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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ren, Yixin Xia, Yewei Zhang, Hao Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Department of Computer Science and Technology Tongji University Shanghai China SIAT Chinese Academy of Sciences Shenzhen China Machine Learning Department MBZUAI Abu Dhabi United Arab Emirates

We propose a novel method to efficiently learn significant Fourier feature pairs for maximizing the power of Hilbert-Schmidt Independence Criterion (HSIC) based independence tests. We first reinterpret HSIC in the frequency domain, which reveals its limited discriminative power due to the inability to adapt to specific frequency-domain features under the current inflexible configuration. To remedy this shortcoming, we introduce a module of learnable Fourier features, thereby developing a new criterion. We then derive a finite sample estimate of the test power by modeling the behavior of the criterion, thus formulating an optimization objective for significant Fourier feature pairs learning. We show that this optimization objective can be computed in linear time (with respect to the sample size n), which ensures fast independence tests. We also prove the convergence property of the optimization objective and establish the consistency of the independence tests. Extensive empirical evaluation on both synthetic and real datasets validates our method's superiority in effectiveness and efficiency, particularly in handling high-dimensional data and dealing with large-scale scenarios. © 2024 Neural information processing systems foundation. All rights reserved.

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Holistic and Historical Instance Comparison for Cervical Cell Detection

Holistic and Historical Instance Comparison for Cervical Cel...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hao Liu, Runsheng Zhou, Yanning Lin, Huangjing Chen, Hao Tencent Ai Lab Shenzhen China Imsight Ai Research Lab Shenzhen China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong The Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

ISBN: (纸本)9798350386226

Cytology screening from Papanicolaou (Pap) smears is a common and effective tool for the preventive clinical management of cervical cancer, where abnormal cell detection from whole slide images serves as the foundation for reporting cervical cytology. However, cervical cell detection remains challenging due to 1) hazily-defined cell types (e.g., ASC-US) with subtle morphological discrepancies caused by the dynamic cancerization process, i.e., cell class ambiguity, and 2) imbalanced class distributions of clinical data may cause missed detection, especially for minor categories, i.e., cell class imbalance. To this end, we propose a holistic and historical instance comparison approach for cervical cell detection. Specifically, we first develop a holistic instance comparison scheme enforcing both RoI-level and class-level cell discrimination. This coarse-to-fine cell comparison encourages the model to learn foreground-distinguishable and class-wise representations. To emphatically improve the distinguishability of minor classes, we then introduce a historical instance comparison scheme with a confident sample selection-based memory bank, which involves comparing current embeddings with historical embeddings for better cell instance discrimination. Extensive experiments and analysis on two large-scale cytology datasets including 42,592 and 114,513 cervical cells demonstrate the effectiveness of our method. The code is available at https://***/hjiangaz/HERO. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Explainable Matrix Factorization with Tags for Multi-Style Explanations 15

Enhancing Explainable Matrix Factorization with Tags for Mul...

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15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Seton, Olurotimi Haghighi, Pegah Sagheb Alshammari, Mohammed Nasraoui, Olfa Knowledge Discovery and Web Mining Lab Computer Science and Engineering Dept University of Louisville United States Department of Computer Science Faculty of Computing and Information Technology Northern Border University Arar Saudi Arabia

ISBN: (纸本)9789897586712

Black-box AI models tend to be more accurate but less transparent and scrutable than white-box models. This poses a limitation for recommender systems that rely on black-box models, such as Matrix Factorization (MF). Explainable Matrix Factorization (EMF) models are "explainable" extensions of Matrix Factorization, a state of the art technique widely used due to its flexibility in learning from sparse data and accuracy. EMF can incorporate explanations derived, by design, from user or item neighborhood graphs, among others, into the model training process, thereby making their recommendations explainable. So far, an EMF model can learn a model that produces only one explanation style, and this in turn limits the number of recommendations with computable explanation scores. In this paper, we propose a framework for EMFs with multiple styles of explanation, based on ratings and tags, by incorporating EMF algorithms that use scores derived from tag-centric graphs to connect rating neighborhood-based EMF techniques to tag-based explanations. We used precalculated explainability scores that have been previously validated in user studies that evaluated user satisfaction with each style individually. Our evaluation experiments show that our proposed methods provide accurate recommendations while providing multiple explanation styles, without sacrificing the accuracy of the recommendations. Copyright © 2023 by SCITEPRESS - science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Matrix algebra

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A secured GA-WPC framework for scheduling the independent tasks in cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 13003-13015页

作者： Dubey, Kalka Sharma, S.C. Kumar, Mohit Kumar, Prashant Nasr, Aida A. Cloud Computing and Wireless Sensor Lab Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais Uttar Pradesh Amethi229304 India Department of Information Technology Dr BR Ambedkar National Institute of Technology Punjab Jalandhar144011 India Information Technology Department Faculty of Computers and Informatics Tanta University Tanta Egypt

Cloud computing proved itself a powerful and popular platform for providing computing services. It offers on-demand services to users with highly efficient resources over the internet. With the exponential increase in the number of internet users and due to the high acceptance rate to use the cloud services in everything, the demand for cloud platforms has also increased significantly. This leads to heavy workloads on the servers, which is a difficult task to manage due to the heterogeneity and uncertainty of the resources. However, several traditional, heuristic, and evolutionary-based approaches have been proposed to solve the problem of scheduling. Existing state-of-art techniques and frameworks have solved the problem up to a limited level. Still, these solutions have not given any effective and concrete solution for the overall resource scheduling problem. Therefore, this paper tries to overcome the bottleneck of existing solutions by presenting a new framework named Genetic Algorithm Water Pressure Change (GAWPC) to solve task scheduling problems efficiently. The GAWPC framework exploited the feature of the Genetic Algorithm (GA) and Water Pressure Change optimization (WPCO) techniques to form a new hybrid mechanism. In the proposed framework, GA has been deployed to find the optimal solution for a particular population. WPCO has been used to optimize fitness function for a fast convergence rate. The computer-based simulation has demonstrated promising benefits like improved computation cost, make-span time, and load balancing when compared with the most recent existing baseline techniques using CloudSim. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Advanced IOT Traffic Light Control System 5

Advanced IOT Traffic Light Control System

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5th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2023

作者： Hamad, Martin Abou El Hajj, Antonio Daou, Roy Abi Zeid Hayek, Ali Haidar, Gaby Abou Lebanese German University Faculty of Public Health Biomedical Technologies Department Lebanon MART Learning Education and Research Center Lebanon University of Kassel Department of Computer Architecture and System Programming Kassel Germany American University of Science and Technology Engineering Faculty Zahle Lebanon

ISBN: (纸本)9798350309959

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy. © 2023 IEEE.

关键词： Traffic congestion

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On-Chip Quantum Dot Lasers Driven High-Speed Optical Neural Networks

On-Chip Quantum Dot Lasers Driven High-Speed Optical Neural ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhou, Zhican Fang, Yuetong Ou, Xiangpeng He, William Wu, Xuhao Xu, Renjing Pan, David Z. Wan, Yating Integrated Photonics Lab King Abdullah University of Science and Technology Thuwal Makkah Saudi Arabia Function Hub The Hong Kong University of Science and Technology Guangzhou China Department of Electrical and Computer Engineering The University of Texas Austin78703 United States

We present an optical neural network architecture driven by on-chip quantum dot lasers, achieving 3.5 TOPS/mm2 theoretical computational density. With an optimized model (reducing programming operations by over 75%), ... 详细信息

关键词： Quantum dot lasers

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Detection of Atherosclerosis Using Deep Learning 13

Detection of Atherosclerosis Using Deep Learning

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Moso, Juliet Chebet Bennai, Mohamed Tahar Guessoum, Zahia Cormier, Stéphane Telalović, Jasminka Hasić Lab-I* UR 4474 Université de Reims Champagne-Ardenne Reims51100 France Department of Computer Science Dedan Kimathi University of Technology Private bag 10143 Nyeri Kenya University Sarajevo School of Science and Technology Sarajevo71000 Bosnia and Herzegovina

Atherosclerosis is one of the major causes of cardiovascular disease, requiring early detection and intervention. Deep learning techniques, particularly transfer learning, offer potential avenues for improving the diagnosis and management of atherosclerosis. In this paper, a transfer learning approach is proposed to enhance the detection of atherosclerotic plaque by adapting pre-trained Convolutional Neural Networks (CNNs). By fine-tuning these models on a dataset of medical images, the study aims to leverage learned representations to improve detection accuracy and efficiency. Additionally, data augmentation is used to enhance model robustness and address data scarcity and class imbalance issues. The findings from our experiments indicate that the ResNet-50 model outperformed others in terms of Recall at 1.0, followed by Inception-v3 at 0.941. In classification accuracy, ResNet-50 achieved 93%, followed by the Inception-v3 model at 86%. Similarly, in AUC-ROC performance, the ResNet-50 model attained the highest score of 0.99, with the Inception-v3 model following closely at 0.966. Our results demonstrate that transfer learning significantly improves the accuracy, sensitivity, and specificity of the detection of atherosclerotic plaque, showcasing its potential as a valuable tool for the detection of atherosclerosis. © 2024 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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On-Chip Quantum Dot Lasers Driven High-Speed Optical Neural Networks

On-Chip Quantum Dot Lasers Driven High-Speed Optical Neural ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Zhou, Zhican Fang, Yuetong Ou, Xiangpeng He, William Wu, Xuhao Xu, Renjing Pan, David Z. Wan, Yating Integrated Photonics lab King Abdullah University of Science and Technology Makkah Thuwal Saudi Arabia Function Hub The Hong Kong University of Science and Technology Guangzhou China Department of Electrical and Computer Engineering The University of Texas Austin78703 United States

ISBN: (纸本)9781957171395

关键词： Quantum dot lasers

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Shaltout, Nawal Abd El-Latif, Ahmed A. Al-Adrousy, Waleed Hammad, Mohamed Ateya, Abdelhamied A. Elmougy, Samir Mansoura University Faculty of Computers and Information Department of Computer Science Mansoura35516 Egypt Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science 32511 Egypt Menoufia University Faculty of Computers and Information Department of Information Technology Shibin El Kom32511 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350351408

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications. © 2024 IEEE.

关键词： Random number generation

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A Comprehensive Study on Disaster Tweet Classification on Social Media Information 6th

A Comprehensive Study on Disaster Tweet Classification on So...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Dasari, Siva Krishna Srinivas, Gorla Prasad Reddy, P.V.G.D. AI Research Lab Department of Computer Science and Engineering GMR Institute of Technology Rajam Vizianagaram India GITAM Deemed To Be University Visakhapatnam India Andhra University Visakhapatnam India

ISBN: (纸本)9789819986279

Twitter is a popular social media platform where people share their opinions. The impact of these opinions plays a critical role when a sudden unexpected situation or any natural disaster may happen. In such a large volume of information, identifying or extracting informative tweets has become challenging in the present case. Many researchers focus on this area in the classification of informative tweets. In this work, we are presenting a comprehensive review based on different approaches. This work mainly focuses on other methods, namely machine learning, deep learning, and transfer learning, for identifying informative tweets on natural disasters. We also concentrate on separate datasets and their performance with other state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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