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Among lattice configurations of densely packed hard ellipses, Monte Carlo simulations are used to identify the so-called parallel and diagonal lattices as the two favorable states. The free energies of these two states are computed for several system sizes employing the Einstein crystal method. An accurate calculation of the free energy difference between the two states reveals the parallel lattice as the state with the lowest free energy. The origin of the entropic difference between the two states is further elucidated by assessing the roles of the translational and rotational degrees of freedom. [ABSTRACT FROM AUTHOR]

Cloud computing is widely applied in modern industrial areas due to its technological advancement, cost reduction, and applicability. Packets (tasks) belonging to different applications (agents) compete to share the common cloud resource through a series of edge nodes (processors) in pursuit of fast transmission. This paper abstracts the cloud computing system as a multi-agent flow-shop scheduling (MAFS) problem. The objective is to minimise the total completion time of several agents with the restriction that the maximum lateness cannot exceed a given bound. Given the complexity of the considered problem, a branch and bound algorithm combined with several pruning rules and lower bounds is proposed to obtain optimal solutions. Furthermore, the considered problem is generalised to a bi-scenario version, and a bi-population cooperative co-evolutionary (BCCE) algorithm is proposed to solve it. A reinforcement learning-based method is presented to generate the initial population. Several problem-specific intensification strategies are constructed to explore promising solutions. Comprehensive experiments verified the effectiveness of the proposed algorithms. The industrial data from the China Earthquake Network Centre further confirmed the superiority of the BCCE algorithm. Overall, the MAFS model and the proposed algorithms effectively enhance the user experience and reasonably guarantee revenue. [ABSTRACT FROM AUTHOR]

Nonlinear dynamical systems such as coupled oscillators are being actively investigated as Ising machines for solving computationally hard problems in combinatorial optimization. Prior works have established the equivalence between the global minima of the cost function describing the coupled oscillator system and the ground state of the Ising Hamiltonian. However, the properties of the oscillator Ising machine (OIM) from a nonlinear control viewpoint, such as the stability of the OIM solutions, remain unexplored. Therefore, in this work, using nonlinear control-theoretic analysis, we (i) identify the conditions required to ensure the functionality of the coupled oscillators as an Ising machine, (ii) show that all globally optimal phase configurations may not always be stable, resulting in some configurations being more favored over others and, thus, creating a biased OIM, and (iii) elucidate the impact of the stability of locally optimal phase configurations on the quality of the solution computed by the system. Our work, fostered through the unique convergence between nonlinear control theory and analog systems for computing, provides a new toolbox for the design and implementation of dynamical system-based computing platforms. [ABSTRACT FROM AUTHOR]

The recently developed Projective Quantum Eigensolver (PQE) offers an elegant procedure to evaluate the ground state energies of molecular systems in quantum computers. However, the noise in available quantum hardware can result in significant errors in computed outcomes, limiting the realization of quantum advantage. Although PQE comes equipped with some degree of inherent noise resilience, any practical implementation with apposite accuracy would require additional routines to eliminate or mitigate the errors further. In this work, we propose a way to enhance the efficiency of PQE by developing an optimal framework for introducing Zero Noise Extrapolation (ZNE) in the nonlinear iterative procedure that outlines the PQE, leading to the formulation of ZNE-PQE. Moreover, we perform a detailed analysis of how various components involved in it affect the accuracy and efficiency of the reciprocated energy convergence trajectory. Additionally, we investigate the underlying mechanism that leads to the improvements observed in ZNE-PQE over conventional PQE by performing a comparative analysis of their residue norm landscape. This approach is expected to facilitate practical applications of quantum computing in fields related to molecular sciences, where it is essential to determine molecular energies accurately. [ABSTRACT FROM AUTHOR]

In 2021, the assessment-data management librarian at Lehman College Library decided to conduct a privacy audit of the Library's public computers and networks. This audit comprised one of the Library's two annual formal assessments of resources and services. The American Library Association's (ALA) Library Privacy Checklist for Public Access Computers and Networks was selected to review 17 key items related to protecting user privacy and confidentiality. Faculty and staff from Circulation, Library Technology, and Online Learning identified 10 indicators needing work. Suggestions are provided for collaboratively resolving these issues and future steps are described to continuously maximize the online security of the campus community. [ABSTRACT FROM AUTHOR]

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks. [ABSTRACT FROM AUTHOR]

Routable Digital Maps represent a recent advancement driven by the growth of Intelligent Transportation System applications. Efforts have been made to develop and utilize maps within computer systems that can autonomously identify optimal routes. Typically, online map visualizations lack essential information such as traffic patterns, construction zones, or lane counts, which are crucial for designing accurate and realistic routes. This research introduces a route planning method that aggregates and reuses various data points to establish a robust framework for developing new routing plans. The primary goal is to analyze the collected data and create a framework for estimating a geometrically smooth centerline of a road map route. This method involves generating a series of parallel curves, known as offset curves, to facilitate the creation of new routes and alternative lanes. The following examples illustrate some empirical results obtained from this approach. [ABSTRACT FROM AUTHOR]

Implementing algorithms purely on digital computing platforms dramatically halts the performance of conventional computing systems. Revolutionary computing systems with extreme energy efficiency and high accuracy are demanded to handle the growing computing tasks. Here, the research on hybrid analog–digital computing platforms enabled by memristor‐based optimization solvers for achieving ultrafast computations is presented. By utilizing tunable memristors as parameters to solve linear programming (LP) and quadratic programming (QP) problems, a real‐time control algorithm for micro air vehicles (MAVs) and a support vector machine (SVM) algorithm for cancer diagnosis are implemented. These experiments demonstrate over 2000x speed‐up compared to conventional digital platforms, with negligible energy consumption, using a memristor‐based system consisting of six memristors. These findings underscore the vast potential of memristor‐based optimization solvers not only in hybrid analog–digital computing platforms but also as a transformative solution for a wide range of modern computing challenges. This approach promises significant advancements in energy efficiency and ultrafast speed, positioning it as a leading contender for next‐generation computing paradigms. [ABSTRACT FROM AUTHOR]

As the educational landscape changes throughout time and with circumstances that forced learning institutions to shift and implement different modalities, maximizing the use of technology in teaching and learning has become an approach to meet the demands of present educational system. This study aimed to investigate the quality of instruction of Computer System Servicing specialization utilizing a strategy called as Pentapartite Gap Analysis; specifically, identifying the issues of learning, utilizing the five key dimensions of the gap analysis which are knowledge, delivery, communication, perception, and service quality to evaluate the said specialization, and proposed an action plan to improve the quality of the specialization across selected public schools. Employing quantitative descriptive research design and utilizing data collection through structured surveys to students and teachers, the study yielded the following results: (1) the respondents perceived the issues faced by the school when first implementing the program in terms of facilities, teachers, and instructional materials; (2) the respondents perceived the extent of the school's capability in offering the specialized program to be at a great extent across various dimensions; and (3) there is a significant difference in the implementation of the said specialization of schools about the assessment of teachers and students. Lastly, there is an importance of an action plan in enhancing the implementation of the specialization program as it served as a strategic guide to strengthen its quality. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption. [ABSTRACT FROM AUTHOR]

Total knee arthroplasty (TKA) is a well‐established treatment for end‐stage knee osteoarthritis. However, in patients with concomitant extra‐articular deformities, conventional TKA techniques may lead to unsatisfactory outcomes and higher complication rates. This review summarizes the application of navigated TKA for treating knee osteoarthritis with extra‐articular deformities. The principles and potential benefits of computer navigation systems, including improved component alignment, soft tissue balancing, and restoration of mechanical axis, are discussed. Research studies demonstrate that navigated TKA can effectively correct deformities, relieve pain, and improve postoperative joint function and quality of life compared with conventional methods. The advantages of navigated TKA in terms of surgical precision, lower complication rates, and superior functional recovery are highlighted. Despite challenges like the learning curve and costs, navigated TKA is an increasingly indispensable tool for achieving satisfactory outcomes in TKA for knee osteoarthritis patients with extra‐articular deformities. [ABSTRACT FROM AUTHOR]

System-on-chip designs for high-performance computing systems widely use network-on-chip (NoC) technology. The critical metrics such as latency, throughput, and total energy directly impact the performance of NoCs. However, a cycle-accurate simulator takes considerable execution time for different values of packet injection rate. This paper proposes a machine learning approach with various regression models to predict the essential NoC performance metrics. The work explores regression models, including decision trees, K-nearest neighbors, random forest, polynomial regression, and support vector regression, alongside baseline linear regression for 2D and 3D NoC-based architectures. Experiment results demonstrate high accuracy, ranging from 96.72 to 99.96% for 2D and up to 99.99% for 3D NoC-based architectures. Root-mean-squared error values vary from 0.2792 to 0.03143 for 2D NoC and 0.1857 to 0.0063 for 3D NoC architecture. The proposed framework achieves impressive speedup, reaching up to 4678.8X and 46152.8X for system sizes 8 × 8 × 4 and 10 × 10 × 20, respectively. [ABSTRACT FROM AUTHOR]

Phase change memory (PCM) is among the most promising candidates for the next generation of storage‐class and main memory systems in the computing era beyond Moore's law. However, the widespread installment of PCM devices is limited by the high price‐per‐bit and complex fabrication process. In this paper, it is shown that functional PCM memory devices can be printed, proving low‐cost avenues for non‐silicon memory technologies. Taking Ge‐Sb‐Te (GST) as a case study, PCM inks are prepared and optimize their structural, rheological, and printing parameters. GST layers are then printed onto PCM devices in the planar configuration, showing excellent performance, such as non‐volatility, resistivity contrast, low cycle‐to‐cycle variability, and endurance of at least 100 cycles. This paper provides a novel approach to liquid‐based engineered PCM devices through inkjet printing, enabling patterned memory layers, lower price‐per‐bit, and customizable multi‐material PCM arrays. [ABSTRACT FROM AUTHOR]

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There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained conditions in this paper. By decomposing and reallocating application functions, optimizing the use of CPU, memory, and network bandwidth, a hierarchical structure of computing units is proposed. The architecture divides the system into layers and components to facilitate resource management. Then, a migration strategy is proposed, which mainly focuses on moving components and data from less critical paths to critical paths and ultimately optimizing the utilization of computing resources. Specifically, the test results suggest that the method can reduce the overall CPU utilization by 27%, memory usage by 6.8%, and network bandwidth occupation by 35%. The practical value of this study lies in providing a theoretical model and implementation method for optimizing resource allocation in scenarios where there is a gap between resource and computing requirements in fixed-resource service architectures. The strategy is compatible for distributed computing architectures and cloud/cloud–edge-computing architectures. [ABSTRACT FROM AUTHOR]

Neural network model compression is very important to achieve model deployment based on the memory and storage available in different computing systems. Generally, the continuous drive for higher accuracy in these models increases their size and complexity, making it challenging to deploy them on resource-constrained computing environments. This article proposes various algorithms for model compression by exploiting weight characteristics and conducts an in-depth study of their performance. The algorithms involve manipulating exponents and mantissa in the floating-point representations of weights. In addition, we also present a retraining method that uses the proposed algorithms to further reduce the size of pre-trained models. The results presented in this article are mainly on BFloat16 floating-point format. The proposed weight manipulation algorithms save at least 20% of memory on state-of-the-art image classification models with very minor accuracy loss. This loss is bridged using the retraining method that saves at least 30% of memory, with potential memory savings of up to 43%. We compare the performance of the proposed methods against the state-of-the-art model compression techniques in terms of accuracy, memory savings, inference time, and energy. [ABSTRACT FROM AUTHOR]

Voltage scaling is one of the most promising approaches for energy efficiency improvement but also brings challenges to fully guaranteeing stable operation in modern VLSI. To tackle such issues, we further extend the DependableHD to the second version DependableHDv2, a HyperDimensional Computing (HDC) system that can tolerate bit-level memory failure in the low voltage region with high robustness. DependableHDv2 introduces the concept of margin enhancement for model retraining and utilizes noise injection to improve the robustness, which is capable of application in most state-of-the-art HDC algorithms. We additionally propose the dimension-swapping technique, which aims at handling the stuck-at errors induced by aggressive voltage scaling in the memory cells. Our experiment shows that under 8% memory stuck-at error, DependableHDv2 exhibits a 2.42% accuracy loss on average, which achieves a 14.1× robustness improvement compared to the baseline HDC solution. The hardware evaluation shows that DependableHDv2 supports the systems to reduce the supply voltage from 430 mV to 340 mV for both item Memory and Associative Memory, which provides a 41.8% energy consumption reduction while maintaining competitive accuracy performance. [ABSTRACT FROM AUTHOR]

In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60–200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs. [ABSTRACT FROM AUTHOR]

Adversarial patches can disrupt computer vision systems, seriously threatening people's lives and property security. Existing defense methods seldom consider the generalization for defending against different patches and the compatibility with various models. Furthermore, the severe security situation necessitates the combination of data defense and model defense to build a comprehensive defense system. To address these issues, we propose a defense method named PatchBreaker, which consists of three components. In data defense, PatchBreaker uses the Semantic-Cutter trained by annotated patch images to cut patches and output incomplete images. Next, the Image-Inpainter trained by clean-incomplete image pairs is used to inpaint these incomplete images and output inpainted images. In model defense, the Adversaril-Classifier will be trained by joint adversarial training with clean images and patch images. Finally, PatchBreaker inputs inpainted images into Adversarial-Classifier to output correct results. Comparative experiments show that PatchBreaker outperforms other comparative defense methods in most cases, which indicates the excellent patch generalization and model compatibility of PatchBreaker. Meanwhile, ablation studies show the effectiveness of combining data defense and model defense. Additionally, PatchBreaker has minimal impact on the clean accuracy (about 1 % ). The background, mechanisms and defense effectiveness of PatchBreaker. Intelligent systems are easily disrupted by adversarial patches. Therefore, we propose the PatchBreaker, which consists of data defense and model defense. In data defense, the Semantic-Cutter cuts patches by BiseNetV2 model and output incomplete images, then the Image-Inpainter inpaints incomplete images by a bilateral image inpainting model. In model defense, we utilize clean and patch images to train the Adversarial-Classifier for classifying inpainted images to output correct results. After defense, the green high-light regions of integrated gradients attribution are more regular, which indicates the effectiveness of PatchBreaker [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) provides a wealth of physiological and psychological information. Decoding EEG signals enables machines to recognize brain activity, a crucial aspect in brain-computer interaction and medical rehabilitation. However, the non-stationarity and inter-individual variability of EEG signals pose challenges for existing EEG signal classification models to achieve the desired level of cross-subject generalization, limiting the practical applications of EEG-based brain computer interface systems. Unsupervised Domain Adaptation (UDA) aims to improve the model's generalization performance on the target domain by minimizing the discrepancies between the source and target domain data distributions. Many researchers treat different subjects as distinct domains and utilize Unsupervised Domain Adaptation (UDA) in transfer learning to guide the model for effective cross-subject EEG Classification. Strategies involve reducing distribution discrepancies between the source and target domains by minimizing well-designed discrepancy metrics or using an adversarial discriminator to capture domain-invariant features. However, neglecting category-discriminatory (abbreviated as discriminative) features leads to the limited effectiveness of these domain-level alignment methods. To tackle these issues, we propose the Discriminative Clustering Domain Adaptation Network (DCDAN), aimed at utilizing an unsupervised approach to construct discriminative information for facilitating Unsupervised Domain Adaptation (UDA) in achieving category-level feature alignment between the source and target domains. Specifically, we employ a clustering algorithm based on adversarial domain adaptation to associate pseudo-labels with target domain samples. Implementing a self-supervised training framework enables the model to acquire discriminative features. Moreover, we introduce the Pseudo Label-Common Spatial Pattern (PL-CSP) component, which integrates the prediction confidence of pseudo-labels from target domain samples with discriminative information from source domain samples through a covariance matrix weighting strategy based on sample confidence. This integration enhances the robustness of the learned spatial filter on the target domain. Experimental results on public datasets demonstrate the effectiveness of our proposed method in extracting more discriminative features. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a crucial technology widely utilized in various sectors in recent years. One significant application is in the healthcare system, particularly in mobile health and remote patient monitoring for individuals with different medical conditions such as kidney disease, heart disease, cancer, hypertension, diabetes, respiratory issues, and stroke. Integrating IoT with cloud computing can enhance the efficiency of healthcare systems and facilitate the creation of novel applications in the future. Load balancing is a significant issue in cloud computing systems that must be addressed. Solving the problem will decrease response time, power usage, and cost and improve server availability. This work consists of developing and executing a healthcare system utilizing IoT and addressing the load-balancing issue in cloud computing using an Improved version of the Grey Wolf Optimization (GWO) algorithm. The suggested method is named Improved GWO Virtual Machine Selector (IGWO-VMS). The proposed method chooses the Optimal Virtual Machine (VM) from a set of VMs based on its fitness value. Various tasks are prioritized and allocated to the most suitable VMs according to their Instruction Millions (IM), with tasks with high IM being assigned to VMs with high fitness values. The results showed that the suggested technique decreases latency and packet loss while maximizing throughput in healthcare systems. The efficiency and success of this technology surpass other state-of-the-art methods in decreasing makespan time and total processing time and ensuring load balancing across virtual machines. The GoCJ dataset used contain a number of jobs in terms of Million Instructions (MI) obtained from the workload behaviour witnessed in google cluster trace. The values of makespan time, throughput time and Standard Deviation (STD) where (23.05), (899.8979) and (177.7675), respectively, in case of applying 500, 600 and 900 tasks. [ABSTRACT FROM AUTHOR]

Programming is a major subject in various Information Systems (IS) programs, with students often finding it a challenging skill to acquire. While there is extensive literature on factors helping students learn to program, most of which focuses on non-IS students. Due to the increasing demand for professionals with programming skills, there is a pressing need for further research on factors that could enhance learning programming skills at the higher education level. One promising approach to address this issue involves examining students' internal characteristics, their programming self-efficacy, and its connection to instructional methods that can enhance it. This study adopted a quantitative research design to evaluate students' programming self-efficacy. A survey was conducted to measure students' beliefs in their programming competence and engagement in various instructional activities, including the value they attributed to learning programming, the time spent practicing, and the frequency they sought guidance from teaching assistants (TA). Through a hierarchical multiple regression analysis, this work investigated how these mentioned variables could predict student-programming self-efficacy. The results indicated that the value students placed on learning programming emerged as the most significant predictor for programming self-efficacy. On the other hand, there was no substantial evidence that the practice time or consulting TA predict programming self-efficacy and the practice time or consulting TA. These findings suggest that educators and instructional designers need to emphasize the practicality and importance of learning how to program to enhance students' perceived value. [ABSTRACT FROM AUTHOR]

Edge computing is gaining prominence as a solution for IoT data management and processing. Task offloading, which distributes the processing load across edge devices, is a key strategy to enhance the efficiency of edge computing. However, traditional methods often overlook the dynamic nature of the edge environment and the interactions between devices. While reinforcement learning-based task offloading shows promise, it can sometimes lead to an imbalance by favoring weaker servers. To address these issues, this paper presents a novel task offloading method for federated learning that leverages the β-skeleton graph in edge computing. This model takes into account spatial and temporal dynamics, optimizing task assignments based on both the processing and communication capabilities of the edge devices. The proposed method significantly outperforms five state-of-the-art methods, showcasing substantial improvements in both initial and long-term performance. Specifically, this method demonstrates a 63.46% improvement over the Binary-SPF-EC method in the initial rounds and achieves an average improvement of 76.518% after 400 rounds. Moreover, it excels in sub-rewards and total latency reduction, underscoring its effectiveness in optimizing edge computing communication and processing tasks. These results underscore the superiority of the proposed method, highlighting its potential to enhance the efficiency and scalability of edge computing systems. This approach, by effectively addressing the dynamic nature of the edge environment and optimizing task offloading, contributes to the development of more robust and efficient edge computing frameworks. This work paves the way for future advancements in federated learning and edge computing integration, promising better management and utilization of IoT data. [ABSTRACT FROM AUTHOR]

Clinicians can rapidly and accurately diagnose disease, learn from experience, and explain their reasoning. Computational Bayesian medical decision-making might replicate this expertise. This paper assesses a computer system for diagnosing cardiac chest pain in the emergency department (ED) that decides whether to admit or discharge a patient.The system can learn likelihood functions by counting data frequency. The computer compares patient and disease data profiles using likelihood. It calculates a Bayesian probabilistic diagnosis and explains its reasoning. A utility function applies the probabilistic diagnosis to produce a numerical BAYES score for making a medical decision.We conducted a pilot study to assess BAYES efficacy in ED chest pain patient disposition. Binary BAYES decisions eliminated patient observation. We compared BAYES to the HEART score. On 100 patients, BAYES reduced HEART’s false positive rate 18-fold from 58.7 to 3.3 %, and improved ROC AUC accuracy from 0.928 to 1.0.The pilot study results were encouraging. The data-driven BAYES score approach could learn from frequency counting, make fast and accurate decisions, and explain its reasoning. The computer replicated these aspects of diagnostic expertise. More research is needed to reproduce and extend these finding to larger diverse patient populations. [ABSTRACT FROM AUTHOR]

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show the results of tuning such parameters to obtain a low-latency and low-energy embedded LSNN (eLSNN) implementation. We demonstrate that it is possible to adapt the firing rate so that the samples belonging to the most frequent class are processed with less spikes. We implemented the eLSNN on a microcontroller-based sensor node and we evaluated its performance and energy consumption using a structural health monitoring application processing a stream of vibrations for damage detection (i.e. binary classification). We obtained a cycle count reduction of 25% and an energy reduction of 22% with respect to a baseline implementation. We also demonstrate that our methodology is applicable to a multi-class scenario, showing that we can reduce spiking activity between 68 and 85% at iso-accuracy. [ABSTRACT FROM AUTHOR]

Existing numerical analysis methods often incur high computational costs when directly solving multi-degree-of-freedom systems due to computer capacity limitations. To address this issue, this paper took the composite laminated concentrically stiffened rectangular plate as the research object. The modal synthesis method was employed to reduce the degrees of freedom of the model, enabling the solution of complex dynamic characteristics. In the research process, the domain decomposition theory was used to divide the research object into plate and rib substructures. Based on the spectral Chebyshev method, the substructure dynamic model of the composite laminated rectangular plate was constructed. Subsequently, the function expressions of the complex load excitation on each substructure were derived, and the fixed modal synthesis method was introduced to construct the spectral Chebyshev reduced model of the substructures. According to the artificial spring theory equivalent, the interface force, and the whole reduction model of the composite laminated concentrically stiffened rectangular plate were obtained by assembling the substructure models. The time–domain and frequency–domain dynamic behaviors of composite laminated concentrically stiffened rectangular plates are yielded by solving the whole reduced model. The novelty of this work lies in combining the spectral Chebyshev method with the modal synthesis method. The established reduced model no longer depended on mesh quality and required fewer matrix dimensions and computation time compared to the whole model, while still achieving sufficiently accurate results. Additionally, based on the reduced model, an effective parametric study scheme was proposed to analyze the impact of dimensions and material properties of rib substructures on the dynamic behaviors of the composite laminated concentrically stiffened rectangular plate. These findings can be used to guide the optimal design of laminated concentrically stiffened rectangular plates in engineering practice. [ABSTRACT FROM AUTHOR]

Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach's efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks. Physical reservoir computing systems often possess a single set of internal dynamics, limiting their computational capabilities. Here, Stenning et. al. create hierarchical neural networks with distinct physical reservoirs, enabling diverse computational performance and learning of small datasets. [ABSTRACT FROM AUTHOR]

Recently, ferroelectric memory utilizing hafnium oxide has emerged as an attractive option compared to existing memory technologies, primarily due to its scalability and energy-efficient advantages. Among them, hafnium zirconium oxide (HZO) is examined for its short-term memory characteristics to achieve a reservoir computing system known to exhibit remarkable polarization properties, being able to switch between distinct polarization states under the influence of an electric field. These unique properties are of utmost importance in ferroelectric memory applications, where they play a pivotal role in the storage and retrieval of binary data. In this study, we identify and experiment with the electrical characteristics of a ferroelectric tunnel junction (FTJ) device with a metal-ferroelectric-semiconductor (MFS) structure using TiN as the top electrode and HZO as the ferroelectric layer. Moreover, we assess the performance of the device by evaluating its maximum 2 P r (remnant polarization) and tunneling electro resistance (TER) values in different conditions of cell area. Furthermore, we analyze and show short-term memory (STM) characteristics and synaptic properties with 5 cycles of potentiation and depression under conditions of stable dynamic range by coordinating identical and incremental pulses. In the case of incremental pulses (>95 %), the MNIST pattern recognition accuracy is higher than in the case of identical pulses (>94 %). Through a sequence of procedures, the synaptic characteristics of FTJs are confirmed to assess their suitability for use as an artificial synaptic device. [ABSTRACT FROM AUTHOR]

The choice between the Schrödinger and Heisenberg pictures can significantly impact the computational resources needed to solve a problem, even though they are equivalent formulations of quantum mechanics. Here, we present a method for analysing Bosonic quantum circuits based on the Heisenberg picture which allows, under certain conditions, a useful factoring of the evolution into signal and noise contributions, similar way to what can be achieved with classical communication systems. We provide examples which suggest that this approach may be particularly useful in analysing quantum computing systems based on the Gottesman–Kitaev–Preskill (GKP) qubits. [ABSTRACT FROM AUTHOR]

Memories play a very important role in computing systems due to the continuous advancements in technology. They are used to store data that is used for proper system operations. Memory architectures that are more intricately designed are more prone to radiation-induced errors such as single bit upsets (SBU) and multiple bit upsets (MBU). Error correction codes (ECC) are used to recover the corrupted data that are stored in memories. H-matrix-based ECC is the commonly used ECC for memories. On the other side, the correction masking (CM) technique was added to ECC to mask the correctable error patterns. CM technique protects the error-free bits while correcting erroneous bits. In this paper, optimized H-matrices are presented. These matrices are used to design an ECC with the CM technique to correct 2-bit to 7-bit adjacent errors. The result shows there is a reduction in the power of 2, 3, and 4 adjacent bit errors by 19.009%, 4.615%, and 27.934% respectively. [ABSTRACT FROM AUTHOR]

This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state sampling based on tracking errors and deviations from the predicted optimal state, thereby enhancing computing performance and system efficiency without compromising the control quality. To further conserve communication resources and improve information transfer efficiency, a quantitative feedback mechanism was employed for sampling and state quantification. The simulation experiments were performed to verify the effectiveness of the method, demonstrating excellent docking trajectory tracking performance, robustness against bounded current interference, and significant reductions in computational and communication burdens. The experimental results demonstrated that the method outperformed in the docking trajectory tracking control performance significantly improved the computational and communication performance, and comprehensively improved the system efficiency. [ABSTRACT FROM AUTHOR]

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole–proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole–proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole–proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems. [ABSTRACT FROM AUTHOR]

A novel and original computer scaling-up system for the counter-rotating twin screw extrusion of polymers was developed. In the system, each scaling parameter (scaling criterion) may be considered as an objective function to be minimized for the single parameters or the functional relationships along the length of the screw. Scaling was based on the process simulation, which was performed using the comprehensive (or global) counter-rotating twin screw extrusion program called TSEM (Twin Screw Extrusion Model). The extrusion process was scaled by applying GASESTWIN (Genetic Algorithms Screw Extrusion Scaling) software developed for this purpose using genetic algorithms. Scaling up the extrusion process was carried out to enhance extrusion process throughput according to the scaling criteria specified by the single quantities of polymer melt temperature at the die exit and relative melting length, and by distributions along the screw length of the extrusion parameters of the polymer melt temperature and polymer plasticating. The global objective function had the lowest value for the selected extrusion parameters, which means the minimal differences between the values of the scaled-up processes and extrusion throughput was significantly increased. The solution to the problem of scaling the counter-rotating process presented in this paper complements the existing solutions for optimizing and scaling basic variants of the extrusion process, i.e., flood-fed and starve (metered)-fed single-screw extrusion, as well as co-rotating and counter-rotating twin-screw extrusion. [ABSTRACT FROM AUTHOR]

It is shown how to approximate the solution of functional differential equations in terms of Bell's polynomials. Some numerical checks are shown, by using the computer algebra system Mathematica © . [ABSTRACT FROM AUTHOR]

With the increasing deployment of renewable energy resources, the scale of DC networks and renewable capacity continues to grow. System security is challenged by the decrease in inertia from traditional synchronous generators. In order to accommodate high‐penetration renewable energy, voltage stability should be considered in the renewable energy integration planning. Herein, first, a voltage stability‐constrained minimum startup index and algorithm for conventional thermal power plants are proposed. Then, based on time series production simulation, a renewable energy integration capacity analysis algorithm is designed considering voltage stability and peak shaving constraints. Finally, based on the boundary conditions of the "Fifteen‐Five Plan", the renewable energy capacity considering voltage stability and peak shaving constraints for Northwest and East China power grids are analysed to verify the effectiveness and engineering practicality of the proposed methodology. The results demonstrate that the proposed method can maintain the renewable energy integration goal outlined in the "Fifteen‐Five Plan" while maintaining the voltage stability in these regions. [ABSTRACT FROM AUTHOR]

Ransomware attacks pose a significant threat to information systems. Server hosts, including cloud infrastructure as a service, are prime targets for ransomware developers. To address this, security mechanisms, such as antivirus software, have proven effective. Moreover, research on ransomware detection advocates for behavior-based finding mechanisms while ransomware is in operation. In response to evolving detections, ransomware developers are now adapting an optimized design tailored for CPU architecture (CPU-optimized ransomware). This variant can rapidly encrypt files, potentially evading detection by traditional antivirus methods that rely on fixed time intervals for file scans. In ransomware detection research, numerous files can be encrypted by CPU-optimized ransomware until malicious activity is detected. This study proposes an early mitigation mechanism named CryptoSniffer, which is designed specifically to counter CPU-optimized ransomware attacks on server hosts. CryptoSniffer focuses on the misuse of CPU architecture-specific encryption instructions for swift file encryption by CPU-optimized ransomware. This can be achieved by capturing the ciphertext in user processes and thwarting file encryption by scrutinizing the content intended for writing. To demonstrate the efficacy of CryptoSniffer, the mechanism was implemented in the latest Linux kernel, and its security and performance were systematically evaluated. The experimental results demonstrate that CryptoSniffer successfully prevents real-world CPU-optimized ransomware, and the performance overhead is well-suited for practical applications. [ABSTRACT FROM AUTHOR]

Along with the rising popularity of pay-as-you-go cloud services, many businesses and communities are deploying their business or scientific workflow applications on cloud-based computing platforms. The primary responsibility of cloud service providers is to reduce the monetary cost and execution time of Infrastructure as a Service (IaaS) cloud services. The majority of current solutions for cost and makespan reduction were developed for conventional cloud platforms and are incompatible with heterogeneous computing systems (HCS) having service-based resource management approaches and pricing models. Fog-cloud infrastructures (FCI) have emerged as desirable target areas for workflow automation across several fields of application. In heterogeneous FCI, the execution of workflows involving tasks having different properties might influence the performance in terms of resource usage. The primary goal of this research is to efficiently offload the computational task and optimally schedule the workflow in such diverse computing environment. In this article, we present a novel strategy for building an environment that includes techniques for offloading and scheduling while balancing competing demands from the user and the resource providers. In order to address the issue of uncertainty, our approach incorporates a fuzzy dominance-based task clustering and offloading technique. To construct a suitable execution sequence of tasks that helps to limit the precedence relationship, by preserving dependency constraints among the tasks, a novel algorithm for tasks segmentation is employed. To simplify the problem of the complexity, a hybrid-heuristics based on Harmony Search Algorithm (HSA) and Genetic Algorithm (GA) for resource scheduling algorithm is used. The multi-objective optimization using three competing objectives is taken into consideration for investigation in heterogeneous FCI. The fitness function derived includes minimization of makespan and cost along with maximization of resource utilization. We performed experimental research using five workflow datasets in order to investigate and verify the efficacy of our proposed technique. We contrasted our proposed strategy with the primary, closely comparable strategies. Extensive testing using scientific workflows confirms the effectiveness of our offloading approach. Our solution provided a substantially better cost-makespan tradeoffs, while achieving significantly less energy consumption and can execute marginally quicker than the existing algorithms. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems. [ABSTRACT FROM AUTHOR]

In the age of high-tech automation and wireless technology, it is possible to make various devices come together in a given environment, enhancing comfort, energy savings, security, and affordability. We can observe environmental indicators remotely and manage relevant equipment with embedded systems' tiny, low-energy consumption. These devices' power consumption and network bandwidth utilization are critical. Thus, a low-power device must transfer data using minimal redundant protocol. Since Wi-Fi is so widely available, linking all devices within the space through one standard gateway makes sense. This study gives an overview of Message Queuing Telemetry Transport (MQTT), an ideal lightweight protocol for such applications because of its low power and bandwidth requirements. The proposed prototype uses MQTT on the ESP8266, a Wi-Fi-based expansion board. In the case of ESP8266, sensors and actuators are connected directly using Cayenne-based MQTT brokers to provide remote control and monitoring. It allows for efficient and effective communication within the network, ensuring stable data transmission and device management while minimizing power consumption and bandwidth usage. The project has shown how possible it is to use MQTT in the ESP8266 to support smart environments, leading to better IoT-integrated solutions. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has significantly advanced artificial intelligence (AI); however, frameworks such as PyTorch, ONNX, and TensorFlow are optimized for general‐purpose GPUs, leading to inefficiencies on specialized accelerators such as neural processing units (NPUs) and processing‐in‐memory (PIM) devices. These accelerators are designed to optimize both throughput and energy efficiency but they require more tailored optimizations. To address these limitations, we propose the NEST compiler (NEST‐C), a novel DL framework that improves the deployment and performance of models across various AI accelerators. NEST‐C leverages profiling‐based quantization, dynamic graph partitioning, and multi‐level intermediate representation (IR) integration for efficient execution on diverse hardware platforms. Our results show that NEST‐C significantly enhances computational efficiency and adaptability across various AI accelerators, achieving higher throughput, lower latency, improved resource utilization, and greater model portability. These benefits contribute to more efficient DL model deployment in modern AI applications. [ABSTRACT FROM AUTHOR]

As computing systems become increasingly larger, high‐performance computing (HPC) is gaining importance. In particular, as hyperscale artificial intelligence (AI) applications, such as large language models emerge, HPC has become important even in the field of AI. Important operations in hyperscale AI and HPC are mainly linear algebraic operations based on tensors. An AB21 supercomputing AI processor has been proposed to accelerate such applications. This study proposes a XEM accelerator to accelerate linear algebraic operations in an AB21 processor effectively. The XEM accelerator has outer product‐based parallel floating‐point units that can efficiently process tensor operations. We provide hardware details of the XEM architecture and introduce new instructions for controlling the XEM accelerator. Additionally, hardware characteristic analyses based on chip fabrication and simulator‐based functional verification are conducted. In the future, the performance and functionalities of the XEM accelerator will be verified using an AB21 processor. [ABSTRACT FROM AUTHOR]

Analog computing based on miniaturized surfaces has gained attention for its high‐speed and low‐power mathematical operations. Building on recent advances, an anisotropic space‐time digital metasurface for parallel and programmable wave‐based mathematical operations is proposed. Using frequency conversions, our metasurface performs 1st‐order and 2nd‐order spatial differentiations, integrodifferential equations, and sharp edge detection in spatially encoded images. The anisotropic nature of the meta‐particle enables independent and simultaneous operations for two orthogonal polarizations. Reconfigurability is achieved through tunable gate biasing of an indium tin oxide layer. Illustrative examples demonstrate that the metasurface's output signals and transfer functions closely match ideal transfer functions, confirming its versatility and effectiveness. Unlike other wave‐based signal processors, the design handles wide spatial frequency bandwidths, even with high spatial frequency inputs. [ABSTRACT FROM AUTHOR]

The development of hardware-based cognitive computing systems critically hinges upon the integration of memristor devices capable of versatile weight expression across a spectrum of resistance levels while preserving consistent electrical properties. This investigation aims to explore the practical implementation of a digit recognition system utilizing memristor devices with minimized weighting levels. Through the process of weight quantization for digits represented by 25 or 49 input signals, the study endeavors to ascertain the feasibility of digit recognition via neural network computation. The integration of memristor devices into the system architecture is poised to streamline the representation of the resistors required for weight expression, thereby facilitating the realization of neural-network-based cognitive systems. To minimize the information corruption in the system caused by weight quantization, we introduce the concept of "weight range" in this work. The weight range is the range between the maximum and minimum values of the weights in the neural network. We found that this has a direct impact on weight quantization, which reduces the number of digits represented by a weight below a certain level. This was found to help maintain the information integrity of the entire system despite the reduction in weight levels. Moreover, to validate the efficacy of the proposed methodology, quantized weights are systematically applied to an array of double-layer neural networks. This validation process involves the construction of cross-point array circuits with dimensions of 25 × 10 and 10 × 10, followed by a meticulous examination of the resultant changes in the recognition rate of randomly generated numbers through device simulations. Such endeavors contribute to advancing the understanding and practical implementation of hardware-based cognitive computing systems leveraging memristor devices and weight quantization techniques. [ABSTRACT FROM AUTHOR]

A Geographic Information System (GIS) is a computer system for gathering, storing, transmitting, and presenting data related to positions on Earth's surface. This research aims to authenticate the GIS data during transmission via internet based on transform-based invisible watermarking. The proposed framework makes use of the singular value decomposition and discrete cosine transformation in the frequency domain. The proposed framework is evaluated on National DEM images obtained from the Geospatial Information Agency's (Badan Informasi Geospasial--BIG) geoportal, under several types of attacks. Two performance metrics (Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE)) were considered for the evaluation of the security of the designed framework. Likewise, Normalized Correlation (NC) was computed to assess the robustness by calculating the similarity between the original and the extracted images. The experimental outcomes show that the extracted logos are readable even if they are altered, which guarantees that the received DEM data are authentic. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested. [ABSTRACT FROM AUTHOR]

Malware remains a major threat to computer systems, with a vast number of new samples being identified and documented regularly. Windows systems are particularly vulnerable to malicious programs like viruses, worms, and trojans. Dynamic analysis, which involves observing malware behavior during execution in a controlled environment, has emerged as a powerful technique for detection. This approach often focuses on analyzing Application Programming Interface (API) calls, which represent the interactions between the malware and the operating system. Recent advances in deep learning have shown promise in improving malware detection accuracy using API call sequence data. However, the potential of Generative Adversarial Networks (GANs) for this purpose remains largely unexplored. This paper proposes a novel hybrid deep learning model combining Gated Recurrent Units (GRUs) and GANs to enhance malware detection based on API call sequences from Windows portable executable files. We evaluate our GRU–GAN model against other approaches like Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) on multiple datasets. Results demonstrated the superior performance of our hybrid model, achieving 98.9% accuracy on the most challenging dataset. It outperformed existing models in resource utilization, with faster training and testing times and low memory usage. [ABSTRACT FROM AUTHOR]