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To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower computation costs. To effectively capture rich contextual dependencies from both domains, we decouple the spatial and spectral contextual attention into two sub-blocks, SaMCA and SeMCA, where depth-wise convolution is employed to contextualize the input keys in the pure dimension. Thereafter, multi-head local attentions are implemented as group processing when the keys are alternately concatenated with the queries. In particular, in the SeMCA block, we group the spatial pixels by evenly sampling and create multi-head channel attention on each sampling set, to reduce the number of the training parameters and avoid the storage increase. In addition, the static contextual keys are fused with the dynamic attentional features in each block to strengthen the capacity of the model in data representation. Finally, the decoupled sub-blocks are weighted and summed together for 3-D attention perception of HSI. The DMuCA module is then plugged into a ResNet to perform HSI classification. Extensive experiments demonstrate that our proposed DMuCA achieves excellent results over several state-of-the-art attention mechanisms with the same backbone.

Abstract In this paper, the effect of correspondence history between contingency and contingency descriptions on the behavior of choice under the self-control paradigm is analyzed in 85 participants between the ages of 10 and 11, through two intrasubject experiments, one carried out in a laboratory context and another in a simulated natural situation. The results indicate that it is possible to affect the behavior of choice between an immediate reinforcer of lesser magnitude and a delayed one of greater magnitude in favor of the second alternative, from an experimental history of correspondence between descriptions of contingencies and contingencies. In both experiments, most of participants chose the delayed reinforcer after undergoing correspondence tests between descriptions and contingencies, and the immediate reinforcer after facing the correspondence absent trials, even though the training situations were formally and functionally different in relation with the task established to evaluate the conduct of choice. The results are analyzed in light of the \"Relational Frame Theory\", specifically, in relation with the alteration of the functions of the language.

Authentication by key is successfully applied in many cases. However, this simple approach raises several problems during contextual authentication, when the final decision depends not only on the knowledge of the key but also on some other independent parameters such as time, distance, values fetched from nearby internet of things (IoT) devices, etc. Such contextual parameters are usually considered an independent phase of the whole authentication process. In this paper, we propose a new method of extended authentication because, instead of using just keys, we also applied some mathematical formulas describing the context to be evaluated as a single cryptographic process.

Cause-related marketing (C-RM) has risen to become a popular strategy to increase business value through profit-motivated giving. Despite the growing number of articles published in the last decade, no comprehensive analysis of the most discussed constructs of cause-related marketing is available. This paper uses an advanced Text Mining methodology (a Bayesian contextual analysis algorithm known as Correlated Topic Model, CTM) to conduct a comprehensive analysis of 246 articles published in 40 different journals between 1988 and 2013 on the subject of cause-related marketing. Text Mining also allows quantitative analyses to be performed on the literature. For instance, it is shown that the most prominent long-term topics discussed since 1988 on the subject are \"brand-cause fit\", \"law and Ethics\", and \"corporate and social identification\", while the most actively discussed topic presently is \"sectors raising social taboos and moral debates\". The paper has two goals: first, it introduces the technique of CTM to the Marketing area, illustrating how Text Mining may guide, simplify, and enhance review processes while providing objective building blocks (topics) to be used in a review; second, it applies CTM to the C-RM field, uncovering and summarizing the most discussed topics. Mining text, however, is not aimed at replacing all subjective decisions that must be taken as part of literature review methodologies.

Scene graph aims to faithfully reveal humans’ perception of image content. When humans look at a scene, they usually focus on their interested parts in a special priority. This innate habit indicates a hierarchical preference about human perception. Therefore, we argue to generate the Scene Graph of Interest which should be hierarchically constructed, so that the important primary content is firstly presented while the secondary one is presented on demand. To achieve this goal, we propose the Tree–Guided Importance Ranking (TGIR) model. We represent the scene with a hierarchical structure by firstly detecting objects in the scene and organizing them into a Hierarchical Entity Tree (HET) according to their spatial scale, considering that larger objects are more likely to be noticed instantly. After that, the scene graph is generated guided by structural information of HET which is modeled by the elaborately designed Hierarchical Contextual Propagation (HCP) module. To further highlight the key relationship in the scene graph, all relationships are re-ranked through additionally estimating their importance by the Relationship Ranking Module (RRM). To train RRM, the most direct way is to collect the key relationship annotation, which is the so-called Direct Supervision scheme. As collecting annotation may be cumbersome, we further utilize two intuitive and effective cues, visual saliency and spatial scale, and treat them as Approximate Supervision, according to the findings that these cues are positively correlated with relationship importance. With these readily available cues, the RRM is still able to estimate the importance even without key relationship annotation. Experiments indicate that our method not only achieves state-of-the-art performances on scene graph generation, but also is expert in mining image-specific relationships which play a great role in serving subsequent tasks such as image captioning and cross-modal retrieval.

With the advancement of neural networks, end-to-end neural automatic speech recognition (ASR) systems have demonstrated significant improvements in identifying contextually biased words. However, the incorporation of bias layers introduces additional computational complexity, requires increased resources, and leads to redundant biases. In this paper, we propose a Context Bias Adaptive Model, which dynamically assesses the presence of biased words in the input and applies context biasing accordingly. Consequently, the bias layer is activated only for input audio containing biased words, rather than indiscriminately introducing contextual bias information for every input. Our findings indicate that the Context Bias Adaptive Model effectively mitigates the adverse effects of contextual bias while substantially reducing computational costs.

A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document’s content that can reflect the authors’ main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors’ provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by the keyphrases that provide the abstract description contained therein. With traditional keyphrase techniques, however, it is difficult to identify relevant information based on context. Several studies in the literature have explored graph-based unsupervised keyphrase extraction techniques for automatic keyphrase extraction. However, there is only limited existing work that embeds contextual information for keyphrase extraction. To understand keyphrases, it is essential to grasp both the concept and the context of the document. Hence, a hybrid unsupervised keyphrase extraction technique is presented in this paper called ContextualRank, which embeds contextual information such as sentences and paragraphs that are relevant to keyphrases in the keyphrase extraction process. We propose a hierarchical topic modeling approach for topic discovery based on aggregating the extracted keyphrases from ContextualRank. Based on the evaluation on two short-text datasets and one long-text dataset, ContextualRank obtains remarkable improvements in performance over other baselines in the short-text datasets.

The increased population and the fast expansion of urbanization were some of the global main characteristics in the past decades. This expansion has shaped the diverse spatial structure in metropolitan areas worldwide. However, these studies have been focused on one or some metropolitan areas within countries or continents and no systematic review, to the best of our knowledge, has ever addressed the spatial structure of metropolitan areas and their contextual factors at a global scale. Thus, this paper attempts to address this gap through a deeper understanding of the evolving spatial structure of metropolitan areas at a global scale and studying the driving and contextual factors affecting them. To this end, the authors examined the empirical evidence conducted in this field using a systematic review method. The statistical society of this article consists of English-based scientific articles published in peer-reviewed journals between 1980 and 2020, which were obtained through a search in scientific databases using the related keywords to the spatial structure of metropolitan areas. After searching for relevant articles and based on the relationship between titles, keywords, and content, 161 articles were selected and carefully examined. The results show that the spatial structure of the metropolitan areas during the last forty years can be categorized into four macro divergent, convergent, homogenous, and heterogeneous patterns. These patterns were not absolute but subject to the centripetal and centrifugal forces experienced different growth trends, leading to various spatial structures.