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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Liangxin Liu, Xuebo Wong, Derek F. Li, Dongfang Wang, Ziyi Hu, Baotian Zhang, Min Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China NLP2CT Lab Department of Computer and Information Science University of Macau China

Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a small, high-quality subset of IT data can significantly enhance the performance of LLMs. Despite this, common approaches often rely on additional models or data, which increases costs and limits widespread adoption. In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself. Specifically, we exploit the intrinsic uncertainty present in LLMs to more effectively select high-quality IT data, without the need for extra resources. Furthermore, we introduce a curated IT dataset, the Selective Alpaca, created by applying SelectIT to the Alpaca-GPT4 dataset. Empirical results demonstrate that IT using Selective Alpaca leads to substantial model ability enhancement. The robustness of SelectIT has also been corroborated in various foundation models and domain-specific tasks. Our findings suggest that longer and more computationally intensive IT data may serve as superior sources of IT, offering valuable insights for future research in this area. Data, code, and scripts are freely available at https://***/Blue-Raincoat/SelectIT. © 2024 Neural information processing systems foundation. All rights reserved.

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Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations 24

Heterogeneity-Aware Federated Deep Multi-View Clustering tow...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jiang, Xiaorui Ma, Zhongyi Fu, Yulin Liao, Yong Zhou, Pengyuan Cccd Key Lab of Ministry of Culture and Tourism University of Science and Technology of China Hefei China Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

ISBN: (纸本)9798400706868

Multi-view clustering has proven to be highly effective in exploring consistency information across multiple views/modalities when dealing with large-scale unlabeled data. However, in the real world, multi-view data is often distributed across multiple entities, and due to privacy concerns, federated multi-view clustering solutions have emerged. Existing federated multi-view clustering algorithms often result in misalignment in feature representations among clients, difficulty in integrating information across multiple views, and poor performance in heterogeneous scenarios. To address these challenges, we propose HFMVC, a heterogeneity-aware federated deep multi-view clustering method. Specifically, HFMVC adaptively perceives the degree of heterogeneity in the environment and employs contrastive learning to explore consistency and complementarity information across clients' multi-view data. Besides, we seek consensus among clients where local data originates from the same view, incorporating a contrastive loss between local models and the global model during local training to adjust consistency among local models. Furthermore, we elucidate the sample representation logic for local clustering in different heterogeneous environments, identifying the degree of heterogeneity by computing the within-cluster sum of squares (WCSS) and the average inter-cluster distance (AICD). Extensive experiments verify the superior performance of HFMVC across both IID and Non-IID settings. © 2024 ACM.

关键词： Federated learning

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Where Are the Constants? New Insights on the Role of Round Constant Addition in the SymSum Distinguisher 1

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25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023

作者： Suryawanshi, Sahiba Saha, Dhiman de.ci.phe.red Lab Department of Computer Science & Engineering Indian Institute of Technology Bhilai492015 India

ISBN: (数字)9783031442742

ISBN: (纸本)9783031442735

The current work makes a systematic attempt to describe the effect of the relative order of round constant (RCon) addition in the round function of an SPN cipher on its algebraic structure. The observations are applied to the SymSum distinguisher, introduced by Saha et al. in FSE 2017 which is one of the best distinguishers on the SHA3 hash function reported in literature. Results show that certain ordering (referred to as Type-LCN) of RCon makes the distinguisher less effective but it still works with some limitations. Results in the form of new SymSum distinguishers are reported on concrete Type-LCN constructions - NIST LWC competition finalist Xoodyak-Hash and its internal permutation Xoodoo. New linear structures are also reported on Xoodoo that augment the distinguisher to penetrate more rounds. Final results include SymSum distinguishers on 7 rounds of Xoodoo and 5 rounds of Xoodyak-Hash with complexity 2128 and 232, respectively. All practical distinguishers have been verified. The characterization encompassing the algebraic structure and effect of RCon provided by the current work improves the understanding of SymSum in general and constitutes one of the first such result on Xoodyak-Hash and Xoodoo. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Classification of Lung Nodule from CT and PET/CT Images Using Artificial Neural Network

Classification of Lung Nodule from CT and PET/CT Images Usin...

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International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Hansdah, Malho Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoid activation functions have been used to supply weights to the model. The present work encompasses detecting and classifying the tumor cells into four categories. The four types of lung cancer nodules are adenocarcinoma, squamous-cell carcinoma, large-cell carcinoma, and small-cell carcinoma. Computed tomography (CT) and Positron emission tomography (PET) scan DICOM images are used for the classification. The proposed approach has been validated with the subset of the original dataset. A total of 6500 images have been taken in the experiment. The approach is to feed the CT scan images into ANNs and classify the image as the correct type. The dataset is provided by The Cancer Imaging Archive (TCIA). The dataset is titled "A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis." The tumor cells are classified using the ANN architecture with 99.6% of validation accuracy and 4.35% loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Mitigating Concept Drift in Distributed Contexts with Dynamic Repository of Federated Models

Mitigating Concept Drift in Distributed Contexts with Dynami...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Tsiporkova, Elena Vis, Michiel De Klein, Sarah Hristoskova, Anna Boeva, Veselka EluciDATA Lab of Sirris Brussels Belgium Digital Services Lab of Sirris Brussels Belgium Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

ISBN: (纸本)9798350324457

This paper proposes a novel federated learning methodology, called FedRepo, that copes with concept drift issues in a statistically heterogeneous distributed learning environment. The proposed horizontal federated learning methodology, based on random forest (RF), can be used for collaborative training and maintenance of a dynamic repository of federated RF models, each one customized to a group of clients/devices. The clients are grouped together if their performance patterns with respect to the global RF model are similar. The performance of the customized RF global models is continuously monitored during the inference phase and the repository is accordingly adapted to mitigate the detected concept drift. The proposed methodology is studied and evaluated against an electricity consumption forecasting use case. The evaluation results demonstrate clearly that the proposed methodology is able to deal with concept drift issues in an efficient and adequate fashion without compromising the overall performance of the distributed environment. © 2023 IEEE.

关键词： clustering concept drift distributed learning federated learning particle swarm optimization random forest

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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

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Second International Conference on Security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer Security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Pulmonary Lung Cancer Classification Using Deep Neural Networks

Pulmonary Lung Cancer Classification Using Deep Neural Netwo...

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International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Goswami, Jagriti Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India

ISBN: (纸本)9789819901883

Lung cancer is the leading cause of cancer-related deaths globally. computer-assisted detection (CAD) systems have previously been used for various disease diagnosis and hence can serve as an efficient tool for lung cancer diagnosis. In this paper, we study the problem of lung cancer classification using chest computed tomography (CT) scans and positron emission tomography–computed tomography (PET-CT). A subset of publicly available Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) is used to train four different deep learning models using transfer learning for classifying three different types of lung cancer: Adenocarcinoma, Small Cell Carcinoma and Squamous Cell Carcinoma, by passing raw nodule patches to the network. The models are evaluated on metrics such as accuracy, precision, recall, F1-score and Cohen’s Kappa score. ROC curves and confusion matrices are also presented to provide a graphical representation of the models’ performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Diagnosis of SARS-CoV-2 Via Rapid Antigen Kits

Diagnosis of SARS-CoV-2 Via Rapid Antigen Kits

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2023 IEEE technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Surathkal Maharshi Patanjali Cps Lab Brics Lab Department of Computer Science and Engineering Bharat Mangalore575025 India

ISBN: (纸本)9798350384659

The recent emergence of SARS-CoV-2, also known as COVID-19, poses a significant health threat and has rapidly spread to 223 countries, affecting more than 755 million people. Globally, more than seven billion COVID-19 tests have been performed. Rapid antigen kits have emerged as valuable detection techniques in the global fight against COVID-19, providing quick and accessible testing capabilities. However, ensuring the accuracy and reliability of these kits is crucial for effective disease management. This paper presents the emergence of different COVID-19 biomarker techniques to detect these viruses. Next, explore the role of the formal verification technique, addressing the trustworthiness of rapid antigen kits. Furthermore, the paper explores the diverse quick antigen kits approved by the Indian Council of Medical Research (ICMR) available in the literature. Formal verification provides substantial implications in the context of rapid antigen kit verification, and it is increasingly becoming a prominent trend in medical diagnostics. © 2023 IEEE.

关键词： Antigens

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Games and Play SIG: Connecting Through Social and Playful Technologies

Games and Play SIG: Connecting Through Social and Playful Te...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Robinson, Raquel B. Mirza-Babaei, Pejman Alvarez, Alberto Garreta Domingo, Muriel Mandryk, Regan L. Isbister, Katherine Uxr Lab Ontario Tech University Canada University of Ontario Institute of Technology Uxr Lab Canada Dept. of Computer Science & Media Technology Malmö University Sweden King Spain Department of Computer Science University of Saskatchewan Canada Social Emotional Technology Lab University of California Santa Cruz United States

ISBN: (纸本)9781450394222

Games have always been popular for connecting people, from local single-player and couch co-op, to massively multiplayer online. Throughout the COVID-19 pandemic, remote games that involved and fostered social interactions and connections were a highlight among strategies for staying connected. For this year's games and play SIG, we come together to discuss the relevant and timely topic of social and playful technologies, and how they can be designed to best foster meaningful social connections over a distance. We bring together attendees from not only the games community, but also those in the broader field of CHI focusing on social and playful technologies. © 2023 Owner/Author.

关键词： COVID-19

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