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arXiv 2024年

作者： Nguyen, Thao Torres-Flores, Tiara Hwang, Changhyun Edwards, Carl Diao, Ying Ji, Heng Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States Department of Chemical & Biomolecular Engineering University of Illinois Urbana-Champaign United States

This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language Descriptors for enhanced PCE prediction. Due to the lack of high-quality experimental data, we collect a dataset consisting of 500 pairs of OPV donor and acceptor molecules along with their corresponding PCE values, which we utilize as the training data for our predictive model. In this low-data regime, GLaD leverages properties learned from large language models (LLMs) pretrained on extensive scientific literature to enrich molecular structural representations, allowing for a multimodal representation of molecules. GLaD achieves precise predictions of PCE, thereby facilitating the synthesis of new OPV molecules with improved efficiency. Furthermore, GLaD showcases versatility, as it applies to a range of molecular property prediction tasks (BBBP, BACE, ClinTox and SIDER (Wu et al., 2018)), not limited to those concerning OPV materials. Especially, GLaD proves valuable for tasks in low-data regimes within the chemical space, as it enriches molecular representations by incorporating molecular property descriptions learned from large-scale pretraining. This capability is significant in real-world scientific endeavors like drug and material discovery, where access to comprehensive data is crucial for informed decision-making and efficient exploration of the chemical space. © 2024, CC BY.

关键词： Molecules

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Performance evaluation of conditional handover in 5G systems under fading scenario

arXiv

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arXiv 2024年

作者： Deb, Souvik Rathod, Megh Balamurugan, Rishi Ghosh, Shankar K. Singh, Rajeev K. Sanyal, Samriddha Department of Computer Science and Engineering Shiv Nadar Institution of Eminence Delhi NCR India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India School of Computing and Data Science FLAME University Pune India

To enhance the handover performance in fifth generation (5G) cellular systems, conditional handover (CHO) has been evolved as a promising solution. Unlike A3 based handover where handover execution is certain after receiving handover command from the serving access network, in CHO, handover execution is conditional on the RSRP measurements from both current and target access networks, as well as on mobility parameters such as preparation and execution offsets. Analytic evaluation of conditional handover performance is unprecedented in literature. In this work, handover performance of CHO has been carried out in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of different mobility parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), UE velocity and channel fading characteristics;has been proposed to characterize handover failure. Results obtained from the analytic model has been validated against extensive simulation results. Our study reveal that optimal configuration of Oexec, Oprep, Texec and Tprep is actually conditional on underlying UE velocity and fading characteristics. This study will be helpful for the mobile operators to choose appropriate thresholds of the mobility parameters under different channel condition and UE velocities. Copyright © 2024, The Authors. All rights reserved.

关键词： Packet loss

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Research on Enhanced Hybrid Whale Optimization Algorithm with Inverse Learning Strategy and Lévy Flight Mechanism

Research on Enhanced Hybrid Whale Optimization Algorithm wit...

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Computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Danyang Guo Yinghao Zheng Jiaxin Lu Sihua Liang Tuyin Chen Jialin Yang Chao Zhou School of Robotics Engineering Guangzhou City University of Technology China School of Materials Science and Engineering Guilin University of Electronic Technology China School of Big Data and Computing Guangdong Baiyun University China

In recent years, bio-inspired optimization algorithms have attained significant success in addressing complex global optimization issues. Nonetheless, a single bio-inspired search strategy may struggle to handle diverse and intricate problems. To surmount this constraint, this paper introduces an enhanced hybrid whale algorithm (MEHWOA) based on reverse learning strategy and Lévy flight mechanism improvement. This approach amalgamates the global search capabilities of the gray wolf algorithm with the local search prowess of the whale algorithm, further augmenting the convergence speed and optimization accuracy of MEHWOA by incorporating reverse learning strategy and Lévy flight mechanism. To assess the performance of MEHWOA, we conducted experiments on 23 general benchmark test functions and compared it with original gray wolf (GWO), whale (WOA), particle swarm (PSO), and sparrow search (SSA) optimization algorithms. The experimental outcomes reveal that MEHWOA exhibits faster convergence speed and superior accuracy across various test functions, including unimodal, multimodal, and composite benchmark test functions. These findings corroborate that MEHWOA possesses considerable potential for solving complex global optimization problems.

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Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure

arXiv

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arXiv 2024年

作者： Deng, Zehang Sun, Ruoxi Xue, Minhui Wen, Sheng Camtepe, Seyit Nepal, Surya Xiang, Yang School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC Australia Cybersecurity and Quantum Systems Group CSIRO’s Data61. Australia

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead. © 2024, CC BY.

关键词： Critical infrastructures

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Alzheimer's Disease Detection using Machine Learning: A Review 3

Alzheimer's Disease Detection using Machine Learning: A Revi...

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3rd International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Dahiya, Savita Vijayalakshmi, S. Sabharwal, Munish Galgotias University School of Computing Science and Engineering Greater Noida India Christ University Department of Data Science Lavasa India Galgotias University School of Computing Science Greater Noida India

ISBN: (纸本)9781665438117

Alzheimer's is a progressive brain disorder which is an untreatable, and inoperable and mostly affect the elderly people. There is a new case of Alzheimer's disease being discovered globally in every four seconds. The outcome is fatal, as it results in death. Timely identification of Alzheimer's disease can be beneficial for us to get necessary care and possibly even avert brain tissue damage by the time. Effective automated techniques are required for detecting Alzheimer's disease at very early stage. Researchers use a variety of novel approaches to classify Alzheimer's disease. machine learning, an AI branch use probabilistic technique that allow system to acquire knowledge from huge amount of data. In this paper we represent a analysis report of the work which is done by researcher in this field. Research has achieved quite promising prediction accuracies however they were evaluated the the non-existent datasets from various imaging modalities which makes it difficult to make the fair comparison with the other methods comparison among them. In this paper, we conducted a study on the effectiveness of using human brain MRI scans to detect Alzheimer's disease and ended with a future discussion of Alzheimer's research trends. © 2021 IEEE.

关键词： Support vector machines

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Design and Promotion of Cost-Effective IOT-Based Heart Rate Monitoring

Design and Promotion of Cost-Effective IOT-Based Heart Rate ...

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2022 International Conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Moshayedi, Ata Jahangir Kolahdooz, Amin Roy, Atanu Shuvam Rostami, Seyyed Ali Latifi Xie, Xiaoyun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Faculty of Technology School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou China

ISBN: (纸本)9781510656673

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate. © 2022 SPIE.

关键词： Cost effectiveness

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Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model

arXiv

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arXiv 2024年

作者： Sinhababu, Nilanjan Parry, Andrew Ganguly, Debasis Samanta, Debasis Mitra, Pabitra Centre for Computational and Data Sciences IIT Kharagpur India School of Computing Science University of Glasgow United Kingdom Department of Computer Science and Engineering IIT Kharagpur India

A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that are capable of working in a zero-shot manner. However, since zero-shot inference does not make use of a training set of pairs of queries and their relevant documents, its performance is mostly worse than that of supervised models, which are trained on such example pairs. Motivated by the existing findings that training examples generally improve zero-shot performance, in our work, we explore if this also applies to ranking models. More specifically, given a query and a pair of documents, the preference prediction task is improved by augmenting examples of preferences for similar queries from a training set. Our proposed pairwise few-shot ranker demonstrates consistent improvements over the zero-shot baseline on both in-domain (TREC DL) and out-domain (BEIR subset) retrieval benchmarks. Our method also achieves a close performance to that of a supervised model without requiring any complex training pipeline. © 2024, CC BY.

关键词： Zero-shot learning

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Semantic Segmentation with Attention Dense U-Net for Lung Extraction from X-ray Images

Semantic Segmentation with Attention Dense U-Net for Lung Ex...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Akib Al Mahmud Auvy Rafiatul Zannah Mahbub-E-Elahi Shezhan Sharif Washik Al Mahmud Jannatun Noor Department of Computer Science and Engineering Computing for Sustainability and Social Good (C2SG) Research Group School of Data and Sciences BRAC University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Deep learning and digital image processing are crucial in medical imaging research. Lung segmentation is particularly challenging, demanding accurate differentiation of complex structures, sophisticated algorithms, and deep learning models for reliable results. In our research, We used the Shen-zhen chest X-ray dataset, comprising 566 frontal chest X-rays focused on pulmonary tuberculosis. This dataset, compiled by the Guangdong Medical College and Shenzhen No. 3 People's Hospital, was released by the United States National Library of Medicine. Images, captured using a Philips DR Digital Diagnostic system, were resized from $3000 \times 3000$ pixels to $512 \times 512$ pixels for computer-aided diagnosis research. We initially used the Attention Dense U-Net to extract lungs from chest X-rays, leveraging attention mechanisms to focus on relevant features and dense blocks to improve feature reuse. Our goal is to assess its performance for chest X-ray segmentation, comparing it with other deep learning models due to limited research in this area. So, the model is compared with four other variants of U-Net architectures to segment lung pixels from an X-ray image. After implementing the approach using a benchmark dataset and comparing it to other existing architectures, we present that Attention Dense U-Net gives the best accuracy for all given parameters, with a result of accuracy: 97.48%, Dice coefficient: 94.87%, and IoU: 93.87%.

关键词： Deep learning Tuberculosis Semantic segmentation Lung Computer architecture Feature extraction Libraries

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CLIP-Driven Multi-Scale Instance Learning for Weakly Supervised Video Anomaly Detection

CLIP-Driven Multi-Scale Instance Learning for Weakly Supervi...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhangbin Qian Jiawei Tan Zhilong Ou Hongxing Wang School of Big Data and Software Engineering Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Existing weakly supervised video anomaly detection methods mainly employ Multiple Instance Learning (MIL) to identify abnormal snippets in untrimmed videos. However, the semantics and presentations of anomalies frequently exhibit ambiguity that MIL is difficult to tackle. Moreover, MIL suffers from false alarms due to its independent optimization of each instance, neglecting temporal correlation between adjacent snippets. Consequently, we badly need to better connect abnormal presentations and their semantics, as well as to enable multi-temporal-scale anomaly discovery. This paper proposes a CLIP-Driven Multi-Scale Instance Learning (CMSIL) framework with two branches including Vision-Language (VL) and Multi-Scale Instance Learning (MSIL). The VL branch leverages the powerful visual concept priors from Contrastive Language-Image Pre-training (CLIP) to generate pseudo anomalies, thereby providing suspected anomaly cues for model training guidance. The MSIL branch utilizes a feature pyramid to fully mine fine-grained temporal dependencies by employing MIL within each pyramid level to learn anomalous patterns across different temporal scales. By collaborating with the two branches, the proposed CMSIL shows better proficiency in handling anomalies with varying durations. Extensive experiments on the XD-Violence and UCF-Crime datasets demonstrate the superior performance of our method. The code is available at https://***/casperZB/CMSIL.

关键词： Training Visualization Correlation Codes Semantics Focusing Streaming media Anomaly detection Optimization Context modeling

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Design of Congestion Control Algorithm based on Switch Queue Length Change Detection

Design of Congestion Control Algorithm based on Switch Queue...

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International Conference on Telecommunications (ICT)

作者： Wei Li Zhaonan Wu Bosheng Zhang Li Zheng Weibo Zhao Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology China

ISBN: (数字)9798350356694

ISBN: (纸本)9798350356700

Remote Direct Memory Access (RDMA) has emerged as the predominant networking technology and plays an important role in the Artificial Intelligence Generated Content (AIGC) industry, which requires effective congestion control algorithms to unleash its performance. Since the data Center Quantized Congestion Notification (DCQCN) protocol was introduced in 2015, it has quickly become the standard congestion control solution across RDMA networks and an essential component of high-speed clusters and data center networks. However, as a congestion control (CC) algorithm that primarily relies on explicit congestion notification (ECN), DCQCN is challenged by delayed bandwidth convergence and sluggish congestion detection. To address these challenges, this paper proposes an optimized congestion control algorithm solution based on DCQCN. Specifically, we propose more accurate switch queue length monitoring, which is divided into two main parts: a congestion detection phase and a congestion control phase. Our algorithm utilizes the change rate of the switch queue length to refine and identify the degree of congestion and adjust the data sending rate after congestion occurs based on this change rate. Experimental results demonstrate that the proposed algorithm achieves better average flow completion time (FCT) in different scenarios including incast and actual network load, compared with DCQCN and other CC algorithms.

关键词： data centers Accuracy Change detection algorithms Transmitters Heuristic algorithms Clustering algorithms Switches

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