131 citations · 866 across the 39 of their papers we have counts for
40 papers
Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Saurabh Bagchi, Hyunseung Kim, Tarek Abdelzaher +20
Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks…
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
Ruijie Wang, Baoyu Li, Yichen Lu +6
This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \t…
Unsupervised Image Classification by Ideological Affiliation from User-Content Interaction Patterns
Xinyi Liu, Jinning Li, Dachun Sun +6
The proliferation of political memes in modern information campaigns calls for efficient solutions for image classification by ideological affiliation. While significant advances h…
MOSAIC: Spatially-Multiplexed Edge AI Optimization over Multiple Concurrent Video Sensing Streams
Ila Gokarn, Hemanth Sabella, Yigong Hu +2
Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase…
Word Embeddings Are Steers for Language Models
Chi Han, Jialiang Xu, Manling Li +5
Language models (LMs) automatically learn word embeddings during pre-training on language corpora. Although word embeddings are usually interpreted as feature vectors for individua…
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
Ruijie Wang, Zheng Li, Jingfeng Yang +4
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages b…
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Yu Meng, Martin Michalski, Jiaxin Huang +3
Recent studies have revealed the intriguing few-shot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount o…
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
Ruijie Wang, Zheng Li, Dachun Sun +4
In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entiti…
Phy-Taylor: Physics-Model-Based Deep Neural Networks
Yanbing Mao, Lui Sha, Huajie Shao +3
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To ad…
Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
Dongxin Liu, Peng Wang, Tianshi Wang +1
This paper presents a semi-supervised learning framework that is new in being designed for automatic modulation classification (AMC). By carefully utilizing unlabeled signal data w…
Cost Function Learning in Memorized Social Networks with Cognitive Behavioral Asymmetry
Yanbing Mao, Jining Li, Naira Hovakimyan +2
This paper investigates the cost function learning in social information networks, wherein the influence of humans' memory on information consumption is explicitly taken into accou…
RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
Ruijie Wang, Zheng Li, Danqing Zhang +4
With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. R…
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving
Jiyang Chen, Simon Yu, Rohan Tabish +4
Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the…
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Jinning Li, Huajie Shao, Dachun Sun +6
This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space a…
DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
Ruijie Wang, Zijie Huang, Shengzhong Liu +7
This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff…
Dynamics of Public Opinion Evolution with Asymmetric Cognitive Bias
Yanbing Mao, Naira Hovakimyan, Tarek Abdelzaher
In this paper, we propose a pubic opinion model with incorporation of asymmetric cognitive bias: confirmation bias and negativity bias. We then investigate the generic modeling gui…
Controllable and Diverse Text Generation in E-commerce
Huajie Shao, Jun Wang, Haohong Lin +4
In E-commerce, a key challenge in text generation is to find a good trade-off between word diversity and accuracy (relevance) in order to make generated text appear more natural an…
Social System Inference from Noisy Observations
Yanbing Mao, Naira Hovakimyan, Tarek Abdelzaher +1
This paper studies social system inference from a single trajectory of public evolving opinions, wherein observation noise leads to the statistical dependence of samples on time an…
Scheduling Real-time Deep Learning Services as Imprecise Computations
Shuochao Yao, Yifan Hao, Yiran Zhao +6
The paper presents an efficient real-time scheduling algorithm for intelligent real-time edge services, defined as those that perform machine intelligence tasks, such as voice reco…
ControlVAE: Tuning, Analytical Properties, and Performance Analysis
Huajie Shao, Zhisheng Xiao, Shuochao Yao +3
This paper reviews the novel concept of controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic proper…
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
Huajie Shao, Haohong Lin, Qinmin Yang +3
This paper challenges the common assumption that the weight , in -VAE, should be larger than in order to effectively disentangle latent factors. We demonstrate that -V…
New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges
Saurabh Bagchi, Tarek F. Abdelzaher, Ramesh Govindan +4
The field of IoT has blossomed and is positively influencing many application domains. In this paper, we bring out the unique challenges this field poses to research in computer sy…
Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
Chaoqi Yang, Ruijie Wang, Shuochao Yao +1
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in inform…
Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US
Chaoqi Yang, Ruijie Wang, Fangwei Gao +3
Recent re-opening policies in the US, following a period of social distancing measures, introduced a significant increase in daily COVID-19 infections, calling for a roll-back or s…