Search

Context: Software regression testing refers to rerunning test cases after the system under test is modified, ascertaining that the changes have not (re-)introduced failures. Not all researchers' approaches consider applicability and scalability concerns, and not many have produced an impact in practice. Objective: One goal is to investigate industrial relevance and applicability of proposed approaches. Another is providing a live review, open to continuous updates by the community. Method: A systematic review of regression testing studies that are clearly motivated by or validated against industrial relevance and applicability is conducted. It is complemented by follow-up surveys with authors of the selected papers and 23 practitioners. Results: A set of 79 primary studies published between 2016-2022 is collected and classified according to approaches and metrics. Aspects relative to their relevance and impact are discussed, also based on their authors' feedback. All the data are made available from the live repository that accompanies the study. Conclusions: While widely motivated by industrial relevance and applicability, not many approaches are evaluated in industrial or large-scale open-source systems, and even fewer approaches have been adopted in practice. Some challenges hindering the implementation of relevant approaches are synthesized, also based on the practitioners' feedback. [ABSTRACT FROM AUTHOR]

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the SemanticWeb community--SemanticWebMachine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology. [ABSTRACT FROM AUTHOR]

Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics. [ABSTRACT FROM AUTHOR]

ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development. [ABSTRACT FROM AUTHOR]

A capability machine is a type of CPU allowing fine-grained privilege separation using capabilities, machine words that represent certain kinds of authority. We present a mathematical model and accompanying proof methods that can be used for formal verification of functional correctness of programs running on a capability machine, even when they invoke and are invoked by unknown (and possibly malicious) code. We use a program logic called Cerise for reasoning about known code, and an associated logical relation, for reasoning about unknown code. The logical relation formally captures the capability safety guarantees provided by the capability machine. The Cerise program logic, logical relation, and all the examples considered in the paper have been mechanized using the Iris program logic framework in the Coq proof assistant. The methodology we present underlies recent work of the authors on formal reasoning about capability machines [Georges et al. 2021; Skorstengaard et al. 2019a; Van Strydonck et al. 2022], but was left somewhat implicit in those publications. In this paper we present a pedagogical introduction to the methodology, in a simpler setting (no exotic capabilities), and starting from minimal examples. We work our way up to new results about a heap-based calling convention and implementations of sophisticated object-capability patterns of the kind previously studied for high-level languages with object-capabilities, demonstrating that the methodology scales to such reasoning. [ABSTRACT FROM AUTHOR]

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multifaceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the past 7 years at major AI and ML conferences that introduce an XAI method. We find that one in three papers evaluate exclusively with anecdotal evidence, and one in five papers evaluate with users. This survey also contributes to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. Our systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark, and compare new and existing XAImethods. The Co-12 categorization scheme and our identified evaluation methods open up opportunities to include quantitative metrics as optimization criteria during model training to optimize for accuracy and interpretability simultaneously. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The resultssuggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published. [ABSTRACT FROM AUTHOR]

We prove novel algorithmic guarantees for several online problems in the smoothed analysis model. In this model, at each time step an adversary chooses an input distribution with density function bounded above pointwise by \(\tfrac{1}{\sigma }\) times that of the uniform distribution; nature then samples an input from this distribution. Here, σ is a parameter that interpolates between the extremes of worst-case and average case analysis. Crucially, our results hold for adaptive adversaries that can base their choice of input distribution on the decisions of the algorithm and the realizations of the inputs in the previous time steps. An adaptive adversary can nontrivially correlate inputs at different time steps with each other and with the algorithm's current state; this appears to rule out the standard proof approaches in smoothed analysis. This paper presents a general technique for proving smoothed algorithmic guarantees against adaptive adversaries, in effect reducing the setting of an adaptive adversary to the much simpler case of an oblivious adversary (i.e., an adversary that commits in advance to the entire sequence of input distributions). We apply this technique to prove strong smoothed guarantees for three different problems: (1) Online learning: We consider the online prediction problem, where instances are generated from an adaptive sequence of σ-smooth distributions and the hypothesis class has VC dimension d. We bound the regret by \(\tilde{O}(\sqrt {T d\ln (1/\sigma)} + d\ln (T/\sigma))\) and provide a near-matching lower bound. Our result shows that under smoothed analysis, learnability against adaptive adversaries is characterized by the finiteness of the VC dimension. This is as opposed to the worst-case analysis, where online learnability is characterized by Littlestone dimension (which is infinite even in the extremely restricted case of one-dimensional threshold functions). Our results fully answer an open question of Rakhlin et al. [64]. (2) Online discrepancy minimization: We consider the setting of the online Komlós problem, where the input is generated from an adaptive sequence of σ-smooth and isotropic distributions on the ℓ2 unit ball. We bound the ℓ∞ norm of the discrepancy vector by \(\tilde{O}(\ln ^2(\frac{nT}{\sigma }))\). This is as opposed to the worst-case analysis, where the tight discrepancy bound is \(\Theta (\sqrt {T/n})\). We show such \(\mathrm{polylog}(nT/\sigma)\) discrepancy guarantees are not achievable for non-isotropic σ-smooth distributions. (3) Dispersion in online optimization: We consider online optimization with piecewise Lipschitz functions where functions with ℓ discontinuities are chosen by a smoothed adaptive adversary and show that the resulting sequence is \(({\sigma }/{\sqrt {T\ell }}, \tilde{O}(\sqrt {T\ell }))\) -dispersed. That is, every ball of radius \({\sigma }/{\sqrt {T\ell }}\) is split by \(\tilde{O}(\sqrt {T\ell })\) of the partitions made by these functions. This result matches the dispersion parameters of Balcan et al. [13] for oblivious smooth adversaries, up to logarithmic factors. On the other hand, worst-case sequences are trivially (0, T)-dispersed.1 [ABSTRACT FROM AUTHOR]

Conceptual modeling is an important part of information systems development and use that involves identifying and representing relevant aspects of reality. Although the past decades have experienced continuous digitalization of services and products that impact business and society, conceptual modeling efforts are still required to support new technologies as they emerge. This paper surveys research on conceptual modeling over the past five decades and shows how its topics and trends continue to evolve to accommodate emerging technologies, while remaining grounded in basic constructs. We survey over 5,300 papers that address conceptual modeling topics from the 1970s to the present, which are collected from 35 multidisciplinary journals and conferences, and use them as the basis from which to analyze the progression of conceptual modeling. The important role that conceptual modeling should play in our evolving digital world is discussed, and future research directions proposed. [ABSTRACT FROM AUTHOR]

Although there are methods of artificial intelligence (AI) applied to virtual reality (VR) solutions, there are few studies in the literature. Thus, to fill this gap, we performed a systematic literature review of these methods. In this review, we apply a methodology proposed in the literature that locates existing studies, selects and evaluates contributions, analyses, and synthesizes data. We used Google Scholar and databases such as Elsevier’s Scopus, ACM Digital Library, and IEEE Xplore Digital Library. A set of inclusion and exclusion criteria were used to select documents. The results showed that when AI methods are used in VR applications, the main advantages are high efficiency and precision of algorithms. Moreover, we observe that machine learning is the most applied AI scientific technique in VR applications. In conclusion, this paper showed that the combination of AI and VR contributes to new trends, opportunities, and applications for human-machine interactive devices, education, agriculture, transport, 3D image reconstruction, and health. We also concluded that the usage of AI in VR provides potential benefits in other fields of the real world such as teleconferencing, emotion interaction, tourist services, and image data extraction. [ABSTRACT FROM AUTHOR]

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 system)atically collected and analysed 311 scientific papers spanning five decades and classified them using the cog)nitive 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 con)cepts 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. [ABSTRACT FROM AUTHOR]

Nearly thirty years ago, it was shown that \(\Omega (\sqrt {n})\) read/write registers are needed to solve randomized wait-free consensus among n processes. This lower bound was improved to n registers in 2018, which exactly matches known algorithms. The \(\Omega (\sqrt {n})\) space complexity lower bound actually applies to a class of objects called historyless objects, which includes registers, test-and-set objects, and readable swap objects. However, every known n-process obstruction-free consensus algorithm from historyless objects uses Ω (n) objects. In this paper, we give the first Ω (n) space complexity lower bounds on consensus algorithms for two kinds of historyless objects. First, we show that any obstruction-free consensus algorithm from swap objects uses at least n-1 objects. More generally, we prove that any obstruction-free k-set agreement algorithm from swap objects uses at least \(\lceil \frac{n}{k}\rceil - 1\) objects. The k-set agreement problem is a generalization of consensus in which processes agree on no more than k different output values. This is the first non-constant lower bound on the space complexity of solving k-set agreement with swap objects when k > 1. We also present an obstruction-free k-set agreement algorithm from n-k swap objects, which exactly matches our lower bound when k=1. Second, we show that any obstruction-free binary consensus algorithm from readable swap objects with domain size b uses at least \(\frac{n-2}{3b+1}\) objects. When b is a constant, this asymptotically matches the best known obstruction-free consensus algorithms from readable swap objects with unbounded domains. Since any historyless object can be simulated by a readable swap object with the same domain, our results imply that any obstruction-free consensus algorithm from historyless objects with domain size b uses at least \(\frac{n-2}{3b+1}\) objects. For b = 2, we show a slightly better lower bound of n-2. There is an obstruction-free binary consensus algorithm using 2n-1 readable swap objects with domain size 2, asymptotically matching our lower bound. [ABSTRACT FROM AUTHOR]

Network traffic workloads are widely utilized in applied research to verify correctness and to measure the impact of novel algorithms, protocols, and network functions. We provide a comprehensive survey of traffic generators referenced by researchers over the last 13 years, providing in-depth classification of the functional behaviors of the most frequently cited generators. These classifications are then used as a critical component of a methodology presented to aid in the selection of generators derived from the workload requirements of future research. [ABSTRACT FROM AUTHOR]

Recent software advances have led to an expansion of the development and usage of application programming interfaces (APIs). From millions of Android packages (APKs) available on Google Store to millions of open-source packages available in Maven, PyPI, and npm, APIs have become an integral part of software development. Like any software artifact, software APIs evolve and suffer from this evolution. Prior research has uncovered many challenges to the development, usage, and evolution of APIs. While some challenges have been studied and solved, many remain. These challenges are scattered in the literature, which hides advances and cloaks the remaining challenges. In this systematic literature review on APIs and API evolution, we uncover and describe publication trends and trending topics.We compile common research goals, evaluation methods, metrics, and subjects.We summarize the current state-of-the-art and outline known existing challenges aswell as newchallenges uncovered during this review. We conclude that the main remaining challenges related to APIs and API evolution are (1) automatically identifying and leveraging factors that drive API changes, (2) creating and using uniform benchmarks for research evaluation, and (3) understanding the impact of API evolution on API developers and users with respect to various programming languages. [ABSTRACT FROM AUTHOR]

Neurodevelopmental Disorders (NDD) are a group of conditions with onset in the developmental period characterized by deficits in the cognitive and social areas. Conversational agents have been increasingly explored to support therapeutic interventions for people with NDD. This survey provides a structured view of the crucial design features of these systems, the types of therapeutic goals they address, and the empirical methods adopted for their evaluation. From this analysis, we elaborate a set of recommendations and highlight the gaps left unsolved in the state of the art, upon which we ground a research agenda on conversational agents for NDD. [ABSTRACT FROM AUTHOR]

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 article 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 categorize the different proposals. Finally, we identify several open research challenges and propose specific action points for the model transformation community. [ABSTRACT FROM AUTHOR]

Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing. In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses, and simplification of manual inspection. We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers. [ABSTRACT FROM AUTHOR]

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. This article investigates the problem of designing a blockchain transaction fee mechanism through the lens of mechanism design. We introduce two new forms of incentive compatibility that capture some of the idiosyncrasies of the blockchain setting, one (MMIC) that protects against deviations by profit-maximizing miners and one (OCA-proofness) that protects against off-chain collusion between miners and users. This study is immediately applicable to a major change to Ethereum's transaction fee mechanism, made on August 5, 2021, based on a proposal called "EIP-1559." Originally, Ethereum's transaction fee mechanism was a first-price (pay-as-bid) auction. EIP-1559 suggested making several tightly coupled changes, including the introduction of variable-size blocks, a history-dependent reserve price, and the burning of a significant portion of the transaction fees. We prove that this new mechanism earns an impressive report card: it satisfies the MMIC and OCA-proofness conditions, and is also dominant-strategy incentive compatible (DSIC) except when there is a sudden demand spike. We also introduce an alternative design, the "tipless mechanism," which offers an incomparable slate of incentive-compatibility guarantees—it is MMIC and DSIC, and OCA-proof unless in the midst of a demand spike. [ABSTRACT FROM AUTHOR]

With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increased trend in the papers that use AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) is one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs models. This paper first categorizes GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs that are not necessarily applied in the video domain in the first run but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models are provided under two main divisions based on existence of a condition. The conditional models are then further classified according to the provided condition into audio, text, video, and image. The paper concludes with the main challenges and limitations of the current video GANs models. [ABSTRACT FROM AUTHOR]

With innovations in the field of gaze and eye tracking, a new concentration of research in the area of gaze-tracked systems and user interfaces has formed in the field of Extended Reality (XR). Eye trackers are being used to explore novel forms of spatial human-computer interaction, to understand human attention and behavior, and to test expectations and human responses. In this article, we review gaze interaction and eye tracking research related to XR that has been published since 1985, which includes a total of 215 publications. We outline efforts to apply eye gaze for direct interaction with virtual content and design of attentive interfaces that adapt the presented content based on eye gaze behavior and discuss how eye gaze has been utilized to improve collaboration in XR. We outline trends and novel directions and discuss representative high-impact papers in detail. [ABSTRACT FROM AUTHOR]

Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network's functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism's data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources. [ABSTRACT FROM AUTHOR]

Military troops rely on maintenance management projects and operations to preserve the materials' ordinary conditions or restore them to combat or military training. Maintenance management in the defense domain has its particularities, such as those related to the type of equipment operated, the environment and operating conditions, the need to maintain equipment readiness in cases of external aggression, and the security of the information. This study aims to understand the challenges, principles, scenarios, techniques, and open questions of predictive maintenance (PdM) in the military domain. We conducted a systematic literature review that resulted in the discussion of 43 articles, leading to the identification of 23 challenges and principles, 4 scenarios where predictive maintenance is crucial, besides discussing techniques used for PdM in the military domain. Our results contribute to understanding the perspective of PdM in the defense context. [ABSTRACT FROM AUTHOR]

Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural Networks (DNN). It offers many significant opportunities for improving DNN performance and efficiency and has been explored in a large body of work. These studies have scattered in many venues across several years. The targets they focus on range from images to videos and texts, and the techniques they use to detect and exploit data redundancy also vary in many aspects. There is not yet a systematic examination and summary of the many efforts, making it difficult for researchers to get a comprehensive view of the prior work, the state of the art, differences and shared principles, and the areas and directions yet to explore. This article tries to fill the void. It surveys hundreds of recent papers on the topic, introduces a novel taxonomy to put the various techniques into a single categorization framework, offers a comprehensive description of the main methods used for exploiting data redundancy in improving multiple kinds of DNNs on data, and points out a set of research opportunities for future exploration. [ABSTRACT FROM AUTHOR]

Recent applications of autonomous agents and robots have brought attention to crucial trust-related challenges associated with the current generation of artificial intelligence (AI) systems. AI systems based on the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. Without symbolic interpretation capabilities, they are ‘black boxes’, which renders their choices or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several approaches to eXplainable Artificial Intelligence (XAI); however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are sparse at this point in time. This paper reviews approaches on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents’ perceptual functions (e.g., senses, vision) and cognitive reasoning (e.g., beliefs, desires, intentions, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency, understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a road map for the possible realization of effective goal-driven explainable agents and robots. [ABSTRACT FROM AUTHOR]

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work. [ABSTRACT FROM AUTHOR]

Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks. [ABSTRACT FROM AUTHOR]

Although testing effort estimation is a very important task in software project management, it is rarely described in the literature. There are many difficulties in finding any useful methods or tools for this purpose. Solutions to many other problems related to testing effort calculation are published much more often. There is also no research focusing on both testing effort estimation and all related areas of software engineering. To fill this gap, we performed a systematic literature review on both questions. Although our primary objective was to find some tools or implementable metods for test effort estimation, we have quickly discovered many other interesting topics related to the main one. The main contribution of this work is the presentation of the testing effort estimation task in a very wide context, indicating the relations with other research fields. This systematic literature review presents a detailed overview of testing effort estimation task, including challenges and approaches to automating it and the solutions proposed in the literature. It also exhaustively investigates related research topics, classifying publications that can be found in connection to the testing effort according to seven criteria formulated on the basis of our research questions. We present here both synthesis of our finding and the deep analysis of the stated research problems. [ABSTRACT FROM AUTHOR]

Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically reviewand synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. Objective: This article aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures. This review also aims at identifying gaps in research on ML-based data exfiltration countermeasures. Method: We used Systematic Literature Review (SLR) method to select and review 92 papers. Results: The review has enabled us to: (a) classify the ML approaches used in the countermeasures into data-driven, and behaviordriven approaches; (b) categorize features into six types: behavioral, content-based, statistical, syntactical, spatial, and temporal; (c) classify the evaluation datasets into simulated, synthesized, and real datasets; and (d) identify 11 performance measures used by these studies. Conclusion: We conclude that: (i) The integration of data-driven and behavior-driven approaches should be explored; (ii) There is a need of developing high quality and large size evaluation datasets; (iii) Incremental ML model training should be incorporated in countermeasures; (iv) Resilience to adversarial learning should be considered and explored during the development of countermeasures to avoid poisoning attacks; and (v) The use of automated feature engineering should be encouraged for efficiently detecting data exfiltration attacks. [ABSTRACT FROM AUTHOR]

Fog computing is a paradigm that brings computational resources and services to the network edge in the vicinity of user devices, lowering latency and connecting with cloud computing resources. Unlike cloud computing, fog resources are based on constrained and heterogeneous nodes whose connectivity can be unstable. In this complex scenario, there is a need to define and implement orchestration processes to ensure that applications and services can be provided, considering the settled agreements. Although some publications have dealt with orchestration in fog computing, there are still some diverse definitions and functional intersection with other areas, such as resource management and monitoring. This article presents a systematic review of the literature with focus on orchestration in fog computing. A generic architecture of fog orchestration is presented, created from the consolidation of the analyzed proposals, bringing to light the essential functionalities addressed in the literature. This work also highlights the main challenges and open research questions. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning architecture have increased its utility in real-life applications. Deep learning models require a large amount of data to train the model. In many application domains, there is a limited set of data available for training neural networks as collecting new data is either not feasible or requires more resources such as in marketing, computer vision, and medical science. These models require a large amount of data to avoid the problem of overfitting. One of the data space solutions to the problem of limited data is data augmentation. The purpose of this study focuses on various data augmentation techniques that can be used to further improve the accuracy of a neural network. This saves the cost and time consumption required to collect new data for the training of deep neural networks by augmenting available data. This also regularizes the model and improves its capability of generalization. The need for large datasets in different fields such as computer vision, natural language processing, security, and healthcare is also covered in this survey paper. The goal of this paper is to provide a comprehensive survey of recent advancements in data augmentation techniques and their application in various domains. [ABSTRACT FROM AUTHOR]

The security and privacy of smart home systems, particularly from a home user's perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this article shows a lack of a more recent literature review on user perspectives of smart home security and privacy since the 2010s. This identified gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is needed; these include holisticmethods that consider a more diverse and heterogeneous range of home devices, interactions between multiple home users, complicated data flowbetween multiple home devices and home users, some less studied demographic factors, and advanced conceptual frameworks. Based on these findings, we recommended key future research directions, e.g., research for a better understanding of security and privacy aspects in different multi-device and multi-user contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home devices and behaviors of different types of home users. [ABSTRACT FROM AUTHOR]

Question Difficulty Estimation from Text (QDET) is the application of Natural Language Processing techniques to the estimation of a value, either numerical or categorical, which represents the difficulty of questions in educational settings. We give an introduction to the field, build a taxonomy based on question characteristics, and present the various approaches that have been proposed in recent years, outlining opportunities for further research. This survey provides an introduction for researchers and practitioners into the domain of question difficulty estimation from text and acts as a point of reference about recent research in this topic to date. [ABSTRACT FROM AUTHOR]

Honeyword (or decoy password) based authentication, first introduced by Juels and Rivest in 2013, has emerged as a security mechanism that can provide security against server-side threats on the password-files. From the theoretical perspective, this security mechanism reduces attackers' efficiency to a great extent as it detects the threat on a password-file so that the system administrator can be notified almost immediately as an attacker tries to take advantage of the compromised file. This paper aims to present a comprehensive survey of the relevant research and technological developments in honeyword-based authentication techniques. We cover twenty-three techniques related to honeyword, reported under different research articles since 2013. This survey paper helps the readers to (i) understand how honeyword based security mechanism works in practice, (ii) get a comparative view on the existing honeyword based techniques, and (iii) identify the existing gaps that have yet to be filled and the emergent research opportunities. [ABSTRACT FROM AUTHOR]