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VDDL: A Deep Learning-Based Vulnerability Detection Model for Smart Contracts 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Jiang, Fan Cao, Yuanlong Xiao, Jianmao Yi, Hui Lei, Gang Liu, Min Deng, Shuiguang Wang, Hao School of Software Jiangxi Normal University Nanchang China College of Computer Science Zhejiang University Hangzhou China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

With the widespread use of smart contracts in various fields, the research on smart contract vulnerability detection has increased yearly. Most of the previous research work is based on symbol detection and comparison with expert-defined error patterns to analyze the possible vulnerabilities of smart contracts. The accuracy and performance of such methods are generally low. In response to this problem, this paper proposes an efficient smart contract vulnerability detection model VDDL (Vulnerability Detection Based on Deep Learning). VDDL uses a multi-layer bidirectional Transformer architecture as the model architecture, involving a multi-head attention mechanism and a masking mechanism. A multi-head attention mechanism is applied in the encoder-decoder layer. The masking mechanism randomly masks the input tokens and combined with the context to predict the masked tokens, a deep bidirectional representation for training is realized. Furthermore, VDDL incorporates CodeBERT, a large bimodal pre-trained model for natural and programming languages, to improve training performance. We collected 47,038 smart contracts as datasets for model training and testing. In the end, the accuracy of VDDL reached 92.35%, the recall reached 81.43%, and the F1-score reached 86.38%, which can efficiently detect possible loopholes in smart contracts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Machine Learning for Forecasting Depression and Anxiety in University Students 1

引用

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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A Social Communication Support Application for Autistic Children Using computer Vision and Large Language Models 19th

A Social Communication Support Application for Autistic Chil...

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19th International Conference on computers Helping People with Special Needs (ICCHP)

作者： Jafri, Rabia King Saud Univ Dept Informat Technol Riyadh Saudi Arabia

ISBN: (纸本)9783031628481;9783031628498

A novel, affordable and accessible software solution that utilizes computer vision tools and Large Language Models (LLMs) to provide communication support to high functioning autistic children during online meetings with the aim of improving their social communication skills is presented. The system displays the remote attendee's facial expressions as distinct emoticons to facilitate the child's understanding of non-verbal social cues and suggests appropriate responses on demand based on the conversational context and the detected expressions. A gamification option for practicing facial expression recognition in an engaging manner is also offered. The application serves as a support platform as well as a teaching tool which autistic children can utilize to connect with friends and caregivers to improve their social communication skills. It is being developed in consultation with therapists who work with autistic children to ensure that its design is compatible with the unique needs of the end users. The system is more cost-effective and sensory friendly as compared to similar robotic and virtual reality-based solutions and has the added advantage that the child converses with a real human being instead of a robot or a virtual agent, thus, increasing the likelihood that the social skills learned would be effectively transferred to co-located face-to-face conversations.

关键词： Autism Spectrum Disorder Facial Expression Recognition Social Skills Communication Skills Education Children Generative AI

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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Exponential Time Complexity of the Complex Weighted Boolean #CSP 29th

Exponential Time Complexity of the Complex Weighted Boolea...

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29th International Computing and Combinatorics Conference, COCOON 2023

作者： Liu, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031491894

Cai, Lu, and Xia [8] proved a dichotomy for complex weighted Boolean #CSP. If the parameter set of Boolean constraint functions F is a subset of either of the affine-type function set A and the product type function set P, then #CSP(F ) is polynomial-time solvable;otherwise, #CSP(F ) is #P-hard. Furthermore, the result holds for # R3 -CSP(F ), which additionally restricts every variable constrained by no more than 3 constraint functions (not necessarily distinct). We strengthen the #P-hardness to the tight sub-exponential time lower bound under the counting Exponential Time Hypothesis (#ETH). We demonstrate that, if #ETH holds, then #CSP(F ) with F A and F P has no sub-exponential time algorithm. The result also holds for # RD -CSP(F ) with some integer D> 0, even D= 3. Additionally, we demonstrate that a vital tool pinning, forcing some variables to be 0 or 1, is still available in the context of # RD -CSP when proving the sub-exponential time lower bound. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Polynomial approximation

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DT-UPD: User Privacy Data Protection Through Distribution Transformation in Unlearning Cloud Service 24th

DT-UPD: User Privacy Data Protection Through Distribution Tr...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Sun, Shouyue Wu, Feng Zhang, Yingzhi Cui, Lei Gao, Longxiang Yu, Shui Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science University of Technology Sydney Ultimo Australia

ISBN: (纸本)9789819615476

Providing machine unlearning services to users in the cloud, known as Machine Unlearning as a Service (MUaaS), has become a prominent privacy protection strategy. However, existing methods primarily focus on the effectiveness of the unlearning algorithm itself, while neglecting the privacy of the data users upload to the cloud for unlearning. To address this problem, we propose a distribution transformation method that maps users’ private data to a distribution (DT-UPD) that is functionally parallel and orthogonal to the original distribution. We then sample from this transformed distribution and use the sampled data instead of the original data to obtain the unlearning service. In detail, DT-UPD first employs the information bottleneck theory to extract the most relevant features for the unlearning service and then maps these features to a normal distribution. Low-dimensional features are then randomly sampled from this normal distribution and expanded into a high-dimensional space using adversarial reconstruction. This sampling and adversarial reconstruction process removes most of the information from the original private data (including unlearning-related and irrelevant information), ensuring that the final reconstructed data used for the unlearning service has a privacy protection effect. Additionally, we introduce Elastic Weight Consolidation (EWC) loss, which effectively avoids ‘catastrophic unlearning’ during the unlearning process. Extensive experimental results show that DT-UPD can achieve a forgetting accuracy (forget ACC) of 18.22% in the forgetting set with no more than a 6% accuracy loss in the test set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios 27th

On the Generalization of WiFi-Based Person-Centric Sensing...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Strohmayer, Julian Kampel, Martin Computer Vision Lab TU Wien Favoritenstr. 9/193-1 Vienna1040 Austria

ISBN: (纸本)9783031783531

In this work, the problem of cross-environment generalization in WiFi Channel State Information (CSI)-based localization and Human Activity Recognition (HAR) models within through-wall scenarios is addressed, highlighting an area that remains underexplored. A comprehensive evaluation is conducted to investigate the effectiveness of various methodologies, including CSI feature selection, feature scaling, dimensionality reduction, and data augmentation techniques, in improving model robustness to environmental variations. The evaluation is based on a dataset collected over three days in environments exhibiting both static and dynamic variations, featuring synchronized CSI and 3D trajectory data of human activities, which is made publicly available at https://***/records/10925351. The findings reveal that plain CSI amplitude features consistently outperform other types in achieving superior generalization in through-wall scenarios. Furthermore, it is found that while dimensionality reduction techniques like PCA, ICA, and UMAP do not enhance model generalization, feature scaling and data augmentation can significantly improve both localization and HAR performance in the presence of static and dynamic environmental variations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wi-Fi

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Safety Assurance Challenges for Autonomous Drones in Underground Mining Environments 25th

Safety Assurance Challenges for Autonomous Drones in Under...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Ryan, Philippa Badyal, Arjun Sze, Samuel Hardin, Benjamin Bin Firoz, Hasan Lewinska, Paulina Hodge, Victoria Department of Computer Science University of York York United Kingdom Oxford Robotics Institute University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031720581

Autonomous drones have been proposed for many industrial inspection roles including building infrastructure, nuclear plants and mining. They have the benefit of accessing hazardous locations, without exposing human operators and other personnel to physical risk. Underground mines are extremely challenging for autonomous drones as there is limited infrastructure for Simultaneous Localisation and Mapping (SLAM), for the drone to navigate. For example, there is no Global Navigation Satellite System (GNSS), poor lighting, and few distinguishing landmarks. Additionally, the physical environment is extremely harsh, affecting the reliability of the drone. This paper describes the impact of these challenges in designing for, and assuring, safety. We illustrate with experience from developing an autonomous Return To Home (RTH) function for an inspection drone. This is initiated when the drone suffers a communications loss whilst surveying newly excavated corridors that are unsafe for personnel. We present some of the key safety assurance challenges we faced, including design constraints and difficulties using simulations for validation and verification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Global positioning system

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An Interpretable Deep Learning Approach for Morphological Script Type Analysis

An Interpretable Deep Learning Approach for Morphological Sc...

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18th International Conference on Document Analysis and Recognition (ICDAR)

作者： Vlachou-Efstathiou, Malamatenia Siglidis, Ioannis Stutzmann, Dominique Aubry, Mathieu Inst Rech & Hist Textes Paris Ile De France France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9783031706417;9783031706424

Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis.

关键词： Latin Palaeography computer Vision Palaeographical Analysis Character Prototypes Textualis Formata

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