Comparative analysis of flexible cryptographic implementations

This paper offers a critique of the application of a systematic literature review (SLR) process during a grounded theory (GT) investigation. The place of the literature review (LR) within GT has long been discussed, contested, and frequently misunderstood. One of GT’s hallmarks is its inductive nature, allowing salient concepts to ‘arise’ rather than being deduced. Hence, there are differing perspectives on the role of the LR concerning enabling or constraining this inductive process. However, scant guidance is available on conducting LRs for GT studies. Wolfswinkel, Furtmueller and Wilderom (2011) offer a five-stage GT method for reviewing literature. This paper will provide insights into the advantages and disadvantages of their method. Though grounded in theoretical principles surrounding the LR’s purpose in GT, this paper gives practical guidance on operationalising this five-stage SLR process. This is done by demonstrating the application of their stages in a doctoral dissertation. A contemporaneous research audit trail was used to document each stage, reflect on challenges, and justify deviations from the original five stages. Excerpts from the audit trail will be used to illustrate how the process was applied. Based on the findings from applying these stages, tentative advancements to the model will be offered so that other researchers will have more detailed guidance on how a SLR may be conducted within GT studies.

Side-channel analysis (SCA) is launched by exploiting the information leaking from the implementation a cryptographic algorithm, e.g., power consumption information. Recently, deep learning-based SCA techniques have also facilitated SCA against software and hardware implementations of various cryptographic algorithms. In this work, we perform SCA using various deep learning (DL) techniques such as Multi-layered Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) on the datasets collected from hardware and software platforms. The objective of this work is to identify the performance of DL techniques in performing SCA for secret key recovery and finding out the best settings for the model to optimize the attack performance in terms of on computation time and SCA efficiency. In our study, we have focused on two open-source AES-128 encryption algorithm databases, ASCAD and DPA contest v2 (DPAv2), where ASCAD database consists of the power traces captured from a software implementation of the AES and DPAv2 database consists of the power traces captured a hardware implementation of the AES. For the first time, we applied hyperparameter tuning with Bayesian Optimization and distributed computing on ASCAD database and we investigated the impact of MLP and RNN along with the distributed computing and hyperparameter tuning with Bayesian optimization on DPAv2 database. Our results show that the CNNs are the best models for performing the attack on software implementation while MLPs are the best for attacking hardware implementation of cryptographic algorithms.

Context: Despite being defined during the first phase of the Systematic Literature Review (SLR) process, the protocol is usually refined when other phases are performed. Several researchers have reported their experiences in applying SLRs in Software Engineering (SE) however, there is still a lack of studies discussing the iterative nature of the protocol definition, especially how it should be perceived by researchers conducting SLRs. Objective: The main goal of this study is to perform a survey aiming to identify: (i) the perception of SE researchers related to protocol definition; (ii) the activities of the review process that typically lead to protocol refinements; and (iii) which protocol items are refined in those activities. Method: A survey was performed with 53 SE researchers. Results: Our results show that: (i) protocol definition and pilot test are the two activities that most lead to further protocol refinements; (ii) data extraction form is the most modified item. Besides that, this study confirmed the iterative nature of the protocol definition. Conclusions: An iterative pilot testcan facilitate refinements in the protocol.

Objective. This paper presents an overview of existing artificial intelligence tools to produce systematic literature reviews. Furthermore, we propose a general framework resulting from combining these techniques to highlight the challenges and possibilities currently existing in this research area. Design/Methodology/Approach. We undertook a scoping review on the systematic literature review steps to automate them via computational techniques. Results/Discussion. The process of creating a literature review is both creative and technical. The technical part of this process is liable to automation. Based on the literature, we chose to divide this technical part into four steps: searching, screening, extraction, and synthesis. For each one of these steps, we presented practical artificial intelligence techniques to carry them out. In addition, we presented the obstacles encountered in the application of each technique. Conclusion. We proposed a framework for automatically creating systematic literature reviews by combining and placing existing techniques in stages where they possess the greatest potential to be useful. Despite still lacking practical assessment in different areas of knowledge, this proposal indicates ways with the potential to reduce the time-consuming and repetitive work embedded in the systematic literature review process. Originality/Value. The paper presents the current possibilities for automating systematic literature reviews and how they can work together to reduce researchers’ operational workload.

Software can significantly facilitate the management of the complete systematic literature review process (SLR). However, most specialized software for use in SLR processes is designed to meet the requirements of the health and medical sciences and software engineering, and there is a need for dedicated software for the specific research requirements of the social sciences. Furthermore, most of the software currently used is closed and the open source code alternatives require personnel with expertise in configuration and setup.We present Buhos, an application for managing the complete process of systematic literature reviews that is web-based and developed in Ruby. It offers functionalities for supporting the process of searching, screening, data extraction and reporting. Buhos can be used locally through an in-house web server, as well as in a distributed manner, integrated with other online services.

Systematic Literature Reviews (SLRs) are an important tool used by software engineering researchers to summarize the state of knowledge about a particular topic. Currently, SLR authors must perform the difficult, time-consuming task in largely manual fashion. To identify barriers faced by SLR authors, we conducted an interactive community workshop prior to ESEM'13. Workshop participants generated a total of 100 ideas that, through group discussions, formed 37 composite barriers to the SLR process. Further analysis reveals the barriers relate to latent themes regarding the SLR process, primary studies, the practitioner community, and tooling. This paper describes the barriers identified during the workshop along with a ranking of those barriers that is based on votes by workshop attendees. The paper concludes by describing the impact of these barriers on three important constituencies: SLR Methodology Researchers, SLR Authors and SLR consumers.

BackgroundTransposable elements (TEs) constitute the most common repeated sequences in eukaryotic genomes. Recent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. Although there are many conventional bioinformatics algorithms for detecting and classifying TEs, none have achieved reliable results on different types of TEs. Machine learning (ML) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems.MethodologyWe followed the Systematic Literature Review (SLR) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. Then search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions.ResultsSeveral ML approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on TEs, despite representing the majority of the nuclear DNA of many organisms. Only 35 articles were found and categorized as relevant in TE or related fields.ConclusionsML is a powerful tool that can be used to address many problems. Although ML techniques have been used widely in other biological tasks, their utilization in TE analyses is still limited. Following the SLR, it was possible to notice that the use of ML for TE analyses (detection and classification) is an open problem, and this new field of research is growing in interest.

The Korean Society for the Study of Obesity has published the 9th edition of its Clinical Practice Guidelines to provide updated, evidence-based recommendations for the management of overweight and obesity. Developed through a structured process of systematic literature review and expert consensus, these guidelines incorporate recent clinical evidence for obesity management. Key clinical questions were formulated using the Population, Intervention, Comparison, and Outcome framework and addressed through comprehensive evaluations of randomized controlled trials, cohort studies, systematic reviews, and existing guidelines. Major updates include the inclusion of the concept of clinical obesity, defined as excessive adiposity with tissue and organ dysfunction. The revised guidelines expand recommendations for pharmacologic treatments and introduce a new section on health functional foods. Emphasis is placed on early assessment of treatment response and individualized therapeutic adjustments to improve long-term outcomes. The guidelines also highlight the importance of recognizing obesity-related health conditions and ensuring equitable access to care. They promote shared decision-making and ethical prescribing practices to support a person-centered approach. All recommendations are evaluated using the Grading of Recommendations Assessment, Development and Evaluation system, which reflects the strength of evidence and the balance of expected benefits and harms. These updates are intended to assist healthcare providers in delivering personalized, evidence-informed care and underscore the evolving role of pharmacotherapy within risk-based obesity management strategies.

The process of developing systematic reviews is a well established method of collecting evidence from publications, where it follows a predefined and explicit protocol design to promote rigour, transparency and repeatability. The process is manual and involves lot of time and needs expertise. The aim of this work is to build an effective framework using machine learning techniques to partially automate the process of systematic literature review by extracting required data elements of anxiety outcome measures. A framework is thus proposed that initially builds a training corpus by extracting different data elements related to anxiety outcome measures from relevant publications. The publications are retrieved from Medline, EMBASE, CINAHL, AHMED and Pyscinfo following a given set of rules defined by a research group in the United Kingdom reviewing comfort interventions in health care. Subsequently, the method trains a machine learning classifier using this training corpus to extract the desired data elements from new publications. The experiments are conducted on 48 publications containing anxiety outcome measures with an aim to automatically extract the sentences stating the mean and standard deviation of the measures of outcomes of different types of interventions to lessen anxiety. The experimental results show that the recall and precision of the proposed method using random forest classifier are respectively 100% and 83%, which indicates that the method is able to extract all required data elements.

PurposeManaging stakeholders' reciprocal interdependencies is always a challenging issue. Stakeholders need to find out different ways to communicate information and coordinate material flows during the supply chain processes. Many recent studies have advanced construction supply chain coordination from multiple perspectives. However, the field still lacks a comprehensive analysis to summarize existing research, to explicitly identify all the possible enablers for coordination and to investigate how the enablers can be carried out at the supply chain interfaces. To fill the gap, this study aims to conduct a systematic review in order to examine the relevant literature.Design/methodology/approachA systematic literature review process was conducted to identify and synthesize relevant publications (published in the past 20 years) concerning the coordination of construction supply chain functions. These publications were coded to link main research findings with specific enabler categories. In addition, how these enablers can be used at the interfaces across supply chain processes was reviewed with an in-depth analysis of reciprocal communications between stakeholders at design-to-production, production-to-logistics and production-to-site-assembly phases.FindingsThe coordination enablers were classified into three categories: (1) contractual enablers (including subtopics on relational contracts and incentive models), (2) procedural enablers (including subtopics on multiagent knowledge sharing systems and the last planner system) and (3) technological enablers (including subtopics on linked databases for design coordination, design for manufacturing software platforms and automated monitoring technologies). It was found that interfacing different functions requires a certain level of integration of stakeholders for quick response and feedback processes. The integration of novel contractual forms with digital technologies, such as smart contracts, however, was not adequately addressed in the state of the art.Research limitations/implicationsThe scope of the systematic review is limited to the static analysis of selected publications. Longitudinal studies should be further included to sharpen the inductions of enablers considering organizational changes and process dynamics in construction projects.Practical implicationsDifferent enablers for coordination were summarized in a concise manner, which provides researchers and project stakeholders with a reinforced understanding of various ways to manage reciprocal interdependencies at different supply chain interfaces.Originality/valueThis study constitutes an important input for research on the construction supply chain by illuminating the thematic topic of coordination from inductively developed review processes, which included a holistic framing of the emerging coordination enablers and their use across supply chain functions. Consequently, it closes some identified knowledge gaps and offers additional insights to improve the supply chain performance of construction projects.

Life sciences industry is witnessing a paradigm shift of evidence-based approach from population to patient level. This shift is being supported by structured evidence generation methodologies like Systematic Literature Review (SLR), Real World Evidence (RWE), and others to support regulatory, clinical and policy level decision making. The amalgamation of SLR, with real world data and quantitative techniques like Meta-Analysis, Network Meta-Analysis represent highest level of evidence generation. However, the current state of generating SLR, involves manual and labor-intensive process with high dependency on developed countries. The various stakeholders worldwide viz: researchers and policy makers are demanding technology levers to optimize the end-to-end SLR process. As the future of evidence generation will be more complex, industry should invest in smart, intelligent, and connected technology and science-based ecosystem to disrupt and transform existing manual process. This article presents the SLR trends, challenges and potential transformation use case leveraging technology, global capability, and capacity harmonization.

Software plays an important role in various aspects of our life. However, recent software development practices have substantial negative impacts (direct and indirect) on economy, society, human beings, and environment. To cope with these issues the concept of Green Software Engineering has boomed recently. Green and sustainable software development aims to create green software that meets the needs of current and future users while minimizing its negative impacts on the environment and society. Green software development is getting attention in Global Software Engineering (GSE). Software development multi-sourcing vendor organizations have focused on the adaptation of green practices in software development projects. The objectives of this study are to identify and scrutinize a list of critical success factors (CSFs) for vendors in the development of green and sustainable software. The required data for conducting the study was taken, via systematic literature review (SLR) process, from a sample of 74 research papers. The SLR findings reveal that ‘green software design and efficient coding ’, ‘power-saving software strategies ’, ‘low carbon emission throughout the software development process ’, ‘efficient resource utilization ’, ‘paperless communication ’, ‘filtration of requirements through green evaluator and e-waste management’ are the critical factors to be implanted by vendors in the development of green and sustainable software.

Efficacy and Toxicity of CD19 Chimeric Antigen Receptor T Cell Therapy for Lymphoma in Solid Organ Transplant Recipients: A Systematic Review and Meta-Analysis

Artificial Intelligence (AI) has evolved from just a theoretical notion of machines possessing the intelligence and capabilities of humans to the tangibility of AI living among humans becoming an integral part of everyday life. Over the last decade, researchers have discovered and developed AI capabilities to recognize human emotions. Many researchers suggested that AI with the capabilities to recognize human emotions, will lead to many potential innovative solutions that can be applied to businesses, industries and societies. This is due to the rising importance of human-computer interaction (HCI). Emotion recognition system (ERS) will enable better awareness, empathy and emotional intelligence, leading to better engagement. This paper aims to review previous studies related to ERS and sustainability. To achieve a comprehensive understanding of previous studies, this paper will adopt a Systematic Literature Review (SLR) process to filter related studies and examine papers that contribute to insights on ERS for sustainability. The findings can help future researchers develop more accurate and impactful ERS technologies that can affect better achievement of sustainability goals.

Cloud computing paved the way to many technical facilities for companies to develop their business needs in more effective and efficient manner. Combining private and public cloud into so called Hybrid cloud has made a positive leap in business by allowing applications and data to be shared among enterprises. However, many challenges and issues have arisen when adopting the hybrid cloud to manage, store and process data. The most critical one of these challenges is the security of the adopted Hybrid cloud. This research presented a comprehensive study about the security challenges in the Hybrid cloud computing as well as the suggested solutions. The study used a Systematic Literature Review (SLR) process to collect, review and summarize published articles from IEEE and Springer Nature databases and between 2020 and 2021. As a result, there were 7 eligible articles selected according to the search criteria and fully reviewed. The findings have revealed that there are four main challenges which are Data Security, Access Control, Privacy, Data Leakage and Cy- ber Attacks. Future studies should be conducted using different databases to have further investigation regarding the security in Hybrid clouds.