pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning
Authors: Xiaoming Qi, Jingyang Zhang, Huazhu Fu, Guanyu Yang, Shuo Li, Yueming Jin
Date: 2025-03-25 00:00:00
Tasks: Continual Learning, Federated Learning

Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, where tasks evolve dynamically and asynchronously across clients, especially in medical scenario. Existing server-side FCL methods in nature domain construct a continually learnable server model by client aggregation on all-involved tasks. However, they are challenged by: (1) Catastrophic forgetting for previously learned tasks, leading to error accumulation in server model, making it difficult to sustain comprehensive knowledge across all tasks. (2) Biased optimization due to asynchronous tasks handled across different clients, leading to the collision of optimization targets of different clients at the same time steps. In this work, we take the first step to propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (FedDAH). It is to facilitate collaborative learning under the distinct and dynamic task streams across clients. To alleviate the catastrophic forgetting, we propose a dynamic allocation hypernetwork (DAHyper) where a continually updated hypernetwork is designed to manage the mapping between task identities and their associated model parameters, enabling the dynamic allocation of the model across clients. For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates, and assign weights to identical tasks across different time steps based on the similarity for continual optimization. Extensive experiments on the AMOS dataset demonstrate the superiority of our FedDAH to other FCL methods on sites with different task streams. The code is available:https://github.com/jinlab-imvr/FedDAH.
Average Peak Age of Information in Underwater Information Collection With Sleep-Scheduling
Authors: Zhengru Fang, Jingjing Wang, Chunxiao Jiang, Xijun Wang, Yong Ren
Date: 2022-05-23 00:00:00
Proceeding: IEEE Transactions on Vehicular Technology 2022 5
Tasks: Management, Scheduling

We investigate the peak age of information (PAoI) in underwater wireless sensor networks (UWSNs), where Internet of underwater things (IoUT) nodes transmit the latest packets to the sink node, which is in charge of adjusting the sleep-scheduling to match network demands. In order to reduce PAoI, we propose active queue management (AQM) policy of the IoUT node, beneficially compresses the packets having large waiting time. Moreover, we deduce the closed-form expressions of the average PAoI as well as the energy cost relying on probability generating function and matrix-geometric solutions. Numerical results verify that the IoUT node relying on the AQM policy has a lower PAoI and energy cost in comparison to those using non-AQM policy.
Scaling Multi-Armed Bandit Algorithms
Authors: Edouard Fouché, Junpei Komiyama, Klemens Böhm
Date: 2019-07-25 00:00:00
Proceeding: KDD 2019 7
Tasks: Multi-Armed Bandits, Sequential Decision Making, Thompson Sampling

The Multi-Armed Bandit (MAB) is a fundamental model capturing
the dilemma between exploration and exploitation in sequential
decision making. At every time step, the decision maker selects a
set of arms and observes a reward from each of the chosen arms.
In this paper, we present a variant of the problem, which we call
the Scaling MAB (S-MAB): The goal of the decision maker is not
only to maximize the cumulative rewards, i.e., choosing the arms
with the highest expected reward, but also to decide how many
arms to select so that, in expectation, the cost of selecting arms
does not exceed the rewards. This problem is relevant to many real-
world applications, e.g., online advertising, financial investments
or data stream monitoring. We propose an extension of Thompson
Sampling, which has strong theoretical guarantees and is reported
to perform well in practice. Our extension dynamically controls the
number of arms to draw. Furthermore, we combine the proposed
method with ADWIN, a state-of-the-art change detector, to deal
with non-static environments. We illustrate the benefits of our
contribution via a real-world use case on predictive maintenance.
Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents
Authors: Kanishk Verma, Maja Popović, Alexandros Poulis, Yelena Cherkasova, Cathal Ó hÓbáin, Angela Mazzone, Tijana Milosevic  and Brian Davis
Date: 2022-09-07 00:00:00
Proceeding: Journal Natural Language Engineering 2022 9
Tasks: Machine Translation, Translation

Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.
MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal
Authors: Ahsan Habib, Shruthi Narayanan Vaniya, Ahsan Khandoker, Chandan Karmakar
Date: 2024-07-04 00:00:00
Proceeding: IEEE Journal of Biomedical and Health Informatics 2024 7
Tasks: Binary Classification, EEG, Heart Rate Variability

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.
Stochastic Optimization-Aided Energy-Efficient Information Collection in Internet of Underwater Things Networks
Authors: Zhengru Fang, Jingjing Wang, Jun Du, Xiangwang Hou, Yong Ren, Zhu Han
Date: 2021-06-10 00:00:00
Proceeding: IEEE Internet of Things Journal 2021 6
Tasks: Stochastic Optimization

In the face of deeply exploring and exploiting marine resources, the Internet of Underwater Things (IoUT) networks have drawn great attention considering its widely distributed low-cost and easy-deployment smart sensing nodes. However, given the hostile underwater environment, it is critical to conceive energy-efficient information collection because of limited underwater energy supply and inefficient artificial recharge methods. Characterized by high flexibility and maneuverability, autonomous underwater vehicles (AUVs) are regarded as a promising solution for information collection in the IoUT relying upon delicate AUVs’ trajectory and information collection strategy design with the spirit of balancing their energy consumption and information processing capability. In this article, we propose a heterogeneous AUV-aided information collection system with the aim of maximizing the energy efficiency of IoUT nodes taking into account AUV trajectory, resource allocation, and the Age of Information (AoI). Moreover, based on the particle swarm optimization (PSO), we obtain the trajectory of AUVs with low time complexity. Additionally, a two-stage joint optimization algorithm based on the Lyapunov optimization is constructed to strike a tradeoff between energy efficiency and system queue backlog iteratively. Finally, simulation results validate the effectiveness and superiority of our proposed strategy.
AoI-inspired collaborative information collection for AUV-assisted internet of underwater things
Authors: Zhengru Fang, Jingjing Wang, Chunxiao Jiang, Qinyu Zhang, Yong Ren
Date: 2021-01-05 00:00:00
Proceeding: IEEE Internet of Things Journal 2021 1

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.
Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective
Authors: Parsa Haddadian, Roya Booryaee, Rooholah Abedian, Ali Moeini
Date: 2024-03-04 00:00:00
Proceeding: International Conference on Distributed Computing and High Performance Computing (DCHPC) 2024 3
Tasks: Graph Classification, Graph Representation Learning, Representation Learning

Over the past ten years, graph representation learning has garnered a lot of attention due to the variety of graph-structured data and its efficiency in both time and space. One essential method for obtaining effective graph representations is graph pooling. Numerous studies on the graph pooling technique have been conducted. Cutting-edge results on a range of graph representation learning tasks were made possible by the combination of graph neural networks and self-attention mechanisms. Nevertheless, the attention mechanism has limitations since it ignores nodes that have no direct connection via an edge but provide valuable network context information. This paper proposes a graph pooling approach based on Personalized PageRank and self-attention, which improves the model to take into account both node properties and graph structure. The experimental findings indicate that, with a suitable number of parameters, the MAGPool approach delivers greater accuracy on the benchmark datasets.
Datrics Text2SQL. A Framework for Natural Language to SQL Query Generation
Authors: Tetiana Gladkykh, Kirykov Kyrylo
Date: 2025-03-15 00:00:00
Proceeding: - 2025 3
Tasks: Natural Language Queries, RAG, Retrieval, Retrieval-augmented Generation, Text to SQL, Text-To-SQL

Datrics Text2SQL is a Text-to-SQL framework using Retrieval-Augmented Generation (RAG) to enhance accuracy and reliability. By integrating domain knowledge, database structure, and example-based learning, it addresses common SQL generation challenges such as incorrect table selection, faulty joins, and misinterpreted business rules. A knowledge base built from database documentation and successful queries is stored in a vector database, enabling retrieval of relevant context for precise SQL generation. The structured process of knowledge collection, context retrieval, and query generation ensures adaptability to various database schemas and query types.
Neuro-Informed Adaptive Learning (NIAL) Algorithm: A Hybrid Deep Learning Approach for ECG Signal Classification
Authors: Sowad Rahman
Date: 2025-03-12 00:00:00
Tasks: Diagnostic

The detection of cardiac abnormalities using electrocardiogram (ECG) signals is crucial for early diagnosis and intervention in cardiovascular diseases. Traditional deep learning models often lack adaptability to varying signal patterns. This study introduces the Neuro-Informed Adaptive Learning (NIAL) algorithm, a hybrid approach integrating convolutional neural networks (CNNs) and transformer-based attention mechanisms to enhance ECG signal classification. The algorithm dynamically adjusts learning rates based on real-time validation performance, ensuring efficient convergence. Using the MIT-BIH Arrhythmia and PTB Diagnostic ECG datasets, our model achieves high classification accuracy, outperforming conventional approaches. These findings highlight the potential of NIAL in real-time cardiovascular monitoring applications.
Toward a Human-Centered AI-assisted Colonoscopy System in Australia
Authors: Hsiang-Ting Chen, Yuan Zhang, Gustavo Carneiro, Rajvinder Singh
Date: 2025-03-15 00:00:00

While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not just algorithmic accuracy. This paper, based on an Australian field study (observations and gastroenterologist interviews), highlights a critical disconnect: current development prioritizes machine learning model performance, overlooking essential aspects of user interface design, workflow integration, and overall user experience. Industry interactions reveal a similar emphasis on data and algorithms. To realize AI's full potential, the HCI community must champion user-centered design, ensuring these systems are usable, support endoscopist expertise, and enhance patient outcomes.
ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant
Authors: John Murzaku, Zifan Liu, Vaishnavi Muppala, Md Mehrab Tanjim, Xiang Chen, Yunyao Li
Date: 2025-03-19 00:00:00
Tasks: Question Generation, Question-Generation

Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolving ambiguities in real-world, enterprise-level interactions, where context and domain-specific knowledge play a crucial role. In this demonstration, we introduce ECLAIR (Enhanced CLArification for Interactive Responses), a multi-agent framework for interactive disambiguation. ECLAIR enhances ambiguous user query clarification through an interactive process where custom agents are defined, ambiguity reasoning is conducted by the agents, clarification questions are generated, and user feedback is leveraged to refine the final response. When tested on real-world customer data, ECLAIR demonstrates significant improvements in clarification question generation compared to standard few-shot methods.
iMedImage Technical Report
Authors: Ran Wei, ZhiXiong Lan, Qing Yan, Ning Song, Ming Lv, Longqing Ye
Date: 2025-03-27 00:00:00
Tasks: Anomaly Detection, Diagnostic, Medical Image Analysis, Mixture-of-Experts, Specificity

Background: Chromosome karyotype analysis is crucial for diagnosing hereditary diseases, yet detecting structural abnormalities remains challenging. While AI has shown promise in medical imaging, its effectiveness varies across modalities. Leveraging advances in Foundation Models that integrate multimodal medical imaging for robust feature extraction and accurate diagnosis, we developed iMedImage, an end-to-end model for general medical image recognition, demonstrating strong performance across multiple imaging tasks, including chromosome abnormality detection. Materials and Methods: We constructed a comprehensive medical image dataset encompassing multiple modalities from common medical domains, including chromosome, cell, pathology, ultrasound, X-ray, CT, and MRI images. Based on this dataset, we developed the iMedImage model, which incorporates the following key features: (1) a unified representation method for diverse modality inputs and medical imaging tasks; (2) multi-level (case-level, image-level, patch-level) image recognition capabilities enhanced by Chain of Thought (CoT) embedding and Mixture of Experts (MoE) strategies. Results: The test set comprised data from 12 institutions across six regions in China, covering three mainstream scanning devices, and included naturally distributed, unscreened abnormal cases. On this diverse dataset, the model achieved a fully automated chromosome analysis workflow, including segmentation, karyotyping, and abnormality detection, reaching a sensitivity of 92.75% and a specificity of 91.5%. Conclusion: We propose iMedImage, an end-to-end foundation model for medical image analysis, demonstrating its superior performance across various medical imaging tasks. iMedImage provides clinicians with a precise imaging analysis tool and contributes to improving diagnostic accuracy and disease screening.
MFH: A Multi-faceted Heuristic Algorithm Selection Approach for Software Verification
Authors: Jie Su, Liansai Deng, Cheng Wen, Rong Wang, Zhi Ma, Nan Zhang, Cong Tian, Zhenhua Duan, Shengchao Qin
Date: 2025-03-28 00:00:00
Tasks: Prediction

Currently, many verification algorithms are available to improve the reliability of software systems. Selecting the appropriate verification algorithm typically demands domain expertise and non-trivial manpower. An automated algorithm selector is thus desired. However, existing selectors, either depend on machine-learned strategies or manually designed heuristics, encounter issues such as reliance on high-quality samples with algorithm labels and limited scalability. In this paper, an automated algorithm selection approach, namely MFH, is proposed for software verification. Our approach leverages the heuristics that verifiers producing correct results typically implement certain appropriate algorithms, and the supported algorithms by these verifiers indirectly reflect which ones are potentially applicable. Specifically, MFH embeds the code property graph (CPG) of a semantic-preserving transformed program to enhance the robustness of the prediction model. Furthermore, our approach decomposes the selection task into the sub-tasks of predicting potentially applicable algorithms and matching the most appropriate verifiers. Additionally, MFH also introduces a feedback loop on incorrect predictions to improve model prediction accuracy. We evaluate MFH on 20 verifiers and over 15,000 verification tasks. Experimental results demonstrate the effectiveness of MFH, achieving a prediction accuracy of 91.47% even without ground truth algorithm labels provided during the training phase. Moreover, the prediction accuracy decreases only by 0.84% when introducing 10 new verifiers, indicating the strong scalability of the proposed approach.
CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving
Authors: Xinwei Gao, Arambam James Singh, Gangadhar Royyuru, Michael Yuhas, Arvind Easwaran
Date: 2025-03-28 00:00:00
Tasks: Autonomous Driving, reinforcement-learning, Reinforcement Learning

Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional RL in efficiency and reliability. Additionally, real-world demonstrations validate its practical value for real-world autonomous driving.
D3PO: Preference-Based Alignment of Discrete Diffusion Models
Authors: Umberto Borso, Davide Paglieri, Jude Wells, Tim Rocktäschel
Date: 2025-03-11 00:00:00
Tasks: Language Modeling, Language Modelling

Diffusion models have achieved state-of-the-art performance across multiple domains, with recent advancements extending their applicability to discrete data. However, aligning discrete diffusion models with task-specific preferences remains challenging, particularly in scenarios where explicit reward functions are unavailable. In this work, we introduce Discrete Diffusion DPO (D3PO), the first adaptation of Direct Preference Optimization (DPO) to discrete diffusion models formulated as continuous-time Markov chains. Our approach derives a novel loss function that directly fine-tunes the generative process using preference data while preserving fidelity to a reference distribution. We validate D3PO on a structured binary sequence generation task, demonstrating that the method effectively aligns model outputs with preferences while maintaining structural validity. Our results highlight that D3PO enables controlled fine-tuning without requiring explicit reward models, making it a practical alternative to reinforcement learning-based approaches. Future research will explore extending D3PO to more complex generative tasks, including language modeling and protein sequence generation, as well as investigating alternative noise schedules, such as uniform noising, to enhance flexibility across different applications.