The Impact of Corporate Philanthropy on Optimising Unequal Allocation of Social Resources

Corporate philanthropy is an effective means to help society reduce the various inequalities phenomena and promote social justice. The present research surveys about this topic focus on corporate donations or management but pay less attention to philanthropy methods and corporate foundations. The establishment and development of corporate foundations have become indispensable means for enterprises to engage the philanthropic activities. Therefore, this paper explores the conceptual development, establishment motives, objectives, operational process, and ultimate effectiveness of corporate philanthropic foundations to address the problem of unequal allocation of social resources. In this research, taking the Tencent Public Philanthropy Foundation as an example, and finds that a joint public welfare approach can better promote the establishment of charitable projects. For instance, Tencent Philanthropy Foundation has partnered with One Foundation, the former provides technical and financial support for future classroom projects, while the latter provides professional services and follow-up. The findings suggest that this joint public welfare collaboration is worthy of being used widely by corporate welfare foundations, because the collaboration can play a positive role due to a clear division of labour and professional means in philanthropy projects, and it also could be a new corporate philanthropic strategy in the future

Experimental PC based TGPID control method for 2D CNC machine

An important problem in the control of circular motion of CNC machine is to let X and Y axes move simultaneously. This article addresses this problem for the performance of desktop-scale CNC milling machine for reducing roundness error (REB), minimizing position time difference (DTt). An approach that can solve those problems will be introduced. Our approach uses a Taguchi–Grey System–Proportional Integral Derivative (TGPID). This method emphasizes an improvement of system performance through this controller’s robustness, such as a faster initialization in gaining as appropriate local minima and also high responsive. In this paper, it is aimed to enhance on multi-performance characteristics, namely actual radius (R_act) and position time (Tt). The improvement of roundness error in counter-clockwise (CCW) direction is from 0.151 mm by default, being 0.140 mm by TPID (Taguchi–PID; without grey system), and 0.133 mm by TGPID. The method can reduce the roundness error significantly, also the difference of position time for 100%. This proposed method also offers a simple experimental-based approach. An improvement of its performance indicated that this proposed approach is applied successfully to multi-linear motion performance optimization which is determined by many parameters at multi-quality performances. Performances of the proposed controller scheme, as well as some practical design aspects, are demonstrated by the control of a circular motion of CNC machine

Descubrimiento de Recursos en un Entorno Grid mediante Ontolog´ıas

Las tecnolog´ıas Grid permiten el uso compartido de recursos heterog´eneos que se encuentran dise-minados geogr´aficamente en una WAN o en Internet. La infraestructura determinada por el uso de estastecnolog´ıas (computaci´on grid) es solamente una parte de un escenario m´as amplio en el que tambi´ense incluye el manejo de informaci´on y soporte para el procesamiento de conocimiento por parte de losprocesos distribuidos. Esta visi´on m´as amplia es adoptada por la grid sem´antica [6] que se describe comouna extensi´on de la computaci´on grid donde la informaci´on y los servicios son descriptos de forma biendefinida, permitiendo trabajar a las personas y a las computadoras de una manera m´as cooperativa.En este art´ıculo se analizan las recientes propuestas en el manejo de ontolog´ıas para describir de formasem´antica los recursos que forman parte del entorno grid, lo cu´al facilita la localizaci´on de los mismoscuando las aplicaciones necesitan de ellos, y se exploran las posibilidades de desarrollo de m´odulos quehagan uso de bases de conocimiento. De esta forma, es posible extender arquitecturas grid existentes conel fin de hacer m´as eficiente la tarea de descubrir recursos en la web y componerlos en forma inteligente

Soka Gakkai in Hong Kong: localizing a Japanese new religion in a Chinese community.

Ng, Ka Shing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 207-214).Abstracts in English and Chinese ; some appendixes in Chinese.Abstracts --- p.iiAcknowledgements --- p.ivTable of Contents --- p.vPreface --- p.1Chapter Chapter 1 --- Soka Gakkai Movement in Japan --- p.13Chapter 1.1 --- The Origins of SG Doctrine --- p.13Chapter 1.2 --- The Development of SG in Prewar Japan --- p.19Chapter 1.3 --- The Development of SG in Postwar Japan --- p.23Chapter 1.4 --- Globalizing SG --- p.27Chapter Chapter 2 --- Soka Gakkai Movement Outside Japan --- p.33Chapter 2.1 --- SG in North America --- p.36SGI-USA --- p.37SGI-Canada --- p.45Chapter 2.2 --- SG in Britain --- p.49Chapter 2.3 --- SG in Southeast Asia --- p.55Chapter 2.4 --- SG in Taiwan --- p.58Chapter Chapter 3 --- Soka Gakkai Movement in Hong Kong --- p.67Chapter 3.1 --- The Origin of HKSGI --- p.67Chapter 3.2 --- HKSGI's Social and Cultural Activities --- p.80Chapter 3.3 --- The Significance of HKSGI: Stepping Stone for SG to Promote in China --- p.88Chapter 3.4 --- The Perception of SG in HK --- p.98Chapter 3.5 --- Comparative Study of SG in Hong Kong and Overseas --- p.113Chapter Chapter 4 --- Localization: Practices and Teachings --- p.116Chapter 4.1 --- How SG Practices are Localized in Hong Kong --- p.119HKSGI Meetings --- p.120HKSGI Leadership --- p.126Overcoming Language Barriers --- p.129HKSGI and Hong Kong Festivals --- p.135HKSGI Wedding and Funeral --- p.139Cultural Festival 2011 --- p.143Why Are There More Women Than Men in HKSGI? --- p.152Chapter 4.2 --- How SG's Teachings are Localized in Hong Kong --- p.157World Peace --- p.158Anti-nuclear Weapon --- p.159Environmental Protection --- p.162Promotion of Education --- p.164Protection of Human Rights --- p.166Improving International Relationships through Dialogues --- p.169Objectives of Chanting More Oriented to Local Situations --- p.171De-politicization --- p.172Localization of SG Concepts --- p.173Chapter 4.3 --- Opportunities and Challenges in Localizing SG --- p.175Chapter Chapter 5 --- Conclusion --- p.184Appendixes --- p.200Chapter 1. --- Cultural Seminars Organized by HKSGI --- p.200Chapter 2. --- Exhibition Organized by HKSGI --- p.202Chapter 3. --- Questionnaire for Non-Member --- p.203Chapter 4. --- Questionnaire for Members --- p.204Chapter 5. --- Sample Interview Questions for Members --- p.206References --- p.20

Cloud service discovery and analysis: a unified framework

Over the past few years, cloud computing has been more and more attractive as a new computing paradigm due to high flexibility for provisioning on-demand computing resources that are used as services through the Internet. The issues around cloud service discovery have considered by many researchers in the recent years. However, in cloud computing, with the highly dynamic, distributed, the lack of standardized description languages, diverse services offered at different levels and non-transparent nature of cloud services, this research area has gained a significant attention. Robust cloud service discovery approaches will assist the promotion and growth of cloud service customers and providers, but will also provide a meaningful contribution to the acceptance and development of cloud computing. In this dissertation, we have proposed an automated cloud service discovery approach of cloud services. We have also conducted extensive experiments to validate our proposed approach. The results demonstrate the applicability of our approach and its capability of effectively identifying and categorizing cloud services on the Internet. Firstly, we develop a novel approach to build cloud service ontology. Cloud service ontology initially is built based on the National Institute of Standards and Technology (NIST) cloud computing standard. Then, we add new concepts to ontology by automatically analyzing real cloud services based on cloud service ontology Algorithm. We also propose cloud service categorization that use Term Frequency to weigh cloud service ontology concepts and calculate cosine similarity to measure the similarity between cloud services. The cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine Learning techniques to identify cloud service in real environment. Our cloud service identifier is built by utilizing cloud service features extracted from the real cloud service providers. We determine several features such as similarity function, semantic ontology, cloud service description and cloud services components, to be used effectively in identifying cloud service on the Web. Also, we build a unified model to expose the cloud service’s features to a cloud service search user to ease the process of searching and comparison among a large amount of cloud services by building cloud service’s profile. Furthermore, we particularly develop a cloud service discovery Engine that has capability to crawl the Web automatically and collect cloud services. The collected datasets include meta-data of nearly 7,500 real-world cloud services providers and nearly 15,000 services (2.45GB). The experimental results show that our approach i) is able to effectively build automatic cloud service ontology, ii) is robust in identifying cloud service in real environment and iii) is more scalable in providing more details about cloud services.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Fault-tolerant data aggregation scheme for monitoring of critical events in grid based healthcare sensor networks

Wireless sensor devices are used for monitoring patients with serious medical conditions. Communication of content-sensitive and context sensitive datasets is crucial for the survival of patients so that informed decisions can be made. The main limitation of sensor devices is that they work on a fixed threshold to notify the relevant Healthcare Professional (HP) about the seriousness of a patient’s current state. Further, these sensor devices have limited processor, memory capabilities and battery. A new grid-based information monitoring architecture is proposed to address the issues of data loss and timely dissemination of critical information to the relevant HP. The proposed approach provides an opportunity to efficiently aggregate datasets of interest by reducing network overhead and minimizing data latency. To narrow down the problem domain, in-network processing of datasets with Grid monitoring capabilities is proposed for the efficient execution of the computational, resource and data intensive tasks. Interactive wireless sensor networks do not guarantee that data gathered from the heterogeneous sources will always arrive at the sink (base) node, but the proposed aggregation technique will provide a fault tolerant solution to the timely notification of a patient’s critical state. Experimental results received are encouraging and clearly show a reduction in the network latency rate

American family entertainment and the only child generation in contemporary urban China

As a result of the economic reform which took place three decades ago, imported American family entertainment had gradually become an important part of the everyday entertainment for Chinese consumers. During the same period, a particular group of Chinese people, generally referred to as the post-80s or the only child generation, had emerged, grown up and become the main contributors to China’s media consumption. In this thesis, a study of the only child generation and the American family entertainment will be presented. The study sees the only child generation as groups of audience exposed to American family entertainment as the media, and the focus of this study is to understand the audience-media relationship between the two. As they are two objects emerged within their own social and cultural boundaries, the thesis will first tackle how the connection between the audience and the media was established. Then, the only child generation will be approached as a social creation. Findings on their social sophistications that are able to influence their relationship to media will be presented. Four case studies form the reset of the thesis. Each of the case studies will focus on one significant aspect of the generation’s social characteristics and how it is connected to the group’s receptions to media texts

Using collaborative tagging for text classification: from text classification to opinion mining

Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment. As a use case, our study is made in the context of text mining evaluation campaign material, related to the classification of cooking recipes tagged by users from a collaborative website. This context makes some of the corpus specificities difficult to model for machine-learning-based systems and keyword or lexical-based systems. In particular, different authors might have different opinions on how to classify a given document. The systems presented hereafter were submitted to the D´Efi Fouille de Textes 2013 evaluation campaign, where they obtained the best overall results, ranking first on task 1 and second on task 2. In this paper, we explain our approach for building relevant and effective systems dealing with such a corpus

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida