Context-aware Model Research Articles

Spatial Context-Aware Time-Series Forecasting for QoS Prediction

With the explosive growth of web services, the increasing sparse and dynamic QoS (quality of service) data pose a great challenge to QoS prediction in service recommendation. How to fully utilize the contextual information of service invocation and their potential relationships becomes the key to improving the accuracy of QoS prediction. In this paper, a spatial context-aware time series forecasting (SCATSF) framework is proposed for QoS prediction by considering both the temporal and the spatial context of users and services. SCATSF has three main parts: time-aware neighbors selection, spatial context-aware interaction learning and time series forecasting for QoS prediction. First, a novel time series similarity (TSS) is proposed to measure the similarity of users or services based on time-varying QoS fluctuation, in order to select appropriate neighbors for the target user or service. Furthermore, the contextual information of neighbors, as well as the contextual information of users and services, are both integrated to enrich the spatial context of service invocation, and a pairwise multi-layer deep network (PMLDN) is developed to consolidate multiple features and to learn feature interactions in pairs. With the help of spatial contextual information and time-aware QoS information, a spatial context-aware GRU model (SCA-GRU) is finally present to complete the time series forecasting for QoS prediction. Experimental results on the prediction of response time in a real-world dataset demonstrate that our SCATSF approach can effectively utilize spatio-temporal contextual information, so that it can achieve higher accuracy than many existing methods.

C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks.

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

A Multi-Level Attentive Context-Aware Trajectory Prediction Algorithm for Mobile Social Users

The prediction of a user’s trajectory is a key problem in mobility prediction, which has been applied to a range of fields such as location-based service recommendations and traffic planning. The impact of users’ social contacts on mobility is not adequately considered in the current trajectory prediction research. Furthermore, the spatial–temporal dependence of long trajectories is difficult to characterize by conventional recurrent neural network models. A multi-level attentive context-aware trajectory prediction model (MACTP) for mobile social users is proposed in this research to address the above problems. Specifically, users’ social preferences are captured by friend-level attention, and different friends are allocated varying weights. The impact of other check-in points in the trajectory on the present check-in point is considered through check-in-level attention. Trajectory-level attention is used to obtain the representation of historical trajectories influenced by current trajectories, as well as the spatial–temporal dependencies of longer trajectories. Experimental results on two real-world datasets demonstrate that the proposed model significantly improves trajectory prediction performance.

Histopathologic Brain Age Estimation via Deep Multiple Instance Learning (P3-6.008)

<h3>Objective:</h3> To identify features of histologic brain aging and clinical correlates of brain age acceleration. <h3>Background:</h3> The discordance between cellular and chronologic aging is useful for understanding diseases in the brain and biology at large. One established method for analyzing the factors that contribute to brain aging is to train machine learning models that predict an individual’s age based on an MRI image of their brain. While this approach has yielded important insights, it is inherently constrained by the information provided by an MRI. However, age-dependent pathologic change has the potential to be assessed at greater detail histologically. Histopathological whole slide images provide more granular information regarding cellular structure, injury, dysfunction, and morphology. Recent technological advances in whole slide image digitization has paved the way for large scale analysis of histologic data via artificially intelligent based computer vision techniques. <h3>Design/Methods:</h3> We leveraged a large novel collection of uniformly processed digitized human post-mortem brain tissue sections to create a histological brain age estimation model. We further investigated the effect of cognitive impairment and exogenous stress on the model. This was accomplished by developing a context-aware attention-based deep multiple instance learning model on 702 human brain tissues sections (ages 50–110 yr) from the hippocampus stained with Luxol Fast Blue counterstained with hematoxylin and eosin on a brain age estimation task. <h3>Results:</h3> This model estimated brain age within a mean absolute error of 5.2 years. Learned attention weights corresponded to neuroanatomical regions vulnerable to age-related change. We found that histopathologic brain age acceleration had significant associations with cognitive impairment, MMSE, p-tau burden, chronic traumatic encephalopathy, and cerebrovascular disease. These associations were not found when using epigenetic-based measures of age acceleration. <h3>Conclusions:</h3> These data indicate that estimated histopathologic brain age can be used as a reliable pathologic correlate to identify factors that contribute to accelerated or decelerated brain aging. <b>Disclosure:</b> Mr. Marx has nothing to disclose. Dr. McKenzie has nothing to disclose. Mr. Kauffman has nothing to disclose. Mr. Koenigsberg has nothing to disclose. The institution of Dr. Farrell has received research support from NIH. Dr. Crary has nothing to disclose.

Context-Aware and Time-Aware Attention-Based Model for Disease Risk Prediction With Interpretability

Thanks to the huge accumulation of Electronic Health Records (EHRs), numerous deep learning based predictive models were proposed for this task. Among them, most of the existing state-of-the-art (SOTA) models were built with recurrent neural networks (RNNs). Regardless of their success, RNN-based models mainly suffer from three limitations. (i) Accuracy: the prediction accuracy of RNN-based models drops quickly as the length of EHR sequences increases. (ii) Efficiency: the recurrence property of RNN-based models makes the computation parallelization impossible, and accordingly hurts the efficiency of such models in practice. (iii) Interpretability: the outputs of RNN-based models are difficult to explain due to the unexplainable nature of deep models. In this paper, we resort to the recently advanced attention mechanism to model the dependencies between inputs and outputs, which overcomes shortages of RNN-based models in accuracy and efficiency. As for interpretability, we model the relationships with two linear mappings from the input to the output, which account for two important factors—one is for context-aware information and the other is for time-aware representation—of capturing discriminative features in learning patient’s representations. We empirically demonstrate the effectiveness of the proposed model in both accuracy and computational efficiency, meanwhile, analyze and discuss the reasonability of each explanation approach.

An Adaptive, Context-Aware, and Stacked Attention Network-Based Recommendation System to Capture Users’ Temporal Preference

Recommendation systems have become more important since the widespread use of the Internet. The large amount of information means that it is difficult for users to discover they really need. However, users preferences can change over time because of the age and the impact of social networks. This study uses context factors as additional information to model users preferences more precisely. For the proposed recommendation system, the context is based on users interaction items within each defined time interval. This study proposes two novel attention mechanisms for the recommendation system. The contextual item attention module captures contextual information, changing pattern in users preference and the importance of items. The multi-head attention module extends the diversity of users preferences and adapts to changing preferences. The recommendation performance is improved using additional items temporal information to model the contextual items representation. Experiments compare the proposed algorithm with several state-of-the-art recommendation methods using three real-world datasets. The experimental results demonstrate that the proposed context-aware recommendation model outperforms traditional methods and demonstrate the effectiveness with which contextual information is captured by the attention mechanism.

Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.

In clinical settings, most automatic recognition systems use visual or sensory data to recognize activities. These systems cannot recognize activities that rely on verbal assessment, lack visual cues, or do not use medical devices. We examined speech-based activity and activity-stage recognition in a clinical domain, making the following contributions. (1) We collected a high-quality dataset representing common activities and activity stages during actual trauma resuscitation events-the initial evaluation and treatment of critically injured patients. (2) We introduced a novel multimodal network based on audio signal and a set of keywords that does not require a high-performing automatic speech recognition (ASR) engine. (3) We designed novel contextual modules to capture dynamic dependencies in team conversations about activities and stages during a complex workflow. (4) We introduced a data augmentation method, which simulates team communication by combining selected utterances and their audio clips, and showed that this method contributed to performance improvement in our data-limited scenario. In offline experiments, our proposed context-aware multimodal model achieved F1-scores of 73.2±0.8% and 78.1±1.1% for activity and activity-stage recognition, respectively. In online experiments, the performance declined about 10% for both recognition types when using utterance-level segmentation of the ASR output. The performance declined about 15% when we omitted the utterance-level segmentation. Our experiments showed the feasibility of speech-based activity and activity-stage recognition during dynamic clinical events.

Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems

Multifidelity methods leverage low-cost surrogate models to speed up computations and make occasional recourse to expensive high-fidelity models to establish accuracy guarantees. Because surrogate and high-fidelity models are used together, poor predictions by surrogate models can be compensated with frequent recourse to high-fidelity models. Thus, there is a trade-off between investing computational resources to improve the accuracy of surrogate models versus simply making more frequent recourse to expensive high-fidelity models; however, this trade-off is ignored by traditional modeling methods that construct surrogate models that are meant to replace high-fidelity models rather than being used together with high-fidelity models. This work considers multifidelity importance sampling and theoretically and computationally trades off increasing the fidelity of surrogate models for constructing more accurate biasing densities and the numbers of samples that are required from the high-fidelity models to compensate poor biasing densities. Numerical examples demonstrate that such context-aware surrogate models for multifidelity importance sampling have lower fidelity than what typically is set as tolerance in traditional model reduction, leading to runtime speedups of up to one order of magnitude in the presented examples.

JEMMA: An extensible Java dataset for ML4Code applications

Machine Learning for Source Code (ML4Code) is an active research field in which extensive experimentation is needed to discover how to best use source code’s richly structured information. With this in mind, we introduce JEMMA: An Extensible Java Dataset for ML4Code Applications, which is a large-scale, diverse, and high-quality dataset targeted at ML4Code. Our goal with JEMMA is to lower the barrier to entry in ML4Code by providing the building blocks to experiment with source code models and tasks. JEMMA comes with a considerable amount of pre-processed information such as metadata, representations (e.g., code tokens, ASTs, graphs), and several properties (e.g., metrics, static analysis results) for 50,000 Java projects from the 50K-C dataset, with over 1.2 million classes and over 8 million methods. JEMMA is also extensible allowing users to add new properties and representations to the dataset, and evaluate tasks on them. Thus, JEMMA becomes a workbench that researchers can use to experiment with novel representations and tasks operating on source code. To demonstrate the utility of the dataset, we also report results from two empirical studies on our data, ultimately showing that significant work lies ahead in the design of context-aware source code models that can reason over a broader network of source code entities in a software project—the very task that JEMMA is designed to help with.

Integrating Representation and Interaction for Context-Aware Document Ranking

Recent studies show that historical behaviors (such as queries and their clicks) contained in a search session can benefit the ranking performance of subsequent queries in the session. Existing neural context-aware ranking models usually rank documents based on either latent representations of user search behaviors or the word-level interactions between the candidate document and each historical behavior in the search session. However, these two kinds of models both have their own drawbacks. Representation-based models neglect fine-grained information on word-level interactions, whereas interaction-based models suffer from the length restriction of session sequence because of the large cost of word-level interactions. To complement the limitations of these two kinds of models, we propose a unified context-aware document ranking model that takes full advantage of both representation and interaction. Specifically, instead of matching a candidate document with every single historical query in a session, we encode the session history into a latent representation and use this representation to enhance the current query and the candidate document. We then just match the enhanced query and candidate document with several matching components to capture the fine-grained information of word-level interactions. Rich experiments on two public query logs prove the effectiveness and efficiency of our model for leveraging representation and interaction.

Context-Aware Machine Learning for Intelligent Transportation Systems: A Survey

Context awareness adds intelligence to and enriches data for applications, services and systems while enabling underlying algorithms to sense dynamic changes in incoming data streams. Context-aware machine learning is often adopted in intelligent services by endowing meaning to Internet of Things(IoT)/ubiquitous data. Intelligent transportation systems (ITS) are at the forefront of applying context awareness with marked success. In contrast to non-context-aware machine learning models, context-aware machine learning models often perform better in traffic prediction/classification and are capable of supporting complex and more intelligent ITS decision-making. This paper presents a comprehensive review of recent studies in context-aware machine learning for intelligent transportation, especially focusing on road transportation systems. State-of-the-art techniques are discussed from several perspectives, including contextual data (e.g., location, time, weather, road condition and events), applications (i.e., traffic prediction and decision making), modes (i.e., specialised and general), learning methods (e.g., supervised, unsupervised, semi-supervised and transfer learning). Two main frameworks of context-aware machine learning models are summarised. In addition, open challenges and future research directions of developing context-aware machine learning models for ITS are discussed, and a novel context-aware machine learning layered engine (CAMILLE) architecture is proposed as a potential solution to address identified gaps in the studied body of knowledge.

Document-Level Neural Machine Translation With Recurrent Context States

Integrating contextual information into sentence-level neural machine translation (NMT) systems has been proven to be effective in generating fluent and coherent translations. However, taking too much context into account slows down these systems, especially when context-aware models are applied to the decoder side. To improve efficiency, we propose a simple and fast method to encode all sentences in an arbitrary large context window. It makes contextual representations in the process of translating each sentence so that the overhead introduced by the context model is almost negligible. We experiment with our method on three widely used English-German document-level translation datasets, which obtain substantial improvements over the sentence-level baseline with almost no loss in efficiency. Moreover, our method also achieves comparable performance with previous strong context-aware baselines and speeds up the inference by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.53\times $ </tex-math></inline-formula> . The speed-up is even larger when more contexts are taken into account. On the ContraPro pronoun translation dataset, it significantly outperforms the strong baseline.

Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification

A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task. While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally. State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

Context-aware modeling for knowledge-intensive medicinal product development processes

Due to their unique characteristics, knowledge-intensive processes (KiPs) are difficult to capture with conventional modeling and management approaches. One such KiP is the advanced therapy medicinal product (ATMP) development process. ATMPs are highly innovative medicinal products that are based on biomedical technology. ATMP development processes need to comply with complex regulatory frameworks. Currently, biomedical scientists that develop ATMPs manage the regulatory aspects of the ATMP development processes in an ad hoc fashion, resulting in inefficiencies such as reworks or even withdrawal of ATMPs from the market. This paper presents an explorative case study in which we use Enterprise Modeling and Context-aware Business Processes to support ATMP scientists in managing the regulatory aspects of ATMP development processes more efficiently and effectively. In our explorative case study, we use enterprise models to describe the important concepts and views in ATMP development processes. By introducing context-awareness to the models, we support ATMP scientists in performing relevant tasks to address the regulatory requirements efficiently and effectively under different contexts. We introduce the novel concept of execution-dependent dynamic context to properly define the context in ATMP development processes. Additionally, this paper takes a broader perspective on the case study by discussing the relevance of the solutions derived for the case study for other KiPs. Thereby this paper aims to present an exemplary approach for context-aware modeling of KiPs. The practical contribution of this paper are the models realized in a real-life ATMP development project. The scientific contribution of this paper is providing an exemplary approach for supporting knowledge workers who perform flexible, KiPs under dynamic contexts and introducing the notion of execution-dependent dynamic context.

Contextualized Recommendation of Aviation Ancillary Services Based on Passenger Portraits

Ancillary services are crucial to Chinese domestic airlines’ profits. Personalized recommendations for ancillary aviation services must consider massive amounts of contextualized online and offline data. The issue of sparsity is more serious in massive data, which makes traditional collaborative filtering (CF) algorithms less accurate. This article adds instant contextualized travel-related factors to the modeling of air passengers’ ancillary services, thus achieving dynamic recommendations. It also proposes a four-tuple to construct a contextual ontology as a conceptual model of ancillary aviation services. The article establishes a context-aware air passenger portrait model based on four tag sets, including passenger attribute tags, ancillary service attribute tags, passenger interaction behavior tags, and contextual attribute tags. Further, it proposes a multidimensional method for calculating contextual portrait similarity; the method recommends a list of the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> items to the target passenger according to the similarity calculation. Compared with the traditional CF recommendation method, the proposed algorithm has better performance, improving the accuracy rate by 4.3% and the recall rate by 2.62%. The final experimental result demonstrates that the contextual recommendation method achieves higher passenger satisfaction, which can provide consolidated data and decision support for the management of aviation ancillary services.

Enhancing Context-Aware Reactive Planning for Unexpected Situations of On-Orbit Spacecraft

Spacecraft software is a complicated software system integrating many subsystems of different disciplines. Supporting on-orbit missions needs the knowledge of industrial information integration. To ensure mission success, reactive planning is a critical function in solving dynamic problems during task execution, relying on the knowledge of situation for optimal decision-making. However, it may unable to correctly identify the operational context in unexpected situations. This article solves the unexpected situation problem for reactive planning. It proposes a context-awareness model enhancement framework to identify the triggers of critical situations based on event evolution-based analysis. The framework includes the process of unexpected situation discovery, context identification, and context refinement. With this approach, preventive operational resilience can be achieved. Moreover, a symbiotic computing paradigm and a flexible inference engine are devised for addressing the on-orbit spacecraft-specific challenges. Also, an agent-based reactive system design and a system integration architecture is provided for implementing the proposed approach from both autonomy and information integration perspective. Experiment results show that the proposed approach is effective and efficient to solve the unexpected situation issue. This article offers a crucial insight to context-aware decision-support in space applications.

A novel intelligent traffic recovery model for emergency vehicles based on context-aware reinforcement learning

Management of traffic emergencies has become very popular in recent years. However, timely response to emergencies and recovering from an emergency is an important problem in itself. The strategies in the current studies merely suggest that after an emergency vehicle passes, the state should iterate to the next phase. Therefore, this paper proposes a novel approach for recovering from an emergency situation at an intersection based on real scenarios. The proposed method is a combination of context-aware and Reinforcement Learning (RL) models that predicts better alternatives for different states rather than just iterating to the next phase. In this regard, a new algorithm, named Interrupt Algorithm, is proposed to predict proper actions for recovering the emergency situation. This algorithm uses a Q-learning-based model that learns from traffic context for an emergency situation and chooses viable action from an action set. The recovery actions are categorized as max, min, and avg, respectively. Test results show that our proposed model outperforms traffic flow over than standard single choice recovering action-based approach by approximately 80%. Based on this, it may be more beneficial to choose different actions and therefore, proposed algorithm with the help of RL presents a more dynamic emergency recovery model.

CAQ: Toward Context-Aware and Self-Adaptive Deep Model Computation for AIoT Applications

Artificial Intelligence of Things (AIoT) has recently accepted significant interests. Remarkably, embedded artificial intelligence (e.g., deep learning) on-device transforms IoT devices into intelligent systems that robustly and privately process data. Quantization technique is widely used to compress deep models for narrowing the resource gap between computation demands and platform supply. However, existing quantization schemes induce unsatisfaction for IoT scenarios since they are oblivious to dynamic changes of application context (e.g., battery and hierarchical memory availability) during the long-term operation. Subsequently, they will mismatch the user-desired resource efficiency and application lifetime. Also, to adapt to the dynamic context, we can neither accept the latency for model retraining with existing hand-crafted quantization nor the overhead for quantization bit width researching with prior on-demand quantization. This article presents a context-aware and self-adaptive deep model quantization (CAQ) system for IoT application scenarios. CAQ integrates a novel switchable multigate quantization framework, optimizing the quantized model accuracy and energy efficiency in diverse contexts. Based on the learned model, CAQ can switch among different gating networks in a context-aware manner and then adopt it to automatically capture the representation importance of various layers for optimal quantization bit-width selection. The experimental results show that CAQ achieves up to 50% storage savings with even 2.61% higher accuracy than the state-of-the-art baselines.

Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic Mapping

Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review.

Context-aware OLAP for textual data warehouses

Decision Support Systems (DSS) that leverage business intelligence are based on numerical data and On-line Analytical Processing (OLAP) is often used to implement it. However, business decisions are increasingly dependent on textual data as well. Existing research work on textual data warehouses has the limitation of capturing contextual relationships when comparing only strongly related documents. This paper proposes an Information System (IS) based context-aware model that uses word embedding in conjunction with agglomerative hierarchical clustering algorithms to dynamically categorize documents in order to form the concept hierarchy. The results of the experimental evaluation provide evidence of the effectiveness of integrating textual data into a data warehouse and improving decision making through various OLAP operations.