Generation Computer Systems Research Articles

TOPCOAT: towards practical two-party Crystals-Dilithium

The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main research focus has been towards protocols constructed for various variants of Crystals-Dilithium, future NIST digital signature standard known as ML-DSA. In this work, we propose TOPCOAT, a two-party lattice-based signature algorithm that embodies Dilithium’s compression techniques. The aforesaid result is achieved by introducing a new hinting mechanism that allows parties to collaboratively calculate HighBits\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {HighBits}$$\\end{document}. Our hinting mechanism allows public key compression similar to Dilithium. Additionally, we suggest an optimization technique to minimize number of restarts both parties need to produce a valid signature. Our approach allows to produce ≈10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 10$$\\end{document} KB signatures within 3 rounds of communication. We prove security of our scheme under MLWE and MSIS assumptions in ROM, and provide implementation of our proposed scheme. As additional contribution, we present vulnerabilities and inconsistencies found in Liu et al. work (Future Generation Computer Systems 2023) which aimed to construct distributed lattice-based signature protocol.

An Efficient Checkpoint Strategy for Federated Learning on Heterogeneous Fault-Prone Nodes

Federated learning (FL) is a distributed machine learning method in which client nodes train deep neural network models locally using their own training data and then send that trained model to a server, which then aggregates all of the trained models into a globally trained model. This protects personal information while enabling machine learning with vast amounts of data through parallel learning. Nodes that train local models are typically mobile or edge devices from which data can be easily obtained. These devices typically run on batteries and use wireless communication, which limits their power, making their computing performance and reliability significantly lower than that of high-performance computing servers. Therefore, training takes a long time, and if something goes wrong, the client may have to start training again from the beginning. If this happens frequently, the training of the global model may slow down and the final performance may deteriorate. In a general computing system, a checkpointing method can be used to solve this problem, but applying an existing checkpointing method to FL may result in excessive overheads. This paper proposes a new FL method for situations with many fault-prone nodes that efficiently utilizes checkpoints.

Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows.Graphical abstract

Juice Jacking: Security Issues and Improvements in USB Technology

For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks.

Modeling Cognitive System with Applied Machine learning in Additive Manufacturing using Fifth Generation Computer Systems

Data mining is known as data, which promotes the growth of knowledge discovery. It is the process of analyzing descriptive data from divergent perspectives and summarizing it into valuable information, which is high-level music processing out of which a machine intends to decipher the Raaga of a frequency or the pitch of the music. One of the ways to approach the task is by comparing selected music features from the spectrum and a Raaga database. Recognizing emotion from music has become one of the active research themes in image processing and applications based on human-computer interaction. This research conducts an experimental study on recognizing facial emotions. The flow of the emotion recognition system includes the basic process in the singular value decomposition system. These include music acquisition, pre-processing of a spectrum, feature detection, feature extraction, classification, and when the emotions are classified, the system assigns the particular user music according to his emotion. The proposed system focuses on live images taken from the music database. This research aims to develop an automatic music recognition system for innovative manufacturing through the additive manufacturing route. The emotions considered for the experiments include happiness, Sadness, Surprise, Fear, Disgust, and Anger that are universally accepted. This paper overviews the progress of applying Additive manufacturing in Applied Machine learning which sustains the capability of disruptive digital manufacturing.

Most Cited Works on Cloud Computing: The ‘Citation Classics’ as Viewed through Dimensions.ai

ABSTRACT Cloud computing is an interdisciplinary field which has witnessed exponential growth in recent years and there is a need to consolidate the research efforts. This study used citation analysis method to identify most cited papers which have achieved the status of citation classics. It is the first study using Dimensions.ai database to identify classics in cloud computing literature. Using a search strategy data was extracted from Dimensions database from 2010 and 2020. Finally 96 papers were identified as citation classics which were cited with at least 400 citations. Data was finally analyzed through Biblioshiny package of R Studio. It was found that Future Generation Computer Systems, IEEE Communication Survey and Tutorials, IEEE Transaction on Parallel and Distributed Systems, Computer and IEEE Access are the key journals contributing to classics. Most papers were contributed from China and USA. R. Buyya is the prolific author who has contributed nine citation classics.

Pattern detection in cloud computing: Bibliometric mapping of publications in the field from past to present

The use of cloud computing has become widespread with the rapid development of technology. In this context, it is important to make a bibliometric analysis to identify developments in cloud computing research and to guide future research. For bibliometric analysis of cloud computing research, this study examines 48692 studies scanned in the Web of Science database as of July 2021, without year and index limits. VOSviewer software was used for bibliometric analysis. Bibliometric analysis showed that the research in the field of cloud computing increased exponentially until 2016, but after 2016, the research decreased and turned to more specific areas. China, USA, and India, respectively, are the countries that contributed the most to the cloud computing literature. According to the keyword co-occurrence analysis, the prominent keywords are fog computing, mobile cloud computing, data security, and trust. In addition, edge computing and blockchain stand out as trending topics in recent years. Future Generation Computer Systems and IEEE Access are the journals that contribute the most to the field. Taken as a whole, the findings of the study describe the field in general and provide important, enlightening, and stimulating information for researchers who are or will be interested in cloud computing.

An Elasticity Framework for Distributed Message Queuing Telemetry Transport Brokers

An Elasticity Framework for Distributed Message Queuing Telemetry Transport Brokers

Blockchain: a framework for membership and access

Blockchain: a framework for membership and access

Future Generation Computer Systems: Publisher’s Note: In recognition of Prof. Peter Sloot on his retirement as Editor-in-Chief

Future Generation Computer Systems: Publisher’s Note: In recognition of Prof. Peter Sloot on his retirement as Editor-in-Chief

Research Progress and Development Trend of Social Media Big Data (SMBD): Knowledge Mapping Analysis Based on CiteSpace

Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took Web of Science (WoS) core collection as the data source, and used traditional statistical methods and CiteSpace software to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field. The results showed that: (1) More and more attention has been paid to SMBD research in academia, and the number of journals published has been increased in recent years, mainly in subjects such as Computer Science Engineering and Telecommunications. The results were published primarily in IEEE Access Sustainability and Future Generation Computer Systems the International Journal of eScience and so on; (2) In terms of contributions, China, the United States, the United Kingdom and other countries (regions) have published the most papers in SMBD, high-yield institutions also mainly from these countries (regions). There were already some excellent teams in the field, such as the Wanggen Wan team at Shanghai University and Haoran Xie team from City University of Hong Kong; (3) we studied the hotspots of SMBD in recent years, and realized the summary of the frontier of SMBD based on the keywords and co-citation literature, including the deep excavation and construction of social media technology, the reflection and concerns about the rapid development of social media, and the role of SMBD in solving human social development problems. These studies could provide values and references for SMBD researchers to understand the research status, hotspots and trends in this field.

“AI for Social Good” and the First AI Arms Race Lessons from Japan’s Fifth Generation Computer Systems (FGCS) Project

“AI for Social Good” and the First AI Arms Race Lessons from Japan’s Fifth Generation Computer Systems (FGCS) Project

Artificial Intelligence and Japan’s Fifth Generation

In 1982, Japan launched its Fifth Generation Computer Systems project (FGCS), designed to develop intelligent software that would run on novel computer hardware. As the first national, large-scale artificial intelligence (AI) research and development (R&D) project to be free from military influence and corporate profit motives, the FGCS was open, international, and oriented around public goods. Although the FGCS did not plan any commercialized technologies, many American computer experts portrayed it as an economic threat to U.S. dominance in computing and the global economy—and policymakers around the developed world believed them and funded AI projects of their own. Later, however, the FGCS was remembered as a failure. Why? This article recasts the FGCS as an interstice in the shift from a state-funded regime of American science organization to the neoliberal privatized regime of R&D now ascendant around the world. By exploring how notions of economic competitiveness and national security shaped R&D, this article reveals AI to be a product of contingent choices by multiple actors—nation-states, government bureaucracies, corporations, and individuals—rather than the outcome of deterministic technological forces.

RETRACTED ARTICLE: Sustainable cost and energy consumption analysis for cloud data centers

The article titled “Sustainable cost and energy consumption analysis for cloud data centers” published in Frontiers of Computer Science is retracted at the request of the author and Editor-in-Chief as it constitutes plagiarism. Part of its content, mainly in Sections 3–8 of the paper including text, tables and figures, is similar to another published article: “An economic and energyaware analysis of the viability of outsourcing cluster computing to a cloud” by Carlos de Alfonso, Miguel Caballer, Fernando Alvarruiz, German Molto, Future Generation Computer Systems, 29 (2013) 704–712, Elsevier.

Guest Editorial: Energy Internet and its Related Technology Research

Guest Editorial: Energy Internet and its Related Technology Research

Cryptoanalysis on ‘A round-optimal lattice-based blind signature scheme for cloud services’

In this note, we review the article published by Zhu et al. in Future Generation Computer Systems in 2017. We show that their construction of a blind signature does not hold the correctness requirement or the blindness requirement.

Two-Phase Flow in a Plant for Production of Cavitation Diamonds

Consideration is given to the regimes of water flow in a hydrodynamic-flow plant with cavitation and hydraulic shock. Calculations are carried out by the CFX hydrodynamic unit of the general physical computational system ANSYS and are compared with experimentally measured values of pressure; flow velocities are also computed. It is shown that the hydraulic shock ensures a growth of the order of 5 atm in the external pressure on a vapor bubble. The possibility of the nanodiamond forming in flows of carbon-containing fluids in such regimes is discussed.

Grid Computing Research from India: A Bibliometric Assessment of Publications during 2008–2017

The paper examines quality and quantity of scientific research in India as reflected in publications output on grid computing on a series of bibliometric indicators. Data were extracted from Scopus database consisting of 1340 publications in grid computing research from India in 10 years during 2008-17 citation impact of 4.33 citations per paper and international collaborative publication share of 12.16%. The paper profiles global publication output and share of 10 most productive countries in grid computing research, 15 most productive Indian organizations and 15 most productive authors on a series of indicators including publications output, number of citations, the relative citation index, citations per paper, h-index and share of international collaborative papers during 2008-17. Computer science, among top 3 subjects, accounted for the largest publication share (90.15%), followed by engineering (26.79%) and mathematics (13.66%). The 15 most leading organizations and authors together contributed 31.57% and 14.25% as their share of Indian publication output and 44.93% and 16.17% as their share of Indian citation output respectively during 2008-17. The most productive Indian organizations were Anna University, Chennai (74 papers) and Thapar University, Patiala (48 papers) The most productive authors were I. Chana of Thapar University, Patiala (21 papers) and N. Mukherjee of Jadavpur University, Kolkata (18 papers). The 15 most productive journals contributed 43.92% share to the Indian journal publication output during 2008-17. The most productive journals were Applied Engineering Research (16 publications), Journal of Grid Computing (14 publications) and Future Generation Computer Systems (13 publications)

Segment and Conflict Aware Page Allocation and Migration in DRAM-PCM Hybrid Main Memory

Phase change memory (PCM), given its nonvolatility, potential high density, and low standby power, is a promising candidate to be used as main memory in next generation computer systems. However, to hide its shortcomings of limited endurance and slow write performance, state-of-the-art solutions tend to construct a dynamic RAM (DRAM)-PCM hybrid memory and place write-intensive pages in DRAM. While existing optimizations to this hybrid architecture focus on tuning DRAM configurations to reduce the number of write operations to PCM, this paper explores the interactions between DRAM and PCM to improve both the performance and the endurance of a DRAM-PCM hybrid main memory. Specifically, it exploits the flexibility of mapping virtual pages to physical pages, and develops a proactive strategy to allocate pages taking both program segments and DRAM conflict misses into consideration, thus distributing those heavily written pages across different DRAM sets. Meanwhile, a lifetime-aware DRAM replacement algorithm and a conflict-aware page remapping strategy are proposed to further reduce DRAM misses and PCM writes. Experiments confirm that the proposed techniques are able to improve average memory hit time and reduce maximum PCM write counts thus enhancing both performance and lifetime of a DRAM-PCM hybrid main memory.

Development of Gesture-based Commands for Natural User Interfaces

Human-computer interfaces are changing to meet the evolving needs of users and overcome limitations of previous generations of computer systems. The current state of computers consists largely of graphical user interfaces (GUI) that incorporate windows, icons, menus, and pointers (WIMPs) as visual representations of computer interactions controlled via user input on a mouse and keyboard. Although this model of interface has dominated human-computer interaction for decades, WIMPs require an extra step between the user’s intent and the computer action, imposing both limitations on the interaction and introducing cognitive demands (van Dam, 1997). Alternatively, natural user interfaces (NUI) employ input methods such as speech, touch, and gesture commands. With NUIs, users can interact directly with the computer without using an intermediary device (e.g., mouse, keyboard). Using the body as an input device may be more “natural” because it allows the user to apply existing knowledge of how to interact with the world (Roupé, Bosch-Sijtsema, & Johansson, 2014). To utilize the potential of natural interfaces, research must first determine what interactions can be considered natural. For the purpose of this paper, we focus on the naturalness of gesture-based interfaces. The purpose of this study was to determine how people perform natural gesture-based computer actions. To answer this question, we first narrowed down potential gestures that would be considered natural for an action. In a previous study, participants ( n=17) were asked how they would gesture to interact with a computer to complete a series of actions. After narrowing down the potential natural gestures by calculating the most frequently performed gestures for each action, we asked participants ( n=188) to rate the naturalness of the gestures in the current study. Participants each watched 26 videos of gestures (3-5 seconds each) and were asked how natural or arbitrary they interpreted each gesture for the series of computer commands (e.g., move object left, shrink object, select object, etc.). The gestures in these videos included the 17 gestures that were most often performed in the previous study in which participants were asked what gesture they would naturally use to complete the computer actions. Nine gestures were also included that were created arbitrarily to act as a comparison to the natural gestures. By analyzing the ratings on a continuum from “Completely Arbitrary” to “Completely Natural,” we found that the natural gestures people produced in the first study were also interpreted as the intended action by this separate sample of participants. All the gestures that were rated as either “Mostly Natural” or “Completely Natural” by participants corresponded to how the object manipulation would be performed physically. For example, the gesture video that depicts a fist closing was rated as “natural” by participants for the action of “selecting an object.” All of the gestures that were created arbitrarily were interpreted as “arbitrary” when they did not correspond to the physical action. Determining how people naturally gesture computer commands and how people interpret those gestures is useful because it can inform the development of NUIs and contributes to the literature on what makes gestures seem “natural.”