| 1. |
- Ahmadilivani, M. H., et al.
(author)
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A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
- 2024
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In: ACM Computing Surveys. - : ASSOC COMPUTING MACHINERY. - 0360-0300 .- 1557-7341. ; 56:6
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Journal article (peer-reviewed)abstract
- Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the past decade, rapid advances in ML have presented Deep Neural Networks (DNNs) consisting of a large number of neurons and layers. DNN Hardware Accelerators (DHAs) are leveraged to deploy DNNs in the target applications. Safety-critical applications, where hardware faults/errors would result in catastrophic consequences, also benefit from DHAs. Therefore, the reliability of DNNs is an essential subject of research. In recent years, several studies have been published accordingly to assess the reliability of DNNs. In this regard, various reliability assessment methods have been proposed on a variety of platforms and applications. Hence, there is a need to summarize the state-of-the-art to identify the gaps in the study of the reliability of DNNs. In this work, we conduct a Systematic Literature Review (SLR) on the reliability assessment methods of DNNs to collect relevant research works as much as possible, present a categorization of them, and address the open challenges. Through this SLR, three kinds of methods for reliability assessment of DNNs are identified, including Fault Injection (FI), Analytical, and Hybrid methods. Since the majority of works assess the DNN reliability by FI, we characterize different approaches and platforms of the FI method comprehensively. Moreover, Analytical and Hybrid methods are propounded. Thus, different reliability assessment methods for DNNs have been elaborated on their conducted DNN platforms and reliability evaluation metrics. Finally, we highlight the advantages and disadvantages of the identified methods and address the open challenges in the research area. We have concluded that Analytical and Hybrid methods are light-weight yet sufficiently accurate and have the potential to be extended in future research and to be utilized in establishing novel DNN reliability assessment frameworks.
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| 2. |
- Bae, Juhee, et al.
(author)
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Interactive clustering : A comprehensive review
- 2020
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In: ACM Computing Surveys. - New York, NY : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 53:1
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Journal article (peer-reviewed)abstract
- In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.
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| 3. |
- Batalla, Jordi Mongay, et al.
(author)
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Secure Smart Homes : Opportunities and Challenges
- 2017
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In: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 50:5, s. p75:1-75:32
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Journal article (peer-reviewed)abstract
- The Smart Home concept integrates smart applications in the daily human life. In recent years, Smart Homes have increased security and management challenges due to the low capacity of small sensors, multiple connectivity to the Internet for efficient applications (use of big data and cloud computing) and heterogeneity of home systems, which require inexpert users to configure devices and micro-systems. This article presents current security and management approaches in Smart Homes and shows the good practices imposed on the market for developing secure systems in houses. At last, we propose future solutions for efficiently and securely managing the Smart Homes
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| 4. |
- Budzisz, Łukasz, et al.
(author)
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A taxonomy and survey of SCTP research
- 2012
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In: ACM Computing Surveys. - New York : ACM Press. - 0360-0300 .- 1557-7341. ; 44:4, s. -36
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Journal article (peer-reviewed)abstract
- The Stream Control Transmission Protocol (SCTP) is a relatively recent general-purpose transport layer protocol for IP networks that has been introduced as a complement to the well-established TCP and UDP transport protocols. Although initially conceived for the transport of PSTN signaling messages over IP networks, the introduction of key features in SCTP, such as multihoming and multistreaming, has spurred considerable research interest surrounding SCTP and its applicability to different networking scenarios. This article aims to provide a detailed survey of one of these new features—multihoming—which, as it is shown, is the subject of evaluation in more than half of all published SCTP-related articles. To this end, the article first summarizes and organizes SCTP-related research conducted so far by developing a four-dimensional taxonomy reflecting the (1) protocol feature examined, (2) application area, (3) network environment, and (4) study approach. Over 430 SCTP-related publications have been analyzed and classified according to the proposed taxonomy. As a result, a clear perspective on this research area in the decade since the first protocol standardization in 2000 is given, covering both current and future research trends. On continuation, a detailed survey of the SCTP multihoming feature is provided, examining possible applications of multihoming, such as robustness, handover support, and loadsharing.
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| 5. |
- Castañeda Lozano, Roberto, 1986-, et al.
(author)
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Survey on Combinatorial Register Allocation and Instruction Scheduling
- 2018
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In: ACM Computing Surveys. - : ACM Press. - 0360-0300 .- 1557-7341.
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Journal article (peer-reviewed)abstract
- Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time.This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization.
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| 6. |
- Castañedalozano, Roberto, et al.
(author)
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Survey on combinatorial register allocation and instruction scheduling
- 2019
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In: ACM Computing Surveys. - : Association for Computing Machinery. - 0360-0300 .- 1557-7341. ; 52:3
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Journal article (peer-reviewed)abstract
- Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the past three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This article provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization. .
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| 7. |
- Ciccozzi, Federico, 1983-, et al.
(author)
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A Comprehensive Exploration of Languages for Parallel Computing
- 2023
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In: ACM Computing Surveys. - : ASSOC COMPUTING MACHINERY. - 0360-0300 .- 1557-7341. ; 55:2
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Journal article (peer-reviewed)abstract
- Software-intensive systems in most domains, from autonomous vehicles to health, are becoming predominantly parallel to efficiently manage large amount of data in short (even real-) time. There is an incredibly rich literature on languages for parallel computing, thus it is difficult for researchers and practitioners, even experienced in this very field, to get a grasp on them. With this work we provide a comprehensive, structured, and detailed snapshot of documented research on those languages to identify trends, technical characteristics, open challenges, and research directions. In this article, we report on planning, execution, and results of our systematic peer-reviewed as well as grey literature review, which aimed at providing such a snapshot by analysing 225 studies.
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| 8. |
- Dai, Hong-Ning, et al.
(author)
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Big Data Analytics for Large-scale Wireless Networks : Challenges and Opportunities
- 2019
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In: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 52:5
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Journal article (peer-reviewed)abstract
- The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.
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| 9. |
- De Boer, Frank, et al.
(author)
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A Survey of Active Object Languages
- 2017
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In: ACM Computing Surveys. - : ASSOC COMPUTING MACHINERY. - 0360-0300 .- 1557-7341. ; 50:5
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Journal article (peer-reviewed)abstract
- To program parallel systems efficiently and easily, a wide range of programming models have been proposed, eachwith different choices concerning synchronization and communication between parallel entities. Among them, the actor model is based on loosely coupled parallel entities that communicate by means of asynchronous messages and mailboxes. Some actor languages provide a strong integration with object-oriented concepts; these are often called active object languages. This article reviews four major actor and active object languages and compares them according to carefully chosen dimensions that cover central aspects of the programming paradigms and their implementation.
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| 10. |
- Dou, Mingliang, et al.
(author)
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Drug-drug interaction relation extraction based on deep learning : A review
- 2024
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In: ACM Computing Surveys. - New York, NY : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 56:6, s. 1-33
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Journal article (peer-reviewed)abstract
- Drug-drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public health. Therefore, using deep learning technology to extract DDI from scientific literature has become a valuable research direction to researchers. In existing DDI datasets, the number of positive instances is relatively small. This makes it difficult for existing deep learning models to obtain sufficient feature information directly from text data. Therefore, existing deep learning models mainly rely on multiple feature supplementation methods to collect sufficient feature information from different types of data. In this study, the general process of DDI relation extraction based on deep learning is introduced first for comprehensive analysis. Next, we summarize the various feature supplement methods and analyze their merits and demerits. We then review the state-of-the-art literature related to DDI extraction from the deep neural network perspective. Finally, all the feature supplement methods are compared, and some suggestions are given to approach the current problems and future research directions. The purpose of this article is to give researchers a more complete understanding of the feature complementation methods used in DDI extraction to be able to rapidly design and implement custom DDI relation extraction methods. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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