Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

생체의 모델링과 Simulation

The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in information and communication technologies to find sustainable solutions to the growing issues. The Internet of Things IoT has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today, infrastructure, platforms and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the IoT. Our objective is to investigate the concept of sensing as a service model in technological, economical and social perspectives and identify the major open challenges and issues. Copyright © 2013 John Wiley & Sons, Ltd.

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Sensing as a service model for smart cities supported by Internet of Things

The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.

Sensing as a Service Model for Smart Cities Supported by Internet of Things

Cloud Computing is becoming a well-known buzzword nowadays. Many companies, such as Amazon, Google, Microsoft and so on, accelerate their paces in developing Cloud Computing systems and enhancing their services to provide for a larger amount of users. However, security and privacy issues present a strong barrier for users to adapt into Cloud Computing systems. In this paper, we investigate several Cloud Computing system providers about their concerns on security and privacy issues. We find those concerns are not adequate and more should be added in terms of five aspects (i.e., availability, confidentiality, data integrity, control, audit) for security. Moreover, released acts on privacy are out of date to protect users' private information in the new environment (i.e., Cloud Computing system environment) since they are no longer applicable to the new relationship between users and providers, which contains three parties (i.e., Cloud service user, Cloud service provider/Cloud user, Cloud provider). Multi located data storage and services (i.e., applications) in the Cloud make privacy issues even worse. Hence, adapting released acts for new scenarios in the Cloud, it will result in more users to step into Cloud. We claim that the prosperity in Cloud Computing literature is to be coming after those security and privacy issues having be resolved.

Security and Privacy in Cloud Computing: A Survey

Cloud Computing is becoming a well-known buzzword nowadays. As a brand new infrastructure to offer services, Cloud Computing systems have many superiorities in comparing to those existed traditional service provisions, such as reduced upfront investment, expected performance, high availability, infinite scalability, tremendous fault-tolerance capability and so on and consequently chased by most of the IT companies, such as Google, Amazon, Microsoft, Salesforce.com. Based on their overwhelming predominance in traditional service provisions and capital accumulation, most of these IT companies have more chance to adapt their services into such a new environment earlier, say Cloud Computing systems. On the other hand, a large number of new companies are spawned with competitive services relayed on those provided Cloud Computing systems. In terms of their provisions, we divide those services into six categories in this paper, say Data as a Service (Daas), Software as a Service (SaaS), Platform as a Service (PaaS), Identity and Policy Management as a Service (IPMaaS), Network as a Service (NaaS), Infrastructure as a Service (IaaS). Detailed analysis to these services are provided, as well as those companies which provide the corresponding service categories.

Services in the Cloud Computing era: A survey

Multi-dimensional data indexing has received much attention in a centralized database. However, not so much work has been done on this topic in the context of Peerto- Peer systems. In this paper, we propose a new Peer-to- Peer framework based on a balanced tree structure overlay, which can support extensible centralized mapping methods and query processing based on a variety of multidimensional tree structures, including R-Tree, X-Tree, SSTree, and M-Tree. Specifically, in a network with N nodes, our framework guarantees that point queries and range queries can be answered within O(logN) hops. We also provide an effective load balancing strategy to allow nodes to balance their work load efficiently. An experimental assessment validates the practicality of our proposal.

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VBI-Tree: A Peer-to-Peer Framework for Supporting Multi-Dimensional Indexing Schemes

Peer-to-Peer systems have recently become a popular means to share resources. Effective search is a critical requirement in such systems, and a number of distributed search structures have been proposed in the literature. Most of these structures provide "log time search" capability, where the logarithm is taken base 2. That is, in a system with N nodes, the cost of the search is O(log2N).In database systems, the importance of large fanout index structures has been well recognized. In P2P search too, the cost could be reduced considerably if this logarithm were taken to a larger base. In this paper, we propose a multi-way tree search structure, which reduces the cost of search to O(logmN), where m is the fanout. The penalty paid is a larger update cost, but we show how to keep this penalty to be no worse than linear in m. We experimentally explore this tradeoff between search and update cost as a function of m, and suggest how to find a good trade-off point.The multi-way tree structure we propose, BATON*, is derived from the BATON structure that has recently been suggested. In addition to multi-way fanout, BATON* also adds support for multi-attribute queries to BATON.

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Speeding up search in peer-to-peer networks with a multi-way tree structure

The automatic construction of large-scale knowledge graphs has received much attention from both academia and industry in the past few years. Notable knowledge graph systems include Google Knowledge Graph, DBPedia, YAGO, NELL, Probase and many others. Knowledge graph organizes the information in a structured way by explicitly describing the relations among entities. Since entity identification and relation extraction are highly depending on language itself, data sources largely determine the way the data are processed, relations are extracted, and ultimately how knowledge graphs are formed, which deeply involves the analysis of lexicon, syntax and semantics of the content. Currently, much progress has been made for knowledge graphs in English language. In this paper, we discuss the challenges facing Chinese knowledge graph construction because Chinese is significantly different from English in various linguistic perspectives. Specifically, we analyze the challenges from three aspects: data sources, taxonomy derivation and knowledge extraction. We also present our insights in addressing these challenges.

Rong Zhang

Papers

Security and Privacy in Cloud Computing: A Survey

Services in the Cloud Computing era: A survey

VBI-Tree: A Peer-to-Peer Framework for Supporting Multi-Dimensional Indexing Schemes

Speeding up search in peer-to-peer networks with a multi-way tree structure

Challenges in Chinese knowledge graph construction