Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features

In recent years many people have begun to express their thoughts and opinions on Twit-ter. Naturally, Twitter has become an ef-fective source to investigate people’s emo-tions for numerous applications. Classifying only positive and negative tweets has been ex-ploited in depth, whereas analyzing finer emo-tions is still a difficult task. More elaborate emotion lexicons should be developed to deal with this problem, but existing lexicon sets are mostly in English. Moreover, building such lexicons is known to be extremely labor-intensive or resource-intensive. Finer-grained features need to be taken into account when determining finer-emotions, but many exist-ing works still utilize coarse features that have been widely used in analyzing only the po-larity of emotion. In this paper, we present a method to automatically build fine-grained emotion lexicon sets and suggest features that improve the performance of machine learning based emotion classification in Korean Twitter texts.

Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning

Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view images do. Hence, owing to the difficulty of applying inductive biases, OCL for single-view images remains challenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHepherding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate locations for slots to focus on to facilitate learning of object-centric representation. We also propose a weak semi-supervision approach for OCL, whilst our proposed framework can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representation and achieves strong performance across four datasets. Code is available at \url{https://github.com/object-understanding/SLASH}

The relationship among transformational leadership, organizational outcomes, and service quality in the five major NCAA conferences

The major purpose of this study was to assess the impact of leadership style on service quality in intercollegiate athletics. Specifically, the study examined the relationship between the athletic directors'ÃÂÃÂ transformational leadership and service quality as perceived by the student athletes via the organizational outcomes including organizational citizenship behavior, organizational commitment, and job satisfaction. To accomplish this purpose, two web-based surveys were utilized to collect data from 927 head coaches and 1,064 student athletes from 53 institutions of the major five conferences in the NCAA during the 2005-06 academic year. The final response rate from the head coaches was 19% (175/927), and from the student athletes was 25% (271/1064). The instrument included basic demographic information, a nine-item to measure the athletic directors'ÃÂÃÂ transformational leadership (Bass, 1985a), a twelve-item measure to assess head coaches'ÃÂÃÂ organizational citizenship behavior (Smith, Organ, & Near, 1983), a six-item measure to capture head coaches' affective commitment (Meyer & Allen, 1997), a three-item measure to assess head coaches'ÃÂÃÂ overall job satisfaction (Cammann, Fichman, Jenkins, & Klesh, 1983), and a fourteen-item measure to assess student athletes' perceived service quality (Harris, 2002). The descriptive data revealed that the athletic directors' charismatic leadership, one dimension of transformational leadership, was the prominent factor, as perceived by the head coaches. Further, the student athletes perceived responsiveness and empathy as the prominent dimensions of service quality. Results from the SEM indicated that the overall athletic directors' transformational leadership was correlated to all organizational outcomes. In the relationship between the transformational leadership and service quality via the organizational outcomes, generalized compliance mediated the relationship between the transformational leadership and service quality

Attitudes of International Music Students from East Asia toward U.S. Higher Education Institutions

Nine universities in the United States with the greatest number of international students and having an accredited music program through the National Association of Schools of Music (NASM) were selected. Survey research methodologies were used to identify the status of the international music students from East Asia in U.S. higher education institutions and to determine their attitudes toward their schools. Among East Asian international music students at US higher education institutions, the results indicated that the professor's reputation, scholarships, and the program's reputation were perceived as the most influential factors impacting the program choice; a good relationship with professors, good feedback from professors, and emotional stability were perceived as the most influential factors impacting academic success; and the professor's teaching, the professor's expertise, and the improvement of musical skills were perceived as the most influential factors impacting students' satisfaction level. The most problematic issues reported were the language barrier and the cultural differences between their host and own countries. In addition, many of the East international music students in this study noted financial difficulties

Large Language Models can Share Images, Too!

This paper explores the image-sharing capability of Large Language Models (LLMs), such as InstructGPT, ChatGPT, and GPT-4, in a zero-shot setting, without the help of visual foundation models. Inspired by the two-stage process of image-sharing in human dialogues, we propose a two-stage framework that allows LLMs to predict potential image-sharing turns and generate related image descriptions using our effective restriction-based prompt template. With extensive experiments, we unlock the \textit{image-sharing} capability of LLMs in zero-shot prompting, with GPT-4 achieving the best performance. Additionally, we uncover the emergent \textit{image-sharing} ability in zero-shot prompting, demonstrating the effectiveness of restriction-based prompts in both stages of our framework. Based on this framework, we augment the PhotoChat dataset with images generated by Stable Diffusion at predicted turns, namely PhotoChat++. To our knowledge, this is the first study to assess the image-sharing ability of LLMs in a zero-shot setting without visual foundation models. The source code and the dataset will be released after publication