Case Study on the Enterprise Microblog Usage: Focusing on Knowledge Learning

Knowledge Management Strategy can be classified by codification strategy and personalization strategy (Hansen et. al., 1999), and how to manage the two strategies were always studied. Also, current studies regarding the knowledge management strategy were targeted mostly for major companies, resulting in lack of studies in how it can be applied on SMEs. This research, with the knowledge management strategy suited for SMEs, sets an Enterprise Microblog (EMB), and with the EMB applied on SMEs’ Knowledge Management Strategy, it is reviewed on the perspective of SMEs’ Codification and Personalization Strategies. Through the advanced research regarding Knowledge Management Strategy and EMB, the hypothesis is set that “Depending on the development of the company, the main application of EMB alters from Codification Strategy to Personalization Strategy.” To check the hypothesis, SME that have used the EMB called ‘Yammer’ was analyzed from the data of their foundation until today. The case study has implemented longitudinal analysis which divides the period when the EMBs were used into three stages and analyzes the contents. As the result of the study, this suggests a substantial implication regarding the application of Knowledge Management Strategy and its Knowledge Management System that is suitable for SME

Generalizations of -Subalgebras in BCK/BCI-Algebras Based on Point -Structures

The aim of this article is to obtain more general forms than the papers of (Jun et al. (2010); Jun et al. (in press)). The notions of -subalgebras of types , and are introduced, and the concepts of -support and -support are also introduced. Several related properties are investigated. Characterizations of -subalgebra of type are discussed, and conditions for an -subalgebra of type to be an -subalgebra of type are considered

Responses of microbial abundance and enzyme activity in integrated vertical-flow constructed wetlands for domestic and secondary wastewater

Although micro-organisms play a significant role in pollutant removal in constructed wetlands, little is known on the effect of wastewater-quality properties on microbial characteristics. In this study, two groups of integrated vertical-flow constructed wetland microcosms were applied to treat synthetic domestic wastewater and synthetic secondary effluent. The effects of wastewater-quality properties on microbial features were assessed. Results showed that higher values of microbial indicators were observed in the systems with domestic wastewater and in down-flow cells. Redundancy analysis revealed that organic matter concentration and temperature were two critical determinants influencing the microbial features

Study of a Vocal Feature Selection Method and Vocal Properties for Discriminating Four Constitution Types

The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy

ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints

Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transformations, leaving viewpoint changes in the 3D world less explored. In general, viewpoint changes are prevalent in various real-world applications (e.g., autonomous driving), making it imperative to evaluate viewpoint robustness. In this paper, we propose a novel method called ViewFool to find adversarial viewpoints that mislead visual recognition models. By encoding real-world objects as neural radiance fields (NeRF), ViewFool characterizes a distribution of diverse adversarial viewpoints under an entropic regularizer, which helps to handle the fluctuations of the real camera pose and mitigate the reality gap between the real objects and their neural representations. Experiments validate that the common image classifiers are extremely vulnerable to the generated adversarial viewpoints, which also exhibit high cross-model transferability. Based on ViewFool, we introduce ImageNet-V, a new out-of-distribution dataset for benchmarking viewpoint robustness of image classifiers. Evaluation results on 40 classifiers with diverse architectures, objective functions, and data augmentations reveal a significant drop in model performance when tested on ImageNet-V, which provides a possibility to leverage ViewFool as an effective data augmentation strategy to improve viewpoint robustness.Comment: NeurIPS 202

CDSD: Chinese Dysarthria Speech Database

We present the Chinese Dysarthria Speech Database (CDSD) as a valuable resource for dysarthria research. This database comprises speech data from 24 participants with dysarthria. Among these participants, one recorded an additional 10 hours of speech data, while each recorded one hour, resulting in 34 hours of speech material. To accommodate participants with varying cognitive levels, our text pool primarily consists of content from the AISHELL-1 dataset and speeches by primary and secondary school students. When participants read these texts, they must use a mobile device or the ZOOM F8n multi-track field recorder to record their speeches. In this paper, we elucidate the data collection and annotation processes and present an approach for establishing a baseline for dysarthric speech recognition. Furthermore, we conducted a speaker-dependent dysarthric speech recognition experiment using an additional 10 hours of speech data from one of our participants. Our research findings indicate that, through extensive data-driven model training, fine-tuning limited quantities of specific individual data yields commendable results in speaker-dependent dysarthric speech recognition. However, we observe significant variations in recognition results among different dysarthric speakers. These insights provide valuable reference points for speaker-dependent dysarthric speech recognition.Comment: 9 pages, 3 figure

Intrinsically Stretchable Three Primary Light-Emitting Films Enabled By Elastomer Blend For Polymer Light-Emitting Diodes

Intrinsically stretchable light-emitting materials are crucial for skin-like wearable displays; however, their color range has been limited to green-like yellow lights owing to the restricted stretchable light-emitting materials (super yellow series materials). To develop skin-like full-color displays, three intrinsically stretchable primary light-emitting materials [red, green, and blue (RGB)] are essential. In this study, we report three highly stretchable primary light-emitting films made from a polymer blend of conventional RGB light-emitting polymers and a nonpolar elastomer. The blend films consist of multidimensional nanodomains of light-emitting polymers that are interconnected in an elastomer matrix for efficient light-emitting under strain. The RGB blend films exhibited over 1000 cd/m2 luminance with low turn-on voltage (Von) and the selectively stretched blend films on rigid substrate maintained stable light-emitting performance up to 100% strain even after 1000 multiple stretching cycles