Computers, Volume 8, Issue 3 (September 2019) – 19 articles

In this paper, we explore a framework for assessing the implementation of IT Strategic planning, grounded in the combination and iteration of different methods. It is a part of an Action Design Research exercise being made up at a leading online European university. The assessment mixes three main dimensions (strategy, performance and governance), extracted from the professional and academic research. Its application to this context through a varied scaffolding of methods, tools and techniques seems robust and helpful to work out with the business and IT senior stakeholders. It allows a quick deployment, even in a complex institutional environment. Full article

Scholars have proposed many approaches to represent and analyze value creation. Value creation in ecosystems such as platform ecosystems often relies on a specific structure of partner alignment. Value modeling techniques can improve the understanding of how ecosystem risks and non-generic complementarities determine value creation and the alignment structures required. First, we conceptualize ecosystem analysis as a tool for alignment in the context of business innovation. Then, we carry out a structured literature review to identify existing techniques, which could support ecosystem analysis. Further, we provide a comprehensive overview of the value modeling techniques and integrate our ecosystem analysis conceptualization with existing classification frameworks. This integrative framework allows researchers and scholars to identify techniques that suit specific needs in terms of internal alignment reach, tooling, innovation phase and ecosystem analysis. Our results show limited support for ecosystem analysis. Still we are able to identify techniques that can provide a useful conceptual or tooling basis to enable ecosystem analysis. Full article

Healthcare is one of the largest business segments in the world and is a critical area for future growth. In order to ensure efficient access to medical and patient-related information, hospitals have invested heavily in improving clinical mobile technologies and spreading their use among doctors towards a more efficient and personalized delivery of care procedures. However, there are also indications that their use may have a negative impact on patient-centeredness and often places many cognitive and physical demands on doctors, making them prone to make medical errors. To tackle this issue, in this paper, we present the main outcomes of the project TESTMED, which aimed at realizing a clinical system that provides operational support to doctors using mobile technologies for delivering care to patients, in a bid to minimize medical errors. The system exploits concepts from Business Process Management (BPM) on how to manage a specific class of care procedures, called clinical guidelines, and how to support their execution and mobile orchestration among doctors. To allow a non-invasive interaction of doctors with the system, we leverage the use of touch and vocal user interfaces. A robust user evaluation performed in a real clinical case study shows the usability and effectiveness of the system. Full article

Security and privacy concerns represent a significant hindrance to the widespread adoption of cloud computing services. While cloud adoption mitigates some of the existing information technology (IT) risks, research shows that it introduces a new set of security risks linked to multi-tenancy, supply chain and system complexity. Assessing and managing cloud risks can be a challenge, even for cloud service providers (CSPs), due to the increased numbers of parties, devices and applications involved in cloud service delivery. The limited visibility of security controls down the supply chain, further exacerbates this risk assessment challenge. As such, we propose the Cloud Supply Chain Cyber Risk Assessment (CSCCRA) model, a quantitative risk assessment model which is supported by supplier security posture assessment and supply chain mapping. Using the CSCCRA model, we assess the risk of a SaaS application, mapping its supply chain, identifying weak links in the chain, evaluating its security risks and presenting the risk value in monetary terms (£), with this, promoting cost-effective risk mitigation and optimal risk prioritisation. We later apply the Core Unified Risk Framework (CURF) in comparing the CSCCRA model with already established methods, as part of evaluating its completeness. Full article

This article presents a case study on the use of open data in the Scandinavian parliaments (Norway, Sweden, and Denmark). While the three countries have all opened the gates and provided access to data—for example, on the voting in parliament, debates, and notes from meetings in committees—the uptake and use of data outside the parliaments is limited. While journalists and academia are users of the open data, hackathons and third-party portals are at an explorative level. Still, there are indicators that hackathons can enhance democracy, and parliamentary data can increase political transparency. Full article

This paper presents an online calculator for bank charges, motivated by information asymmetry in the market for payment accounts. The calculator provides users with a personalized list of the most suitable bank accounts based on required services and monthly fee criteria. This paper outlines the conceptual foundation, workflows, and matrix of the data for the underlying logic of the calculator, as well as the design of the user interface. The proposed calculator was validated by performing an interaction cost analysis. This paper presents a novel methodology for conducting this analysis, including rules for expressing interactions in graphs for the objective evaluation of the usability of the user interface. Scenarios were defined and analyzed with the intended goal of choosing the best bank account. The interaction cost analysis then confirmed the differences in cost between traditional approaches (interacting with various web interfaces) and using a specialized online service (the calculator). The consistency of the layout and navigation contributed significantly to the final results being in favor of the proposed bank charges calculator. These conclusions are applicable not just within the selected market, but also in many others that are prone to problems arising from price information asymmetry. Full article

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization. Full article

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions. Full article

Once a botnet is constructed over the network, a bot master and bots start communicating by periodically exchanging messages, which is known as botnet C&C communication, in order to send botnet commands to bots, collect critical information stored in bots, upgrade software functions of malwares installed in bots, and so on. For this reason, most existing botnet detection techniques focus on monitoring and capturing suspicious communications between the bot master and bots. Meanwhile, botnets continue to evolve to hide their C&C communication. Recently, a novel type of botnet using image steganography techniques and SNS (Social Network Service) platforms, which is known as image steganography-based botnet or stegobotnet, has emerged to make its C&C communications undetectable by existing botnet detection systems. In stegobotnets, image files used in SNSs carry messages (between the bot master and bots) which are hidden in them by using image steganography techniques. In this paper, we first investigate whether major SNS platforms such as KakaoTalk, Facebook, and Twitter can be suitable for constructing image steganography-based botnets. Next, we construct a part of stegobotnet based on KakaoTalk, and conduct extensive experiments including digital forensic analysis (1) to validate stegobotnet C&C communication can be successful in KakaoTalk and (2) to examine its performance in terms of C&C communication reliability. Full article

On Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, the quality of information about the same topic depends on the language. Any interested user can improve an article and that improvement may depend on the popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper, we also analyze how popular selected topics are among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we selected 27 main multilingual categories and analyzed all their connections with sub-categories based on information extracted from over 10 million categories in 55 language versions. To classify the articles to one of the 27 main categories, we took into account over 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach, we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content. Full article

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods. Full article

In this paper, we propose an intrusion detection system (IDS) and Blockchain-based delivery framework, called DeliveryCoin, for drone-delivered services. The DeliveryCoin framework consists of four phases, including system initialization phase, creating the block, updating the blockchain, and intrusion detection phase. To achieve privacy-preservation, the DeliveryCoin framework employs hash functions and short signatures without random oracles and the Strong Diffie–Hellman (SDH) assumption in bilinear groups. To achieve consensus inside the blockchain-based delivery platform, we introduce a UAV-aided forwarding mechanism, named pBFTF. We also propose an IDS system in each macro eNB (5G) for detecting self-driving network attacks as well as false transactions between self-driving nodes. Furthermore, extensive simulations are conducted, and results confirm the efficiency of our proposed DeliveryCoin framework in terms of latency of blockchain consensus and accuracy. Full article

The growing adoption of Radio-frequency Identification (RFID) systems, particularly in the healthcare field, demonstrates that RFID is a positive asset for healthcare institutions. RFID offers the ability to save organizations time and costs by enabling data of traceability, identification, communication, temperature and location in real time for both people and resources. However, the RFID systems challenges are financial, technical, organizational and above all privacy and security. For this reason, recent works focus on attribute-based access control (ABAC) schemes. Currently, ABAC are based on mostly centralized models, which in environments such as the supply chain can present problems of scalability, synchronization and trust between the parties. In this manuscript, we implement an ABAC model in RFID systems based on a decentralized model such as blockchain. Common criteria for the selection of the appropriate blockchain are detailed. Our access control policies are executed through the decentralized application (DApp), which interfaces with the blockchain through the smart contract. Smart contracts and blockchain technology, on the one hand, solve current centralized systems issues as well as being flexible infrastructures that represent the relationship of trust and support essential in the ABAC model in order to provide the security of RFID systems. Our system has been designed for a supply chain environment with an use case suitable for healthcare systems, so that assets such as surgical instruments containing an associated RFID tag can only access to specific areas. Our system is deployed in both a local and Testnet environment in order to stablish a deep comparison and determining the technical feasibility. Full article

Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expenses. Full article

The growth of ecommerce has triggered online reviews as a rich source of product information. Revealing consumer sentiment from the reviews through Sentiment Analysis (SA) is an important task of online product review analysis. Two popular approaches of SA are the supervised approach and the lexicon-based approach. In supervised approach, the employed machine learning (ML) algorithm is not the only one to influence the results of SA. The utilized text features also handle an important role in determining the performance of SA tasks. In this regard, we proposed a method to extract text features that takes into account semantic of words. We argue that this semantic feature is capable of augmenting the results of supervised SA tasks compared to commonly utilized features, i.e., bag-of-words (BoW). To extract the features, we assigned the correct sense of the word in reviewing the sentence by adopting a Word Sense Disambiguation (WSD) technique. Several WordNet similarity algorithms were involved, and correct sentiment values were assigned to words. Accordingly, we generated text features for product review documents. To evaluate the performance of our text features in the supervised approach, we conducted experiments using several ML algorithms and feature selection methods. The results of the experiments using 10-fold cross-validation indicated that our proposed semantic features favorably increased the performance of SA by 10.9%, 9.2%, and 10.6% of precision, recall, and F-Measure, respectively, compared with baseline methods. Full article

We perform a theoretical and empirical analysis of a set of Cascading Style Sheets (CSS) document complexity metrics. The metrics are validated using a practical framework that demonstrates their viability. The theoretical analysis is performed using the Weyuker’s properties−a widely adopted approach to conducting empirical validations of metrics proposals. The empirical analysis is conducted using visual and statistical analysis of distribution of metric values, Cliff’s delta, Chi-square and Liliefors statistical normality tests, and correlation analysis on our own dataset of CSS documents. The results show that five out of the nine metrics (56%) satisfy Weyuker’s properties except for the Number of Attributes Defined per Rule Block (NADRB) metric, which satisfies six out of nine (67%) properties. In addition, the results from the statistical analysis show good statistical distribution characteristics (only the Number of Extended Rule Blocks (NERB) metric exceeds the rule-of-thumb threshold value of the Cliff’s delta). The correlation between the metric values and the size of the CSS documents is insignificant, suggesting that the presented metrics are indeed complexity rather than size metrics. The practical application of the presented CSS complexity metric suite is to assess the risk of CSS documents. The proposed CSS complexity metrics suite allows identification of CSS files that require immediate attention of software maintenance personnel. Full article

In 1947, N. Herlofson proposed a modification to the 1884 Heinrich Hertz’s Emagram with the goal of getting more precise hand-made weather forecasts providing larger angles between isotherms and adiabats. Since then, the Herlofson’s nomogram has been used every day to visualize the results of about 800 radiosonde balloons that, twice a day, are globally released, sounding the atmosphere and reading pressure, altitude, temperature, dew point, and wind velocity. Relevant weather forecasts use such pieces of information to predict fog, cloud height, rain, thunderstorms, etc. However, despite its diffusion, non-technical people (e.g., private gliding pilots) do not use the Herlofson’s nomogram because they often consider it hard to interpret and confusing. This paper copes with this problem presenting a visualization based environment that presents the Herlofson’s nomogram in an easier to interpret way, allowing the selection of the right level of detail and at the same time inspection of the sounding row data and the plotted diagram. Our visual environment was compared with the classic way of representing the Herlofson’s nomogram in a formal user study, demonstrating the higher efficacy and better comprehensibility of the proposed solution. Full article

Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture. Full article

Several countries have invested in building their identity management systems to equip citizens with infrastructures and tools to benefit from e-services. However, current systems still lack the interoperability requirement, which is the core issue that could lower the wide benefits of having an identity management system. In fact, in the existing systems, the user is allowed to choose only one partial identity from an identity provider (IdP) during a single session with a service provider (SP). However, in some scenarios, an SP needs to retrieve information about user’s identities managed by multiple IdPs. The potential method to tackle these shortcomings is attribute aggregation from multiple identity providers. A number of initiatives and projects on attribute aggregation have been explored. Nevertheless, these constructions do not fulfill some identity management requirements. This paper describes a new flexible model that aims to provide the necessary mechanisms to ensure attribute aggregation in order to meet the interoperability challenges of current identity management systems. The proposed scheme is a scalable solution, based on identity federation technologies, that introduces a new IdP called an account linking provider (ALP). The purpose of this ALP is to link together different accounts, holding end users’ attributes, whenever more than one source of data is needed to grant access to the requested web resource in a single session. Furthermore, the proposed identity federation system is based on a streamlined, cost-effective, and interoperable architecture, which makes this model suitable for large-scale identity federation environments. Full article