193 research outputs found
BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
Sentiment analysis in conversations has gained increasing attention in recent
years for the growing amount of applications it can serve, e.g., sentiment
analysis, recommender systems, and human-robot interaction. The main difference
between conversational sentiment analysis and single sentence sentiment
analysis is the existence of context information which may influence the
sentiment of an utterance in a dialogue. How to effectively encode contextual
information in dialogues, however, remains a challenge. Existing approaches
employ complicated deep learning structures to distinguish different parties in
a conversation and then model the context information. In this paper, we
propose a fast, compact and parameter-efficient party-ignorant framework named
bidirectional emotional recurrent unit for conversational sentiment analysis.
In our system, a generalized neural tensor block followed by a two-channel
classifier is designed to perform context compositionality and sentiment
classification, respectively. Extensive experiments on three standard datasets
demonstrate that our model outperforms the state of the art in most cases.Comment: 9 pages, 7 figure
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
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Neural conversational models learn to generate responses by taking into
account the dialog history. These models are typically optimized over the
query-response pairs with a maximum likelihood estimation objective. However,
the query-response tuples are naturally loosely coupled, and there exist
multiple responses that can respond to a given query, which leads the
conversational model learning burdensome. Besides, the general dull response
problem is even worsened when the model is confronted with meaningless response
training instances. Intuitively, a high-quality response not only responds to
the given query but also links up to the future conversations, in this paper,
we leverage the query-response-future turn triples to induce the generated
responses that consider both the given context and the future conversations. To
facilitate the modeling of these triples, we further propose a novel
encoder-decoder based generative adversarial learning framework, Posterior
Generative Adversarial Network (Posterior-GAN), which consists of a forward and
a backward generative discriminator to cooperatively encourage the generated
response to be informative and coherent by two complementary assessment
perspectives. Experimental results demonstrate that our method effectively
boosts the informativeness and coherence of the generated response on both
automatic and human evaluation, which verifies the advantages of considering
two assessment perspectives.Comment: Accepted by AAAI 202
Coping with COVID-19 using contact tracing mobile apps
Purpose To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs. Design/methodology/approach Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents. Findings The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not. Originality/value This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities.publishedVersionPeer reviewe
Adaptive fuzzy sliding mode algorithm-based decentralised control for a permanent magnet spherical actuator
<p>The dynamic model of multi-degree-of-freedom permanent magnet (PM) spherical actuators is multivariate and nonlinear due to strong inter-axis couplings, which affects the trajectory tracking performance of the system. In this paper, a decentralised control strategy based on adaptive fuzzy sliding mode (AFSM) algorithm is developed for a PM spherical actuator to enhance its trajectory tracking performance. In this algorithm, the coupling terms are separated as subsystems from the entire system. The AFSM algorithm is applied in such a way that the fuzzy logic systems are used to approximate the subsystem with uncertainties. A sliding mode term is introduced to compensate for the effect of coupling terms and fuzzy approximation error. The stability of the proposed method is guaranteed by choosing the appropriate Lyapunov function. Both simulation and experimental results show that the proposed control algorithm can effectively handle various uncertainties and inter-axis couplings, and improve the trajectory tracking precision of the spherical actuator.</p
Heat Pump-Based Novel Energy System for High-Power LED Lamp Cooling and Waste Heat Recovery
Unlike incandescent light bulb, which radiates heat into the surroundings by infrared rays, light emitting diode (LED) traps heat inside the lamp. This fact increases the difficulty of cooling LED lamps, while it facilitates the recovery of the generated heat. We propose a novel energy system that merges high-power LED lamp cooling with the heat pump use; the heat pump can cool the LED lamp and at the same time recover the waste heat. In this way, a high percentage of the energy consumed by the LED lamp can be utilized. In this work, we developed a prototype of this energy system and conducted a series of experimental studies to determine the effect of several parameters, such as cooling water flow rate and LED power, on the LED leadframe temperature, compressor power consumption, and system performance. The experimental results clearly indicate that the energy system can lead to substantial energy savings
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