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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Enhanced Word Embedding with CNN using Word Order Information For Sentiment Analysis

Enhanced Word Embedding with CNN using Word Order Informatio...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Peter Atandoh Zhang Fengli Paul Atandoh Hakeem Edward Mensah Acheampong Daniel Addo Michael Appiah-Twum Daniel Adu-Gyamfi Richard Safo Blankson Tohye Tewodros Giza Wi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Mathematics Mercer University Macon GA USA School of Resources and Environment University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology C. K. Tedam University of Technology and Applied Sciences Navrongo UE/R Ghana Summit Economics LLC Colorado USA

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7 % , 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE.

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Fusion Dilated CNN for Encrypted Web Traffic Classification

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International Journal of Network Security 2022年第4期24卷 733-740页

作者： Appiah, Benjamin Sackey, Anthony Kingsley Kwabena, Owusu-Agyemang Kanpogninge, Ansuura JohnBosco Aristotle Buah, Peter Antwi Department of Computer Science Ho Technical University1 Ho Volta Region Ghana School of Information and Software Engineering University of Electronic Science and Technology of China China C. K. Tedam University of Technology Applied Sciences Ghana Department of Civil Engineering Southwest Jiaotong University China

A Growing number of conventional Convolutional neural network (CNN) models have been employed for encrypted web traffic characterization. However, the application of CNN models is confronted with two significant challenges;a) they possess short reflective fields that don’t gather much-encrypted traffic information for effective and accurate predictions. b) these models are not adaptive to the diverse nature of traffic flow because of their singlehead architecture. This paper alleviates these problems using the fusion of dilated Convolutional neural networks dubbed FDCNN. FDCNN architecture supports exponentially large receptive fields and captures local dependencies in encrypted traffic data. The experimental results on public datasets, ISCX VPNnon-VPN Traffic datasets, indicate that FDCNN architecture is practical and achieves higher accuracy. © Institute of Mathematical Statistics, 2022

关键词： Convolution

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DeceFL:a principled fully decentralized federated learning framework

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National science Open 2023年第1期2卷 35-51页

作者： Ye Yuan Jun Liu Dou Jin Zuogong Yue Tao Yang Ruijuan Chen Maolin Wang Chuan Sun Lei Xu Feng Hua Yuqi Guo Xiuchuan Tang Xin He Xinlei Yi Dong Li Guanghui Wen Wenwu Yu Hai-Tao Zhang Tianyou Chai Shaochun Sui Han Ding School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Department of Applied Mathematics University of WaterlooWaterloo N2L 3G1Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Mathematical and Physical Sciences Wuhan Textile UniversityWuhan 430200China School of Electrical Engineering and Computer Science and Digital FuturesKTH Royal Institute of TechnologyStockholm 10044Sweden Shenyang Institute of Automation(SIA) Chinese Academy of SciencesShenyang 110169China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu 610091China

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently *** a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy *** there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central *** we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning *** has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly ***,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.

关键词： decentralized federated learning smart manufacturing control systems privacy

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Precise Fermi level engineering in a topological Weyl semimetal via fast ion implantation

arXiv

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arXiv 2023年

作者： Mandal, Manasi Chotrattanapituk, Abhijatmedhi Woller, Kevin Xu, Haowei Mao, Nannan Okabe, Ryotaro Boonkird, Artittaya Nguyen, Thanh Drucker, Nathan C. Momiki, Takashi Li, Ju Kong, Jing Li, Mingda Quantum Measurement Group MIT CambridgeMA02139 United States Department of Nuclear Science and Engineering MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Department of Chemistry MIT CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Aisin Technical Center of America Inc CA95110 United States Department of Materials Science and Engineering MIT CambridgeMA02139 United States

The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to the Weyl nodes and unlock exotic electronic and optoelectronic effects associated with the divergent Berry curvature. However, in contrast to 2D materials, where the Fermi level can be controlled through various techniques, the situation for bulk crystals beyond laborious chemical doping poses significant challenges. Here, we report the meV-level ultra-fine-tuning of the Fermi level of bulk topological Weyl semimetal TaP using accelerator-based high-energy hydrogen implantation and theory-driven planning. By calculating the desired carrier density and controlling the accelerator profiles, the Fermi level can be fine-tuned from 5 meV to only ∼0.5 meV (DFT calculations) away from the Weyl nodes. The Weyl nodes are preserved, while the carrier mobility is largely retained. Our work demonstrates the viability of this generic approach to tune the Fermi level in semimetal systems and could serve to achieve property fine-tuning for other bulk quantum materials with ultrahigh precision. © 2023, CC BY.

关键词： Fermi level

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FPGA Implementation of SIMON-128 Cryptographic Algorithm Using Artix-7

arXiv

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arXiv 2023年

作者： Ghayoula, Ridha Fattahi, Jaouhar Smida, Amor Gmati, Issam El Pricop, Emil Ziadia, Marwa Unit of Research in High Frequency Electronic Circuits and Systems University of Tunis El Manar Tunis2092 Tunisia Department of Electrical and Computer Engineering Laval University Quebec Canada Department of Computer Science and Software Engineering Laval University Quebec Canada Department of Medical Equipment Technology College of Applied Medical Sciences Majmaah University Majmaah11952 Saudi Arabia College of Engineering Al Gunfudha Umm Al Qura University Saudi Arabia Automatic Control Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON’s block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization

arXiv

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arXiv 2025年

作者： Maus, Natalie Kim, Kyurae Zeng, Yimeng Jones, Haydn Thomas Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of T black-box objective functions, f1, . . . fT , simultaneously. Traditional approaches often seek a single Pareto-optimal set that balances trade-offs among all objectives. In this work, we introduce a novel problem setting that departs from this paradigm: finding a smaller set of K solutions, where K 1, . . . fT , there is at least one good solution. A motivating example for this problem setting occurs in drug design. For example, we may have T pathogens and aim to identify a set of K © 2025, CC BY-SA.

关键词： Pathogens

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Spreading Social Influence with both Positive and Negative Opinions in Online Networks

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Big Data Mining and Analytics 2019年第2期2卷 100-117页

作者： Jing (Selena) He Meng Han Shouling Ji Tianyu Du Zhao Li College of Computing and Software Engineering at Kennesaw State University Department of Computer Science at Zhejiang University Alibaba Group

Social networks are important media for spreading information, ideas, and influence among *** existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the "word-of-mouth" effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set(MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of ?. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.

关键词： influence spread social networks positive influential node set greedy algorithm positive and negative influences

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Heart Sound Classification using Residual Neural Network and Convolution Block Attention Module

Heart Sound Classification using Residual Neural Network and...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Enoch Adjei Frimpong Qin Zhiguang Tenagyei Edwin Kwadwo Patamia Agbeshi Rutherford Edward Y. Baagyere Regina Esi Turkson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu P.R. China Department of Computer Science and Technology Cape Coast Technical University Cape Coast Ghana Department of Computer Science C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Computer Science and Information Technology University of Cape Coast PMB University Post Office Cape Coast Ghana

ISBN: (纸本)9781665493901

Listening to the heart sound with digital or manual stethoscopes has become one of the practical ways to identify heart diseases in recent years. It's still difficult because of its manual approach and the fact that only experienced healthcare practitioners can use it to diagnose anomalies. The automatic extraction of heart sound features to aid in classification has been explored, however there is still potential for improvement. This paper proposes a residual neural network integrated with a convolutional block attention module (CBAM) for heart sound analysis, using generated Mel-spectrograms as input for our network. We tested our model using the Pascal Heart Sound Challenge dataset, and it performed favorably to other cutting-edge models.

关键词： Heart Convolution Manuals Stethoscope Medical services Media Feature extraction

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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