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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Khaliq, Faiza Ilyas, Amna Rehman, Abd Ur Shahryar Akbar, Syed Satatr, Omar Riphah School of Computing & Innovation Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Riphah Interntional University Riphah School of Computing Lahore Pakistan University City Sharjah Skylin University College General Education Department Sharjah1797 United Arab Emirates

ISBN: (纸本)9798350335644

Data mining is the concept of collecting raw data and convert into useful information. Data mining is used for everything from what customers want to buy a thing and which thing is recommended to customers. In this paper, the proposed algorithm used is Neural Network. A Neural Network algorithm is used to recognize the hidden relationship between the data set by adopting the process of how the human brain performs functioning. In this research paper, a different algorithm is also used, and compare the result of this algorithm. The algorithm is applied to the data set of books which may result in the book being recommended or not on the different features and also the Accuracy of the algorithm. © 2023 IEEE.

关键词： Recommender systems

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Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction

arXiv

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arXiv 2023年

作者： Xu, Yangyang Yang, Yibo Ghanem, Bernard Zhang, Lefei Du, Bo Tao, Dacheng School of Computer Science Wuhan University Wuhan China Visual Computing Center King Abdullah University of Science and Technology Jeddah Saudi Arabia Sydney Ai Centre School of Computer Science The University of Sydney Sydney Australia

Convolution neural networks (CNNs) and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Most of the current studies on MTL solely rely on CNN or Transformer. In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer with shared gating for multi-task learning of dense prediction. This combination may offer a simple and efficient solution owing to its powerful and flexible task-specific learning and advantages of lower cost, less complexity and smaller parameters than the traditional MTL methods. We introduce deformable mixer Transformer with gating (DeMTG), a simple and effective encoder-decoder architecture up-to-date that incorporates the convolution and attention mechanism in a unified network for MTL. It is exquisitely designed to use advantages of each block, and provide deformable and comprehensive features for all tasks from local and global perspective. First, the deformable mixer encoder contains two types of operators: the channel-aware mixing operator leveraged to allow communication among different channels, and the spatial-aware deformable operator with deformable convolution applied to efficiently sample more informative spatial locations. Second, the taskaware gating transformer decoder is used to perform the task-specific predictions, in which task interaction block integrated with self-attention is applied to capture task interaction features, and the task query block integrated with gating attention is leveraged to select corresponding task-specific features. Further, the experiment results demonstrate that the proposed DeMTG uses fewer GFLOPs and significantly outperforms current Transformer-based and CNN-based competitive models on a variety of metrics on three dense prediction datasets (i.e., NYUD-v2, PASCAL-Context, and Cityscapes). For example, by using Swin-L as a backbone, our method achieves 57.55 mIoU segmentation

关键词： Forecasting

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PokkahScan - An Intelligent Drone for detection of Pokkah Boeng Disease in Sugarcane Using Transfer Learning and eXplainable AI 15

PokkahScan - An Intelligent Drone for detection of Pokkah Bo...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ravikumar, Rahul Sujith Kumaar, M.V. Krishna, Shirish Jayasurkanth, M. Yasshwanth, V.R. Saravanan, Sanjay Kishore Thangavel, Senthil Kumar Thangeswari, S. Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Center for Computational Engineering & Networking Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Aerospace Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Tamilnadu Agricultural University Department of Plant Pathology Coimbatore India Amrita School of Physical Sciences Department of Mathematics Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning becomes apparent as it allows for leveraging pre-existing knowledge from large datasets to improve the performance of models trained on smaller, domain-specific datasets. IoT has also played a significant role in our automation process. Transfer learning leveraging MobilenetV2 is particularly beneficial for this problem of detecting Pokkah Boeng disease in sugarcane crops, as it enables the adaptation of pretrained models, such as MobileNetV2, to accurately identify Pokkah Boeng disease even with limited set of data. LIME is employed to provide transparent explanations for the model's predictions, enhancing its interpretability and facilitating informed decision-making in agricultural management. By combining transfer learning with xAI and aerial drone technology, having add-ons of IoT and facilitating a comprehensive comparative study, PokkahScan offers a comprehensive solution for timely disease detection and crop management in sugarcane farming. © 2024 IEEE.

关键词： Agricultural Analytics Crop Management Disease Detection Early Detection IoT MobilenetV2 Pokkah Boeng Disease Sugarcane Crop Transfer Learning xAI

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ALPHAZERO-BASED PROOF COST NETWORK TO AID GAME SOLVING 10

ALPHAZERO-BASED PROOF COST NETWORK TO AID GAME SOLVING

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10th International Conference on Learning Representations, ICLR 2022

作者： Wu, Ti-Rong Shih, Chung-Chin Wei, Ting Han Tsai, Meng-Yu Hsu, Wei-Yuan Wu, I-Chen Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Computing Science University of Alberta Edmonton Canada

The AlphaZero algorithm learns and plays games without hand-crafted expert knowledge. However, since its objective is to play well, we hypothesize that a better objective can be defined for the related but separate task of solving games. This paper proposes a novel approach to solving problems by modifying the training target of the AlphaZero algorithm, such that it prioritizes solving the game quickly, rather than winning. We train a Proof Cost Network (PCN), where proof cost is a heuristic that estimates the amount of work required to solve problems. This matches the general concept of the so-called proof number from proof number search, which has been shown to be well-suited for game solving. We propose two specific training targets. The first finds the shortest path to a solution, while the second estimates the proof cost. We conduct experiments on solving 15x15 Gomoku and 9x9 Killall-Go problems with both MCTS-based and focused depth-first proof number search solvers. Comparisons between using AlphaZero networks and PCN as heuristics show that PCN can solve more problems. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Cost estimating

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FaceA-Net: Facial Attribute-Driven ID Preserving Image Generation Network 39

FaceA-Net: Facial Attribute-Driven ID Preserving Image Gener...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Jiayu Yu, Yue Chen, Jingjing Dai, Qi Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Research Asia China

ISBN: (纸本)157735897X

Recent advances in diffusion-based generative models have demonstrated superior performance in subject-driven image generation. Identity (ID) preserving image generation, as a subtask of subject-driven image generation, aims to generate customized images for specific human identity and has broad application potential. However, this task remains challenging due to the requirement for high ID fidelity and precise detail preservation. Additionally, generating high-quality context presents another challenge, as existing methods struggle to achieve both high ID fidelity and satisfactory context simultaneously. To address the issues of insufficient ID fidelity, we introduce a simple yet effective test-time fine-tuning approach. Specifically, we propose an attribute-driven training method that establishes global-level and local-level tasks to learn the global face feature and fine-grained attribute features, respectively. Furthermore, we introduce a novel ID-context decoupling framework that decouples image context generation from human ID generation, ensuring the quality of contextual content as well as facilitating the learning of ID information. Through extensive experiments, we demonstrate the effectiveness of the proposed method and showcase its capabilities across various applications. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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Canonical Saliency Maps: Decoding Deep Face Models

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2021年第4期3卷 561-572页

作者： John, Thrupthi Ann Balasubramanian, Vineeth N. Jawahar, C.V. Center for Visual Information Technology International Institute of Information Technology Hyderabad Hyderabad India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

As Deep Neural Network models for face processing tasks approach human-like performance, their deployment in critical applications such as law enforcement and access control has seen an upswing, where any failure may have far-reaching consequences. We need methods to build trust in deployed systems by making their working as transparent as possible. Existing visualization algorithms are designed for object recognition and do not give insightful results when applied' to the face domain. In this work, we present 'Canonical Saliency Maps', a new method which highlights relevant facial areas by projecting saliency maps onto a canonical face model. We present two kinds of Canonical Saliency Maps: image-level maps and model-level maps. Image-level maps highlight facial features responsible for the decision made by a deep face model on a given image, thus helping to understand how a DNN made a prediction on the image. Model-level maps provide an understanding of what the entire DNN model focuses on in each task, and thus can be used to detect biases in the model. Our qualitative and quantitative results show the usefulness of the proposed canonical saliency maps, which can be used on any deep face model regardless of the architecture. © 2019 IEEE.

关键词： Deep neural networks

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How to Detect AI-Generated Texts? 14

How to Detect AI-Generated Texts?

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14th IEEE Annual Ubiquitous computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Nguyen, Trung T. Hatua, Amartya Sung, Andrew H. Winona State University Department of Computer Science WinonaMN55987 United States AI Center of Excellence Fidelity Investments BostonMA02210 United States The University of Southern Mississippi School of Computing Sciences and Computer Engineering HattiesburgMS39401 United States

ISBN: (纸本)9798350304138

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to ***% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research. © 2023 IEEE.

关键词： ChatGPT document similarity explanation models feature engineering feature importance LLM

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MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior

arXiv

引用

arXiv 2023年

作者： Zhou, Shili Jiang, Xuhao Tan, Weimin He, Ruian Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

In recent years, many deep learning-based methods have been proposed to tackle the problem of optical flow estimation and achieved promising results. However, they hardly consider that most videos are compressed and thus ignore the pre-computed information in compressed video streams. Motion vectors, one of the compression information, record the motion of the video frames. They can be directly extracted from the compression code stream without computational cost and serve as a solid prior for optical flow estimation. Therefore, we propose an optical flow model, MVFlow, which uses motion vectors to improve the speed and accuracy of optical flow estimation for compressed videos. In detail, MVFlow includes a key Motion-Vector Converting Module, which ensures that the motion vectors can be transformed into the same domain of optical flow and then be utilized fully by the flow estimation module. Meanwhile, we construct four optical flow datasets for compressed videos containing frames and motion vectors in pairs. The experimental results demonstrate the superiority of our proposed MVFlow, which can reduce the AEPE by 1.09 compared to existing models or save 52% time to achieve similar accuracy to existing models. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical flows

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