Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this...
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ISBN:
(纸本)9798350323481
Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.
There is now a large body of techniques, many based on formal methods, for describing and realizing complex robotics tasks, including those involving a variety of rich goals and time-extended behavior. This paper expl...
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Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services w...
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Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.
Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are...
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The purpose of this research is to develop a method for vehicle collision prediction from visual sizes of objects. There are three main stages in the proposed method. The first stage is object detection and classifica...
The purpose of this research is to develop a method for vehicle collision prediction from visual sizes of objects. There are three main stages in the proposed method. The first stage is object detection and classification using You Only Look Once version 7 (YOLOv7) and object tracking using Simple Online and Realtime Tracking with a deep association metric (DeepSORT). Then, linear regression is applied with the data gathered from the first stage to predict the shape of the bounding box and the coordinates of the centroid of the bounding box of the object at various time intervals. The last stage is vehicle collision prediction along with the time interval in which the collision may occur. If the predicted bounding box of the object overlaps with the defined area in the video, the proposed method predicts that the collision will occur. From the experimental results, the proposed method can trigger collision warning and help reduce the risk of road accidents with car dash camera.
This paper proposes a constraint-aware safety control approach via adaptive dynamic programming (ADP) to address the control optimization issues for discrete-time systems subjected to state constraints. First, the con...
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Internet of Things (IoT) connects objects and allows data exchange between smart things and the Internet. Web of Things (WoT) further integrates smart things into the Web architecture by leveraging existing standardiz...
Internet of Things (IoT) connects objects and allows data exchange between smart things and the Internet. Web of Things (WoT) further integrates smart things into the Web architecture by leveraging existing standardized Web technologies. In this paper, we envision Semantic Web of Things (SWoT), aiming to exchange semantically rich and machine understandable information with smart things. This article discusses the landscape of IoT and WoT through the past and its future prospects, presents a SWoT reference architecture, and conducts a gap analysis. Aiming to highlight the deficiencies of existing solutions, we carry out a gap analysis with respect to (i) extension of WoT, (ii) semantic knowledge base, (iii) semantic reasoning, (iv) semantic interoperability, and (v) semantic service composition. Based on the results of our analysis, we conclude this article with a list of recommendations in order to fill the gaps. The main contributions pertain to a SWoT reference architecture with key SWoT building blocks and an analysis of technologies to support SWoT.
We theoretically study the influence of frequency uncertainties on the operation of a Kerr-cat qubit. As the mean photon number increases, Kerr-cat qubits provide an increasing level of protection against phase errors...
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Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly ...
ISBN:
(纸本)9798331314385
Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, current quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to quantize models. This paper boldly quantizes the weight matrices of LLMs to 1-bit, paving the way for the extremely low bit-width deployment of LLMs. For this target, we introduce a 1-bit model compressing framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the quantization framework. Sufficient experimental results indicate that OneBit achieves good performance (at least 81% of the non-quantized performance on LLaMA models) with robust training processes when only using 1-bit weight matrices. Code and checkpoints are available at https://***/xuyuzhuang11/OneBit
Neural Machine Translation (NMT) is one of the advanced approaches of Machine Translation (MT) that has recently gained popularity. A significant amount of parallel corpus is required to achieve a sound translation sy...
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