In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modelled as graph processing. Graph processing, especially the processing of the large scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This paper surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping and specific GPU programming. In this paper, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in details, and explore the research opportunities in future.
Organizations are exposed to threats that increase the risk factor of their ICT systems and the assurance of their protection is crucial, as their reliance on information technology is a continuing challenge for both security experts and chief executives. To tackle down the threats decision makers should be provided with information needed to understand and mitigate them. Risk assessment forms a means of providing such information and facilitates the development of a security strategy. This paper aims at addressing the problem of selection an appropriate risk assessment method to assess and manage information security risks, by proposing a set of 17 criteria, grouped in 4 categories, for comparing such methods and provide a comparison of the 10 most popular methods based upon them. Finally, the comparison presented in the paper could be utilized by organizations to determine which method is more suitable for their needs.
Smartphone applications to support healthcare are proliferating. A growing and important subset of these apps supports emergency medical intervention to address a wide range of illness-related emergencies in order to speed the arrival of relevant treatment. The emergency response characteristics and strategies employed by these apps are the focus in this study resulting in an mHealth Emergency Strategy Index (MESI). While a growing body of knowledge focuses on usability, safety and privacy aspects that characterize such apps, studies that map the various emergency intervention strategies and suggest criteria to evaluate their role as emergency agents are limited. We survey an extensive range of mHealth apps designed for emergency response along with the related assessment literature and present an index for mobile-based medical emergency intervention apps that can address assessment needs of future mHealth apps.
Stylometry, or the analysis of authorial writing style, relies on the assumption that this style is quantifiable and distinct. However, deriving a universal style representation has plagued researchers for nearly 200 years, resulting in several methods and tools to address various challenges, such as use of limited training samples for accurate author recognition. Research has since concentrated on the fine tuning of these techniques and the role of stylometry in preserving and/or exposing privacy and anonymity. This survey covers these methods with emphasis on stylometry-related sub-problems. Additionally, while previous surveys neglect to include adversarial stylometric techniques, methods specifically designed to counter authorship detection are discussed. Many experimental models and databases are defined and discussion of various research approaches which employ each are provided. Finally, several research challenges and descriptions of various open-source and commercial software are provided.
Making cities smarter help improve city services and increase citizens quality of life. Information and communication technologies (ICT) are fundamental for progressing towards smarter city environments. Smart City software platforms potentially support the development and integration of Smart City applications. However, the ICT community must overcome current signicant technological and scientic challenges before these platforms can be widely used. This paper surveys the state-of-the-art in software platforms for Smart Cities. We analyzed 23 projects with respect to the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, InternetofThings,BigData,andCloudComputing.Basedontheseresults,wederivedareferencearchitecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges, and discussed future opportunities. This survey gives important references for helping application developers, city managers, system operators, end-users, and Smart City researchers to make project, investment, and research decisions.
The main achievements of spatio-temporal modelling in the field of Geographic Information Science over the past three decades are surveyed. This article offers an overview of: (i) the origins and history of Temporal Geographic Information Systems (T-GIS); (ii) relevant spatio-temporal data models proposed; (iii) the evolution of spatio-temporal modelling trends; and (iv) an analysis of the future trends and developments in T-GIS. It also presents some current theories and concepts that have emerged from the research performed, as well as a summary of the current progress and the upcoming challenges and potential research directions for T-GIS. One relevant result of this survey is the proposed taxonomy of spatio-temporal modelling trends, which classifies 186 modelling proposals surveyed from more than 1400 articles.
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. Applying linear algebra, this tutorial explains the theory of canonical correlation analysis including its regularised, kernel, and sparse variants. Together with the numerical examples, this overview provides a coherent compendium on the applicabilities of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.
Cyber risk management largely reduces to a race for information between defenders and attackers. Defenders can gain advantage in this race by sharing cyber risk information with each other. Yet, defenders often share less than is socially desirable, because sharing decisions are guided by selfish rather than altruistic reasons. A growing line of research studies these strategic aspects that drive defenders' sharing decisions. The present survey systematizes these works in a novel framework. It provides a consolidated understanding of defenders' strategies to privately or publicly share information, and enables us to distill trends in the literature and identify future research directions. The review also reveals that many theoretical works assume cyber risk information sharing to be beneficial, while corresponding empirical validations are missing.
The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.
Wearable computing is rapidly getting deployed in many commercial, medical and personal domains of day-to-day life. Wearable devices appear in various forms, shapes and sizes and facilitate a wide variety of applications in many domains of life. However, wearables raise unique security and privacy concerns. Wearables also hold the promise to help enhance the existing security, privacy and safety paradigms in unique ways while preserving systems usability. The contribution of this research literature survey is three-fold. First, as a background, we identify a wide range of existing as well as upcoming wearable devices and investigate their broad applications. Second, we provide an exposition of the security and privacy of wearable computing, studying dual aspects, i.e., both attacks and defenses. Third, we provide a comprehensive study of the potential security, privacy and safety enhancements to existing systems based on the emergence of wearable technology. Although several research works have emerged exploring different offensive and defensive uses of wearables, there is a lack of a broad and precise literature review systematizing all those security and privacy aspects and the underlying threat models. This research survey also analyzes current and emerging research trends, and provides directions for future research.
It is unlikely that an hacker is able to compromise sensitive data that is stored in an encrypted form. However, when data is to be processed, it has to be decrypted, becoming vulnerable to attacks. Homomorphic encryption fixes this vulnerability by allowing one to compute directly on encrypted data. In this survey, both previous and current Somewhat Homomorphic Encryption (SHE) schemes are reviewed, and the more powerful and recent Fully Homomorphic Encryption (FHE) schemes are comprehensively studied. The concepts that support these schemes are presented, and their performance and security are analyzed from an engineering standpoint.
The Experience Sampling Method (ESM) is used by scientists from various disciplines to gather insights into the intrapsychic elements of human life. Researchers have used the ESM in a wide variety of studies, with the method seeing increased popularity. Mobile technologies have enabled new possibilities for the use of the ESM, while simultaneously leading to new conceptual, methodological, and technological challenges. In this survey, we provide an overview of the history of the ESM, usage of this methodology in the computer science discipline, as well as its evolution over time. Next, we identify and discuss important considerations for ESM studies on mobile devices, and analyse the particular methodological parameters scientists should consider in their study design. We reflect on the existing tools that support the ESM methodology and discuss the future development of such tools. Finally, we discuss the effect of future technological developments on the use of the ESM and identify areas requiring further investigation.
A new form of caching, namely application-level caching, has been recently employed in web applications to improve their performance and increase scalability. It consists of the insertion of caching logic into the application base code to temporarily store processed content in memory, and then decrease the response time of web requests by reusing this content. However, caching at this level demands knowledge of the domain and application specificities to achieve caching benefits, given that this information supports decisions such as what and when to cache content. Developers thus must manually manage the cache, possibly with the help of existing libraries and frameworks. Given the increasing popularity of application-level caching, we thus provide a survey of approaches proposed in this context. We provide a comprehensive introduction to web caching and application-level caching, and present state-of-the-art work on designing, implementing and managing application-level caching. Our focus is not only on static solutions but also approaches that adaptively adjust caching solutions to avoid the gradual performance decay that caching can suffer over time. This survey can be used as a start point for researchers and developers, who aim to improve application-level caching or need guidance in designing application-level caching solutions, possibly with humans out-of-the-loop.
Despite the rapid growth of hardware capacity and popularity in mobile devices, limited resources in battery and processing capacity still lack the ability to meet the increasing mobile users' demands. Both conventional techniques and emerging approaches are brought together to fill this gap between the user demand and mobile device's limited capacity. The cloud computing is an uprising topic in both business and academia in recent years to eliminate the gap. Augmentation techniques such as computation outsourcing and service oriented architectures are proposed by the proposed works, and new challenges regarding the augmentation techniques, energy efficiency, etc, needs to be studied. In this paper, we aim to provide a comprehensive taxonomy and survey of the existing techniques and frameworks for mobile cloud augmentation in terms of both computation and storage. Different from the existing taxonomies in this field, we focus on the techniques aspect, following the idea of realizing a complete mobile cloud computing system. The objective of this survey is to provide a guide on what available augmentation techniques can be adopted in mobile cloud computing systems as well as supporting mechanisms such as decision making and fault tolerance policies for realizing reliable mobile cloud services.
Firewalls are network security components that handle incoming and outgoing network traffic based on a set of rules. The process of correctly configuring a firewall is complicated and prone to error, and it worsens as the network complexity grows. A poorly configured firewall may result in major security threats; in case of a network firewall, an organizations security could be endangered, and in the case of a personal firewall, an individual computers security is threatened. A major reason of poorly configured firewalls, as pointed out in the literature, is usability issues. Our aim is to identify existing solutions that help professional and non- professional users to create and manage firewall configuration files, and to analyze the proposals in respect of usability. A systematic literature review with a focus on usability of firewall configuration is presented in the paper. Its main goal is to explore what has already been done in this field. In the primary selection procedure, 1,202 papers were retrieved and then screened. The secondary selection led us to 35 papers carefully chosen for further investigation, of which, 14 papers were selected and summarized....
Storage as a Service (StaaS) forms a critical component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Despite many open challenges within a cloud-based data store, application providers deploy Geo-replicated data stores in order to obtain higher availability, lower response time, and more cost efficiency. The deployment of Geo-replicated data stores is in its infancy and poses vital challenges for researchers. In this paper, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data cost optimization. Finally, we map various cloud-based data store projects to our proposed taxonomy not only to validate the taxonomy but also to identify areas for future research.
Optical on-chip data transmission enabled by silicon photonics is widely considered a key technology to overcome the bandwidth and energy limitations of electrical interconnects. The possibility of utilizing optical links in the on-chip communication fabric has paved the way to a fascinating new research field - Optical Networks-on-Chip (ONoCs) - which has been gaining large interest in the community. Nanophotonic devices and materials, however, are still evolving, and dealing with optical data transmission on chip makes designers and researchers face a whole new set of obstacles and challenges. Designing efficient ONoCs is a challenging task and requires a detailed knowledge from on-chip traffic demands and patterns down to the physical layout and implications of integrating both electronic and photonic devices. In this paper, we provide an exhaustive review of recent ONoC proposals, discuss their strengths and weaknesses, and outline outstanding research questions. Moreover, we discuss recent research efforts in key enabling technologies, such as on-chip and adaptive laser sources, automatic synthesis tools, and ring heating techniques, which are essential to enable a widespread commercial adoption of ONoCs in the future.
Nano-crossbar arrays have emerged as a promising and viable technology to improve computing performance of electronic circuits beyond the limits of current CMOS. Arrays offer both structural efficiency with reconfiguration and prospective capability of integration with different technologies. However, certain problems need to be addressed and the most important one is the prevailing occurrence of faults. Considering fault rate projections as high as 20\% that is much higher than those of CMOS, it is fair to expect sophisticated fault tolerance methods. The focus of this survey paper is the assessment and evaluation of these methods and related algorithms applied in logic mapping and configuration processes. As a start, we concisely explain reconfigurable nano-crossbar arrays with their fault characteristics and models. Following that, we demonstrate configuration techniques of the arrays in the presence of permanent faults and elaborate on two main fault tolerance methodologies, namely defect-unaware and defect-aware approaches, with a short review on advantages and disadvantages. Next, we overview fault tolerance approaches for transient faults. In the experimental results section, we give detailed results of the algorithms regarding their strengths and weaknesses with a comprehensive yield, success rate, and runtime analysis. As a conclusion, we overview the proposed algorithms with future directions and upcoming challenges.
Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. The objectives include: building simpler and more comprehensible models, improving data mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities of feature selection algorithms. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. In particular, we revisit feature selection research from a data perspective, and review representative feature selection algorithms for generic data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for generic data, we generally categorize them into four groups: similarity based, information theoretical based, sparse learning based and statistical based methods. Finally, to facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (http://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At last, we also have a discussion about some open problems and challenges that need to be paid more attention in future research.
Approximate computing has gained research attention recently as a way to increase energy efficiency and/or performance by exploiting some applications' intrinsic error resiliency. However, little attention has been given to its potential for tackling the communication bottleneck which remains as one of the looming challenges to be tackled for efficient parallelism. This paper seeks to explore the potential benefits of approximate computing for communication reduction by surveying four promising techniques for approximate communication - compression, relaxed synchronization, value prediction, and accelerators. The techniques are compared based on an evaluation framework composed of: communication cost reduction, performance, energy reduction, application domain, overheads, and output degradation. Comparison results show that lossy link compression and approximate value prediction are good choices for reducing the communication bottleneck in bandwidth constrained applications, while relaxed synchronization and approximate accelerators can achieve greater speedups on applications amenable to these techniques. Finally, this paper also includes several suggestions for future research on approximate communication techniques.
Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to auto-scale the resources provisioned to their applications under dynamic workload in order to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this paper, we comprehensively analyze the challenges remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scaling systems according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weakness in this field. Moreover, based on the analysis, we propose new future directions.
Authenticated encryption (AE) has long been a vital operation in cryptography due to its ability to provide confidentiality, integrity and authenticity at the same time. Its use has soared in parallel with widespread use of Internet and has led to several new schemes. There have already been studies investigating software performance of various schemes. However, the same is yet to be done for hardware. In this paper, we present a comprehensive survey of hardware performance of the most commonly used authenticated encryption schemes in literature. These schemes include encrypt-then-MAC combination, block cipher based AE modes, relatively new authenticated encryption ciphers and the recently-introduced permutation-based AE scheme. For completeness, we implemented each scheme with various standardized block ciphers and/or hash algorithms, and their lightweight versions. In our evaluation, we targeted minimizing the time-area product while maximizing the throughput on ASIC platforms. 45nm NANGATE Open Cell Library was used for syntheses. In the results, we present area, speed, time-area product, throughput, and power figures for both standard and lightweight versions of each scheme. Finally, we provide an unbiased discussion on the impact of the structure and complexity of each scheme on hardware implementation, together with recommendations on hardware-friendly authenticated encryption scheme design.
Shape-changing interfaces are physically tangible, interactive devices, surfaces or spaces. Over the last fifteen years, research has produced functional prototypes over many use-applications, and reviews have identified themes and possible future directions but have not yet looked at possible design or application based research. Here we gather this information together to provide a reference for designers and researchers wishing to build upon existing prototyping work, using synthesis and discussion of existing shape-changing interface reviews and comprehensive analysis and classification of 78 shape-changing interfaces. Eight categories of prototype are identified, alongside recommendations for the field.
Crowd-centric research is receiving increasingly more attention as data sets on crowd behavior are becoming readily available. We have come to a point that many of the models on pedestrian analytics introduced in the last decade, which have mostly not been validated, can now be tested using real-world data sets. In this survey we concentrate exclusively on automatically gathering such data sets, which we refer to as sensing the behavior of pedestrians. We roughly distinguish two approaches: one that requires users to explicitly use local applications and wearables, and one that scans the presence of handheld devices such as smartphones. We come to the conclusion that despite the numerous reports in popular media, relatively few groups have been looking into practical solutions for sensing pedestrian behavior. Moreover, we find that much work is still needed, in particular when it comes to combing privacy, transparency, scalability, and ease of deployment. We report on over 90 relevant articles and discuss and compare in detail 30 reports on sensing pedestrian behavior.
Geomagnetism has recently attracted considerable attention for indoor localization due to its pervasiveness and unreliance on extra infrastructure. Its location signature has been observed to be temporally stable and spatially discernible for localization purposes. This survey investigates the recent challenges and advances in geomagnetism-based indoor localization using smartphones. We first study smartphone-based geomagnetism measurements. We then review recent efforts in database construction and computation reduction, followed by state-of-the-art schemes in localizing the target. For each category, we identify practical deployment challenges and compare related studies. Finally, we summarize future directions and provide guideline for new researchers in this field.
The last decades have seen a growing interest and demand for collaborative systems and platforms. These systems and platforms aim to provide an environment in which users can collaboratively create, share and manage resources. While offering attractive opportunities for online collaboration and information sharing, they also open several security and privacy issues. This has attracted several research efforts towards the design and implementation of novel access control solutions that can handle the complexity introduced by collaboration. Despite these efforts, transition to practice has been hindered by the lack of maturity of the proposed solutions. The access control solutions typically adopted by commercial collaborative systems like online social network websites and collaborative editing platforms, are still rather rudimentary and do not provide users with a sufficient control over their resources. This survey examines the growing literature on access control for collaborative systems centered on communities, and identifies the main challenges to be addressed in order to facilitate the adoption of collaborative access control solutions in real-life settings. Based on the literature study, we delineate a roadmap for future research in the area of access control for community-centered collaborative systems.
This article presents an annotated bibliography on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs, without human intervention. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages and security. Furthermore, it provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature.
High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing traditional scientific applications and analytics business services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from dedicated on-premise environments to shared public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make it easier its usage. Moreover, the discussion on the right pricing and contractual models that will fit both small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.
Vehicular networks and their associated technologies enable an extremely varied plethora of applications and therefore attract increasing attention from a wide audience. However vehicular networks also have many challenges that arise mainly due to their dynamic and complex environment. Fuzzy Logic, known for its ability to deal with complexity, imprecision and model non-deterministic problems, is a very promising technology for use in such a dynamic and complex context. This paper presents the first comprehensive survey of research on Fuzzy Logic approaches in the context of vehicular networks, and provides fundamental information which enables readers to design their own Fuzzy Logic systems in this context. As such, the paper describes the Fuzzy Logic concepts with emphasis on their implementation in vehicular networks, includes a classification and thorough analysis of the Fuzzy Logic-based solutions in vehicular networks and discusses how Fuzzy Logic could empower the key research directions in the 5G-enabled vehicular networks, the next generation of vehicular communications.
Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this paper, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.
This article presents a comprehensive survey on parallel I/O. This is an important field for High Performance Computing because of the historic gap between processing power and storage latencies, which causes applications performance to be impaired when accessing or generating large amounts of data. As the available processing power and amount of data increase, I/O remains a central issue for the scientific community. In this survey, we present background concepts everyone could benefit from. Moreover, through the comprehensive study of publications from the most important conferences and journals in a five-year time window, we discuss the state of the art of I/O optimization approaches, access pattern extraction techniques, and performance modeling, in addition to general aspects of parallel I/O research. Through this approach, we aim at identifying the general characteristics of the field and the main current and future research topics.
Robots are sophisticated machines that are susceptible to different types of faults. These faults have to be detected and diagnosed in time to allow recovery and continuous operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. Yet, the study of FDD for robotics is relatively new, and only few surveys were presented. These surveys have focused on traditional FDD approaches and how they may broadly apply to a generic type of robots. Yet, robotic systems can be identified by fundamental characteristics, which pose different constraints and requirements from FDD. In this paper, we aim to provide the reader with useful insights regarding the use of FDD approaches which best suit the different characteristics of robotic systems. We elaborate on the advantages and the challenges these approaches must face. We use two perspectives: (1) FDD from the perspective of the different characteristics of robotic systems, and (2) FDD from the perspective of the different approaches. Finally, we describe research opportunities. With these three contributions readers from both the FDD and the robotics research communities are introduced to this subject.
Designing an optimal distributed database is an extremely complex process due to many factors like large number of relations, data transmission costs, number of network sites, communication costs between sites and query response time. In the sake of achieving an optimal design, fragmentation, replication and data allocation techniques are the key factors for providing a high rendering and supporting data access and sharing at different sites. It is worth saying, however, that these techniques often treated separately and rarely processed together. Some researches sought to find only optimal allocation methods regardless of how the fragmentation technique is performed or replication process is adopted. In contrast, others attempt to find the best fragment solution without considering how allocation would be performed. In this paper, most of different fragmentation, replication and allocation techniques are extensively and precisely scrutinized in contemporary literature for both centralized and distributed databases. Furthermore, some of these techniques presented as cases study for well-analyzed fragmentation and allocation models. These cases are cited as evidence proving that a well designed distributed database can result in significant reduction in communication costs, response time and substantial boost in performance outperforming over centralized systems for geographically distributed sites.
This survey covers research on the topic of mixed criticality systems that has been published since Vestal's seminal paper in 2007. It covers the period up to and including July 2015. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including job-based, task-based, fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, formal treatments, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as fault tolerant scheduling, hierarchical scheduling, cyber physical systems, and probabilistic hard real-time systems. An appendix lists funded projects in the area of mixed criticality.
In recent years, eye-tracking has been used by researchers in the field of programming education to analyse and understand tasks such as code comprehension, debugging, collaborative programming, tractability and the comprehension of non-code programming representations. Eye-trackers are used to gain more insights into the cognitive process of programmers and programming techniques. In this paper, we perform a systematic literature review (SLR) on existing research using eye-tracking in computer programming. We identify, evaluate, and report 65 studies, published between 1990 and 2015. Participants in these studies were mainly students and faculty members with the common programming language used are Java and UML representation. We also report on a range of eye-trackers and attention tracking tools utilized in these studies and found that the Tobii eye-trackers are more preferred among researchers. In this SLR, we report the findings based on the materials, participant sample, and eye-tracking device used in each experiment.
Online judges are systems designed for the reliable evaluation of algorithm source code submitted by users, which is next compiled and tested in a homogeneous environment. Online judges are becoming popular in various applications. Thus, we would like to review the state of the art for these systems. We classify them according to their principal objectives into systems supporting organization of competitive programming contests, enhancing education and recruitment processes, or facilitating the solving of data mining challenges, online compilers and development platforms integrated as components of other custom systems. Moreover, we present the Optil.io platform, which has been proposed for the solving of complex optimization problems. We also present the advantages of our system by analysis of the competition results conducted using the proposed platform. The competition proved that this platform, strengthened by crowdsourcing concepts, can be successfully applied to accurately and efficiently solve complex industrial- and science-driven challenges.
With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. Lots of research work have been done in the multimedia area, targeting at different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey which targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.
The presence construct, most commonly defined as the sense of "being there", has driven research and development of virtual environments (VEs) for decades. Despite that, there is not widespread agreement on how to define or operationalize this construct. The literature contains many different definitions of presence, and many proposed measures for it. This article reviews many of the definitions, measures, and models of presence from the literature. We also discuss several related constructs, including immersion, agency, transportation, and reality judgment. We also present a meta-analysis of existing models of presence informed by Slater's Place Illusion and Plausibility Illusion constructs.
We present a survey of multi-robot assembly applications and methods, and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.
Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.
Context: Recent years have seen growing interest in open-ended interactive tools such as games. One of the most crucial factors in developing games is to model and predict individual behavior. Although model-based approaches have been considered a standard way for this purpose, their application is often extremely difficult due to a huge space of actions can be created by games. For this reason, data-driven approaches have shown promise, in part because they are not completely reliant on expert knowledge. Objective: This study seeks to systematically review the existing research on the use of data-driven approaches in game player modeling. Method: We have carefully surveyed a nine-year sample (2008-2016) of experimental studies conducted on data-driven approaches in game player modeling, and thereby found 36 studies that addressed four primary research questions, and so we analyzed and classified the questions, methods, and findings of these published works, which we evaluated and drew conclusions from based on non-statistical methods. Results: We found that there are three primary avenues in which data-driven approaches have been studied in games research. In conclusion, we highlight critical future challenges in the area and offer directions for future study
Modern cloud environments support a relatively high degree of automation in service provisioning, which allows cloud users to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by todays cloud environments and support different application scenarios, e.g. migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate on what a CML is and what aspects of a cloud application and the target cloud environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grained level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support cloud users in selecting the CML which fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda for the future.
Distributed and multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work together to develop a course of action that satisfies the goals of the group. This paper reviews the most relevant approaches to MAP, including the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to the key features of the solvers, distribution and coordination.