Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty

Pan, Zhenyu; Sharma, Anshujit; Hu, Jerry Yao-Chieh; Liu, Zhuo; Li, Ang; Liu, Ran; Huang, Michael C.; Geng, Tong

doi:10.1609/aaai.v37i8.26121

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Recent studies developed deep learning models, such as bidirectional transformers(BERT) [10](which are widely using in review tasks [13]), recurrent neural network(RNN), [27] recursive neural networks (RvNN) [39], Long-Short Term Memory (LSTM), generative adversarial network (GAN) [16,56], transformer [7,20] and Convolutional Neural Networks (CNN) [11,17], to learn sequential features from information propagation patterns over time. [1] These models also have widespread applications in other fields, such as data security [21,22], vision learning [18,52], material analysis [15,51] , compiling [33] and hardware designing [34], E-commerce [37], image segmentation [43], traffic controlling [38], communication [28,32] and Aerial Search [30] . These methods, however, only learn the correlations from local neighbors in the structure of information propagation while ignore the global structures of rumor dispersion.…”

Section: Introductionmentioning

confidence: 99%

Rumor Detection with A Novel Graph Neural Network Approach

Liu,

Cai,

et al. 2024

AJST

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The wide spread of rumors[3] on social media has caused a negative impact on people's daily life, leading to potential panic, fear and mental health problems for the public.[47] How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detect rumors[6], while very few works focus on correlation among users that they may coordinate to spread rumors in order to gain a large popularity. In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media. Specifically, we leverage graph neural networks to learn the representations of user correlation from a bipartite graph[5] that describes the correlations between users and source tweets[12], and the representations of information propagation with a tree structure. Then we combine the learned representations from these two modules to classify the rumors. Since malicious users intend to subvert our model after deployment, we further develop a greedy attack scheme to analyze the cost of three adversarial attacks: graph attack, comment attack and joint attack. Evaluation results on two public datasets illustrate that the proposed MODEL outperforms the state-of-the-art rumor detection models. We also demonstrate our method performs well for early rumor detection.[8] Moreover, the proposed detection method is more robust to adversarial attacks compared to the best existing method. Importantly, we show that it requires high cost for attackers to subvert user correlation pattern, demonstrating the importance of considering user correlation for rumor detection.

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Section: Introductionmentioning

confidence: 99%

Rumor Detection with A Novel Graph Neural Network Approach

Liu,

Cai,

et al. 2024

AJST

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Augmenting an electronic Ising machine to effectively solve boolean satisfiability

Sharma,

Burns,

Hahn

et al. 2023

Sci Rep

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With the slowdown of improvement in conventional von Neumann systems, increasing attention is paid to novel paradigms such as Ising machines. They have very different approach to solving combinatorial optimization problems. Ising machines have shown great potential in solving binary optimization problems like MaxCut. In this paper, we present an analysis of these systems in boolean satisfiability (SAT) problems. We demonstrate that, in the case of 3-SAT, a basic architecture fails to produce meaningful acceleration, largely due to the relentless progress made in conventional SAT solvers. Nevertheless, careful analysis attributes part of the failure to the lack of two important components: cubic interactions and efficient randomization heuristics. To overcome these limitations, we add proper architectural support for cubic interaction on a state-of-the-art Ising machine. More importantly, we propose a novel semantic-aware annealing schedule that makes the search-space navigation much more efficient than existing annealing heuristics. Using numerical simulations, we show that such an “Augmented” Ising Machine for SAT is projected to outperform state-of-the-art software-based, GPU-based and conventional hardware SAT solvers by orders of magnitude.

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Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

Nofal

2024

Sci Rep

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We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic status (i.e., low, mild, or high) after 6 h on a given street can be predicted, provided the hourly traffic status of the street in the last 19 h. To pursue our objectives, we exploited historical hourly traffic data from Google Maps for a main street in the capital city of Jordan, Amman. We employ several machine learning algorithms to construct our predictive models: neural networks, gradient boosting, support vector machines, AdaBoost, and nearest neighbors. Our experimental results confirm our investigations positively, such that our models have an accuracy of around 98–99% in predicting vehicle travel time and traffic status on our study’s street for the target hour (i.e., after 6 h from a specific point in time). Moreover, given our time series traffic data and our constructed predictive models, we inspect the most critical indicators of street traffic status and vehicle travel time after 6 h on our study’s street. However, as we elaborate in the article, our predictive models do not agree on the degree of importance of our data features.

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Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty

Cited by 9 publications

References 24 publications

Rumor Detection with A Novel Graph Neural Network Approach

Rumor Detection with A Novel Graph Neural Network Approach

Augmenting an electronic Ising machine to effectively solve boolean satisfiability

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

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