Android dialogue system for customer service using prompt-based topic control and compliments generation

This paper describes a dialogue system developed for the Dialogue Robot Competition 2023 that achieves topic control for trip planning by inserting text into prompts using the ChatGPT-API. We built a system that is capable of generating compliments for the user based on recognition of the user's appearance and creating travel plans by extracting the knowledge about the user's preference from the history of the user's utterances. Complements and planning based on preference are the elements required to maintain the quality of customer service. A preliminary round was held at a travel agency's actual store, where real customers experienced and evaluated the system. This system was evaluated first in the preliminary round and participated in the final round. The results of the preliminary round showed the effectiveness of the proposed system.Comment: This paper is part of the proceedings of the Dialogue Robot Competition 202

DETECTION OF FOOT CONTACT AND TOEIOFF USING KINEMATIC DATA FOR TREADMILL RUNNING OVER A WIDE RANGE OF SPEEDS

The purposes of this study were to propose a method for simply and accurately detecting the foot contact and toe-off events during treadmill running over a wide range of speeds and to examine the validity and accuracy of the method. Three patterns of foot-strike were distinguished from the foot kinematic data. The thresholds for foot contact and toe-off were determined inductively based on the minimum height of the heel or metatarsal for each foot-strike pattern with regard to running speed. The estimate for foot contact and toe off indicates that this method can apply over a wide range of speeds with high accuracy

IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge

As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. To train the proposed model, it is essential to design features that are effective for signature classification. Experts classify signatures with predefined if-then rules. An if-then rule returns a label of low, medium, high, or unknown importance based on keyword matching of the elements in the signature. Therefore, we first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as term frequency-inverse document frequency (TF-IDF) features of the message text in the signatures. The features are obtained by web scraping from the referenced external attack identification systems described in the signature. Because failure needs to be minimized in the classification of IDPS signatures, as in the medical field, we consider introducing a RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts: a dataset that can be classified with if-then rules and a dataset with elements that do not match an if-then rule. In the experiment, the proposed model is evaluated. In both cases, the combined SFs and WMFs performed better than the combined SFs and KFs. In addition, we also performed feature analysis.Comment: 9 pages, 5 figures, 3 table

Personality Trait Classification via Co-Occurrent Multiparty Multimodal Event Discovery

This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant’s nonverbal activity and group activity. This feature representationenables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent co-occurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from groupmeetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the co-occurrence features improve the accuracy from 2% up to 17%

FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection

The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients' data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy.Comment: accepted in IEEE-ASRU202

Characteristics and mechanism of low temperature dehydrochlorination of poly(vinyl chloride) in the presence of zinc(II) oxide

Characteristics of low temperature (473 K) dehydrochlorination of PVC powder in the presence of ZnO were studied in nitrogen flow and a reaction mechanism was proposed. It was revealed that a large portion of chlorine (ca. 70%) in PVC powder reacts with ZnO accompanying evolution of water and a slight amount of HCl (ca. 0.5%) without any organic gas. Analysis of the solid product by SEM, XRD and IR suggested that an apparent solid-solid reaction proceeds at 473 K via formation of a liquid phase which acts as reaction promoter and that the solid product is abundant in aliphatic (CH)(n) polymer. It was concluded that the first step of the dehydrochlorination is cross-linking C-C single bond formation in a polymer chain or among polymer chains followed by isomerization to polyene and aromatics.ArticlePOLYMER DEGRADATION AND STABILITY. 97(4):584-591 (2012)journal articl

A higher intramuscular fat in vastus medialis is associated with functional disabilities and symptoms in early stage of knee osteoarthritis: a case-control study

[Background] The characteristics of muscle degeneration in individual quadriceps in early knee osteoarthritis (OA) and the association of muscle quantity and quality on knee dysfunction remain unclear. This study aimed to clarify the characteristics of muscle degeneration in individual quadriceps muscles in early knee OA and elucidate the association of muscle volume and intramuscular adipose tissue (intraMAT) with knee dysfunction, including functional disabilities, symptoms, and joint morphology. [Methods] Fifty participants were categorized into early knee OA and healthy control groups. 3.0 T magnetic resonance imaging (MRI) using T1-weighted and Dixon methods and 3D SPACE in the thigh muscle and knee joint regions was performed. Quadriceps muscle volume, intraMAT, and whole-organ MRI score (WORMS) were assessed. The Knee Society Score (KSS) was used to evaluate functional disabilities and knee symptoms. Univariate analysis of variance was conducted with covariates to clarify the differences in muscle volume and intraMAT between the two groups. Multiple linear regression analyses were performed using the KSS function and symptom subcategories and WORMS as dependent variables and muscle volume, intraMAT, and the presence of early knee OA as independent variables, such as potential confounders. [Results] The quadriceps intraMAT, especially in the vastus medialis (VM), was significantly higher in patients with early knee OA than in healthy controls. The VM intraMAT, not muscle volume, was significantly associated with KSS function [B =  − 3.47; 95% confidence interval [CI],  − 5.24 to − 1.71; p < 0.001] and symptom scores [B =  − 0.63; 95% CI,  − 1.09 to − 0.17; p = 0.008], but not with WORMS. [Conclusion] These findings suggest that higher VM intraMAT is characteristic of quadriceps muscle degeneration in early knee OA and its increase is associated with functional disabilities and symptoms