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Conference on Information and Communication technology (CICT)

作者： Alireza Maleki Mahsa Lavaei Mohsen Bagheritabar Salar Beigzad Zahra Abadi Department of Technology Management University of Tehran Tehran Iran Electrical and Computer Engineering University of Tehran Tehran Iran Electrical Engineering Department University of Cincinnati Ohio USA Software and Engineering University of St. Thomas Minnesota Minnesota USA Electrical Engineering Department Tehran University Tehran Iran

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Deep learning techniques have proven highly effective in image classification, but their deployment in resource-constrained environments remains challenging due to high computational demands. Furthermore, their interpretability is of high importance which demands even more available resources. In this work, we introduce an approach that combines saliency-guided training with quantization techniques to create an interpretable and resource-efficient model without compromising accuracy. We utilize Parameterized Clipping Activation (PACT) to perform quantization-aware training, specifically targeting activations and weights to optimize precision while minimizing resource usage. Concurrently, saliency-guided training is employed to enhance interpretability by iteratively masking features with low gradient values, leading to more focused and meaningful saliency maps. This training procedure helps in mitigating noisy gradients and yields models that provide clearer, more interpretable insights into their decision-making processes. To evaluate the impact of our approach, we conduct experiments using famous Convo-lutional Neural Networks (CNN) architecture on the MNIST and CIFAR-10 benchmark datasets as two popular datasets. We compare the saliency maps generated by standard and quantized models to assess the influence of quantization on both interpretability and classification accuracy. Our results demonstrate that the combined use of saliency-guided training and PACT-based quantization not only maintains classification performance but also produces models that are significantly more efficient and interpretable, making them suitable for deployment in resource-limited settings.

关键词： Training Deep learning Accuracy Quantization (signal) Computational modeling Decision making Refining computer architecture Predictive models Standards

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Local-Global History-Aware Contrastive Learning for Temporal Knowledge Graph Reasoning

Local-Global History-Aware Contrastive Learning for Temporal...

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International Conference on Data engineering

作者： Wei Chen Huaiyu Wan Yuting Wu Shuyuan Zhao Jiayaqi Cheng Yuxin Li Youfang Lin School of Computer and Information Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future un-known facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information; (2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise. To this end, we propose a novel Local-global history-aware Contrastive Learning model (LogCL) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs a local-global query contrast module, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines. The code of LogCL is available at https://***/WeiChen3690/LoeCL.

关键词： Knowledge engineering Extrapolation Noise Contrastive learning Knowledge graphs Resists Interference

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation

arXiv

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arXiv 2023年

作者： Zhang, Yang Xiong, Chenyun Liu, Junjie Ye, Xuhui Sun, Guodong The School of Mechanical Engineering Hubei University of Technology Wuhan430068 China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information. According to previous studies, depth information can provide corresponding geometric relationships for objects and scenes, but actual depth data usually exist as noise. To avoid unfavorable effects on segmentation accuracy and computation, it is necessary to design an efficient framework to leverage cross-modal correlations and complementary cues. In this paper, we propose an efficient lightweight encoder-decoder network that reduces the computational parameters and guarantees the robustness of the algorithm. Working with channel and spatial fusion attention modules, our network effectively captures multi-level RGB-D features. A globally guided local affinity context module is proposed to obtain sufficient high-level context information. The decoder utilizes a lightweight residual unit that combines short- and long-distance information with a few redundant computations. Experimental results on NYUv2, SUN RGB-D, and Cityscapes datasets show that our method achieves a better trade-off among segmentation accuracy, inference time, and parameters than the state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Semantic Segmentation

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Deep Learning based Brain Tumor Classification for MR Images using ResNet50

Deep Learning based Brain Tumor Classification for MR Images...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Ömer Miraç Kökçam Aytuğ Boyaci Muhammed Emre Çolak Department of Software Engineering Faculty of Technology Fırat University Elazığ Türkiye Department of Computer Engineering Air Force Academy National Defence University İstanbul Türkiye Department of Artificial Intelligence and Data Science Engineering Faculty of Engineering Fırat University Elazığ Türkiye

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, “Early Diagnosis Saves Lives”. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis.

关键词： Deep learning Image recognition Brain modeling Internet Security Artificial intelligence Medical diagnostic imaging

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The Sophisticated Prognostication of Migraine Aura Using Machine Learning

The Sophisticated Prognostication of Migraine Aura Using Mac...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Samiullah Abdul Rehman Attique Ur Rehman Sabeen Javaid Tahir Mohammad Ali Azka Mir Yadaiah Nirsanametla Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait Division of Research and Development Lovely Professional University Phagwara Punjab KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Migraine is one of the most disabling diseases in the world and impacting more than one billion individuals. The symptoms such as intensity, Nausea, Vomit, Phonophobia, Photophobia, Visual, Dysphasia, Dysarthria, Vertigo, Sensory, intense to sound, occurs before the migraine. Migraine drains the quality of persons' life. Furthermore, the study of this research was to train machine learning model on migraine aura dataset using different modern approaches that could assist medical patients before occurring migraine and could also give the indications related to migraine. Likewise, by applying random forest algorithm, we got 99.5 percent accuracy using google colab which will be enough to deploy on application for future Project. Moreover, we leveraged traditional framework to complete the research incorporating, Data collection (Online free Kaggle), Preprocessing, Classification, Model training and Comparison of models. As a result, our peak algorithm precision was including, Naive Bayes 94.52%, decision tree 98.3%, K-nearest neighbours 98.6%, random forest 99.5%, Similarly, Rapid miner and Google Colab software are used for the comparison of algorithms and best one is chosen.

关键词： Training Visualization Machine learning algorithms software algorithms Migraine Data models software Classification algorithms Internet Random forests

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A Channel Mix Method for Fine-Grained Cross-Modal Retrieval

A Channel Mix Method for Fine-Grained Cross-Modal Retrieval

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Hu, Hanxu Chen, Zhipeng School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China University of Edinburgh Edinburgh United Kingdom Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities. Specifically, in order to enhance information interaction in different modalities for fine-grained objects, a channel mix method is developed and performed upon the channels of deep activations across dif-ferent modalities. After that, a 1 x 1 convolution is employed to aggregate the mixed channels into a unified feature vector. Moreover, equipped with a novel fine-grained cross-modal cen-ter loss, our method can further improve the intra-class separa-bility as well as inter-class compactness for multi-modalities. Experiments are conducted on the fine-grained cross-modal benchmark dataset and show our superiority over competing methods. Meanwhile, ablation studies also demonstrate the effectiveness of our proposals. © 2022 IEEE.

关键词： computer vision

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems

Robust and Reliable Liveness Detection Models for Facial Rec...

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Frontiers of Information technology (FIT)

作者： Haris Anjum Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Abu Bakar Siddique Muhammad Imad Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model’s accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system.

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A Review of ChatGPT Applications in Education, Marketing, software engineering, and Healthcare: Benefits, Drawbacks, and Research Directions

arXiv

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arXiv 2023年

作者： Fraiwan, Mohammad Khasawneh, Natheer Department of Computer Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid22110 Jordan Department of Software Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid22110 Jordan

ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, plethora of possible applications, and the great possibility for abuse. At the time of writing this work, several other language models (e.g., Google’s Bard and Meta’s LLaMA) just came out in an attempt to get a foothold in the vast possible market. These models have the ability to revolutionize the way we interact with computers and have potential applications in many fields, including education, software engineering, healthcare, and marketing. In this paper, we will discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields. We first start with a brief introduction and the development timeline of artificial intelligence-based language models, then we go through possible applications of such models, after that we discuss the limitations and drawbacks of the current technological state of the art, and finally we point out future possible research directions. © 2023, CC BY.

关键词： Marketing

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Adversarial Machine Learning for Detecting Advanced Threats Inspired by StuxNet in Critical Infrastructure Networks

Adversarial Machine Learning for Detecting Advanced Threats ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Hafiz Bilal Ahmad HaiChang Gao Naila Latif Abdul Aziiz Muhammad Auraangzeb Muhammad Tanveer Khan School of Computer Science and Technology Xidian University Shaanxi P.R.China School of Telecommunications and Engineering Xidian University Shaanxi P.R.China School of Computer Science and Engineering Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Software Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Inspired by the notorious StuxNet malware, this paper presents a unique and resilient architecture to address the imminent issue of identifying advanced threats within critical infrastructure networks. We use adversarial machine learning techniques to keep ahead of ever-changing threats. Our defense system's fundamental component is a Multi-Layer Perceptron (MLP) architecture that we've dubbed “StuxNet.” This innovative method combines threat intelligence, anomaly detection, and deep learning to forecast and counter sophisticated attacks. The effectiveness of our method in bolstering the security of critical infrastructure networks is supported by empirical evaluations and real-world case studies. Importantly, the StuxNet-MLP architecture has a 98 percent detection accuracy for sophisticated threats. These results highlight the paramount significance of preventative cybersecurity measures in protecting mission-critical systems and provide useful insights for fending off sophisticated attacks. To ensure a safer and more resilient digital future, this study highlights the need to stay ahead in the continuous battle to secure key infrastructure.

关键词： Training computer worms Neural networks Mission critical systems Adversarial machine learning Threat assessment Robustness

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