Catálogo de publicaciones - revistas

<jats:p>Formal methods are mathematically based techniques for the rigorous development of software-intensive systems. The railway signaling domain is a field in which formal methods have traditionally been applied, with several success stories. This article reports on a mapping study that surveys the landscape of research on applications of formal methods to the development of railway systems. Following the guidelines of systematic reviews, we identify 328 relevant primary studies, and extract information about their demographics, the characteristics of formal methods used and railway-specific aspects. Our main results are as follows: (i) we identify a total of 328 primary studies relevant to our scope published between 1989 and 2020, of which 44% published during the last 5 years and 24% involving industry; (ii) the majority of studies are evaluated through Examples (41%) and Experience Reports (38%), while full-fledged Case Studies are limited (1.5%); (iii) Model checking is the most commonly adopted technique (47%), followed by simulation (27%) and theorem proving (19.5%); (iv) the dominant languages are UML (18%) and B (15%), while frequently used tools are ProB (9%), NuSMV (8%) and UPPAAL (7%); however, a diverse landscape of languages and tools is employed; (v) the majority of systems are interlocking products (40%), followed by models of high-level control logic (27%); (vi) most of the studies focus on the Architecture (66%) and Detailed Design (45%) development phases. Based on these findings, we highlight current research gaps and expected actions. In particular, the need to focus on more empirically sound research methods, such as Case Studies and Controlled Experiments, and to lower the degree of abstraction, by applying formal methods and tools to development phases that are closer to software development. Our study contributes with an empirically based perspective on the future of research and practice in formal methods applications for railways. It can be used by formal methods researchers to better focus their scientific inquiries, and by railway practitioners for an improved understanding of the interplay between formal methods and their specific application domain.</jats:p>

<jats:p>The quality of large and complex Systems-of-Systems (SoS) that have emerged in critical application domains depends on the quality of their architectures, which are inherently dynamic in terms of reorganization at runtime to comply with domain needs. However, the way to model and evaluate the quality of these architectures is not clear. This article presents the state of the art regarding how SoS architectures have been evaluated. For this, we systematically examined the literature and, as a result, we discovered and summarized relevant architectural evaluation methods and associated modeling techniques and quality attributes, the maturity of these methods, as well as the benefits and costs of adopting them. We also address open issues and research opportunities and recommend that the mindset for SoS architecture evaluation must be changed to ensure the quality of SoS in critical domains.</jats:p>

<jats:p>Machine learning has become the de-facto approach for various scientific domains such as computer vision and natural language processing. Despite recent breakthroughs, machine learning has only made its way into the fundamental challenges in computer systems and networking recently. This paper attempts to shed light on recent literature that appeals for machine learning based solutions to traditional problems in computer systems and networking. To this end, we first introduce a taxonomy based on a set of major research problem domains. Then, we present a comprehensive review per domain, where we compare the traditional approaches against the machine learning based ones. Finally, we discuss the general limitations of machine learning for computer systems and networking, including lack of training data, training overhead, real-time performance, and explainability, and reveal future research directions targeting these limitations.</jats:p>

<jats:p>Cognition plays a fundamental role in most software engineering activities. This article provides a taxonomy of cognitive concepts and a survey of the literature since the beginning of the Software Engineering discipline. The taxonomy comprises the top-level concepts of perception, attention, memory, cognitive load, reasoning, cognitive biases, knowledge, social cognition, cognitive control, and errors, and procedures to assess them both qualitatively and quantitatively. The taxonomy provides a useful tool to filter existing studies, classify new studies, and support researchers in getting familiar with a (sub) area. In the literature survey, we systematically collected and analysed 311 scientific papers spanning five decades and classified them using the cognitive concepts from the taxonomy. Our analysis shows that the most developed areas of research correspond to the four life-cycle stages, software requirements, design, construction, and maintenance. Most research is quantitative and focuses on knowledge, cognitive load, memory, and reasoning. Overall, the state of the art appears fragmented when viewed from the perspective of cognition. There is a lack of use of cognitive concepts that would represent a coherent picture of the cognitive processes active in specific tasks. Accordingly, we discuss the research gap in each cognitive concept and provide recommendations for future research.</jats:p>

<jats:p>The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with the ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of machine learning applications in any domain can only be ensured if intelligent capabilities cover all its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support distributed learning architecture, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. Firstly, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Secondly, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned above be addressed with improved ML architecture and agile operations and propose ML pipeline-based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain and multi-layer environment and provide decision-making on a global or holistic rather than individual function level.</jats:p>

<jats:p>Model transformations are the key technique in Model-Driven Engineering (MDE) to manipulate and construct models. As a consequence, the correctness of software systems built with MDE approaches relies mainly on the correctness of model transformations, and thus, detecting and locating bugs in model transformations have been popular research topics in recent years. This surge of work has led to a vast literature on model transformation testing and debugging, which makes it challenging to gain a comprehensive view of the current state of the art. This is an obstacle for newcomers to this topic and MDE practitioners to apply these approaches. This paper presents a survey on testing and debugging model transformations based on the analysis of 140 papers on the topics. We explore the trends, advances, and evolution over the years, bringing together previously disparate streams of work and providing a comprehensive view of these thriving areas. In addition, we present a conceptual framework to understand and categorise the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community.</jats:p>

<jats:p> The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the “big picture”—the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: <jats:italic>Crime</jats:italic> , <jats:italic>Disasters</jats:italic> , <jats:italic>Finance</jats:italic> , <jats:italic>Physical Environment</jats:italic> , <jats:italic>Politics</jats:italic> , and <jats:italic>Health and Population</jats:italic> . We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions. </jats:p>

<jats:p>Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.</jats:p>

<jats:p>Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that ML models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. MIAs on ML models can directly lead to a privacy breach. For example, via identifying the fact that a clinical record that has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease with a high chance. In recent years, MIAs have been shown to be effective on various ML models, e.g., classification models and generative models. Meanwhile, many defense methods have been proposed to mitigate MIAs. Although MIAs on ML models form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this paper, we conduct the first comprehensive survey on membership inference attacks and defenses. We provide the taxonomies for both attacks and defenses, based on their characterizations, and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain. To further help the researchers, we have created an online resource repository, which we will keep updated with future relevant work. Interested readers can find the repository at https://github.com/HongshengHu/membership-inference-machine-learning-literature.</jats:p>

<jats:p>Which samples should be labelled in a large data set is one of the most important problems for the training of deep learning. So far, a variety of active sample selection strategies related to deep learning have been proposed in many literatures. We defined them as Active Deep Learning (ADL) only if their predictor or selector is a deep model, where the basic learner is called as the predictor and the labeling schemes are called as the selector. In this survey, we categorize ADL into model-driven ADL and data-driven ADL, by whether its selector is model-driven or data-driven. We also detailed introduce the different characteristics of the two major types of ADL respectively. We summarized three fundamental factors in the designation of a selector. We pointed out that, with the development of deep learning, the selector in ADL also is experiencing the stage from model-driven to data-driven. The advantages and disadvantages between data-driven ADL and model-driven ADL are thoroughly analyzed. Furthermore, different sub-classes of data-drive or model-driven ADL are also summarized and discussed emphatically. Finally, we survey the trend of ADL from model-driven to data-driven.</jats:p>