Different Types Of Contextual Information Research Articles

Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

The graph neural network (GNN) based approaches have attracted more and more attention in session-based recommendation tasks. However, most of the existing methods do not fully take advantage of context information in session when capturing user’s interest, and the research on context adaptation is even less. Furthermore, hypergraph has potential to express complex relations among items, but it has remained unexplored. Therefore, this paper proposes an adaptive context-embedded hypergraph convolutional network (AC-HCN) for session-based recommendation. At first, the data of sessions is constructed as session hypergraph. Then, the representation of each item in session hypergraph is learned using an adaptive context-embedded hypergraph convolution. In the convolution, different types of context information from both current item itself and the item’s neighborhoods are adaptively integrated into the representation updating of current item. Meanwhile, an adaptive transformation function is employed to effectively eliminate the effects of irrelevant items. Then, the learned item representations are combined with time interval embeddings and reversed position embeddings to fully reflect time interval information and sequential information between items in session. Finally, based on learned item representations in session, a soft attention mechanism is used to obtain user’s interest, and then a recommendation list is given. Extensive experiments on the real-world datasets show that the proposed model has significantly improvement compared with the state-of-arts methods.

Rethinking Activity Awareness: The Design, Evaluation and Implication of Integrating Activity Awareness into Mobile Messaging

Nowadays, different types of context information are integrated into mobile messaging to increase expressiveness and awareness, including mobile device setting, location, activity, and heart rate. Due to low recognition accuracy, sometimes users cannot accurately infer others’ status through activity awareness. Recently, activity recognition technology has advanced. However, the user behaviors of activity awareness with improved technology have not been studied. In this study, we design ActAware, a mobile instant messaging application that integrates activity awareness based on improved activity recognition technology, i.e., improved recognition accuracy and the addition of activity transition notification. We conduct a field study to explore user behaviors and found that activity awareness allows users to speculate on the reasons for chat interruption, plan communication, speculate on whether the chat partner is departing/arriving, and deepen the understanding of living patterns. Compared with disclosing other types of context information, users have fewer privacy concerns about disclosing activity information in ActAware. Based on these findings, we provide design recommendations for mobile messaging to better support activity awareness.

PF‐ITS: Intelligent traffic service recommendation based on DeepAFM model

Due to the progressive complexity of traffic networks, the traffic pressure increases sharply and traffic accidents occur frequently, which is largely posed by imprecise traffic information service provided in existing Intelligent traffic service systems (ITSSs). To relieve traffic information trek encountered in complex traffic networks, the personalized traffic information recommendation based on click through rate (CTR) prediction has attracted extensive attention. However, the data sparsity and cold start problems in traditional recommendations hinder their real-time applications in ITSSs. In this paper, the multisource data with different types of context information are utilized to construct a personalized fine-grained recommendation method for intelligent traffic services (PF-ITS), which includes three components: a road condition optimization strategy (RCOS) to capture users' behavior preferences and traffic patterns, an encoder–decoder long short-term memory (LSTM) model to address the data sparsity and cold start problems with rich context information, and an improved DeepFM model based on attention mechanism (DeepAFM) to exert personalized fine-grained traffic information recommendation with embeddings of multisource data. More specifically, the RCOS is proposed to perform coarse-grained route recommendation based on path planning theory, in which the traffic data and drivers' preferences are fully utilized for comprehensive modeling. The encoder–decoder LSTM model is employed for representation learning, in which the traffic sequences and driving behaviors are mapped into dense distributed representations with rich semantic information. The DeepAFM is utilized to achieve effective driving safety guarantee according to an individual's requirements, in which the weighted low-order feature combinations and high-order feature interactions are incorporated for personalized fine-grained recommendation. We also conduct extensive experiments on public data sets and in real-world scenarios. The experimental results demonstrate that the PF-ITS method based on RCOS and DeepAFM outperforms the state-of-the-art baseline models in effectiveness and efficiency.

An Attention-Based Spatiotemporal LSTM Network for Next POI Recommendation

Next point-of-interest (POI) recommendation, also known as a natural extension of general POI recommendation, is recently proposed to predict user's next destination and has attracted considerable research interest. It focuses on learning users’ sequential patterns of check-in behavior and on training personalized recommendation models using different types of contextual information. Unfortunately, most of the previous studies failed to incorporate the spatiotemporal contextual information, which plays a critical role in analyzing user check-in behavior, into recommending the next POI. In recent years, embedding learning and recurrent neural network (RNN) based approaches show promising performance for modeling sequential patterns of check-in behavior in next POI recommendation. However, not all of the historical check-in records contribute equally to the next-step check-in behavior. To provide better next POI recommendation performance, we first proposed a spatiotemporal long and short-term memory (ST-LSTM) network. By feeding the spatiotemporal contextual information into the LSTM network in each step, ST-LSTM can model the spatial and temporal information better. Also, we developed an attention-based spatiotemporal LSTM (ATST-LSTM) network for next POI recommendation. By using the attention mechanism, ATST-LSTM can focus on the relevant historical check-in records in a check-in sequence selectively using the spatiotemporal contextual information. Besides, we conducted a comprehensive performance evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely Gowalla and Brightkite. Experimental results indicated that the proposed ATST-LSTM network outperformed two state-of-the-art next POI recommendation approaches regarding three commonly-used evaluation metrics.

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

Games in context: Equilibrium under ambiguity for belief functions

We propose a new solution concept, called Context-Dependent Equilibrium Under Ambiguity (CD-EUA), for strategic games where players' beliefs may be influenced by exogenous context-related information. Players' beliefs about the strategic behavior of their opponents are represented by belief functions. The notion of belief functions allows us to combine exogenous context information in the spirit of Schelling (1960) with endogenous equilibrium beliefs about the opponents' behavior in analogy to the standard Nash equilibrium. For any finite strategic game, we prove existence of a CD-EUA for any context information and any degree of confidence in it. Moreover, we show continuity of the equilibrium correspondence. Finally, we illustrate how CD-EUA can be applied to different types of context information in games by explaining some stylized facts from experimental research on coordination.

An attention‐based category‐aware GRU model for the next POI recommendation

With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.

Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information

An automatic stress detection system that uses unobtrusive smart bands will contribute to human health and well-being by alleviating the effects of high stress levels. However, there are a number of challenges for detecting stress in unrestricted daily life which results in lower performances of such systems when compared to semi-restricted and laboratory environment studies. The addition of contextual information such as physical activity level, activity type and weather to the physiological signals can improve the classification accuracies of these systems. We developed an automatic stress detection system that employs smart bands for physiological data collection. In this study, we monitored the stress levels of 16 participants of an EU project training every day throughout the eight days long event by using our system. We collected 1440 hours of physiological data and 2780 self-report questions from the participants who are from diverse countries. The project midterm presentations (see Figure 3) in front of a jury at the end of the event were the source of significant real stress. Different types of contextual information, along with the physiological data, were recorded to determine the perceived stress levels of individuals. We further analyze the physiological signals in this event to infer long term perceived stress levels which we obtained from baseline PSS-14 questionnaires. Session-based, daily and long-term perceived stress levels could be identified by using the proposed system successfully.

A Smart Home Context-aware Model Based On UML and Colored Petri Net

Smart Home is one of the main application domains of Pervasive Computing, and it can provide Context-aware Services to homeowners. As context information is obtained from different sources in the Smart Home environment, the context information may be heterogeneous, inaccurate and dynamic. How to effectively model these different types of context information is a main research issue. To address this issue, we propose a hybrid Context-aware Modeling approach based on UML and Colored Petri Net in this paper. We also model leave home scenario using this approach, and use Coverability Graph to verify the leave home scenario model.

Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities.

In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of today’s technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

Good Samaritans on social network services: Effects of shared context information on social supports for strangers

Technological developments in smart phones and various sensors have been growing at a rapid pace, which leads people to share their context information with other users and support strangers in social network services (SNS). This allows us to study the effects of shared context information on socially supportive behaviors of individuals (Good Samaritans) in SNS. Our aim of this study is to investigate the pattern of social support amongst strangers in SNS. We aim to find out which types and forms of context information induce high levels of willingness to provide social support among strangers. We focus on four different types of context information (location, activity, emotion, or physical environment), which vary by degree of self-disclosure. We also investigated two forms of context information (subjective vs. objective), which differ depending on the provision of human interpretation.In order to achieve our research goals, we first constructed a causal model between context information and social support mediated by the theory of mind, which consists of simulation theory (empathy) and theory theory (rational reasoning). To verify the model, we conducted two exploratory pre-studies and a controlled main experiment. Our results indicated that types and forms of context information affect social support, simulation theory and theory theory. First, we found that a high level of self-disclosure positively effects social support. Context information in the subjective form induced more social supports compared to the objective form even though the information content itself is the same. Emotional context information presented in the subjective form has the strongest positive effect on social support. Furthermore, a high level of self-disclosure was found to positively affect context form's effect on simulation theory and social supports. In regard to theory theory, we discovered that high levels of self-disclosure have a positive effect.Both practical and theoretical implications of the study results have been presented. Theoretically, a conceptual model of the effects of context information on intention to provide social supports has been proposed and empirically verified. Practically, the interplay between context information types and forms can be utilized to construct social network services efficiently promoting social supports among fellow users. This paper ends with study limitations and implications.

Agency and Facial Emotion Judgment in Context

Past research showed that East Asians' belief in holism was expressed as their tendencies to include background facial emotions into the evaluation of target faces more than North Americans. However, this pattern can be interpreted as North Americans' tendency to downplay background facial emotions due to their conceptualization of facial emotion as volitional expression of internal states. Examining this alternative explanation, we investigated whether different types of contextual information produce varying degrees of effect on one's face evaluation across cultures. In three studies, European Canadians and East Asians rated the intensity of target facial emotions surrounded with either affectively salient landscape sceneries or background facial emotions. The results showed that, although affectively salient landscapes influenced the judgment of both cultural groups, only European Canadians downplayed the background facial emotions. The role of agency as differently conceptualized across cultures and multilayered systems of cultural meanings are discussed.

Exploring Tiny Images: The Roles of Appearance and Contextual Information for Machine and Human Object Recognition

Typically, object recognition is performed based solely on the appearance of the object. However, relevant information also exists in the scene surrounding the object. In this paper, we explore the roles that appearance and contextual information play in object recognition. Through machine experiments and human studies, we show that the importance of contextual information varies with the quality of the appearance information, such as an image's resolution. Our machine experiments explicitly model context between object categories through the use of relative location and relative scale, in addition to co-occurrence. With the use of our context model, our algorithm achieves state-of-the-art performance on the MSRC and Corel data sets. We perform recognition tests for machines and human subjects on low and high resolution images, which vary significantly in the amount of appearance information present, using just the object appearance information, the combination of appearance and context, as well as just context without object appearance information (blind recognition). We also explore the impact of the different sources of context (co-occurrence, relative-location, and relative-scale). We find that the importance of different types of contextual information varies significantly across data sets such as MSRC and PASCAL.

Quantifying and Transferring Contextual Information in Object Detection

Context is critical for reducing the uncertainty in object detection. However, context modeling is challenging because there are often many different types of contextual information coexisting with different degrees of relevance to the detection of target object(s) in different images. It is therefore crucial to devise a context model to automatically quantify and select the most effective contextual information for assisting in detecting the target object. Nevertheless, the diversity of contextual information means that learning a robust context model requires a larger training set than learning the target object appearance model, which may not be available in practice. In this work, a novel context modeling framework is proposed without the need for any prior scene segmentation or context annotation. We formulate a polar geometric context descriptor for representing multiple types of contextual information. In order to quantify context, we propose a new maximum margin context (MMC) model to evaluate and measure the usefulness of contextual information directly and explicitly through a discriminant context inference method. Furthermore, to address the problem of context learning with limited data, we exploit the idea of transfer learning based on the observation that although two categories of objects can have very different visual appearance, there can be similarity in their context and/or the way contextual information helps to distinguish target objects from nontarget objects. To that end, two novel context transfer learning models are proposed which utilize training samples from source object classes to improve the learning of the context model for a target object class based on a joint maximum margin learning framework. Experiments are carried out on PASCAL VOC2005 and VOC2007 data sets, a luggage detection data set extracted from the i-LIDS data set, and a vehicle detection data set extracted from outdoor surveillance footage. Our results validate the effectiveness of the proposed models for quantifying and transferring contextual information, and demonstrate that they outperform related alternative context models.

Context caches in the Clouds

In context-aware systems, the contextual information about human and computing situations has a strong temporal aspect i.e. it remains valid for a period of time. This temporal property can be exploited in caching mechanisms that aim to exploit such locality of reference. However, different types of contextual information have varying temporal validity durations and a varied spectrum of access frequencies as well. Such variation affects the suitability of a single caching strategy and an ideal caching mechanism should utilize dynamic strategies based on the type of context data, quality of service heuristics and access patterns and frequencies of context consuming applications. This paper presents an investigation into the utility of various context-caching strategies and proposes a novel bipartite caching mechanism in a Cloud-based context provisioning system. The results demonstrate the relative benefits of different caching strategies under varying context usage scenarios. The utility of the bipartite context caching mechanism is established both through simulation and deployment in a Cloud platform.

Hybrid reasoning technique for improving context-aware applications

With the rapid adoption of GPS enabled smart phones and the fact that users are almost permanently connected to the Internet, an evolution is observed toward applications and services that adapt themselves using the user’s context, a.o. taking into account location information. To facilitate the development of such new intelligent applications, new enabling platforms are needed to collect, distribute, and exchange context information. An important aspect of such platforms is the derivation of new, high-level knowledge by combining different types of context information using reasoning techniques. In this paper, we present a new approach to derive context information by combining case-based and rule-based reasoning. Two use cases are detailed where both reasoners are used to derive extra useful information. For the desk sharing office use case, the combination of rule-based and case-based reasoning allows to automatically learn typical trajectories of a user and improve localization on such trajects with 42%. In both use cases, the hybrid approach is shown to provide a significant improvement.

Context based object categorization: A critical survey

The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the same scene. Several models for object categorization use appearance and context information from objects to improve recognition accuracy. Appearance information, based on visual cues, can successfully identify object classes up to a certain extent. Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. In this work we address the problem of incorporating different types of contextual information for robust object categorization in computer vision. We review different ways of using contextual information in the field of object categorization, considering the most common levels of extraction of context and the different levels of contextual interactions. We also examine common machine learning models that integrate context information into object recognition frameworks and discuss scalability, optimizations and possible future approaches.

A Conditional Random Field Approach for Named Entity Recognition in Bengali and Hindi

This paper describes the development of Named Entity Recognition (NER) systems for two leading Indian languages, namely Bengali and Hindi, using the Conditional Random Field (CRF) framework. The system makes use of different types of contextual information along with a variety of features that are helpful in predicting the different named entity (NE) classes. This set of features includes language independent as well as language dependent components. We have used the annotated corpora of 122,467 tokens for Bengali and 502,974 tokens for Hindi tagged with a tag set of twelve different NE classes, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL). We have considered only the tags that denote person names, location names, organization names, number expressions, time expressions and measurement expressions. A number of experiments have been carried out in order to find out the most suitable features for NER in Bengali and Hindi. The system has been tested with the gold standard test sets of 35K for Bengali and 50K tokens for Hindi. Evaluation results in overall f-score values of 81.15% for Bengali and 78.29% for Hindi for the test sets. 10-fold cross validation tests yield f-score values of 83.89% for Bengali and 80.93% for Hindi. ANOVA analysis is performed to show that the performance improvement due to the use of language dependent features is statistically significant.

Context attributes in memory are bound to item information, but not to one another

In four experiments, we compared binding between item and context information with binding between different types of context information. Participants studied line drawings of common objects that appeared in both different colors and different locations. We explored the effects of reinstating one type of information on recognition memory for another type, and we also tested the participants' ability to discriminate intact from rearranged pairings of two types of information. Results showed that different contextual dimensions (i.e., color and location) were bound to item information (i.e., object information), but not to each other.

Aging, source memory, and emotion.

In 2 experiments we assessed younger and older adults' ability to remember contextual information about an event. Each experiment examined memory for 3 different types of contextual information: (a) perceptual information (e.g., location of an item); (b) conceptual, nonemotional information (e.g., quality of an item); and (c) conceptual, emotional information (e.g., safety of an item). Consistent with a large literature on aging and source memory, younger adults outperformed older adults when the contextual information was perceptual in nature and when it was conceptual, but not emotional. Age differences in source memory were eliminated, however, when participants recalled emotional source information. These findings suggest that emotional information differentially engages older adults, possibly evoking enhanced elaborations and associations. The data are also consistent with a growing literature, suggesting that emotional processing remains stable with age (e.g., Carstensen & Turk-Charles, 1994, 1998; Isaacowitz, Charles, & Carstensen, 2000).