Abstract

Newton’s laws of motion perfectly explain or approximate physical phenomena in our everyday life. Are there any laws that explain or approximate technology’s rise or fall? After reviewing thirteen information technologies that succeeded, this article concludes three laws of technology and derives five corollaries to explain or approximate the rise or fall of technology. Three laws are the laws of technology inertia, technology change force, and technology action and reaction. Five corollaries are the corollaries of measurement of technology change force, technology breakthrough, technology monopoly, technology openness, and technology business opportunity. I present how to use the laws and the corollaries to analyze an emerging technology—the open-source RISC-V processor. Also, I elaborate on benchmarks’ role in applying those laws.

Newton’s laws of motion perfectly explain or approximate physical phenomena in our everyday life. Are there any laws that explain or approximate technology’s rise or fall? After reviewing thirteen information technologies that succeeded, this article concludes three laws of technology and derives five corollaries to explain or approximate the rise or fall of technology. Three laws are the laws of technology inertia, technology change force, and technology action and reaction. Five corollaries are the corollaries of measurement of technology change force, technology breakthrough, technology monopoly, technology openness, and technology business opportunity. I present how to use the laws and the corollaries to analyze an emerging technology—the open-source RISC-V processor. Also, I elaborate on benchmarks’ role in applying those laws.

Abstract

The global community faces many pressing and uncertain challenges like pandemics and global climate change. Information technology (IT) infrastructure has become the enabler to addressing those challenges. Unfortunately, IT decoupling has distracted and weakened the international community’s ability to handle those challenges. This article initiates an open-source computer system (OSCS) initiative to tackle the challenges of IT decoupling. The OSCS movement is where open-source software converges with open-source hardware. Its essential is to utilize the inherent characteristics of a class of representative workloads and propose innovative abstraction and methodology to co-explore the software and hardware design spaces of high-end computer systems, attaining peak performance, security, and other fundamental dimensions. I discuss its four challenges, including the system complexity, the tradeoff between universal and ideal systems, guaranteeing quality of computation results and performance under different conditions, e.g., best-case, worst-case, or average-case, and balancing legal, patent, and license issues. Inspired by the philosophy of building large systems out of smaller functions, I propose the funclet abstraction and methodology to tackle the first challenge. The funclet abstraction is a well-defined, evolvable, reusable, independently deployable, and testable functionality with modest complexity. Each funclet interoperates with other funclets through standard bus interfaces or interconnections. Four funclet building blocks: chiplet, HWlet, envlet, and servlet at the chip, hardware, environment management, and service layers form the four-layer funclet architecture. The advantages of the funclet abstraction and architecture are discussed. The project’s website is publicly available from https://www.opensourcecomputer.org or https://www.computercouncil.org.

The global community faces many pressing and uncertain challenges like pandemics and global climate change. Information technology (IT) infrastructure has become the enabler to addressing those challenges. Unfortunately, IT decoupling has distracted and weakened the international community’s ability to handle those challenges. This article initiates an open-source computer system (OSCS) initiative to tackle the challenges of IT decoupling. The OSCS movement is where open-source software converges with open-source hardware. Its essential is to utilize the inherent characteristics of a class of representative workloads and propose innovative abstraction and methodology to co-explore the software and hardware design spaces of high-end computer systems, attaining peak performance, security, and other fundamental dimensions. I discuss its four challenges, including the system complexity, the tradeoff between universal and ideal systems, guaranteeing quality of computation results and performance under different conditions, e.g., best-case, worst-case, or average-case, and balancing legal, patent, and license issues. Inspired by the philosophy of building large systems out of smaller functions, I propose the funclet abstraction and methodology to tackle the first challenge. The funclet abstraction is a well-defined, evolvable, reusable, independently deployable, and testable functionality with modest complexity. Each funclet interoperates with other funclets through standard bus interfaces or interconnections. Four funclet building blocks: chiplet, HWlet, envlet, and servlet at the chip, hardware, environment management, and service layers form the four-layer funclet architecture. The advantages of the funclet abstraction and architecture are discussed. The project’s website is publicly available from https://www.opensourcecomputer.org or https://www.computercouncil.org.

Abstract

With the rapid development of distributed transactional databases in recent years, there is an urgent need for fair performance evaluation and comparison. Though there are various open-source benchmarks built for databases, it is lack of a comprehensive study about the applicability for distributed transactional databases. This paper presents a review of the state-of-art benchmarks with respect to distributed transactional databases. We first summarize the representative architectures of distributed transactional databases and then provide an overview about the chock points in distributed transactional databases. Then, we classify the classic transactional benchmarks based on their characteristics and design purposes. Finally, we review these benchmarks from schema and data definition, workload generation, and evaluation and metrics to check whether they are still applicable to distributed transactional databases with respect to the chock points. This paper exposes a potential research direction to motivate future benchmark designs in the area of distributed transactional databases.

With the rapid development of distributed transactional databases in recent years, there is an urgent need for fair performance evaluation and comparison. Though there are various open-source benchmarks built for databases, it is lack of a comprehensive study about the applicability for distributed transactional databases. This paper presents a review of the state-of-art benchmarks with respect to distributed transactional databases. We first summarize the representative architectures of distributed transactional databases and then provide an overview about the chock points in distributed transactional databases. Then, we classify the classic transactional benchmarks based on their characteristics and design purposes. Finally, we review these benchmarks from schema and data definition, workload generation, and evaluation and metrics to check whether they are still applicable to distributed transactional databases with respect to the chock points. This paper exposes a potential research direction to motivate future benchmark designs in the area of distributed transactional databases.

Abstract

The use of social media has accelerated information sharing and instantaneous communications. The low barrier to enter social media enables more users to participate and makes them stay engaged longer, while incentivizing individuals with a hidden agenda to use disinformation to manipulate information and influence opinions. Disinformation, such as fake news, hoaxes, and conspiracy theories, has increasingly been weaponized to divide people and create detrimental societal effects. Therefore, it is imperative to understand disinformation and systematically investigate how we can improve resistance against it, taking into account the tension between the need for information and the need for security and protection against disinformation. In this survey, we look into the concepts, methods, and recent advancements of detecting disinformation from a computational perspective. We will also discuss open issues and future research directions for combating disinformation on social media.

The use of social media has accelerated information sharing and instantaneous communications. The low barrier to enter social media enables more users to participate and makes them stay engaged longer, while incentivizing individuals with a hidden agenda to use disinformation to manipulate information and influence opinions. Disinformation, such as fake news, hoaxes, and conspiracy theories, has increasingly been weaponized to divide people and create detrimental societal effects. Therefore, it is imperative to understand disinformation and systematically investigate how we can improve resistance against it, taking into account the tension between the need for information and the need for security and protection against disinformation. In this survey, we look into the concepts, methods, and recent advancements of detecting disinformation from a computational perspective. We will also discuss open issues and future research directions for combating disinformation on social media.

Abstract

Cyber-Physical Systems (CPS) refer to systems where some intelligence is embedded into devices that interact with their environment. Using wireless technology in such systems is desirable for better flexibility, improved maintainability, and cost reduction, among others. Moreover, CPS applications often specify deadlines; that is, maximal tolerable delays between the execution of distributed tasks. Systems that guarantee to meet such deadlines are called real-time systems. In the past few years, a technique known as synchronous transmissions (ST) has been shown to enable reliable and energy efficient communication, which is promising for the design of real-time wireless CPS. We identify at least three issues that limit the adoption of ST in this domain: (i) ST is difficult to use due to stringent time synchronization requirements (in the order of ). There is a lack of tools to facilitate the implementation of ST by CPS engineers, which are often not wireless communication experts. (ii) There are only few examples showcasing the use of ST for CPS applications and academic works based on ST tend to focus on communication rather than applications. Convincing proof-of-concept CPS applications are missing. (iii) The inherent variability of the wireless environment makes performance evaluation challenging. The lack of an agreed-upon methodology hinders experiment reproducibility and limits the confidence in the performance claims. This paper synthesizes recent advances what address these three problems, thereby enabling significant progress for future applications of low-power wireless technology in real-time CPS.

Cyber-Physical Systems (CPS) refer to systems where some intelligence is embedded into devices that interact with their environment. Using wireless technology in such systems is desirable for better flexibility, improved maintainability, and cost reduction, among others. Moreover, CPS applications often specify deadlines; that is, maximal tolerable delays between the execution of distributed tasks. Systems that guarantee to meet such deadlines are called real-time systems. In the past few years, a technique known as synchronous transmissions (ST) has been shown to enable reliable and energy efficient communication, which is promising for the design of real-time wireless CPS. We identify at least three issues that limit the adoption of ST in this domain: (i) ST is difficult to use due to stringent time synchronization requirements (in the order of ). There is a lack of tools to facilitate the implementation of ST by CPS engineers, which are often not wireless communication experts. (ii) There are only few examples showcasing the use of ST for CPS applications and academic works based on ST tend to focus on communication rather than applications. Convincing proof-of-concept CPS applications are missing. (iii) The inherent variability of the wireless environment makes performance evaluation challenging. The lack of an agreed-upon methodology hinders experiment reproducibility and limits the confidence in the performance claims. This paper synthesizes recent advances what address these three problems, thereby enabling significant progress for future applications of low-power wireless technology in real-time CPS.

Abstract

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer’s disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://www.benchcouncil.org/clinical_aibench/.

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer’s disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://www.benchcouncil.org/clinical_aibench/.