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Knowledge is the most important resource in software development. The success of software development relies on knowledge sharing between software developers working across the globe. Global software development has brought many benefits to the software industry; however, at the same, time knowledge sharing across diverse team members is one of the main concerns of global software development organizations. This paper provides a systematic literature review of 42 studies on knowledge sharing barriers and facilitators from 2010 to 2017 and classifies them into five main categories: Individual, Organizational, Technological, Cultural, and Geographical. In order to synthesize and represent the complexity of the knowledge sharing factors in a more manageable and visual manner, this paper proposes concept maps for each category. The identified factors can be strategically used as the guidelines in the global software development organizations to boost the culture of knowledge sharing. INDEX TERMS Knowledge sharing barriers, knowledge sharing facilitators, global software development organizations, cultural barriers, geographical barriers.

The k-means algorithm of the partitioning clustering method is used to analyze cloud migration strategies in this study. The extent of assistance required to be provided to organizations while working on migration strategies was investigated for each cloud service model and concrete clusters were formed. This investigation is intended to aid cloud consumers in selecting their required cloud migration strategy. It is not easy for businessmen to select the most appropriate cloud migration strategy, and therefore, we proposed a suitable model to solve this problem. This model comprises a web of migration strategies, which provides an unambiguous visualization of the selected migration strategy. The cloud migration strategy targets the technical aspects linked with cloud facilities and measures the critical realization factors for cloud acceptance. Based on similar features, a correlation among the migration strategies is suggested, and three main clusters are formed accordingly. This helps to link the cloud migration strategies across the cloud service models (software as a service, platform as a service, and infrastructure as a service). This correlation was justified using the digital logic approach. This study is useful for the academia and industry as the proposed migration strategy selection process aids cloud consumers in efficiently selecting a cloud migration strategy for their legacy applications.

Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.