Environmental Context Information Research Articles

Seeking the public health angle in Missouri newspaper coverage of the opioid crisis

The public health model of reporting calls for public health professionals and journalists to work together to provide social and environmental context, risk factors, and prevention information in news coverage of public health issues. This quantitative content analysis describes the prevalence of contextual information in Missouri newspaper coverage of the opioid crisis. Results indicate Missouri newspapers are not depicting the opioid crisis as a public health issue. The study has implications for research and practice.

Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition.

Grasp intention recognition plays a crucial role in controlling assistive robots to aid older people and individuals with limited mobility in restoring arm and hand function. Among the various modalities used for intention recognition, the eye-gaze movement has emerged as a promising approach due to its simplicity, intuitiveness, and effectiveness. Existing gaze-based approaches insufficiently integrate gaze data with environmental context and underuse temporal information, leading to inadequate intention recognition performance. The objective of this study is to eliminate the proposed deficiency and establish a gaze-based framework for object detection and its associated intention recognition. A novel gaze-based grasp intention recognition and sequential decision fusion framework (GIRSDF) is proposed. The GIRSDF comprises three main components: gaze attention map generation, the Gaze-YOLO grasp intention recognition model, and sequential decision fusion models (HMM, LSTM, and GRU). To evaluate the performance of GIRSDF, a dataset named Invisible containing data from healthy individuals and hemiplegic patients is established. GIRSDF is validated by trial-based and subject-based experiments on Invisible and outperforms the previous gaze-based grasp intention recognition methods. In terms of running efficiency, the proposed framework can run at a frequency of about 22 Hz, which ensures real-time grasp intention recognition. This study is expected to inspire additional gaze-related grasp intention recognition works.

LIGHTWEIGHT MOBILE WEB SERVICE PROVISIONING FOR THE INTERNET OF THINGS MEDIATION

Emerging sensor-embedded smartphones motivated the mobile Internet of Things research. With the integrated embedded hardware and software sensor components, and mobile network technologies, smartphones are capable of providing various environmental context information via embedded mobile device-hosted Web services (MWS). MWS enhances the capability of various mobile sensing applications such as mobile crowdsensing, real time mobile health monitoring, mobile social network in proximity and so on. Although recent smartphones are quite capable in terms of mobile data transmission speed and computation power, the frequent usage of high performance multi-core mobile CPU and the high speed 3G/4G mobile Internet data transmission will quickly drain the battery power of the mobile device. Although numerous previous researchers have tried to overcome the resource intensive issues in mobile embedded service provisioning domain, most of the efforts were constrained because of the underlying resource intensive technologies. This paper presents a lightweight mobile Web service provisioning framework for mobile sensing which utilises the protocols that were designed for constrained Internet of Things environment. The prototype experimental results show that the proposed framework can provide higher throughput and less resource consumption than the traditional mobile Web service frameworks.

RESEARCH ON BEHAVIORAL MOTIVATION INFERENCE METHOD FOR GEOGRAPHICAL SPATIOTEMPORAL LARGE DATA

Abstract. Along with the rapid development of Internet technology, GNSS technology and mobile terminals, a large amount of information including geographical location and time attributes has been generated. Faced with large and complex Internet geospatial data, how to quickly and accurately extract valuable reference information becomes an urgent problem to be solved. And the user's demand for personalized information of recommendation information is getting stronger and stronger, and researching efficient and accurate personalized recommendation system has good application value. In this paper, based on the application requirements of personalized recommendation information, the GIS platform and related recommendation algorithms are used to fully exploit the user and location based on geographic space-time big dataIt is divided into user explicit interest and user implicit interest, and then establishes a scientific and efficient user behavior motivation prediction model based on geographic situation. User interest information can be obtained from explicit interest information, implicit interest information and geographic situation interest information. Geographical environment, geographic location and other related context information. By introducing time factors, it is used to update and improve the user real-time interest model to achieve accurate prediction of user behavior motives under geographic spatio-temporal big data. Use Apriori algorithm to calculate the support and determine the current Frequent itemsets of user interest in geographic context, using frequent itemsets to generate strong association rules, and realizing the analysis of user behavior motives based on geography context. For geographic spatio-temporal big data, this paper proposes a personalized hybrid recommendation algorithm, which is based on users. Effective combination of collaborative filtering algorithms and association rules for geographic context-user behavioral interest adaptation.

Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network

Traffic sign detection plays an important role in intelligent transportation systems. But traffic signs are still not well-detected by deep convolution neural network-based methods because the sizes of their feature maps are constrained, and the environmental context information has not been fully exploited by other researchers. What we need is a way to incorporate relevant context detail from the neighboring layers into the detection architecture. We have developed a novel traffic sign detection approach based on recurrent attention for multi-scale analysis and use of local context in the image. Experiments on the German traffic sign detection benchmark and the Tsinghua-Tencent 100K data set demonstrated that our approach obtained an accuracy comparable to the state-of-the-art approaches in traffic sign detection.

An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study.

Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter.

Prediction of crime occurrence from multi-modal data using deep learning.

In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models.

Lightweight Mobile Web Service Provisioning for the Internet of Things Mediation

Emerging sensor-embedded smartphones motivated the mobile Internet of Things research. With the integrated embedded hardware and software sensor components, and mobile network technologies, smartphones are capable of providing various environmental context information via embedded mobile device-hosted Web services (MWS). MWS enhances the capability of various mobile sensing applications such as mobile crowdsensing, real time mobile health monitoring, mobile social network in proximity and so on. Although recent smartphones are quite capable in terms of mobile data transmission speed and computation power, the frequent usage of high performance multi-core mobile CPU and the high speed 3G/4G mobile Internet data transmission will quickly drain the battery power of the mobile device. Although numerous previous researchers have tried to overcome the resource intensive issues in mobile embedded service provisioning domain, most of the efforts were constrained because of the underlying resource intensive technologies. This paper presents a lightweight mobile Web service provisioning framework for mobile sensing which utilises the protocols that were designed for constrained Internet of Things environment. The prototype experimental results show that the proposed framework can provide higher throughput and less resource consumption than the traditional mobile Web service frameworks.

Spatial Navigation and the Central Complex: Sensory Acquisition, Orientation, and Motor Control.

Cockroaches are scavengers that forage through dark, maze-like environments. Like other foraging animals, for instance rats, they must continually asses their situation to keep track of targets and negotiate barriers. While navigating a complex environment, all animals need to integrate sensory information in order to produce appropriate motor commands. The integrated sensory cues can be used to provide the animal with an environmental and contextual reference frame for the behavior. To successfully reach a goal location, navigational cues continuously derived from sensory inputs have to be utilized in the spatial guidance of motor commands. The sensory processes, contextual and spatial mechanisms, and motor outputs contributing to navigation have been heavily studied in rats. In contrast, many insect studies focused on the sensory and/or motor components of navigation, and our knowledge of the abstract representation of environmental context and spatial information in the insect brain is relatively limited. Recent reports from several laboratories have explored the role of the central complex (CX), a sensorimotor region of the insect brain, in navigational processes by recording the activity of CX neurons in freely-moving insects and in more constrained, experimenter-controlled situations. The results of these studies indicate that the CX participates in processing the temporal and spatial components of sensory cues, and utilizes these cues in creating an internal representation of orientation and context, while also directing motor control. Although these studies led to a better understanding of the CX's role in insect navigation, there are still major voids in the literature regarding the underlying mechanisms and brain regions involved in spatial navigation. The main goal of this review is to place the above listed findings in the wider context of animal navigation by providing an overview of the neural mechanisms of navigation in rats and summarizing and comparing our current knowledge on the CX's role in insect navigation to these processes. By doing so, we aimed to highlight some of the missing puzzle pieces in insect navigation and provide a different perspective for future directions.

Aproximación a un modelo contextual para calidad de datos en agricultura de precisión

Precision agriculture is a farming management concept, based on the crop variability in the field; it comprises several stages: data collection, information processing and decision-making. After an extensive review of the literature, it appears that data quality control is an important process in precision agriculture and can be considered in the data collection process. This paper makes an approach to data architecture quality control by applying the contextual information of the acquisition system (sad) and environment context information. This approach can provide the sad the capability to understand the situations of their environment in order to improve the quality of data for decision-making.

Slope Based Intelligent 3D Disaster Simulation Using Physics Engine

In the modern society, the forms of disasters are getting larger and more diverse by abnormal weather phenomena due to rapid social changes. As human and economic damage is increasing by these disasters, interest in disaster observation and prevention is increasing and studies have been carried out in various fields. In order to minimize disaster damage, National Disaster Information Center provides disaster-related information such as disaster situations, risk index, local situation and so on. At present, however, various descriptive studies on action tips in the event of a disaster, disaster alert, disaster communications network have been in progress but there are few studies on prediction or simulation for preventing disasters etc. Therefore, this paper proposes a slope-based intelligent 3D disaster simulation using a physics engine. The proposed method is to infer a variety of disaster situations that may occur in reality through ontology and apply it to 3D simulation. Disaster situation ontology constitutes context information of outside, inside, disasters etc. and generates rule bases using Jena 2.0 inference engine. Inference results are derived in the form of XML and applicable to a variety of devices. The expression of 3D simulation virtual space is divided into interior and exterior. Interior constitutes the index information based on actually measured terrain and then expresses it into 3D compound type terrain data. Exterior expresses six planes into a hexahedron by using Sky Box technique. Simulation operates object collision, movement, surroundings etc. in real time by using a physics engine and represents a disaster situation in 3D virtual space by using particles function. Based on ontology based context awareness, environment context information on slope, including landslides, snow, rain, fogs, fire, explosion, and smoke, is developed into the slope based intelligent 3D disaster simulation. As a result, depending on geomorphic characteristics, disaster management service can realistically and visually be offered to users in 3D virtual reality. In addition, it is possible to experimentally apply disaster situations virtually through 3D virtual reality, and thereby judge their potential risk.

Context-based user grouping for multi-casting in heterogeneous radio networks

Abstract. Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity of social networks, these data are complemented by profile information about individual users. Making use of this information by classifying users in wireless networks enables targeted content and advertisement delivery as well as optimizing network resources, in particular bandwidth utilization, by facilitating group-based multi-casting. In this paper, we present the design and implementation of a web service for advanced user classification based on user, network, and environmental context information. The service employs simple and advanced clustering algorithms for forming classes of users. Available service functionalities include group formation, context-aware adaptation, and deletion as well as the exposure of group characteristics. Moreover, the results of a performance evaluation, where the service has been integrated in a simulator modeling user behavior in heterogeneous wireless systems, are presented.

Middleware for Smart Environments Management

This paper introduces a self-configuring middleware that manages the processes of context information acquisition and representation from smart closed environments, targeting the development of context aware applications. The environment context information is modeled using three sets: context resources, context actors and context policies. The context model artifacts are generated and administrated at run time by a management infrastructure based on intelligent software agents. The self-configuring property is enforced by monitoring the closed environment in order to detect variations or conditions for which the context model artifacts must be updated. The middleware was tested and validated within the premises of our Distributed Systems Research Laboratory smart environment.

Multiple Object Category Detection and Localization Using Generative and Discriminative Models

This paper proposes an integrated approach to simultaneous detection and localization of multiple object categories using both generative and discriminative models. Our approach consists of first generating a set of hypotheses for each object category using a generative model (pLSA) with a bag of visual words representing each object. Based on the variation of objects within a category, the pLSA model automatically fits to an optimal number of topics. Then, the discriminative part verifies each hypothesis using a multi-class SVM classifier with merging features that combines spatial shape and appearance of an object. In the post-processing stage, environmental context information along with the probabilistic output of the SVM classifier is used to improve the overall performance of the system. Our integrated approach with merging features and context information allows reliable detection and localization of various object categories in the same image. The performance of the proposed framework is evaluated on the various standards (MIT-CSAIL, UIUC, TUD etc.) and the authors' own datasets. In experiments we achieved superior results to some state of the art methods over a number of standard datasets. An extensive experimental evaluation on up to ten diverse object categories over thousands of images demonstrates that our system works for detecting and localizing multiple objects within an image in the presence of cluttered background, substantial occlusion, and significant scale changes.

A comparison of two techniques for reducing context-dependent forgetting.

Recall is poorer when tested in a new environment than when tested in the original learning context. Two techniques for reducing this context-dependent forgetting were compared. One technique involved instructing subjects to recall their learning room(s), and the other attempted to establish multiple environmental retrieval cues by presenting lists in multiple rooms rather than all in the same room. Subjects were given three word lists to study in one or three rooms. All subjects were given a free-recall test in a new room, and half were asked to use remembered environmental context (EC) information to facilitate word memory. Multiple input contexts benefited only subjects who were uninstructed in the use of EC cues. Subjects given EC-recall instructions, however, recalled somewhat less in the three-room condition than in the one-room condition. The facilitative effects of the two techniques were not additive: EC-recall instructions benefited only one-room subjects. The results suggest that both EC-recall instructions and mul­ tiple learning contexts induce subjects to use contextual retrieval cues that are otherwise not spontaneously utilized, and that the greater the number of context cues stored in memory, the less accessible those cues become. Context-dependent memory (COM) has referred typically to the common finding that recall tested in a new context is poorer than recall tested in the orig­