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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Adelshiyev, Syrym Kurmashev, Dias Department of Computational and Data Sciences Computer Science Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

In recent years, the advancement of renewable energy technologies has gained significant momentum, with wind energy emerging as a prominent source of clean electricity generation. The efficient operation and maintenance of wind turbines are crucial for maximizing energy production while minimizing costs. This paper presents the development of a machine learning (ML) model tailored specifically for wind turbine performance optimization. The proposed ML model integrates various data sources including meteorological conditions, turbine sensor data, historical performance records, and maintenance logs. Leveraging these datasets, the model employs sophisticated algorithms such as neural networks, decision trees, and ensemble methods to predict turbine performance, detect anomalies, and optimize maintenance schedules. ©2024 IEEE |

关键词： Wind turbines

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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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CoverGAN: cover photo generation from text story using layout guided GAN

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Soft Computing 2025年第1期29卷 405-423页

作者： Cheema, Adeel Naeem, M. Asif Department of Data Science and Artificial Intelligence National University of Computer and Emerging Sciences Islamabad Pakistan

Generating cover photos from story text is a non trivial challenge to solve. Existing approaches focus on generating only images from given text prompt. To the best of our knowledge, non of these approaches focus on generating cover photos from a text story. The paper addresses this issue by introducing multi-object image generation with text title from a text story. We split the problem into three steps:- understanding semantics of text story, predicting layout of objects, and generating a cover photo. At start, a semantic relation was encoded between text story objects using a Scene graph, then features from graph neural network were concatenated with single object features from scene graph to create an object layout. All of these features were then passed on to the image generating part. Image generation was further divided into two phases. In the first phase, the image is generated using a scene graph image generation model. While in the second phase, the results of first phase were further enhanced using image translation model conditioned on the object layout. In final phase we generated title of the given story based on generated image. In our experiments, we used custom dataset of text stories with three animal categories along with the COCO dataset. For the image generating part, we evaluated our approach with state of the art models known as scene_gen and sg2im. Our method generated high-resolution informative cover photo with story title by positioning the objects at right locations as specified in the text story. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Graph neural networks

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A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Williams, Lorrayya Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.;(ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites. © 2024 IEEE.

关键词： Convolutional neural networks

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Fuzzy knowledge based intelligent decision support system for ground based air defence

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2317-2340页

作者： Ahmad, Adnan Amjad, Rawan Basharat, Amna Farhan, Asma Ahmad Abbas, Ali Ezad Department of Computer Science School of Computing National University of Computer and Emerging Sciences Islamabad Pakistan Department of Data Science School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Islamabad Pakistan

This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A Fire Control Officer is responsible for assessing threats and assigning the most appropriate weapon to neutralize them. However, the decision-making process can be prone to errors, risking resource wastage and endangering DA protection. To address this problem, this research proposes a hybrid approach that combines a knowledge-driven fuzzy inference system with machine learning models to optimize resource allocation while incorporating expert knowledge in the decision-making process. Since sensory data obtained from multiple radars may be incomplete or incorrect, a fuzzy knowledge graph-based system is used for data fusion and providing it to the connected modules. Feature selection is optimized by including the most important parameters, such as the vitality of defended assets and threat score, in the threat evaluation. The results from these subsystems are visualized using a Geographical Information System, allowing for real-time mapping of the GBAD environment and displaying the results in a user-friendly web interface. The proposed system has undergone rigorous testing and evaluation, resulting in an efficient and accurate weapon assignment model with a low RMSE value of 0.037. Overall, this Intelligent Decision Support System provides an effective solution for optimizing decision-making processes in GBAD environments and can significantly improve DA protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graphic methods

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Combining Checkpoint/Restart and Replication for Fault Tolerance with High Performance 31

Combining Checkpoint/Restart and Replication for Fault Toler...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Joshi, Sarthak Vadhiyar, Sathish Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

As we have entered Exascale computing, the faults in high-performance systems are expected to increase considerably. To compensate for a higher failure rate, the standard checkpoint/restart technique would need to create checkpoints at a much higher frequency, resulting in an excessive amount of overhead, which would not be sustainable for many scientific applications. To improve application efficiency in such high-failure environments, the mechanism of replication of MPI processes was proposed. Replication allows for fast recovery from failures by simply dropping the failed processes and using their replicas to continue the regular operation of the application. We have implemented FTHP-MPI (Fault Tolerance and High-Performance MPI), a novel fault-tolerant MPI library that augments checkpoint/restart with replication to provide resilience from failures. The novelty of our work is that it is designed to provide fault tolerance in a native MPI library. This lets application developers achieve fault tolerance at high failure rates while also using efficient communication protocols in the native MPI libraries that are generally fine-tuned for specific HPC platforms. Our work employs various concepts, including MPI-agnostic checkpointing from DMTCP and communicator shrinking from ULFM, and implements them together without any code modifications on the user end. We have also implemented efficient parallel communication techniques that involve replicas. © 2024 IEEE

关键词： Failure rate

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A Preliminary Performance Analysis of LLM Inference on Edge Accelerators 31

A Preliminary Performance Analysis of LLM Inference on Edge ...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Arya, Mayank Simmhan, Yogesh Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

Large Language Models (LLMs) have revolutionized machine learning by efficiently handling tasks like translation, text generation, summarization, and classification, making them ideal for applications such as virtual assistants and agentic work-flows. However, their substantial computational requirements limit their wide adoption, particularly for smaller organizations lacking the computing infrastructure. Current LLM serving is done on the cloud, which imposes cost, latency and privacy concerns. Cloud-edge collaboration also struggles with network bottlenecks and model partitioning complexities. This work explores the potential for using edge accelerators, like Nvidia’s Jetson, for LLM inference. These compact devices have 100-1000s of GPU cores and 10+GB of shared GPU memory, allowing real-time edge inference close to the data source. We analyze and evaluate the efficiency of edge devices for LLM inference for different models and power modes, and make a case for tuning power modes to reduce power usage by ≈ 4-6% while matching the throughput of the highest performance mode. © 2024 IEEE

关键词： Graphics processing unit

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Performance Trade-offs in GNN Inference: An Early Study on Hardware and Sampling Configurations 31

Performance Trade-offs in GNN Inference: An Early Study on H...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Naman, Pranjal Simmhan, Yogesh Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

Inferencing over Graph Neural Network (GNN) has recently gained traction in applications like recommender systems and fraud detection. However, building a high throughput, low latency GNN serving system for large graphs in real-world applications is challenging due to the irregular memory access patterns and high host-device data transfer times. In this poster, we study the effects of various hardware and sampling configurations on the latency and performance of GNN serving, which will help build scalable GNN serving systems. Our results show that sampling on CPU and inference on GPU, popular for training, does not do well for inferencing. Smaller fan-outs give reasonable accuracies but suffer from high variability while GPU-based sampling fails with high fan-outs, making Unified Virtual Addressing (UVA) based sampling a preferred alternative. © 2024 IEEE

关键词： Virtual addresses

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Towards a Generalized SDK for a Programmable Drones-as-a-Service 31

Towards a Generalized SDK for a Programmable Drones-as-a-Ser...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Raj, Suman Singh, Rajdeep Astu, Kautuk Simmhan, Yogesh Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

The proliferation of Unmanned Aerial Vehicles (UAVs) in spatio-temporal applications has given rise to the need for Drone-as-a-Service (DaaS) platforms that support diverse tasks, ranging from urban air mobility to precision agriculture. However, existing DaaS platforms are often tightly-coupled to hardware-specific frameworks that lack programmability and flexibility for custom applications. For instance, using UAVs to help a Visually Impaired Person (VIP) navigate requires a suite of applications, such as obstacle avoidance, route planning, and real-time tracking. In this poster, we propose Ocularone SDK (O-SDK) that integrates on our prior Ocularone stack for VIP applications with Aerostack2, which generalizes the hardware abstraction. O-SDK offers an analytics layer as a novel contribution to enable developers to easily integrate Deep Neural Network (DNN)-based models, and interface swarms of UAVs for complex tasks like navigating the visually impaired. Our evaluations show that the minimal overheads of using O-SDK for real-time experiments using Tello drones and Jetson Orin Nano. This opens the potential to develop a domain-neutral, programmable and customizable DaaS Software Development Kit (SDK) in future. © 2024 IEEE

关键词： Drones

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Toward Responsible Artificial Intelligence Systems: Safety and Trustworthiness 8th

Toward Responsible Artificial Intelligence Systems: Safety a...

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8th International Conference on Engineering of computer-Based Systems, ECBS 2023

作者： Herrera, Francisco Department Computer Sciences and Artificial Intelligence Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada Spain

ISBN: (纸本)9783031492518

This short paper associated to the invited lectures introduces two key concepts essential to artificial intelligence (AI), the area of trustworthy AI and the concept of responsible AI systems, fundamental to understand the technological, ethical and legal context of the current framework of debate and regulation of AI. The aim is to understand their dimension and their interrelation with the rest of the elements involved in the regulation and auditability of AI algorithms in order to achieve safe and trusted AI. We highlight concepts in bold in order to fix the moment when they are described in context. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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