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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Ullah, Atta Parchin, Naser Ojaroudi Danjuma, Isah Musa Abd-Alhameed, Raed A. University of Bradford Faculty of Engineering and Informatics BradfordBD7 1DP United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Nigerian Defence Academy Faculty of Engineering and Technology Kaduna Nigeria

ISBN: (纸本)9781665442282

Broadband phased array antennas with wide-scan and smart beam-steering are desirable to meet the requirements of future cellular networks. This paper discusses the characteristics of a new beam-steerable phased array design having broad bandwidth for 5G communications. The discussion is focused on smartphones, due to their small size and complexity. The design procedure is straightforward and accomplished on a mainboard of future smart handheld devices. The introduced phased array design involves eight circular-dipole resonators with a 1× 8 linear form. The resonators have small sizes with microstrip-line feedings. For -10 dB, the design represents 28-38 GHz covering multiple bands in the 5G spectra. © 2023 IEEE.

关键词： Resonators

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Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster

Hadar: Heterogeneity-Aware Optimization-Based Online Schedul...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Abeda Sultana Fei Xu Xu Yuan Li Chen Nian-Feng Tzeng School of Computing and Informatics University of Louisiana at Lafayette USA School of Computer Science and Technology East China Normal University China Department of Computer and Information Sciences University of Delaware USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

With the wide adoption of deep neural network (DNN) models for various applications, enterprises, and cloud providers have built deep learning clusters and increasingly deployed specialized accelerators, such as GPUs and TPUs, for DNN training jobs. To arbitrate cluster resources among multi-user jobs, existing schedulers fall short, either lacking fine-grained heterogeneity awareness or hardly generalizable to various scheduling policies. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an online optimization framework that can express other scheduling algorithms. Hadar leverages the performance traits of DNN jobs on a heterogeneous cluster, characterizes the task-level performance heterogeneity in the optimization problem, and makes scheduling decisions across both spatial and temporal dimensions. The primal-dual framework is employed, with our design of a dual subroutine, to solve the optimization problem and guide the scheduling design. Extensive trace-driven simulations with representative DNN models have been conducted to demonstrate that Hadar improves the average job completion time (JCT) by 3× over an Apache YARN-based resource manager used in production. Moreover, Hadar outperforms Gavel [1], the state-of-the-art heterogeneity-aware scheduler, by 2.5× for the average JCT, shortens the queuing delay by 13%, and improves FTF (Finish-Time-Fairness) by 1.5%.

关键词： Deep learning Training Runtime Scheduling algorithms Prototypes Artificial neural networks Optimal scheduling

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Raeed AL-Sabri Jianliang Gao Jiamin Chen Babatounde Moctard Oloulade Zhenpeng Wu Monir Abdullah Xiaohua Hu School of Computer Science and Engineering Central South University Changsha China Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khlifa University Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.

关键词： Representation learning Biological system modeling Manuals Predictive models Neural architecture search Topology Bioinformatics Faces

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Advancements in cheating detection algorithms in FPS games

Advancements in cheating detection algorithms in FPS games

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2024 International Conference on Computer Application and Information Security, ICCAIS 2024

作者： Xu, Ziwen Yu, Ruotong Liang, Yan Chen, Zihan Wan, Ye School of Computer Science and Technology Tongji University Shanghai201804 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai519087 China Luddy School of Informatics Computing and Engineering Indiana University of Bloomington Indiana BloomingtonIN47405 United States Guangdong Guangya High School Guangdong Guangzhou510000 China School of Computer Science and Technology Northeastern University Hebei Qinhuangdao066000 China

ISBN: (纸本)9781510689312

Over the past two decades, the increasing variety of cheating techniques in FPS games has led to the development of cheating detection algorithms. This literature review provides a chronological review of advancements in cheating detection techniques. Early methods, such as avatar trajectory-based detection, focused on behavioral analysis and identified bots by analyzing movement patterns. A remote anomaly-based system called Fides was presented to detect client-side manipulations with client-server architectures. With the emergence of machine learning, a variety of supervised learning methods have been introduced into the field of game detection to achieve real-time detection. More recent studies have been more innovative. Traps like the "bait-target" technique were introduced to detect cheating mechanisms that mimic human behavior. Methods combining deep learning and computer vision were proposed to detect complex cheating based on visual images. Overall, this review shows the diversity of cheat detection algorithms that have evolved to maintain the fairness of competitive gaming environments. © The Authors.

关键词： Supervised learning

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Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations

arXiv

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arXiv 2024年

作者： Zhou, Ao Liu, Bin Wang, Jin Tsoumakas, Grigorios Key Laboratory of Data Engineering and Visual Computing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has been demonstrated that batch selection algorithms preferring samples with higher uncertainty achieve better performance than difficulty-based methods. Although there are two batch selection methods tailored for multi-label data, none of them leverage important uncertainty information. Adapting the concept of uncertainty to multi-label data is not a trivial task, since there are two issues that should be tackled. First, traditional variance or entropy-based uncertainty measures ignore fluctuations of predictions within sliding windows and the importance of the current model state. Second, existing multi-label methods do not explicitly exploit the label correlations, particularly the uncertainty-based label correlations that evolve during the training process. In this paper, we propose an uncertainty-based multi-label batch selection algorithm. It assesses uncertainty for each label by considering differences between successive predictions and the confidence of current outputs, and further leverages dynamic uncertainty-based label correlations to emphasize instances whose uncertainty is synergistically expressed across multiple labels. Empirical studies demonstrate the effectiveness of our method in improving the performance and accelerating the convergence of various multi-label deep learning models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Combination of Boruta's Algorithm and Lasso Logistic Regression in Establishing Blood-Based Biomarkers for Alzheimer's Disease

Combination of Boruta's Algorithm and Lasso Logistic Regress...

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Mohammad Nasir Abdullah Yap Bee Wah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Tapah Perak Malaysia School of Information Technology UNITAR International University Petaling Jaya Selangor Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Alzheimer's disease (AD) is the leading neurodegenerative disorder worldwide, significantly impacting morbidity, disability, and mortality rates. This study aims to enhance the early diagnosis of AD by identifying blood-based biomarkers using Boruta's algorithm and lasso logistic regression. The Gene Expression Omnibus (GEO) dataset GSE140829 identified 47,008 potential biomarkers, which preprocessing reduced to 22,372. Boruta's algorithm further narrowed these to 21 significant biomarkers. Lasso logistic regression was applied to this balanced dataset, resulting in six key biomarkers: CST7, DPAGT1, DZIP1, MOSC1, TACC3, and YDJC. The final model, validated with a C-index of 0.763, demonstrated good discriminative power and clinical utility. This research offers a robust method for predicting AD, potentially improving early diagnosis and guiding treatment decisions.

关键词： Logistic regression Analytical models Biological system modeling Biomarkers Prediction algorithms Calibration Gene expression Alzheimer's disease Artificial intelligence Logistics

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FedClust: Optimizing Federated Learning on Non-IID Data Through Weight-Driven Client Clustering

FedClust: Optimizing Federated Learning on Non-IID Data Thro...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Md Sirajul Islam Simin Javaherian Fei Xu Xu Yuan Li Chen Nian-Feng Tzeng School of Computing and Informatics University of Louisiana Lafayette USA School of Computer Science and Technology East China Normal University China Department of Computer and Information Sciences University of Delaware USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions. However, existing CFL approaches require a large number of communication rounds for stable cluster formation and rely on a predefined number of clusters, thus limiting their flexibility and adaptability. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions. FedClust groups clients into clusters in a one-shot manner using strategically selected partial model weights and dynamically accommodates newcomers in real-time. Experimental results demonstrate FedClust outperforms baseline approaches in terms of accuracy and communication costs.

关键词： Training Adaptation models Distributed processing Limiting Costs Correlation Federated learning

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Towards an Integrated Rat Llm Framework for Contextually Appropriate Complex Clinical Recommendations

SSRN

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SSRN 2024年

作者： Mukalazi, Serugunda Henry Jianquan, Ouyang Namatovu, Hasifah K. Kimbugwe, Nasser Department of Networks China Department of Information Systems Department of Information Technology Uganda School of Computer Science Xiangtan University Hunan Province Xiangtan China School of Computing and Informatics Technology Makerere University Uganda Uganda

The coexistence of multiple chronic diseases, known as multimorbidity or complex chronic conditions, still poses a global challenge and contributes to a significant portion of worldwide deaths. Managing complex chronic diseases is challenging both for patients and healthcare providers, as patients are required to adhere to long-term treatments and monitor their ever-changing health status and potential adverse drug effects. Medical practitioners face difficulties due to a lack of integrated clinical guidelines, often resulting in patients receiving recommendations from disintegrated healthcare providers. This disintegration puts patients at risk of confusing and conflicting information, hence increasing their vulnerability to errors and adverse drug reactions. Given the complexity of managing complex chronic conditions, there is a need for innovative solutions that can be utilized and harnessed by healthcare practitioners and patients. Large language models (LLMs) such as ChatGPT, PaLM, LaMDA, Bing Chat, and Llama have emerged as powerful tools presenting new healthcare transformative opportunities with the potential to enhance clinical decision-making. Despite their promise, leveraging LLMs for contextually accurate clinical recommendations faces challenges such as a lack of domain-specific knowledge, lack of up-to-date information, and biases in training data, with limitations in representing low-income settings perspectives. This article proposes an Integrated Retrieval-Augmented and Tuned (RAT) LLM framework to enhance LLMs' ability to generate contextually accurate clinical recommendations. © 2024, The Authors. All rights reserved.

关键词： Decision making

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On the Power of Quantum Distributed Proofs

arXiv

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arXiv 2024年

作者： Hasegawa, Atsuya Kundu, Srijita Nishimura, Harumichi Graduate School of Information Science and Technology The University of Tokyo Japan Institute for Quantum Computing Department of Combinatorics and Optimization University of Waterloo Canada Graduate School of Informatics Nagoya University Japan

Quantum nondeterministic distributed computing was recently introduced as dQMA (distributed quantum Merlin-Arthur) protocols by Fraigniaud, Le Gall, Nishimura and Paz (ITCS 2021). In dQMA protocols, with the help of quantum proofs and local communication, nodes on a network verify a global property of the network. Fraigniaud et al. showed that, when the network size is small, there exists an exponential separation in proof size between distributed classical and quantum verification protocols, for the equality problem, where the verifiers check if all the data owned by a subset of them are identical. In this paper, we further investigate and characterize the power of the dQMA protocols for various decision problems. First, we give a more efficient dQMA protocol for the equality problem with a simpler analysis. This is done by adding a symmetrization step on each node and exploiting properties of the permutation test, which is a generalization of the SWAP test. We also show a quantum advantage for the equality problem on path networks still persists even when the network size is large, by considering "relay points" between extreme nodes. Second, we show that even in a general network, there exist efficient dQMA protocols for the ranking verification problem, the Hamming distance problem, and more problems that derive from efficient quantum one-way communication protocols. Third, in a line network, we construct an efficient dQMA protocol for a problem that has an efficient two-party QMA communication protocol. Finally, we obtain the first lower bounds on the proof and communication cost of dQMA protocols. To prove a lower bound on the equality problem, we show any dQMA protocol with an entangled proof between nodes can be simulated with a dQMA protocol with a separable proof between nodes by using a QMA communication-complete problem introduced by Raz and Shpilka (CCC 2004). Copyright © 2024, The Authors. All rights reserved.

关键词： Hamming distance

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UWB NLOS Identification and Mitigation based on Bidirectional Encoder Representations from Transformer (BERT) Deep Learning

UWB NLOS Identification and Mitigation based on Bidirectiona...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Hongchao Yang Yunjia Wang CheeKiat Seow Meng Sun David Plets School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China School of Computing Science University of Glasgow Glasgow U.K Information Technology imec-Waves Ghent University Ghent Belgium

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

The Non-Line-of-Sight (NLOS) phenomenon can hinder signal propagation and significantly reduce the accuracy of UWB for indoor positioning and navigation. The Channel Impulse Response (CIR) sequence generated during UWB ranging is widely used for channel identification. However, existing deep learning algorithms struggle to balance the local and global features of the CIR sequence effectively. To address this, this paper constructs a Bidirectional Encoder Representations from Transformers (BERT) channel identification model using the self-attention mechanism to improve the NLOS identification. The identification Accuracy, LOS recall, and F2 scores in multiple scenarios are 96.65%, 97.13%, and 0.9703 respectively. Comparing to state-of-art algorithms such as LS-SVM, CNN, and LSTM, our algorithm outperformed by 17.9%, 11.86%, and 10.80% respectively. For NLOS ranging errors, a fine-grained classification model is constructed with error correction model based on BERT. In multiple scenarios, the average NLOS ranging error is reduced by 41.97% and outperforms LS-SVM, CNN, and LSTM by 34.04%, 31.99%, and 16.81% respectively. In the overall positioning experiment, our algorithm achieves better performance than the existing algorithms by 32.13%.

关键词： Accuracy Heuristic algorithms Prevention and mitigation Bidirectional control Transformers Encoding Distance measurement Classification algorithms Channel impulse response Long short term memory

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