This paper summarizes the application and security situation of the current mainstream database system in China through analyzing the database security problems faced by China's current traffic building field. Tak...
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ISBN:
(数字)9798350360745
ISBN:
(纸本)9798350371536
This paper summarizes the application and security situation of the current mainstream database system in China through analyzing the database security problems faced by China's current traffic building field. Taking OpenGuss domestic database in the traffic building field as the application background, this paper analyzes its security architecture and existing security problems, and proposes a security detection AI model that conforms to OpenGuss AI architecture, This model can effectively solve the random and uncertain RPC security access problem in the distributed information system of large traffic buildings, improve the monitoring performance of API access exceptions at the data level, and has very important significance and value in promoting the application of domestic database systems in the field of traffic buildings and solving the current security problems they face.
Similar to local file system checkers such as e2fsck for Ext4, a parallel file system (PFS) checker ensures the file system's correctness. The basic idea of file system checkers is straightforward: important metad...
Similar to local file system checkers such as e2fsck for Ext4, a parallel file system (PFS) checker ensures the file system's correctness. The basic idea of file system checkers is straightforward: important metadata are stored redundantly in separate places for cross-checking; inconsistent metadata will be repaired or overwritten by its ‘more correct' counterpart, which is defined by the developers. Unfortunately, implementing the idea for PFSes is non-trivial due to the system complexity. Although many popular parallel file systems already contain dedicated checkers (e.g., LFSCK for Lustre, BeeGFS-FSCK for BeeGFS, mmfsck for GPFS), the existing checkers often cannot detect or repair inconsistencies accurately due to one fundamental limitation: they rely on a fixed set of consistency rules predefined by developers, which cannot cover the various failure scenarios that may occur in *** this study, we propose a new graph-based method to build PFS checkers. Specifically, we model important PFS metadata into graphs, then generalize the logic of cross-checking and repairing into graph analytic tasks. We design a new graph algorithm, FaultyRank, to quantitatively calculate the correctness of each metadata object. By leveraging the calculated correctness, we are able to recommend the most promising repairs to users. Based on the idea, we implement a prototype of FaultyRank on Lustre, one of the most widely used parallel file systems, and compare it with Lustre's default file system checker LFSCK. Our experiments show that FaultyRank can achieve the same checking and repairing logic as LFSCK. Moreover, it is capable of detecting and repairing complicated PFS consistency issues that LFSCK can not handle. We also show the performance advantage of FaultyRank compared with LFSCK. Through this study, we believe FaultyRank opens a new opportunity for building PFS checkers effectively and efficiently.
Ordered key-value stores (or sorted maps/dictionaries) are a fundamental building block in a large variety of both sequential and parallel/distributed algorithms. However, most state-of-art approaches are either based...
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Ordered key-value stores (or sorted maps/dictionaries) are a fundamental building block in a large variety of both sequential and parallel/distributed algorithms. However, most state-of-art approaches are either based on ephemeral in-memory representations that are difficult to persist and/or not scalable enough under concurrent access (e.g., red-black trees, skip lists), and/or not lightweight enough (e.g. database engines). Furthermore, there is an increasing need to provide versioning support, which is needed in a variety of scenarios: introspection, provenance tracking, revisiting previous intermediate results. To address these challenges, we propose a new lightweight dictionary data structure that simultaneously provides support for multi-versioning, persistency and scalability under concurrent access. We demonstrate its effectiveness through a series of experiments, in which it outperforms several state-of-art approaches, both in terms of vertical and horizontal scalability.
Internet of things system is generating huge amounts of data all the time, and the accompanying challenge is the difficulty of real-time processing and analysis. In this paper, we propose a real-time subspace learning...
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ISBN:
(数字)9798331540241
ISBN:
(纸本)9798331540258
Internet of things system is generating huge amounts of data all the time, and the accompanying challenge is the difficulty of real-time processing and analysis. In this paper, we propose a real-time subspace learning framework based on federated learning (RTFed), in which participating devices communicate in over-air mode. RTFed reduces the influence of missing data and outliers on distributed learning by using subspace tracking method under the condition of data sharing constraints. Moreover, the proposed algorithm effectively alleviates the common channel noise problem in over-air broadcasting by using compressive sensing method. In addition, RTFed uses the multi-core parallelism of CPUs and GPUs to enhance the processing of real-time data, ensuring timely updates for applications such as recommendation systems and social networks. The performance of the framework was demonstrated through extensive numerical experiments and real-world data evaluation.
Federated Learning (FL) is a popular distributed machine learning paradigm that enables devices to work together to train a centralized model without transmitting raw data. However, when the model becomes complex, mob...
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Federated Learning (FL) is a popular distributed machine learning paradigm that enables devices to work together to train a centralized model without transmitting raw data. However, when the model becomes complex, mobile devices’ communication overhead can be unacceptably large in traditional FL methods. To address this problem, Federated Distillation (FD) is proposed as a federated version of knowledge distillation. Most of the recent FD methods calculate the model output (logits) of each client as the local knowledge on a public proxy dataset and do distillation with the average of the clients’ logits on the server side. Nevertheless, these FD methods are not robust and perform poorly in the non-IID (data is nonindependent and non-identically distributed) scenario such as Federated Recommendation (FR). In order to eliminate the non-IID problem and apply FD in FR, we proposed a novel method named FedDyn to construct a proxy dataset and extract local knowledge dynamically in this paper. In this method, we replaced the average strategy with focus distillation to strengthen reliable knowledge, which solved the non-IID problem that the local model has biased knowledge. The average strategy is a dilution and perturbation of knowledge since it treats reliable and unreliable knowledge equally important. In addition, to prevent inference of private user information from local knowledge, we used a method like local differential privacy techniques to protect this knowledge on the client side. The experimental results showed that our method has a faster convergence speed and lower communication overhead than the baselines on three datasets, including MovieLens-10OK, MovieLens-IM and Pinterest.
We study several fundamental problems in the k-machine model, a message-passing model for large-scale distributed computations where k >= 2 machines jointly perform computations on a large input of size N, (typical...
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ISBN:
(纸本)9781665440660
We study several fundamental problems in the k-machine model, a message-passing model for large-scale distributed computations where k >= 2 machines jointly perform computations on a large input of size N, (typically, N >> k). The input is initially partitioned (randomly or in a balanced fashion) among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds of the computation. Our main result is a general technique for designing efficient deterministic distributed algorithms in the k-machine model using PRAM algorithms. Our technique works by efficiently simulating PRAM algorithms in the k-machine model in a deterministic way. This simulation allows us to arrive at new algorithms in the k-machine model for some problems for which no efficient k-machine algorithms are known before and also improve on existing results in the k-machine model for some problems. While our simulation allows us to obtain k-machine algorithms for any problem with a known PRAM algorithm, we mainly focus on graph problems. For an input graph on n vertices and m edges, we obtain (O) over tilde (m/k(2)) round(4) algorithms for various graph problems such as r-connectivity for r = 1,2,3,4, minimum spanning tree (MST), maximal independent set (MIS), (Delta + 1)-coloring, maximal matching, ear decomposition, and spanners under the assmuption that the edges of the input graph are partitioned (randomly, or in an arbitrary, but balanced, fashion) among the k machines. For problems such as connectivity and MST, the above bound is (essentially) the best possible (up to logarithmic factors). Our simulation technique allows us to obtain the first known efficient deterministic algorithms in the k-machine model for other problems with known deterministic PRAM algorithms.
Vision-based autonomous navigation is pivotal in mobile robot technology, involving autonomous movement, path planning, obstacle avoidance, and target reaching in unknown environments. Reinforcement Learning (RL) offe...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Vision-based autonomous navigation is pivotal in mobile robot technology, involving autonomous movement, path planning, obstacle avoidance, and target reaching in unknown environments. Reinforcement Learning (RL) offers a trial-and-error-driven learning method for autonomous navigation. However, training RL models directly on physical robots is time-intensive and poses potential risks. Simulators accelerate RL training efficiency and reduce costs, but they typically provide only approximate models of robot dynamics and their interactions with the environment. This leads to a simulation-to-reality gap (Sim2Real Gap), where strategies trained in simulation under-perform in real-world applications, sometimes resulting in task failures. To address the issue of reducing the Sim2Real Gap in visual sampling training, this paper establishes a bridging plugin between the simulator and ROS, enabling the subscription to interaction data between the ROS-based robot and its real-world environment. We propose a Sim2Real domain adaptation method based on CycleGAN, which generates effective visual observations for autonomous navigation learning. The experimental results demonstrate that this domain adaptation-based method, by utilizing minimal real-world data, significantly reduces the Sim2Real Gap compared to approaches relying solely on simulation data, achieving a performance improvement of 62.38%.
Protocol implementations are stateful and reactive systems, where the protocol process communicates with the client through a session. Complex state changes of implementations limit the efficiency of fuzzing. Current ...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Protocol implementations are stateful and reactive systems, where the protocol process communicates with the client through a session. Complex state changes of implementations limit the efficiency of fuzzing. Current methods typically send a mutated message sequence to the protocol implementation, then terminate the session and evaluate the message sequence based on the feedback. We observe the following issues: (1) The state changes of the protocol implementation are random because of mutated messages, and message schedule methods that do not consider the real-time state of the target are blind; (2) The value of a message can vary significantly in different states, making seeds value evaluation method based on the entire message sequence insufficiently precise. (3) Existing fuzzing approaches actively terminate sessions after a test, resulting in resource *** solve these problem, we propose SSFuzz, a state sensitive fuzzing approach for protocol implementations. First, it monitors the state transitions and path feedback of the protocol implementation in real-time. Second, it dynamically schedules messages based on real-time state transitions during testing. According to the state transitions and path feedback after processing a message, it then conducts more precise evaluation of the messages. Last, we achieve testing multiple states within one session by reusing session. We believe that our approach better adapts to the characteristics of protocol implementations. We validated our approach on 9 widely used protocol implementations from ProFuzzBench. Compared to the state-of-the-art network protocol greybox fuzzing tool AFLnet, SSFuzz can increase discovered branch coverage on average 1.31% and discovers 25.28% more unique crashes within 24 hours.
Maximum power point tracking (MPPT) is a crucial technology for enhancing photovoltaic (PV) array power generation efficiency. Under scenarios with partial shading, the output power characteristic curve of a PV array ...
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ISBN:
(数字)9798350361001
ISBN:
(纸本)9798350361018
Maximum power point tracking (MPPT) is a crucial technology for enhancing photovoltaic (PV) array power generation efficiency. Under scenarios with partial shading, the output power characteristic curve of a PV array often displays multiple peaks, necessitating the use of global peak-seeking techniques. Particle Swarm Optimization (PSO) and its advanced variants have been widely adopted to address global optimization challenges. However, the current landscape primarily includes PSO based MPPT methods designed for single inverters, which tend to exhibit slow optimization speeds. This paper presents an Internet of Things (IoT)-based multi- inverters approach for global MPPT. In this approach, all inverters within the network are treated as a single particle. This transformation shifts the conventional serial PSO operation into a parallel one, enabling data exchange among inverters to jointly track the maximum power point. Simulation results demonstrate that the proposed methodology significantly accelerates the optimization process, effectively mitigating the limitations of traditional approaches.
Coarse-Grained Reconfigurable Arrays (CGRAs) are promising accelerators in the rapidly evolving field of high-performance computing (HPC). However, their potential is limited by the inability of compilers to efficient...
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ISBN:
(数字)9798350364606
ISBN:
(纸本)9798350364613
Coarse-Grained Reconfigurable Arrays (CGRAs) are promising accelerators in the rapidly evolving field of high-performance computing (HPC). However, their potential is limited by the inability of compilers to efficiently map complex application kernels to architectures. In this paper, we propose an architecture-agnostic mapping framework called AGILE, which has a loosely coupled flow that contains architecture modeling and dataflow intermediate representation (IR) generation, hierarchical mapping and design space exploration (DSE). We extend mapping to end-to-end flow for better evaluation of architectures, flexible modeling and IR allow for adapting to and exploring various architectures, and hierarchical dataflow mapping methodology enables better evaluation of proposed architectures. The experiments show that our partitioning algorithm adapts to large-scale DFGs, and divide-and-conquer reduces the problem size. Our framework achieves a 1.86 × and 1.4 × improvement in throughput over the baseline CGRA when compared to CGRA-ME and LISA, respectively, and offers significant mapping acceleration, being 133 × and 33.3 × faster. On the Hycube architecture, we improve utilization and throughput by 4× compared to Morpher. Moreover, the DSE of a range of architectures demonstrates the effectiveness of our approach.
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