27 research outputs found
A Hierarchical Approach for Investigating Social Features of a City from Mobile Phone Call Detail Records
Cellphone service-providers continuously collect Call Detail Records (CDR) as
a usage log containing spatio-temporal traces of phone users. We proposed a
multi-layered hierarchical analytical model for large spatio-temporal datasets
and applied that for the progressive exploration of social features of a city,
e.g., social activities, relationships, and groups, from CDR. This approach
utilizes CDR as the preliminary input for the initial layer, and analytical
results from consecutive layers are added to the knowledge-base to be used in
the subsequent layers to explore more detailed social features. Each subsequent
layer uses the results from previous layers, facilitating the discovery of more
in-depth social features not predictable in a single-layered approach using
only raw CDR. This model starts with exploring aggregated overviews of the
social features and gradually focuses on comprehensive details of social
relationships and groups, which facilitates a novel approach for investigating
CDR datasets for the progressive exploration of social features in a
densely-populated city
Understanding Social Structures from Contemporary Literary Fiction using Character Interaction Graph -- Half Century Chronology of Influential Bengali Writers
Social structures and real-world incidents often influence contemporary
literary fiction. Existing research in literary fiction analysis explains these
real-world phenomena through the manual critical analysis of stories.
Conventional Natural Language Processing (NLP) methodologies, including
sentiment analysis, narrative summarization, and topic modeling, have
demonstrated substantial efficacy in analyzing and identifying similarities
within fictional works. However, the intricate dynamics of character
interactions within fiction necessitate a more nuanced approach that
incorporates visualization techniques. Character interaction graphs (or
networks) emerge as a highly suitable means for visualization and information
retrieval from the realm of fiction. Therefore, we leverage character
interaction graphs with NLP-derived features to explore a diverse spectrum of
societal inquiries about contemporary culture's impact on the landscape of
literary fiction. Our study involves constructing character interaction graphs
from fiction, extracting relevant graph features, and exploiting these features
to resolve various real-life queries. Experimental evaluation of influential
Bengali fiction over half a century demonstrates that character interaction
graphs can be highly effective in specific assessments and information
retrieval from literary fiction. Our data and codebase are available at
https://cutt.ly/fbMgGEMComment: 8 pages, 11 figures, 6 pages appendi
The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis
Analyzing the writing styles of authors and articles is a key to supporting
various literary analyses such as author attribution and genre detection. Over
the years, rich sets of features that include stylometry, bag-of-words, n-grams
have been widely used to perform such analysis. However, the effectiveness of
these features largely depends on the linguistic aspects of a particular
language and datasets specific characteristics. Consequently, techniques based
on these feature sets cannot give desired results across domains. In this
paper, we propose a novel Word2vec graph based modeling of a document that can
rightly capture both context and style of the document. By using these Word2vec
graph based features, we perform classification to perform author attribution
and genre detection tasks. Our detailed experimental study with a comprehensive
set of literary writings shows the effectiveness of this method over
traditional feature based approaches. Our code and data are publicly available
at https://cutt.ly/svLjSgkComment: 12 pages, 6 figure
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Knowledge Graph (KG)-to-Text generation aims at generating fluent
natural-language text that accurately represents the information of a given
knowledge graph. While significant progress has been made in this task by
exploiting the power of pre-trained language models (PLMs) with appropriate
graph structure-aware modules, existing models still fall short of generating
faithful text, especially when the ground-truth natural-language text contains
additional information that is not present in the graph. In this paper, we
develop a KG-to-text generation model that can generate faithful
natural-language text from a given graph, in the presence of noisy reference
text. Our framework incorporates two core ideas: Firstly, we utilize
contrastive learning to enhance the model's ability to differentiate between
faithful and hallucinated information in the text, thereby encouraging the
decoder to generate text that aligns with the input graph. Secondly, we empower
the decoder to control the level of hallucination in the generated text by
employing a controllable text generation technique. We evaluate our model's
performance through the standard quantitative metrics as well as a
ChatGPT-based quantitative and qualitative analysis. Our evaluation
demonstrates the superior performance of our model over state-of-the-art
KG-to-text models on faithfulness
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
In this paper, we propose a novel contrastive learning based deep learning
framework for patient similarity search using physiological signals. We use a
contrastive learning based approach to learn similar embeddings of patients
with similar physiological signal data. We also introduce a number of neighbor
selection algorithms to determine the patients with the highest similarity on
the generated embeddings. To validate the effectiveness of our framework for
measuring patient similarity, we select the detection of Atrial Fibrillation
(AF) through photoplethysmography (PPG) signals obtained from smartwatch
devices as our case study. We present extensive experimentation of our
framework on a dataset of over 170 individuals and compare the performance of
our framework with other baseline methods on this dataset.Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in
Biology and Medicin