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

Trust: The Social Virtues and the Creation of Prosperity

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

We are on the verge of ubiquitously adopting Augmented Reality (AR) technologies to enhance our percep- tion and help us see, hear, and feel our environments in new and enriched ways. AR will support us in fields such as education, maintenance, design and reconnaissance, to name but a few. This paper describes the field of AR, including a brief definition and development history, the enabling technologies and their characteristics. It surveys the state of the art by reviewing some recent applications of AR technology as well as some known limitations regarding human factors in the use of AR systems that developers will need to overcome.

/pdf/a-survey-of-augmented-reality-technologies-applications-and-3d9hf1tnc4.pdf

A Survey of Augmented Reality Technologies, Applications and Limitations

A systematic literature review of blockchain-based applications: Current status, classification and open issues

Privacy-friendly platform for healthcare data in cloud based on blockchain environment

Healthcare data are grabbing the interest of cyber attackers in recent years. Annihilating consequences of healthcare data could be alleviated through decentralization. A peer to peer (P2P) network enables the property of decentralization, where different parties can store and run computation while keeping the sensitive health data private. Blockchain technology leverages decentralized or distributed process, which ensures the accountability and integrity of its use. This paper presents a patient centric healthcare data management system by using Blockchain as storage to attain privacy. Pseudonymity is ensured by using the cryptographic functions to protect patient’s data.

MediBchain: A Blockchain Based Privacy Preserving Platform for Healthcare Data

We present an educational application that allows users to interact with 3D web content using virtual and augmented reality (AR). This enables us to explore the potential benefits of Web3D and AR technologies in engineering education and learning. A lecturer’s traditional delivery can be enriched by viewing multimedia content locally or over the Internet, as well as in a table-top AR environment. The implemented framework is composed an XML data repository, an XML based communications server, and an XML based client visualisation application. In this paper we illustrate the architecture by configuring it to deliver multimedia content related to the teaching of mechanical engineering. We illustrate four mechanical engineering themes (machines, vehicles, platonic solids and tools) to demonstrate use of the system to support learning through Web3D.

http://www.wiete.com.au/journals/WTE&TE/Pages/Vol.3,%20No.1%20(2004)/04_Liarokapis.pdf

Web3D and Augmented Reality to support engineering education

In this paper, we discuss the current situation with respect to simulation usage in P2P research, testing the available P2P simulators against a proposed set of requirements, and surveying over 280 papers to discover what simulators are already being used. We found that no simulator currently meets all our requirements, and that simulation results are generally reported in the literature in a fashion that precludes any reproduction of results. We hope that this paper will give rise to further discussion and knowledge sharing among those of the P2P and network simulation research communities, so that a simulator that meets the needs of rigorous P2P research can be developed

/pdf/the-state-of-peer-to-peer-simulators-and-simulations-2b01vjqbpy.pdf

The state of peer-to-peer simulators and simulations

As scientists, our research must be tested and evaluated in order for it to be shown to be valid. Part of this process includes providing reproducible results so that peers are able to confirm any findings. The techniques used to achieve this include analytical solutions, simulators and experiments with the actual system, however in the area of Peer-to-Peer (P2P) computing, this gives rise to a number of challenges. In this paper, we focus on P2P simulators by surveying a number of P2P simulators with respect to important criteria when simulating a P2P system. Simulators are compared, usage experiences are given and limitations are discussed. The survey shows that while there are a plethora of simulators, there are many issues such as poor scalability, little or no documentation, steep learning curves, and system limitations that makes using these simulators a convoluted process.

Anirban Basu

Papers

Privacy-friendly platform for healthcare data in cloud based on blockchain environment

MediBchain: A Blockchain Based Privacy Preserving Platform for Healthcare Data

Web3D and Augmented Reality to support engineering education

The state of peer-to-peer simulators and simulations

A Survey of Peer-to-Peer Network Simulators