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

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

The Grid 2: Blueprint for a New Computing Infrastructure

The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

/pdf/internet-of-things-architectures-protocols-and-applications-46rpfbd58i.pdf

Internet of Things: Architectures, Protocols, and Applications

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

To accurately map weak D2-Ne long-range interactions, we have studied rotationally inelastic cold scattering of D2 prepared in the vibrationally excited (v = 4) and rotationally aligned (j = 2, m) quantum state within the moving frame of a supersonically expanded mixed molecular beam. In contrast to earlier high energy D2-Ne collision experiments, the (j = 2 → j' = 0) cold scattering produced highly symmetric angular distributions that strongly suggest a resonant quasi-bound collision complex that lives long enough to make a few rotations. Our partial wave analysis indicates that the scattering dynamics is dominated by a single resonant l = 2 orbital, even in the presence of a broad temperature (0-5 K) distribution that allows incoming orbitals up to l = 5. The dominance of a single orbital suggests that the resonant complex stabilizes through the coupling of the internal (j = 2) and orbital (l = 2) angular momentum to produce a total angular momentum of J = 0 for the D2-Ne complex.

Resonant cold scattering of highly vibrationally excited D2 with Ne.

During the recent COVID-19 pandemic, healthcare workers are trying to manage this medical emergency at war footing. It is the need of the time to have a COVID-19 detection procedure that can be used to stothe kiosk-based operation is shownp the spread among the general people. This paper proposes an ontology-based technique to detect and preliminarily diagnose suspected COVID-19 patients without direct intervention of healthcare professionals. The proposed procedure works as a part of remote healthcare services and tries to respond timely to the situation. The ontology is used for handling general people, rather than patients, to detect COVID-19 primarily.

Ontology for Preliminary Detection of COVID-19

Energy saving is one of the biggest challenges in wireless sensor networks (WSNs). The original Directed Diffusion algorithm chooses the best (shortest) path to transmit data from the source node to the destination (sink) node. In this algorithm, a particular set of nodes could be used more frequently, for message transmission. So, owing to high congestions, the energy of those nodes might get exhausted and an energy hole may be created. But if the message transmission load is distributed by considering both the parameters, namely the remaining energy and the remaining path length, then the problem could be solved. In this paper, we suggest a scheme for reducing energy dissipation in WSN. The scheme is an extension of the concept introduced in Hwang et al. (2006). We provide theoretical analysis to prove the efficiency of the proposed scheme. We also provide theoretical basis of choosing the values of certain parameters for optimising energy dissipation in the network.

A study on efficient path selection algorithms for propagating data messages with a goal of optimising energy dissipation in WSN

Grid computing has made it possible for users to create complex applications by combining globally distributed data and analysis components and deploy them on geographically distributed resources for execution. Introduction of ad-hoc Virtual Organizations based on on-demand service provisioning further enhances this architectural concept. Job-based paradigms or reliance on relatively static UDDI lead to a failure in offering the complete dynamism of a heterogeneous distributed Grid. A possible alternative is the use of dynamic peer-to-peer (P2P) techniques within a Web Service based Grid to introduce the ability of the network to adapt to resource volatility already established in P2P-based content-delivery models. In this paper, we present the architecture of a demand-driven Web Service deployment framework that allows sharing of data and computing capacity using P2P technology as its backbone. We focus on various issues such as resource availability, scalability and abstraction. Demand-driven resource allocation is based on request parameters and availability of the resources to create the basis for a fully dynamic virtual market place of computational resources.

An Architecture for Dynamic Web Service Provisioning Using Peer-to-Peer Networks

As the world is moving towards the “Internet of Things” [1], Internet Protocol enabled wireless sensor network is becoming an important research area. In order to make it possible and to facilitate transmission of IPv6 packets over low powered networks, 6Lowpan [2,3] has been introduced. Since the IPWSN application domain is expanded to real-time applications such as health-care [17] and surveillance systems, a fast and seamless handover becomes an important criterion for supporting mobility in 6LoWPAN [21]. In this paper, we have studied mobility characteristics along with design issues under different mobility scenarios. We propose a scheme to manage the mobility of nodes. The 6LoWPAN based wireless sensor network will be integrated with grid computing establishing a Sensor-grid which may also be used widely in various applications.

Nandini Mukherjee

Papers

Resonant cold scattering of highly vibrationally excited D2 with Ne.

Ontology for Preliminary Detection of COVID-19

A study on efficient path selection algorithms for propagating data messages with a goal of optimising energy dissipation in WSN

An Architecture for Dynamic Web Service Provisioning Using Peer-to-Peer Networks

Mobility of IP based Wireless Sensor Nodes in Sensor-Grid infrastructure