Implicit Context Research Articles

Refining urban morphology: An explainable machine learning method for estimating footprint-level building height

Fine-grained 3D buildings with diverse morphology are a cornerstone of urban physical structures and have profound implications for sustainable city development. However, accurately estimating building height at the footprint-level is a challenge. This study bridged this gap by using random forest models to integrate the elevation, geometry and shape attributes of individual buildings, further refining those with spatial aggregation. It considered over one million buildings across 10 large Chinese cities and trained two-types models that demonstrated commendable performance in city-specific (the mean absolute error (MAE) ranged from 3.43 m to 5.06 m) and combined (MAE = 4.68 m) models. Results revealed that the current dataset had a finer urban morphology compared with existing datasets and showed outstanding generalisability in method transfer and feature ablation tests. By incorporating Shapley values, we explored the features' global and local impacts. The explainable results demonstrated that building area was the most impactful feature, and the elevation-dimension features were particularly beneficial in estimating high-rise buildings. Using the fine-grained 3D buildings, we explored the connections between explicit morphology differences and implicit contexts in cities. Overall, our work is an endeavour to estimate footprint-level building height as fuel for refining urban morphology and enabling sustainable city studies.

The Phenomenon of Feedback in the Educational Process of Higher Education Institution: Explicit and Implicit Contexts

The Phenomenon of Feedback in the Educational Process of Higher Education Institution: Explicit and Implicit Contexts

Masked and Permuted Implicit Context Learning for Scene Text Recognition

Masked and Permuted Implicit Context Learning for Scene Text Recognition

Media Archaeology Experiences: Method, Meaning and Amusement

In this paper I make four interventions in favour of the seductive value of experiential media archaeology. 1) The constellation of material artefacts that mediate between us and the world are an implicit context for historic writing on perception, representation and epistemology. Engagement with the materiality of these often forgotten artefacts offers insight into the meanings of texts that exclusively text-based scholarship would otherwise miss. 2) Tacit, artisanal knowledge embedded in artefacts sometimes exceeds that which can be found in written texts. I argue that an effective way of accessing this material logic is to re-build old artefacts to see how they work. Applying the theory of extended cognition to this process, I make a case for its unique epistemological value. 3) I show how the seductive intimacy of these objects can be amplified by re-imagining their aesthetic possibilities. 4) I discuss the educational value of the “rational recreation” with media artifacts as “philosophical toys.”&nbsp

Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.

In today's world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user's social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the "News Headline Data set" for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.

Inducing a mental context for associative memory formation with real-time fMRI neurofeedback.

Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.

Exploring the impacts of implicit context association and arithmetic booster in impulsivity reduction.

People have a higher preference for immediate over delayed rewards, and it is suggested that such an impulsive tendency is governed by one’s ability to simulate future rewards. Consistent with this view, recent studies have shown that enforcing individuals to focus on episodic future thoughts reduces their impulsivity. Inspired by these reports, we hypothesized that administration of a simple cognitive task linked to future thinking might effectively modulate individuals’ delay discounting. Specifically, we used one associative memory task targeting intervention of context information, and one working memory task targeting enhancement of individual’s ability to construct a coherent future event. To measure whether each type of cognitive task reduces individuals’ impulsivity, a classic intertemporal choice task was used to quantify individuals’ baseline and post-intervention impulsivity. Across two experiments and data from 216 healthy young adult participants, we observed that the impacts of intervention tasks were inconsistent. Still, we observed a significant task repetition effect such that the participants showed more patient choices in the second impulsivity assessment. In conclusion, there was no clear evidence supporting that our suggested intervention tasks reduce individuals’ impulsivity, and that the current results call attention to the importance of taking into account task repetition effects in studying the impacts of cognitive training and intervention.

Палатка в контексте византийской символики власти X–XII веков

ntroduction. The tent (σκηνή, τέντα) was a simple and everyday object of Byzantine life. Diplomats, merchants, pilgrims, soldiers, travelers, and simply wanderers spent a considerable part of their lives in a tent. It was a natural element of the Byzantine landscape, and geographical mobility was an important part of the lifestyle of the Byzantine elite and its psychology. But this simple, everyday thing in a certain context took on an extremely important meaning and turned into an important religious and social symbol. A simple object could indicate complicated social and ideological constructions of the 10th–12th centuries. The task of the study is to reveal the implicit power context which in certain cases endowed simple everyday objects (like a tent) with an extremely important meaning. Methods. The article is written in the general context of structuralist methodologies. We regarded the tent as a simple sign indicating the complex representations that lie behind its content. Structuralist methods allow for a correct reconstruction of Byzantine everyday representations in different strata of society. Analysis and Results. The rich tent in Byzantine society of the 10th–12th centuries was not just a part of the daily military life of the aristocracy, but also an important element of power relations. Tents defined the social status of their owners, emphasized their power and importance. Aristocratic tents of that time were a space where power decisions were made and court life took place. The tent as a power symbol relied on a broad religious context. The Tabernacle of Moses, which was the model for every tent in the Byzantine Empire, was also created by the Lord’s will, with Moses himself acting as “royal scribe”. The folkloric tent of Charos in the Acritic songs turns out to be the center of the “lower” world in which Charos ruthlessly reigns. Behind each reading of the symbolic meaning of the tent lie different social practices of different groups of Byzantine society, but they are all filled with their own understanding of the essence of power.

A Study on the In-Vehicle Voice Interaction Structure Considering Implicit context with Persistence of Conversation

A Study on the In-Vehicle Voice Interaction Structure Considering Implicit context with Persistence of Conversation

FODA: Building Change Detection in High-Resolution Remote Sensing Images Based on Feature–Output Space Dual-Alignment

In building change detection task, factors such as phenological changes, illumination changes, and registration errors will cause unchanged areas in remote sensing images to have obvious differences in pixels, which will lead to pseudochanges in results. Existing methods focus on the change information of multi-temporal remote sensing images, ignoring the exploration of pseudochange problems. Therefore, feature-output space dual-alignment (FODA) method is proposed to reduce the negative effect of the pseudochange problem by paying attention to the relationship between unchanged areas of multitemporal images. On the one hand, FODA narrows the distance between the features of the unchanged areas in the feature space, increasing its feature extraction ability of pseudo-changed areas. On the other hand, given the spatial context of image scene implicit in the output space, the ability to recognize pseudochanges of the FODA is improved through an adversarial learning procedure. Due to its simplicity and effectiveness, FODA achieves 88.73% and 82.75% F1 scores on the LEVIR-CD dataset and WHU-CD dataset respectively. Compared with state-of-the-art methods, FODA can effectively reduce the problem of pseudo-changes and significantly improve the effect of change detection even only based on a simple backbone model.

Nondefinitivity of Poetic Text Units in View of Intertextuality Theory

The article considers the problem of finding unmarked intertextual units in fiction. The typology of intertextual relations is represented. These relations are divided into autotextuality, paratextuality, intextuality and archtextuality. Intertextual units are viewed as marked and unmarked ones. The phenomenon of intertextual crosslinking and the exchange of contexts between identical address texts units is discussed. The pattern of nondefinitivity is researched. It is divided into three subpatterns: the context type (explicit / implicit), valance, intertextual potential. A textual unit being in the implicit context has several dictionary and non-dictionary meanings at the same time. A textual unit being in the explicit context has only one dictionary meaning. Valance is discussed from the perspective of the textual aspect. The terms of polysemy and multisense are defined and differentiated. Intertextual potential bases on the examples of well-known and unknown texts, which are linked together by intertextual units. On the other hand, intertextual potential bases on the examples of references to a referent with the use of personal pronouns and common nouns, which do not nominate a person or an object. An unmarked unit of intertextuality has the following features: the implicit type of context, polysemy, intertextual potential equals to 1. The works of R. Frost, R. Lowell, L. Hughes, W. Blake, E. E. Cummings provide practical material, which illustrates the pattern of nondefinitivity.

The Cerebellum and Implicit Sequencing: Evidence from Cerebellar Ataxia.

The cerebellum recognizes sequences from prior experiences and uses this information to generate internal models that predict future outcomes in a feedforward manner [Front Hum Neurosci 8: 475, 2014; Cortex 47: 137-44, 2011; Cerebellum 7: 611-5, 2008; J Neurosci 26: 9107-16, 2006]. This process has been well documented in the motor domain, but the cerebellum's role in cognitive sequencing, within the context of implicit versus explicit processes, is not well characterized. In this study, we tested individuals with cerebellar ataxia and healthy controls to clarify the role of the cerebellum sequencing using variations on implicit versus explicit and motor versus cognitive demands across five experiments. Converging results across these studies suggest that cerebellar feedforward mechanisms may be necessary for sequencing in the implicit domain only. In the ataxia group, rhythmic tapping, rate of motor learning, and implicit sequence learning were impaired. However, for cognitive sequencing that could be accomplished using explicit strategies, the cerebellar group performed normally, as though they shifted to extra-cerebellar mechanisms to compensate. For example, when cognitive and motor functions relied on cerebellar function simultaneously, the ataxia group's motor function was unaffected, in contrast to that of controls whose motor performance declined as a function of cognitive load. These findings indicated that the cerebellum is not critical for all forms of sequencing per se. Instead, it plays a fundamental role for sequencing within the implicit domain, whether functions are motor or cognitive. Moreover, individuals with cerebellar ataxia are generally able to compensate for cognitive sequencing when explicit strategies are available in order to preserve resources for motor function.

The ability of young learners to construct word meaning in context

This study examines young English readers’ ability to infer word meanings in context and to use metacognitive knowledge for constructing word meanings in relation to their reading performance. The participants were 61 fourth-grade students in the United States, comprising 24 monolingual English-speaking (ME) students and 37 English-as-a-second-language (L2) students; each group was also divided into strong and emergent readers in English. Participants were asked to read aloud paragraphs containing words unfamiliar to them in two different contextual conditions (i.e., explicit and implicit conditions), to guess the unfamiliar word meanings, and to tell a teacher how they arrived at the inferred meanings. Quantitative analyses found significant differences between strong and emergent readers in their oral fluency as well as in their ability to infer word meanings and articulate their use of metacognitive knowledge. Although significant differences were found in the ability to infer word meanings and the use of metacognitive reasoning between ME and L2 students, such differences disappeared after controlling for the size of students’ receptive vocabulary. Qualitative analyses also revealed differences in the kinds of knowledge and strategies that strong and emergent readers relied on when constructing the meaning of unknown words in both explicit and implicit contexts.

Cultural Familiarity and Individual Musical Taste Differently Affect Social Bonding when Moving to Music

Social bonds are essential for our health and well-being. Music provides a unique and implicit context for social bonding by introducing temporal and affective frameworks, which facilitate movement synchronization and increase affiliation. How these frameworks are modulated by cultural familiarity and individual musical preferences remain open questions. In three experiments, we operationalized the affective aspects of social interactions as ratings of interpersonal closeness between two walking stick-figures in a video. These figures represented a virtual self and a virtual other person. The temporal aspects of social interactions were manipulated by movement synchrony: while the virtual self always moved in time with the beat of instrumental music, the virtual other moved either synchronously or asynchronously. When the context-providing music was more enjoyed, social closeness increased strongly with a synchronized virtual other, but only weakly with an asynchronized virtual other. When the music was more familiar, social closeness was higher independent of movement synchrony. We conclude that the social context provided by music can strengthen interpersonal closeness by increasing temporal and affective self-other overlaps. Individual musical preferences might be more relevant for the influence of movement synchrony on social bonding than musical familiarity.

Matrix factorization with heterogeneous multiclass preference context

The spreading use of the Internet and big data technology has spawned the need for recommendation systems. However, to alleviate public anxiety about privacy, this paper advocates making recommendation with internal context, which refers to the implicit context hidden beneath the rating matrix only. Inspired by a recent work that embeds neighborhood information among users (as represented by multiclass preference context, MPC) into a factorization-based method, we extend it to both user-oriented and item-oriented, and put forward both user-oriented MPC and item-oriented MPC into a generic factorization framework called matrix factorization with heterogeneous MPC (MF-HMPC). In particular, we derive two specific recommendation methods from MF-HMPC with different structures, including MF with dual MPC (MF-DMPC) and MF with pipelined MPC (MF-PMPC), which are corresponding to concurrent structure and sequential structure, respectively. Extensive empirical studies on four public datasets show that our two forms of MF-HMPC outperform the referenced state-of-the-art MF methods as well as two representative deep learning methods. Moreover, we also discover some interesting facts about how these two kinds of internal contextual information complement each other. The main advantage of our methods is that they manage to strike a good balance between user-oriented neighborhood information and item-oriented neighborhood information.

Quantum-Information Conservation. The Problem About ‘Hidden Variables’, or the ‘Conservation of Energy Conservation’ in Quantum Mechanics: A Historical Lesson for Future Discoveries

The explicit history of the “hidden variables” problem is well-known and established. The main events of its chronology are traced. An implicit context of that history is suggested. It links the problem with the “conservation of energy conservation” in quantum mechanics. Bohr, Kramers, and Slaters (1924) admitted its violation being due to the “fourth Heisenberg uncertainty”, that of energy in relation to time. Wolfgang Pauli rejected the conjecture and even forecast the existence of a new and unknown then elementary particle, neutrino, on the ground of energy conservation in quantum mechanics, afterwards confirmed experimentally. Bohr recognized his defeat and Pauli’s truth: the paradigm of elementary particles (furthermore underlying the Standard model) dominates nowadays. However, the reason of energy conservation in quantum mechanics is quite different from that in classical mechanics (the Lie group of all translations in time). Even more, if the reason was the latter, Bohr, Cramers, and Slatters’s argument would be valid. The link between the “conservation of energy conservation” and the problem of hidden variables is the following: the former is equivalent to their absence. The same can be verified historically by the unification of Heisenberg’s matrix mechanics and Schrodinger’s wave mechanics in the contemporary quantum mechanics by means of the separable complex Hilbert space. The Heisenberg version relies on the vector interpretation of Hilbert space, and the Schrodinger one, on the wave-function interpretation. However the both are equivalent to each other only under the additional condition that a certain well-ordering is equivalent to the corresponding ordinal number (as in Neumann’s definition of “ordinal number”). The same condition interpreted in the proper terms of quantum mechanics means its “unitarity”, therefore the “conservation of energy conservation”. In other words, the “conservation of energy conservation” is postulated in the foundations of quantum mechanics by means of the concept of the separable complex Hilbert space, which furthermore is equivalent to postulating the absence of hidden variables in quantum mechanics (directly deducible from the properties of that Hilbert space). Further, the lesson of that unification (of Heisenberg’s approach and Schrodinger’s version) can be directly interpreted in terms of the unification of general relativity and quantum mechanics in the cherished “quantum gravity” as well as a “manual” of how one can do this considering them as isomorphic to each other in a new mathematical structure corresponding to quantum information. Even more, the condition of the unification is analogical to that in the historical precedent of the unifying mathematical structure (namely the separable complex Hilbert space of quantum mechanics) and consists in the class of equivalence of any smooth deformations of the pseudo-Riemannian space of general relativity: each element of that class is a wave function and vice versa as well. Thus, quantum mechanics can be considered as a “thermodynamic version” of general relativity, after which the universe is observed as if “outside” (similarly to a phenomenological thermodynamic system observable only “outside” as a whole). The statistical approach to that “phenomenological thermodynamics” of quantum mechanics implies Gibbs classes of equivalence of all states of the universe, furthermore re-presentable in Boltzmann’s manner implying general relativity properly … The meta-lesson is that the historical lesson can serve for future discoveries.

Context-sensitive and attribute-based sentiment classification of online consumer-generated content

Purpose Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC. Design/methodology/approach A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words. Findings Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains. Practical implications Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach. Originality/value This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing

With the growing popularity of cloud manufacturing (CMfg), an increasing number of functionally equivalent cloud services are available on the Internet, which makes cloud service recommendation an important topic recently. Since the quality-of-service (QoS) varies largely in cloud environment, many Collaborative Filtering (CF) approaches are recently proposed to predict the uncertain QoS by utilizing the historical QoS records of similar users. In addition to the data sparsity problem, however, most existing approaches also ignore the task similarity among different users, while dissimilar tasks requested by similar users may lead to quite different QoS prediction results, and thus cannot achieve higher prediction accuracy and recommendation quality. To address this problem, we develop a novel clustering-based and trust-aware approach. Firstly, a set of similar users can be identified from the view of task similarity using clustering-based algorithm, where task similarity can be computed by incorporating both explicit textual information and rating information as well as implicit context information. Secondly, considering the fact that the QoS values may be contributed by unreliable users, we design a trust-aware CF approach by merging local and global trust values, to reconstruct trust network of the clustered users. Finally, we combine the clustering-based algorithm and trust-aware approach to make a more personalized QoS prediction and reliable cloud service recommendation for the active user in CMfg. Experimental results on two real-world data sets demonstrate the effectiveness of the proposed approach compared with other state-of-the-art approaches.

Urban Design of Old Town from the Perspective of Context Continuation

The current urban development in China has generally entered the stage of urban renewal. Facing the urban mix of old and new, different forms of collage picture, the urban design exploration of continuing the urban context has important theoretical value and practical significance for the urban renewal of the old urban area. Based on the analysis of the explicit and implicit context, this paper raises six strategies for urban design in the old city, namely texture improvement, building integration, interface reconstruction, information implantation, functional compounding, and benefit balancing. As an empirical evidence, the urban design of the old city further discusses the importance of the continuity of the context for urban renewal in the old city.

A dynamic account of lian…dou in Chinese verb doubling cleft construction

This paper deals with the puzzling Chinese verb doubling construction in the schema of lian…dou (verb doubling lian…dou construction, VDLD) from a parsing perspective. Within the framework of Dynamic Syntax (Kempson et al., 2001; Cann et al., 2005) which allows the interaction between structural, semantic and pragmatic information, it is shown that VDLD can receive a straightforward explanation on the assumption that the construction is licensed by an explicit or implicit context. Specifically, it is argued that (i) the first verb marked by lian is a topicalized focus, whose parse involves reusing the structure projected by a scalar implicature; (ii) dou is an intensifier-like adverb, whose parse suggests that the alternatives projected by a scalar implicature should be true in the relevant context.