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Macao SAR, China is one of the more densely-populated territories in the world, and as such necessarily struggles with Soundscape quality. Nonetheless, the territory has already been identified as a unique location for to function as a Soundscape living lab (Cordeiro et al., 2014), since it has a very small manageable area that includes many types of geographical varieties, from extremely high density urban areas to natural environments with dense vegetation highland or varied water front typologies. In addition, Macao has extremely wide multicultural population with a broad range of subjects that have diverse cultural perceptions and thresholds in regards to sonic cognition. The potential impact of this diversity has already been noticed in both tourism (To & Chung, 2019) and research (Chung et al., 2016). The concept of Soundscape itself is garnering increased awareness as a viable alternative to assess the quality of the sonic environment, of use to policy management and legislation, shown not only by the increasing numbers of scientific articles on the subject (Moscoso et al., 2018), but also by recent international standardisation efforts in measuring it (ISO,2018). In this talk we shall give a preliminary description and illustration of the Soundscape in a territory that is rich in diversity and has huge potential for citizen participation. This includes approaches like noise mapping, sound mapping, Soundwalks, grounded theory efforts for detailed descriptions of the environment and use of alternative objective metrics. We will describe how to use the richness of this gathered data in developing artificial-intelligence algorithms to autonomously assess and predict the evaluation of a given Soundscape based on recordings alone. This goal will alleviate the intense human effort in subjective assessment, and may prove to be an effective and substantial diagnostics tool in planning the soundscape for prospective built environments, functioning not only as an analysis and diagnostics tool, but as a design strategy for a sustainable sonic future.
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Over the years Macao has been exposed to different cultures and has been influenced by various political and economic interests. The booming casino economy has ultimately transformed the city into the largest gambling hub in the world. In spite of the consensus about Macao's shiny future, there are factors (such as the large reliance on a single industry) and socio-economic problems (such as labor shortage, unequal income distribution, and inflationary pressure) that moderate the optimism. By making use of the Chatterjee and Nankervis' convergent and divergent process model for management, this paper examines how global, regional, and local forces have impacted the economic development process, form and type of organizations in Macao. The paper also suggests that the government implement a framework that develops and diversifies the economy but also takes into consideration the social needs of the community.
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Citizens' trust in eGovernment is crucial for the successful implementation of new electronic services. This relationship in the Greater Bay Area (GBA) plays an essential role since the Government services rely on mobile mini-programs This study investigates the trust towards government service mini-programs in WeChat and Alipay. A user feedback questionnaire was designed, and a total of 609 valid samples were collected from Shenzhen, Guangzhou, Hong Kong, and Macau. The findings imply that competence, integrity, and benevolence are the key components of trust in e-government (TIEG). TIEG positively influences perceived value (PV), which positively affects citizens' Intention to adopt service mini-programs. PV significantly mediates the relationship between TIEG and Intention. Although TIEG does not effectively reduce perceived risk (PR), risk issues cannot be ignored in the adoption process. Finally, this article proposes relevant implications and suggestions for the GBA government agents and policy makers.
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Crime fiction in China emerged in the 1890s in translations of Western works, and evolved from the mere imitation of Western crime fiction to becoming an autonomous literary genre. Despite fluctuations in popularity, the genre of Chinese crime fiction, the plots of which are based on true cases, has retained a reasonably constant presence on the literary scene, and has captured the popular imagination in contemporary China and, more recently, across the world. After the demise of Mao, under whose governance the genre was banned, the government of the early Deng regime began to favor so-called “legal system literature” (fazhi wenxue), and aimed to use it to propagate moral principles and maintain political control in opposition to writers who strived for independence and originality. Since the mid and late 1980s, which were considered the heyday of Chinese crime fiction, and the expansion of the legal system and legal institutions, crime fiction has served to illuminate the role of law and to display new social perceptions. To investigate these attitudes, I focus on works of contemporary Chinese crime fiction by arguing that they are expressions of a confluence of cultural exchange and new trends. Several factors may have contributed to such a change, from the impact of the cinema and television serials in China to the celebrity status of Chinese detectives, lawyers and judges both as crime solvers and writers in the Chinese mainland and amongst the Chinese writing diaspora. An important finding is that besides giving detailed descriptions of legal procedures, all of the works studied have clearly shifted away from the traditional formula of Chinese crime fiction, that is, of the quest of a hero for justice, punishment, and revenge, to focus on the process of solving crime and the rendering of justice through legal processes. It seems that crime fiction is becoming crucial in conveying a new understanding of citizen’s rights in an attempt to fit into ongoing contemporary debates on universalistic notions of justice and the competence of legal institutions to provide justice to increasingly marginalized sectors of contemporary China.
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This paper argues for paradigm pluralism in computing education research. The value of mixing paradigms, and the choice of methodological eclecticism and mixed methods is explored using pragmatic knowledge claims. A research study, which focused on the design of an introductory object-oriented programming (OOP) course for undergraduate students, is introduced as an illustration of paradigm pluralism. The study demonstrates methodological eclecticism and use of mixed methods for data collection and analysis. Meaningful outcomes resulting from the choice of the research design are described. A framework that focuses on the research problem and research questions to guide research design is presented as the outcome of the study. Through the discussion and demonstration of paradigm pluralism, this paper contributes to increased awareness of theoretically anchored research in computer science. © 2012, Australian Computer Society, Inc.
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Technology research offers several theories and models to explain how individuals accept and use technology innovations. While these often focus on the technical aspects of the innovation, they tend to downplay the affective component of technology. Recognizing that the adoption of technology is also determined by what it means and represents to the users, this paper aims to fill the gap in the literature by studying the effects of social influence and image on the behavioral intention to adopt a technology. We used structural equation modeling (SmartPLS) to analyze data collected from 238 self-administrated surveys regarding the behavioral intention of Macau residents to use battery electric vehicles. The result showed significant relationships among the variables in the model and depicted the construct of image as a strong factor in the adoption decision. Our findings suggest that social influence may not exhibit substantial impact in the case of innovations in their initial phase and, more importantly, the construct of image could be included as a key predictor of behavioral intention in technology acceptance models, particularly in contexts where the choices that consumers make are public, and therefore subject to judgments from the members of the community.
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The decision to accept and use technology innovations has long been a source of debate across disciplines due to the complexity involved in predicting behavior. Recognizing that the subject is vast and fragmented, this paper examines the mainstream technology works to assist researchers to understand, conceptualize and select the most appropriate theoretical framework for their study. Starting with the pioneering effort on Diffusion of Innovations (DOI/IDT), the analysis considers the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM/TAM-2/TAM-3), the Value-based Acceptance Model (VAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT-2) among the most important. A review of the key literature is vital to assessing and identifying research trends, as well as contributing to the discussion of emerging technologies such as Artificial Intelligence (AI), Augmented Reality (AR), Blockchain, Cloud Computing, Internet of Things (IoT), Mobile Apps, etc. Suggestions for future research paths are also provided.
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Association Rule Mining by Aprior method has been one of the popular data mining techniques for decades, where knowledge in the form of item-association rules is harvested from a dataset. The quality of item-association rules nevertheless depends on the concentration of frequent items from the input dataset. When the dataset becomes large, the items are scattered far apart. It is known from previous literature that clustering helps produce some data groups which are concentrated with frequent items. Among all the data clusters generated by a clustering algorithm, there must be one or more clusters which contain suitable and frequent items. In turn, the association rules that are mined from such clusters would be assured of better qualities in terms of high confidence than those mined from the whole dataset. However, it is not known in advance which cluster is the suitable one until all the clusters are tried by association rule mining. It is time consuming if they were to be tested by brute-force. In this paper, a statistical property called prior probability is investigated with respect to selecting the best out of many clusters by a clustering algorithm as a pre-processing step before association rule mining. Experiment results indicate that there is correlation between prior probability of the best cluster and the relatively high quality of association rules generated from that cluster. The results are significant as it is possible to know which cluster should be best used for association rule mining instead of testing them all out exhaustively.
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