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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ali, Wasif Sajid, Ahthasham Ghodke, Tanvi Ajit Malik, Rida Malik, Neeru Kaushik, Keshav Riphah Institute of System Engineering Riphah International University Department of Cyber Security Islamabad Pakistan Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra India School of Engineering & Technology Pimpri Chinchwad University Maharashtra Pune India Amity School of Engineering and Technology Amity University Mohali India

ISBN: (纸本)9798331518592

This research study addresses the importance of honeypots at multiple levels, which includes but not limited to placement in network or group of networks working alongside in an organization. This paper talks about the usage of multiple honeypots working alongside to log useful data for the mitigation of ongoing bot or actual attack this approach helps the organization better understand the threat they are dealing and the techniques which they can use to overcome the gap of the unseen security risks. The comparison will shed better light to the working and attack patterns of the ongoing attacks, gaining more confidence on using the honeypot for better protection. This allows strategic, optimized and critical insight to the evolving attack patterns. © 2024 IEEE.

关键词： Network intrusion

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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RSP-gcForest: A Distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Li, Mark Junjie Cai, Wenzhu Lin, Yigang Huang, Sunjie Huang, Joshua Zhexue Peng, Patrick Xiaogang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen Academy of Inspection and Quarantine China

ISBN: (纸本)9798350324457

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing. © 2023 IEEE.

关键词： Deep forest Distributed Computing Ensemble Learning Random Sample Partition

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Design of syntactic adaptive interactive system based on human perception state estimation within scenario context

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16933-16948页

作者： Jin, Dian Yu, Weiwei Srivastava, Gautam Yang, Xinliang School of Mechanical Engineering Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China Unmanned System Research Institute Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Math Lebanese American University Beirut Lebanon

Adaptive interactive systems can be divided into two categories: Semantic adaptations affect the system’s function, and syntactic adaptations affect the presentation of information through the operator interface without modifying the system behavior. Although automation is becoming more and more important, in many situations, human operators still play an irreplaceable role, making the development of syntactic adaptations indispensable. However, existing syntactic adaptations usually do not relate to the task context, which is a critical factor to achieve syntactic adaptations. Besides, existing studies do not fully consider the operator’s information perception state under highly dynamic scenarios, thus cannot dynamically prompt accurate information to the operators. Aiming at the problems above, a syntactic adaptive interactive system architecture based on perception state estimation and context segmentation is proposed in this paper. To address the real-time context segmentation, a time series segmentation algorithm is proposed, which achieves real-time estimation of the scene and produces highly interpretable results. To address the problem of dynamic perception state estimation, for the first time, the concept of a perception stack is proposed, which can be used to estimate the perception bias in the operator’s working memory based on the eye movement data and generate proper prompt information according to the current context. The proposed design method of an adaptive syntactic interactive system was verified in a flight simulator. The system improves information prompts accuracy and reduces redundancy through these innovative mechanisms mentioned above. Comparative experiments confirm significant improvements in the operator’s control accuracy and control stability according to our evaluation criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： State estimation

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Track Slab Crack Detection Algorithm with Few Samples Based on Deep Learning 8

Track Slab Crack Detection Algorithm with Few Samples Based ...

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Liu, Zhongjie Li, Wenju Gao, Xiaosong Liu, Cui Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Crrc Ro-Intelligent System. Co. Ltd. Shanghai China

ISBN: (纸本)9798350369564

In order to solve the problem of track slab crack detection under few samples' conditions, this paper proposes a track slab crack detection algorithm with few samples based on deep learning. Firstly, after extracting multi-scale query and support features through the Swin Transformer backbone, the similarity between query pixels and support pixels is computed according to the scaled dot-product attention of the Transformer architecture, and the query masks are aggregated;in order to filter a large amount of irrelevant background information in the crack image, multi-scale prototypes are computed by using prototypical learning, highlighting the class information of the cracks, and introducing a lightweight attention module to generate class-representative attention feature maps using support features at different scales, which guides the network to focus its attention on the target region;finally, the final prediction of cracks is realized by the feature fusion module. The experimental results show that the track slab crack IoU reaches 66.94% and the FB-IoU reaches 83.02% under the few samples' condition and exhibits strong generalization performance on the crack set of other scenarios as well. © 2023 IEEE.

关键词： Crack detection

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CLMSI: A Novel Image-Text Named Entity Recognition Method Based on Contrast Learning and Multimodal Semantic Interaction 11

CLMSI: A Novel Image-Text Named Entity Recognition Method Ba...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Ma, Jiangtao Jin, Wenduo Chen, Yonggang Zhang, Fan Qian, Shenyi Qiao, Yaqiong College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The State Information Center China China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin300350 China

ISBN: (纸本)9798331531904

Most existing multimodal named entity recognition (MNER) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNER accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNER benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI. © 2024 IEEE.

关键词： Contrastive Learning

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Towards Demystifying Android Adware: Dataset and Payload Location 39

Towards Demystifying Android Adware: Dataset and Payload Loc...

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39th ACM/IEEE International Conference on Automated Software engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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A Traffic Sign Recognition Method with Bi-Level Routing Attention 8

A Traffic Sign Recognition Method with Bi-Level Routing Atte...

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Liu, Ziqiong Li, Wenju Cui, Liu Gao, Xiaosong Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China ShangHai CRRC Ro-Intelligent System. Co. Ltd. Shanghai China

ISBN: (纸本)9798350369564

Traffic sign recognition has long been an essential part of automatic driving. Accurate traffic sign recognition algorithms are conducive to the development of automatic driving. However, traffic signs in natural scenes are often small targets, which contain less feature information and are difficult to detect and recognize. Aiming at the problem of low recognition accuracy of small targets in traffic sign recognition, a traffic sign recognition algorithm based on a Bi-Level Routing Attention is proposed. It utilizes a single-stage detector YOLOv5 as the basic network, with a double-layer routing attention mechanism introduced, realizing more flexible computing allocation and content awareness through double-layer routing, which could capture long-range data without incurring an increasing computational overhead and memory usage. Furthermore, our approach achieves noteworthy recognition accuracy by extracting fine-grained information of small targets. In particular, the experiment shows the appealing results on the TT100K dataset, which detection accuracy map of reaches 92.9%, and the frame processing rate is 119.8FPS. © 2023 IEEE.

关键词： Object detection

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Gaze Estimation Based on Single Objective Natural Conditions 4

Gaze Estimation Based on Single Objective Natural Conditions

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2022 4th International Conference on Electronic engineering and Informatics, EEI 2022

作者： Liu, Ziming Li, Tianjun Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China

ISBN: (纸本)9783800759231

Appearance based gaze estimation has great application value in the field of human-computer interaction. Nowadays, most deep learning methods require a lot of calculation and time. In this experiment, I designed a new mapping algorithm from three-dimensional coordinates to two-dimensional coordinates, and made the fixation point control the mouse movement to achieve the function of human-computer interaction. When the value 1 represents the maximum accuracy, the fixation accuracy of this experiment can be maintained above 0.8 when the head posture is not moving, High accuracy can also be maintained in the case of head posture movement. Compared with the depth learning method, our accuracy is equivalent, and compared with the traditional method, our accuracy is better. Moreover, the method designed in this paper has high fixation mapping efficiency and lower equipment requirements. The method proposed in this paper has great prospects in human eye control computer interaction. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Human computer interaction

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Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第4期16卷 30-45页

作者： Abuaddous, Hayfa. Y. Saleh, Ashraf Mousa Enaizan, Odai Ghabban, Fahad Al-Badareen, Anas Bassam Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Department of Management Information System University of Tabuk Tabuk Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Software Engineering Aqaba University of Technology Aqaba Jordan

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most fre-quently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods. © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Automation

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