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This study examined the effects of storytelling with or without contextual information on children with autism spectrum disorder (ASD) and typical development (TD) using eye-tracker. They were randomized into two groups—the stories included and did not include social contextual information respectively. Training was delivered in groups, with eight sessions across four weeks, 30 min/session. Participants’ fixation duration, visit duration, and fixation count on human faces from 20 photos and a video were recorded. Our findings revealed that storytelling with social contextual information enhanced participants’ eye gazes on eyes/ faces in static information (photos) for both children with ASD and TD, but the same advantage could not be seen for children with ASD in regard to dynamic information (videos).Clinical Trial Registration Number (URL: http://www.clinicaltrials.gov): NCT04587557

Recently, visual attention mechanisms have been employed in CNN-based crowd counting methods to overcome the interference of background noise and have achieved good performance. However, the existing methods usually focus on designing complex attention structures and extracting pixel-level contextual information, while ignoring global contextual information extraction at different scales. In this paper, to overcome scale variation and complex background noise, we propose a novel scale-aware and global contextual network (SGCNet) that employs multi-scale attention mechanisms to selectively strengthen features with different network scales. The key component of SGCNet is a multi-scale global contextual block that consists of multi-scale feature selection and global contextual information extraction, where global contextual information is adopted as guidance to weight features at different scales. Compared with the previous methods that ignore scale information injected into the attention mechanism, SGCNet achieves better counting performance via multi-scale contextual information extraction. Extensive experiments on four crowd counting datasets (ShanghaiTech, UCF_CC_50, UCF-QNRF, UCSD) demonstrate the effectiveness and superiority of the proposed method in highly congested noisy crowd scenes.

Temporal trajectories of land-cover change provide important information on landscape dynamics that are critical to our understanding of complex human-environment adaptive systems. The increasing availability of long time series of satellite images, especially the recent free release of multi-decadal Landsat satellite archive, presents a great opportunity to improve our ability to detect land-cover change over multiple dates and advance land change science. In this article, a spatial-temporal modeling approach is developed for reconstructing land-cover change trajectories from time series of satellite images. The change detection method represents an enhancement to the conventional post-classification comparison. The key innovation lies in the use of Markov random field theory to model spatial-temporal contextual information explicitly in the classification of time series images. When evaluated using a time series of seven Landsat images in a case study of southeast Ohio, the spatial-temporal modeling approach yielded significantly more accurate and consistent trajectories of land-cover change than conventional non-contextual approaches. The results from the case study demonstrate the effectiveness of the change detection method in reconstructing land-cover change trajectories and also highlight the utility of spatial-temporal contextual information in improving the accuracy and consistency of land-cover classifications across space and time.

A variety of theorists have recently argued against the explanation of the semantic content of a sentence as a minimal proposition claiming that intentional aspects of the context are often needed to obtain a minimal proposition. Minimalists such as Borg, however, still defend intention-insensitive minimal propositions for sentences in a narrow context and provide solutions or dissolutions against incompleteness objections. In this paper, we show that these putative defences of propositionalism do not serve to avoid some additional genuine objections which arise from compositional context-sensitivity. We aim to show that there are complex expressions which compositionally demand intention-sensitive pragmatic effects in a mandatory way and, for that reason, they provide us with evidence against the type of propositionalism that substantiates the defence of semantic minimalism. Algunos teóricos han rechazado recientemente la concepción del contenido semántico de una oración como proposición mínima afirmando que para conseguir una proposición mínima a menudo se necesitan aspectos intencionales del contexto. Sin embargo, minimalistas como Borg siguen defendiendo que las oraciones en un contexto estrecho expresan proposiciones mínimas sin tener en cuenta las intenciones y lo defienden resolviendo o disolviendo las objeciones de incompletitud. En este artículo mostramos que esas supuestas defensas del proposicionalismo no sirven para evitar otras objeciones genuinas que dependen de la sensibilidad contextual composicional. Nuestro objetivo es mostrar que hay expresiones complejas que demandan composicionalmente de modo obligatorio efectos pragmáticos cuya recuperación depende de las intenciones y, por ello, proporcionan evidencia contra el tipo de proposicionalismo que fundamenta la defensa del minimismo semántico.

Political scientists designing experiments often face the question of how abstract or detailed their experimental stimuli should be. Typically, this question is framed in terms of trade-offs relating to experimental control and generalizability: the more context introduced into studies, the less control, and the more difficulty generalizing the results. Yet, we have reason to question this trade-off, and there is relatively little systematic evidence to rely on when calibrating the degree of abstraction in studies. We make two contributions. First, we provide a theoretical framework that identifies and considers the consequences of three dimensions of abstraction in experimental design: situational hypotheticality, actor identity, and contextual detail. Second, we replicate and extend three survey experiments, varying these levels of abstraction. We find no evidence that situational hypotheticality substantively changes results in any of our studies, but do find that increased contextual detail dampens treatment effects, and that the salience of actor identities moderates results in our endorsement experiment.

We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training (GPT) and its variants, Transformer-XL, Cross-lingual Language Models (XLM), and the Bidirectional Encoder Representations from Transformers (BERT). Considering BERT’s strength and popularity in text-based emotion detection, the paper discusses recent works in which researchers proposed various BERT-based models. The survey presents its contributions, results, limitations, and datasets used. We have also provided future research directions to encourage research in text-based emotion detection using these models.

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

Informational resources have significantly expanded as a result of the growth of the internet. Consequently, making personalized suggestions about different types of information, goods, and services is the best strategy to assist customers in solving the issue of information overload. As a result, recommendation systems are employed to aid clients in locating the products most appropriate to their interests. The majority of traditional recommender systems rely on a traditional model that just takes into account user-item-rating interactions without taking context into account. It has been demonstrated that context-aware recommender systems deliver improved predicted performance across a variety of areas by attempting to adapt to users' preferences across various settings. This study presents a proposed system to help the recommender system solve its difficulties in producing accurate predictions that are relevant to the user's preferences. The system is the Contextual Pre-filtering Based Collaborative Filtering (CPBCF) model, which is based on splitting items. To decrease the time and space needed for processing correlations, it depends on the recommended splitting approach utilizing the variance equation, which decreases the dataset depending on the most important attributes. In the proposed system experiments, the performance of CPBCF with and without contextual pre-filtering was enhanced by (5-7%) for the precision, (7-8%) for the recall, and (7-8%) for the f1-measuer. While the complexity time has enhanced by (3-4 sec). The effectiveness of the CPBCF model was evaluated using various numbers of neighbors. We can observe that neighborhood size does have an effect on forecast accuracy.

We consider the problem of multiple-hypothesis testing with generic side information: for each hypothesis Hi we observe both a p-value pi and some predictor xi encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple-testing procedures. We propose a general iterative framework for this problem, the adaptive p-value thresholding procedure which we call AdaPT, which adaptively estimates a Bayes optimal p-value rejection threshold and controls the false discovery rate in finite samples. At each iteration of the procedure, the analyst proposes a rejection threshold and observes partially censored p-values, estimates the false discovery proportion below the threshold and proposes another threshold, until the estimated false discovery proportion is below α. Our procedure is adaptive in an unusually strong sense, permitting the analyst to use any statistical or machine learning method she chooses to estimate the optimal threshold, and to switch between different models at each iteration as information accrues. We demonstrate the favourable performance of AdaPT by comparing it with state of the art methods in five real applications and two simulation studies.

•29 studies in 14 disciplines demonstrate influence of confirmation bias.•These studies support three improvements to improve accuracy of analyses, including:•Reduce access to unnecessary information.•Use multiple comparison samples.•Repeat analysis blinded to previous conclusions. The extent to which cognitive biases may influence decision-making in forensic science is an important question with implications for training and practice. We conducted a systematic review of the literature on cognitive biases in forensic science disciplines. The initial literature search including electronic searching of three databases (two social science, one science) and manual review of reference lists in identified articles. An initial screening of title and abstract by two independent reviewers followed by full text review resulted in the identification of 29 primary source (research) studies. A critical methodological deficiency, serious enough to make the study too problematic to provide useful evidence, was identified in two of the studies. Most (n = 22) conducted analyses limited to practitioners (n = 17), forensic science trainees (n = 2), or both forensic science practitioners and students (n = 3); other analyses were based on university student or general population participants. Latent fingerprint analysis was examined in 11 studies, with 1–3 other studies found in 13 other disciplines or domains. This set of studies provides a robust database, with evidence of the influence of confirmation bias on analysts conclusions, specifically among the studies with practitioners or trainees presented with case-specific information about the “suspect” or crime scenario (in 9 of 11 studies examining this question), procedures regarding use of exemplar(s) (in 4 of 4 studies), or knowledge of a previous decision (in 4 of 4 studies). The available research supports the idea of susceptibility of forensic science practitioners to various types of confirmation bias and of the potential value of procedures designed to reduce access to unnecessary information and control the order of providing relevant information, use of multiple comparison samples rather than a single suspect exemplar, and replication of results by analysts blinded to previous results.