Concept Of Computing Research Articles

Perspective on unconventional computing using magnetic skyrmions

Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.

The effects of a role-play-based micro-game strategy on students’ computational thinking, learning engagement and learning motivation

ABSTRACT The game-based learning approach to developing students’ computational thinking (CT) current has received attention from researchers. However, the compatibility between games and instruction is often insufficient to accommodate the entertaining and educational nature of the curriculum entirely, and the benefits of game-based learning could be compromised. Therefore, this study proposed a role-playing-based micro-game strategy for developing students’ CT in a Python programming course, and designed a quasi-experiment to verify the effectiveness of the role-playing-based micro-game strategy. A total of 133 eighth-grade students from schools in eastern China were randomly divided into experimental and control groups. The experimental group used a role-playing-based micro-game strategy, and the control group used a traditional instructional strategy. The results showed that the experimental group scored significantly higher than the control group on the CT test, especially in the dimensions of computational concepts and computational practices. The learning engagement of the experimental group was significantly higher than that of the control group, and the learning motivation of the experimental group was significantly improved before and after the experiment. In addition, qualitative analysis of the interview data indicated that micro-game strategy on role-playing could promote classroom learning and meet the needs of the course knowledge system.

Modeling and stability analysis of a sliding bead from a problem-based learning perspective

In this work, we adopt a problem-based learning approach to develop an integrative bachelor project that relies on competences acquired from basic courses in mathematics, mechanics and computation. In this sense, the proposed project tries to pave the way from “apparently disconnected” concepts gained through previous studies toward the field of computational mechanics. On this basis, we study the motion of a particle along an arbitrary curve in space subject only to the gravitational field, the so-called sliding bead. Although this is a classic problem in mechanics, it has a substantial richness from a theoretical and practical point of view where the student reinforcing abstract skills and exploring different aspects of its numerical solution are allowed. Along this path, we start with the modeling process. This is followed by a stability analysis of the governing differential equations. Later on, we present a moderate introduction to classical numerical time integration by considering several numerical schemes and the well-known Matlab add-on Simulink. Finally, we present a brief discussion about how the proposed project allows articulating concepts of mathematics, mechanics, and computation in engineering programs of studies at horizontal and vertical levels.

Computational concept for human food choice and eating behaviour

An inadequate diet can cause a number of illnesses with some of them posing major threats for humanity. Poor diet largely originates from behavioral and social issues rather than environmental factors. With simulation being a grand tool to analyze and address behavior issues, relatively few studies focus on computational modeling of nutrition at behavioural level. Furthermore, we have overviewed several popular approaches to computational modeling and simulating dietary decision-making and found no clear favorite. Further still, modelers rarely pay attention to one of the key behavioural factors – motivation. In the vast majority of models, either motivation is assumed to be exogenously given and, hence, is left out of the model, or motivation is not taken into account in any form, even though ignoring incentives significantly reduces adaptive capabilities of any human-like goal-directed model entity. We aimed to outline a modelling approach that would fit into the food choice topic and would improve on the available models. This implies offering an intelligible algorithm that would be easily applied to statistical data yet offering a depth of analysis despite its seeming simplicity. Thus, we present our view of the food choice simulation problem which employs eating incentives and an original choice mechanism that is different both from traditional maximizing approaches common to economics and artificial intelligence research and from the dominant psychological computational approaches. We outlined the programming conceptual algorithm that involves sequential incentive (which can result from the biological necessities, social, intellectual or spiritual needs alike) selection, incentive-foodstuff coupling (a relation can be either fixed or dynamic) and elimination of undesirable food options based on incentives ranking (qualitative ranking seems to be preferable over quantitative ranking, forasmuch as it resembles the way of thinking of a regular person more closely) supplemented by pseudocode segments. The algorithm suits agent-based simulation paradigm, yet it is not tied to it and can be fitted with other simulation approaches as well. The algorithm is supposed to be implemented in Java. Since the offered algorithm is conceptual it requires an implementation to bring about robust conclusions which is our goal to reach next.

Fast Progressive Differentiable Architecture Search based on adaptive task granularity reorganization

Shrinkage methods reduce the search space of a Differentiable Architecture Search (DARTS) by progressively discarding candidates, which accelerates the search speed. However, their shrinkage strategy suffers from the vulnerability of too fine task granularity. In other words, they drop only one of the least promising candidates per round of shrinkage, which is suboptimal in terms of performance and efficiency. In this study, we introduce the concept of Granular Computing (GrC) into the shrinkage method and present a Fast Progressive Differentiable Architecture Search (FP-DARTS) method. This method effectively reduces the computational complexity of each round of shrinkage, thereby improving the efficiency and performance of the algorithm. FP-DARTS can be divided into three stages: adaptive granularity division and selection, granular-channel performance evaluation, and progressive shrinkage. In the first stage, to reorganize the task granularity, we cluster the candidate operations into granular-channels and adaptively select the appropriate task granularity. We also propose a dynamic clustering strategy to avoid introducing additional computation. In the second stage, we train the architecture parameters to measure the potential of the granular-channels. In the third stage, to improve the stability of the shrinkage results, we introduce a channel annealing mechanism to smoothly discard unpromising granular-channels. We conducted systematic experiments on CIFAR-10 and ImageNet and achieved a test accuracy of 97.56% on CIFAR-10 with 0.04 GPU-days, and a test accuracy of 75.5% on ImageNet with 1.2 GPU-days. We also conducted experiments on the search space of NAS-Bench-201, and obtained test accuracies of 94.22, 73.07, and 46.23% for CIFAR-10, CIFAR-100 and ImageNet16-120, respectively. The above experimental results demonstrate that FP-DARTS achieves higher search speed and competitive performance compared to other state-of-the-art shrinkage methods and non-shrinkage methods.

FastSpline: Automatic Generation of Interpolants for Lattice Samplings

Interpolation is a foundational concept in scientific computing and is at the heart of many scientific visualization techniques. There is usually a tradeoff between the approximation capabilities of an interpolation scheme and its evaluation efficiency. For many applications, it is important for a user to navigate their data in real time. In practice, evaluation efficiency outweighs any incremental improvements in reconstruction fidelity. We first analyze, from a general standpoint, the use of compact piece-wise polynomial basis functions to efficiently interpolate data that is sampled on a lattice. We then detail our automatic code-generation framework on both CPU and GPU architectures. Specifically, we propose a general framework that can produce a fast evaluation scheme by analyzing the algebro-geometric structure of the convolution sum for a given lattice and basis function combination. We demonstrate the utility and generality of our framework by providing fast implementations of various box splines on the Body Centered and Face Centered Cubic lattices, as well as some non-separable box splines on the Cartesian lattice. We also provide fast implementations for certain Voronoi-splines that have not yet appeared in the literature. Finally, we demonstrate that this framework may also be used for non-Cartesian lattices in 4D.

On enhancing transmission performance for iov based on improved greedy algorithm

Nowadays, the development of 5G and IoT technology can be observed from abundance of their applications, such as VR/AR/MR, communication, healthcare, transportation and so on. Some of these applications are in need of quality improvement like delay and resource consumption decrease since the need of users and devices about the relatively developed technique gradually increase as well. About this kind of QoS improvement in the field of VANET, this paper first has a brief review on this concept and related key word and makes use of the concept of Edge Computing, eventually provides information about the research that focuses on the optimization of Task Offloading function by proposing a new algorithm. The new algorithm consists of 3 C++ programs and determines the relatively effective task offloading strategy by taking both calculating resource consumption and communication latency into consideration. In order to prove the developed performance of the proposed algorithm, paper uses other two kinds of algorithms to have comparison, finally reaches the conclusion that proposed algorithm has the best system cost performance among the comparison objects.

Blockchain-Aided Edge Computing Market: Smart Contract and Consensus Mechanisms

Building upon the concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and show that the proposed mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness-driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair reward allocation. We formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities.

Leveraging Morphological Computation for Controlling Soft Robots: Learning from Nature to Control Soft Robots

Traditional robot designs typically employ rigid body parts and high-torque servo motors. This helps to obtain simple reproducible models and, therefore, to facilitate control. Soft robotics is a new research field that deliberately expands the design toolbox to a wide range of smart and, often, soft materials. This approach is generally inspired by the remarkable performance of biological systems, which use soft structures to interact successfully with noisy and hard-to-model environments and, as a result, outperform state-of-the-art robots in open-world scenarios. However, using soft bodies comes with a significant disadvantage. Soft materials often have complex and nonlinear dynamics, which makes them hard to model and therefore difficult to control. To fulfill the potential of soft robotics to achieve performances close to biological systems, this control problem has to be solved. A promising solution is another bioinspired principle called morphological computation, which proposes to outsource functionality directly to the body morphology. From this point of view, the seemingly undesired nonlinear dynamics become a resource for implementing nonlinear functionalities. This extends the control design problem to the question of how to design the body morphology of the robot. While there exist proofs of concept that demonstrate the potential of this approach, the existing work (for the most part) is lacking mathematical rigor and a general framework. We believe that the control community has the right set of tools to support the development of a design framework for morphological computation. The goal of this article (see “Summary”) is to provide an introduction to the concepts of soft robotics and morphological computation, explain how they can work together, and (with the help of examples) illustrate their potential for control. The hope is to inspire members of the control community to develop novel control frameworks for the next generation of soft robots.

Two-Way Concept-Cognitive Learning Method: A Fuzzy-Based Progressive Learning

Granular computing (GrC) and two-way concept learning (TCL) are influential studies of knowledge processing and cognitive learning. A central notion of two-way concept learning is learning concepts from an arbitrary information granule. Although TCL has been widely adopted for concept learning and formal concept analysis in a fuzzy context, the existing studies of TCL still have some issues: 1) the sufficient and necessary granule concept is only obtained from the necessary granule or sufficient granule concept; 2) the cognitive mechanism ignores integrating past experiences into itself to deal with dynamic data. Meanwhile, concept-cognitive learning (CCL) method still faces challenges, such as incomplete cognitive and weak generalization ability. This paper proposes a novel two-way concept-cognitive learning method for dynamic concept learning in a fuzzy context for these problems and challenges. Unlike TCL, fuzzy-based twoway concept-cognitive learning (F-TCCL) is more flexible and less time-consuming to learn granule concepts from the given clue, and meanwhile, it is good at dynamic concept learning. Moreover, we design a fuzzy-based progressive learning mechanism within this framework under the dynamic environment. Some numerical experiments on public datasets verify the effectiveness of our proposed method. The considered framework can provide a convenient novel method for researching two-way learning and concept-cognitive learning.

Virtualization of Research in Terminology. Cloud-based Terminology Management Tools

Developing our previous research in the field, the present paper approaches the evolution of IT and terminology software. It explores the concepts of virtualization and cloud-based computing and its influence in the virtualization of terminology and development of cloud-based terminology management tools. The goal of our paper is to provide actualized information about the existing cloud-based terminology tools and about similar ongoing projects for experts, professors and students.

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.

Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.

Research on quantum computing and its development

Based on the rapid development of quantum computing and quantum communication all over the world, this paper studies the latest development of quantum computing on the basis of consulting a large number of literatures. By analyzing literature and summarizing, the basic knowledge and concept of quantum computing, the theory of time and data complexity, quantum computer, and the technical route of quantum computing are studied. Through the research, it is found that quantum computing is still in the primary stage, and the hardware of quantum computing, quantum error correction, anti-noise quantum computing and quantum simulation all need further research to improve quantum theory.

Quantum Power Electronics: From Theory to Implementation

While impressive progress has been already achieved in wide-bandgap (WBG) semiconductors such as 4H-SiC and GaN technologies, the lack of intelligent methodologies to control the gate drivers has prevented exploitation of the maximum potential of semiconductor chips from obtaining the desired device operations. Thus, a potent ongoing trend is to design a fast gate driver switching scheme to upgrade the performance of electronic equipment at the system level. To address this issue, this work proposed a novel intelligent scheme for the control of gate driver switching using the concept of quantum computation in machine learning. In particular, the quantum principle was incorporated into deep reinforcement learning (DRL) to address the hardware limitations of conventional computers and the growing amount of data sets. Taking potential benefit of the quantum theory, the DRL algorithm influenced by quantum specifications (referred to as QDRL) not only ameliorates the performance of the native algorithm on traditional computers but also enhances the progress of relevant research fields like quantum computing and machine learning. To test the practicability and usefulness of QDRL, a dc/dc parallel boost converter feeding constant power loads (CPLs) was chosen as the case study, and several power hardware-in-the-loop (PHiL) experiments and comparative analysis were performed.

Enhancing Linear Algebra Learning through Computational Thinking: A Project-Based Approach

This article discusses the integration of computational thinking concepts, including algorithm, coding, abstraction, decomposition, debugging, and pattern recognition, into a Linear Algebra course in a community college in the fall of 2022. Through the implementation of project-based learning (PBL), we aimed to enhance students' understanding of linear algebra topics while familiarizing them with essential computational thinking concepts. A pre-and post-survey assessed the students' familiarity with the concepts. The results indicated a significant improvement in the students' understanding of computational thinking concepts and their application in linear algebra.

Topos of Noise

This paper focuses on the significance of the concept of noise for cognition and computation. The concept of noise was massively transformed in the twentieth century with the advent of information theory, cybernetics, and computer science, all of which provide formal accounts of information and noise centrally concerned with contingency. We show how the concept has changed from these classical formulations, through developments in mathematics (topology and topos theory), computing (interactive computing and univalent foundations), and cognitive science (predictive processing and cognitive morphodynamics). Ultimately it argues for the central importance of noise not only within a topological conception of cognition and computation, but also in the transcendental-empirical torsion of image schemata and the social interactive elaboration of freedom.

Real-Time FaaS: Towards a Latency Bounded Serverless Cloud

Today, Function-as-a-Service is the most promising concept of serverless cloud computing. It makes possible for developers to focus on application development without any system management effort: FaaS ensures resource allocation, fast response time, schedulability, scalability, resiliency, and upgradability. Applications of 5G, IoT, and Industry 4.0 raise the idea to open cloud-edge computing infrastructures for time-critical applications too, i.e., there is a strong desire to pose real-time requirements for computing systems like FaaS. However, multi-node systems make real-time scheduling significantly complex since guaranteeing real-time task execution and communication is challenging even on one computing node with multi-core processors. In this paper, we present an analytical model and a heuristic partitioning scheduling algorithm suitable for real-time FaaS platforms of multi-node clusters. We show that our task scheduling heuristics could outperform existing algorithms by 55%. Furthermore, we propose three conceptual designs to enable the necessary real-time communications. We present the architecture of the envisioned real-time FaaS platform, emphasize its benefits and the requirements for the underlying network and nodes, and survey the related work that could meet these demands.

Physicality, modeling, and agency in a computational physics class

Computation is intertwined with essentially all aspects of physics research and is invaluable for physicists’ careers. Despite its disciplinary importance, integration of computation into physics education remains a challenge and, moreover, has tended to be constructed narrowly as a route to solving physics problems. Here, we broaden Physics Education Research’s conception of computation by constructing a metamodel—a model of modeling—incorporating insights on computational modeling from the philosophy of science and prior work. The metamodel is formulated in terms of practices, things physicists do, and how these inform one another. We operationalize this metamodel in an educational environment that incorporates making, the creation of shared physical and digital artifacts, intended to promote students’ agency, creativity, and self-expression alongside doing physics. We present a content analysis of student work from initial implementations of this approach to illustrate the very complex epistemic maneuvers students make as they engaged in computational modeling. We demonstrate how our metamodel can be used to understand student practices and conclude with implications of the metamodel for instruction and future research.Received 8 March 2022Accepted 31 January 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.010121Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEpistemology, attitudes, & beliefsInstructional strategiesLearning environmentScientific reasoning & problem solvingTechnologyProfessional TopicsUpper undergraduate studentsPhysics Education Research

Hybrid Analog Computer for Modeling Nonlinear Dynamical Systems: The Complete Cookbook

This paper describes a design process for a universal development kit based on an analog computer concept that can model the dynamics of an arbitrarily complex dynamical system up to the fourth order. The constructed development kit contains digital blocks and associated analog-to-digital and digital-to-analog converters (ADCs and DAC), such that multiple-segmented piecewise-linear input-output characteristics can be used for the synthesis of the prescribed mathematical model. Polynomial input-output curves can be implemented easily by four-quadrant analog multipliers. The proposed kit was verified through several experimental scenarios, starting with simple sinusoidal oscillators and ending with generators of continuous-time robust chaotic attractors. The description of each individual part of the development kit is accompanied by links to technical documentation, allowing skilled readers in the construction of electronic systems to replicate the proposed functional example. For this purpose, the electrical scheme of the hybrid analog computer and all important source codes are available online.

Edge computing-based digital management system of game events in the era of Internet of Things

With the great development of Internet of Things (IoT) and edge computing, the development of sports activities depends on the development of information technology and it is inevitable to pay attention to the combination and optimization of resources. The combination of IoT and edge computing will be critical in sports activities. This paper elaborates on the application of network skill in sports event information management, that is, through the effective gathering of sports event data, to realize the use of sports event information, to achieve the purpose of information and digitization. Furthermore, the goal is to investigate the effect of sports event in the era of IoT. The impact of sports events on the economy and culture of the hosting city is investigated using IoT concept of edge computing. By analyzing the advantages and disadvantages of traditional centralized optimization method, we present a series of performance indicators and utility functions and show that the method is effective and achieves the optimal purpose. Through vital research, it is found that with the development of the edge computing and IoT industry, the scale of sports events is constantly expanding. By 2019, there has been a scale of 1,271 billion yuan. An increase of 981 billion yuan, compared with 290 billion yuan in 2013. Therefore, the use of the IoT technology in combination with edge computing to manage sports events will greatly encourage the expansion of sports activities. Furthermore, the holding of sporting events reflects a city’s overall strength and enhances the city’s exposure and fame. The investigation offers a certain reference point for cities looking to increase their influence through events.