15 research outputs found
Disentangled Variational Auto-Encoder for Semi-supervised Learning
Semi-supervised learning is attracting increasing attention due to the fact
that datasets of many domains lack enough labeled data. Variational
Auto-Encoder (VAE), in particular, has demonstrated the benefits of
semi-supervised learning. The majority of existing semi-supervised VAEs utilize
a classifier to exploit label information, where the parameters of the
classifier are introduced to the VAE. Given the limited labeled data, learning
the parameters for the classifiers may not be an optimal solution for
exploiting label information. Therefore, in this paper, we develop a novel
approach for semi-supervised VAE without classifier. Specifically, we propose a
new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the
input data into disentangled representation and non-interpretable
representation, then the category information is directly utilized to
regularize the disentangled representation via the equality constraint. To
further enhance the feature learning ability of the proposed VAE, we
incorporate reinforcement learning to relieve the lack of data. The dynamic
framework is capable of dealing with both image and text data with its
corresponding encoder and decoder networks. Extensive experiments on image and
text datasets demonstrate the effectiveness of the proposed framework.Comment: 6 figures, 10 pages, Information Sciences 201
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders
could sometimes turn to suicidal ideation without effective treatment. Early
detection of mental disorders and suicidal ideation from social content
provides a potential way for effective social intervention. However,
classifying suicidal ideation and other mental disorders is challenging as they
share similar patterns in language usage and sentimental polarity. This paper
enhances text representation with lexicon-based sentiment scores and latent
topics and proposes using relation networks to detect suicidal ideation and
mental disorders with related risk indicators. The relation module is further
equipped with the attention mechanism to prioritize more critical relational
features. Through experiments on three real-world datasets, our model
outperforms most of its counterparts
CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
Generating multiple categories of texts is a challenging task and draws more
and more attention. Since generative adversarial nets (GANs) have shown
competitive results on general text generation, they are extended for category
text generation in some previous works. However, the complicated model
structures and learning strategies limit their performance and exacerbate the
training instability. This paper proposes a category-aware GAN (CatGAN) which
consists of an efficient category-aware model for category text generation and
a hierarchical evolutionary learning algorithm for training our model. The
category-aware model directly measures the gap between real samples and
generated samples on each category, then reducing this gap will guide the model
to generate high-quality category samples. The Gumbel-Softmax relaxation
further frees our model from complicated learning strategies for updating
CatGAN on discrete data. Moreover, only focusing on the sample quality normally
leads the mode collapse problem, thus a hierarchical evolutionary learning
algorithm is introduced to stabilize the training procedure and obtain the
trade-off between quality and diversity while training CatGAN. Experimental
results demonstrate that CatGAN outperforms most of the existing
state-of-the-art methods.Comment: 15 pages, 4 figures. Accepted by AAAI 202
Feature-aware conditional GAN for category text generation
Category text generation receives considerable attentions since it is
beneficial for various natural language processing tasks. Recently, the
generative adversarial network (GAN) has attained promising performance in text
generation, attributed to its adversarial training process. However, there are
several issues in text GANs, including discreteness, training instability, mode
collapse, lack of diversity and controllability etc. To address these issues,
this paper proposes a novel GAN framework, the feature-aware conditional GAN
(FA-GAN), for controllable category text generation. In FA-GAN, the generator
has a sequence-to-sequence structure for improving sentence diversity, which
consists of three encoders including a special feature-aware encoder and a
category-aware encoder, and one relational-memory-core-based decoder with the
Gumbel SoftMax activation function. The discriminator has an additional
category classification head. To generate sentences with specified categories,
the multi-class classification loss is supplemented in the adversarial
training. Comprehensive experiments have been conducted, and the results show
that FA-GAN consistently outperforms 10 state-of-the-art text generation
approaches on 6 text classification datasets. The case study demonstrates that
the synthetic sentences generated by FA-GAN can match the required categories
and are aware of the features of conditioned sentences, with good readability,
fluency, and text authenticity.Comment: 27 pages, 8 figure
Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward
Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency
Generative AI in the Construction Industry: Opportunities & Challenges
In the last decade, despite rapid advancements in artificial intelligence
(AI) transforming many industry practices, construction largely lags in
adoption. Recently, the emergence and rapid adoption of advanced large language
models (LLM) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown
great potential and sparked considerable global interest. However, the current
surge lacks a study investigating the opportunities and challenges of
implementing Generative AI (GenAI) in the construction sector, creating a
critical knowledge gap for researchers and practitioners. This underlines the
necessity to explore the prospects and complexities of GenAI integration.
Bridging this gap is fundamental to optimizing GenAI's early-stage adoption
within the construction sector. Given GenAI's unprecedented capabilities to
generate human-like content based on learning from existing content, we reflect
on two guiding questions: What will the future bring for GenAI in the
construction industry? What are the potential opportunities and challenges in
implementing GenAI in the construction industry? This study delves into
reflected perception in literature, analyzes the industry perception using
programming-based word cloud and frequency analysis, and integrates authors'
opinions to answer these questions. This paper recommends a conceptual GenAI
implementation framework, provides practical recommendations, summarizes future
research questions, and builds foundational literature to foster subsequent
research expansion in GenAI within the construction and its allied architecture
& engineering domains