1,720,983 research outputs found
Multi-Local Attention for Speech-Based Depression Detection
Get PDFThis article shows that an attention mechanism, the Multi-Local Attention, can improve a depression detection approach based on Long Short-Term Memory Networks. Besides leading to higher performance metrics (e.g., Accuracy and F1 Score), Multi-Local Attention improves two other aspects of the approach, both important from an application point of view. The first is the effectiveness of a confidence score associated to the detection outcome at identifying speakers more likely to be classified correctly. The second is the amount of speaking time needed to classify a speaker as depressed or non-depressed. The experiments were performed over read speech and involved 109 participants (including 55 diagnosed with depression by professional psychiatrists). The results show accuracies up to 88.0% (F1 Score 88.0%)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Towards context-aware image semantic representation via modality relational reasoning and embedding
Get PDFRepresentation learning is a machine learning technique aimed at automatically discovering the most informative features of raw data, transforming it into a representation that captures the essential characteristics relevant to a specific task. Instead of relying on manual feature engineering, representation learning enables models to learn these features directly from the data, often leading to more accurate and robust performance across various artificial intelligence (AI) applications. In contexts like computer vision (CV) or natural language processing (NLP), etc., representation learning helps models understand complex, high-dimensional data by focusing on meaningful patterns and structures within the input. This approach is fundamental for enabling deep learning models to generalize effectively and adapt to diverse challenges in real-world scenarios.
Unlike other modalities such as text or speech with explicit semantic expressions, image data is inherently complex and ambiguous, requiring the extraction of more complex spatial and contextual information. In particular, factors such as the diversity and complexity of entities and corresponding relations and the ambiguity of semantic expressions make it more challenging to accurately capture and represent the features of images. In unimodal visual representation learning or multimodal joint representation learning that includes vision, visual representation learning presents unique challenges. Consequently, effective visual representation learning demands more sophisticated techniques to overcome these challenges and achieve robust performance.
This thesis is geared towards context-aware image semantic representation learning via modality relational reasoning and embedding methods. Our research aims to advance understanding and methodologies of combining contextual relationship information from a uni-visual modality or multiple joint modalities to enhance visual semantic representations. Two different tasks are studied in depth, namely unimodal facial action unit (FAU) recognition and multimodal image-sentence retrieval (ISR). We explore the effectiveness of various visual relational reasoning and embedding approaches in these two tasks. On the one hand, we explore the effectiveness of relational reasoning and information transfer between different muscle regions to improve the final visual facial representations in the FAU recognition task. We first propose a biLSTM-based implicit relational reasoning and embedding method with skipping connections (Skip-BiLSTM) and verify the effectiveness of relational reasoning for face representation. Then, we explore the encoding of explicit muscle relations into muscle features and propose a Graph Neural Network (GNN) model with local-global interactions to further enhance the face representation capability. In our latest work, we introduce language-guided supervision for FAU recognition, which introduces language-level local and global relational reasoning for face representation learning, and we achieve better AU recognition performance in the final.
On the other hand, we explore the effectiveness of different multimodal relationship reasoning and encoding approaches to improve representation learning ability, especially for complex images, in multimodal interaction tasks. We first explore the contribution of a novel multimodal tree-structured relational reasoning and embedding to the multimodal feature representation learning in the image-sentence retrieval task. Moreover, we introduce scene recognition for semantic relational preprocessing of complex image scenes and utilize graph convolutional neural networks (GCNs) for further relational reasoning and embedding (termed relationshipaware GCNs), which further improves the multimodal feature representation capability, especially for complex visual representations. Finally, we explore the effectiveness of a semantic and spatial relation-based salient object enhancement approach within the visual modality for image-sentence retrieval during multimodal alignment optimization.
Experimental results demonstrate that visual representation learning based on relational reasoning and embedding can effectively promote the visual feature representation ability and further enhance the semantic and relational expression of fundamental visual features, whether for unimodal FAU recognition or multimodal image-sentence retrieval tasks
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