Abstract Android users install various apps, such as banking apps, on their smart devices dealing with user‐sensitive information. The Android framework, via Inter‐Component Communication (ICC) mechanism, ensures that app components (inside the same app or on different apps) can communicate. The literature works have shown that this mechanism can cause security issues, such as app security policy violations, especially in the case of Inter‐App Communication (IAC). Despite the plethora of research on detecting security issues in IAC, detection techniques face fundamental ICC challenges for improving the precision of static analysis. Challenges include providing comprehensive and scalable modeling of app specification, capturing all potential ICC paths, and enabling more effective IAC analysis. To overcome such challenges, in this paper, we propose a framework called VAnDroid2, as an extension of our previous work, to address the security issues in multiple components at both intra‐ and inter‐app analysis levels. VAnDroid2, based on Model‐Driven Reverse Engineering, has extended our previous work as per following: (1) providing a comprehensive Intermediate Representation (IR) of the app which supports extracting all the ICC information from the app, (2) extracting high‐level representations of the apps and their interactions by omitting the details that are not relevant to inter‐app security analysis, and (3) enabling more effective IAC security analysis. This framework is implemented as an Eclipse‐based tool. The results of evaluating VAnDroid2 w.r.t. correctness, scalability, and run‐time performance, and comparing with state‐of‐the‐art analysis tools well indicate that VAnDroid2 is a promising framework in the field of Android inter‐app security analysis.

Abstract With the advent of technology and the development of more complex software systems, the size of logs generated by these systems has increasingly risen so that the anomaly detection for remediating common errors has been more difficult than ever. The cloud emergence in the information technology (IT) industry has led to the immigration of enterprises toward it, which has extended the application of cloud management stacks such as OpenStack. By using the OpenStack platform, users can access resource infrastructure and manage virtual machines (VMs). The anomaly detection in OpenStack logs is not realized conveniently due to the substantial size of logs, and it is required to automate this process. Since there is no appropriate open‐source dataset for OpenStack logs, we have generated 25,000 logs by injecting three types of anomalies to propose a more efficient technique in terms of performance and time in detecting anomalies in OpenStack logs relative to recent studies by proper OpenStack log parsing and analyzing these logs by data mining algorithms. To this end, compared to the previous research study, we could improve the anomaly detection performance in terms of F1 score, recall, and precision by 9%, 4%, and 14%, respectively, and decrease the running time relative to the log size by at least 30 s.

Abstract Background and ObjectiveMachine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre‐trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal.MethodsThe DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding.ResultsWe developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant. The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre‐processing and post‐processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service.ConclusionThe DeepPlayer plugin allows running deep learning models easily and interactively. Therefore, experts and non‐AI experts alike can explore and apply deep learning models for (biomedical) signal processing. Its ease of use and interactivity might also contribute to a better understanding and acceptance of AI methods in biomedicine.

Abstract Different software development approaches (SDAs) are developed with broad portfolios of development processes. Each of the approaches has certain exclusive principles, practices, thinking, and values, which are informally represented, implemented, and improperly institutionalized. Ontologies are developed for the representation, assessment, and adaptation of SDAs separately without having a shared terminology which may lead to terminological conflict and confusion affecting the simultaneous representation and implementation in software development industry and academia. The software engineering approaches does not consider and support sustainability as priority concern. However, the approaches have capabilities of supporting sustainable software development in different sustainability aspects. This research article aims for the designing and development of an integrated ontology of software engineering approaches (i.e., agile, lean, and green) named OntoSuSD (ontology for sustainable software development) to support sustainable software development knowledge, awareness, and implementation. The goal of OntoSuSD is to propose, design and develop a formal, generic, consistent, and shared knowledge base containing semantic terminology and description of concepts and relationships generated around the representation and implementation of lean, agile, and green approaches in software development processes, which will facilitate their simultaneous implementation and assessment for sustainable software development. The OntoSuSD is developed using practical ontology engineering methodology by reusing relevant ontologies and explicit concepts and properties are defined to fulfill knowledge requirements and representations of the domain. The OntoSuSD is evaluated, and results infer OntoSuSD has high ontological design, good domain coverage, potential applications and achieves purpose of the ontology development.

Abstract The coronavirus pandemic, which first began in December 2019, has completely changed our daily lives. In particular, daily online digital content consumption over the world has soared since the start of the coronavirus pandemic. For preventing illegal digital content distribution, various technologies have been applied, but current copyright protection technologies are still lacking to provide secure digital content distribution while preserving privacy. In this article, we introduce a new digital content distribution system, which utilizes blockchain‐based decentralized identifier and format‐preserving encryption to enable user privacy preserved digital content tracking. We present the system architecture with detailed message flows of the proposal and provide its analysis result in terms of security and overhead.

Abstract The cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

Abstract Recent works showed that implementations of quicksort using vector CPU instructions can outperform the non‐vectorized algorithms in widespread use. However, these implementations are typically single‐threaded, implemented for a particular instruction set, and restricted to a small set of key types. We lift these three restrictions: our proposedvqsortalgorithm integrates into the state‐of‐the‐art parallel sorter , with a geometric mean speedup of 1.59. The same implementation works on seven instruction sets (including SVE and RISC‐V V) across four platforms. It also supports floating‐point and 16–128 bit integer keys. To the best of our knowledge, this is the fastest sort for large arrays of non‐tuple keys on CPUs, up to 20 times as fast as the sorting algorithms implemented in standard libraries. This article focuses on the practical engineering aspects enabling the speed and portability, which we have not yet seen demonstrated for a quicksort implementation. Furthermore, we introduce compact and transpose‐free sorting networks for in‐register sorting of small arrays, and a vector‐friendly pivot sampling strategy that is robust against adversarial input.

Abstract As a recently predominant architecture style, MicroService Architecture (MSA) is likely to suffer the issues of poor maintainability due to inappropriate microservice boundaries. Architectural Smell (AS), as a metaphor for potential architectural issues that may have negative impacts on software maintenance, can be used to pinpoint refactoring opportunity for evolving microservice boundary. However, existing studies mostly focus on AS detection with little further investigation on the possible impacts, causes, and solutions of AS, which does little help in addressing the bad smells in architecture. Our goal in this study is to bridge this gap by investigating the possible impacts, causes, and solutions of AS in MSA‐based systems. An industrial case study is carried out to collect repository data and practitioners' views on six typical ASes in a real MSA‐based telecommunication system. Statistical Analysis and Coding techniques are used in the analyses of quantitative and qualitative data respectively. The results show that AS influences the modularity, modifiability, analyzability, and testability of the MSA‐based system, which further induce extra cross‐team communication, change‐ and fault‐prone microservices. To explore the causes for AS, a five‐aspect conceptual classification with technology, project, organization, business, and professional is proposed, in which the business and organization aspects take the major roles. Both technical and non‐technical solutions are distilled to deal with ASes despite potential constraints. These results and their comparison to current literature are discussed, which provide practical implications in coping with AS in microservices.