49 research outputs found
LAnoBERT : System Log Anomaly Detection based on BERT Masked Language Model
The system log generated in a computer system refers to large-scale data that
are collected simultaneously and used as the basic data for determining simple
errors and detecting external adversarial intrusion or the abnormal behaviors
of insiders. The aim of system log anomaly detection is to promptly identify
anomalies while minimizing human intervention, which is a critical problem in
the industry. Previous studies performed anomaly detection through algorithms
after converting various forms of log data into a standardized template using a
parser. These methods involved generating a template for refining the log key.
Particularly, a template corresponding to a specific event should be defined in
advance for all the log data using which the information within the log key may
get lost.In this study, we propose LAnoBERT, a parser free system log anomaly
detection method that uses the BERT model, exhibiting excellent natural
language processing performance. The proposed method, LAnoBERT, learns the
model through masked language modeling, which is a BERT-based pre-training
method, and proceeds with unsupervised learning-based anomaly detection using
the masked language modeling loss function per log key word during the
inference process. LAnoBERT achieved better performance compared to previous
methodology in an experiment conducted using benchmark log datasets, HDFS, and
BGL, and also compared to certain supervised learning-based models
Box Office Forecasting considering Competitive Environment and Word-of-Mouth in Social Networks: A Case Study of Korean Film Market
Accurate box office forecasting models are developed by considering competition and word-of-mouth (WOM) effects in addition to screening-related information. Nationality, genre, ratings, and distributors of motion pictures running concurrently with the target motion picture are used to describe the competition, whereas the numbers of informative, positive, and negative mentions posted on social network services (SNS) are used to gauge the atmosphere spread by WOM. Among these candidate variables, only significant variables are selected by genetic algorithm (GA), based on which machine learning algorithms are trained to build forecasting models. The forecasts are combined to improve forecasting performance. Experimental results on the Korean film market show that the forecasting accuracy in early screening periods can be significantly improved by considering competition. In addition, WOM has a stronger influence on total box office forecasting. Considering both competition and WOM improves forecasting performance to a larger extent than when only one of them is considered
DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training
Dialogue State Tracking (DST) is critical for comprehensively interpreting
user and system utterances, thereby forming the cornerstone of efficient
dialogue systems. Despite past research efforts focused on enhancing DST
performance through alterations to the model structure or integrating
additional features like graph relations, they often require additional
pre-training with external dialogue corpora. In this study, we propose DSTEA,
improving Dialogue State Tracking via Entity Adaptive pre-training, which can
enhance the encoder through by intensively training key entities in dialogue
utterances. DSTEA identifies these pivotal entities from input dialogues
utilizing four different methods: ontology information, named-entity
recognition, the spaCy, and the flair library. Subsequently, it employs
selective knowledge masking to train the model effectively. Remarkably, DSTEA
only requires pre-training without the direct infusion of extra knowledge into
the DST model. This approach resulted in substantial performance improvements
of four robust DST models on MultiWOZ 2.0, 2.1, and 2.2, with joint goal
accuracy witnessing an increase of up to 2.69% (from 52.41% to 55.10%). Further
validation of DSTEA's efficacy was provided through comparative experiments
considering various entity types and different entity adaptive pre-training
configurations such as masking strategy and masking rate
Model eye imaging by closed-loop accumulation of single scattering (CLASS) microscopy
‘Closed-loop accumulation of single scattering (CLASS)’ microscopy provides novel solutions to the problems of light scattering and aberration in optical imaging, providing increased imaging depth while maintaining diffraction limited resolution. This method has a great potential to increase imaging depth and resolution of current eye imaging. In this presentation, the strength and weakness of the CLASS microscopy over the current adaptive optical microscopy will be discussed. Important factors to apply CLASS microscopy to eye imaging and the possibility to imaging retina in turbid condition will be discussed by using model eye
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
As the e-commerce market continues to expand and online transactions
proliferate, customer reviews have emerged as a critical element in shaping the
purchasing decisions of prospective buyers. Previous studies have endeavored to
identify key aspects of customer reviews through the development of sentiment
analysis models and topic models. However, extracting specific dissatisfaction
factors remains a challenging task. In this study, we delineate the pain point
detection problem and propose Painsight, an unsupervised framework for
automatically extracting distinct dissatisfaction factors from customer reviews
without relying on ground truth labels. Painsight employs pre-trained language
models to construct sentiment analysis and topic models, leveraging attribution
scores derived from model gradients to extract dissatisfaction factors. Upon
application of the proposed methodology to customer review data spanning five
product categories, we successfully identified and categorized dissatisfaction
factors within each group, as well as isolated factors for each type. Notably,
Painsight outperformed benchmark methods, achieving substantial performance
enhancements and exceptional results in human evaluations.Comment: WASSA at ACL 202