pwc-archive/papers-with-abstracts huggingface dataset:
Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data
Authors: Hui Li, Jian Ni, Fangzhu Yang
Date: 2024-05-24 00:00:00
Tasks: Generative Adversarial Network

The rise of generative artificial intelligence (AI) has facilitated automated product design but often neglects valuable consumer preference data within companies' internal datasets. Additionally, external sources such as social media and user-generated content (UGC) platforms contain substantial untapped information on product design and consumer preferences, yet remain underutilized. We propose a novel framework that transforms the product design paradigm to be data-driven, automated, and consumer-centric. Our method employs a semi-supervised deep generative architecture that systematically integrates multidimensional consumer preferences and heterogeneous external data. The framework is both generative and preference-aware, enabling companies to produce consumer-aligned designs with enhanced cost efficiency. Our framework trains a specialized predictor model to comprehend consumer preferences and utilizes predicted popularity metrics to guide a continuous conditional generative adversarial network (CcGAN). The trained CcGAN can directionally generate consumer-preferred designs, circumventing the expenditure associated with testing suboptimal candidates. Using external data, our framework offers particular advantages for start-ups or other resource-constrained companies confronting the ``cold-start" problem. We demonstrate the framework's efficacy through an empirical application with a self-operated photography chain, where our model successfully generated superior photo template designs. We also conduct web-based experiments to verify our method and confirm its effectiveness across varying design contexts.
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention
Authors: Shuai Shao, Yu Guan, Victor Sanchez
Date: 2024-05-21 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series

Human Activity Recognition (HAR) has become increasingly popular with ubiquitous computing, driven by the popularity of wearable sensors in fields like healthcare and sports. While Convolutional Neural Networks (ConvNets) have significantly contributed to HAR, they often adopt a frame-by-frame analysis, concentrating on individual frames and potentially overlooking the broader temporal dynamics inherent in human activities. To address this, we propose the intra- and inter-frame attention model. This model captures both the nuances within individual frames and the broader contextual relationships across multiple frames, offering a comprehensive perspective on sequential data. We further enrich the temporal understanding by proposing a novel time-sequential batch learning strategy. This learning strategy preserves the chronological sequence of time-series data within each batch, ensuring the continuity and integrity of temporal patterns in sensor-based HAR.
Delay-Doppler Domain Pulse Design for OTFS-NOMA
Authors: Michel Kulhandjian, Hovannes Kulhandjian, Gunes Karabulut Kurt, Halim Yanikomeroglu
Date: 2024-05-29 00:00:00

We address the challenge of developing an orthogonal time-frequency space (OTFS)-based non-orthogonal multiple access (NOMA) system where each user is modulated using orthogonal pulses in the delay Doppler domain. Building upon the concept of the sufficient (bi)orthogonality train-pulse [1], we extend this idea by introducing Hermite functions, known for their orthogonality properties. Simulation results demonstrate that our proposed Hermite functions outperform the traditional OTFS-NOMA schemes, including power-domain (PDM) NOMA and code-domain (CDM) NOMA, in terms of bit error rate (BER) over a high-mobility channel. The algorithm's complexity is minimal, primarily involving the demodulation of OTFS. The spectrum efficiency of Hermite-based OTFS-NOMA is K times that of OTFS-CDM-NOMA scheme, where K is the spreading length of the NOMA waveform.
RA: A machine based rational agent, Part 2, Preliminary test
Authors: G. Pantelis
Date: 2024-05-26 00:00:00

A preliminary test of the software package RA is presented. The main focus of this test is to assess RA`s reasoning capabilities that are based on the formal system PECR. Particular attention is given to the finite computational resources of the real-world machine that define the environment within which programs are to be executed.
Rotations of Gödel algebras with modal operators
Authors: Tommaso Flaminio, Lluis Godo, Paula Menchón, Ricardo O. Rodriguez
Date: 2024-05-23 00:00:00
Tasks: Negation

The present paper is devoted to study the effect of connected and disconnected rotations of G\"odel algebras with operators grounded on directly indecomposable structures. The structures resulting from this construction we will present are nilpotent minimum (with or without negation fixpoint, depending on whether the rotation is connected or disconnected) with special modal operators defined on a directly indecomposable algebra. In this paper we will present a (quasi-)equational definition of these latter structures. Our main results show that directly indecomposable nilpotent minimum algebras (with or without negation fixpoint) with modal operators are fully characterized as connected and disconnected rotations of directly indecomposable G\"odel algebras endowed with modal operators.
Defeaters and Eliminative Argumentation in Assurance 2.0
Authors: Robin Bloomfield, Kate Netkachova, John Rushby
Date: 2024-05-16 00:00:00
Tasks: valid

A traditional assurance case employs a positive argument in which reasoning steps, grounded on evidence and assumptions, sustain a top claim that has external significance. Human judgement is required to check the evidence, the assumptions, and the narrative justifications for the reasoning steps; if all are assessed good, then the top claim can be accepted. A valid concern about this process is that human judgement is fallible and prone to confirmation bias. The best defense against this concern is vigorous and skeptical debate and discussion in the manner of a dialectic or Socratic dialog. There is merit in recording aspects of this discussion for the benefit of subsequent developers and assessors. Defeaters are a means doing this: they express doubts about aspects of the argument and can be developed into subcases that confirm or refute the doubts, and can record them as documentation to assist future consideration. This report describes how defeaters, and multiple levels of defeaters, should be represented and assessed in Assurance 2.0 and its Clarissa/ASCE tool support. These mechanisms also support eliminative argumentation, which is a contrary approach to assurance, favored by some, that uses a negative argument to refute all reasons why the top claim could be false.
Finite Groundings for ASP with Functions: A Journey through Consistency
Authors: Lukas Gerlach, David Carral, Markus Hecher
Date: 2024-05-08 00:00:00

Answer set programming (ASP) is a logic programming formalism used in various areas of artificial intelligence like combinatorial problem solving and knowledge representation and reasoning. It is known that enhancing ASP with function symbols makes basic reasoning problems highly undecidable. However, even in simple cases, state of the art reasoners, specifically those relying on a ground-and-solve approach, fail to produce a result. Therefore, we reconsider consistency as a basic reasoning problem for ASP. We show reductions that give an intuition for the high level of undecidability. These insights allow for a more fine-grained analysis where we characterize ASP programs as "frugal" and "non-proliferous". For such programs, we are not only able to semi-decide consistency but we also propose a grounding procedure that yields finite groundings on more ASP programs with the concept of "forbidden" facts.
Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning
Authors: Miguel Angel Mendez-Lucero, Enrique Bojorquez Gallardo, Vaishak Belle
Date: 2024-05-03 00:00:00
Tasks: Knowledge Distillation

Issues of safety, explainability, and efficiency are of increasing concern in learning systems deployed with hard and soft constraints. Symbolic Constrained Learning and Knowledge Distillation techniques have shown promising results in this area, by embedding and extracting knowledge, as well as providing logical constraints during neural network training. Although many frameworks exist to date, through an integration of logic and information geometry, we provide a construction and theoretical framework for these tasks that generalize many approaches. We propose a loss-based method that embeds knowledge-enforces logical constraints-into a machine learning model that outputs probability distributions. This is done by constructing a distribution from the external knowledge/logic formula and constructing a loss function as a linear combination of the original loss function with the Fisher-Rao distance or Kullback-Leibler divergence to the constraint distribution. This construction includes logical constraints in the form of propositional formulas (Boolean variables), formulas of a first-order language with finite variables over a model with compact domain (categorical and continuous variables), and in general, likely applicable to any statistical model that was pretrained with semantic information. We evaluate our method on a variety of learning tasks, including classification tasks with logic constraints, transferring knowledge from logic formulas, and knowledge distillation from general distributions.
Distributed Continual Learning
Authors: Long Le, Marcel Hussing, Eric Eaton
Date: 2024-05-23 00:00:00
Tasks: Continual Learning, Federated Learning

This work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share knowledge. We introduce a mathematical framework capturing the essential aspects of distributed continual learning, including agent model and statistical heterogeneity, continual distribution shift, network topology, and communication constraints. Operating on the thesis that distributed continual learning enhances individual agent performance over single-agent learning, we identify three modes of information exchange: data instances, full model parameters, and modular (partial) model parameters. We develop algorithms for each sharing mode and conduct extensive empirical investigations across various datasets, topology structures, and communication limits. Our findings reveal three key insights: sharing parameters is more efficient than sharing data as tasks become more complex; modular parameter sharing yields the best performance while minimizing communication costs; and combining sharing modes can cumulatively improve performance.
Data Complexity Estimates for Operator Learning
Authors: Nikola B. Kovachki, Samuel Lanthaler, Hrushikesh Mhaskar
Date: 2024-05-25 00:00:00
Tasks: Operator learning

Operator learning has emerged as a new paradigm for the data-driven approximation of nonlinear operators. Despite its empirical success, the theoretical underpinnings governing the conditions for efficient operator learning remain incomplete. The present work develops theory to study the data complexity of operator learning, complementing existing research on the parametric complexity. We investigate the fundamental question: How many input/output samples are needed in operator learning to achieve a desired accuracy $\epsilon$? This question is addressed from the point of view of $n$-widths, and this work makes two key contributions. The first contribution is to derive lower bounds on $n$-widths for general classes of Lipschitz and Fr\'echet differentiable operators. These bounds rigorously demonstrate a ``curse of data-complexity'', revealing that learning on such general classes requires a sample size exponential in the inverse of the desired accuracy $\epsilon$. The second contribution of this work is to show that ``parametric efficiency'' implies ``data efficiency''; using the Fourier neural operator (FNO) as a case study, we show rigorously that on a narrower class of operators, efficiently approximated by FNO in terms of the number of tunable parameters, efficient operator learning is attainable in data complexity as well. Specifically, we show that if only an algebraically increasing number of tunable parameters is needed to reach a desired approximation accuracy, then an algebraically bounded number of data samples is also sufficient to achieve the same accuracy.
Throughput Requirements for RAN Functional Splits in 3D-Networks
Authors: MohammadAmin Vakilifard, Tim Düe, Mohammad Rihan, Maik Röper, Dirk Wübben, Carsten Bockelmann, Armin Dekorsy
Date: 2024-05-24 00:00:00

The rapid growth of non-terrestrial communication necessitates its integration with existing terrestrial networks, as highlighted in 3GPP Releases 16 and 17. This paper analyses the concept of functional splits in 3D-Networks. To manage this complex structure effectively, the adoption of a Radio Access Network (RAN) architecture with Functional Split (FS) offers advantages in flexibility, scalability, and cost-efficiency. RAN achieves this by disaggregating functionalities into three separate units. Analogous to the terrestrial network approach, 3GPP is extending this concept to non-terrestrial platforms as well. This work presents a general analysis of the requested Fronthaul (FH) data rate on feeder link between a non-terrestrial platform and the ground-station. Each split option is a trade-of between FH data rate and the respected complexity. Since flying nodes face more limitations regarding power consumption and complexity on board in comparison to terrestrial ones, we are investigating the split options between lower and higher physical layer.
Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges
Authors: Fahime Khoramnejad, Ekram Hossain
Date: 2024-05-22 00:00:00

Next-generation (xG) wireless networks, with their complex and dynamic nature, present significant challenges to using traditional optimization techniques. Generative AI (GAI) emerges as a powerful tool due to its unique strengths. Unlike traditional optimization techniques and other machine learning methods, GAI excels at learning from real-world network data, capturing its intricacies. This enables safe, offline exploration of various configurations and generation of diverse, unseen scenarios, empowering proactive, data-driven exploration and optimization for xG networks. Additionally, GAI's scalability makes it ideal for large-scale xG networks. This paper surveys how GAI-based models unlock optimization opportunities in xG wireless networks. We begin by providing a review of GAI models and some of the major communication paradigms of xG (e.g., 6G) wireless networks. We then delve into exploring how GAI can be used to improve resource allocation and enhance overall network performance. Additionally, we briefly review the networking requirements for supporting GAI applications in xG wireless networks. The paper further discusses the key challenges and future research directions in leveraging GAI for network optimization. Finally, a case study demonstrates the application of a diffusion-based GAI model for load balancing, carrier aggregation, and backhauling optimization in non-terrestrial networks, a core technology of xG networks. This case study serves as a practical example of how the combination of reinforcement learning and GAI can be implemented to address real-world network optimization problems.
Leveraging Machine Learning for Accurate IoT Device Identification in Dynamic Wireless Contexts
Authors: Bhagyashri Tushir, Vikram K Ramanna, Yuhong Liu, Behnam Dezfouli
Date: 2024-05-15 00:00:00
Tasks: IoT Device Identification

Identifying IoT devices is crucial for network monitoring, security enforcement, and inventory tracking. However, most existing identification methods rely on deep packet inspection, which raises privacy concerns and adds computational complexity. More importantly, existing works overlook the impact of wireless channel dynamics on the accuracy of layer-2 features, thereby limiting their effectiveness in real-world scenarios. In this work, we define and use the latency of specific probe-response packet exchanges, referred to as "device latency," as the main feature for device identification. Additionally, we reveal the critical impact of wireless channel dynamics on the accuracy of device identification based on device latency. Specifically, this work introduces "accumulation score" as a novel approach to capturing fine-grained channel dynamics and their impact on device latency when training machine learning models. We implement the proposed methods and measure the accuracy and overhead of device identification in real-world scenarios. The results confirm that by incorporating the accumulation score for balanced data collection and training machine learning algorithms, we achieve an F1 score of over 97% for device identification, even amidst wireless channel dynamics, a significant improvement over the 75% F1 score achieved by disregarding the impact of channel dynamics on data collection and device latency.
When Large Language Models Meet Optical Networks: Paving the Way for Automation
Authors: Danshi Wang, Yidi Wang, Xiaotian Jiang, Yao Zhang, Yue Pang, Min Zhang
Date: 2024-05-14 00:00:00
Tasks: AI Agent, Prompt Engineering

Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art performance on numerous areas. However, LLMs are considered to be general-purpose models for NLP tasks, which may encounter challenges when applied to complex tasks in specialized fields such as optical networks. In this study, we propose a framework of LLM-empowered optical networks, facilitating intelligent control of the physical layer and efficient interaction with the application layer through an LLM-driven agent (AI-Agent) deployed in the control layer. The AI-Agent can leverage external tools and extract domain knowledge from a comprehensive resource library specifically established for optical networks. This is achieved through user input and well-crafted prompts, enabling the generation of control instructions and result representations for autonomous operation and maintenance in optical networks. To improve LLM's capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study. Moreover, the proposed framework is verified on two typical tasks: network alarm analysis and network performance optimization. The good response accuracies and sematic similarities of 2,400 test situations exhibit the great potential of LLM in optical networks.
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
Authors: Hao Zhou, Chengming Hu, Xue Liu
Date: 2024-05-09 00:00:00
Tasks: Hierarchical Reinforcement Learning, Management, Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Reinforcement Learning

Reconfigurable intelligent surface (RIS) becomes a promising technique for 6G networks by reshaping signal propagation in smart radio environments. However, it also leads to significant complexity for network management due to the large number of elements and dedicated phase-shift optimization. In this work, we provide an overview of machine learning (ML)-enabled optimization for RIS-aided 6G networks. In particular, we focus on various reinforcement learning (RL) techniques, e.g., deep Q-learning, multi-agent reinforcement learning, transfer reinforcement learning, hierarchical reinforcement learning, and offline reinforcement learning. Different from existing studies, this work further discusses how large language models (LLMs) can be combined with RL to handle network optimization problems. It shows that LLM offers new opportunities to enhance the capabilities of RL algorithms in terms of generalization, reward function design, multi-modal information processing, etc. Finally, we identify the future challenges and directions of ML-enabled optimization for RIS-aided 6G networks.
Obtaining physical layer data of latest generation networks for investigating adversary attacks
Authors: M. V. Ushakova, Yu. A. Ushakov, L. V. Legashev
Date: 2024-05-02 00:00:00

The field of machine learning is developing rapidly and is being used in various fields of science and technology. In this way, machine learning can be used to optimize the functions of latest generation data networks such as 5G and 6G. This also applies to functions at a lower level. A feature of the use of machine learning in the radio path for targeted radiation generation in modern ultra-massive MIMO, reconfigurable intelligent interfaces and other technologies is the complex acquisition and processing of data from the physical layer. Additionally, adversarial measures that manipulate the behaviour of intelligent machine learning models are becoming a major concern, as many machine learning models are sensitive to incorrect input data. To obtain data on attacks directly from processing service information, a simulation model is proposed that works in conjunction with machine learning applications.
Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network
Authors: Chong Zheng, Yongming Huang, Cheng Zhang, Tony Q. S. Quek
Date: 2024-05-02 00:00:00
Tasks: Graph Neural Network, reinforcement-learning, Reinforcement Learning

In this paper, we aim to maximize the SSR for heterogeneous service demands in the cooperative MEC-assisted RAN slicing system by jointly considering the multi-node computing resources cooperation and allocation, the transmission resource blocks (RBs) allocation, and the time-varying dynamicity of the system. To this end, we abstract the system into a weighted undirected topology graph and, then propose a recurrent graph reinforcement learning (RGRL) algorithm to intelligently learn the optimal hybrid RA policy. Therein, the graph neural network (GCN) and the deep deterministic policy gradient (DDPG) is combined to effectively extract spatial features from the equivalent topology graph. Furthermore, a novel time recurrent reinforcement learning framework is designed in the proposed RGRL algorithm by incorporating the action output of the policy network at the previous moment into the state input of the policy network at the subsequent moment, so as to cope with the time-varying and contextual network environment. In addition, we explore two use case scenarios to discuss the universal superiority of the proposed RGRL algorithm. Simulation results demonstrate the superiority of the proposed algorithm in terms of the average SSR, the performance stability, and the network complexity.