Cloud computing services: taxonomy and comparison

Cloud computing is a highly discussed topic in the technical and economic world, and many of the big players of the software industry have entered the development of cloud services. Several companies what to explore the possibilities and benefits of incorporating such cloud computing services in their business, as well as the possibilities to offer own cloud services. However, with the amount of cloud computing services increasing quickly, the need for a taxonomy framework rises. This paper examines the available cloud computing services and identifies and explains their main characteristics. Next, this paper organizes these characteristics and proposes a tree-structured taxonomy. This taxonomy allows quick classifications of the different cloud computing services and makes it easier to compare them. Based on existing taxonomies, this taxonomy provides more detailed characteristics and hierarchies. Additionally, the taxonomy offers a common terminology and baseline information for easy communication. Finally, the taxonomy is explained and verified using existing cloud services as examples

Tomato Grading: A New Approach for Classifying and Predicting Tomato Quality based on Visual Features

Increased awareness about nourishing and healthy lifestyles to propel the consumption of vegetables in order to meet diverse dietary and nutritional needs. The global tomato market was valued to register a Compound Annual Growth Rate of more than 3.8% over the projection horizon of 2021-2026. The planned approach that calculates the grade of the tomato in regard to its external features. Grading is sorting or categorization of tomatoes into different grades according to the size, shape, colour etc and is one of the foremost necessary processes in post harvesting, however this procedure is sometimes administered manually, that is not economical as a result it needs huge estimate of enrollment, and have an inclination to human error. The grading method is performed by capturing the tomato image using web camera which calculates the percentage of ripeness based on unique set of features that are utilized to train the neural network. Color emerges as an extremely prominent feature for recognizing defect and matureness of the tomato. The major objective is to check the tomato quality with high speed for evaluating maximal count of tomatoes in least amount of time. For spoiled tomatoes, the proposed system helps in identification of tomato plant disease and allocate countermeasures that can be used as a fortification mechanism against the disease. The tomato plant disease detection can be done by observing the spots on the leaves of the diseased plant. In order to detect plant diseases, the approach we are endorsing is image processing using Convolution neural network (CNN)

Security Assurance in DevOps Methodologies and Related Environments

The biggest software development companies conduct daily more than hundreds deployments which influence currently operating IT (Information Technology) systems. This is possible due to the availability of automatic mechanisms which are providing their functional testing and later applications deployment. Unfortunately, nowadays, there are no tools or even a set of good practices related to the problem on how to include IT security issues into the whole production and deployment processes. This paper describes how to deal with this problem in the large mobile telecommunication operator environment.

Security Assurance in DevOps Methodologies and Related Environments

The biggest software development companies conduct daily more than hundreds deployments which influence currently operating IT (Information Technology) systems. This is possible due to the availability of automatic mechanisms which are providing their functional testing and later applications deployment. Unfortunately, nowadays, there are no tools or even a set of good practices related to the problem on how to include IT security issues into the whole production and deployment processes. This paper describes how to deal with this problem in the large mobile telecommunication operator environment.

Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge.

Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area

An automated essay evaluation system using natural language processing and sentiment analysi

An automated essay evaluation system is a machine-based approach leveraging long short-term memory (LSTM) model to award grades to essays written in English language. natural language processing (NLP) is used to extract feature representations from the essays. The LSTM network learns from the extracted features and generates parameters for testing and validation. The main objectives of the research include proposing and training an LSTM model using a dataset of manually graded essays with scores. Sentiment analysis is performed to determine the sentiment of the essay as either positive, negative or neutral. The twitter sample dataset is used to build sentiment classifier that analyzes the sentiment based on the student’s approach towards a topic. Additionally, each essay is subjected to detection of syntactical errors as well as plagiarism check to detect the novelty of the essay. The overall grade is calculated based on the quality of the essay, the number of syntactic errors, the percentage of plagiarism found and sentiment of the essay. The corrected essay is provided as feedback to the students. This essay grading model has gained an average quadratic weighted kappa (QWK) score of 0.911 with 99.4% accuracy for the sentiment analysis classifier

Cloud services, interoperability and analytics within a ROLE-enabled personal learning environment

The ROLE project (Responsive Open Learning Environments, EU 7th Framework Programme, grant agreement no.: 231396, 2009-2013) was focused on the next generation of Personal Learning Environments (PLEs). A ROLE PLE is a bundle of interoperating widgets - often realised as cloud services - used for teaching and learning. In this paper, we first describe the creation of new ROLE widgets and widget bundles at Galileo University, Guatemala, within a cloud-based infrastructure. We introduce an initial architecture for cloud interoperability services including the means for collecting interaction data as needed for learning analytics. Furthermore, we describe the newly implemented widgets, namely a social networking tool, a mind-mapping tool and an online document editor, as well as the modification of existing widgets. The newly created and modified widgets have been combined in two different bundles that have been evaluated in two web-based courses at Galileo University, with participants from three different Latin-American countries. We measured emotional aspects, motivation, usability and attitudes towards the environment. The results demonstrated the readiness of cloud-based education solutions, and how ROLE can bring together such an environment from a PLE perspective

Reframing technical change: Livestock Fodder Scarcity Revisited as Innovation Capacity Scarcity: Part 3. Tools for Diagnosis and Institutional Change in Innovation Systems

The exploration of fodder innovation capacity requires tools to undertake the following tasks: (i) Diagnosis of fodder innovation capacity to identify project starting points, including micro and macro elements (ii) Socio-economic benchmarking, and follow-up studies (iii) Pilot innovation cloud process learning/ process-driven intervention correction (iv) Comparative analysis of institutional change processes (iv) Project team process learning And (iv) Project evaluation. There is a wide range of existing tools available to investigate institutional change. This paper reviews these and recommends that an eclectic approach of mixing and matching tools to the emerging circumstances of the research is the best way forward.Technological Change, Agricultural Technology, Livestock, Poverty Reduction, Evaluation, Benchmarking

Fuzzy logic approach to modelling trust in cloud computing

Despite the growing deployment of mission critical applications on computing systems, trust and security continues to hinder its full adoption and deployment on cloud computing platforms. In addition to accountability and non-repudiation on the cloud deployment, end-users want to be confident of availability and reliability of services. For any cloud platform to be secure and trusted, the individual layers of the platform must be secure as there is no 'one fits all solution' for securing all the layers. This work presents a multi-layer trust security model (MLTSM) based on unified cloud platform trust that employs a fuzzy logic combination of on-demand states of several different security mechanisms, such as identification, direct and in-direct trust, across all cloud layers. In addition, results from a MATLAB-based simulation of the model are also presented. A MLTSM can improve the secure deployment of cloud infrastructure in mission critical sectors such as electrical power system operation, as it provides empirical evidence that allows direct (on-demand) determination and verification of the trust state of any given cloud computing platform or service. Such a modelling approach is useful for comparison, classification and improving end-user confidence in selecting or consuming cloud computing resources

Soil Stabilization Manual 2014 Update

Soil Stabilization is used for a variety of activities including temporary wearing curses, working platforms, improving poor subgrade materials, upgrading marginal materials, dust control, and recycling old roads containing marginal materials. There are a number methods of stabilizing soils including modifying the gradation, the use of asphalt or cement stabilizers, geofiber stabilization and chemical stabilization. Selection of the method depends on the soil type, environment and application. This manual provide tools and guidance in the selection of the proper stabilization method and information on how to apply the method. A major portion of this manual is devoted to the use of stabilizing agents. The methods described here are considered best practices for Alaska.State of Alaska, Alaska Dept. of Transportation and Public Facilitie