Development and applications of novel and practical fluorination reagents.

The fluorine (F) atom has distinctive properties such as the highest electronegativity, small size, low polarizability, and strong C–F bond strength. Not surprisingly, fluorinated organic compounds have garnered significant attention in the pharmaceutical, agrochemical, and material fields. Since organofluorides are very rare in nature, we need fluorination reagents to help us transfer fluorine into the organic molecules. In past decades, a vast number of fluorination reagents have been developed and many of them are commercially available now. However, there is still much room for the improvement of these reagents. Our target is to develop novel and practical fluorination reagents to make up for the shortcomings of the currently popular ones. We have developed a novel fluorinating agent, N-fluoro-N-(tert-butyl)-tert-butanesulfonamide (NFBB), which was prepared in excellent yield using N-fluorobenzenesulfonimide (NFSI) or F2/N2 and was purified by simple distillation. Its easy preparation and purification made NFBB a reagent suitable for large scale production. NFBB provided unprecedented high-yielding fluorination of highly basic organolithium species, which has been an unsolved problem for a long time. With NFBB, a conceptually new base-catalyzed, self-sustaining fluorination of active methylene compounds was discovered. NFBB also fluorinated other carbanions such as Grignard reagents and enolates in good yields. NFBB is expected to play an important role in the preparation of many useful fluorinated compounds and it is now commercialized by Tokyo Chemical Industry Co., Ltd. (TCI). The trifluoromethylthio (CF3S) group has the highest lipophilicity among the common fluorine-containing moieties. Therefore, introducing a fluorine atom or a CF3S group into a bioactive molecule could produce dramatic effects on its physical, chemical, and biological properties. We have developed a novel trifluoromethylthiolating reagent, S-trifluoromethyl trifluoromethanesulfonothioate (TTST). Unlike conventional CF3S reagents, TTST can be easily prepared in one step from commercially inexpensive sodium trifluoromethanesulfinate and triflic anhydride. TTST is a highly reactive, versatile, and atom-efficient reagent that can generate CF3S+, CF3S-, and CF3S•/CF3• reactive species. Many kinds of C, O, S, and N-nucleophiles were trifluoromethylthiolated by TTST in high yields. Notably, TTST reacted with sodium phenoxides to provide a new series of hitherto difficult to prepare aryl trifluoromethanesulfenates that were found to undergo a novel acid-catalyzed CF3S(II)-rearrangement reaction. By means of Cu or TDAE/Ph3P combination, TTST generated two CF3S anion species that are useful to prepare trifluoromethylthio compounds in high atom-economy fashion. Photocatalytic radical trifluoromethyl-trifluoromethylthiolation of alkenes with only one equivalent of TTST was achieved in high yield as well as in high atom-efficiency. TTST is expected to be a compelling alternative to the current CF3S reagents in terms of preparation, reactivity, and practicality. We also found a novel application of the fluorinated solvent, 1,1,1,3,3,3-hexafluoroisopropanol (HFIP), which assisted the hydrohalogenation of alkenes with inactive aqueous hydrogen halide solutions via hydrogen bonding. Dynamic studies showed this reaction is hydrogen bond acidity-dominated and dilution-accelerated. Both aqueous HCl, HBr, and HI provided good yields of the hydrohalogenated products in this system

Protecting attributes and contents in online social networks

With the extreme popularity of online social networks, security and privacy issues become critical. In particular, it is important to protect user privacy without preventing them from normal socialization. User privacy in the context of data publishing and structural re-identification attacks has been well studied. However, protection of attributes and data content was mostly neglected in the research community. While social network data is rarely published, billions of messages are shared in various social networks on a daily basis. Therefore, it is more important to protect attributes and textual content in social networks. We first study the vulnerabilities of user attributes and contents, in particular, the identifiability of the users when the adversary learns a small piece of information about the target. We have presented two attribute-reidentification attacks that exploit information retrieval and web search techniques. We have shown that large portions of users with online presence are very identifiable, even with a small piece of seed information, and the seed information could be inaccurate. To protect user attributes and content, we adopt the social circle model derived from the concepts of "privacy as user perception" and "information boundary". Users will have different social circles, and share different information in different circles. We introduce a social circle discovery approach using multi-view clustering. We present our observations on the key features of social circles, including friendship links, content similarity and social interactions. We treat each feature as one view, and propose a one-side co-trained spectral clustering technique, which is tailored for the sparse nature of our data. We also propose two evaluation measurements. One is based on the quantitative measure of similarity ratio, while the other employs human evaluators to examine pairs of users, who are selected by the max-risk active evaluation approach. We evaluate our approach on ego networks of twitter users, and present our clustering results. We also compare our proposed clustering technique with single-view clustering and original co-trained spectral clustering techniques. Our results show that multi-view clustering is more accurate for social circle detection; and our proposed approach gains significantly higher similarity ratio than the original multi-view clustering approach. In addition, we build a proof-of-concept implementation of automatic circle detection and recommendation methods. For a user, the system will return its circle detection result from our proposed multi-view clustering technique, and the key words for each circle are also presented. Users can also enter a message they want to post, and the system will suggest which circle to disseminate the message

Exploring Users’ Adoption of MOOCs from the Perspective of the Institutional theory

MOOCs, which stands for Massive Open Online Courses, have attracted millions of users around the world and it has a promise to be a very important part of future education. However, there is little research on users’ adoption of MOOCs. This paper aims to improve the understanding of users’ behavior intention to use MOOCs. The proposed research model is an extension of technology acceptance model with three factors from the institutional theory. And an empirical study with 247 subjects was conducted to test this model. The results indicate that both perceived usefulness and perceived ease of use directly affect users’ behavior intention to use MOOCs significantly. Another interesting finding is that mimetic pressures also have a significant positive influence on users’ behavior intention to use MOOCs

Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.Comment: AAAI 2019, 10 page

An Examination of the Determinants of Customer Loyalty in Online Group-buying Context in China

With the rapidly development of e-commerce, online shopping becomes very popular among Chinese customers. In the past few years, online group-buying has experienced big rise and fall, thus, how to retain customers and improve customer loyalty should be considered by the practitioners. This study aims to explore potential factors which contribute to customer loyalty in the online group-buying context. Based on the literature review, we proposed a research model included five factors which directly or indirectly affect customer loyalty of online group-buying. The model was empirically evaluated using survey data collected from 352 users, and the data was analyzed by the structural equation modeling technology. Six research hypotheses were proposed in the study. Four research hypotheses were positively significant supported, while two research hypotheses were rejected in this study. The result shows that both customer satisfaction and switching costs have positive effects on customer loyalty in the online group-buying context, and the effect from switching costs is stronger. Furthermore, structural assurances in the process of online group-buying have strong effect on customers’ trust, while customer satisfaction is directly affected by trust. In addition, both theoretical and practical implications of this study are discussed at last