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Hallucinations in Large Language Models (LLMs) 11

Hallucinations in Large Language Models (LLMs)

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11th ieee open conference of Electrical, Electronic and Information Sciences, eStream 2024

作者： Reddy, G. Pradeep Pavan Kumar, Y.V. Prakash, K. Purna Kookmin University Seoul02703 Korea Republic of School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350352412

The recent advancements in neural network architectures, particularly transformers, have played a crucial role in the rapid progress of Large Language Models (LLMs). LLMs are trained on many parameters. By training these parameters on vast amounts of text data, LLMs can learn to generate reactions to a wide variety of prompts. These models have enabled machines to generate new data (human-like), driving significant developments in Natural Language Processing (NLP). They have demonstrated remarkable capabilities in producing new content. Besides their impressive performance, LLMs occasionally generate hallucinatory responses that produce nonsensical or inaccurate information. In simple terms, hallucinations in LLMs happen when the model generates information that may sound believable but is actually wrong. It can make up details or go beyond what it has learned from the training data, resulting in inaccurate output. These hallucinatory responses appear to be authentic but lack grounding in reality. Such hallucinations can include fabrications such as facts, events, or statements that lack support from real-world data. Addressing this issue is important to enhance the reliability of AI-generated content. Hallucinations can be a significant challenge in critical applications such as healthcare, law, etc. In this view, this paper delves into the phenomenon of hallucinations in the context of LLMs. The objective is to understand the causes, explore the implications, and discuss potential strategies for mitigation. © 2024 ieee.

关键词： network architecture

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A novel dynamic service function chaining to enable Ultra-Reliable Low Latency Communication in NFV 17

A novel dynamic service function chaining to enable Ultra-Re...

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17th International conference on Telecommunications (ConTEL)

作者： Erbati, Mohammad Mohammadi Schiele, Gregor Deutsch Telekom Techn GmbH Fixed Line Device Engn Darmstadt Germany Univ Duisburg Essen Embedded Syst Grp Duisburg Germany

ISBN: (纸本)9798350302233

network Function Virtualization (NFV) is a revolutionary technology that can make traditional network architectures more efficient and flexible by decoupling software from hardware. In recent years, the use of Ultra-Reliable Low Latency Communication (URLLC) applications such as driverless vehicles, tele-operated driving, remote surgery, virtual reality, and industrial automation has increased substantially. Communication Service Providers (CSPs) must meet the requirements of URLLC applications. Providing a network service with high reliability and low latency is extremely challenging, and the limited physical network resources available to CSPs increase the complexity. To enable URLLC in an NFV-enabled environment, we provide a set of novel heuristic algorithms for dynamic service function chaining that simultaneously optimizes reliability and latency without using backup techniques. We employ flow prioritization and provide an adjustable priority coefficient factor (mu) to reserve a specific amount of physical network resources (bandwidth, CPU, and memory) exclusively for the deployment of ultra-reliable low latency flows. Our proposed algorithms outperform existing methods in terms of end-to-end delay, reliability, bandwidth usage, and SFC acceptance rate, as demonstrated by the results.

关键词： Service function chaining (SFC) network function virtualization (NFV) software defined network (SDN) reliability quality of service (QoS) virtual network function (VNF) ultra-reliable low latency communication (URLLC)

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Integrating Optical and Mobile networks: A Comprehensive End-to-End Simulation Platform 24

Integrating Optical and Mobile Networks: A Comprehensive End...

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24th International conference on Transparent Optical networks (ICTON)

作者： Rodriguez, Micaela Pirri, Maximo Ingles, Lucas Rattaro, Claudina Castro, Alberto Univ Republica Sch Engn Montevideo Uruguay

ISBN: (纸本)9798350377330;9798350377323

Communications are crucial in human interactions, the economy, education, and accessing and democratizing various services and information. With a vast physical infrastructure deployed, optical networks have been fundamental in end-to-end broadband communications. They are considered the foundation for future communications in modern society. Next-generation optical networks are expected to provide broadband services with massive capacity, lower latencies, and improved reliability, supporting various ultra-high bandwidth applications: cloud/edge networking, 8K video services, and digital twins. Optical networks have been playing an increasingly important role in mobile communications. They will be key for 5G/6G networks, given their bandwidth, coverage, synchronization, and low latency requirements. Various technologies are being developed for the convergence of optical-mobile networks to enhance performance, improve energy efficiency, and simplify the network's design, implementation, and operation. Researchers, vendors, and operators must be able to evaluate these new solutions effectively. They require a robust simulation framework that seamlessly implements the mobile and optical domains and their interactions. As far as we know, no open-source simulator has fulfilled these requirements to date. For this reason, in this paper, we present an optical-mobile unified simulation platform that addresses the convergence of 5G/6G technology and optical networks. This tool enables academia and industry members to test new optical-mobile network architectures and end-to-end (multi-technology domain) algorithms for dynamic resource allocation or restoration, advancing the capabilities and understanding of next-generation network technologies.

关键词： 5G 6G network Simulation Optical-Mobile networks

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Design of network Monitoring System for IoT and Autonomous network using Python 2

Design of Network Monitoring System for IoT and Autonomous N...

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2nd ieee International conference on Vision Towards Emerging Trends in Communication and networking Technologies, ViTECoN 2023

作者： Suchitra, V. Kumar, Rajesh Saha, Pradip Nayak, D.B. Ratha, Hare Krishna DRDO Communication Directorate Integrated Test Range Chandipur India

ISBN: (纸本)9798350347982

The design and development of an open standard, scalable platform, and interoperable, network Monitoring System (NMS) is always an inspiration and challenging for IoT and Autonomous systems which are namely deployed over a local and remote environment. The developed system is a novel approach towards scalable and open platform-based NMS using Python programming language. It has a feature to mark the performance and faults of all connected nodes over a network. It utilizes Simple network Management Protocol (SNMP) and a Syslog-based monitoring module. With the quality of the real-time monitoring system, it ensures an individual link status and its summary and depicts its updates in real-time GUI updater, and store data in MySQL database. It provides easy visualization of network stations and allows the concerned to take appropriate action accordingly. © 2023 ieee.

关键词： Python

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Scalable Forward Reachability Analysis of Multi-Agent Systems with Neural network Controllers 62

Scalable Forward Reachability Analysis of Multi-Agent System...

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62nd ieee conference on Decision and Control (CDC)

作者： Gates, Oliver Newton, Matthew Gatsis, Konstantinos Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9798350301243

Neural networks (NNs) have been shown to learn complex control laws successfully, often with performance advantages or decreased computational cost compared to alternative methods. Neural network controllers (NNCs) are, however, highly sensitive to disturbances and uncertainty, meaning that it can be challenging to make satisfactory robustness guarantees for systems with these controllers. This problem is exacerbated when considering multi-agent NN-controlled systems, as existing reachability methods often scale poorly for large systems. This paper addresses the problem of finding overapproximations of forward reachable sets for discrete-time uncertain multi-agent systems with distributed NNC architectures. We first reformulate the dynamics, making the system more amenable to reachablility analysis. Next, we take advantage of the distributed architecture to split the overall reachability problem into smaller problems, significantly reducing computation time. We use quadratic constraints, along with a convex representation of uncertainty in each agent's model, to form semidefinite programs, the solutions of which give overapproximations of forward reachable sets for each agent. Finally, the methodology is tested on two realistic examples: a platoon of vehicles and a power network system.

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GraNNDis: Fast Distributed Graph Neural network Training Framework for Multi-Server Clusters 24

GraNNDis: Fast Distributed Graph Neural Network Training Fra...

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International conference on Parallel architectures and Compilation Techniques (PACT)

作者： Song, Jaeyong Jang, Hongsun Lim, Hunseong Jung, Jaewon Kim, Youngsok Lee, Jinho Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9798400706318

ISBN: (纸本)9798400706318

Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitations when applied to multi-server clusters. 1) They suffer from an inter-server communication bottleneck because they do not consider the inter-/intra-server bandwidth gap, a representative characteristic of multi-server clusters. 2) Redundant memory usage and computation hinder the scalability of the distributed frameworks. 3) Sampling methods, de facto standard in mini-batch training, incur unnecessary errors in multi-server clusters. We found that these limitations can be addressed by exploiting the characteristics of multi-server clusters. Here, we propose GraNNDis, a fast distributed GNN training framework for multi-server clusters. Firstly, we present Flexible Preloading, which preloads the essential vertex dependencies server-wise to reduce the low-bandwidth inter-server communications. Secondly, we introduce Cooperative Batching, which enables memory-efficient, less redundant mini-batch training by utilizing high-bandwidth intra-server communications. Thirdly, we propose Expansion-aware Sampling, a cluster-aware sampling method, which samples the edges that affect the system speedup. As sampling the intra-server dependencies does not contribute much to the speedup as they are communicated through fast intra-server links, it only targets a server boundary to be sampled. Lastly, we introduce One-Hop Graph Masking, a computation and communication structure to realize the above methods in multi-server environments. We evaluated GraNNDis on multi-server clusters, and it provided significant speedup over the state-of-the-art distributed GNN training frameworks. GraNNDis is open-sourced at https://***/AISSNU/GraNNDis_Artifact to facilitate its use.

关键词： Distributed Systems Systems for Deep Learning Graph Neural network Training Distributed Graph Neural network Training

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Soft open Point Planning Method for Active Distribution network Based on Generative Adversarial network 7

Soft Open Point Planning Method for Active Distribution Netw...

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7th ieee conference on Energy Internet and Energy System Integration, EI2 2023

作者： Wang, Hao Lin, Qiyou Wang, Hongxing Ge, Yuan Yu, Nuo Wang, Shinong School of Electrical Engineering Anhui Polytechnic University Wuhu China State Grid Anhui Electric Power Co. Ltd Wuhu Fanchang District Power Supply Company Wuhu China State Grid Anhui Electric Power Co. Ltd Wuwei City Power Supply Company Wuhu China School of Electrical Engineering Anhui Polytechnic University Anhui Institute of Future Technology Wuhu China

ISBN: (纸本)9798350345094

With the continuous increase of the penetration rate of distributed generation (DG) in the distribution network, higher requirements have been put forward for the dependable and cost-effective operation of the distribution network. In order to address the impact caused by the intermittent output of a large number of DGs on the distribution network, this article proposes a planning method for soft open point (SOP) using generative adversarial network (GAN). First, to accurately simulate the operating characteristics of DG, a GAN is employed for sce-nario generation. Then, operational constraints for distribution networks with SOP are proposed, with the objective function being the minimization of annual total cost. By relaxing and linearizing the original non-convex and non-linear constraints, a second-order cone programming model is derived, and the objective function is solved under the constraints. Finally, the proposed scenario generation algorithm and planning model are analyzed and veriffed on the ieee 33- node test system. © 2023 ieee.

关键词： Second-order cone programming

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Memory-Guided Coordinate Encoding network for Anomaly Detection

Memory-Guided Coordinate Encoding Network for Anomaly Detect...

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2023 ieee International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Xingang Zhang, Hong Cao, Rui Zhou, Jinyan Lu, Xingchao Jinan China

ISBN: (纸本)9798350337020

Video anomaly detection remains challenging due to the complexity of visual scenes. Video surveillance is often fixed lens, and existing approaches, either using autoencoder architectures or generative adversarial network models, lack encoding of dynamic and static information in feature extraction to emphasize information-rich features. Based on this, we improve the encoder architecture and propose a memory-guided coordinate encoding network based on extensive experiments to introduce a coordinate attention module to improve the U-Net network and enhance dynamic entity representation. Considering the diversity of abnormal events, we use the memory module to record the prototype patterns of normal features and propose feature discretization loss and feature aggregation loss to make a compact representation between features and separation between feature terms to improve the accessibility and prediction accuracy of the memory module. Our experimental results on three open standard datasets show that our model outperforms the state-of-the-art methods. © 2023 ieee.

关键词： anomaly detection attention coordinate encoding memory module

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Real-Time Tracking Control Using Adaptive Dynamic programming for Underwater Vehicles 21

Real-Time Tracking Control Using Adaptive Dynamic Programmin...

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21st International conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024

作者： Duran, Claudia Rigel Palacios, Filiberto Munoz Espinoza, Eduardo S. Trinidad, Benjamin Nicolas DIEM CINVESTAV Mexico City Mexico DIEM IxM CONAHCYT-CINVESTAV Mexico City Mexico

ISBN: (纸本)9798350377545

This manuscript presents the real-Time experiments of a model-free nonlinear controller based on Adaptive Dynamic programming (ADP) for an Autonomous Underwater Vehicle (AUV). These types of vehicles exhibit significant coupled nonlinearities and parameter uncertainties. To address this, our approach introduces a data-driven scheme, where open-loop data are collected and used to train a differential neural network for identifying the vehicle's dynamics. Based on the identified model, an ADP scheme with an actor-critic structure is implemented to conduct trajectory tracking experiments for yaw and depth dynamics. Additionally, this study examines the implications of using the ADP algorithm and offers insights for necessary adaptations to achieve successful implementation on an open-source commercial underwater vehicle within the ROS environment, thereby bridging the gap between theoretical concepts and practical experimentation. © 2024 ieee.

关键词： Linear systems

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ProSLICE: An open RAN-based approach to Programmable RAN Slicing

ProSLICE: An Open RAN-based approach to Programmable RAN Sli...

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ieee Global Communications conference (GLOBECOM)

作者： Kak, Ahan Pham, Van-Quan Thieu, Huu-Trung Choi, Nakjung Nokia Bell Labs Network Syst & Secur Res Murray Hill NJ 07974 USA

ISBN: (纸本)9781665435406

The increasing popularity of programmable wireless networks has led to efforts by both academia as well as industry to redesign access networks based on the disaggregated open Radio Access network (O-RAN) concept. However, the absence of a development platform for prototyping O-RAN use cases and the perceived complexity of the O-RAN specification have led to a stagnation of systems research efforts in this domain with a disproportionate focus on monolithic RAN architectures. With a view to overcoming these challenges, this paper introduces the ProSLICE platform, a full-scale realization of the complete O-RAN specification based on purely open source components along with several enhancements and extensions for fine-grained network control and ease of use. Key contributions include a fully disaggregated O-RAN-compliant RAN, a custom O-RAN service model in support of network slicing, statistics and configuration applications for the O-RAN RAN Intelligent Controller (RIC), and a comprehensive use case-based performance evaluation.

关键词： Specifications

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