Types Of Contextual Information Research Articles

Hyperion: A Highly Effective Page and PC Based Delta Prefetcher

Hardware prefetching plays an important role in modern processors for hiding memory access latency. Delta prefetchers show great potential at the L1D cache level, as they can impose small storage overhead by recording deltas. Furthermore, local delta prefetchers, such as Berti, have been shown to achieve high L1D accuracy. However, there is still room for improving the L1D coverage of existing delta prefetchers. Our goal is to develop a delta prefetcher capable of achieving both high L1D coverage and accuracy. We explore delta prefetchers trained on various types of contextual information, ranging from coarse-grained to fine-grained, and analyze their L1D coverage and accuracy. Our findings indicate that training deltas based on the access histories of both PCs and memory pages for individual PCs and memory pages can lead to increased L1D coverage alongside high accuracy. Therefore, we introduce Hyperion, a highly efficient Page and PC-based delta prefetcher. In terms of the vital component of recording access histories, we implement three different structures and engage in a detailed discussion about them. Furthermore, Hyperion utilizes micro-architecture information (e.g., L1D hits or misses, PQ occupancy) and real-time L1D accuracy to dynamically adjust its issuing mechanism, further enhancing performance and L1D accuracy. Our results show that Hyperion achieves an L1D accuracy of 92.4% and an L1D coverage of 51.9%, along with an L2C coverage of 63.0% and an LLC coverage of 67.5% across a diverse range of applications, including SPEC CPU2006, SPEC CPU2017, GAP, and PARSEC, with a baseline of no prefetching. Regarding performance, Hyperion achieves a 50.1% performance gain, outperforming the state-of-the-art delta prefetcher Berti by 5.0% over baseline across all memory-intensive traces from the four benchmark suites.

Contextual information needs of people in life transitions struggling with obsessive-compulsive disorder

Introduction. Obsessive-compulsive Disorder (OCD) is a severe chronic mental health issue that significantly impacts people’s everyday lives. This study aims to reveal the contextual information needs of the people living with OCD during life transitions and how these needs dynamically evolve across various transitional stages. Method. The study collected 1,500 posts from an OCD online forum of a well-known Chinese social media platform, Douban. A subsequent content analysis was performed on 324 selected valid posts. Analysis. Guiding by the information behaviour theory of transitions, two rounds of open-coding analyses were conducted on the posts. Results. Preliminary findings indicate that people with OCD have various types of contextual information needs during different transitional stages, namely understanding, negotiating, and resolving, suggested by the existing theory. Conclusion. Information needs are context-specific, complex and continuously changing during life transitions of people with OCD. Our conclusion can provide theoretical insights for future research exploring the dynamic information needs during the mental health transitions. Additionally, it serves as a preliminary conceptual framework for various stakeholders to offer informational support to people with OCD.

The role of cognitive control and referential complexity on adults' choice of referring expressions: Testing and expanding the referential complexity scale.

This study aims to advance our understanding of the nature and source(s) of individual differences in pragmatic language behavior over the adult lifespan. Across four story continuation experiments, we probed adults' (N = 496 participants, ages 18-82) choice of referential forms (i.e., names vs. pronouns to refer to the main character). Our manipulations were based on Fossard et al.'s (2018) scale of referential complexity which varies according to the visual properties of the scene: low complexity (one character), intermediate complexity (two characters of different genders), and high complexity (two characters of the same gender). Since pronouns signal topic continuity (i.e., that the discourse will continue to be about the same referent), the use of pronouns is expected to decrease as referential complexity increases. The choice of names versus pronouns, therefore, provides insight into participants' perception of the topicality of a referent, and whether that varies by age and cognitive capacity. In Experiment 1, we used the scale to test the association between referential choice, aging, and cognition, identifying a link between older adults' switching skills and optimal referential choice. In Experiments 2-4, we tested novel manipulations that could impact the scale and found both the TIMING of a competitor referent's presence and EMPHASIS placed on competitors modulated referential choice, leading us to refine the scale for future use. Collectively, Experiments 1-4 highlight what type of contextual information is prioritized at different ages, revealing older adults' preserved sensitivity to (visual) scene complexity but reduced sensitivity to linguistic prominence cues, compared to younger adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

The impact of contextual information on aesthetic engagement of artworks

Art is embedded in its historical, social, political, and cultural context, and rarely evaluated in isolation. The semantic context created by providing text-based information about an artwork influences how an artwork will be evaluated. In the current study, we investigated how contextual information influences the aesthetic appreciation of artworks. Experiment 1 explored whether contextual information such as artist or technique information influenced aesthetic judgments of abstract artworks by Jackson Pollock. The combination of artist and technique information increased liking and interest for the artworks. Experiment 2 investigated whether contextual information about the artist, technique, or content of representational artworks by Indian and European/American artists influenced aesthetic responses of Northern American participants. We found that artist, content, and technique information compared to no information influenced the aesthetic experience of representational artworks. For both experiments, the effect of contextual information was stronger in participants with little art experience, and those more open to experience, and for artworks from another culture compared to one’s own. In sum, along-with theories of empirical and neuro-aesthetics, the current findings also have implications for aesthetics education and museum curation. It seems crucial to consider the type of artwork, the type of contextual information, its potential to enhance aesthetic experience, and the curatorial background of the museum or exhibition, as well as individual differences of viewers. Artworks that are unfamiliar to its viewers might require more contextual information to have an impact on the viewers, and may lower viewers’ prejudices against artworks/artists originating from an out-group.

Influence of context on users’ views about explanations for decision-tree predictions

We consider the influence of two types of contextual information, background information available to users and users’ goals, on users’ views and preferences regarding textual explanations generated for the outcomes predicted by Decision Trees (DTs). To investigate the influence of background information, we generate contrastive explanations that address potential conflicts between aspects of DT predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. To investigate the influence of users’ goals, we employ an interactive setting where given a goal and an initial explanation for a predicted outcome, users select follow-up questions, and assess the explanations that answer these questions. Here, we offer algorithms to generate explanations that address six types of follow-up questions.The main result from both user studies is that explanations which have a contrastive aspect about a predicted class are generally preferred by users. In addition, the results from the first study indicate that these explanations are deemed especially valuable when users expectations differ from predicted outcomes; and the results from the second study indicate that contrastive explanations which describe how to change a predicted outcome are particularly well regarded in terms of helping users achieve this goal, and they are also popular in terms of helping users achieve other goals.

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.

Representing Context in FrameNet: A Multidimensional, Multimodal Approach.

Frame Semantics includes context as a central aspect of the theory. Frames themselves can be regarded as a representation of the immediate context against which meaning is to be construed. Moreover, the notion of frame invocation includes context as one possible source of information comprehenders use to construe meaning. As the original implementation of Frame Semantics, Berkeley FrameNet is capable of providing computational representations of some aspects of context, but not all of them. In this article, we present FrameNet Brasil: a framenet enriched with qualia relations and capable of taking other semiotic modes as input data, namely pictures and videos. We claim that such an enriched model is capable of addressing other types of contextual information in a framenet, namely sentence-level cotext and commonsense knowledge. We demonstrate how the FrameNet Brasil software infrastructure addresses contextual information in both database construction and corpora annotation. We present the guidelines for the construction of two multimodal datasets whose annotations represent contextual information and also report on two experiments: (i) the identification of frame-evoking lexical units in sentences and (ii) a methodology for domain adaptation in Neural Machine Translation that leverages frames and qualia for representing sentence-level context. Experimental results emphasize the importance of computationally representing contextual information in a principled structured fashion as opposed to trying to derive it from the manipulation of linguistic form alone.

Putting the Pieces Together: Mental Construction of Semantically Congruent and Incongruent Scenes in Dementia

Scene construction refers to the process by which humans generate richly detailed and spatially cohesive scenes in the mind’s eye. The cognitive processes that underwrite this capacity remain unclear, particularly when the envisaged scene calls for the integration of various types of contextual information. Here, we explored social and non-social forms of scene construction in Alzheimer’s disease (AD; n = 11) and the behavioural variant of frontotemporal dementia (bvFTD; n = 15) relative to healthy older control participants (n = 16) using a novel adaptation of the scene construction task. Participants mentally constructed detailed scenes in response to scene–object cues that varied in terms of their sociality (social; non-social) and congruence (congruent; incongruent). A significant group × sociality × congruence interaction was found whereby performance on the incongruent social scene condition was significantly disrupted in both patient groups relative to controls. Moreover, bvFTD patients produced significantly less contextual detail in social relative to non-social incongruent scenes. Construction of social and non-social incongruent scenes in the patient groups combined was significantly associated with independent measures of semantic processing and visuospatial memory. Our findings demonstrate the influence of schema-incongruency on scene construction performance and reinforce the importance of episodic–semantic interactions during novel event construction.

Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.

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.

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes.

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

SHEDR: An End-to-End Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the Information System (IS) literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (Social Me-dia-based Hyperlocal Event Detection & Recommendation), an end-to-end neural event detection and recommendation framework on Twitter to facilitate residents’ information-seeking of hyperlo-cal events. The key innovation in SHDER lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, CNN and LSTM, in a joint CNN-LSTM model to characterize spatial-temporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pair-wise ranking algorithm for recommending detected hyperlocal events t resi-dents based on their interests. To alleviate the sparsity issue and improve personalization, our algo-rithm incorporates several types of contextual information covering topic, social and geographical proximities. We perform comprehensive evaluations based on two large scale datasets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our frame-work in comparison to several state-of-the-art approaches. We show that our hyperlocal event de-tection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores.

Is color source retrieval sensitive to emotion? Electrophysiological evidence from old/new effects

It has been proved that item memory and source memory are two dissociable processes, as reflected by differential influence at behavioral and electrophysiological levels, the latter being evidenced by the ERP old/new effects. Specially for source memory, the retrieval of color source may be unique from recollecting other types of contextual information, which can be seen from the late posterior negativity (LPN). However, the mediation of emotional valence on the old/new effects for verbal stimuli encoded in colors remains unknown. Adopting words of three emotional valences (i.e., positive, neutral, and negative), with their displayed colors serving as sources, the current experiment aimed to explore the sensitivity of old/new effects to emotion for both item memory and source memory. Results demonstrated that: the FN400 that reflects familiarity was recorded and it was sensitive to emotional valence across both memory tasks; the mediation of emotional valence was absent in recollection-reflected LPC, neither for item memory nor for source memory; an association between LPN and color source retrieval was confirmed, with reliable amplitudes for neutral words but not for emotional words. These data were discussed in terms of the dual-process model and other accounts. Future research directions were recommended.

Deep Neural Network-Based Indoor Emergency Awareness Using Contextual Information From Sound, Human Activity, and Indoor Position on Mobile Device

Context-aware computing, which gathers information about the environment at a given time and adapts its behavior accordingly, can be effectively applied to a monitoring system that automatically detects and copes with emergency situations. In this paper, we propose an indoor emergency awareness alarm system using a deep neural network on a mobile device for at-risk people, such as the elderly and children. The proposed system detects emergency situations and quickly delivers the state of the person being monitored to a guardian using a mobile transmission system. To do this, three types of contextual information, including sound, human activity, and indoor location, are instantly recognized by the protected person’s mobile device. Both sound and human activity are used to improve the recognition accuracy of emergency situations provided to guardians’ mobile devices. Sound events are detected by a residual neural network, and human activities are recognized by applying the accelerometer and gyroscope signals of the mobile device to a deep spiking neural network. In addition, the indoor location where the emergency occurred is detected by the deep spiking neural network. The experimental results show the high accuracy of the indoor emergency awareness framework.

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.