Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering

Question answering (QA) has significantly benefitted from deep learning techniques in recent years. However, domain-specific QA remains a challenge due to the significant amount of data required to train a neural network. This paper studies the answer sentence selection task in the Bible domain and answer questions by selecting relevant verses from the Bible. For this purpose, we create a new dataset BibleQA based on bible trivia questions and propose three neural network models for our task. We pre-train our models on a large-scale QA dataset, SQuAD, and investigate the effect of transferring weights on model accuracy. Furthermore, we also measure the model accuracies with different answer context lengths and different Bible translations. We affirm that transfer learning has a noticeable improvement in the model accuracy. We achieve relatively good results with shorter context lengths, whereas longer context lengths decreased model accuracy. We also find that using a more modern Bible translation in the dataset has a positive effect on the task.Comment: The paper has been accepted at IJCNN 201

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Yellow River Piano Concerto: A Synthesis of Western and Chinese Characteristics

The Yellow River Piano Concerto is a valuable addition to the piano concerto repertoire for both historical and theoretical reasons. It has been performed frequently by Chinese pianists on many important occasions, such as National Day Concerts, New Year Concerts, and Spring Festival Galas. The Concerto is not only standard repertoire on Chinese stages, but it is also performed and recorded by leading international orchestras, such as the Slovak Radio Symphony Orchestra, Philadelphia Orchestra, and New Zealand Symphony Orchestra. One of the possible reasons that made this Concerto successful is that it combines characteristics of Chinese and Western cultures. The primary purpose of this study is to address its Western cultural influences, its Chinese nationalistic traits, and how traditional Chinese aesthetics shaped the work

Low-Frequency Raman Modes and Electronic Excitations In Atomically Thin MoS2 Crystals

Atomically thin MoS$_{2}$ crystals have been recognized as a quasi-2D semiconductor with remarkable physics properties. This letter reports our Raman scattering measurements on multilayer and monolayer MoS$_{2}$, especially in the low-frequency range ($<$50 cm$^{-1}$). We find two low-frequency Raman modes with contrasting thickness dependence. With increasing the number of MoS$_{2}$ layers, one shows a significant increase in frequency while the other decreases following a 1/N (N denotes layer-number) trend. With the aid of first-principle calculations we assign the former as the shear mode $E_{2g}^{2}$ and the latter as the compression vibrational mode. The opposite evolution of the two modes with thickness demonstrates novel vibrational modes in atomically thin crystal as well as a new and more precise way to characterize thickness of atomically thin MoS$_{2}$ films. In addition, we observe a broad feature around 38 cm$^{-1}$ (~5 meV) which is visible only under near-resonance excitation and pinned at the fixed energy independent of thickness. We interpret the feature as an electronic Raman scattering associated with the spin-orbit coupling induced splitting in conduction band at K points in their Brillouin zone.Comment: 5 pages, 4 figure

Investigating the Relationship between Effectiveness of App Evolution and App Continuance Intention: An Empirical Study of the U.S. App Market

App evolution has been shown to continuously lead to app success from the developer perspective. However, few studies have explored app success from the user perspective, which limits our understanding of the role of app evolution in app success. Building on app evolution literature and the technology acceptance model (TAM), the authors investigate the influence of the effectiveness of app evolution on users’ perceived app usefulness and ease of use and their app continuance intention, which is a proxy of app success from the user perspective. Survey data were collected from 299 app users on both the Google Play and AppStore platforms in the U.S. The findings indicate that the effectiveness of strategic evolution and effectiveness of evolution speed directly affect a user’s perceived app usefulness, while effectiveness of operational evolution and effectiveness of evolution speed directly affect a user’s perceived app ease of use. In addition, perceived app usefulness and perceived app ease of use are two keys that lead to users’ app continuance intention. A user’s perceived app ease of use affects app continuance intention both directly and indirectly through perceived app usefulness. This study enhances our understanding of the relationship between effectiveness of app evolution and app continuance intention. This is especially important in helping app developers that are small firms or startups with limited resources understand how to retain app users. Limitations and directions for future research are also discussed

Investigating the Relationship between the Effectiveness of App Evolution and App Continuance Intention: An Empirical Study of the U.S. App Market

Researchers have shown app evolution to continuously lead to app success from the developer perspective. However, few studies have explored app success from the user perspective, which limits our knowledge about the role that app evolution has in app success. Building on app evolution literature and the technology acceptance model (TAM), we investigate the influence that effectiveness of app evolution has on perceived app usefulness, perceived ease of use, and app continuance intention (a proxy for app success from the user perspective). We collected survey data from 299 app users on both the Google Play and Apple’s App Store platforms in the United States. Our findings indicate that effectiveness of strategic evolution and effectiveness of evolution speed directly affect perceived app usefulness, while effectiveness of operational evolution and effectiveness of evolution speed directly affect perceived app ease of use. In addition, perceived app usefulness and perceived app ease of use constitute two key factors that lead to app continuance intention. Perceived ease of use affects users’ app continuance intention both directly and indirectly through perceived app usefulness. This study enhances our knowledge about the relationship between effectiveness of app evolution and app continuance intention. Such knowledge has particular importance in helping small firms or startups with limited resources understand how to retain app users. We also discuss limitations and directions for future research

MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images

In the real world, knowledge often exists in a multimodal and heterogeneous form. Addressing the task of question answering with hybrid data types, including text, tables, and images, is a challenging task (MMHQA). Recently, with the rise of large language models (LLM), in-context learning (ICL) has become the most popular way to solve QA problems. We propose MMHQA-ICL framework for addressing this problems, which includes stronger heterogeneous data retriever and an image caption module. Most importantly, we propose a Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage their powerful performance in this task. We are the first to use end-to-end LLM prompting method for this task. Experimental results demonstrate that our framework outperforms all baselines and methods trained on the full dataset, achieving state-of-the-art results under the few-shot setting on the MultimodalQA dataset

HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering

Answering numerical questions over hybrid contents from the given tables and text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs) have gained significant attention in the NLP community. With the emergence of large language models, In-Context Learning and Chain-of-Thought prompting have become two particularly popular research topics in this field. In this paper, we introduce a new prompting strategy called Hybrid prompt strategy and Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt the model to develop the ability of retrieval thinking when dealing with hybrid data. Our method achieves superior performance compared to the fully-supervised SOTA on the MultiHiertt dataset in the few-shot setting