– The purpose of this paper is to investigate the factors that affect the adoption of cloud computing by firms belonging to the high‐tech industry. The eight factors examined in this study are relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure., – A questionnaire‐based survey was used to collect data from 111 firms belonging to the high‐tech industry in Taiwan. Relevant hypotheses were derived and tested by logistic regression analysis., – The findings revealed that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have a significant effect on the adoption of cloud computing., – The research was conducted in the high‐tech industry, which may limit the generalisability of the findings., – The findings offer cloud computing service providers with a better understanding of what affects cloud computing adoption characteristics, with relevant insight on current promotions., – The research contributes to the application of new technology cloud computing adoption in the high‐tech industry through the use of a wide range of variables. The findings also help firms consider their information technologies investments when implementing cloud computing.

Understanding the determinants of cloud computing adoption

A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

Progress in Outlier Detection Techniques: A Survey

We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions.

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A Descriptive Literature Review and Classification of Cloud Computing Research

In this review, the applications of molecularly imprinted polymer (MIP) materials in the area of electrochemical sensors have been explored. The designs of the MIPs containing different polymers, their preparation and their immobilization on the transducer surface have been discussed. Further, the employment of various transducers containing the MIPs based on different electrochemical techniques for determining analytes has been assessed. In addition, the general protocols for getting the electrochemical signal based on the binding ability of analyte with the MIPs have been given. The review ends with describing scope and limitations of the above electrochemical based MIP sensors.

Molecularly Imprinted Electrochemical Sensors

Trust and trustworthiness of web services and organizations is calculated as scalar values. But there is still a certain level of risk for the overall reliability of this value. In this article, we focus on calculating trust values as intervals between upper and lower bounds based on predefined confidence values through an additional confidence probability. This will give us a more realistic approach to the trust assessments between individuals and organizations. We also developed a web-based software tool, TAST (Trust Assessment Software Tool) that collects the web services’ evaluation of different customer groups for similar organizations through the user interface and calculates the trust intervals for predefined and previously selected confidence values. Our model uses a weighted calculation of mean and variances of customer groups in specific periods and analyses the total and incremental trust of different customer groups.

Formal Trust Assessment with Confidence Probability

Data anomaly detection in wireless sensor networks, which is one of the important technologies and study areas, is a method that enhances data quality and data reliability. Besides data enhancing methods such as estimating missing data, deduplication, noise removal; anomaly detection is important in terms of finding data patterns which are out of normal data. This stage influences next analysis and decision processes and plays an important role in determining events, faults or unexcepted but meaningful patterns. This study proposes the Histogram Based Outlier Score (HBOS) method to detect anomalies in data acquired by wireless sensor networks. In respect to anomaly detection methods used in this area, such as data classification, data clustering, statistical, distance based and support vector machines based approaches, histogram based algorithms are unsupervised and provide fast solutions.

Anomaly Detection in Wireless Sensor Networks Data by Using Histogram Based Outlier Score Method

Terrorism is the greatest threat to national security and cannot be defeated by conventional military force alone. In critical areas such as Iraq, Afghanistan and Turkey, regular forces cannot reach these hostile/terrorist groups, the instigators of terrorism. These groups have a clear understanding of the relative ineffectiveness of counter-guerrilla operations and rely on guerrilla warfare to avoid major combat as their primary means of continuing the conflict with the governmental structures. In Internal Security Operations, detection of terrorist and hostile groups in their hiding places such as caves, lairs, etc. can only be achieved by professionally trained people such as Special Forces or intelligence units with the necessary experience and tools suitable for collecting accurate information in these often harsh, rugged and mountainous countries. To assist these forces, commercial micro-sensors with wireless interfaces could be utilized to study and monitor a variety of phenomena and environments from a certain distance for military purposes. In order to locate hidden terrorist groups and enable more effective use of conventional military resources, this paper proposes an active remote sensing model implanted into animals capable of living in these environments. By using these mobile sensor devices, improving communications for data transfer from the source, and developing better ways to monitor and detect threats, terrorist ability to carry out attacks can be severely disrupted.

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Detection of Hidden Hostile/Terrorist Groups in Harsh Territories by Using Animals as Mobile Biological Sensors

The ultimate goal of all businesses no matter in which sectors they operate is to maintain their profitability and growth. There is a relationship among the capital, investment, and the personnel in order to achieve objectives for the performance of the organizational systems and it must be maintained without any deterioration. Companies have a chance to compete with other firms in the national and international arena through this relationship, and of course within the limits specified by their capitals. The level of success targeted with the suitable investments, qualified personnel and the capabilities of today’s technology is possible for today, at least with a fixed capital for a certain period of time in this relationship. Companies can provide many benefits by using and managing the capabilities offered by virtualization. In this study, we present the results of a survey conducted in 100 small and medium-sized companies in the transportation and services sectors located in Izmir (Turkey). The analysis of this survey will enable a better understanding of virtualization services and motivate especially Information Technologies (IT) staff as a detailed framework for the companies working in different industrial sectors.

	 

	Key words: Virtualization, outsourcing, cloud computing, small and medium-sized businesses.

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Tuncay Ercan

Papers

Formal Trust Assessment with Confidence Probability

Anomaly Detection in Wireless Sensor Networks Data by Using Histogram Based Outlier Score Method

Detection of Hidden Hostile/Terrorist Groups in Harsh Territories by Using Animals as Mobile Biological Sensors

Towards virtualization: A competitive business continuity

A desktop application to learn the actual workload of the students