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arXiv 2024年

作者： Matejek, Brian Elenius, Daniel Gentry, Cale Stoker, David Cobb, Adam Computer Science Laboratory SRI International United States Advanced Technology and Systems Division SRI International United States

We propose a resource-constrained heuristic for instances of Max-SAT that iteratively decomposes a larger problem into smaller subcomponents that can be solved by optimized solvers and hardware. The unconstrained outer loop maintains the state space of a given problem and selects a subset of the SAT variables for optimization independent of previous calls. The resource-constrained inner loop maximizes the number of satisfiable clauses in the "sub-SAT" problem. Our outer loop is agnostic to the mechanisms of the inner loop, allowing for the use of traditional solvers for the optimization step. However, we can also transform the selected "sub-SAT" problem into a quadratic unconstrained binary optimization (QUBO) one and use specialized hardware for optimization. In contrast to existing solutions that convert a SAT instance into a QUBO one before decomposition, we choose a subset of the SAT variables before QUBO optimization. We analyze a set of variable selection methods, including a novel graph-based method that exploits the structure of a given SAT instance. The number of QUBO variables needed to encode a (sub-)SAT problem varies, so we additionally learn a model that predicts the size of sub-SAT problems that will fit a fixed-size QUBO solver. We empirically demonstrate our results on a set of randomly generated Max-SAT instances as well as real world examples from the Max-SAT evaluation benchmarks and outperform existing QUBO decomposer solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Exploring Auditory Hand Guidance for Eyes-free 3D Path Tracing

Exploring Auditory Hand Guidance for Eyes-free 3D Path Traci...

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Abe, Yuki Hara, Kotaro Sakamoto, Daisuke Ono, Tetsuo Human-Computer Interaction Lab. Hokkaido University Hokkaido Sapporo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Division of Computer Science and Information Technology Hokkaido University Hokkaido Sapporo Japan Faculty of Engineering Kyoto Tachibana University Kyoto Japan

ISBN: (纸本)9798400713958

Guiding a user’s hand along a 3D path can help individuals avoid obstacles and manipulate everyday items with eyes-free. While prior work focused on haptic approaches using robots, auditory approaches for 3D path guidance remain underexplored. We prototyped and assessed two auditory hand guidance techniques for 3D path tracing: (1) VERBAL repeating spoken directional prompts;(2) Follow-Your-Finger (FYF) using the user’s index finger in moving hand as an embodied reference and sonifying whether the user should follow the index finger direction. In a controlled pilot study with 12 sighted participants under an eyes-free condition, VERBAL yielded about 1.5 times faster performance than FYF, while FYF achieved an error rate of less than half VERBAL’s. We thus recommend using VERBAL for wider paths with fewer obstacles and FYF for narrower paths with higher collision risks. We also discuss the research direction for applying these findings to real-world tasks. © 2025 Copyright held by the owner/author(s).

关键词： Three dimensional computer graphics

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SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning 27

SETTP: Style Extraction and Tunable Inference via Dual-Level...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaïane, Osmar Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

ISBN: (纸本)9781643685489

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. © 2024 The Authors.

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Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction

Predicting Drug Effects from High-Dimensional, Asymmetric Dr...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Avishek Bose Guojing Cong Learning Systems Group Data and AI Systems Section Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Graph neural networks (GNNs) have emerged as one of the most effective ML techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using datasets that contain high-dimensional, asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects are impractical. Therefore, an opportunity exists to address this challenge as multitarget prediction problems and predict all drug effects at a time. We developed standard and hybrid GNNs to perform two separate tasks: multiregression for continuous values and multilabel classification for categorical values contained in our datasets. Because multilabel classification makes the target data even more sparse and introduces asymmetric label co-occurrence, learning these models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multilabel classification performances on all the given imbalanced molecular graph datasets. Using the technique, we improve the data imbalance ratio of the drug effects while protecting the datasets' integrity. Finally, we evaluate the multilabel classification performance of the best-performing hybrid GNN model on all the oversampled datasets obtained from the proposed oversampling technique. In all the evaluation metrics (i.e., precision, recall, and F1 score), this model significantly outperforms other ML models, including GNN models when they are trained on the original datasets or oversampled datasets with MLSMOTE, which is a well-known oversampling technique.

关键词： Drugs Training Measurement Correlation Multi label classification Machine learning Predictive models Graph neural networks Data models Standards

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A Lightweight Encoder-Decoder Framework for Carpooling Route Planning

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IEEE Transactions on Mobile computing 2025年

作者： Gao, Yucen Ma, Li Yu, Zhemeng Zhang, Songjian Gao, Hui Fang, Jun Gao, Xiaofeng Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems College of Computer Science and Engineering Shanghai200240 China Didi Global Inc. Beijing100190 China

Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, called Multi-Candidate Carpooling Route Planning (MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets. Our code is available at https://***/GaoYucen/Lite-GD © 2002-2012 IEEE.

关键词： Network coding

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Devil in the Noise: Detecting Advanced Persistent Threats with Backbone Extraction

Devil in the Noise: Detecting Advanced Persistent Threats wi...

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IEEE Symposium on computers and Communications (ISCC)

作者： Caio M. C. Viana Carlos H. G. Ferreira Fabricio Murai Aldri Luiz Dos Santos Lourenço Alves Pereira Júnior Computer Science Division Aeronautics Institute of Technology Brazil Department of Computing and Systems Federal University of Ouro Preto Brazil Data Science & Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Federal University of Minas Gerais Brazil

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

The use of host intrusion detection systems shows promising results in detecting APT campaigns due to the use of systems logs as source data to get more information about system environment. However, dealing with the increase of logs in time while tracking the execution context is a challenge for security analysts. Therefore, this work presents backbone extraction as a crucial preprocessing step, filtering out irrelevant logs. As the logs are modeled as provenance graphs, we discard spurious edges to detect residuals with distinctive node and edge distributions that indicate security threats. By applying our methodology to state-of-the-art benchmark datasets, we observed an increase in the performance of one-class classifiers by up to 62% on F1-score and 48% on recall in the Streamspot dataset and by up to 40% on F1-score and 33% on recall in the DARPA3 THEIA dataset. Moreover, our results indicate mitigation of the dependency explosion problem and underscore the ability of our methodology to improve the detection landscape by shrinking graph sizes without losing essential aspects to characterize attacks.

关键词： computers Filtering Image edge detection Prevention and mitigation Noise Intrusion detection Benchmark testing Explosions Security

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Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling 24

Scalable and Consistent Graph Neural Networks for Distribute...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Shivam Barwey Riccardo Balin Bethany Lusch Saumil Patel Ramesh Balakrishnan Pinaki Pal Romit Maulik Venkatram Vishwanath Transportation and Power Systems Argonne National Laboratory Lemont USA Leadership Computing Facility Argonne National Laboratory Lemont USA Computational Science Division Argonne National Laboratory Lemont USA Information Sciences and Technology Pennsylvania State University University Park USA Mathematics and Computer Science Argonne National Laboratory Lemont USA

ISBN: (纸本)9798350355543

This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer.

关键词： graph neural networks

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On the Security of Double-Block-Length Hash Functions with Rate 1

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Chinese Journal of Electronics 2023年第3期21卷 566-570页

作者： GONG Zheng LUO Yiyuan LAI Xuejia CHEN Kefei School of Computer Science South China Normal University Guangzhou China Department of Computer Science Shanghai Jiaotong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

The security of double-block-length hash functions with rate 1, which are based on a block cipher with a block length of n bits and a key length of 2n bits, was analyzed by Satoh et al. and Hirose. In this paper, we reconsider the security of this general class of hash functions (named FDBL-II for brevity). The new counter-examples and attacks are presented on FDBL-II, which reveal some flaws in the necessary conditions proposed by Satoh et al. and Hirose. Moreover, our analysis shows that all rate-1 hash functions in FDBL-II fail to be optimally (second) preimage resistant. Finally, the necessary conditions are revised for ensuring that a subclass of hash functions in FDBL-II can be optimally secure against collision attacks.

关键词： Hash functions Resistance Ciphers Education Security

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EnFed: An Energy-aware Opportunistic Federated Learning in Resource Constrained Environments for Human Activity Recognition

arXiv

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arXiv 2024年

作者： Mukherjee, Anwesha Buyya, Rajkumar Department of Computer Science Mahishadal Raj College West Bengal Mahishadal721628 India Laboratory School of Computing and Information Systems University of Melbourne Australia

This paper proposes an energy-efficient federated learning method and its application in human activity monitoring and recognition. In the proposed approach, the device that needs a model for an application requests its nearby devices for collaboration. The nearby devices that accept the request, send their model updates to the requesting device. The device receives the model updates from the collaborators and performs aggregation to build its model. As mobile devices have limited battery life, the number of rounds is decided based on the desired accuracy level and battery level of the requesting device. The performance of the proposed approach is evaluated with respect to prediction accuracy, training time, training energy consumption of the device, and response time. We have used two different datasets for performance evaluation. The first dataset contains different types of physical activities and the respective calorie burn. The second dataset is a human activity recognition dataset that considers six types of physical activities. The experimental results show that using the proposed method the training time and training energy consumption of the device are reduced by approximately 59% and 19% for the first and second datasets respectively, than the decentralized federated learning approach, while using LSTM as the underlying data analysis model. The results also present that the proposed method reduces the training time and energy consumption by approximately 55% and 72% for the first and second datasets respectively, than the decentralized federated learning approach while using MLP as the underlying data analysis model. © 2024, CC BY.

关键词： Federated learning

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A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge computing

arXiv

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arXiv 2024年

作者： Mukherjee, Anwesha Buyya, Rajkumar The Department of Computer Science Mahishadal Raj College West Bengal Mahishadal721628 India Laboratory School of Computing and Information Systems University of Melbourne Australia

Computation offloading at lower time and lower energy consumption is crucial for resource limited mobile devices. This paper proposes an offloading decision-making model using federated learning. Based on the task type and the user input, the proposed decision-making model predicts whether the task is computationally intensive or not. If the predicted result is computationally intensive, then based on the network parameters the proposed decision-making model predicts whether to offload or locally execute the task. According to the predicted result the task is either locally executed or offloaded to the edge server. The proposed method is implemented in a real-time environment, and the experimental results show that the proposed method has achieved above 90% prediction accuracy in offloading decision-making. The experimental results also present that the proposed offloading method reduces the response time and energy consumption of the user device by ∼11-31% for computationally intensive tasks. A partial computation offloading method for federated learning is also proposed and implemented in this paper, where the devices which are unable to analyse the huge number of data samples, offload a part of their local datasets to the edge server. For secure data transmission, cryptography is used. The experimental results present that using encryption and decryption the total time is increased by only 0.05-0.16%. The results also present that the proposed partial computation offloading method for federated learning has achieved a prediction accuracy of above 98% for the global model. © 2024, CC BY.

关键词： Federated learning

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