pwc-archive/papers-with-abstracts huggingface dataset:
Covariate Assisted Entity Ranking with Sparse Intrinsic Scores
Authors: Jianqing Fan, Jikai Hou, Mengxin Yu
Date: 2024-07-09 00:00:00

This paper addresses the item ranking problem with associate covariates, focusing on scenarios where the preference scores can not be fully explained by covariates, and the remaining intrinsic scores, are sparse. Specifically, we extend the pioneering Bradley-Terry-Luce (BTL) model by incorporating covariate information and considering sparse individual intrinsic scores. Our work introduces novel model identification conditions and examines the regularized penalized Maximum Likelihood Estimator (MLE) statistical rates. We then construct a debiased estimator for the penalized MLE and analyze its distributional properties. Additionally, we apply our method to the goodness-of-fit test for models with no latent intrinsic scores, namely, the covariates fully explaining the preference scores of individual items. We also offer confidence intervals for ranks. Our numerical studies lend further support to our theoretical findings, demonstrating validation for our proposed method
PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
Authors: Saber Zerhoudi, Michael Granitzer
Date: 2024-07-12 00:00:00
Tasks: Information Retrieval, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) models address this by enhancing LLMs with external knowledge, but often fail to personalize the retrieval process. This paper introduces PersonaRAG, a novel framework incorporating user-centric agents to adapt retrieval and generation based on real-time user data and interactions. Evaluated across various question answering datasets, PersonaRAG demonstrates superiority over baseline models, providing tailored answers to user needs. The results suggest promising directions for user-adapted information retrieval systems.
AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments
Authors: Tomislav Duricic, Peter Müllner, Nicole Weidinger, Neven ElSayed, Dominik Kowald, Eduardo Veas
Date: 2024-07-12 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Speech-to-Text

Many industrial sectors rely on well-trained employees that are able to operate complex machinery. In this work, we demonstrate an AI-powered immersive assistance system that supports users in performing complex tasks in industrial environments. Specifically, our system leverages a VR environment that resembles a juice mixer setup. This digital twin of a physical setup simulates complex industrial machinery used to mix preparations or liquids (e.g., similar to the pharmaceutical industry) and includes various containers, sensors, pumps, and flow controllers. This setup demonstrates our system's capabilities in a controlled environment while acting as a proof-of-concept for broader industrial applications. The core components of our multimodal AI assistant are a large language model and a speech-to-text model that process a video and audio recording of an expert performing the task in a VR environment. The video and speech input extracted from the expert's video enables it to provide step-by-step guidance to support users in executing complex tasks. This demonstration showcases the potential of our AI-powered assistant to reduce cognitive load, increase productivity, and enhance safety in industrial environments.
Distinct citation distributions complicate research evaluations. A single indicator that universally reveals research efficiency cannot be formulated
Authors: Alonso Rodríguez-Navarro
Date: 2024-07-12 00:00:00

Purpose: Analyze the diversity of citation distributions to publications in different research topics to investigate the accuracy of size-independent, rank-based indicators. Top percentile-based indicators are the most common indicators of this type, and the evaluations of Japan are the most evident misjudgments. Design/methodology/approach: The distributions of citations to publications from countries and in journals in several research topics were analyzed along with the corresponding global publications using histograms with logarithmic binning, double rank plots, and normal probability plots of log-transformed numbers of citations. Findings: Size-independent, top percentile-based indicators are accurate when the global ranks of local publications fit a power law, but deviations in the least cited papers are frequent in countries and occur in all journals with high impact factors. In these cases, a single indicator is misleading. Comparisons of proportions of uncited papers are the best way to predict these deviations. Research limitations: The study is fundamentally analytical; its results describe mathematical facts that are self-evident. Practical implications: Respectable institutions, such as the OECD, European Commission, US National Science Board, and others, produce research country rankings and individual evaluations using size-independent percentile indicators that are misleading in many countries. These misleading evaluations should be discontinued because they cause confusion among research policymakers and lead to incorrect research policies. Originality/value: Studies linking the lower tail of citation distribution, including uncited papers, to percentile research indicators have not been performed previously. The present results demonstrate that studies of this type are necessary to find reliable procedures for research assessments.
Toward Automatic Group Membership Annotation for Group Fairness Evaluation
Authors: Fumian Chen, Dayu Yang, Hui Fang
Date: 2024-07-12 00:00:00
Tasks: Fairness, Information Retrieval, Retrieval

With the increasing research attention on fairness in information retrieval systems, more and more fairness-aware algorithms have been proposed to ensure fairness for a sustainable and healthy retrieval ecosystem. However, as the most adopted measurement of fairness-aware algorithms, group fairness evaluation metrics, require group membership information that needs massive human annotations and is barely available for general information retrieval datasets. This data sparsity significantly impedes the development of fairness-aware information retrieval studies. Hence, a practical, scalable, low-cost group membership annotation method is needed to assist or replace human annotations. This study explored how to leverage language models to automatically annotate group membership for group fairness evaluations, focusing on annotation accuracy and its impact. Our experimental results show that BERT-based models outperformed state-of-the-art large language models, including GPT and Mistral, achieving promising annotation accuracy with minimal supervision in recent fair-ranking datasets. Our impact-oriented evaluations reveal that minimal annotation error will not degrade the effectiveness and robustness of group fairness evaluation. The proposed annotation method reduces tremendous human efforts and expands the frontier of fairness-aware studies to more datasets.
Application of Data-Driven Model Predictive Control for Autonomous Vehicle Steering
Authors: Jiarui Zhang, Aijing Kong, Yu Tang, Zhichao Lv, Lulu Guo, Peng Hang
Date: 2024-07-11 00:00:00
Tasks: Autonomous Driving, Model Predictive Control, Steering Control

With the development of autonomous driving technology, there are increasing demands for vehicle control, and MPC has become a widely researched topic in both industry and academia. Existing MPC control methods based on vehicle kinematics or dynamics have challenges such as difficult modeling, numerous parameters, strong nonlinearity, and high computational cost. To address these issues, this paper adapts an existing Data-driven MPC control method and applies it to autonomous vehicle steering control. This method avoids the need for complex vehicle system modeling and achieves trajectory tracking with relatively low computational time and small errors. We validate the control effectiveness of the algorithm in specific scenario through CarSim-Simulink simulation and perform comparative analysis with PID and vehicle kinematics MPC, confirming the feasibility and superiority of it for vehicle steering control.
MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank
Authors: Fan Cheng, Wei Guo, and Xingyi Zhang
Date: 2018-12-02 00:00:00
Proceeding: wiley online library 2018 12
Tasks: Collaborative Filtering, feature selection, Learning-To-Rank

Learning to rank has attracted increasing interest in the past decade, due to its wide applications in the areas like document retrieval
and collaborative filtering. Feature selection for learning to rank is to select a small number of features from the original large set
of features which can ensure a high ranking accuracy, since in many real ranking applications many features are redundant or even
irrelevant. To this end, in this paper, amultiobjective evolutionary algorithm, termedMOFSRank, is proposed for feature selection
in learning to rank which consists of three components. First, an instance selection strategy is suggested to choose the informative
instances from the ranking training set, by which the redundant data is removed and the training efficiency is enhanced. Then
on the selected instance subsets, a multiobjective feature selection algorithm with an adaptive mutation is developed, where good
feature subsets are obtained by selecting the features with high ranking accuracy and low redundancy. Finally, an ensemble strategy
is also designed inMOFSRank, which utilizes these obtained feature subsets to produce a set of better features. Experimental results
on benchmark data sets confirm the advantage of the proposed method in comparison with the state-of-the-arts.
Joint Load and Capacity Scheduling for Flexible Radio Resource Management of High-Throughput Satellites
Authors: Jia Zhuoya, Xiong Wei, Hao Hongxing, Liu Zheng, Han Chi
Date: 2024-07-11 00:00:00
Tasks: Management, Scheduling

This work first explores using flexible beam-user mapping to optimize the beam service range and beam position, in order to adapt the non-uniform traffic demand to offer in high-throughput satellite (HTS) systems. Second, on this basis, the joint flexible bandwidth allocation is adopted to adapt the offer to demand at the same time. This strategy allows both beam capacity and load to be adjusted to cope with the traffic demand. The new information generated during the load transfer process of flexible beam-user mapping can guide the direction of beam optimization. Then, the proposed strategies are tested against joint power-bandwidth allocation and joint optimization of bandwidth and beam-user mapping under different traffic profiles. Numerical results are obtained for various non-uniform traffic distributions to evaluate the performance of the solutions. Results show that flexible joint load and capacity scheduling are superior to other strategies in terms of demand satisfaction with acceptable complexity. Our source code along with results are available at crystal-zwz/HTS_RRM_Joint-Load-and-Capacity-Scheduling (github.com).
GSLB: The Graph Structure Learning Benchmark
Authors: ZHIXUN LI, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu
Date: 2023-10-08 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph structure learning

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://github.com/GSL-Benchmark/GSLB.
OASL: Orientation-aware adaptive sampling learning for arbitrary oriented object detection
Authors: Zifei Zhao, Shengyang Li
Date: 2024-04-01 00:00:00
Proceeding: International Journal of Applied Earth Observation and Geoinformation 2024 4
Tasks: object-detection, Object Detection, Object Detection In Aerial Images, Oriented Object Detection

Arbitrary oriented object detection (AOOD) is a fundamental task in aeiral image interpretation, which is commonly implemented by optimizing three subtasks: classification, localization, and orientation. The consistency of classification, localization, and orientation is crucial for achieving high performance of AOOD detectors. However, independent prediction between subtasks and differences in quality metrics for task-specific samples reduces the accuracy of high-precision detection. In this paper, we propose a novel dense anchor-free detector Orientation-aware Adaptive Sampling Learning (OASL) that includes two core modules: Orientation-aware Structured Information Extraction Module (O-SIEM) and Weights adaptive Sample Information Richness Metric (W-SIRM). Specifically, O-SIEM applies the global and local receptive fields of objects to explicitly extract spatial contextual features of oriented and densely distributed objects, which helps to strike a balance between capturing task-interactive and task-specific features, ultimately leading to learning common patterns between subtasks. W-SIRM integrates information from three subtasks and achieves dynamic evaluation of sample quality and adaptive adjustment of sample weights based on the information richness of samples. Extensive experiments demonstrate that the proposed OASL method can effectively handle the consistency problem and enhance the validity of the sample quality metric.
Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning
Authors: Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, Sajal K. Das
Date: 2024-03-20 00:00:00
Proceeding: IEEE Transactions on Parallel and Distributed Systems 2024 3
Tasks: Clustering, Federated Learning, Knowledge Distillation

Federated Learning is a training framework that
enables multiple participants to collaboratively train a shared
model while preserving data privacy. The heterogeneity of devices
and networking resources of the participants delay the training
and aggregation. The paper introduces a novel approach to federated learning by incorporating resource-aware clustering. This
method addresses the challenges posed by the diverse devices and
networking resources among participants. Unlike static clustering
approaches, this paper proposes a dynamic method to determine
the optimal number of clusters using Dunn Indices. It enables
adaptability to the varying heterogeneity levels among participants,
ensuring a responsive and customized approach to clustering. Next,
the paper goes beyond empirical observations by providing a mathematical derivation of the communication rounds for convergence
within each cluster. Further, the participant assignment mechanism
adds a layer of sophistication and ensures that devices and networking resources are allocated optimally. Afterwards, we incorporate
a leader-follower technique, particularly through knowledge distillation, which improves the performance of lightweight models
within clusters. Finally, experiments are conducted to validate
the approach and to compare it with state-of-the-art. The results
demonstrated an accuracy improvement of over 3% compared to
its closest competitor and a reduction in communication rounds of
around 10%.
EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model
Authors: Ismail Hossain, Md Jahangir Alam, Sai Puppala, Sajedul Talukder
Date: 2024-07-12 00:00:00
Tasks: Decoder, Recommendation Systems

Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they continually expand their network of friends or followers, freely interacting with others by posting, commenting, and sharing content. Over time, user behavior evolves based on demographic attributes and the networks they establish. In this research, we propose a predictive method to understand how a user evolves on social media throughout their life and to forecast the next stage of their evolution. We fine-tune a GPT-like decoder-only model (we named it E-GPT: Evolution-GPT) to predict the future stages of a user's evolution in online social media. We evaluate the performance of these models and demonstrate how user attributes influence changes within their network by predicting future connections and shifts in user activities on social media, which also addresses other social media challenges such as recommendation systems.
SocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Media
Authors: Ismail Hossain, Sai Puppala, Md Jahangir Alam, Sajedul Talukder
Date: 2024-07-13 00:00:00
Tasks: Collaborative Filtering

User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level of interest on social media such as Facebook, Twitter, and Reddit. Our objective is to analyze user history over time, including their posts and engagement on various topics. Additionally, we take into account the user's profile, seeking connections between their activities and social media platforms. By integrating user history, engagement, and persona, we aim to assess recommendation scores based on relevant item sharing by Hit Rate (HR) and the quality of the ranking system by Normalized Discounted Cumulative Gain (NDCG), where we achieve the highest for NeuMF 0.80 and 0.6 respectively. Our hybrid approach solves the cold-start problem when there is a new user, for new items cold-start problem will never occur, as we consider the post category values. To improve the performance of the model during cold-start we introduce collaborative filtering by looking for similar users and ranking the users based on the highest similarity scores.
xLSTMTime : Long-term Time Series Forecasting With xLSTM
Authors: Musleh Alharthi, Ausif Mahmood
Date: 2024-07-14 00:00:00
Tasks: Time Series, Time Series Forecasting

In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies. The emergence of LTSF-Linear, with its straightforward linear architecture, has notably outperformed transformer-based counterparts, prompting a reevaluation of the transformer's utility in time series forecasting. In response, this paper presents an adaptation of a recent architecture termed extended LSTM (xLSTM) for LTSF. xLSTM incorporates exponential gating and a revised memory structure with higher capacity that has good potential for LTSF. Our adopted architecture for LTSF termed as xLSTMTime surpasses current approaches. We compare xLSTMTime's performance against various state-of-the-art models across multiple real-world da-tasets, demonstrating superior forecasting capabilities. Our findings suggest that refined recurrent architectures can offer competitive alternatives to transformer-based models in LTSF tasks, po-tentially redefining the landscape of time series forecasting.
Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
Authors: Mateusz Pabian, Dominik Rzepka, Mirosław Pawlak
Date: 2024-06-03 00:00:00
Tasks: Twitter Bot Detection

This study proposes a framework that extends existing time-coding time-to-first-spike spiking neural network (SNN) models to allow processing information changing over time. We explain spike propagation through a model with multiple input and output spikes at each neuron, as well as design training rules for end-to-end backpropagation. This strategy enables us to process information changing over time. The model is trained and evaluated on a Twitter bot detection task where the time of events (tweets and retweets) is the primary carrier of information. This task was chosen to evaluate how the proposed SNN deals with spike train data composed of hundreds of events occurring at timescales differing by almost five orders of magnitude. The impact of various parameters on model properties, performance and training-time stability is analyzed.
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects
Authors: Javier Poyatos, Javier Del Ser, Salvador Garcia, Hisao Ishibuchi, Daniel Molina, Isaac Triguero, Bing Xue, Xin Yao, Francisco Herrera
Date: 2024-06-03 00:00:00

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purpose Artificial Intelligence Systems (GPAIS) poses model configuration and adaptability challenges at far greater complexity scales than the optimal design of traditional Machine Learning models. Evolutionary Computation (EC) has been a useful tool for both the design and optimization of Machine Learning models, endowing them with the capability to configure and/or adapt themselves to the task under consideration. Therefore, their application to GPAIS is a natural choice. This paper aims to analyze the role of EC in the field of GPAIS, exploring the use of EC for their design or enrichment. We also match GPAIS properties to Machine Learning areas in which EC has had a notable contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we discuss the challenges of harnessing the benefits of EC for GPAIS, presenting different strategies to both design and improve GPAIS with EC, covering tangential areas, identifying research niches, and outlining potential research directions for EC and GPAIS.