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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Aditya Putra Perdana Prasetyo Mohd. Yazid Idris Hanif Fakhrurroja Deris Stiawan Department of Computer Engineering Universitas Sriwijaya Palembang Indonesia Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

As urban areas face increasing challenges related to air pollution, the integration of edge computing and artificial intelligence (AI) offers a viable solution for real-time monitoring and control. This research focuses on the development of preprocessing techniques that enhance data collection and analysis at the edge, addressing critical issues such as energy consumption and data latency. By proposing a lightweight framework, the study aims to facilitate intelligent processing and data sharing in air quality applications. Additionally, the paper highlights the significance of real-time data in managing indoor air quality (IAQ) and its impact on occupant health and productivity. The ongoing work seeks to create a comprehensive framework that not only improves air quality monitoring but also incorporates predictive analytics to forecast air quality trends, thereby informing policy and operational decisions. Ultimately, this research underscores the potential of edge intelligence to transform air quality management into a proactive and adaptive system, contributing to healthier urban living environments.

关键词： Reviews Urban areas Transforms Air quality Market research Real-time systems Stakeholders Sustainable development Research and development Edge computing

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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Retraction Note: High performance reconciliation for practical quantum key distribution systems

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Optical and Quantum Electronics 2024年第12期56卷 1-2页

作者： Mao, ao-Kun Li, Qiong Hao, Peng-Lei Abd-El-Atty, Bassem Iliyasu, Abdullah M. Department of Computer Science and Technology Harbin Institute of Technology Harbin China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei China Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al- Kharj Saudi Arabia School of Computing Tokyo Institute of Technology Yokohama Japan School of Computer Science and Technology Changchun University of Science and Technology Changchun China

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Decoupling semantic similarity from spatial alignment for neural networks 24

Decoupling semantic similarity from spatial alignment for ne...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tassilo Wald Constantin Ulrich Gregor Köhler David Zimmerer Stefan Denner Michael Baumgartner Fabian Isensee Priyank Jaini Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany Google Deepmind Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and Pattern Analysis and Learning Group Department of Radiation Oncology

ISBN: (纸本)9798331314385

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities.

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Rethinking Super-Resolution as Text-Guided Details Generation

arXiv

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arXiv 2022年

作者： Ma, Chenxi Yan, Bo Lin, Qing Tan, Weimin Chen, Siming School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China School of Data Science Fudan University China

Deep neural networks have greatly promoted the performance of single image super-resolution (SISR). Conventional methods still resort to restoring the single high-resolution (HR) solution only based on the input of image modality. However, the image-level information is insufficient to predict adequate details and photo-realistic visual quality facing large upscaling factors (×8, ×16). In this paper, we propose a new perspective that regards the SISR as a semantic image detail enhancement problem to generate semantically reasonable HR image that are faithful to the ground truth. To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities. Different from existing methods, the proposed TGSR could generate HR image details that match the text descriptions through a coarse-to-fine process. Extensive experiments and ablation studies demonstrate the effect of the TGSR, which exploits the text reference to recover realistic images. © 2022, CC BY.

关键词： Deep neural networks

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IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Shih-Chun Chou Yi-Shen Chen Ping-Xiang Chen Yuan-Hao Chang Ming-Chang Yang Tei-Wei Kuo Yu-Fang Chen Yu-Ming Chang Department of Computer Science and Information Engineering National Taiwan University Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Department of Computer Science National Tsing Hua University Institute of Information Science Academia Sinica Department of Computer Science and Engineering The Chinese University of Hong Kong Mohamed bin Zayed University of Artificial Intelligence High Performance and Scientific Computing Center National Taiwan University Wolley Inc.

Recently, solid-state drives (SSDs) have been widely used in modern storage systems because of their excellent access performance and high energy efficiency. Therefore, secure deletion is critical for users to protect their data privacy against unauthorized access. However, the inherent access characteristics of SSDs bring in a grand challenge to thoroughly remove the sensitive data from the storage devices or make them inaccessible permanently. Moreover, the existing erase mechanism of SSDs may indirectly reduce the SSD lifetime when performing a secure deletion because of the massive block erases. To address these issues, we propose FSD, a file-related secure deletion that exploits the file information hints via multi-streamed SSDs to optimize the data allocation to alleviate the impact of secure deletion for prolonging SSD lifetime. The extensive experiments show that FSD satisfies the requirement of secure deletion and significantly prolongs the SSD lifetime by mitigating about 80% of block erases as well as extra data movement.

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Consensus based optimization with memory effects: random selection and applications

arXiv

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arXiv 2023年

作者： Borghi, Giacomo Grassi, Sara Pareschi, Lorenzo RWTH Aachen University Institute for Geometry and Applied Mathematics Aachen Germany University of Ferrara Department of Mathematics and Computer Science Center for Modelling Computing and Statistics Ferrara Italy

In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm iteratively updates a population of particles according to a consensus dynamics inspired by social interactions among individuals. The consensus point is computed taking into account the past positions of all particles. While sharing features with the popular Particle Swarm Optimization (PSO) method, the exploratory behavior is fundamentally different and allows better control over the convergence of the particle system. We discuss some implementation aspects which lead to an increased efficiency while preserving the success rate in the optimization process. In particular, we show how employing a random selection strategy to discard particles during the computation improves the overall performance. Several benchmark problems and applications to image segmentation and Neural Networks training are used to validate and test the proposed method. A theoretical analysis allows to recover convergence guarantees under mild assumptions on the objective function. This is done by first approximating the particles evolution with a continuous-in-time dynamics, and then by taking the mean-field limit of such dynamics. Convergence to a global minimizer is finally proved at the mean-field level. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiong Wang Yuxin Chen Yuqing Li Xiaofei Liao Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients’ selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal ${{\mathcal{O}}}\left({{T^{ - 2/3}}}\right)$ convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques.

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LEARNING CAUSAL ALIGNMENT FOR RELIABLE DISEASE DIAGNOSIS

arXiv

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arXiv 2023年

作者： Liu, Mingzhou Lee, Ching-Wen Sun, Xinwei Yu, Xueqing Yu, Qiao Wang, Yizhou School of Computer Science Peking University China School of Data Science Fudan University China Computer Science Department Shanghai Jiao Tong University China Center on Frontiers of Computing Studies Peking University China Institute for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Peking University China State Key Lab. of General Artificial Intelligence Peking University China

Aligning the decision-making process of machine learning algorithms with that of experienced radiologists is crucial for reliable diagnosis. While existing methods have attempted to align their diagnosis behaviors to those of radiologists reflected in the training data, this alignment is primarily associational rather than causal, resulting in pseudo-correlations that may not transfer well. In this paper, we propose a causality-based alignment framework towards aligning the model’s decision process with that of experts. Specifically, we first employ counterfactual generation to identify the causal chain of model decisions. To align this causal chain with that of experts, we propose a causal alignment loss that enforces the model to focus on causal factors underlying each decision step in the whole causal chain. To optimize this loss that involves the counterfactual generator as an implicit function of the model’s parameters, we employ the implicit function theorem equipped with the conjugate gradient method for efficient estimation. We demonstrate the effectiveness of our method on two medical diagnosis applications, showcasing faithful alignment to radiologists. Code is publicly available at https://***/lmz123321/Causal_alignment. © 2023, CC BY.

关键词： Radiology

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