Computational Paradigm Research Articles

Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures

Computer vision-based gait recognition (CVGR) is a technology that has gained considerable attention in recent years due to its non-invasive, unobtrusive, and difficult-to-conceal nature. Beyond its applications in biometrics, CVGR holds significant potential for healthcare and human–computer interaction. Current CVGR systems often transmit collected data to a cloud server for machine learning-based gait pattern recognition. While effective, this cloud-centric approach can result in increased system response times. Alternatively, the emerging paradigm of edge computing, which involves moving computational processes to local devices, offers the potential to reduce latency, enable real-time surveillance, and eliminate reliance on internet connectivity. Furthermore, recent advancements in low-cost, compact microcomputers capable of handling complex inference tasks (e.g., Jetson Nano Orin, Jetson Xavier NX, and Khadas VIM4) have created exciting opportunities for deploying CVGR systems at the edge. This paper reports the state of the art in gait data acquisition modalities, feature representations, models, and architectures for CVGR systems suitable for edge computing. Additionally, this paper addresses the general limitations and highlights new avenues for future research in the promising intersection of CVGR and edge computing.

Mobile Cloud Computing Paradigm: A Survey of Operational Concerns, Challenges and Open Issues

ABSTRACTThe Mobile Cloud Computing paradigm has revolutionized the concepts of mobile computing and the Internet of Things (IoT). This paradigm allows outsourcing the workload of mobile devices, or other connected “things,” to be computed in the Cloud. Currently, outsourcing possibilities have been widely developed making available computing platforms at different network layers. In a consequence of that, a virtual increasing of the performance and a homogenization of the computing capabilities of the devices are produced. The research described in this work presents a review of the state of the art about recent works, the main operational concerns, challenges, and open issues of this paradigm in order to update the border of knowledge on this topic. As a result, a critical analysis is conducted, and new research directions are discussed. The findings provide value‐added to the scientific community and, therefore it could be helpful for other researches in these topics, especially given the rising popularity of IoT platforms.

GPU Based Virtual Machine for Sleptsov Net Computing

Novel paradigm of Sleptsov Net Computing (SNC) mends imperfections of modern HPC architecture with computing memory implementation and provides a vivid graphical language, fine granulation of concurrent processes, and wide application of formal methods for reliable software design. IDE and VM for SNC have been developed and described in early papers. In the present paper, we considerably reduce GPU memory and thread consumption of the previous prototype implementation, introducing a matrix with condensed columns (MCC), and enhance performance, using the first fireable transition choice on the transition sequence reordered according to the lattice of priorities. MCC takes into consideration procedures of Sleptsov net (SN) arcs processing to enhance performance with rather little overhead compared to traditional sparse matrix formats. We represent a matrix of SN arcs by a pair of considerably smaller matrices, having the same number of columns, with the number of rows equal to the maximal number of nonzero elements over the source matrix columns. The first matrix contains the indexes within the source matrix, the second matrix contains the values within the source matrix. To run an SN, we use the corresponding rectangular matrix of GPU threads, with very few indirect memory accesses via MCC. As a result, we increase VM performance in 2–4 times, and reduce by hundred times GPU memory and thread consumption on programs from the SN software collection.

AIHO: Enhancing task offloading and reducing latency in serverless multi-edge-to-cloud systems

Serverless edge computing provides a lightweight and easily scalable new paradigm for edge computing, which is widespread in many fields. However, its characteristics of fine-grained tasks, short startup times, and fast execution speed bring new challenges in task offloading and latency reduction. In this paper, we consider the task offloading problem of serverless functions in a multi-edge-to-cloud environment. A new hybrid offloading algorithm, Average latency constrained Independent task Hybrid Offloading (AIHO), is proposed aimed at reducing latency so as to enhance serverless computing in edge environment. AIHO integrates a serverless-based three-layer system framework, enabling strategic deployment of serverless functions closer to end devices, and includes four critical components: offloading decision, task sorting, path selection, and function replacement. The proposed algorithm is evaluated by comparing it to other three baselines for similar problems on the same datasets. By focusing more on horizontal collaboration among edge nodes based on the multi-hop communication mechanism, AIHO exhibits higher performance than other baselines. Experimental results demonstrate that it can significantly reduce average latency, optimize resource usage, and enhance overall resilience and efficiency of edge computing systems, marking a substantial advancement in serverless and edge computing integration.

Angle‐Based Neuromorphic Wave Normal Sensing

AbstractAngle‐based wavefront sensing has a rich historical background in measuring optical aberrations. The Shack–Hartmann wavefront sensor is widely employed in adaptive optics systems due to its high optical efficiency and high robustness. However, simultaneously achieving high sensitivity and large dynamic range is still challenging, limiting the performance of diagnosing fast‐changing turbulence. To overcome this limitation, angle‐based neuromorphic wave normal sensing, which serves as a differentiable framework developed on the asynchronous event modality is proposed. Herein, it is illustrated that the emerging computational neuromorphic imaging paradigm enables a direct perception of a high‐dimensional wave normal from the highly efficient temporal diversity measurement. To the best of available knowledge, the proposed scheme is the first to successfully surpass the spot‐overlapping issue caused by the curvature constraint in classical angle‐based wavefront sensing setups under challenging dynamic scenarios.

Characterizing Behavioral Dynamics in Bipolar Disorder with Computational Ethology.

New technologies for the quantification of behavior have revolutionized animal studies in social, cognitive, and pharmacological neurosciences. However, comparable studies in understanding human behavior, especially in psychiatry, are lacking. In this study, we utilized data-driven machine learning to analyze natural, spontaneous open-field human behaviors from people with euthymic bipolar disorder (BD) and non-BD participants. Our computational paradigm identified representations of distinct sets of actions ( motifs ) that capture the physical activities of both groups of participants. We propose novel measures for quantifying dynamics, variability, and stereotypy in BD behaviors. These fine-grained behavioral features reflect patterns of cognitive functions of BD and better predict BD compared with traditional ethological and psychiatric measures and action recognition approaches. This research represents a significant computational advancement in human ethology, enabling the quantification of complex behaviors in real-world conditions and opening new avenues for characterizing neuropsychiatric conditions from behavior.

Harnessing the Distributed Computing Paradigm for Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy allows fast and versatile elemental analysis, standing as a promising technique for a wide range of applications both at the research and industry levels. Yet, its high operation speed comes with a high throughput of data, which introduces some challenges at the level of the data processing domain, mainly due to the large computational load and data volume. In this work, we analyze and discuss opportunities of distributed computing paradigms and resources to address some of these challenges, covering most of the procedures usually employed in typical applications. We infer the possible impact of such computing resources by presenting some metrics of simple processing prototypes running in state-of-the-art computing facilities. Our results allow us to conclude that, while underexplored so far, these computing resources may allow for the development of tools for timely research and analysis in demanding applications and introduce novel solutions toward a more agile working environment.

DeepSecure: A computational design science approach for interpretable threat hunting in cybersecurity decision making

Businesses and industries are placing a greater emphasis on information systems for cybersecurity decision-making due to the rising cybersecurity threat landscape and the critical need to protect their digital assets. Threat hunting provides a data-driven and proactive approach to cybersecurity, enabling organizations to efficiently detect, analyze, and respond to cyber threats in real-time. Despite playing a crucial role, these systems face several obstacles, including the manual analysis of technical threat intelligence, the non-Gaussian nature of real-world data, the high rate of false positives produced during threat hunting, and the lack of interpretation and justification for these complex models. This article adopts the computational design science paradigm to develop a novel IT artifact for threat-hunting named DeepSecure. First, to automatically extract latent patterns from multivariate time series datasets, we propose a dynamic vector quantized variational autoencoder technique. Second, a multiscale hierarchical attention bi-directional gated recurrent unit-based threat-hunting mechanism is designed. Finally, we provide the visualization of attention scores to aid in model interpretation. We evaluate the DeepSecure against state-of-the-art benchmarks on two publicly available datasets, namely, ToN-IoT and CSE-CIC-IDS2018. The experimental evaluation proves that our model can efficiently identify threat types. Beyond demonstrating practical utility, the proposed framework can help address the lack of interpretation and justification for complex models in cyber threat detection and will allow organizations to respond to potential security incidents quickly.

A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm

In the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing such services, as it can meet computation requirements and enhance resource utilization. This study delves into efficient and intelligent dynamic edge-server placement by taking into account time-varying network scenarios and deployment costs. Firstly, edge servers are classified into static and dynamic ones. Subsequently, an improved snake optimization algorithm is proposed to determine the number and placement locations of dynamic servers while adhering to delay requirements. Finally, a minimum placement-cost algorithm is put forward to further reduce the service cost. Experimental results demonstrate that compared to classic algorithms, the proposed algorithms can achieve a reduction in latency of 5% to 12%. And compared to the state-of-the-art methods, they can reduce service costs by 20% to 43%. This research offers an effective solution for dynamic edge-server placement and holds great theoretical and practical significance.

Provable bounds for noise-free expectation values computed from noisy samples

Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today’s quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions.

A Block-chain Based Mechanism for Securely Storing Data on Cloud and IOT

The burgeoning paradigm of cloud computing has made a number of tools available to researchers. It is a cutting-edge technology that keeps revolutionizing many sectors in every imaginable method. It is a promising technology that gives users and researchers new perspectives, development, and many amazing factors. The integration of cloud and block-chain technology and internet of things (IOT). The benefits both service providers and customers by enabling the use of resources that can be authenticated while protecting anonymity. This joining method is distinct and environmentally. This cooperative method sheds light on the different paths or facets of computers that can solve issues other than security-related ones. The primary problem at the moment is cloud security. For service providers, it's critical to protect client privacy and avoid data loss. Using client data privacy solutions, the current review study focused on block-chain with combination of IOT and data security. In order to strengthen security and expand the power or utility of cloud systems, a comprehensive examination is also carried out. Block-chain technology has the ability to increase speed and anonymity while providing numerous advantages to cloud-based and IOT based applications.

Computational elucidation of nonverbal behavior and body language in music therapy.

Music therapy has shown efficacy in serious and chronic conditions, mental disorders, and disabilities. However, there is still much to explore regarding the mechanisms through which music interventions exert their effects. A typical session involves interactions between the therapist, the client, and the musical work itself, and to help address the challenges of capturing and comprehending its dynamics, we extend our general computational paradigm (CP) for analyzing the expressive and social behavioral processes in arts therapies. The extension includes bodily and nonverbal aspects of the behavior, offering additional insights into the client's emotional states and engagement. We have used this version of the CP, which employs AI pose estimation technology, image processing, and audio analysis, to capture therapy-related psychometrics and their intra- and inter-session analysis. The CP is applied in a real-world proof-of-concept study, and the results enable us to pinpoint meaningful events and emergent properties not captured by the human eye, complementing the therapist's interpretations. The resulting data may also be useful in other scientific and clinical areas.

Mapping quantum circuits to shallow-depth measurement patterns based on graph states

Abstract The paradigm of measurement-based quantum computing (MBQC) starts from a highly entangled resource state on which unitary operations are executed through adaptive measurements and corrections ensuring determinism. This is set in contrast to the more common quantum circuit model, in which unitary operations are directly implemented through quantum gates prior to final measurements. In this work, we incorporate concepts from MBQC into the circuit model to create a hybrid simulation technique, permitting us to split any quantum circuit into a classically efficiently simulatable Clifford-part and a second part consisting of a stabilizer state and local (adaptive) measurement instructions—a so-called standard form—which is executed on a quantum computer. We further process the stabilizer state with the graph state formalism, thus, enabling a significant decrease in circuit depth for certain applications. We show that groups of mutually-commuting operators can be implemented using fully-parallel, i.e. non-adaptive, measurements within our protocol. In addition, we discuss how groups of mutually commuting observables can be simulatenously measured by adjusting the resource state, rather than performing a costly basis transformation prior to the measurement as it is done in the circuit model. Finally, we demonstrate the utility of our technique on two examples of high practical relevance—the Quantum Approximate Optimization Algorithm and the Variational Quantum Eigensolver (VQE) for the ground-state energy estimation of the water molecule. For the VQE, we find a reduction of the depth by a factor of 4 to 5 using measurement patterns vs. the standard circuit model. At the same time, since we incorporate the simultaneous measurements, our patterns allow us to save shots by a factor of at least 3.5 compared to measuring Pauli strings individually in the circuit model.

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems.

An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks

The Internet has become the primary vehicle for doing almost everything online, and smartphones are needed for almost everyone to live their daily lives. As a result, cybersecurity is a top priority in today’s world. As Internet usage has grown exponentially with billions of users and the proliferation of Internet of Things (IoT) devices, cybersecurity has become a cat-and-mouse game between attackers and defenders. Cyberattacks on systems are commonplace, and defense mechanisms are continually updated to prevent them. Based on a literature review of cybersecurity vulnerabilities, attacks, and preventive measures, we find that cybersecurity problems are rooted in computer system architectures, operating systems, network protocols, design options, heterogeneity, complexity, evolution, open systems, open-source software vulnerabilities, user convenience, ease of Internet access, global users, advertisements, business needs, and the global market. We investigate common cybersecurity vulnerabilities and find that the bare machine computing (BMC) paradigm is a possible solution to address and eliminate their root causes at many levels. We study 22 common cyberattacks, identify their root causes, and investigate preventive mechanisms currently used to address them. We compare conventional and bare machine characteristics and evaluate the BMC paradigm and its applications with respect to these attacks. Our study finds that BMC applications are resilient to most cyberattacks, except for a few physical attacks. We also find that BMC applications have inherent security at all computer and information system levels. Further research is needed to validate the security strengths of BMC systems and applications.

Blind Quantum Machine Learning with Quantum Bipartite Correlator.

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this Letter, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.

Toward security quantification of serverless computing

Serverless computing is one of the recent compelling paradigms in cloud computing. Serverless computing can quickly run user applications and services regardless of the underlying server architecture. Despite the availability of several commercial and open-source serverless platforms, there are still some open issues and challenges to address. One of the key concerns in serverless computing platforms is security. Therefore, in this paper, we present a multi-layer abstract model of serverless computing for an security investigation. We conduct a quantitative analysis of security risks for each layer. We observe that the Attack Tree and Attack-Defense Tree methodologies are viable approaches in this regard. Consequently, we make use of the Attack Tree and the Attack-Defense Tree to quantify the security risks and countermeasures of serverless computing. We also propose a novel measure called the Relative Risk Matrix (RRM) to quantify the probability of attack success. Stakeholders including application developers, researchers, and cloud providers can potentially apply these findings and implications to better understand and further enhance the security of serverless computing.

U-DeepONet: U-Net enhanced deep operator network for geologic carbon sequestration

Learning operators with deep neural networks is an emerging paradigm for scientific computing. Deep Operator Network (DeepONet) is a modular operator learning framework that allows for flexibility in choosing the kind of neural network to be used in the trunk and/or branch of the DeepONet. This is beneficial as it has been shown many times that different types of problems require different kinds of network architectures for effective learning. In this work, we design an efficient neural operator based on the DeepONet architecture. We introduce U-Net enhanced DeepONet (U-DeepONet) for learning the solution operator of highly complex CO2-water two-phase flow in heterogeneous porous media. The U-DeepONet is more accurate in predicting gas saturation and pressure buildup than the state-of-the-art U-Net based Fourier Neural Operator (U-FNO) and the Fourier-enhanced Multiple-Input Operator (Fourier-MIONet) trained on the same dataset. Moreover, our U-DeepONet is significantly more efficient in training times than both the U-FNO (more than 18 times faster) and the Fourier-MIONet (more than 5 times faster), while consuming less computational resources. We also show that the U-DeepONet is more data efficient and better at generalization than both the U-FNO and the Fourier-MIONet.

Imperative Genetic Programming

Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.

ASF-Net: Robust video deraining via temporal alignment and online adaptive learning

In recent times, learning-based methods for video deraining have demonstrated commendable results. However, there are two critical challenges that these methods are yet to address: exploiting temporal correlations among adjacent frames and ensuring adaptability to unknown real-world scenarios. To overcome these challenges, we explore video deraining from a paradigm design perspective to learning strategy construction. Specifically, we propose a new computational paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a temporal shift module. This module is novel to this field and provides deeper exploration of temporal information by facilitating the exchange of channel-level information within the feature space. To fully discharge the model’s characterization capability, we further construct a LArge-scale RAiny video dataset (LARA) which also supports the development of this community. On the basis of the newly-constructed dataset, we explore the parameters learning process by developing an innovative re-degraded learning strategy. This strategy bridges the gap between synthetic and real-world scenes, resulting in stronger scene adaptability. Our approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios, underscoring its efficacy. The code is available at https://github.com/vis-opt-group/ASF-Net.