makers face a number of challenges with the shift towards operating within diverse, multi-dimensional, and unconventional environments. Leaders require a deeper understanding of the broader social and civil context in which operations occur, including the underlying factors that contribute to instability and the drivers of conflict. This understanding is often derived through the analysis of textual data. Both traditional and non-traditional sources – such as news articles, blog entries, and tweets – represent a vast amount of data that can be brought to bear on problems ranging from measuring the progress of missions to forecasting important changes in the environment. The volume and velocity of this data requires processing tools that can help users understand the concepts and events being discussed. Additionally, these data are inherently ambiguous, and the automated processing techniques necessary for aggregating and analyzing data may introduce further uncertainty. The validity and veracity of data, sources, assumptions, and conclusions must be carefully considered prior to action. Planners and decision-makers require new, data-driven tools that aid in selecting the best course of action through interactive exploration and assessment processes. In this paper, we describe an ongoing research and development effort to create a context-driven, web-based tool that aids planning and decision-making by providing a more comprehensive understanding of the civil component of operational environments. This tool allows users to rapidly find, organize, and assess complex data across multiple phases of civil information management. This includes: (1) researching and assessing civil vulnerabilities; (2) developing plans to address identified vulnerabilities; and (3) tracking ongoing trends and progress towards goals and objectives. Our tool assists the user in each phase by collecting, processing, and recommending data and analyses that are contextually relevant to their task. By offloading data collection, supporting data organization, and providing personalized recommendations, our tool allows users to focus their efforts on verifying, interpreting, and assessing the information needed to recommend the best course of action.

Context-aware decision support systems based on typical knowledge integration models

A methodology for designing context-aware decision support systems based on typical knowledge integration models is proposed. These models describe the functional capabilities of the system at different stages of its usage. The models are used to specify requirements for information and knowledge from the side of the context-aware system. The comparison of these requirements with the user requirements for the system functional capabilities and user restrictions allows us to obtain the functional capabilities that are available for a given user.

Models of context-aware decision support systems in dynamic structured domains

Some existing approaches to representation and organization of contexts in different information systems are analyzed. A two-level context management model for intelligent decision support in dynamic structured domains is proposed. A model for description of information resources of an open information environment is given. A technology model of context-aware decision support system is designed.

Patterns for context-based knowledge fusion in decision support systems

The here presented research focuses on the context-based knowledge fusion patterns. Patterns are discovered based on an analysis and investigation of knowledge fusion processes in a context aware decision support system at the operational stage of the system functioning. At this stage the context-based knowledge fusion processes are manifested around the context. The patterns are generalized in regard to the following three aspects: (1) the effects that the knowledge fusion processes produce in the system; (2) the preservation of internal structures for the context and multiple sources the information/knowledge is fused from; and (3) the preservation of multiple sources and the context autonomies. At that, seven knowledge fusion patterns have been discovered: simple fusion, extension, instantiated fusion, configured fusion, adaptation, flat fusion, and historical fusion.

Design and development of a context-aware decision support system for real-time accident handling in logistics

This paper describes the design and development of a context-aware fleet management system (CFMS) prototype for real-time accident handling in logistics using a design science approach. One of the most important decisions in fleet management is the optimization of vehicle scheduling during an accident, such as a vehicle breakdown and mechanical failure during delivery. The schedule planner has to assign another vehicle to take over the task; thus, accident handling needs the reassignment or re-scheduling of vehicles. The large number of available vehicles for reassignment and numerous trips in a day make rescheduling complicated and difficult to resolve. In this paper, we propose a CFMS integrated with global positioning system (GPS) for real-time vehicle positioning and eSeal enabled by the RFID technology, to help human planners with rescheduling. A CFMS prototype was built and evaluated in a real-world setting. The system prototype was satisfactory during evaluation. The system was found to be more effective by its potential users and field logistics experts in aiding real-time accident handling in logistics. The design science approach used to develop the prototype could form a basis for further research.

Design and development of an intelligent context-aware decision support system for real-time monitoring of container terminal operations

This paper describes a case study of the research and development of an intelligent context-aware decision support system (ICADSS) prototype for real-time monitoring of container terminal operations in Hong Kong. We present the system design and development of the prototype system, and discuss the experiences and lessons learned. To the best of our knowledge, this study is the first identifiable application of an intelligent context-aware decision support system for the real-time monitoring of container terminal operations reported in the academic literature. The intelligent context-aware decision support system employs ZigBee-based ubiquitous sensor network (USN) technology. In this study, an ICADSS prototype was built and implemented in a real world setting. The results of the system prototype evaluation are satisfactory and support the contention that it is more effective in supporting the real-time tracking and tracing of container trucks, quay cranes, and rubber-tired gantry cranes in a container terminal. The results also validate the practical viability of the proposed system architecture. Given the contextual details of the study, we present the lessons learned from developing and operating the system in a container terminal and provide suggestions for further research. We hope that the proposed system architecture and developed prototype system can help both practitioners and academics in the further use and research of intelligent context-aware decision support systems.

Context-aware decision support in knowledge-intensive collaborative e-Work

Abstract In organisation-based work groups, experts often approach problem solving by combining explicit domain knowledge and information with their practice-based knowledge in ways that are largely driven by their specific work context. In collaborative ework, such common grounds for decision making offered by a shared work context hardly exist. Designing context-aware systems to support decision making in collaborative e-work, thus, poses a huge challenge because of the inherent difficulty in establishing a shared context of work and users adequate for supporting cohesive collaboration and knowledge sharing among experts across organisational and geographical boundaries. To address this problem, this paper proposes a framework, which incorporates an explicit model of context between the domain model of an application and the activity landscapes of various individuals, workgroups and organisations collaborating across borders, and between these landscapes and the knowledge resource space model in an intelligent ubiquitous environment. We argue that an explicit context model will enable a clearer understanding of the way experts integrate knowledge during problem solving, and thus provide common grounds for decision making and knowledge sharing during collaborative e-work. We demonstrate how a system based on our proposed model can be applied to support the reactive, collaborative and proactive modes of decision support in collaborative e-work.

Modelling situation awareness for Context-aware Decision Support

Situation awareness modelling is popularly used in the command and control domain for situation assessment and decision support. However, situation models in real-world applications are typically complex and not easy to use. This paper presents a Context-aware Decision Support (CaDS) system, which consists of a situation model for shared situation awareness modelling and a group of entity agents, one for each individual user, for focused and customized decision support. By incorporating a rule-based inference engine, the entity agents provide functions including event classification, action recommendation, and proactive decision making. The implementation and the performance of the proposed system are demonstrated through a case study on a simulated command and control application.