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Advancements in computing (ICAC), International Conference on

作者： W.H.A. Eishan Dinuka A.Y.S. Wickramasinghe W W.S. Weerasinghe H K.P. Karunaratne G Chethana Liyanapathirana Lakmal Rupasinghe Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users’ feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users’ preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly.

关键词： Neural networks Manuals Predictive models Developing countries Chatbots Prediction algorithms User experience

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LinNet: linear network for efficient point cloud representation learning 24

LinNet: linear network for efficient point cloud representat...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hao Deng Kunlei Jing Shengmei Cheng Cheng Liu Jiawei Ru Jiang Bo Lin Wang State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University and Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality School of Software Engineering Xi'an Jiaotong University and Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9798331314385

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of down-sampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet.

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MT-PART: Metamorphic-Testing-Based Adaptive Random Testing Through Partitioning

MT-PART: Metamorphic-Testing-Based Adaptive Random Testing T...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Zhihao Ying Dave Towey Tsong Yueh Chen Zhi Quan Zhou School of Computer Science University of Nottingham Ningbo China Zhejiang People's Republic of China Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia School of Computing and Information Technology University of Wollongong Wollongong NSW Australia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Metamorphic Testing (MT) has been repeatedly proven effective in detecting software faults. MT detects faults by checking the Metamorphic Relations (MRs) among Source Test Cases (STCs) and Follow-up Test Cases (FTCs) and the corresponding outputs. Metamorphic Groups (MGs) denote the associated STCs and FTCs. The performance of MT relates strongly to the MRs and MGs. However, previous studies that on MG generation mainly focused on improving the effectiveness (i.e. fault-detection capability) of MT, but to some extent overlooked the efficiency. This paper proposes a new kind of MG generation algorithms called Metamorphic-Testing-based Adaptive Random Testing through Partitioning (MT-PART). These algorithms at-tempt to improve both the effectiveness and the efficiency of MT by dynamically partitioning the input domain and generating new STCs and FTCs that are uniformly distributed over their corresponding input domains. Through empirical experiments, we found that our algorithms are able to significantly outper-form other existing MG generation algorithms in terms of test efficiency, while maintaining good test effectiveness.

关键词： Heuristic algorithms software algorithms software Partitioning algorithms Testing

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A Smart City Concept for Botswana in the Sub Saharan Region of Africa

A Smart City Concept for Botswana in the Sub Saharan Region ...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Leo John Baptist Andrews Annamalai Alagappan Sampath Kumar Venkatachary Sarathkumar D Raymon Antony Raj Department of Software Engineering Faculty of Computing Botho University Botswana Department of Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana Grant Thornton Gaborone Botswana Department of Electrical and Electronics Engineering Kongu Engineering College Tamilnadu India

ISBN: (纸本)9781665474177

The Internet of Things (IoT) as a technology and its adaptation into mainstreams by various governments across the globe has set developmental pace for many new innovations in IoT. IoTs are transforming cities across globe in the form of ensuring healthcare, understanding traffic, building better and efficient services in the form of delivery in various sectors like energy, water and sanitation etc while improving quality of life and to establish the smart city. This paper place aims to understand some of these technologies and how it can be adopted as technology into the Sub-Sharan African region with a focus on Botswana to make a smart city. The article attempts to highlight key issues that must be resolved to achieve improved IoT adoption for Botswana's development of smart cities in Sub-Saharan Africa.

关键词： Technological innovation Smart cities Government Buildings Africa Medical services Internet of Things

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Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning

arXiv

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arXiv 2024年

作者： Yang, Fuchao Cheng, Jianhong Liu, Hui Dong, Yongqiang Jia, Yuheng Hou, Junhui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Yam Pak Charitable Foundation School of Computing and Information Sciences Saint Francis University Hong Kong Department of Computer Science City University of Hong Kong Hong Kong China

In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On the other side, it brings high label ambiguity as the noisy labels are quite undistinguishable from the ground-truth label. To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. Moreover, to reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information to disambiguate the candidate label set. Extensive experimental comparisons with twelve methods on six benchmark data sets, including four fine-grained data sets, demonstrate the effectiveness of the proposed method. The code implementation is publicly available at https://***/Yangfc-ML/CEL. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Status Update on Phishing Emails Awareness: Jordanian Case 21

Status Update on Phishing Emails Awareness: Jordanian Case

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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Qasaimeh, Malik Al-Manaseer, Hana Al-Manaseer, Haya Alghanim, Firas Software Engineering Department Computer Information Technology Jordan University of Science and Technology Irbid22110 Jordan King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman11941 Jordan

ISBN: (纸本)9781450390446

This study is a response to the rapid proliferation of high-risk phishing emails, representing one of the most dangerous cybercrimes and the primary medium for the deception of online users. This study aims to investigate the various characteristics of phishing email messages, and to measure Jordanian internet users' awareness and knowledge about these threats. To achieve these objectives, a quantitative approach was used based on an online survey that included a total of 1531 participants. The results showed that only a few participants had the ability to distinguish illegitimate from legitimate emails, as the majority of participants could not recognize illegitimate emails by answering with "legitimate email"or "I don't know". Similarly, only a few participants could distinguish phishing emails, with the majority being unable to recognize phishing emails by answering with "real email"or "I don't know". Therefore, more work to increase public awareness of phishing emails is recommended at the individual and institutional levels, so as to protect users from any possible phishing email threats. This study focuses on the aspects that are related to email phishing that include techniques used to generate such phishing emails on the basis of which we will identity preventive measures to protect Jordanian users and increase their awareness of phishing emails. © 2021 ACM.

关键词： Electronic mail

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

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Cybersecurity Risks Mitigation in the Internet of Things

Cybersecurity Risks Mitigation in the Internet of Things

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Annamalai Alagappan Leo John Baptist Andrews Sampath Kumar Venkatachary Sarathkumar D Raymon Antony Raj Department of Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana Department of Software Engineering Faculty of Computing Botho University Botswana Grant Thornton Gaborone Botswana Department of Electrical and Electronics Engineering Kongu Engineering College Tamilnadu India

ISBN: (纸本)9781665474177

Internet of Things is a dynamic platform and involves data which in turn attracts cybersecurity threats. Numerous organisations are increasingly placing its focus towards understanding cyber risks in a dynamic and complex environment while also trying to quantify their exposure. This article focuses on addressing the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach using machine learning algorithms while highlighting and discussing the traditional cyber risk assessment techniques at length. Built on the statistics analysis, it can be decided that the Bayesian algorithm performs in estimating the potential risks at 82% and in comparison, the CAQ model using J48 classifies at 94%. The Bayes algorithm also helps in quantifying the complex risks originating from different sources and helps in understanding how the risk factors emerge and connect in an IoT environment and what controls are required for mitigating them.

关键词： Analytical models Machine learning algorithms Hardware Classification algorithms Bayes methods Recording Internet of Things

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Cybersecurity Risks Quantification in the Internet of Things

Cybersecurity Risks Quantification in the Internet of Things

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Annamalai Alagappan Leo John Baptist Andrews Sampath Kumar V Raymon Antony Raj Sarathkumar D Department of Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana Department of Software Engineering Faculty of Computing Botho University Botswana Grant Thornton Gaborone Botswana Department of Electrical and Electronics Engineering Kongu Engineering College Tamilnadu India

A dynamic platform, the Internet of Things uses data, which in turn invites cybersecurity risks. In a dynamic and complicated world, many organisations are putting more of their attention into understanding cyber threats and attempting to quantify their exposure. This paper highlights and goes into great detail on standard cyber risk assessment methods while focusing on the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach employing machine learning algorithms. Based on the statistical research, it can be determined that the CAQ model utilising J48 classifies at 94 percent, while the Bayesian method performs in evaluating possible risks at 82 percent. The Bayes algorithm also aids in estimating complicated risks coming from various sources, aids in comprehending how risk variables originate and connect in an IoT environment, and aids in determining what controls are necessary for quantifying and reducing them.

关键词： Technological innovation Machine learning algorithms Data analysis Focusing Hardware Bayes methods Internet of Things

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