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

Data Mining Practical Machine Learning Tools and Techniques

物件導向軟體之架構(Object-Oriented Software Construction)探討

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Mobile systems have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. These restrictions may be alleviated by computation offloading: sending heavy computation to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade. This survey paper provides an overview of the background, techniques, systems, and research areas for offloading computation. We also describe directions for future research.

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A Survey of Computation Offloading for Mobile Systems

We present the design, implementation, and evaluation of PROMISE, a novel peer-to-peer media streaming system encompassing the key functions of peer lookup, peer-based aggregated streaming, and dynamic adaptations to network and peer conditions. Particularly, PROMISE is based on a new application level P2P service called CollectCast. CollectCast performs three main functions: (1) inferring and leveraging the underlying network topology and performance information for the selection of senders; (2) monitoring the status of peers and connections and reacting to peer/connection failure or degradation with low overhead; (3) dynamically switching active senders and standby senders, so that the collective network performance out of the active senders remains satisfactory. Based on both real-world measurement and simulation, we evaluate the performance of PROMISE, and discuss lessons learned from our experience with respect to the practicality and further optimization of PROMISE.

/pdf/promise-peer-to-peer-media-streaming-using-collectcast-2mibneus2t.pdf

PROMISE: peer-to-peer media streaming using CollectCast

Multimedia data secmity is important for multimedia commerce. Previous cryptography studies have focused on text data. The encryption algorithms devdoped to secure text data may not be suitable to multimedia applications becattse of large data sizes and real time constraint. For multimedia applications, light weight encryption algorithms are attractive. We present a novel MPEG Video Encryption Algorithm, called VEA The basic idea of VEA is to use a secret key randomly changing the sign bits of all of the DCT coefficients of MPEG video. VEA’S encryption effects are achieved by the IDCT during MPEG video decompression processing. VEA adds minimum overhead to MPEG codecj one Mm&e XOR operation to each none zero DCT coefficient. A software implementation of VEA is fast enough to meet the real time requirement of MPEG video applications. Our experimental results show that VEA achieves satisfying results. We believe that it can be used to secure video-on-demand, tideo conferencing and video email applications.

A fast MPEG video encryption algorithm

Providing secure and efficient access to large scale outsourced data is an important component of cloud computing. In this paper, we propose a mechanism to solve this problem in owner-write-users-read applications. We propose to encrypt every data block with a different key so that flexible cryptography-based access control can be achieved. Through the adoption of key derivation methods, the owner needs to maintain only a few secrets. Analysis shows that the key derivation procedure using hash functions will introduce very limited computation overhead. We propose to use over-encryption and/or lazy revocation to prevent revoked users from getting access to updated data blocks. We design mechanisms to handle both updates to outsourced data and changes in user access rights. We investigate the overhead and safety of the proposed approach, and study mechanisms to improve data access efficiency.

/pdf/secure-and-efficient-access-to-outsourced-data-yk05rx2t62.pdf

Secure and efficient access to outsourced data

Several protocols have been proposed to defend against wormholes in ad hoc networks by adopting positioning devices, synchronized clocks, or directional antennas. In this paper, we propose a mechanism, MDS-VOW, to detect wormholes in a sensor network. MDS-VOW first reconstructs the layout of the sensors using multi-dimensional scaling. To compensate the distortions caused by distance measurement errors, a surface smoothing scheme is adopted. MDS-VOW then detects the wormhole by visualizing the anomalies introduced by the attack. The anomalies, which are caused by the fake connections through the wormhole, bend the reconstructed surface to pull the sensors that are faraway to each other. Through detecting the bending feature, the wormhole is located and the fake connections are identified. The contributions of MDS-VOW are: (1) it does not require the sensors to be equipped with special hardware, (2) it adopts and combines the techniques from social science, computer graphics, and scientific visualization to attack the problem in network security. We examine the accuracy of the proposed mechanism when the sensors are deployed in a circle area and one wormhole exists in the network. The results show that MDS-VOW has a low false alarm ratio when the distance measurement errors are not large.

/pdf/visualization-of-wormholes-in-sensor-networks-3pq2ybismh.pdf

Bharat Bhargava

Papers

A Survey of Computation Offloading for Mobile Systems

PROMISE: peer-to-peer media streaming using CollectCast

A fast MPEG video encryption algorithm

Secure and efficient access to outsourced data

Visualization of wormholes in sensor networks