User Interaction Model Research Articles

Towards digitalisation of socially sustainable neighbourhood design

ABSTRACT Digital tools for performance-assessment are commonly used to shorten the feedback loop in testing designs for buildings and neighbourhoods. However, these tools do not extend to the social dimension in the same way as the economic and environmental dimensions. This paper aims to contribute to the development of digital tools to design socially sustainable neighbourhoods. We analyse 115 academic articles to establish a theoretical understanding of Social Sustainability (SoSu). Based on these results, we propose a digital user-interaction model to operationalise SoSu in the digital design process of buildings. In the literature, we observe a lack of consensus on the theoretical discussion on SoSu. Several extrinsic and intrinsic factors are identified contributing to this fuzziness; the dependency on stakeholder value systems, the qualitative nature of social indicators, and comparison to environmental sustainability being the most common. Still, we distinguish two overarching categories, social equity and social capital, that are further divided into sub-themes. Having mapped the categories and hierarchies of social themes, we propose a user-interaction model that incorporates these findings into a digital environment. The user-interaction model creates a guided decision-making framework for architects and urban planners by enabling stakeholders to make conscious and informed decisions grounded in theory.

Multimodal Risk-Based Path Planning for Neurosurgical Interventions

AbstractImage-guided neurosurgical interventions are challenging due to the complex anatomy of the brain and the inherent risk of damaging vital structures. This paper presents a neurosurgical planning tool for safe and effective neurosurgical interventions, minimizing the risk through optimized access planning. The strengths of the proposed system are the integration of multiple risk structures combined into a holistic model for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons to quickly determine the most appropriate surgical entry point and trajectory through the brain with minimized risk. The user interface guides a user through the decision-making process and may save planning time of neurosurgical interventions. The navigation tool has been interfaced to the Robot Operating System, which allows the integration into automated workflows and the planning of linear and nonlinear trajectories. Determined risk structures and trajectories can be visualized intuitively as a projection map on the skin or cortical surface. Two risk calculation modes (strict and joint) are offered to the neurosurgeons, depending on the intracranial procedure's type and complexity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system.

Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks

The way users interact on social media can indicate their well-being. When depressed, people’s feelings tend to be more evident, affecting how users interact and demonstrating their feelings on social media. This paper presents a new approach for the temporal assessment of emotional behavior and interaction among depressed users on social networks. We start by modeling user interactions using complex networks, grouping users through time using the Clauset-Newman-Moore greedy modularity maximization. We evaluate the built networks using metrics such as assortativity, density, clustering, diameter, and shortest path length, closeness, and coverage. Then, we propose EMUS, a method for establishing an emotional user score based on the extraction of emotional features in texts of posts and comments. To extract emotional features, we combine the use of the Empath framework and VADER lexicon. Finally, based on the standard deviation among users, we establish a metric for assessing mood levels. We evaluated users for 33 days, and the results show a sequence of mixed emotional behaviors with high correlations between the number of active users in the network communities, and the form and quality of interactions. The developed approach can be further applied to other database graphs, for different sequential pattern analysis and text-mining contexts.

Game theoretic approach for bandwidth sharing in future generation networks

With the advent of 4G LTE networks, the demand for bandwidth has rapidly increased among users of mobile devices, especially handheld smartphones. Consequently, the 4G data plans offered by mobile network operators have become expensive in many countries around the world. Moreover, due to the discrete tiers of the data plans, users often experience shortage or surplus. In order to facilitate sharing of bandwidth among users, we propose a game theoretic framework where users share bandwidth with each other. The sharing mechanism is based on the distribution of surplus bandwidth of some users to overcome the shortage of others. All users sometimes serve as providers (i.e., offering bandwidth to others) and sometimes as receivers (i.e., receiving bandwidth from the providers), thus constituting a user-provided network (UPN). The sharing game is designed in such a way that those receiving bandwidth are obligated to offer the same to others at a later point of time. Unused bandwidth of a user is shared to other users who require bandwidth at that instance, who must return the bandwidth that they use to the provider of the bandwidth at a later time based on requirement, by a coordination-based model of user interaction in the UPN that consists of these entities. Further, we have developed a coordination-based game model to better simulate user interaction in this context. In order to validate the proposed framework, we conducted simulations experiments as well as conducted experiments with handheld devices where bandwidth from a 4G LTE commercial network provider was shared among the devices. Results reveal that a Nash Equilibrium is reached for the overall set of interactions between users sharing and receiving bandwidth, provided that a Nash Equilibrium is reached in the interactions between each pair of users.

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy.

Background and purposeAuto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring.Materials and methodsA total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019–April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour.ResultsThe median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour.ConclusionAuto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines.

Modeling user interaction with app-based reward system: A graphical model approach integrated with max-margin learning

In recent years, there has been a rapid growth of smart apps that could interact with users and implement personalized rewards to coordinate and change user behavior. Understanding user behavior is an enabling factor for the success of these promising apps. However, existing statistical models for modeling user behavior encounter limitations. Choice models based on Random Utility Maximization (RUM) commonly assume that the data collection is independent with the human behavior. However, when users interact with the apps, the real potential and also the real challenge for modeling user behavior is that the apps not merely are data collection tools, but also change users’ behaviors. In this work, we model the user behavior as a graphical model, examine our hypothesis that existing choice models are not suitable, and develop an interesting computational strategy using max-margin formulation to overcome the learning challenge of the our proposed graphical model that is named the Latent Decision Threshold (LDT) model.

사회적 거리두기 환경 속에서의 온라인 홈 트레이닝 서비스 유형 및 사용자 인터랙션 모형

전 세계적으로 전염병 확산을 막기 위해 사회적 거리두기 캠페인이 실행됨으로써 실외활동에 제약이 생기고 집에서 생활하는 시간이 길어지고 있다. 그 결과 신체 활동이 저하되면서 피로감과 무력감, 면역저하를 호소하는 사람들이 많아지고 있다. 실내 생활이 장기화 되자 집에서 할 수 있는 다양한 방식의 홈 트레이닝이 활성화되고 있다. 본 연구는 온라인 홈 트레이닝 서비스의 사례를 조사한 후 서비스디자인 관점으로 분석하여 세 가지 유형으로 분류하였다. 연구 결과 특정 플랫폼에 제공자가 사용자에게 일방적으로 서비스를 제공하는 ‘기본형’, 제공자와 사용자가 서비스를 매개로 양방향 소통을 하는 ‘연결형’, 제공자와 사용자가 직접적으로 상호작용을 하지 않고 사용자와 서비스가 상호작용을 하는 ‘반연결형’ 세 가지 유형으로 분류할 수 있었다. 연결형은 ‘경험 공유’를, 반연결형은 ‘기술 주도’를 특징으로 한다. 시간이 흐름에 따라 경험 공유의 연결형과 기술 주도의 반 연결형이 자연스럽게 결합하여 온라인 홈 트레이닝 서비스의 새로운 패러다임을 만들 것으로 기대된다.

Behavioral Link Analytics on Heterogeneous Human Interaction Networks

For designing and modeling Artificial Intelligence (AI) systems in the area of human-machine interaction, suitable approaches for user modeling are important in order to both capture user characteristics. Using multimodal data, this can be performed from various perspectives. Specifically, for modeling user interactions in human interaction networks, appropriate approaches for capturing those interactions, as well as to analyze them in order to extract meaningful patterns are important. Specifically, for modeling user behavior for the respective AI systems, we can make use of diverse heterogeneous data sources. This paper investigates face-to-face as well as socio-spatial interaction networks for modeling user interactions from three perspectives: We analyze preferences and perceptions of human social interactions in relation to the interactions observed using wearable sensors, i. e., face-to-face as well as socio-spatial interactions fo the respective actors. For that, we investigate the correspondence of according networks, in order to identify conformance, exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of proximity interactions coupled with self-report questionnaires about preferences and perception of those interactions. The different networks, and according perspectives then provide different options for user modeling and integration into AI systems modeling such user behavior.

Web 3D: a CityGML viewer for cross-domain problem resolution

3D urban models are a key component of diverse applications based on geospatial data, such as urban management and urban planning. CityGML models (OGC standards) are interoperable and allow the integration and dissemination of data within the spatial data infrastructures (SDI) objectives. 3D web viewers must allow model visualization and user interaction with geospatial data. This paper presents a system architecture for web visualization of a CityGML urban model. The system allows geospatial data structuring and storage as well as model visualization and access through a Web3D viewer. During the development of the system, the cross-domain problem was solved using GeoJSON. This change in format improved user access to the 3D urban model and its data. This standard also enabled the integrated storage of geospatial information and allows access to information from other geospatial data servers without cross-domain problems.

Testing the Usability and Accessibility of Smart TV Applications Using an Automated Model-Based Approach

As the popularity of Smart Televisions (TVs) and interactive Smart TV applications (apps) has recently grown, the usability of these apps has become an important quality characteristic. Previous studies examined Smart TV apps from a usability perspective. However, these methods are mainly manual, and the potential of automated model-based testing methods for usability testing purposes has not yet been fully explored. In this article, we propose an approach to test the usability of Smart TV apps based on the automated generation of a Smart TV user interaction model from an existing app by a specialized automated crawler. By means of this model, defined user tasks in the Smart TV app can be evaluated automatically in terms of their feasibility and estimated user effort, which reflects the usability of the analyzed app. This analysis can be applied in the context of regular users and users with various specific needs. The findings from this model-based automated analysis approach can be used to optimize the user interface of a Smart TV app to increase its usability, accessibility, and quality.

Google Users as Sequences: A Robust Hierarchical Cluster Analysis Study

In this era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. By understanding user behavior in constructing applications and interacting with typical cloud infrastructures, cloud managers can develop better systems that improve the users’ experience. In this paper, we analyze a large dataset of a Google cluster to characterize the users into distinct groups of similar usage behavior. We used a wide range of measured metrics to model user behavior in composing applications from the perspective of actions around application architecting, capacity planning, and workload type planning and to model user interaction behavior around the session view. The trajectories of users’ actions are represented as sequences using categorical and proportional encoding schemes. We used techniques from the sequence analysis paradigm to quantify dissimilarity among users. We employed a robust cluster analysis procedure based on the agglomerative hierarchical methods to optimally classify users into 12 classes. We used a variety of formal indices and visual aids to confirm the quality and stability of the outcomes. By visual inspection, we regrouped the obtained clusters into 5 main groups that reveal interesting insights about the characteristics which underline different groups’ utilization behavior.

Analysis of notations for modeling user interaction scenarios in ubiquitous collaborative systems

Ubiquitous collaborative systems are difficult to design; particularly those where the participants are human beings and there is not a pre-established workflow to coordinate the activities conducted by them. A key challenge for designers of these systems is to envision and represent scenarios where the interaction among users can take place, and thus provide appropriate services to the supporting application. A few modeling languages and notations have been proposed for specifying interaction scenarios among users, but none of them has been broadly adopted by systems’ designers, probably because there is not clear evidence that helps engineers decide what notation to use. This paper reviews the three main visual notations proposed to model computer-mediated interaction scenarios and presents an experimental study that analyses not only the usability and usefulness of these notations, but also the tensions among these aspects. The study results help designers identify suitable user interaction representations to support the requirement elicitation and analysis during the development of ubiquitous collaborative systems. The results can also be used to improve the usability and usefulness of other visual notations, and the relationship between these two aspects. In this sense, designers of modeling languages can take advantage of the study findings to generate new proposals or improve the existing ones.

Boosting learning to rank with user dynamics and continuation methods

Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting different objective functions that can effectively drive the algorithm towards promising areas of the search space. We enrich the iterative process followed by the learning algorithm in two ways: (1) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (2) by designing a learning path across different loss functions that can capture different signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically significant margins.

Modeling hierarchical usage context for software exceptions based on interaction data

Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production—software fault reports consisting of software exceptions and stack traces. The model described in this paper aims to improve developers’ comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. Therefore, we propose a probabilistic unsupervised learning approach, adapting the nested hierarchical Dirichlet process, which is a Bayesian non-parametric hierarchical topic model originally applied to natural language data. This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions. We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.

KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation.

Measuring drug-drug similarity is important but challenging. Significant progresses have been made in drugs whose labeled training data is sufficient and available. However, handling data skewness and incompleteness with domain-specific knowledge graph, is still a relatively new territory and an under-explored prospect. In this paper, we present a system KGDDS for node-link-based bio-medical Knowledge Graph curation and visualization, aiding Drug-Drug Similarity measure. Specifically, we reuse existing knowledge bases to alleviate the difficulties in building a high-quality knowledge graph, ranging in size up to 7 million edges. Then we design a prediction model to explore the pharmacology features and knowledge graph features. Finally, we propose a user interaction model to allow the user to better understand the drug properties from a drug similarity perspective and gain insights that are not easily observable in individual drugs. Visual result demonstration and experimental results indicate that KGDDS can bridge the user/caregiver gap by facilitating antibiotics prescription knowledge, and has remarkable applicability, outperforming existing state-of-the-art drug similarity measures.

Interactive map matching and its visualisation: techniques and system

The trajectory data of taxies is an important kind of traffic data. Many traffic applications need to perform processing and analysis on trajectory data. Visualising trajectory data of vehicles on road maps is an important measure of reflecting and demonstrating the trend of traffic variation, where map matching from trajectory data to road network plays the most crucial role in such a visualisation process. We design and implement a novel interactive visualised map matching system in this paper, namely MMatchingVis, which provides multiple front-end functions including road selection, data extraction, map matching algorithm selection and result display, based on web techniques and Baidu Map. MMatchingVis employs the JStorm platform for trajectory data processing. We evaluate MMatchingVis' map matching results with the trajectory dataset collected from 6,599 taxies in Kunming, and evaluation results show that MMatchingVis could efficiently process and analyse trajectory data, support multiple user interaction models, and provide fine-grained visualisation presentation.

Interactive map matching and its visualisation: techniques and system

The trajectory data of taxies is an important kind of traffic data. Many traffic applications need to perform processing and analysis on trajectory data. Visualising trajectory data of vehicles on road maps is an important measure of reflecting and demonstrating the trend of traffic variation, where map matching from trajectory data to road network plays the most crucial role in such a visualisation process. We design and implement a novel interactive visualised map matching system in this paper, namely MMatchingVis, which provides multiple front-end functions including road selection, data extraction, map matching algorithm selection and result display, based on web techniques and Baidu Map. MMatchingVis employs the JStorm platform for trajectory data processing. We evaluate MMatchingVis' map matching results with the trajectory dataset collected from 6,599 taxies in Kunming, and evaluation results show that MMatchingVis could efficiently process and analyse trajectory data, support multiple user interaction models, and provide fine-grained visualisation presentation.

HARSAM: A Hybrid Model for Recommendation Supported by Self-Attention Mechanism

Collaborative filtering is one of the most commonly used methods in recommendation systems. However, the sparsity of the rating matrix, cold start-up, and most recommendation algorithms only consider the users while neglecting the relationship between the products, all of what limit the effectiveness of the recommendation algorithms. In this paper, based on the self-attention mechanism, a deep learning model, named HARSAM, is proposed for modeling user interaction data and learning the user’s latent preference expression. HARSAM partitions the user’s latent feedback data in different time granularity and employs the self-attention mechanism to extract the correlation among the data in each partition. Moreover, the model learns the user’s latent preferences through the deep neural network. Simultaneously, the model learns the item latent representation by making use of the stacked denoising autoencoder to model the item’s rating data. As the result, the model recommends items to users according to the similarities between user’s preference and items. Experiments conducted on the public data demonstrate the effectiveness of the proposed model.

A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures.

PurposeCatheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X‐ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction.MethodsFor the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire‐like structures in the X‐ray images. After applying adaptive binarization method, the centerlines of wire‐like objects were extracted. Finally, the catheters and guidewires were detected as a smooth path which is reconstructed from centerlines of target wire‐like objects. In order to classify electrode catheters which are mainly used in electrophysiology procedures, additional steps were proposed. First, a blob detection method, which is embedded in vessel enhancement filter with no additional computational cost, localizes electrode positions on catheters. Then the type of electrode catheters can be recognized by detecting the number of electrodes and also the shape created by a series of electrodes. Furthermore, for detecting guiding catheters or guidewires, a localized machine learning algorithm is added into the framework to distinguish between target wire objects and other wire‐like artifacts. The proposed framework were tested on total 10,624 images which are from 102 image sequences acquired from 63 clinical cases.ResultsDetection errors for the coronary sinus (CS) catheter, lasso catheter ring and lasso catheter body are 0.56 ± 0.28 mm, 0.64 ± 0.36 mm, and 0.66 ± 0.32 mm, respectively, as well as success rates of 91.4%, 86.3%, and 84.8% were achieved. Detection errors for guidewires and guiding catheters are 0.62 ± 0.48 mm and success rates are 83.5%.ConclusionThe proposed computational framework do not require any user interaction or prior models and it can detect multiple catheters or guidewires simultaneously and in real‐time. The accuracy of the proposed framework is sub‐mm and the methods are robust toward low‐dose X‐ray fluoroscopic images, which are mainly used during procedures to maintain low radiation dose.

A Robust Utility Learning Framework via Inverse Optimization

In many smart infrastructure applications, flexibility in achieving sustainability goals can be gained by engaging end users. However, these users often have heterogeneous preferences that are unknown to the decision maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between noncooperative players, we propose a robust parametric utility learning framework that employs constrained feasible generalized least squares estimation with heteroskedastic inference. To improve forecasting performance, we extend the robust utility learning scheme by employing bootstrapping with bagging, bumping, and gradient boosting ensemble methods. Moreover, we estimate the noise covariance, which provides approximated correlations between players, which we leverage to develop a novel correlated utility learning framework. We apply the proposed methods both to a toy example arising from Bertrand-Nash competition between two firms and to data from a social game experiment designed to encourage energy efficient behavior among smart building occupants. Using occupant voting data for shared resources such as lighting, we simulate the game defined by the estimated utility functions to demonstrate the performance of the proposed methods.