Computing Surveys (CSUR)

Latest Articles

GeoStreams: A Survey

Current and Future Trends in Mobile Device Forensics: A Survey

A Survey on Multidimensional Scaling

A Survey of Random Forest Based Methods for Intrusion Detection Systems

Co-Simulation: A Survey

A Survey of Symbolic Execution Techniques

A Survey on Routing in Anonymous Communication Protocols

Provenance Analytics for Workflow-Based Computational Experiments: A Survey

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions


About CSUR

ACM Computing Surveys (CSUR) publishes comprehensive, readable tutorials and survey papers that give guided tours through the literature and explain topics to those who seek to learn the basics of areas outside their specialties. These carefully planned and presented introductions are also an excellent way for professionals to develop perspectives on, and identify trends in complex technologies. Recent issues have covered image understanding, software reusability, and object and relational database topics. 

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Forthcoming Articles

A Survey on Malicious Domains Detection through DNS Data Analysis

Triclustering Algorithms for Three-Dimensional Data Analysis: A Comprehensive Survey

Three-dimensional data are increasingly prevalent across biomedical and social domains. Notable examples are gene-sample-time, individual-feature-time or node-node-time data, generally referred observation-attribute-context data. The unsupervised analysis of three-dimensional data can be pursued to discover putative biological modules, disease progression patterns or communities of individuals with coherent behavior, thus being key to enhance the understanding of complex biological, individual and societal systems. Although clustering can be applied to group observations, it is of limited potential since observations in three-dimensional data domains are typically only meaningfully correlated on subspaces of the overall space. Biclustering tackles this challenge but disregards the third dimension of data. In this context, triclustering -- the discovery of coherent subspaces within three-dimensional data -- has been largely researched to tackle these problems. Despite the diversity of contributions in this field, there is still lacking a structured view on the major requirements of this task, allowed homogeneity criteria (including coherency, structure, quality, locality and orthonormality criteria) and algorithmic approaches. In this context, this work formalizes the triclustering task and its scope; introduces a taxonomy to categorize the contributions on the field; provides a comprehensive comparison of state-of-the-art triclustering algorithms according to their behavior and output; and lists relevant real-world applications.

Evaluation in Contextual Information Retrieval: Foundations and Recent Advances within the Challenges of Context Dynamicity and Data Privacy

Context such as user's search history, demographics, devices and surroundings, has become prevalent in various domains of information seeking and retrieval such as mobile search, task-based search and social search. While evaluation is central and has a long history in information retrieval, it faces the big challenge of designing an appropriate methodology that embeds the context into the evaluation settings. In this survey, we summarize in a unified view, a wide range of main and recent progress in contextual information retrieval evaluation that leverages diverse context dimensions and uses different principles, methodologies and levels of measurements. More specifically, this survey aims to fill two main gaps in the literature: first, it provides a critical summary and comparison of existing contextual information retrieval evaluation methodologies and metrics according to a simple stratification model; second, it points out the impact of context dynamicity and data privacy on the evaluation design. Finally, we recommend promising research directions for future investigations.

Host-based Intrusion Detection System with System Calls: Review and Future Trends

The increasing amount of contemporary Linux applications in a data center often generate large quantity of real-time system call traces, which are not well-suited for traditional host-based intrusion detection system deployed on each single host. Training and testing data mining models with system call traces on a single host that has static computing and storage capacity is time-consuming and intermediate datasets are not capable to be handled. It is cumbersome for the maintenance and update of HIDS installed on each physical or virtual host and distributed system call analysis can hardly be performed to detect complicated and distributed attacks among multiple hosts. This paper provides a review of the development of system call based HIDS as well as future research directions. Algorithms and techniques relevant to system call based HIDS are evaluated, including feature extraction methods and various data mining algorithms. Modern application areas for system call based HIDS are summarized and related works are investigated. The HIDS dataset issues including current available datasets with system calls and approaches for researchers to generate their own datasets are listed. Potential HIDS solutions in regard to cloud computing and big data tools are provided in this paper.

A Survey on Brain Biometrics

Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to impersonation, sensitivity to emotional and mental state, continuous nature, and cancellability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure person identification and authentication. Although existing research on brain biometrics have obtained some intriguing insights, a lot of work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability.

Large Scale Ontology Matching: State of the Art Analysis

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

Stress is a major concern in daily life that imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination which can lead to life-threatening situations as a large number of road traffic crashes occur every year due to driver stress. In addition, the rate of many general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than many other occupational groups. Therefore, an early warning system for drivers stress level in car is needed to continuously predict dangerous driving situations and alert the driver pro-actively from the perspective of safety and comfortable driving. With recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, it is becoming feasible to comfortably measure combinations of different sensed modalities to recognise driver stress automatically. This survey reviews the most recent researches on automatic driver stress level detection domain based on different sensors and data. Different computational techniques which have been used in this domain for data analysis are investigated. The important methodological issues that hinder the implementation of such a system are discussed and future research directions are offered.

Deception Techniques In Computer Security: A Research Perspective

A recent trend both in academia and industry is to explore the use of deception techniques to achieve proactive attack detection and defense  to the point of marketing intrusion deception solutions as zero-false-positive intrusion detection. However, there is still a general lack of understanding of deception techniques from a research perspective and it is not clear how the effectiveness of these solution can be measured and compared with other security approaches. To shed light on this topic, we introduce a comprehensive classification of existing solutions, and survey the current application of deception techniques in computer security. Furthermore, we analyze several open research directions, including the design of strategies to help defenders to design and integrate deception within a target architecture, the study of automated ways to deploy deception in complex systems and, most importantly, the design of new techniques and experiments to evaluate the effectiveness of the existing deception techniques. Finally, we discuss the limitations of existing solutions and provide insights for further research on this topic.

Reproducibility in Scientific Computing

Reproducibility is widely considered to be an essential requirement of the scientific process. However, a number of serious concerns have been raised recently, questioning whether today's computational work is adequately reproducible. In principle, it should be possible to specify a computation to sufficient detail that anyone should be able to reproduce it exactly. But in practice, there are fundamental, technical, and social barriers to doing so. The many objectives and meanings of reproducibility are discussed within the context of scientific computing. Many technical barriers to reproducibility are described, extant approaches surveyed, and open areas of research are identified.

Automated Regression Test Case Generation for Web Application: A Survey

Testing is one of the most important phase in the development of any product or software. Various types of software testing exists which has to be done to meet the need of the software. Regression testing is one of the crucial phases of testing where testing of program is done for the original test build along with the modifications. Very few approaches and methodologies have been found which provides the real tool for test case generation. There is a requirement of a tool which can accept requirements, generate test cases for first version and then for versions with the change. Various studies proposed by the authors have been analyzed focusing on test cases generation and their approach towards web application. We have provided the detailed study of Regression Test Case Generation and its approach towards web application. It has been found that there is a need of an Automated Regression tool which can generate Regression test cases based on user requirements directly taken by a tool and the inputs required for testing it. These test cases have to be generated and implemented by the tool so that the reduction in the overall effort and cost can be achieved.

Brownout Approach for Adaptive Management of Resources and Applications in Cloud Computing Systems: A Taxonomy and Future Directions

Cloud computing has been regarded as an emerging approach to provisioning resources and managing applications. It provides attractive features, such as on-demand model, scalability enhancement, and management costs reduction. However, cloud computing systems continue to face problems such as hardware failures, overloads caused by unexpected workloads, or the waste of energy due to inefficient resource utilization, which all result to resource shortages and application issues such as delays or saturated eventually. A paradigm named brownout has been applied to handle these issues by adaptively activating or deactivating optional parts of applications or services to manage resource usage in cloud computing system. Brownout has successfully shown it can avoid overloads due to changes in the workload and achieve better load balancing and energy saving effects. This paper proposes a taxonomy of brownout approach for managing resources and applications adaptively in cloud computing systems and carries out a comprehensive survey. It identifies open challenges and offers future research directions.

Sequence-Aware Recommender Systems

Recommender systems are one of the most successful applications of data mining and machine learning technology in practice and significant technological advances were made over the last two decades. Academic research in the field in the recent past was strongly fueled by the increasing availability of large datasets containing user-item rating matrices. Many of these works were therefore based on a problem abstraction where only one single user-item interaction is considered in the recommendation process. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works has shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user-item interaction logs when recommending. In addition, we discuss problem settings where the sequence in which items can be recommended is subject to strict or weak ordering constraints. We propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call recommender systems, and outline open challenges in the area.

Who watches the watchmen: A security-focused review on current state-of-the-art techniques, tools and methods for systems and binary analysis on modern platforms.

Malicious software are still threatening users on a daily basis and their evolution goes from social- engineering-based bankers to advanced persistent threats (APTs). Recent research and discoveries have presented us to a wide range of anti-analysis and evasion techniques, in-memory attacks, such as Returned Oriented Programming (ROP), and systems subversion, including BIOS and hypervisors. This work presents a survey on techniques able to detect, mitigate and analyze these kinds of attacks, which require transparent and fine-grained environments as analysis resources. We cover current tools limitations, such as not being fully-transparent, and introduce systems and techniques to overcome and/or mitigate these constraints. The work presents approaches based on hypervisor introspection, System Managment Mode (SMM) instrumen- tation as well as some hardware-based ones. We also present some threats based on the same techniques. Our main goal is to give to the reader a broader and more comprehensive understanding of recently-surfaced tools and techniques.

Engagement in HCI: Conception, Theory and Measurement

The design of usable and useful products and services is supported by an understanding of users needs and experiences. For designers of products and services of every kind, engaging users is a priority. However, to date, there has been limited analysis of how HCI and computer science research deals with engagement. Questions persist concerning the conception, abstraction, and measurement of this concept. This paper presents a systematic review of engagement, drawing upon a final corpus of 351 papers and 102 unique definitions of engagement. We describe the diversity of interpretation, theory, and measurement of engage- ment, along with strategies for the design of engaging experiences. We map the current state of engagement research, discuss the value of the concept and its relationship to other popular terms, and present a set of guidelines and opportunities for future research.

Scalable Metadata Management Techniques for Ultra Large Distributed Storage Systems A Systematic Review

In an ultra large scale storage system, data is distributed on multiple nodes. The access to this distributed data is through its metadata, maintained by multiple metadata servers. This Metadata has information about the physical position of distributed data and access privileges. The efficiency of a storage system depends upon the effective management of such metadata. Inefficient metadata management may lead to higher lookup time and large memory overheads. In this paper an extensive systematic literature review of scalable metadata management techniques in general and a very large distributed storage system in particular. The different metadata distribution techniques lead to different taxonomies. The evaluation of these techniques has been carried out for metadata management. Metadata management, its benets and challenges in different techniques are reported. Further, it brings much awareness in potential benets of metadata management in large distributed storage system, and identify the need to develop efficient metadata management techniques. The paper investigates the techniques based on parameters such as metadata distribution, in-memory caching, load balancing, migration cost, lookup time, memory overheads, metadata operation costs, scalability, availability and locality etc. Finally, it discusses the existing challenges for researchers to stay abreast and for carrying out research in the metadata management techniques.

Group Deviation Detection Techniques: A Survey

Pointwise anomaly detection and change detection focus on the study of individual data instances however an emerging area of research involves groups or collections of observations. From applications of high energy particle physics to healthcare collusion, group deviation detection techniques result in novel research discoveries, mitigation of risks, prevention of malicious collaborative activities and other interesting explanatory insights. In particular, static group anomaly detection is the process of identifying groups that are not consistent with regular group patterns while dynamic group change detection assesses significant differences in the state of a group over a period of time. Since both group anomaly detection and group change detection share fundamental ideas, this survey paper provides a clearer and deeper understanding of group deviation detection research in static and dynamic situations.

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (assigning variables to processor registers or memory) and instruction scheduling (reordering instructions to increase throughput) in a compiler are essential tasks for generating efficient assembly code. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can generate optimal code, can accurately capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This survey reviews combinatorial optimization for register allocation and instruction scheduling. It focuses on integer programming, constraint programming, and partitioned Boolean quadratic programming as combinatorial techniques that are used in the area, are based on models, and can generate provably optimal code. A detailed, multidimensional classification of the surveyed approaches based on optimization technique, scope, model accuracy, and practical scalability enables to critically compare them and to highlight developments, trends, and challenges.

A Survey of End-system Optimizations for High Speed Networks

The gap is widening between the processor clock speed of end-system architectures and network throughput capabilities. It is now physically possible to provide single-flow throughput of speeds up to 100 Gbps, and 400 Gbps will soon be possible. Most current research into high-speed data networking focuses on managing expanding network capabilities within datacenter Local-Area Networks (LANs) or efficiently multiplexing millions of relatively small flows through a Wide-Area Network (WAN). However, datacenter hyper-convergence places high-throughput networking workloads on general-purpose hardware, and distributed High-Performance Computing (HPC) applications require time-sensitive, high-throughput end-to-end flows (also referred to as elephant flows) to occur over WANs. For these applications, the bottleneck is often the end-system, and not the intervening network. Since the problem of the end-system bottleneck was uncovered, many techniques have been developed which address this mismatch with varying degrees of effectiveness. In this survey, we describe the most promising techniques, beginning with network architecturesand NIC design, continuing with operating and end-system architectures, and concluding with clean-slate protocol design.

A Survey on Self-Adaptive Security for Large Scale Open Environments

Contemporary software systems operate in heterogeneous, dynamic, and distributed environments, where security needs changes at runtime. The security solutions for such systems need to be adaptive for the continuous satisfaction of the security goals of software systems. Whilst existing research on self-adaptive security has made notable advancement towards designing and engineering self-adaptive security solutions, there exists little work on taxonomic analysis and understanding of the reported research on architectural level solutions and research gaps. We propose an architecture-centric taxonomy for mapping and comparing the current research and identifying the future research directions in this field. The proposed taxonomy has been used to review the representative work on the architectural characteristics that self-adaptive security systems must maintain for their effective application in large-scale open environments.We reflect on the findings from the taxonomic analysis and discuss the design principles, research challenges and limitations reported in the-state-of-the-art and practice. We outline the directions for the future research on architectural level support for self-adaptive security systems for large-scale open environments.

A Survey on Compiler Autotuning using Machine Learning

Since the mid 1980s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (i) selecting the best optimizations and (ii) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.

A Survey on Homomorphic Encryption Schemes: Theory, Implementation, and Applications

Legacy encryption systems depend on sharing the key (public or private) between the users, which is the norm for many systems today. However, this approach poses privacy concerns. The users or service providers with the key have exclusive rights on the data. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Moreover, untrusted servers, providers, cloud operators can keep physically identifying elements of users long after the user ends the relationship with the services. Indeed, Homomorphic Encryption (HE) is a special kind of encryption scheme, which allows any third party to operate on the encrypted data without decrypting it in advance. The first plausible and achievable Fully Homomorphic Encryption (FHE) scheme was introduced by Craig Gentry in 2009. In this article, we survey the literature from the first HE scheme RSA in 1978 to recent improvements in FHE schemes proposed primarily to improve the practicality of Gentrys original scheme. To conclude, implementations and potential applications are shown while discussing about the future directions in the area of HE.

A survey on automatic detection of hate speech in text

Hate speech detection is defined as the automatic classification of a text message as containing hate speech or not. This survey organizes and describes the current state of the field, by providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and context, and provides a unifying definition. This area has an unquestionably potential for societal impact and the development of shared resources, such as annotated datasets in multiple languages, is a crucial step for its development.

Machine Learning for Survival Analysis: A Survey

Accurately predicting the occurrence time for the event of interest is a critical problem in longitudinal data analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after some time point or the instances did not experience any event during the monitoring period. Such a phenomenon is called censoring which is typically handled using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to effectively overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle survival data and tackle other challenging problems that arise from real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and give a well-organized comprehensive taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application fields. We hope that this paper will provide a better understanding of the recent advances on survival analysis and offer some guidelines on how to apply these approaches for solving new problems that arise in applications with censored data.

A Survey on Gait Recognition

Recognizing people by their gait has become more and more popular nowadays, due to the following reasons. First, gait recognition can work well remotely. Second, gait recognition can be done from low resolution videos and with simple instrumentation. Third, gait recognition can be done without the cooperation of individuals. Fourth, gait recognition can work well while other features such as face and fingerprint are hidden. Finally, gait features are typically difficult to be impersonated. Recent ubiquity of smartphones that capture gait patterns through accelerometers and gyroscope and advances in machine learning has opened new research directions and applications in gait recognition. A timely survey that addresses current advances is missing. In this article, we survey research works in gait recognition. In addition to recognition based on video, we address new modalities, such as recognition based on floor sensor, radar, and accelerometer; new approaches that include machine learning methods; and examine challenges and vulnerabilities in this field. In addition, we propose a set of future research directions. Our review reveals the current state-of-art, and can be helpful to both experts and newcomers of gait recognition. Moreover, it lists future works and publicly available databases in gait recognition for researchers.

A Survey on Deep Learning: Algorithms, Techniques, and Applications

Deep learning is a machine learning technique that uses multiple layers to represent the abstractions of data to build computational models. The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. The deep learning algorithms and techniques have already enabled several discoveries in video, image, audio, text, etc. to such an extent that our perception of information processing and communication has completely changed. However, there exists an aperture of understanding behind this tremendously fast paced domain, because it was never quite represented from a multi-scope perspective previously. Thus, this article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, speech, and audio processing, social network analysis, and natural language processing; followed by the in-depth analysis on pivoting and groundbreaking recent advances in deep learning applications. Moreover, deep learning has repeatedly perceived as a silver bullet to all stumbling blocks in machine learning. Therefore, it was undertaken to also review the issues faced in deep learning such as unsupervised learning, black box models, and online learning, and a case was made on how these challenges can be transformed into prolific future research avenues.

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. Creation of new concepts is a central challenge for any creative system. In this paper, we outline different approaches to concept creation and then review conceptual representations relevant to concept creation. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. These two distinctions are orthogonal. Additionally, conceptual representations used in particular creative domains, i.e. language, music, image and emotion, are reviewed separately. For each representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.

A Survey of Machine Learning for Big Code and Naturalness

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

Systematically Understanding the Cyber Attack Business: A Survey

Cyber attacks are increasingly menacing businesses. Based on literature review and publicly available reports, this paper develops a comprehensive and systematic framework of the cybercrime business. A value chain model is constructed and used to describe 25 key value-added activities, which can be offered "as a service" for use in a cyber attack. Understanding the specialization, commercialization, and cooperation of services for cyber attacks helps to anticipate emerging cyber attack services. Finally, this framework can help to build a more cyber immune system by targeting cybercrime control-points and assigning defense responsibilities to encourage collaboration.

A Survey of Physics-Based Attack Detection in Cyber-Physical Systems

Monitoring the ``physics'' of control systems to detect attacks is a growing area of research. In its basic form a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements in order to identify potentially false control commands or false sensor readings. In this paper, we review previous work on physics-based anomaly detection based on a unified taxonomy that allows us to identify limitations and unexplored challenges, and propose new solutions.

Is Multimedia Multisensorial? - A Review of Mulsemedia Systems

Mulsemedia - multiple sensorial media - makes possible the inclusion of layered sensory stimulation and the interaction through multiple sensory channels. e recent upsurge in technology and wearables provides mulsemedia researchers a vehicle for boundless choice. However, in order to build systems that integrate various senses there are still some issues that need to be addressed. This review deals with mulsemedia topics remained insufficiently explored by previous work with a focus on multi-multi (multiple media - multiple senses) perspective, where multiple types of media engage multiple senses. Moreover, it addresses the evolution of previously identified challenges in this area and it formulates new exploration directions.

Analytics for the Internet of Things: A Survey

The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.

Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art

Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future, and a short list of recommended readings are suggested.

A Survey on Game Theoretic Approaches for Intrusion Detection and Response Optimization

Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. However, the efficiency of an IDS depends primarily on both its configuration and its precision. The large amount of network traffic that needs to be analyzed, in addition to the increase in attacks sophistication, renders the optimization of intrusion detection an important requirement for infrastructure security, and a very active research subject. In the state of the art, a number of approaches have been proposed to improve the efficiency of intrusion detection and response systems. In this paper, we review the works relying on decision-making techniques focused on game theory and Markov Decision Processes to analyze the interactions between the attacker and the defender, and classify them according to the type of the optimization problem they address. While these works provide valuable insights for decision making, we discuss the limitations of these solutions as a whole, in particular regarding the hypotheses in the models and the validation methods. We also propose future research directions to improve the integration of game theoretic approaches into IDS optimization techniques.

FPGA Dynamic and Partial Reconfiguration: A Survey of Architectures, Methods, and Applications

Dynamic and partial reconfiguration are key differentiating capabilities of field programmable gate arrays (FPGAs). While they have been studied extensively in academic literature, they find limited use in deployed systems. We review FPGA reconfiguration, looking at architectures built for the purpose, and the properties of modern commercial architectures. We then investigate design flows, and identify the key challenges in making reconfigurable FPGA systems easier to design. Finally, we look at applications where reconfiguration has found use, as well as proposing new areas where this capability places FPGAs in a unique position for adoption.

RDF Data Storage and Query Processing Schemes: A Survey

The size of Linked Data is growing fast, thus a Linked Data management system must to be able to deal with increasing amounts of data. Even though physically handling Linked Data using a relational table is possible, querying a giant triple table becomes very costly due to the multiple nested joins required for typical queries. In addition, the heterogeneity of Linked Data poses entirely new challenges to database systems. This article provides a comprehensive study of the state of the art in storing and querying RDF data. In particular, we focus on data storage techniques, indexing strategies, and query execution mechanisms. In addition, we provide a classification of existing systems and approaches. We also provide an overview of the various benchmarking efforts in this context and discuss some of the open problems in this domain.

Voice Disguise in Automatic Speaker Recognition

We, humans, are able to identify other people even in voice disguise conditions. However, we are not immune to all voice changes when trying to identifying people from voice. Likewise, automatic speaker recognition systems can also be deceived by voice imitation and other types of disguise. Taking into account the voice disguise classification into the combination of two different categories (deliberate/non-deliberate and electronic/non-electronic), this survey provides a literature review on the influence of voice disguise in the automatic speaker recognition task and the robustness of these systems to such voice changes. Additionally, the survey addresses existing applications dealing with voice disguise and analyses some issues for future research.

Knee articular cartilage segmentation from MR images: A review

Articular cartilage (AC) is flexible, soft yet stiff tissue that can be visualized and interpreted using magnetic resonance imaging (MRI) for the assessment of knee osteoarthritis (OA). Segmentation of AC from MR images is a challenging task that has been investigated widely. The development of computational methods to segment AC is highly dependent on various image parameters, quality, tissue structure and acquisition protocol involved. This review focuses on the challenges faced during AC segmentation from MR images followed by the discussion on computational methods for semi/fully automated approaches whilst performances parameters and their significances have also been explored. Furthermore, hybrid approaches used to segment AC are reviewed. This review indicates that despite the challenges in AC segmentation, semi-automated method utilizing advanced computational methods such as active contour and clustering have shown significant accuracy. Fully automated AC segmentation methods have obtained moderate accuracy and shows suitability for extensive clinical studies whilst advanced methods are being investigated that has led to achieve significantly better sensitivity. In conclusion, this review indicates that research in AC segmentation from MR images is moving towards the development of fully automated methods using advanced multi-level, multi-data and multi-approach techniques to provide assistance in clinical studies.


Publication Years 1969-2018
Publication Count 1738
Citation Count 52264
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First Name Last Name Award
El Saddik Abdulmotaleb ACM Distinguished Member (2010)
ACM Senior Member (2008)
Serge Abiteboul ACM Fellows (2011)
Gregory Abowd ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics (2009)
ACM Fellows (2008)
ACM Senior Member (2008)
Pankaj Agarwal ACM Fellows (2002)
Charu Chandra Aggarwal ACM Fellows (2013)
Gail-Joon Ahn ACM Distinguished Member (2015)
ACM Senior Member (2007)
Alfred V Aho ACM Fellows (1996)
Narendra Ahuja ACM Fellows (1996)
Rajeev Alur ACM Fellows (2007)
Lorenzo Alvisi ACM Fellows (2010)
Nancy M Amato ACM Fellows (2015)
ACM Distinguished Member (2012)
Gregory R Andrews ACM Fellows (1998)
Owen Astrachan ACM Karl V. Karlstrom Outstanding Educator Award (2016)
ACM Distinguished Member (2013)
David F Bacon ACM Fellows (2009)
ACM Distinguished Member (2006)
Ricardo A Baeza-Yates ACM Fellows (2009)
Ruzena R Bajcsy ACM Distinguished Service Award (2003)
ACM AAAI Allen Newell Award (2001)
ACM Fellows (1996)
Victor Basili ACM Fellows (1997)
Benjamin B Bederson ACM Distinguished Member (2011)
Philip A Bernstein ACM Fellows (2001)
Fernando Berzal Galiano ACM Senior Member (2008)
Jose A Blakeley ACM Fellows (2009)
Michael W Blasgen ACM Fellows (1994)
ACM Programming Systems and Languages Paper Award (1982)
Guy Blelloch ACM Fellows (2011)
Daniel Bobrow ACM Fellows (1994)
ACM Software System Award (1992)
ACM Programming Systems and Languages Paper Award (1974)
Gregor V Bochmann ACM Fellows (1996)
Ronald J. Brachman ACM Fellows (1999)
Eric A. Brewer ACM Prize in Computing (2009)
ACM Fellows (2007)
Frederick Brooks ACM-IEEE CS Eckert-Mauchly Award (2004)
ACM A. M. Turing Award (1999)
ACM AAAI Allen Newell Award (1994)
ACM Fellows (1994)
ACM Distinguished Service Award (1987)
Peter Brusilovsky ACM Senior Member (2008)
Randal E Bryant ACM Fellows (2000)
ACM Paris Kanellakis Theory and Practice Award (1998)
Douglas C Burger ACM Fellows (2010)
ACM Distinguished Member (2008)
ACM Senior Member (2006)
Margaret Burnett ACM Fellows (2017)
ACM Distinguished Member (2015)
Rajkumar Buyya ACM Senior Member (2008)
Robert Cailliau ACM Software System Award (1995)
Murray S Campbell ACM Distinguished Member (2008)
Mario Cannataro ACM Senior Member (2009)
Jiannong Cao ACM Distinguished Member (2017)
Luca Cardelli ACM Fellows (2004)
Robert S Cartwright ACM Fellows (1998)
Godwin V Caruana ACM Senior Member (2013)
Carlos A. Castillo ACM Senior Member (2014)
Rick Cattell ACM Fellows (2012)
Stefano Ceri ACM Fellows (2013)
Donald Chamberlin ACM Fellows (1994)
ACM Software System Award (1988)
Ramalingam Chellappa ACM Fellows (2013)
Danny Z Chen ACM Distinguished Member (2014)
ACM Senior Member (2011)
Peter Chen ACM Fellows (2010)
Alok Choudhary ACM Fellows (2009)
Michael G Christel ACM Senior Member (2010)
David D Clark ACM Fellows (2001)
Charles L A Clarke ACM Distinguished Member (2015)
Edmund Clarke ACM A. M. Turing Award (2007)
ACM Fellows (1998)
ACM Paris Kanellakis Theory and Practice Award (1998)
Lori Clarke ACM Fellows (1998)
Ed Coffman ACM Distinguished Service Award (2004)
ACM Fellows (1994)
Outstanding Contribution to ACM Award (1987)
Tom Cormen ACM Distinguished Member (2009)
J D Couger ACM Fellows (1997)
Lorrie Faith Cranor ACM Fellows (2014)
ACM Senior Member (2006)
Fabio Crestani ACM Senior Member (2009)
Ron Cytron ACM Fellows (2010)
Ernesto Damiani ACM Distinguished Member (2008)
Susan B Davidson ACM Fellows (2001)
Umeshwar Dayal ACM Fellows (2008)
David DeWitt ACM Software System Award (2008)
ACM Fellows (1995)
Thomas L Dean ACM Fellows (2009)
Rina Dechter ACM Fellows (2013)
Alberto Del Bimbo ACM Distinguished Member (2016)
Richard Demillo ACM Fellows (2003)
Dorothy E Denning ACM Fellows (1995)
Peter J Denning Outstanding Contribution to ACM Award (1998)
ACM Karl V. Karlstrom Outstanding Educator Award (1996)
ACM Fellows (1994)
ACM Distinguished Service Award (1989)
ACM Programming Systems and Languages Paper Award (1971)
Jack Dennis ACM Fellows (1994)
ACM-IEEE CS Eckert-Mauchly Award (1984)
Narsingh Deo ACM Fellows (1996)
Steven J Derose ACM Senior Member (2007)
Thomas G Dietterich ACM Fellows (2002)
George Dodd ACM Fellows (1996)
Michel Dubois ACM Fellows (2005)
Matthew B Dwyer ACM Distinguished Member (2007)
David S Ebert ACM Senior Member (2009)
Faith Ellen ACM Fellows (2014)
Carla S. Ellis ACM Fellows (2010)
ACM Distinguished Member (2009)
Ahmed Elmagarmid ACM Fellows (2012)
ACM Distinguished Member (2009)
Joel Emer ACM-IEEE CS Eckert-Mauchly Award (2009)
ACM Fellows (2004)
Douglas Engelbart ACM A. M. Turing Award (1997)
ACM Software System Award (1990)
Martin Erwig ACM Senior Member (2008)
Vladimir Estivill-Castro ACM Senior Member (2015)
Christos Faloutsos ACM Fellows (2010)
Matthias Felleisen ACM Karl V. Karlstrom Outstanding Educator Award (2009)
ACM Fellows (2006)
Tse-Yun Feng ACM Fellows (1994)
Paulo Ferreira ACM Senior Member (2013)
Aaron Finerman ACM Fellows (1994)
Stephen Fink ACM Distinguished Member (2011)
ACM Senior Member (2008)
Michael J Flynn ACM Fellows (1994)
ACM-IEEE CS Eckert-Mauchly Award (1992)
Patrick J Flynn ACM Distinguished Member (2011)
ACM Senior Member (2009)
James D Foley ACM Fellows (1999)
Stephanie Forrest ACM AAAI Allen Newell Award (2011)
Edward Fox ACM Fellows (2017)
Michael Franz ACM Fellows (2015)
ACM Distinguished Member (2006)
Herbert Freeman ACM Fellows (1997)
Peter A Freeman ACM Fellows (2000)
Ophir Frieder ACM Fellows (2005)
Benjamin Fung ACM Senior Member (2013)
Zvi Galil ACM Fellows (1995)
Wen Gao ACM Fellows (2013)
Hector Garcia-Molina ACM Fellows (1997)
David Garlan ACM Fellows (2013)
ACM Senior Member (2008)
Minos Garofalakis ACM Distinguished Member (2011)
ACM Senior Member (2009)
Marco Aurelio Gerosa ACM Senior Member (2017)
Garth A Gibson ACM Fellows (2012)
ACM Doctoral Dissertation Award
Series Winner (1991)
David K Gifford ACM Fellows (2011)
Robert L Glass ACM Fellows (1999)
Adele Goldberg ACM Fellows (1994)
ACM Software System Award (1987)
Michael T Goodrich ACM Fellows (2009)
ACM Distinguished Member (2006)
Georg Gottlob ACM Fellows (2007)
Goetz Graefe ACM Software System Award (2008)
Susan L Graham ACM-IEEE CS Ken Kennedy Award (2011)
ACM Distinguished Service Award (2006)
ACM Fellows (1994)
Jim Gray ACM A. M. Turing Award (1998)
ACM Fellows (1994)
ACM Software System Award (1988)
ACM Programming Systems and Languages Paper Award (1982)
Cordell Green ACM Fellows (1994)
ACM Grace Murray Hopper Award (1985)
Robert L. Grossman ACM Fellows (2016)
Barbara J Grosz ACM AAAI Allen Newell Award (2008)
ACM Fellows (2003)
Jonathan Grudin ACM Fellows (2012)
Rachid Guerraoui ACM Fellows (2012)
Leonidas Guibas ACM AAAI Allen Newell Award (2007)
ACM Fellows (1999)
Gopal Krishna Gupta ACM Fellows (1998)
Rajiv Gupta ACM Fellows (2009)
Carl Gutwin ACM Distinguished Member (2015)
Brent T Hailpern ACM Fellows (2003)
Wendy Hall Outstanding Contribution to ACM Award (2014)
ACM Fellows (2010)
Lynda Hardman ACM Distinguished Member (2014)
ACM Senior Member (2013)
Robert Harper ACM Fellows (2005)
Juris Hartmanis ACM Distinguished Service Award (2013)
ACM Fellows (1994)
ACM A. M. Turing Award (1993)
Marti Hearst ACM Fellows (2013)
John L Hennessy ACM A. M. Turing Award (2017)
ACM-IEEE CS Eckert-Mauchly Award (2001)
ACM Fellows (1997)
Thomas A Henzinger ACM Fellows (2006)
John Hershberger ACM Fellows (2012)
C. Antony R. Hoare ACM A. M. Turing Award (1980)
ACM Programming Systems and Languages Paper Award (1973)
Lance Hoffman ACM Fellows (1995)
Gerard J. Holzmann ACM Fellows (2011)
ACM Paris Kanellakis Theory and Practice Award (2005)
ACM Software System Award (2001)
James Jay Horning ACM Fellows (1998)
Tiejun Huang ACM Senior Member (2013)
Jean-Pierre Hubaux ACM Fellows (2010)
Paul Hudak ACM Fellows (2003)
Richard Hull ACM Fellows (2007)
Ihab F. Ilyas ACM Distinguished Member (2014)
Yannis E Ioannidis ACM Fellows (2004)
Sitharama Iyengar ACM Fellows (2001)
Robert J.K. Jacob ACM Fellows (2016)
Anil K Jain ACM Fellows (2003)
Ramesh C Jain ACM Fellows (2003)
Matthias Jarke ACM Fellows (2013)
Christian S. Jensen ACM Fellows (2011)
Anita K Jones ACM Fellows (1996)
Cliff B Jones ACM Fellows (1995)
Neil Jones ACM Fellows (1998)
Michael I. Jordan ACM Fellows (2010)
ACM AAAI Allen Newell Award (2009)
Joaquim Jorge ACM Distinguished Member (2017)
ACM Senior Member (2007)
Randy H. Katz ACM Karl V. Karlstrom Outstanding Educator Award (1999)
ACM Fellows (1996)
Arie E Kaufman ACM Fellows (2009)
Lydia Kavraki ACM Athena Lecturer Award (2017)
ACM Fellows (2010)
ACM Grace Murray Hopper Award (2000)
Stephen T Kent ACM Fellows (1998)
Anne-Marie Kermarrec ACM Fellows (2016)
Angelos Dennis Keromytis ACM Fellows (2017)
ACM Distinguished Member (2012)
ACM Senior Member (2007)
Gregor Kiczales ACM Fellows (2012)
Won Kim ACM Distinguished Service Award (2001)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM Fellows (2013)
ACM Prize in Computing (2008)
Craig Knoblock ACM Fellows (2017)
ACM Distinguished Member (2008)
Donald E Knuth ACM Fellows (1994)
ACM Software System Award (1986)
ACM A. M. Turing Award (1974)
ACM Grace Murray Hopper Award (1971)
Daphne Koller ACM Prize in Computing (2007)
Fabio Kon ACM Distinguished Member (2016)
ACM Senior Member (2009)
Henry F Korth ACM Fellows (2000)
Donald Kossmann ACM Fellows (2010)
David Kotz ACM Senior Member (2006)
David J Kuck ACM-IEEE CS Ken Kennedy Award (2010)
ACM Fellows (1994)
ACM-IEEE CS Eckert-Mauchly Award (1993)
Vipin Kumar ACM Fellows (2005)
James Kurose ACM Fellows (2001)
Douglas S Lea ACM Fellows (2013)
Der-Tsai Lee ACM Fellows (1997)
Insup Lee ACM Fellows (2017)
Peter Lee ACM Fellows (2004)
K. Rustan M. Leino ACM Fellows (2016)
Abraham Lempel ACM Paris Kanellakis Theory and Practice Award (1997)
Maurizio Lenzerini ACM Fellows (2009)
George Lepouras ACM Senior Member (2008)
Nancy Leveson ACM AAAI Allen Newell Award (1999)
ACM Fellows (1995)
Roy Levin ACM Fellows (2008)
Ming C Lin ACM Fellows (2011)
Bruce Lindsay ACM Fellows (1994)
ACM Programming Systems and Languages Paper Award (1982)
Witold Litwin ACM Fellows (2001)
Yunhao Liu ACM Fellows (2015)
Yunhao Liu ACM Presidential Award (2013)
David B Lomet ACM Fellows (2002)
Javier Lopez ACM Senior Member (2012)
Raymond A Lorie ACM Fellows (2000)
ACM Software System Award (1988)
ACM Programming Systems and Languages Paper Award (1982)
David Maier ACM Fellows (1998)
Alessio Malizia ACM Senior Member (2014)
Igor Markov ACM Distinguished Member (2011)
ACM Senior Member (2007)
Steven R. McCanne ACM Doctoral Dissertation Award (1997)
Paul R McJones ACM Fellows (1994)
ACM Programming Systems and Languages Paper Award (1982)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Panagiotis Metaxas ACM Senior Member (2009)
Barton P Miller ACM Fellows (2001)
Dejan Milojicic ACM Distinguished Member (2008)
Jack Minker ACM AAAI Allen Newell Award (2005)
ACM Fellows (1994)
Outstanding Contribution to ACM Award (1985)
John C. Mitchell ACM Fellows (2008)
Edmundo Monteiro ACM Senior Member (2013)
Thomas Moran ACM Fellows (2003)
James H Morris ACM Fellows (2000)
Rajeev Motwani ACM Fellows (2007)
Trevor Mudge ACM Fellows (2016)
ACM-IEEE CS Eckert-Mauchly Award (2014)
Richard R Muntz ACM Fellows (1996)
S. Muthukrishnan ACM Fellows (2010)
Brad A Myers ACM Fellows (2005)
Klara Nahrstedt ACM Fellows (2012)
Shamkant Navathe ACM Fellows (2014)
J Nievergelt ACM Fellows (1995)
David S Notkin ACM Fellows (1998)
Martin Odersky ACM Fellows (2007)
Beng Chin Ooi ACM Fellows (2011)
Leon J Osterweil ACM Fellows (1998)
M. Tamer Ozsu ACM Fellows (2006)
Yale Patt ACM Fellows (2001)
ACM Karl V. Karlstrom Outstanding Educator Award (2000)
ACM-IEEE CS Eckert-Mauchly Award (1996)
David Patterson ACM A. M. Turing Award (2017)
ACM-IEEE CS Eckert-Mauchly Award (2008)
ACM Distinguished Service Award (2007)
ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1991)
Randy Pausch ACM Fellows (2007)
ACM Karl V. Karlstrom Outstanding Educator Award (2007)
Judea Pearl ACM Fellows (2015)
ACM A. M. Turing Award (2011)
ACM AAAI Allen Newell Award (2003)
Fernando Pereira ACM Fellows (2010)
Ron Perrott ACM Distinguished Service Award (2015)
ACM Fellows (1997)
Frank Pfenning ACM Fellows (2015)
ACM Senior Member (2007)
Gian Pietro Picco ACM Senior Member (2014)
Andre Platzer ACM Senior Member (2017)
ACM Doctoral Dissertation Award
Honorable Mention (2009)
Vaughan Ronald Pratt ACM Fellows (1997)
Thomas G Price ACM Programming Systems and Languages Paper Award (1982)
Roy F Rada ACM Fellows (1995)
Prabhakar Raghavan ACM Fellows (2001)
Raghu Ramakrishnan ACM Fellows (2001)
Krithivasan Ramamritham ACM Fellows (2001)
Brian Randell ACM Fellows (2008)
Rajeev Ramnarain Rastogi ACM Fellows (2012)
Raj Reddy ACM Fellows (2012)
ACM A. M. Turing Award (1994)
Daniel A Reed ACM Fellows (2003)
Edward M Reingold ACM Fellows (1996)
Aristides Requicha ACM Fellows (2007)
John C Reynolds ACM Fellows (2001)
ACM Programming Systems and Languages Paper Award (1971)
John Rice ACM Fellows (1996)
Dirk Riehle ACM Senior Member (2008)
Eric S Roberts ACM Karl V. Karlstrom Outstanding Educator Award (2012)
ACM Fellows (2007)
Joel Jose Rodrigues ACM Senior Member (2011)
Saul Rosen ACM Distinguished Service Award (1984)
Arnold Rosenberg ACM Fellows (1996)
David S. Rosenblum ACM Fellows (2010)
Azriel Rosenfeld ACM Fellows (1994)
Nick Roussopoulos ACM Fellows (2001)
Daniela Rus ACM Fellows (2014)
Barbara Gershon Ryder ACM Presidential Award (2008)
ACM Fellows (1998)
Rizos Sakellariou ACM Senior Member (2015)
Sherif Sakr ACM Senior Member (2017)
Ahmed Sameh ACM Fellows (1999)
Hanan Samet ACM Paris Kanellakis Theory and Practice Award (2011)
ACM Fellows (1996)
Ravinderpal S Sandhu ACM Fellows (2001)
Vijay Saraswat ACM Doctoral Dissertation Award (1989)
Mahadev Satyanarayanan ACM Software System Award (2016)
ACM Fellows (2002)
John E Savage ACM Fellows (1996)
Hans-Joerg Schek ACM Fellows (2001)
William L Scherlis ACM Senior Member (2009)
Fred B Schneider ACM Fellows (1995)
Robert Sedgewick ACM Fellows (1997)
Bart Selman ACM Fellows (2012)
Lui Sha ACM Fellows (2005)
Marc Shapiro ACM Senior Member (2009)
Micha Sharir ACM Fellows (1997)
Dennis E Shasha ACM Fellows (2013)
Alan C Shaw ACM Fellows (1997)
Mary M Shaw ACM Fellows (1996)
Prashant J Shenoy ACM Distinguished Member (2009)
ACM Senior Member (2006)
Stuart Shieber ACM Fellows (2014)
Kang Shin ACM Fellows (2001)
Frank Shipman ACM Distinguished Member (2009)
Ben Shneiderman ACM Fellows (1997)
Yoav Shoham ACM AAAI Allen Newell Award (2012)
ACM Fellows (2012)
Howard Siegel ACM Fellows (1998)
Joseph Sifakis ACM Fellows (2012)
ACM A. M. Turing Award (2007)
Abraham Silberschatz ACM Karl V. Karlstrom Outstanding Educator Award (1998)
ACM Fellows (1996)
Yogesh Simmhan ACM Senior Member (2013)
Kevin Skadron ACM Fellows (2015)
ACM Distinguished Member (2010)
ACM Senior Member (2007)
Jacob Slonim ACM Doctoral Dissertation Award (1980)
Alan Smith ACM Fellows (2001)
Richard T Snodgrass Outstanding Contribution to ACM Award (2004)
ACM Fellows (1999)
Leonel Sousa ACM Distinguished Member (2015)
ACM Senior Member (2009)
Diomidis Spinellis ACM Senior Member (2006)
Richard Stallman ACM Software System Award (2015)
ACM Grace Murray Hopper Award (1990)
John A Stankovic ACM Fellows (1996)
John Stasko ACM Distinguished Member (2011)
ACM Senior Member (2011)
Giancarlo Succi ACM Senior Member (2008)
S. Sudarshan ACM Fellows (2014)
Subhash Suri ACM Fellows (2010)
ACM Distinguished Member (2007)
Ivan Sutherland ACM Fellows (1994)
ACM Software System Award (1993)
ACM A. M. Turing Award (1988)
Jeffrey Victor Sutherland ACM Senior Member (2009)
Roberto Tamassia ACM Fellows (2012)
Andrew S Tanenbaum ACM Fellows (1996)
Andrew S. Tanenbaum ACM Karl V. Karlstrom Outstanding Educator Award (1994)
Robert W Taylor ACM Fellows (1994)
ACM Software System Award (1984)
Bent Thomsen ACM Senior Member (2016)
Irv Traiger ACM Fellows (1994)
ACM Software System Award (1988)
ACM Programming Systems and Languages Paper Award (1982)
ACM Programming Systems and Languages Paper Award (1971)
Allen Tucker ACM Fellows (1994)
Outstanding Contribution to ACM Award (1991)
Jeffrey D Ullman ACM Karl V. Karlstrom Outstanding Educator Award (1997)
ACM Fellows (1995)
Patrick Valduriez ACM Fellows (2012)
Mateo Valero ACM Distinguished Service Award (2012)
ACM-IEEE CS Eckert-Mauchly Award (2007)
ACM Fellows (2002)
Paul Van Oorschot ACM Fellows (2016)
Robbert Van Renesse ACM Fellows (2009)
C J Van Rijsbergen ACM Fellows (2003)
Moshe Y Vardi ACM Presidential Award (2017)
Outstanding Contribution to ACM Award (2009)
ACM Presidential Award (2008)
ACM Paris Kanellakis Theory and Practice Award (2005)
ACM Fellows (2000)
Sebastian Ventura ACM Senior Member (2013)
Jeffrey S Vetter ACM Distinguished Member (2012)
ACM Gordon Bell Prize
Performance (2010)
Philip Wadler ACM Fellows (2007)
David L Waltz ACM Fellows (1999)
Yu Wang ACM Senior Member (2011)
Anthony I Wasserman ACM Fellows (1996)
Peter Wegner ACM Distinguished Service Award (2000)
ACM Fellows (1995)
Bruce W Weide ACM Senior Member (2009)
Fred W Weingarten ACM Fellows (1996)
Edgar Weippl ACM Senior Member (2008)
Michael Wellman ACM Fellows (2005)
John R. White ACM Presidential Award (2015)
ACM Fellows (1995)
Outstanding Contribution to ACM Award (1994)
Jennifer Widom ACM Athena Lecturer Award (2015)
ACM Fellows (2005)
Gio Wiederhold ACM Fellows (1995)
Reinhard Wilhelm ACM Distinguished Service Award (2010)
ACM Fellows (2000)
John Wilkes ACM Fellows (2002)
Maurice V. Wilkes ACM Fellows (1994)
ACM-IEEE CS Eckert-Mauchly Award (1980)
ACM A. M. Turing Award (1967)
Jeannette M. Wing ACM Distinguished Service Award (2014)
ACM Fellows (1998)
Niklaus E Wirth ACM Fellows (1994)
ACM A. M. Turing Award (1984)
ACM Programming Systems and Languages Paper Award (1978)
Ian Witten ACM Fellows (1996)
Ouri Wolfson ACM Fellows (2001)
Pierre Wolper ACM Paris Kanellakis Theory and Practice Award (2005)
Jie Wu ACM Senior Member (2006)
William A Wulf ACM Policy Award (2017)
ACM Karl V. Karlstrom Outstanding Educator Award (2014)
ACM Distinguished Service Award (2011)
ACM Fellows (1994)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Bulent Yener ACM Senior Member (2013)
Philip S Yu ACM Fellows (1997)
Pamela Zave ACM Fellows (2001)
HongJiang Zhang ACM Fellows (2007)
Lixia Zhang ACM Fellows (2006)
Feng Zhao ACM Distinguished Member (2006)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)
Albert Zomaya ACM Distinguished Member (2006)
Stuart Zweben ACM Fellows (1998)
Outstanding Contribution to ACM Award (1997)
Andries van Dam ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1993)
Andries van Dam ACM Fellows (1994)
ACM Karl V. Karlstrom Outstanding Educator Award (1993)

First Name Last Name Paper Counts
Peter Denning 21
Adele Goldberg 18
Rajkumar Buyya 11
William Dorn 9
Anthony Wasserman 8
Sparsh Mittal 7
Rachid Guerraoui 6
Helen Ashman 6
Andries Van Dam 6
Peter Wegner 6
Jeffrey Vitter 5
Rance Cleaveland 5
Yannis Ioannidis 5
Azzedine Boukerche 5
Sameeullah Khan 5
Jacques Cohen 5
Sundaraja Iyengar 5
Andrew Tanenbaum 5
Elliott Organick 5
Corrado Priami 4
Gonzalo Navarro 4
Wendy Hall 4
Peter Lee 4
James Fry 4
David Lilja 4
James Peterson 4
Pierpaolo Degano 4
Albert Zomaya 4
Jayadev Misra 4
Peter Wegner 4
Fred Schneider 4
Jens Palsberg 4
Richard Muntz 4
Hanan Samet 4
Azriel Rosenfeld 4
Brad Myers 4
Narendra Ahuja 4
Tamer Özsu 4
Luca Cardelli 4
Scott Smolka 4
Jeffrey Buzen 4
Gregory Andrews 4
Mohamed Fayad 4
Roberto Gorrieri 3
Isabel Cruz 3
Joseph Pasquale 3
Ben Shneiderman 3
Boualem Benatallah 3
Michael Marcotty 3
David De Roure 3
Jacques Noyé 3
Pankaj Agarwal 3
Charles Consel 3
Nabil Adam 3
David Schmidt 3
Toby Teorey 3
Bruce Weide 3
Johnleslie King 3
Robert Rosin 3
Miroslaw Malek 3
Dick Bulterman 3
Avi Silberschatz 3
Antônio Loureiro 3
Ingrid Carlbom 3
Peter Larsen 3
Leslie Carr 3
Michael Bieber 3
Abdullah Gani 3
Goetz Graefe 3
Robyn Kozierok 3
Jonathan Grudin 3
Thomas Dean 3
Alberto Pettorossi 3
Patrick Cousot 3
Chris Hankin 3
Cristina Nita-Rotaru 3
Rodrigo Calheiros 3
Mark Harman 3
Werner Retschitzegger 3
Cinzia Cappiello 3
John Rice 3
Maurizio Proietti 3
John Stankovic 3
Fabrício Enembreck 3
Michael, Loui 3
Quan Sheng 3
Raghu Ramakrishnan 3
Garth Gibson 3
Jon Doyle 3
Emil Lupu 3
Mahadevan Ganapathi 3
Dirk Riehle 3
Udo Pooch 3
Marvin Zelkowitz 3
George Nagy 3
Alexander Thomasian 3
Kotagiri Ramamohanarao 3
Laurie Damianos 3
Alan Burns 3
Flemming Nielson 3
Frank Manola 3
Henry Ledgard 3
Roberto Tamassia 3
Hanne Nielson 3
Fátima Nunes 3
Zimu Zhou 3
Gabriel Muntean 3
Carlo Furia 3
Francky Catthoor 3
Randy Katz 3
Maarten Van Steen 3
Tiziana Catarci 2
John Mitchell 2
Serge Demeyer 2
Bruce Schachter 2
David Embley 2
Andrew Turpin 2
Philip Bernstein 2
Alípio Jorge 2
Dennis Severance 2
Ronald Loui 2
Philip Wadler 2
Joseph Paciorek 2
John Hennessy 2
Peter Triantafillou 2
Vipin Kumar 2
Gerti Kappel 2
Eduardo Miranda 2
Benjamin Bustos 2
Jörg Rothe 2
Elisha Sacks 2
Giuseppe Anastasi 2
Sergio Ilarri 2
Hafedh Mili 2
Matteo Rossi 2
William Kent 2
Carl Landwehr 2
Jochen Huber 2
Daniel Keim 2
Kevin Crowston 2
Ahmed Elmagarmid 2
Georgia Psychou 2
Tobias Noll 2
Jürg Nievergelt 2
Michael Mannino 2
Gilles Muller 2
Kim Bruce 2
Sarvnaz Karimi 2
Siba Mohanty 2
Levente Buttyán 2
Yimin Yang 2
Charu Aggarwal 2
Huan Liu 2
Narain Gehani 2
Ann Fitzsimmons 2
Tom Love 2
Erik Sandewall 2
Chunyan Miao 2
Maxime Crochemore 2
Gary Sockut 2
Frank Piessens 2
Philippe Navaux 2
Daniel Schwabe 2
John Roddick 2
Sabri Mahmoud 2
Jones Granatyr 2
Jean Hubaux 2
Dale Miller 2
Fabio Vitali 2
Umesh Bellur 2
Jeffrey Kurtz 2
Patrick Hall 2
Donald Knuth 2
Robert Sproull 2
Daniel Le Métayer 2
Lori Clarke 2
Karl Lieberherr 2
Catherine McGeoch 2
Fabio Crestani 2
Stephen Taylor 2
Manuel Wimmer 2
James Church 2
Philip Treleaven 2
Brian Randell 2
Anne Kermarrec 2
James Stanier 2
William Smyth 2
Rosemary Simpson 2
Mikel Luján 2
Mário Dantas 2
Davide Brugali 2
George Lueker 2
Edson Scalabrin 2
Niall Murray 2
Asuman Dogac 2
Abraham Silberschatz 2
C Hoare 2
Richard Millar 2
Max Mühlhäuser 2
David Brailsford 2
Kevin Martin 2
Cornelia Fermüller 2
Jonathan Walpole 2
Laura Lafave 2
Scott Thibault 2
André Schiper 2
Alexander Brodsky 2
Simon Kasif 2
Richard Snodgrass 2
Kenneth Neves 2
Sungho Kang 2
Klara Nahrstedt 2
H Li 2
James Foley 2
Ricardo Baeza-Yates 2
Tomáš Skopal 2
Rami Bahsoon 2
Rahim Tafazolli 2
Katia Sycara 2
Gustavo Rossi 2
Sajjad Madani 2
Yonathan Bard 2
Kenneth Kraemer 2
Zhiqiang Lin 2
Alistair Moffat 2
Gary Leavens 2
Theodor Nelson 2
Harri Oinas-Kukkonen 2
Brian Davison 2
Norman Meyrowitz 2
Luke Hornof 2
Axel Jantsch 2
George Cybenko 2
Yale Patt 2
Mehmet Akşit 2
Barbara Ryder 2
Dennis Volpano 2
John Wilkes 2
Pascal Van Hentenryck 2
Sushil Jajodia 2
Albert Bifet 2
Imran Ghani 2
Bruce Leverett 2
Matthias Jarke 2
Andy Cockburn 2
Andrew Newell 2
Jason Alexander 2
Shuching Chen 2
Ilia Pietri 2
Jiliang Tang 2
Eduardofreire Nakamura 2
Olivier Danvy 2
Alan Feuer 2
Raymond Yeh 2
Tom Van Cutsem 2
Giovanni Romano 2
Inderveer Chana 2
Christos Faloutsos 2
Sitharama Iyengar 2
Andreas Bulling 2
Alexander Gluhak 2
Justin Zobel 2
Natarajan Shankar 2
Sivan Toledo 2
Chunqiu Zeng 2
Andrzej Jajszczyk 2
Michael Franz 2
Julia Lawall 2
Ivona Brandić 2
Michael Wellman 2
Edmund Clarke 2
Jeannette Wing 2
Krithi Ramamritham 2
Ronald Brachman 2
Edward Reingold 2
Marjan Mernik 2
Wubai Zhou 2
Yue Huang 2
Lynette Hirschman 2
Robbert Van Renesse 2
John Hatcliff 2
João Gama 2
Charles Hoare 2
Howard Wactlar 2
Bernhard Steffen 2
Douglas Schmidt 2
Francis Sullivan 2
Steve Bryson 2
Robert Harper 2
Paul Hudak 2
Subhash Suri 2
Ugo Montanari 2
Vijay Saraswat 2
Joseph Sifakis 2
Lionel Ni 2
Niki Pissinou 2
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Subject index 1
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