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In recent years, action recognition has been an essential branch of video understanding and a hot research direction. Among them, the graph convolutional network (GCN) is widely used in skeleton-based action recognition and has achieved remarkable performance. However, in practical situations, recognizing human action often depends on the movement of a part of the joints. In the existing GCN-based methods, the size of a single frame of the skeleton graph is fixed, and all joints of the human body will participate in the whole operation process, so the critical joints in the moving process cannot be flexibly selected. Therefore, this paper takes the adaptive graph convolutional network (AGCN) as the baseline and uses the graph-pooling method to select the critical joints in the human moving process. We design two new networks: Pooling-AGCN and U-AGCN and use them to form the multi-stream P&U AGCNs for action recognition. Extensive experiments show the complementarity between the two networks and that the method proposed in this paper outperforms the recent work on the three large-scale public datasets (NTU-RGB+D 60, NTU-RGB+D 120, Kinetics-Skeleton). [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

Large number factorization is not only the most critical entry point for Rivest–Shamir–Adleman (RSA) security analysis, but also the most direct means of attacking the asymmetric encryption algorithm RSA. In this paper, the factorization methods of large numbers are summarized and analysed: classical integer factoring algorithms, Shor's circuit model algorithm, quantum adiabatic methods (integer factorization based on a quantum nuclear magnetic resonance (NMR) platform and D-Wave quantum annealing), and hybrid quantum-classical computing. Finally, the feasibility of integer factorization based on quantum adiabatics is discussed. In this paper, quantum annealing is regarded as a quantum attack method that is completely different from the famous Shor algorithm, and the potential of D-Wave factorization of large numbers to crack RSA cryptography is verified, which provides a new idea for a quantum attack on RSA public key cryptography. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables distributed training of AI models without data sharing, thereby promoting privacy by design. However, it is essential to acknowledge that FL only offers a partial solution to safeguard the confidentiality of AI and machine learning (ML) models. Unfortunately, many studies fail to report the results of privacy measurement when applying FL, mainly due to assumptions that privacy is implicitly achieved as FL is a privacy-by-design approach. This trend can also be attributed to the complexity of understanding privacy measurement metrics and methods. This paper presents a survey of privacy measurement in FL, aimed at evaluating its effectiveness in protecting the privacy of sensitive data during the training of AI and ML models. While FL is a promising approach for preserving privacy during model training, ensuring privacy is genuinely achieved in practice is crucial. By evaluating privacy measurement metrics and methods in FL, we can identify the gaps in existing approaches and propose new techniques to enhance FL's privacy. A comprehensive study investigating "privacy measurement and metrics" in FL is therefore required to support the field's growth. Our survey provides a critical analysis of the current state of privacy measurement in FL, identifies gaps in existing research, and offers insights into potential research directions. Moreover, this paper presents a case study that evaluates the effectiveness of various privacy techniques in a specific FL scenario. This case study serves as tangible evidence of the real-world implications of privacy measurements, providing insightful and practical guidelines for researchers and practitioners to optimize privacy preservation while balancing other crucial factors such as communication overhead and accuracy. Finally, our paper outlines a future roadmap for advancing privacy in FL, combining traditional techniques with innovative technologies such as quantum computing and Trusted Execution Environments to fortify data protection. [ABSTRACT FROM AUTHOR]

In order to meet ever-increasing demands for reliable parallel and distributed systems, it is crucial to evaluate the reliability and fault tolerance of their underlying interconnection networks. Such an interconnection network is usually modeled as a connected graph G, where the vertex set and edge set represent the processors and links between processors in the network, respectively. In this paper, we combine Fàbrega's idea about h-extra edge-connectivity and Sampathkumar's concept about r-component edge-connectivity to introduce a more refined parameter for characterizing fault tolerance of interconnection networks, named as h-extra r-component edge-connectivity. Given a connected graph G, for two integers h ≥ 1 and r ≥ 2 , the h-extra r-component edge-connectivity of G, denoted as c λ r h (G) , is the minimum cardinality among all edge subsets F ⊂ E (G) , if any, such that G - F has at least r components, and each component has at least h vertices. As an enhancement on hypercube, the n-dimensional augmented cube AQ n , introduced by Choudum and Sunitha in 2002, reserves several excellent topological properties. As | V (AQ n) | = 2 n , the h-extra three-component edge-connectivity of AQ n is well-defined for each integer h with 1 ≤ h ≤ ⌊ 2 n / 3 ⌋ . In this paper, a generalization of Xu et al.'s conclusion is obtained that finds an upper bound for the exact value of general h-extra three-component edge-connectivity of AQ n and shows that it is sharp for 1 ≤ h ≤ 2 n 2 - 1 and h = 2 c where 1 ≤ c ≤ n - 2 . Let h = ∑ i = 0 s 2 t i be a positive integer with t 0 > t 1 > ⋯ > t s ≥ 0 . Let δ = 0 if h is even and δ = 1 if h is odd. Specifically, c λ 3 h (AQ n) = (4 n - 4) h - 2 ∑ i = 0 s (2 t i - 1) 2 t i - 2 ∑ i = 0 s 4 i · 2 t i - δ for n ≥ 4 , h ≤ 2 n 2 - 1 , and c λ 3 2 c (AQ n) = (2 n - 2 c - 1) 2 c + 1 for n ≥ 4 and 1 ≤ c ≤ n - 2 . [ABSTRACT FROM AUTHOR]

Forest fire is natural disasters that are sudden, destructive and difficult to handle and rescue, with millions of hectares of forests burned every year all over the world, causing serious ecological damage, loss of life and property. Therefore, timely detection and treatment of early fire is of positive and important significance for forest fire early control. The fire detection method based on image processing is one of the most important means of preventing the occurrence of large-scale forest fires at present by extracting the flame and smoke features in the image and quickly determining the location of the fire. The current deep learning-based forest fire early detection methods have problems such as high false alarm rate due to small detection targets and complex environmental background, and large number of detection model parameters. In view of this, this paper proposes a lightweight early forest fire and smoke detection method based on GS-YOLOv5. Firstly, this paper proposes a novel Super-SPPF structure to replace the SPPF structure in YOLOv5, by which the output of the feature extraction network is used as input, and the input is divided into two branches to retain more semantic information, and the GhostConv operation is performed separately to reduce the number of model parameters. The Super-SPPF structure performs the serial MaxPooling operation on one of the branches, which improves the computation speed by choosing a smaller pooling kernel, and then fuses the outputs of the two branches to reduce the false alarm rate of the detection model. Secondly, C3Ghost is utilized instead of the C3 module in YOLOv5 to further reduce the number of detection model parameters. Finally, the coordinate attention (CA) module is introduced in backbone of YOLOv5 to obtain the relationship between channels and space, which enables the network to obtain the location information of interest more accurately and further improves the detection accuracy of early fires. In this paper, a self-constructed DL-Fire dataset is used to verify the performance of the GS-YOLOv5 detection model by collecting environmental interference samples and combining them with the D-Fire dataset. The experimental results show that the detection accuracy of GS-YOLOv5 is 95.9%, and the model size is 10.58 mb. Compared with YOLOv5, false alarm rate is reduced from 12% to 6% and computational complexity is reduced from 16.0 GFlops to 12.8 GFlops by GS-YOLOv5. [ABSTRACT FROM AUTHOR]

Reconfigurable hardware, extensively employed in mission-critical digital applications like space and military electronics due to its adaptability, encounters the issue of soft errors, especially in control path elements, which could result in functional failure. Various system-level fault tolerance methodologies exist, and this paper implements a bio-inspired fault tolerance technique called evolvable hardware (EHW). The preferred implementation of the EHW system involves hosting the evolutionary algorithm on the processor alongside the reconfigurable hardware. However, this approach encounters delays in the intercommunication of the evolved circuit between the reconfigurable hardware and the processor. To address this issue, the paper proposes a two-tier architecture to achieve absolute fault mitigation in the controller. In this architecture, Tier-1 involves the digital implementation of the genetic algorithm on the reconfigurable hardware to mitigate errors in the controller, while Tier-2 focuses on mitigating errors occurring in Tier-1. The aim is to establish an absolute and self-resilient controller hardware to mitigate faults. The study simulates faults at the target circuit and genetic module as a proof of concept. The proposed two-tier single event upset (SEU) mitigation is deployed on Microsemi's ProAsic3e FPGA (Field Programmable Gate Array), achieving an average efficiency of 91%. This efficiency is accompanied by ten times lesser resource utilization compared to traditional methodologies and a 30% accelerated speed when compared to hybrid evolvable systems. [ABSTRACT FROM AUTHOR]

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight; an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed; and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers. [ABSTRACT FROM AUTHOR]