Search Results - Theory of computation Theory and algorithms for application domains

HIFI: Explaining and Mitigating Algorithmic Bias Through the Lens of Game-Theoretic Interactions by Zhang, Lingfeng, Wang, Zhaohui, Zhang, Yueling, Zhang, Min, Wang, Jiangtao

“…Machine Learning (ML) algorithms are increasingly used in decision-making process across various social-critical domains, but they often somewhat inherit and amplify bias from their training data, leading to unfair and unethical outcomes…”
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Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System by Huang, Jun, Yang, Yang, Yu, Hang, Li, Jianguo, Zheng, Xiao

“…Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services…”
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Making Fair ML Software using Trustworthy Explanation by Chakraborty, Joymallya, Peng, Kewen, Menzies, Tim

“…Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice…”
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Invited: Algorithms and Architectures for Accelerating Long Read Sequence Analysis by Gamaarachchi, Hasindu, Liyanage, Kisaru, Parameswaran, Sri

“…; and three, novel algorithms and domain-specific architectures for rapid in situ analysis of third-generation sequencing data…”
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Holistic Design towards Resource-Stringent Binary Vector Symbolic Architecture by Duan, Shijin, Narkthong, Nuntipat, Luo, Yukui, Ren, Shaolei, Xu, Xiaolin

“… This paper introduces UniVSA, a co-optimized binary VSA framework for both algorithm and hardware…”
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Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining by Shen, Weijun, Li, Yanhui, Chen, Lin, Han, Yuanlei, Zhou, Yuming, Xu, Baowen

“…With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models…”
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Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders by Ludmir, Jason Zev, Rebello, Sophia, Ruiz, Jacob, Patel, Tirthak

“…Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum…”
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Optimal Memory Allocation and Scheduling for DMA Data Transfers under the LET Paradigm by Pazzaglia, Paolo, Casini, Daniel, Biondi, Alessandro, Natale, Marco Di

“…The Logical Execution Time (LET) paradigm is increasingly used to achieve predictable communications in modern multicore automotive applications…”
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ParGNN: A Scalable Graph Neural Network Training Framework on multi-GPUs by Gu, Junyu, Li, Shunde, Cao, Rongqiang, Wang, Jue, Wang, Zijian, Liang, Zhiqiang, Liu, Fang, Li, Shigang, Zhou, Chunbao, Wang, Yangang, Chi, Xuebin

“… over-partition to alleviate load imbalance. Based on the over-partition results, we present a subgraph pipeline algorithm to overlap communication and computation while maintaining the accuracy of GNN training…”
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A Universal Method for Task Allocation on FP-FPS Multiprocessor Systems with Spin Locks by Zhao, Shuai, Chen, Nan, Fang, Yinjie, Li, Zhao, Chang, Wanli

“…Many complex real-time systems, such as increasingly automated vehicles and 5G wireless base stations, contain a large amount of shared resources that must be…”
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Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation by Cheng, Jinglei, Zhou, Ruilin, Gan, Yuhang, Qian, Chen, Liu, Junyu

“…We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD…”
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A Multiple Representation Transformer with Optimized Abstract Syntax Tree for Efficient Code Clone Detection by Yu, Tianchen, Yuan, Li, Lin, Liannan, He, Hongkui

“…Over the past decade, the application of deep learning in code clone detection has produced remarkable results…”
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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers by Li, Zhen, Zhang, Ruqian, Zou, Deqing, Wang, Ning, Li, Yating, Xu, Shouhuai, Chen, Chen, Jin, Hai

“…Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship…”
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SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice by Ren, Rui, Yang, Jingbang, Yang, Linxiao, Gu, Xinyue, Sun, Liang

“…) approach to select rules iteratively, maximizing a non-monotone submodular lower bound. Compared with existing fault localization algorithms, our algorithm can adapt…”
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MAGIKA: AI-Powered Content-Type Detection by Fratantonio, Yanick, Invernizzi, Luca, Farah, Loua, Thomas, Kurt, Zhang, Marina, Albertini, Ange, Galilee, Francois, Metitieri, Giancarlo, Cretin, Julien, Petit-Bianco, Alex, Tao, David, Bursztein, Elie

“…, and a variety of security applications. In this paper, we introduce Magika, a novel AI-powered content-type detection tool…”
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ADAMAS: Adaptive Domain-Aware Performance Anomaly Detection in Cloud Service Systems by Gu, Wenwei, Gu, Jiazhen, Liu, Jinyang, Chen, Zhuangbin, Zhang, Jianping, Kuang, Jinxi, Feng, Cong, Yang, Yongqiang, Lyu, Michael R.

“…A common practice in the reliability engineering of cloud services involves the collection of monitoring metrics, followed by comprehensive analysis to…”
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Famos: Fault Diagnosis for Microservice Systems Through Effective Multi-Modal Data Fusion by Duan, Chiming, Yang, Yong, Jia, Tong, Liu, Guiyang, Liu, Jinbu, Zhang, Huxing, Zhou, Qi, Li, Ying, Huang, Gang

“…Accurately diagnosing the fault that causes the failure is crucial for maintaining the reliability of a microservice system after a failure occurs. Mainstream…”
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IFHE: Intermediate-Feature Heterogeneity Enhancement for Image Synthesis in Data-Free Knowledge Distillation by Chen, Yi, Liu, Ning, Ren, Ao, Yang, Tao, Liu, Duo

“…Data-free knowledge distillation (DFKD) explores training a compact student network only by a pre-trained teacher without real data. Prevailing DFKD methods…”
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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy by Jiang, Zihan, Mao, Fubing, Guo, Yapu, Liu, Xu, Liu, Haikun, Liao, Xiaofei, Jin, Hai, Zhang, Wei

“…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…”
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DuQTTA: Dual Quantized Tensor-Train Adaptation with Decoupling Magnitude-Direction for Efficient Fine-Tuning of LLMs by Dong, Haoyan, Chen, Hai-Bao, Chang, Jingjing, Yang, Yixin, Gao, Ziyang, Ji, Zhigang, Wang, Runsheng, Huang, Ru

“…Recent parameter-efficient fine-tuning (PEFT) techniques have enabled large language models (LLMs) to be efficiently fine-tuned for specific tasks, while…”
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