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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Xingtong Shen, Meng Cui, Laizhong Ye, Ke Jia, Jizhe Yue, Guangchun School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Shenzhen University College of Computer Science and Software Engineering Shenzhen China School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350346558

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively *** this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency. © 2022 IEEE.

关键词： Knowledge management

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Anti-money laundering method based on hierarchical risk control knowledge graph 5

Anti-money laundering method based on hierarchical risk cont...

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5th International Conference on Artificial Intelligence and computer science, AICS 2023

作者： Tang, Zichen Haihong, E. Sun, Mingzhi Zhao, Lamei Wang, Rongrong Song, Meina School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China Beijing Trusfort Technology Co. Ltd. Beijing100089 China

ISBN: (纸本)9781510668621

With the development of the Internet and advances in technology, financial transactions have become more convenient, but money laundering has also become more concealed, complex, and can be quickly adjusted according to regulatory policies, making traditional methods based on rule libraries and blacklists difficult to cope with the current Anti-Money Laundering (AML) challenges. To improve the efficiency and accuracy of AML work, various machine learning methods have been proposed. However, these methods are often difficult to be widely used due to their complex models and poor interpretability. To overcome the shortcomings of existing methods and adapt to the changing trends in money laundering while ensuring good interpretability, this paper proposes an AML method based on Hierarchical Risk Control Knowledge Graph (HRCKG). Firstly, a simulated AML dataset is generated based on a small amount of real data and money laundering patterns. Next, various transaction features are extracted to construct graded risk control indicators, which are used to automatically search for and filter risk control rules to form a rule library, and then construct an HRCKG. Finally, integrating rule-based reasoning and graph-based reasoning, this paper uses rule matching, node importance measurement, and community discovery algorithms to evaluate the money laundering risks of accounts, identify money laundering accounts, and discover money laundering groups. Through comparison with other machine learning methods and analysis of experimental results, the effectiveness and application value of the proposed method is demonstrated. © 2023 SPIE.

关键词： Knowledge graph

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A Stimulus Analysis for Sustaining Attention of Video Lecture Learners 15th

A Stimulus Analysis for Sustaining Attention of Video Lectur...

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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Roh, Kyoungeui Park, Ji Su Kang, Jungho Shon, Jin Gon Advanced Convergence Technology Laboratory Korea National Open University Seoul Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-si Korea Republic of Department of Software Engineering Baewha Woman University Seoul Korea Republic of Department of Computer Science Graduate School Korea National Open University Seoul Korea Republic of

ISBN: (纸本)9789819724468

There is a concern about the learner effectiveness in video lecture. The ability of learners to effectively engage is influenced by the time they can sustain their attention. However, learners watching video lectures often struggle to maintain their attention after a certain period of time. This is primarily because video lectures are created without taking into consideration the time learners can sustain their attention. This study aims to investigate what kind of stimuli could be presented to learners during video lectures to help them sustain their attention. This study designs five experimental stimuli to be presented during video lectures playback and measures the time learners sustain attention for each stimulus. The experimental results show that when video lecture learners are received simultaneous visual stimulus in the form of dynamic text and auditory stimulus in the form of voiceover, the time they sustain attention in video lectures is the longest. This suggests that presenting dynamic text and voiceover simultaneously during video lectures can be the most effective strategy to sustain learners’ attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of computer science & Technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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Rural road environment segmentation of LiDAR dataset with deep learning 13

Rural road environment segmentation of LiDAR dataset with de...

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13th International Conference on Signal Processing Systems, ICSPS 2021

作者： Zakria, Z. Deng, Jianhua He, Jiani Cai, Jingye Khokhar, Muhammad Saddam School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Unstructured road segmentation is a key task in self-driving technology and it's still a challenging problem. Mostly available point cloud datasets focus on data collected from urban areas, and approaches are evaluated for structured roads or urban areas, which has considerable limitations in rural areas such as fails at night, road without boundary lines, and no markings. In this regard, we present a new large-scale aerial LiDAR dataset of rural roads with hand-labeled points spanning 500 km2 of road and nine object categories. Our dataset is the most extensive dataset contains a critical number of expert-verified hand-labeled points for analyzing 3D deep learning algorithms, allowing existing algorithms to shift their focus to unstructured road data. The nature of our data, the annotation methodology, and the performance of existing state-of-the-art algorithms on our dataset are all described in detail. Furthermore, challenges and applications of rural area road semantic segmentation are discussed in detail. © COPYRIGHT SPIE.

关键词： Data acquisition

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Multi-scale pulmonary nodule classification with deep feature fusion via residual network

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14829-14840页

作者： Zhang, Guokai Zhu, Dandan Liu, Xiao Chen, Mingle Itti, Laurent Luo, Ye Lu, Jianwei School of Software Engineering Tongji University Shanghai China High School Affiliated to Fudan University Shanghai China Department of Computer Science and Neuroscience Program University of Southern California Los Angeles United States Institute of Translational Medicine Tongji University Shanghai China

The early stage detection of benign and malignant pulmonary nodules plays an important role in clinical diagnosis. The malignancy risk assessment is usually used to guide the doctor in identifying the cancer stage and making follow-up prognosis plan. However, due to the variance of nodules on size, shape, and location, it has been a big challenge to classify the nodules in computer aided diagnosis system. In this paper, we design a novel model based on convolution neural network to achieve automatic pulmonary nodule malignancy classification. By using our model, the multi-scale features are extracted through the multi-convolution process, and the structure of residual blocks allows the network to capture more high-level and semantic information. Moreover, a strategy is proposed to fuse the features from the last avg-pooling layer and the ones from the last residual block to further enhance the performance of our model. Experimental results on the public Lung Image Database Consortium dataset demonstrate that our model can achieve a lung nodule classification accuracy of 87.5 % which outperforms state-of-the-art methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rojas, Fredy Madanian, Samaneh Templeton, John Michael Poellabauer, Christian Schneider, Sandra L. Department of Computer Science and Software Engineering Auckland New Zealand University of South Florida Department of Computer Science and Engineering TampaFL United States Florida International University School of Computing and Information Sciences MiamiFL United States Saint Mary's College Department of Communicative Sciences & Disorders Notre DameIN United States

ISBN: (纸本)9798350362480

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings. © 2024 IEEE.

关键词： Speech analysis

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EventLens: Leveraging Event-Aware Pretraining and Cross-modal Linking Enhances Visual Commonsense Reasoning

arXiv

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arXiv 2024年

作者： Ma, Mingjie Yu, Zhihuan Ma, Yichao Li, Guohui School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions requiring human commonsense, and to provide rationales explaining why the answers are correct. With emergence of Large Language Models (LLMs), it is natural and imperative to explore their applicability to VCR. However, VCR task demands more external knowledge to tackle its challenging questions, necessitating special designs to activate LLMs’ commonsense reasoning abilities. Also, most existing Multimodal LLMs adopted an abstraction of entire input image, which makes it difficult to comprehend VCR’s unique co-reference tags between image regions and text, posing challenges for fine-grained alignment. To address these issues, we propose EventLens that leverages Event-Aware Pretraining and Cross-modal Linking and EnhanceS VCR. First, by emulating the cognitive process of human reasoning, an Event-Aware Pretraining auxiliary task is introduced to better activate LLM’s global comprehension of intricate scenarios. Second, during fine-tuning, we further utilize reference tags to bridge RoI features with texts, while preserving both modality semantics. Finally, we use instruct-style prompts to narrow the gap between pretraining and fine-tuning, and task-specific adapters to better integrate LLM’s inherent knowledge with new commonsense. Experimental results show the effectiveness of our proposed auxiliary task and fine-grained linking strategy. © 2024, CC BY.

关键词： Semantics

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GAN and IEC Approach for Image Generation 6

GAN and IEC Approach for Image Generation

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Shukla, Amar Mahdi, Hussain Falih Tandon, Ishita Singh, Rajkamal Choudhury, Tanupriya School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

Generative adversarial networks (GANs) are capable of learning deep representations efficiently without requiring a significant amount of annotation. Through Backpropagation, signals are derived from one network by another through a competitive process. This article presents a machine learning framework that incorporates generative adversarial networks (GANs) and interactive evolutionary computation (IEC) to generate images and to resample those images for the detection of fake images. In our study, we found that GANs trained on a particular domain could produce reliable and compact phenotypic maps. Through the use of a user research method, participants able to produce images closely matched target images, demonstrating the advantages of this unique approach. © 2022 IEEE.

关键词： Generative adversarial networks

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A Web-Based Vaccine Distribution System for Covid-19 Using Vaxallot 4th

A Web-Based Vaccine Distribution System for Covid-19 Using V...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Valarmathi, B. Srinivasa Gupta, N. Prakash, G. BarathyKolappan, A. Padmavathy, N. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Systems Engineer Tata Consultancy Services Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488900

Vaxallot seeks to implement a system to distribute vaccines across high-risk groups accounting for various parameters and prove to be superior to what conventional systems are capable of today. It is a Python flask-based tool backed by infrastructure and data resources from the Covid India central repository;all it needs is a single channel input and a single parameter of the value produced, and the algorithm will take care of the rest. Since it’s Python-based and has an active integration with google sheets, live value updating could be possible for the real-time output of the distribution. The novelty of the proposed mechanism is the unique priority index, a score that accounts for an array of factors associated with the pandemic and is computed for regions in question here;this makes way for better distribution of vaccines. The application has an exclusive segment centered on handling excess units, if any. Moreover, since the application is developed to suit the needs of dynamic demographics, any region can roll out this application for purposes they desire to serve the masses. Since it isn’t bound by a coronavirus, it can be used by the healthcare industry as they deem fit. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Coronavirus

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