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44th Annual Conference on European Association for computer graphics, EUROgraphics 2023

作者： Firat, Elif E. Lang, Colm Srinivas, Bhumika Peng, Ilena Laramee, Robert S. Joshi, Alark Department of Computer Engineering Cukurova University Turkey Visualization and Graphics Lab Department of Computer Science University of San Francisco CA United States School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9783038682103

Treemaps are a popular representation to show hierarchical as well as part-to-whole relationships in data. While most students are aware of node-link representations/network diagrams based on their K-12 education, treemaps are often a novel representation to them. We present our experience of developing a software using principles from constructivism to help students understand treemaps using linked, side-by-side views of a node-link diagram and a treemap of the same data. Based on the qualitative survey conducted at the end of the intervention, students found the linked views to be beneficial for understanding hierarchical representation of data using treemaps. © 2023 The Authors.

关键词： Students

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-contextual Analysis

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SN computer science 2023年第1期4卷 18页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Department of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC)-Florianópolis Santa Catarina Brazil

Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar

关键词： Autonomous vehicles Deep learning Inertial sensors Intelligent transport systems Road conditions Speed bump

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Bio-assembled smart nanocapsules for targeted delivery of KRAS shRNA and cancer cell bioimage

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Chinese Chemical Letters 2023年第1期34卷 270-273页

作者： Maonan Wang Zengchao Guo Jiayu Zeng Liu Liu Yihan Wang Jinpeng Wang Hongbing Lu Haijun Zhang Hui Jiang Xuemei Wang State Key Laboratory of Bioelectronics(Chien-Shiung Wu Lab) School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Department of Biomedical Engineering/Computer Application Fourth Military Medical UniversityXi’an 710032China Department of Oncology Zhongda HospitalMedical School of Southeast UniversityNanjing 210009China

The five-year survival rate for pancreatic cancer is less than 5%. However, the current clinical multimodal therapy combined with first-line chemotherapy drugs only increases the patient’s median survival from 5.0 months to 7.2 months. Consequently, a new strategy of cancer treatments is urgently needed to overcome this high-fatality disease. Through a series of biometric analyses, we found that KRAS is highly expressed in the tumor of pancreatic cancer patients, and this high expression is closely related to the poor prognosis of patients. It shows that inhibiting the expression of KRAS has great potential in gene therapy for pancreatic cancer. Given those above, we have exploited the possibility of targeted delivery of KRAS shRNA with the intelligent and bio-responsive nanomedicine to detect the special oxidative stress microenvironment of cancer cells and realize efficient cancer theranostics. Our observations demonstrate that by designing the smart self-assembled nanocapsules of melanin with fluorescent nanoclusters we can readily achieve the bio-recognition and bioimaging of cancer cells in biological solution or *** self-assembled nanocapsules can make a significant bio-response to the oxidative stress microenvironment of cancer cells and generate fluorescent zinc oxide Nanoclusters in situ for targeted cell bioimaging. Moreover, it can also readily facilitate cancer cell suppression through the targeted delivery of KRAS shRNA and low-temperature hyperthermia. This raises the possibility to provide a promising theranostics platform and self-assembled nanomedicine for targeted cancer diagnostics and treatments through special oxidative stress-responsive effects of cancer cells.

关键词： Smart bio-responsive nanocapsules Intelligent nanomedicine shRNA KRAS Fluorescent bioimaging Oxidative stress response

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Stereo Conversion with Disparity-Aware Warping, Compositing and Inpainting

Stereo Conversion with Disparity-Aware Warping, Compositing ...

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IEEE Workshop on applications of computer Vision (WACV)

作者： Lukas Mehl Andrés Bruhn Markus Gross Christopher Schroers Institute for Visualization and Interactive Systems University of Stuttgart Department of Computer Science Computer Graphics Lab ETH Zurich DisneyResearch|Studios Switzerland

Despite of exciting advances in image-based rendering and novel view synthesis, it is still challenging to achieve high-resolution results that can reach production-level quality when applying such methods to the task of stereo conversion. At the same time, only very few dedicated stereo conversion approaches exist, which also fall short in terms of the required quality. Hence, in this paper, we present a novel method for high-resolution 2D-to-3D conversion. It is fully differentiable in all of its stages and performs disparity-informed warping, consistent foreground-background compositing, and background-aware inpainting. To enable temporal consistency in the resulting video, we propose a strategy to integrate information from additional video frames. Extensive ablation studies validate our design choices, leading to a fully automatic model that outperforms existing approaches by a large margin (49-70% LPIPS error reduction). Finally, inspired from current practices in manual stereo conversion, we introduce optional interactive tools into our model, which allow to steer the conversion process and make it significantly more applicable for 3D film production.

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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Innovative Design of Hybrid Teaching for Data Structure 24

Innovative Design of Hybrid Teaching for Data Structure

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7th International Conference on Educational Technology Management, ICETM 2024

作者： Zhang, Dan Zhao, Dong Ma, Xiujuan Wang, Jiawei School of Computer Science Qinghai Normal University Qinghai Xining China State Key Lab of Tibetan Intelligent Informntion Processing and Application Co-Established by Province and Ministry Qinghai Normal University Qinghai Xining China Talent and Personnel Department Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400717468

This paper explores the core position of Data Structure in information-related education and the teaching challenges it faces. By adopting the hybrid teaching education concept of"gradual progress, truth-seeking and innovation" and combining it with the modern educational thinking of"Internet +", this paper is committed to building a hybrid teaching model to improve students’ learning efficiency, understanding and practical skills. By analyzing and improving existing teaching methods, this report proposes a series of innovative strategies to cultivate high-quality talents who can adapt to the needs of future technological development. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Few Shot Contrastive Spectral Clustering with Meta Learning and Neighbor Mining

Few Shot Contrastive Spectral Clustering with Meta Learning ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Nongxiao Wang Xulun Ye Jieyu Zhao Qing Wang Department of Computer Science and Technology Ningbo University China Key Lab of Mobile Network Application Technology of Zhejiang Province Ningbo China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Spectral clustering is widely used in the field of unsupervised learning and has been successfully applied in various data analysis tasks. However, in practical scenarios, it’s common to get few labeled data at low cost, resulting limited labeled data in few categories, while the rest are completely unlabeled. Relying solely on the unsupervised clustering framework can’t leverage the existing supervised information. Using a supervised meta-learning paradigm alone poses challenges when dealing with the majority categories of completely unlabeled data. Therefore, we propose a few-shot contrastive spectral clustering combined with meta learning and neighbor mining framework (FCSMN). First we leverage few shot labeled information to obtain a meta-learning feature embedding with strong generalization capabilities, and acquire semantic feature spaces using an unsupervised contrastive learning model. Then, we mine neighbors in these two feature spaces separately and align them. Next, we jointly reinforce intra-cluster compactness and inter-cluster separability at both instance and cluster level. To satisfies the conditions of spectral clustering, we impose orthogonality constraints at the last layer. Experiments on three datasets demonstrate the effectiveness our proposed method.

关键词： Metalearning Costs Semantics Neural networks Contrastive learning Data augmentation Data mining

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Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms 2

Medical Image Fusion Solid Works Supporting Multiple Techniq...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Mutha, Rakhi Pathak, Devendra Nath Ahuja, Vandana Bhandari, Prabhakar Ramesh Rahi, Pankaj Mamodiya, Udit Lovely Professional University Department of Computer Application Punjab India Poornima College of Engineering Department of Computer Science and Engineering Rajasthan Jaipur India Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India Sevadal Mahila Mahavidyalaya Department of Microbiology Umred Road Maharashtra Nagpur India Poornima Institute of Engineering and Technology Department of Artificial Intelligence & Data Science Rajasthan Jaipur India Piet Aicte Idea Lab Poornima Institute of Engineering and Technology Department of Electrical Engineering Rajasthan Jaipur India

ISBN: (纸本)9781665491990

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image. © 2023 IEEE.

关键词： Image resolution

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RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models

arXiv

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arXiv 2024年

作者： Kim, Jangyeong Kang, Donggoo Choi, Junyoung Wi, Jeonga Gwon, Junho Bae, Jiun Yoon, Dumim Han, Junghyun Graphics AI Lab. NCSOFT Korea Republic of Department of Computer Science and Engineering Korea University Korea Republic of Department of Image Chung-Ang University Korea Republic of

Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper, we propose a robust text-to-texture method for generating consistent and seamless textures that are well aligned with the mesh. Our method leverages state-of-the-art 2D diffusion models, including SDXL and multiple ControlNets, to capture structural features and intricate details in the generated textures. The method also employs a symmetrical view synthesis strategy combined with regional prompts for enhancing view consistency. Additionally, it introduces novel texture blending and soft-inpainting techniques, which significantly reduce the seam regions. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods. © 2024, CC BY-NC-ND.

关键词： Mesh generation

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