pwc-archive/papers-with-abstracts huggingface dataset:
A Machine Learning Model for Crowd Density Classification in Hajj Video Frames
Authors: Afnan A. Shah
Date: 2025-01-09 00:00:00
Tasks: Texture Classification

Managing the massive annual gatherings of Hajj and Umrah presents significant challenges, particularly as the Saudi government aims to increase the number of pilgrims. Currently, around two million pilgrims attend Hajj and 26 million attend Umrah making crowd control especially in critical areas like the Grand Mosque during Tawaf, a major concern. Additional risks arise in managing dense crowds at key sites such as Arafat where the potential for stampedes, fires and pandemics poses serious threats to public safety. This research proposes a machine learning model to classify crowd density into three levels: moderate crowd, overcrowded and very dense crowd in video frames recorded during Hajj, with a flashing red light to alert organizers in real-time when a very dense crowd is detected. While current research efforts in processing Hajj surveillance videos focus solely on using CNN to detect abnormal behaviors, this research focuses more on high-risk crowds that can lead to disasters. Hazardous crowd conditions require a robust method, as incorrect classification could trigger unnecessary alerts and government intervention, while failure to classify could result in disaster. The proposed model integrates Local Binary Pattern (LBP) texture analysis, which enhances feature extraction for differentiating crowd density levels, along with edge density and area-based features. The model was tested on the KAU-Smart Crowd 'HAJJv2' dataset which contains 18 videos from various key locations during Hajj including 'Massaa', 'Jamarat', 'Arafat' and 'Tawaf'. The model achieved an accuracy rate of 87% with a 2.14% error percentage (misclassification rate), demonstrating its ability to detect and classify various crowd conditions effectively. That contributes to enhanced crowd management and safety during large-scale events like Hajj.
Traits of a Leader: User Influence Level Prediction through Sociolinguistic Modeling
Authors: Denys Katerenchuk, Rivka Levitan
Date: 2025-01-05 00:00:00
Tasks: Misinformation

Recognition of a user's influence level has attracted much attention as human interactions move online. Influential users have the ability to sway others' opinions to achieve some goals. As a result, predicting users' level of influence can help to understand social networks, forecast trends, prevent misinformation, etc. However, predicting user influence is a challenging problem because the concept of influence is specific to a situation or a domain, and user communications are limited to text. In this work, we define user influence level as a function of community endorsement and develop a model that significantly outperforms the baseline by leveraging demographic and personality data. This approach consistently improves RankDCG scores across eight different domains.
Toward Inclusive Educational AI: Auditing Frontier LLMs through a Multiplexity Lens
Authors: Abdullah Mushtaq, Muhammad Rafay Naeem, Muhammad Imran Taj, Ibrahim Ghaznavi, Junaid Qadir
Date: 2025-01-02 00:00:00
Tasks: Sentiment Analysis

As large language models (LLMs) like GPT-4 and Llama 3 become integral to educational contexts, concerns are mounting over the cultural biases, power imbalances, and ethical limitations embedded within these technologies. Though generative AI tools aim to enhance learning experiences, they often reflect values rooted in Western, Educated, Industrialized, Rich, and Democratic (WEIRD) cultural paradigms, potentially sidelining diverse global perspectives. This paper proposes a framework to assess and mitigate cultural bias within LLMs through the lens of applied multiplexity. Multiplexity, inspired by Senturk et al. and rooted in Islamic and other wisdom traditions, emphasizes the coexistence of diverse cultural viewpoints, supporting a multi-layered epistemology that integrates both empirical sciences and normative values. Our analysis reveals that LLMs frequently exhibit cultural polarization, with biases appearing in both overt responses and subtle contextual cues. To address inherent biases and incorporate multiplexity in LLMs, we propose two strategies: \textit{Contextually-Implemented Multiplex LLMs}, which embed multiplex principles directly into the system prompt, influencing LLM outputs at a foundational level and independent of individual prompts, and \textit{Multi-Agent System (MAS)-Implemented Multiplex LLMs}, where multiple LLM agents, each representing distinct cultural viewpoints, collaboratively generate a balanced, synthesized response. Our findings demonstrate that as mitigation strategies evolve from contextual prompting to MAS-implementation, cultural inclusivity markedly improves, evidenced by a significant rise in the Perspectives Distribution Score (PDS) and a PDS Entropy increase from 3.25\% at baseline to 98\% with the MAS-Implemented Multiplex LLMs. Sentiment analysis further shows a shift towards positive sentiment across cultures,...
AICat: An AI Cataloguing Approach to Support the EU AI Act
Authors: Delaram Golpayegani, Harshvardhan J. Pandit, Dave Lewis
Date: 2024-12-19 00:00:00

The European Union's Artificial Intelligence Act (AI Act) requires providers and deployers of high-risk AI applications to register their systems into the EU database, wherein the information should be represented and maintained in an easily-navigable and machine-readable manner. Given the uptake of open data and Semantic Web-based approaches for other EU repositories, in particular the use of the Data Catalogue vocabulary Application Profile (DCAT-AP), a similar solution for managing the EU database of high-risk AI systems is needed. This paper introduces AICat - an extension of DCAT for representing catalogues of AI systems that provides consistency, machine-readability, searchability, and interoperability in managing open metadata regarding AI systems. This open approach to cataloguing ensures transparency, traceability, and accountability in AI application markets beyond the immediate needs of high-risk AI compliance in the EU. AICat is available online at https://w3id.org/aicat under the CC-BY-4.0 license.
A Generative AI-driven Metadata Modelling Approach
Authors: Mayukh Bagchi
Date: 2024-12-13 00:00:00
Tasks: Large Language Model

Since decades, the modelling of metadata has been core to the functioning of any academic library. Its importance has only enhanced with the increasing pervasiveness of Generative Artificial Intelligence (AI)-driven information activities and services which constitute a library's outreach. However, with the rising importance of metadata, there arose several outstanding problems with the process of designing a library metadata model impacting its reusability, crosswalk and interoperability with other metadata models. This paper posits that the above problems stem from an underlying thesis that there should only be a few core metadata models which would be necessary and sufficient for any information service using them, irrespective of the heterogeneity of intra-domain or inter-domain settings. To that end, this paper advances a contrary view of the above thesis and substantiates its argument in three key steps. First, it introduces a novel way of thinking about a library metadata model as an ontology-driven composition of five functionally interlinked representation levels from perception to its intensional definition via properties. Second, it introduces the representational manifoldness implicit in each of the five levels which cumulatively contributes to a conceptually entangled library metadata model. Finally, and most importantly, it proposes a Generative AI-driven Human-Large Language Model (LLM) collaboration based metadata modelling approach to disentangle the entanglement inherent in each representation level leading to the generation of a conceptually disentangled metadata model. Throughout the paper, the arguments are exemplified by motivating scenarios and examples from representative libraries handling cancer information.
Beam Domain Channel Estimation for Spatial Non-Stationary Massive MIMO Systems
Authors: Lin Hou, Hengtai Chang, Cheng-Xiang Wang, Jie Huang, Songjiang Yang
Date: 2025-01-08 00:00:00

In massive multiple-input multiple-output (MIMO) systems, the channel estimation scheme is subject to the spatial non-stationarity and inevitably power leakage in the beam domain. In this paper, a beam domain channel estimation scheme is investigated for spatial non-stationary (SNS) massive MIMO systems considering power leakage. %a novel beam domain channel estimation scheme is proposed for spatial non-stationary (SNS) massive MIMO systems. Specifically, a realistic massive MIMO beam domain channel model (BDCM) is introduced to capture the spatial non-stationarity considering power leakage by introducing the illustration of visibility region (VR). Then, a beam domain structure-based sparsity adaptive matching pursuit (BDS-SAMP) scheme is proposed based on the cross-block sparse structure and power ratio threshold of beam domain channel. Finally, the simulation results validate the accuracy of proposed BDS-SAMP scheme with low pilot overhead and reasonable complexity by comparing with conventional schemes.
A Novel Non-Stationary Channel Emulator for 6G MIMO Wireless Channels
Authors: Yuan Zong, Lijian Xin, Jie Huang, Cheng-Xiang Wang
Date: 2025-01-08 00:00:00

The performance evaluation of sixth generation (6G) communication systems is anticipated to be a controlled and repeatable process in the lab, which brings up the demand for wireless channel emulators. However, channel emulation for 6G space-time-frequency (STF) non-stationary channels is missing currently. In this paper, a non-stationary multiple-input multiple-output (MIMO) geometry-based stochastic model (GBSM) that accurately characterizes the channel STF properties is introduced firstly. Then, a subspace-based method is proposed for reconstructing the channel fading obtained from the GBSM and a channel emulator architecture with frequency domain processing is presented for 6G MIMO systems. Moreover, the spatial time-varying channel transfer functions (CTFs) of the channel simulation and the channel emulation are compared and analyzed. The Doppler power spectral density (PSD) and delay PSD are further derived and compared between the channel model simulation and subspace-based emulation. The results demonstrate that the proposed channel emulator is capable of reproducing the non-stationary channel characteristics.
Approximation Rates in Fréchet Metrics: Barron Spaces, Paley-Wiener Spaces, and Fourier Multipliers
Authors: Ahmed Abdeljawad, Thomas Dittrich
Date: 2024-12-27 00:00:00
Tasks: Operator learning

Operator learning is a recent development in the simulation of Partial Differential Equations (PDEs) by means of neural networks. The idea behind this approach is to learn the behavior of an operator, such that the resulting neural network is an (approximate) mapping in infinite-dimensional spaces that is capable of (approximately) simulating the solution operator governed by the PDE. In our work, we study some general approximation capabilities for linear differential operators by approximating the corresponding symbol in the Fourier domain. Analogous to the structure of the class of H\"ormander-Symbols, we consider the approximation with respect to a topology that is induced by a sequence of semi-norms. In that sense, we measure the approximation error in terms of a Fr\'echet metric, and our main result identifies sufficient conditions for achieving a predefined approximation error. Secondly, we then focus on a natural extension of our main theorem, in which we manage to reduce the assumptions on the sequence of semi-norms. Based on existing approximation results for the exponential spectral Barron space, we then present a concrete example of symbols that can be approximated well.
Video Summarisation with Incident and Context Information using Generative AI
Authors: Ulindu De Silva, Leon Fernando, Kalinga Bandara, Rashmika Nawaratne
Date: 2025-01-08 00:00:00
Tasks: Action Recognition, Navigate, object-detection, Object Detection

The proliferation of video content production has led to vast amounts of data, posing substantial challenges in terms of analysis efficiency and resource utilization. Addressing this issue calls for the development of robust video analysis tools. This paper proposes a novel approach leveraging Generative Artificial Intelligence (GenAI) to facilitate streamlined video analysis. Our tool aims to deliver tailored textual summaries of user-defined queries, offering a focused insight amidst extensive video datasets. Unlike conventional frameworks that offer generic summaries or limited action recognition, our method harnesses the power of GenAI to distil relevant information, enhancing analysis precision and efficiency. Employing YOLO-V8 for object detection and Gemini for comprehensive video and text analysis, our solution achieves heightened contextual accuracy. By combining YOLO with Gemini, our approach furnishes textual summaries extracted from extensive CCTV footage, enabling users to swiftly navigate and verify pertinent events without the need for exhaustive manual review. The quantitative evaluation revealed a similarity of 72.8%, while the qualitative assessment rated an accuracy of 85%, demonstrating the capability of the proposed method.
Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech Recognition
Authors: Rui Liu, Hongyu Yuan, Haizhou Li
Date: 2025-01-03 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Video Compression, Visual Speech Recognition

Unlike traditional Automatic Speech Recognition (ASR), Audio-Visual Speech Recognition (AVSR) takes audio and visual signals simultaneously to infer the transcription. Recent studies have shown that Large Language Models (LLMs) can be effectively used for Generative Error Correction (GER) in ASR by predicting the best transcription from ASR-generated N-best hypotheses. However, these LLMs lack the ability to simultaneously understand audio and visual, making the GER approach challenging to apply in AVSR. In this work, we propose a novel GER paradigm for AVSR, termed AVGER, that follows the concept of ``listening and seeing again''. Specifically, we first use the powerful AVSR system to read the audio and visual signals to get the N-Best hypotheses, and then use the Q-former-based Multimodal Synchronous Encoder to read the audio and visual information again and convert them into an audio and video compression representation respectively that can be understood by LLM. Afterward, the audio-visual compression representation and the N-Best hypothesis together constitute a Cross-modal Prompt to guide the LLM in producing the best transcription. In addition, we also proposed a Multi-Level Consistency Constraint training criterion, including logits-level, utterance-level and representations-level, to improve the correction accuracy while enhancing the interpretability of audio and visual compression representations. The experimental results on the LRS3 dataset show that our method outperforms current mainstream AVSR systems. The proposed AVGER can reduce the Word Error Rate (WER) by 24% compared to them. Code and models can be found at: https://github.com/CircleRedRain/AVGER.
FlexCache: Flexible Approximate Cache System for Video Diffusion
Authors: Desen Sun, Henry Tian, Tim Lu, Sihang Liu
Date: 2024-12-18 00:00:00
Tasks: Video Generation

Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a single video. While prior research has addressed the computational overhead in text-to-image diffusion models, the techniques developed are not directly suitable for video diffusion models due to the significantly larger cache requirements and enhanced computational demands associated with video generation. We present FlexCache, a flexible approximate cache system that addresses the challenges in two main designs. First, we compress the caches before saving them to storage. Our compression strategy can reduce 6.7 times consumption on average. Then we find that the approximate cache system can achieve higher hit rate and computation savings by decoupling the object and background. We further design a tailored cache replacement policy to support the two techniques mentioned above better. Through our evaluation, FlexCache reaches 1.26 times higher throughput and 25% lower cost compared to the state-of-the-art diffusion approximate cache system.
SEIHRDV: a multi-age multi-group epidemiological model and its validation on the COVID-19 epidemics in Italy
Authors: Luca Dede', Nicola Parolini, Alfio Quarteroni, Giulia Villani, Giovanni Ziarelli
Date: 2025-01-07 00:00:00
Tasks: Decision Making

We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of the COVID-19 epidemic, which we validate using data from Italy starting in September 2020. SEIHRDV features the following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D) and Vaccinated (V). The model is age-stratified, as it considers the population split into 15 age groups. Moreover, it takes into account 7 different contexts of exposition to the infection (family, home, school, work, transport, leisure, other contexts), which impact on the transmission mechanism. Thanks to these features, the model can address the analysis of the epidemics and the efficacy of non-pharmaceutical interventions, as well as possible vaccination strategies and the introduction of the Green Pass, a containment measure introduced in Italy in 2021. By leveraging on the SEIHRDV model, we successfully analyzed epidemic trends during the COVID-19 outbreak from September 2020 to July 2021. The model proved instrumental in conducting comprehensive what-if studies and scenario analyses tailored to Italy and its regions. Furthermore, SEIHRDV facilitated accurate forecasting of the future potential trajectory of the epidemic, providing critical information for informed decision making and public health strategies.
Optimize the parameters of the PID Controller using Genetic Algorithm for Robot Manipulators
Authors: Vu Ngoc Son, Pham Van Cuong, Nguyen Duy Minh, Phi Hoang Nha
Date: 2025-01-08 00:00:00

This paper presents the design a Proportional-Integral-Derivative (PID) controller with optimized parameters for a two-degree-of-freedom robotic arm. A genetic algorithm (GA) is proposed to optimize the controller parameters, addressing the challenges in determining PID controller parameters for highly nonlinear systems like robotic arms compared to traditional methods. The GA-optimized PID controller significantly improves control accuracy and performance over traditional control methods. Simulation results demonstrate that the robotic arm system operates with high precision and stability. Additionally, the shortened trajectory tracking response time enhances the feasibility of applying this control algorithm in realworld scenarios. This research not only confirms the suitability of PID-GA for robotic arms and similar systems but also opens new avenues for applying this algorithm to real physical systems.
Development of an Adaptive Sliding Mode Controller using Neural Networks for Trajectory Tracking of a Cylindrical Manipulator
Authors: TieuNien Le, VanCuong Pham, NgocSon Vu
Date: 2025-01-08 00:00:00

Cylindrical manipulators are extensively used in industrial automation, especially in emerging technologies like 3D printing, which represents a significant future trend. However, controlling the trajectory of nonlinear models with system uncertainties remains a critical challenge, often leading to reduced accuracy and reliability. To address this, the study develops an Adaptive Sliding Mode Controller (ASMC) integrated with Neural Networks (NNs) to improve trajectory tracking for cylindrical manipulators. The ASMC leverages the robustness of sliding mode control and the adaptability of neural networks to handle uncertainties and dynamic variations effectively. Simulation results validate that the proposed ASMC-NN achieves high trajectory tracking accuracy, fast response time, and enhanced reliability, making it a promising solution for applications in 3D printing and beyond.
Probing Speaker-specific Features in Speaker Representations
Authors: Aemon Yat Fei Chiu, Paco Kei Ching Fung, Roger Tsz Yeung Li, Jingyu Li, Tan Lee
Date: 2025-01-09 00:00:00
Tasks: Self-Supervised Learning, Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This study explores speaker-specific features encoded in speaker embeddings and intermediate layers of speech self-supervised learning (SSL) models. By utilising a probing method, we analyse features such as pitch, tempo, and energy across prominent speaker embedding models and speech SSL models, including HuBERT, WavLM, and Wav2vec 2.0. The results reveal that speaker embeddings like CAM++ excel in energy classification, while speech SSL models demonstrate superior performance across multiple features due to their hierarchical feature encoding. Intermediate layers effectively capture a mix of acoustic and para-linguistic information, with deeper layers refining these representations. This investigation provides insights into model design and highlights the potential of these representations for downstream applications, such as speaker verification and text-to-speech synthesis, while laying the groundwork for exploring additional features and advanced probing methods.
Comparison of fundamental frequency estimators with subharmonic voice signals
Authors: Takeshi Ikuma, Melda Kunduk, Andrew J. McWhorter
Date: 2025-01-08 00:00:00

In clinical voice signal analysis, mishandling of subharmonic voicing may cause an acoustic parameter to signal false negatives. As such, the ability of a fundamental frequency estimator to identify speaking fundamental frequency is critical. This paper presents a sustained-vowel study, which used a quality-of-estimate classification to identify subharmonic errors and subharmonics-to-harmonics ratio (SHR) to measure the strength of subharmonic voicing. Five estimators were studied with a sustained vowel dataset: Praat, YAAPT, Harvest, CREPE, and FCN-F0. FCN-F0, a deep-learning model, performed the best both in overall accuracy and in correctly resolving subharmonic signals. CREPE and Harvest are also highly capable estimators for sustained vowel analysis.
FleSpeech: Flexibly Controllable Speech Generation with Various Prompts
Authors: Hanzhao Li, Yuke Li, Xinsheng Wang, Jingbin Hu, Qicong Xie, Shan Yang, Lei Xie
Date: 2025-01-08 00:00:00
Tasks: Speech Synthesis

Controllable speech generation methods typically rely on single or fixed prompts, hindering creativity and flexibility. These limitations make it difficult to meet specific user needs in certain scenarios, such as adjusting the style while preserving a selected speaker's timbre, or choosing a style and generating a voice that matches a character's visual appearance. To overcome these challenges, we propose \textit{FleSpeech}, a novel multi-stage speech generation framework that allows for more flexible manipulation of speech attributes by integrating various forms of control. FleSpeech employs a multimodal prompt encoder that processes and unifies different text, audio, and visual prompts into a cohesive representation. This approach enhances the adaptability of speech synthesis and supports creative and precise control over the generated speech. Additionally, we develop a data collection pipeline for multimodal datasets to facilitate further research and applications in this field. Comprehensive subjective and objective experiments demonstrate the effectiveness of FleSpeech. Audio samples are available at https://kkksuper.github.io/FleSpeech/