Pieces Of Contextual Information Research Articles

Design and Analysis of a Lightweight Context Fusion CNN Scheme for Crowd Counting.

Crowd counting, which is widely used in disaster management, traffic monitoring, and other fields of urban security, is a challenging task that is attracting increasing interest from researchers. For better accuracy, most methods have attempted to handle the scale variation explicitly. which results in huge scale changes of the object size. However, earlier methods based on convolutional neural networks (CNN) have focused primarily on improving accuracy while ignoring the complexity of the model. This paper proposes a novel method based on a lightweight CNN-based network for estimating crowd counting and generating density maps under resource constraints. The network is composed of three components: a basic feature extractor (BFE), a stacked à trous convolution module (SACM), and a context fusion module (CFM). The BFE encodes basic feature information with reduced spatial resolution for further refining. Various pieces of contextual information are generated through a short pipeline in SACM. To generate a context fusion density map, CFM distills feature maps from the above components. The whole network is trained in an end-to-end fashion and uses a compression factor to restrict its size. Experiments on three highly-challenging datasets demonstrate that the proposed method delivers attractive performance.

The Ethical Implications of the Intentional Fallacy

I argue that biographical information is akin to other non-aesthetic, social, historical, or political information. As such, artist’s biographies are always relevant and important when interpreting art. While the meaning and value of a piece of art is not determined by any single piece of contextual information, neither is its meaning and value ever entirely separated from context. In some cases, however, a piece of art that is technically magnificent may be experienced as repugnant when the artist has committed egregious acts.

Logical Activity Recognition for Understanding of Human Activities

Recognizing human activities based on user’s contextual information is becoming more important to the success of context-aware pervasive computing. In this paper, we define activities and introduce a six-layer activity model. We also describe our algorithm to perform activity recognition. The key contextual information is location, and other pieces of contextual information are also used in our study. To evaluate our algorithm we collected user data from twenty test users while they performed daily activities. We present a comprehensive experimental evaluation and discuss the results.

Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System

Context-aware recommender system (CARS) is a highly researched and implemented way of providing a personalized service that helps users to find their desired content. One of the remaining issues is how to decide which contextual information to acquire and how to incorporate it into CARS. While the relevant contextual information will improve the recommendations, the irrelevant contextual information could have a negative impact on the recommendation accuracy. By testing the independence between the contextual variable on the users' ratings for items, we can detect its relevance and impact on the feedback for the item consumed in that specific context. In this article, we propose several new theoretical concepts that should help deciding which information to use, as well as a methodology for detecting which contextual information contributes to explaining the variance in the ratings, based on statistical testing. The experiment was conducted on the real movie dataset that contains 12 different pieces of contextual information. We used two statistical tests with power analysis for the detection, and three contextualized matrix-factorization algorithms with slightly different reasoning for the prediction of ratings. The results showed a significant difference in the prediction of ratings in the context that was detected as relevant by our method, and the one that was detected as irrelevant, pointing to the importance of the power analysis and the benefits of the proposed method in the case of a small dataset.

CA5W1HOnto: Ontological Context-Aware Model Based on 5W1H

Ubiquitous computing is necessary to define models for broad contextual information arising out of surrounding environment. It also helps comprehend how to model a mechanism of selectively collecting useful pieces of contextual information and of providing relevant intelligent services. Further, studies are also required on how to process contextual information, its maintenance, and reasoning. However, current context-aware researches are still in need of modeling techniques reflecting ontological characteristics. As a result, it is impossible to effectively provide relevant intelligent services. They are limited as well in terms of contextual reasoning and interoperability across different pieces of contextual information. Aware of the issues, this study proposes an ontology-based context-aware modeling technique, along with a relevant framework, in order to enable efficient specification of contextual information and, thereby, further to provide intelligent context-aware services for context management and reasoning. Moreover, we mobilize the maxim of “five Ws and one H” to process physical and logical contextual information and to support our proposed technique. The maxim-applied modeling technique sets forth an intuitive context-aware schema and demonstrates high applicability to sharing and integration of contextual information. Meanwhile, the ontology-based modeling supports reasoning on contextual information and facilitates more intelligent and reliable services.

Awareness of context and privacy

AbstractEditor's SummaryAdding contextual information enhances the content and value of communications, yet it can also introduce risk and threaten privacy. A common piece of contextual information is location, but context extends to identity, user profile, e‐mail address, time and more. Understanding context from the standpoint of privacy awareness requires a systematic conceptualization of the concepts of privacy and context, personally identifiable information and the ways information flows, from processing and creation through transfer and acceptance. Numerous examples illustrate the potential chain of connections that could be revealed between a personal subject and context. Such information, made explicit, can undermine privacy policies. Integrating context‐ and location‐aware services in software should be approached cautiously and with full understanding of the implications. Diagrammed scenarios provided can inform considerations and software specification building.

Interaction between grammar and pragmatics: The case of implicit arguments, implicit predicates and co-composition in Hungarian

After an overview of previous approaches to the relation between grammar and pragmatics we define grammar and pragmatics as two separate but not independent components of the theory of language modeling grammatical and pragmatic competences, respectively. In addition to assuming their co-operation in contexts of language use, we also accept that detached from their contexts, some pieces of contextual information can become context-independent. Furthermore, such kind of encyclopedic information and information concerning the use of language can be considered an integral part of lexical-semantic representations as well. The analysis of three types of Hungarian utterances, namely utterances containing implicit arguments, implicit predicates and co-composition, in the framework we propose demonstrates that grammatical factors (including lexical-semantic representations and morphosyntactic properties) and pragmatic factors (general and context-dependent pragmatic knowledge) thoroughly and intensively interact in various ways. On the basis of the analysis it can also be concluded that, in the absence of pragmatic information, use or interpretation predicted by grammar can often be evaluated as only typical or even underspecified, and, if the context is more specific, it can determine the utterance meaning to a greater degree.

Making sense of standardized survey questions: The influence of reference periods and their repetition

To provide an informative answer to a question, communicators need to determine its intended meaning which often requires extensive inferences based on contextual information. The current studies test if a given piece of contextual information is more influential when it is unique to the question asked rather than shared by several questions. Two experiments demonstrate that respondents draw on the length of the reference period in interpreting frequency questions. When the same question is presented with a long reference period (six months), respondents interpret it as pertaining to less frequent and more extreme instances of a behavior than when it is presented with a short reference period (one day). As predicted, however, this influence of the reference period on question interpretation is only observed when the reference period is unique to the target question. When the same reference period is repeatedly used in multiple questions, its use is no longer considered informative and its influence on question interpretation is eliminated. Theoretical and methodological implications are discussed.

A Statistical Dialogue Analysis Model Based on Speech Acts for Dialogue Machine Translation

In some cases, to make a proper translation of an utterance in a dialogue, different pieces of contextual information are needed. Interpreting such utterances often requires dialogue analysis including speech acts and discourse analysis. In this paper, a statistical dialogue analysis model for Korean–English dialogue machine translation based on speech acts is proposed. The model uses syntactic patterns and n-grams of speech acts. The syntactic patterns include surface syntactic features which are related to the language-dependent expressions of speech acts. Speech-act n-grams are used to approximate the context of utterances. The key feature is the use of speech-act n-grams based on hierarchical recency. Experimental results with trigrams show that the proposed model achieves an accuracy of 66.87% for the top candidate and 82.35% for the top three candidates. It indicates that the proposed model based on hierarchical recency outperforms the model based on linear recency.