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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia Computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy

arXiv

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arXiv 2024年

作者： Moses, Isaiah A. Reinhart, Wesley F. Materials Research Institute The Pennsylvania State University University Park PA16802 United States Department of Materials Science and Engineering The Pennsylvania State University University Park PA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University Park PA16802 United States

Deep learning models based on atomic force microscopy (AFM) enhance efficiency in inverse design and characterization of materials. However, the limited and imbalanced data of experimental materials that are typically available is a major challenge. Also important is the need to interpret trained models, which are normally complex enough to be uninterpretable by humans. Here, we present a systemic evaluation of transfer learning strategies to accommodate low-data scenarios in materials synthesis and a model latent feature analysis to draw connections to the human-interpretable characteristics of the samples. While we imagine this framework can be used in downstream analysis tasks such as quantitative characterization, we demonstrate the strategies on a multi-material classification task for which the ground truth labels are readily available. Our models show accurate predictions in five classes of transition metal dichalcogenides (TMDs) (MoS2, WS2, WSe2,MoSe2, and Mo−WSe2) with up to 89% accuracy on held-out test samples. Analysis of the latent features reveals a correlation with physical characteristics such as grain density, Difference of Gaussian blob, and local variation. The transfer learning optimization modality and the exploration of the correlation between the latent and physical features provide important frameworks that can be applied to other classes of materials beyond TMDs to enhance the models’ performance and explainability which can accelerate the inverse design of materials for technological applications. © 2024, CC BY.

关键词： Layered semiconductors

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What affects the performance of POD for the simulation of heat transfer through building component? 8

What affects the performance of POD for the simulation of he...

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8th International Building Physics Conference, IBPC 2021

作者： Hou, Tianfeng Roels, Staf Janssen, Hans KU Leuven Department of Civil Engineering Building Physics Section Leuven3001 Belgium Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan RIKEN Prediction Science Laboratory Kobe Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program Kobe Japan

The capability of the proper orthogonal decomposition for the simulation of heat transfer in building components is investigated via three applications: linear heat transfer (not coupled to mass transfer), mildly non-linear heat transfer (coupled to air and moisture transfer (hygroscopic)) and highly non-linear heat transfer (coupled to moisture transfer (capillary)). It is shown that increasing non-linearity leads to an increasing number of required construction modes. To further investigate the reason for this degrading performance of POD, the singular values' decay progress from the different training snapshots is addressed in this paper. The results confirm that a fast decay of the singular values implies a high interrelation of the snapshots and a better performance of the POD method. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Heat transfer

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Weakly Supervised IDH-Status Glioma Classification from H&E-Stained Whole Slide Images

Weakly Supervised IDH-Status Glioma Classification from H&E-...

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IEEE International Symposium on Biomedical Imaging

作者： Shubham Innani Bhakti Baheti MacLean P. Nasrallah Spyridon Bakas Department of Pathology and Laboratory Medicine School of Medicine Division of Computational Pathology Indiana University Indianapolis IN USA Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurological Surgery School of Medicine Indiana University Indianapolis IN USA Department of Biostatistics & Health Data Science School of Medicine Indiana University Indianapolis IN USA Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Gliomas are the most common malignant primary brain tumors in the Central Nervous System. Isocitrate dehydrogenase (IDH) mutation is a key driver of gliomagenesis in infiltrating gliomas and a diagnostic criterion with prognostic relevance. Determining IDH status from hematoxylin and eosin (H&E) stained Whole Slide Images (WSI) would be a valuable tool in precision oncology as it would expedite diagnosis and downstream patient treatment. We seek a computational approach to predict IDH status from digitized histopathology glioma sections (WSI). The proposed approach first applies a comprehensive curation step to remove artifactual content on each H&E-stained WSI, followed by a classification mechanism based on weakly supervised attention-based multiple instance learning. Quantitative evaluation on 1,534 digitized tissue sections from 799 cases from the publicly available TCGA-GBM and TCGA-LGG dataset revealed AUC of 0.92 and a balanced accuracy of 0.85 on a 10-fold cross-validation (10CV) schema. Histological prediction of IDH status represents an ideal prototype to interrogate WSI towards identifying clinically-relevant tumor biomarkers.

关键词： Accuracy Histopathology Prototypes Medical treatment Central nervous system Biomarkers Oncology

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Computerized tomography

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Devider: Long-Read Reconstruction of Many Diverse Haplotypes 29th

Devider: Long-Read Reconstruction of Many Diverse Haplotype...

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29th International Conference on Research in computational Molecular Biology, RECOMB 2025

作者： Shaw, Jim Boucher, Christina Yu, Yun William Noyes, Noelle Li, Heng Department of Data Science Dana-Farber Cancer Institute BostonMA02215 United States Department of Biomedical Informatics Harvard Medical School BostonMA02215 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States Department of Veterinary Population Medicine University of Minnesota St. PaulMN55421 United States

ISBN: (纸本)9783031902512

Reconstructing haplotypes through sequencing of a mixture of similar sequences is a fundamental problem. Long-read sequencing technologies can connect distant alleles to disentangle similar haplotypes, but handling elevated sequencing error rates requires specialized techniques. We present devider, an algorithm for haplotyping small sequences—such as viruses or genes—from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. Benchmarking on synthetic mixtures of antimicrobial resistance (AMR) genes showed that devider recovered 83% of haplotypes, 23% points higher than the next best method. On real PacBio and Nanopore datasets, devider recapitulates previously known results in seconds, disentangling a bacterial community with >10 strains and an HIV-1 co-infection dataset. We used devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering a history of recombination for diverse AMR gene haplotypes and showcasing devider ’s ability to unveil ecological signals for heterogeneous mixtures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammals

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DFT-FE 1.0: A massively parallel hybrid CPU-GPU density functional theory code using finite-element discretization

arXiv

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arXiv 2022年

作者： Das, Sambit Motamarri, Phani Subramanian, Vishal Rogers, David M. Gavini, Vikram Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Computational and Data Sciences Indian Institute of Science Bangalore India National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Materials Science & Engineering University of Michigan Ann ArborMI48109 United States

We present DFT-FE 1.0, building on DFT-FE 0.6 [Comput. Phys. Commun. 246, 106853 (2020)], to conduct fast and accurate large-scale density functional theory (DFT) calculations (reaching ∼ 100, 000 electrons) on both many-core CPU and hybrid CPU-GPU computing architectures. This work involves improvements in the real-space formulation-via an improved treatment of the electrostatic interactions that substantially enhances the computational efficiency-as well high-performance computing aspects, including the GPU acceleration of all the key compute kernels in DFT-FE. We demonstrate the accuracy by comparing the ground-state energies, ionic forces and cell stresses on a wide-range of benchmark systems against those obtained from widely used DFT codes. Further, we demonstrate the numerical efficiency of our implementation, which yields ∼ 20× CPU-GPU speed-up by using GPU acceleration on hybrid CPU-GPU nodes. Notably, owing to the parallel-scaling of the GPU implementation, we obtain wall-times of 80−140 seconds for full ground-state calculations, with stringent accuracy, on benchmark systems containing ∼ 6, 000 − 15, 000 electrons. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Adversarially robust clustering with optimality guarantees

arXiv

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arXiv 2023年

作者： Jana, Soham Yang, Kun Kulkarni, Sanjeev The Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN United States The Data Science Institute Center For Research Computing Southern Methodist University DallasTX United States The Department of Operations Research and Financial Engineering Princeton University PrincetonNJ United States

We consider the problem of clustering data points coming from sub-Gaussian mixtures. Existing methods that provably achieve the optimal mislabeling error, such as the Lloyd algorithm, are usually vulnerable to outliers. In contrast, clustering methods seemingly robust to adversarial perturbations are not known to satisfy the optimal statistical guarantees. We propose a simple robust algorithm based on the coordinatewise median that obtains the optimal mislabeling rate even when we allow adversarial outliers to be present. Our algorithm achieves the optimal error rate in constant iterations when a weak initialization condition is satisfied. In the absence of outliers, in fixed dimensions, our theoretical guarantees are similar to that of the Lloyd algorithm. Extensive experiments on various simulated and public datasets are conducted to support the theoretical guarantees of our *** Codes 62G30, 62G35, 62H30 Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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