Use Of Contextual Information Research Articles

Category Level Object Pose Estimation via Global High-Order Pooling

Category level 6D object pose estimation aims to predict the rotation, translation and size of object instances in any scene. In current research methods, global average pooling (first-order) is usually used to explore geometric features, which can only capture the first-order statistical information of the features and do not fully utilize the potential of the network. In this work, we propose a new high-order pose estimation network (HoPENet), which enhances feature representation by collecting high-order statistics to model high-order geometric features at each stage of the network. HoPENet introduces a global high-order enhancement module and utilizes global high-order pooling operations to capture the correlation between features and fuse global information. In addition, this module can capture long-term statistical correlations and make full use of contextual information. The entire network finally obtains a more discriminative feature representation. Experiments on two benchmarks, the virtual dataset CAMERA25 and the real dataset REAL275, demonstrate the effectiveness of HoPENet, achieving state-of-the-art (SOTA) pose estimation performance.

On the use of contextual information for machine learning based test case prioritization in continuous integration development

Context:In most software organizations, Continuous Integration (CI) is a common practice usually subject to some budgets. Consequently, prioritizing test cases to be executed in the CI cycle is fundamental. The idea is first to execute test cases with higher failure-proneness to provide rapid feedback and decrease costs. To perform this task approaches in the literature adopt failure history and Machine Learning (ML). However, in addition to the failure history, it is also important to consider information from the CI context of the organizations and the application domain. Objective:For this end, we introduce a contextual information approach for ML algorithms. Such an approach considers information from the testing activity that can be easily collected, such as test case execution time, size, and complexity. We implement the approach by introducing two contextual versions of the algorithms: Multi-Armed Bandit (MAB) and Random Forest (RF). Method:Six systems are used to compare both contextual algorithms and to evaluate their performance regarding their corresponding non-contextual versions, considering three different budgets. Results:Contextual algorithms perform better when indicators related to test time reduction are considered, as the contextual information they use is related to execution time. Regarding NAPFD and APFDc, the non-contextual algorithms have better general performance, but both contextual versions obtain competitive results. Conclusions:The contextual versions implemented can capture the desired context information in the prioritization without negatively impacting their performance regarding fault-detection.

On the use of historical data in context-aware multimedia documents adaptation processes

Playing multimedia documents in ubiquitous systems may require content adaptation based on gathered context information and accumulated historical data. Several approaches have already been proposed, in which adaptation actions are performed to provide adapted documents. Nevertheless, these approaches focus mainly on efficient use of context information without involving historical users data to improve the adaptation process. Thus, this paper allows for consideration of historical users data during the execution of the adaptation process. To do so, the context elements and the adaptation actions are first modeled using the oriented-object approach and then converted into relational and NoSQL databases schemes. Finally, algorithms for storing, retrieving and analysing data are designed. The proposal is validated by implementing scenarios through a real prototype. At a first step, the performances are measured to estimate the cost of data processing. The experiments show that NoSQL databases excel in data storage and ease of implementation, while relational databases perform well in data retrieve. At a second step, the proposal usefulness is highlighted by showing how historical data contribute to adaptation rules personalization using datadriven rule learning mechanisms rather than defining them explicitly. The analysis algorithm could retain personalized adaptation rules with confidence degree greater than 90%. Overall, the results are satisfactory.

An effective graph embedded YOLOv5 model for forest fire detection

AbstractThe existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework.

Interfacility Referral Communication for PICU Transfer.

For patients requiring transfer to a higher level of care, excellent interfacility communication is essential. Our objective was to characterize verbal handoffs for urgent interfacility transfers of children to the PICU and compare these characteristics with known elements of high-quality intrahospital shift-to-shift handoffs. Mixed methods retrospective study of audio-recorded referral calls between referring clinicians and receiving PICU physicians for urgent interfacility PICU transfers. Academic tertiary referral PICU. Children 0-18 years old admitted to a single PICU following interfacility transfer over a 4-month period (October 2019 to January 2020). None. We reviewed interfacility referral phone calls for 49 patients. Referral calls between clinicians lasted a median of 9.7 minutes (interquartile range, 6.8-14.5 min). Most referring clinicians provided information on history (96%), physical examination (94%), test results (94%), and interventions (98%). Fewer clinicians provided assessments of illness severity (87%) or code status (19%). Seventy-seven percent of referring clinicians and 6% of receiving PICU physicians stated the working diagnosis. Only 9% of PICU physicians summarized information received. Interfacility handoffs usually involved: 1) indirect references to illness severity and diagnosis rather than explicit discussions, 2) justifications for PICU admission, 3) statements communicating and addressing uncertainty, and 4) statements indicating the referring hospital's reliance on PICU resources. Interfacility referral communication was similar to intrahospital shift-to-shift handoffs with some key differences: 1) use of contextual information for appropriate PICU triage, 2) difference in expertise between communicating clinicians, and 3) reliance of referring clinicians and PICU physicians on each other for accurate information and medical/transport guidance. Interfacility PICU referral communication shared characteristics with intrahospital shift-to-shift handoffs; however, communication did not adhere to known elements of high-quality handovers. Structured tools specific to PICU interfacility referral communication must be developed and investigated for effectiveness in improving communication and patient outcomes.

The effect of domain-specific exercise on high- and low-level cognitive processing during anticipation

ABSTRACT Skilled anticipation is underpinned by the ability to synthesise high- (e.g., context) and low-level (e.g., biological motion) processes. While researchers have highlighted the effect of physiological load on the pick-up of biological motion from an opponent’s kinematics, how such stress affects the use of contextual information has remained unexplored. In this paper, we conducted an experiment to examine how a fatigue-inducing, cricket-specific exercise protocol affects the use of contextual and kinematic information during anticipation. Altogether, 13 skilled batters completed a validated simulated cricket batting task designed to induce physiological responses representative of competition. Before, after, and during the exercise protocol, participants anticipated the end location of bowling deliveries that presented either opponent kinematics, contextual information (field positioning and game situation), or both. Anticipation responses were more accurate during the exercise protocol than at rest. Also, responses were more accurate when contextual information was available compared with when only kinematic cues were presented. Moreover, from the beginning to the end of the protocol, anticipation responses decreased in accuracy in those conditions in which contextual information was presented and increased in accuracy when only kinematic cues were available. We interpret the findings relative to Attentional Control Theory (Eysenck et al., 2007). Findings highlight the complex nature of expert sports performance and indicate that the effect of domain-specific exercise on anticipation is dependent on the physiological load experienced and the type of information available.

Two‐dimensional medical image segmentation based on U‐shaped structure

AbstractWith rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder‐decoder‐based U‐Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual information due to continuous pooling operations or strided convolution when decreasing image resolution, and make less use of contextual information and global information under different receptive fields. To overcome this shortcoming, this paper proposes a novel structure called RAAU‐Net. In our proposed RAAU‐Net structure, which is a modified U‐shaped architecture, we aim to capture high‐level information while preserving spatial information and focusing on the regions of interest. RAAU‐Net comprises three main components: a feature encoder module that utilizes a pre‐trained ResNet‐18 model as a fixed feature extractor, a multi‐receptive field extraction module that we developed, and a feature decoder module. We have tested our method on several 2D medical image segmentation tasks such as retinal nerve, breast tumor, skin lesion, lung, gland, and polyp segmentation. All the indexes of the model reached the best in the dataset of skin lesions, in which Accuracy, Precision, IoU, Recall, and Dice Score were 3.26%, 5.42%, 9.92%, 6.52%, and 5.95% higher than UNet.

CSI-CSI: Comparing several investigative approaches toward crime scene improvement

Crime scene investigations are highly complex environments that require the CSI to engage in complex decision-making. CSIs must rely on personal experience, context information, and scientific knowledge about the fundamental principles of forensic science to both find and correctly interpret ambiguous traces and accurately reconstruct a scene. Differences in CSI decision making can arise in multiple stages of a crime scene investigation. Given its crucial role in forensic investigation, CSI decision-making must be further studied to understand how differences may arise during the stages of a crime scene investigation. The following exploratory research project is a first step at comparing how crime scene investigations of violent robberies are conducted between 25 crime scene investigators from nine countries across the world.Through a mock crime scene and semi-structured interview, we observed that CSIs have adopted a variety of investigation approaches. The results show that CSIs have different working strategies and make different decisions when it comes to the construction of relevant hypotheses, their search strategy, and the collection of traces. These different decisions may, amongst other factors, be due to the use of prior information, a CSI’s knowledge and experience, and the perceived goal of their investigation. We suggest the development of more practical guidelines to aid CSIs through a hypothetico-deductive reasoning process, where (a) CSIs are supported in the correct use of contextual information, (b) outside knowledge and expertise are integrated into this process, and (c) CSIs are guided in the evaluation of the utility of their traces.

Internal Cognitive Load Differentially Influences Acoustic and Lexical Context Effects in Speech Perception: Evidence From a Population With Attention-Deficit/Hyperactivity Disorder.

To overcome variability in spoken language, listeners utilize various types of context information for disambiguating speech sounds. Context effects have been shown to be affected by cognitive load. However, previous results are mixed regarding the influence of cognitive load on the use of context information in speech perception. We tested a population characterized by an attention-deficit/hyperactivity disorder (ADHD) to better understand the relationship between attention (or internal cognitive load) and context effects. The use of acoustic versus lexical properties of the surrounding signal to disambiguate speech sounds was examined in listeners with ADHD and neurotypical listeners. Compared to neurotypicals, individuals with ADHD relied more strongly on lexical context for speech perception; however, reliance on acoustic context information from speech rate did not differ. These findings confirm that cognitive load impacts the use of high-level but not low-level context information in speech and imply that speech recognition deficits in ADHD likely arise due to impaired higher order cognitive processes.

Bridging Multi-Scale Context-Aware Representation for Object Detection

Feature Pyramid Network (FPN) exploits multi-scale fusion representation to deal with scale variances in object detection. However, it ignores the context information gap across different levels. In this paper, we develop a plug-and-play detector, the multi-scale context-aware feature pyramid network to unleash the power of feature pyramid representation. Based on the dilated feature map at the highest level of the backbone, we propose the cross-scale context aggregation block to make full use of context information in the feature pyramid. Moreover, we extract discriminative features among different levels by the adaptive context aggregation block for robust object detection. Comprehensive experiments on MS-COCO demonstrate the effectiveness and efficiency of the proposed network, where about 1.0 ~ 3.0 AP improvements are achieved compared with existing FPN-based methods. In addition, we also conduct extensive experiments on pixel-level prediction tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., instance segmentation, semantic segmentation, and panoptic segmentation, which further verify the effectiveness of the proposed method.

Context-Based Aspect-Oriented Requirement Engineering Model

Mobile applications are context-oriented systems that involve the use of context information while operating. Mobile applications demand tackling context information in the early phase of software engineering. A context-aware system demands a different approach to handling the influence of the context on a system's requirements. Aspect-oriented Requirement Engineering separates concerns throughout requirements, called crosscutting concerns, in the early phase of software development to improve the modularity of complex applications. Capturing requirements embedded within context is a challenging procedure. This study aimed to identify such contextual characteristics of requirements in the early phase of software engineering, using natural language processing techniques, by proposing Context-Based Aspect-Oriented Requirement Engineering (CB-AORE) to visualize the existence of crosscutting concerns. CB-AORE performs context modeling to analyze the context dependency with base requirements and helps the analyst to visualize the correlation of functional and non-functional requirements with context. A case study analyzed the identification of context and its use to identify crosscutting concerns.

Deep Neural Network with Embedding Fusion for Chinese Named Entity Recognition

Chinese Named Entity Recognition (NER) is an essential task in natural language processing, and its performance directly impacts the downstream tasks. The main challenges in Chinese NER are the high dependence of named entities on context and the lack of word boundary information. Therefore, how to integrate relevant knowledge into the corresponding entity has become the primary task for Chinese NER. Both the lattice LSTM model and the WC-LSTM model did not make excellent use of contextual information. Additionally, the lattice LSTM model had a complex structure and did not exploit the word information well. To address the preceding problems, we propose a Chinese NER method based on the deep neural network with multiple ways of embedding fusion. First, we use a convolutional neural network to combine the contextual information of the input sequence and apply a self-attention mechanism to integrate lexicon knowledge, compensating for the lack of word boundaries. The word feature, context feature, bigram feature, and bigram context feature are obtained for each character. Second, four different features are used to fuse information at the embedding layer. As a result, four different word embeddings are obtained through cascading. Last, the fused feature information is input to the encoding and decoding layer. Experiments on three datasets show that our model can effectively improve the performance of Chinese NER.

Methodological approaches to study context in intervention implementation studies: an evidence gap map

BackgroundWithin implementation science studies, contextual analysis is increasingly recognized as foundational to interventions' successful and sustainable implementation. However, inconsistencies between methodological approaches currently limit progress in studying context and guidance to standardize the use of those approaches is scant. Therefore, this study's objective was to systematically review and map current methodological approaches to contextual analysis in intervention implementation studies. The results would help us both to systematize the process of contextual analysis and identify gaps in the current evidence.MethodsWe conducted an evidence gap map (EGM) based on literature data via a stepwise approach. First, using an empirically developed search string, we randomly sampled 20% of all intervention implementation studies available from PubMed per year (2015–2020). Second, we assessed included studies that conducted a contextual analysis. Data extraction and evaluation followed the Basel Approach for CoNtextual ANAlysis (BANANA), using a color-coded rating scheme. Also based on BANANA and on the Context and Implementation of Complex Interventions (CICI) framework–an implementation framework that pays ample attention to context– we created visual maps of various approaches to contextual analysis.ResultsOf 15, 286 identified intervention implementation studies and study protocols, 3017 were screened for inclusion. Of those, 110 warranted close examination, revealing 22% that reported on contextual analysis.Only one study explicitly applied a framework for contextual analysis. Data were most commonly collected via surveys (n = 15) and individual interviews (n = 13). Ten studies reported mixed-methods analyses. Twenty-two assessed meso-level contextual and setting factors, with socio-cultural aspects most commonly studied. Eighteen described the use of contextual information for subsequent project phases (e.g., intervention development/adaption, selecting implementation strategies). Nine reported contextual factors' influences on implementation and/or effectiveness outcomes.ConclusionsThis study describes current approaches to contextual analysis in implementation science and provides a novel framework for evaluating and mapping it. By synthesizing our findings graphically in figures, we provide an initial evidence base framework that can incorporate new findings as necessary. We strongly recommend further development of methodological approaches both to conduct contextual analysis and to systematize the reporting of it. These actions will increase the quality and consistency of implementation science research.

Spatial Relationship Extraction of Geographic Entities Based on BERT Model

Geographic entity relationship extraction from text is an important way to acquire geographic knowledge. Entity relations in Chinese text are difficult to discover because of implicit representations of relations between entities in Chinese text. Therefore, using existing pattern matching and machine learning methods to extract entity relations from Chinese text often has problems such as insufficient artificial features, poor generality, inability to resolve word polysemy, and difficulty in making full use of contextual information. However, deep learning methods can better solve the above problems. This paper takes the spatial relation between geographic entities as the main research object to build a geographic entity spatial relation corpus, and propose a geographic entity relationship extraction method that combines BERT and attention mechanism. The method is trained and tested on the corpus. The results show that the F1 value of the BERT-BiGRU-Attention model proposed in this paper reaches 85.2% on the test set, which is a great improvement in relation extraction ability compared with other baseline models. The geographic entity relationship extraction method based on the BERT model can effectively learn the context information in Chinese text sentences, improve the accuracy of relationship extraction, and provide support for geographic knowledge graph construction and geographic entity information retrieval.

The effects of aging and perceived loneliness on lexical ambiguity resolution.

Language is central to the interactional nature of the social life within which it is situated. To react or respond in a particular situation, we must be able to recognize the social situation. Growing evidence has demonstrated the negative impact of perceived loneliness on late-life executive functions. Yet little is known about how social factors impact language processing for older people. The current study aims to fill this gap, first by assessing age-related changes in lexical processing during Chinese word reading, second, by examining whether older adults' individual differences, such as processing speed and verbal abilities, modulate meaning retrieval and, third, by investigating whether perceived loneliness can hinder word reading. The use of compound words in Chinese enables significant sublexical ambiguity, requiring varying executive load during word recognition: when a word's constituent characters carry multiple meanings, readers must consider the meaning contributions of both constituent characters and use top-down word information to determine the most accurate meaning of the ambiguous character, a process termed "sublexical ambiguity resolution." In this study, adults read real Chinese words (including both sublexically ambiguous and unambiguous words) and pseudowords, and they were asked to make lexical decisions. Older adults exhibited greater lexicality effects (i.e., real words were easier to be identified than pseudowords) and similar sublexical ambiguity effects compared with young adults. Among older participants, processing speed could account for their ability to differentiate between words and pseudowords. In contrast, the level of perceived loneliness modulated the efficacy of sublexical ambiguity resolution: the participants with higher perceived loneliness displayed a greater sublexical ambiguity disadvantage effect. These results indicate that perceived loneliness may affect the use of contextual information in meaning retrieval during reading. The findings provide an important link between social connections and language processing.

Reading Behaviors through Patterns of Finger-Tracking in Italian Children with Autism Spectrum Disorder.

The paper proposes an ecological and portable protocol for the large-scale collection of reading data in high-functioning autism spectrum disorder (ASD) children based on recording the finger movements of a subject reading a text displayed on a tablet touchscreen. By capitalizing on recent evidence that movements of a finger that points to a scene or text during visual exploration or reading may approximate eye fixations, we focus on recognition of written content and function words, pace of reading, and accuracy in reading comprehension. The analysis showed significant differences between typically developing and ASD children, with the latter group exhibiting greater variation in levels of reading ability, slower developmental pace in reading speed, less accurate comprehension, greater dependency on word length and word frequency, less significant prediction-based processing, as well as a monotonous, steady reading pace with reduced attention to weak punctuation. Finger-tracking patterns provides evidence that ASD readers may fail to integrate single word processing into major syntactic structures and lends support to the hypothesis of an impaired use of contextual information to predict upcoming stimuli, suggesting that difficulties in perception may arise as difficulties in prediction.

Fractal pyramid low-light image enhancement network with illumination information

Low-light images suffer from many problems, including low contrast, low brightness, color distortion, blurred details, and noise, which adversely affect the performance of many advanced computer vision tasks. There have been a variety of deep-learning-based methods used to enhance low-light images in recent years. These methods, however, fail to calculate the illumination information and neglect the relationship between multi-scale features and contextual information, which lead to not only poor model generalization but also poor color and details enhancement. To address these concerns, we propose a two-stage low-light image enhancement network called the fractal pyramid network with illumination information (FPN-IL). On the one hand, we use a code network added spatial channel attention mechanism to extract the lighting information in case of uneven exposure and overexposure. On the other hand, we combine the fractal and pyramid networks to construct a new coding method. By having multiple processing paths for information, the FPN-IL is able to make full use of contextual information and interactions of features at different scales. Thus, the image’s details could be abundant. The results demonstrate the advantages of our method compared with other methods, from both qualitative and quantitative perspectives.

Deep-Learning-Based Financial Message Sentiment Classification in Business Management.

A deep-learning-based financial text sentiment classification method is proposed in this paper, which can provide a reference for business management. In the proposed method, domain adaptation is adopted to solve the common problem of insufficient labeled samples in the financial textual domain. Specifically, in the classification process, the seq2seq model is firstly adopted to extract the abstract from the financial message, which can reduce the influence of invalid information and speed up processing. In the process of sentiment classification, a bidirectional LSTM model is adopted for classification, which can more comprehensively make use of context information. Experiments are carried out to testify the proposed method through the open-source data set. It can be seen that the proposed method can effectively transfer from the reduced Amazon data set to the StockTwits financial text data set. Compared with the parameter-frozen-based method and the SDA-based method, the recognition rates have improved by 0.5% and 6.8%, respectively. If the target domain data set can be directly adopted for training, the recognition rate of the proposed method is higher than that of the SVM method and the LSTM method by 8.3% and 4.5%, respectively.

Context Awareness in Recognition of Affective States: A Systematic Mapping of the Literature

Studies indicate the growth and relevance of systems that can recognize affective states in the most different areas. A tendency to more natural environments could be noticed that considering physical or physiological signs and the individual’s relationship with the environment, other people, and daily activities. Therefore, what is the importance of combining ubiquitous computing, affective computing, and the contributions of context-aware information to provide more accurate and intelligent affective systems? This study presents a systematic literature mapping about the use of contextual information to identify affective states, which covered articles published between 2010 and October 2021, resulting in 1.638 studies. After applying filters, which we explain further in this article, we selected 49 works to answer a set of research questions. The results indicate that physiological data was the main parameter for recognizing affective signs (62.3%, 33/53), followed by visual data (32.1%, 17/53). The links between context and affective signs presented a more significant occurrence in the combination of contexts related to activities and physiological data (34%, 18/53).

AB-ResUNet+: Improving Multiple Cardiovascular Structure Segmentation from Computed Tomography Angiography Images

Accurate segmentation of cardiovascular structures plays an important role in many clinical applications. Recently, fully convolutional networks (FCNs), led by the UNet architecture, have significantly improved the accuracy and speed of semantic segmentation tasks, greatly improving medical segmentation and analysis tasks. The UNet architecture makes heavy use of contextual information. However, useful channel features are not fully exploited. In this work, we present an improved UNet architecture that exploits residual learning, squeeze and excitation operations, Atrous Spatial Pyramid Pooling (ASPP), and the attention mechanism for accurate and effective segmentation of complex cardiovascular structures and name it AB-ResUNet+. The channel attention block is inserted into the skip connection to optimize the coding ability of each layer. The ASPP block is located at the bottom of the network and acts as a bridge between the encoder and decoder. This increases the field of view of the filters and allows them to include a wider context. The proposed AB-ResUNet+ is evaluated on eleven datasets of different cardiovascular structures, including coronary sinus (CS), descending aorta (DA), inferior vena cava (IVC), left atrial appendage (LAA), left atrial wall (LAW), papillary muscle (PM), posterior mitral leaflet (PML), proximal ascending aorta (PAA), pulmonary aorta (PA), right ventricular wall (RVW), and superior vena cava (SVC). Our experimental evaluations show that the proposed AB-ResUNet+ significantly outperforms the UNet, ResUNet, and ResUNet++ architecture by achieving higher values in terms of Dice coefficient and mIoU.