A Lite Hierarchical Model for Dialogue Summarization with Multi-Granularity Decoder

Abstract dialogue summarization generation has recently attracted considerable research attention, especially in using hierarchical models to accomplish abstract dialogue summarization tasks successfully. However, problems in recent studies often include an excessive amount of model parameters and long training time mainly because existing dialogue summaries of hierarchical models are typically generated by adding extra encoders and attention layers in the decoder to enhance learning and summarization generation ability of the model. Hence, designing an increasingly lightweight hierarchical model is necessary. A lightweight hierarchical model named ALH-BART is proposed in this study to generate high-accuracy dialogue summaries rapidly. The proposed hierarchical model includes word and turn encoders, which enhance the ability of the model to understand dialogue. A multigranularity decoder in the model is also proposed to decode word- and turn-level information in the decoder at the same time. Encoder parameters in multihead self-attention are provided for each corresponding multihead self-attention to reduce the number of model parameters and improve the speed of model learning effectively. Finally, the effectiveness of the model is verified on SAMSum and DialogSum datasets

Double Deep Features for Apparel Recommendation System

This study describes a recommendation system embedded in the double features extracted by convolutional neural networks (CNNs). Several probabilistic models, such as probabilistic matrix factorization (PMF)-based approaches, have been utilized for recommendation systems based on a CNN model. Each recommendation algorithm utilizes a single CNN model to extract precise features about documents and pictures, and the systems with CNN have contributed in improving the performance in rating prediction. Meanwhile, the systems for some items should consider at least two precise features simultaneously, and the extension to embed multiple CNN models is necessary. However, methods that integrate multiple CNN-based features into existing recommendation systems, such as PMF, are not available. Thus, this study proposes a novel probabilistic model that integrates double CNNs into PMF. For apparel goods, two trained CNNs from document and image shape features are combined, and the latent variables of users and items are optimized based on the vectorized features of CNNs and rating. Extensive experiments demonstrate that our model outperforms other recommendation models

FML-based Prediction Agent and Its Application to Game of Go

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.Comment: 6 pages, 12 figures, Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 201

Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance

[Purpose]Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).[Materials and methods]We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwentNM-MRI, and performed manual ROI-based analysis. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in ten healthy volunteers were compared among images with a number of excitations (NEX) of 1 (NEX1), NEX1 images with dDLR (NEX1+dDLR) and 5-NEX images (NEX5). Acquisition times for NEX1 and NEX5 were 3 min 12 s and 15 min 58 s, respectively. Diagnostic performances using the contrast ratio (CR) of the SN (CR_SN) and LC (CR_LC) and those by visual assessment for diferentiating PD from non-PD were also compared between NEX1 and NEX1+dDLR.[Results]Image quality analyses revealed that SNRs and CNRs of the SN and LC in NEX1+dDLR were signifcantly higherthan in NEX1, and comparable to those in NEX5. In diagnostic performance analysis, areas under the receiver operating characteristic curve (AUC) using CR_SN and CR_LC of NEX1+dDLR were 0.87 and 0.75, respectively, which had no signifcant diference with those of NEX1. Visual assessment showed improvement of diagnostic performance by applying dDLR.[Conclusion]Image quality for NEX1+dDLR was comparable to that of NEX5. dDLR has the potential to reduce scan time of NM-MRI without degrading image quality. Both 1-NEX NM-MRI with and without dDLR showed high AUCs for diagnosing PD by CR. The results of visual assessment suggest advantages of dDLR. Further tuning of dDLR would be expected to provide clinical merits in diagnosing PD

Evolutionary-Edge Bundling with Concatenation Process of Control Points

Edge bundling is one of the information visualization techniques, which bundle the edges of a network diagram based on certain rules to increase the visibility of the network diagram and facilitate the analysis of key relationships among nodes. As one of evolutionary-based edge bundling, genetic algorithm-based edge bundling (called GABEB) is proposed which uses a genetic algorithm to optimize the placement of edges based on aesthetic criteria. However, it does not sufficiently consider the bundling between neighboring edges, and thus visual clutter issues still remain. Based on the above background, we propose an improved bundling method that considers the concatenating of control points at each edge using GABEB

Text-based Causality Modeling with a Conceptual Label in a Hierarchical Topic Structure Using Bayesian Rose Trees

This paper describes a method for constructing a causality model from review text data. Review text data include the evaluation factors of rating, and causality model extraction from text data is important for understanding the evaluation factors and their relationships. Several methods are available for extracting causality models by using a topic model. In particular, the method based on hierarchical latent Dirichlet allocation is useful for hierarchically comprehending causality structure. However, the depth of each topic in a hierarchical structure is forcefully pruned even if granularities differ for each topic. Thus, interpreting a hierarchical topic structure is difficult. To solve these problems, we construct a hierarchical topic structure with different depths by using Bayesian rose trees. Furthermore, we use conceptual labeling to add explicit semantics for each topic for interpretation. An experiment confirms that this model is accurate and interpretable using actual data

Collaborative Filtering versus Personal Log based Filtering: Experimental Comparison for Hotel Room Selection

In order to support decision making in e-commerce domain, recommender system has attractive features such as collaborative filtering and personal log based filtering for products/services. As experimental study, this paper compares these filtering for hotel room selection. Differently from commodity items, products/services as hotel rooms have three features: many attributes, multiformity and high-frequency update. Noting that we cannot use explicit rating data assigned by users, this paper describes how to derive implicit rating from sales records. Numerical simulation shows how accuracy between two filtering exists, where our case data consist of 10,000 users, 400,000 personal log and 160,000 room plans.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlIFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2075, Kobe, JapanSymposium 2, Session 3 : Creation of Agent-Based Social Systems Sciences Formal System

Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

With the rise in meteorological disasters, improving evaluation strategies for disaster response agencies is critical. This shift from expert scoring to data-driven approaches is challenged by sample imbalance in the data, affecting accurate capability assessments. This study proposes a solution integrating adaptive focal loss into the cross-entropy loss function to address sample distribution imbalances, facilitating nuanced evaluations. A key aspect of this solution is the Encoder-Adaptive-Focal deep learning model coupled with a custom training algorithm, adept at handling the data complexities of meteorological disaster response agencies. The model proficiently extracts and optimizes capability features from time series data, directing the evaluative focus toward more complex samples, thus mitigating sample imbalance issues. Comparative analysis with existing methods like UAE-NaiveBayes, UAE-SVM, and UAE-RandomForest illustrates the superior performance of our model in ability evaluation, positioning it as a robust tool for dynamic capability evaluation. This work aims to enhance disaster management strategies, contributing to mitigating the impacts of meteorological disasters