pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Authors: Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar
Date: 2024-07-25 00:00:00
Tasks: Multiple Instance Learning, whole slide images

Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single resolution. Multi-resolution graph models leverage the spatial relationships of patches at multiple magnifications, learning the context for each patch. In this study, we conduct the most thorough validation of a graph model for ovarian cancer subtyping to date. Seven models were tuned and trained using five-fold cross-validation on a set of 1864 whole slide images (WSIs) from 434 patients treated at Leeds Teaching Hospitals NHS Trust. The cross-validation models were ensembled and evaluated using a balanced hold-out test set of 100 WSIs from 30 patients, and an external validation set of 80 WSIs from 80 patients in the Transcanadian Study. The best-performing model, a graph model using 10x+20x magnification data, gave balanced accuracies of 73%, 88%, and 99% in cross-validation, hold-out testing, and external validation, respectively. However, this only exceeded the performance of attention-based multiple instance learning in external validation, with a 93% balanced accuracy. Graph models benefitted greatly from using the UNI foundation model rather than an ImageNet-pretrained ResNet50 for feature extraction, with this having a much greater effect on performance than changing the subsequent classification approach. The accuracy of the combined foundation model and multi-resolution graph network offers a step towards the clinical applicability of these models, with a new highest-reported performance for this task, though further validations are still required to ensure the robustness and usability of the models.
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review
Authors: Adel ElZemity, Budi Arief
Date: 2024-07-25 00:00:00
Tasks: Federated Learning, Privacy Preserving, Systematic Literature Review

Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, ACM, and Science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures -- specifically within the context of IoT -- using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions.
PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
Authors: Christopher Clarke, Yuzhao Heng, Lingjia Tang, Jason Mars
Date: 2024-07-25 00:00:00
Tasks: Diversity, parameter-efficient fine-tuning

The recent emergence of Large Language Models (LLMs) has heralded a new era of human-AI interaction. These sophisticated models, exemplified by Chat-GPT and its successors, have exhibited remarkable capabilities in language understanding. However, as these LLMs have undergone exponential growth, a crucial dimension that remains understudied is the personalization of these models. Large foundation models such as GPT-3 etc. focus on creating a universal model that serves a broad range of tasks and users. This approach emphasizes the model's generalization capabilities, treating users as a collective rather than as distinct individuals. While practical for many common applications, this one-size-fits-all approach often fails to address the rich tapestry of human diversity and individual needs. To explore this issue we introduce the PEFT-U Benchmark: a new dataset for building and evaluating NLP models for user personalization. \datasetname{} consists of a series of user-centered tasks containing diverse and individualized expressions where the preferences of users can potentially differ for the same input. Using PEFT-U, we explore the challenge of efficiently personalizing LLMs to accommodate user-specific preferences in the context of diverse user-centered tasks.
Difficulty Estimation and Simplification of French Text Using LLMs
Authors: Henri Jamet, Yash Raj Shrestha, Michalis Vlachos
Date: 2024-07-25 00:00:00
Tasks: Transfer Learning

We leverage generative large language models for language learning applications, focusing on estimating the difficulty of foreign language texts and simplifying them to lower difficulty levels. We frame both tasks as prediction problems and develop a difficulty classification model using labeled examples, transfer learning, and large language models, demonstrating superior accuracy compared to previous approaches. For simplification, we evaluate the trade-off between simplification quality and meaning preservation, comparing zero-shot and fine-tuned performances of large language models. We show that meaningful text simplifications can be obtained with limited fine-tuning. Our experiments are conducted on French texts, but our methods are language-agnostic and directly applicable to other foreign languages.
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution
Authors: Jintong Hu, Bin Xia, Bin Chen, Wenming Yang, Lei Zhang
Date: 2024-07-25 00:00:00
Tasks: Decoder, Image Super-Resolution, Super-Resolution

Implicit neural representations (INRs) have significantly advanced the field of arbitrary-scale super-resolution (ASSR) of images. Most existing INR-based ASSR networks first extract features from the given low-resolution image using an encoder, and then render the super-resolved result via a multi-layer perceptron decoder. Although these approaches have shown promising results, their performance is constrained by the limited representation ability of discrete latent codes in the encoded features. In this paper, we propose a novel ASSR method named GaussianSR that overcomes this limitation through 2D Gaussian Splatting (2DGS). Unlike traditional methods that treat pixels as discrete points, GaussianSR represents each pixel as a continuous Gaussian field. The encoded features are simultaneously refined and upsampled by rendering the mutually stacked Gaussian fields. As a result, long-range dependencies are established to enhance representation ability. In addition, a classifier is developed to dynamically assign Gaussian kernels to all pixels to further improve flexibility. All components of GaussianSR (i.e., encoder, classifier, Gaussian kernels, and decoder) are jointly learned end-to-end. Experiments demonstrate that GaussianSR achieves superior ASSR performance with fewer parameters than existing methods while enjoying interpretable and content-aware feature aggregations.
Peak-Controlled Logits Poisoning Attack in Federated Distillation
Authors: Yuhan Tang, Aoxu Zhang, Zhiyuan Wu, Bo Gao, Tian Wen, Yuwei Wang, Sheng Sun
Date: 2024-07-25 00:00:00
Tasks: Knowledge Distillation, Transfer Learning

Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to a central server. While FD has gained popularity, its vulnerability to poisoning attacks remains underexplored. To address this gap, we previously introduced FDLA (Federated Distillation Logits Attack), a method that manipulates logits communication to mislead and degrade the performance of client models. However, the impact of FDLA on participants with different identities and the effects of malicious modifications at various stages of knowledge transfer remain unexplored. To this end, we present PCFDLA (Peak-Controlled Federated Distillation Logits Attack), an advanced and more stealthy logits poisoning attack method for FD. PCFDLA enhances the effectiveness of FDLA by carefully controlling the peak values of logits to create highly misleading yet inconspicuous modifications. Furthermore, we introduce a novel metric for better evaluating attack efficacy, demonstrating that PCFDLA maintains stealth while being significantly more disruptive to victim models compared to its predecessors. Experimental results across various datasets confirm the superior impact of PCFDLA on model accuracy, solidifying its potential threat in federated distillation systems.
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models
Authors: Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Sixiang Chen, Tian Ye, Renjing Pei, Kaiwen Zhou, Fenglong Song, Lei Zhu
Date: 2024-07-25 00:00:00
Tasks: Image Restoration, Low-Light Image Enhancement

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, which is time-consuming and may yield suboptimal results. All-in-one models, though capable of handling multiple tasks, typically support only a limited range and often produce overly smooth, low-fidelity outcomes due to their broad data distribution fitting. To address these challenges, we first define a new pipeline for restoring images with multiple degradations, and then introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts. Furthermore, the system modular design facilitates the fast integration of new tasks and models, enhancing its flexibility and scalability for various applications.
AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild
Authors: Junho Park, Kyeongbo Kong, Suk-Ju Kang
Date: 2024-07-25 00:00:00
Tasks: 3D Hand Pose Estimation, 3D Interacting Hand Pose Estimation, Text-to-Image Generation

Recently, there has been a significant amount of research conducted on 3D hand reconstruction to use various forms of human-computer interaction. However, 3D hand reconstruction in the wild is challenging due to extreme lack of in-the-wild 3D hand datasets. Especially, when hands are in complex pose such as interacting hands, the problems like appearance similarity, self-handed occclusion and depth ambiguity make it more difficult. To overcome these issues, we propose AttentionHand, a novel method for text-driven controllable hand image generation. Since AttentionHand can generate various and numerous in-the-wild hand images well-aligned with 3D hand label, we can acquire a new 3D hand dataset, and can relieve the domain gap between indoor and outdoor scenes. Our method needs easy-to-use four modalities (i.e, an RGB image, a hand mesh image from 3D label, a bounding box, and a text prompt). These modalities are embedded into the latent space by the encoding phase. Then, through the text attention stage, hand-related tokens from the given text prompt are attended to highlight hand-related regions of the latent embedding. After the highlighted embedding is fed to the visual attention stage, hand-related regions in the embedding are attended by conditioning global and local hand mesh images with the diffusion-based pipeline. In the decoding phase, the final feature is decoded to new hand images, which are well-aligned with the given hand mesh image and text prompt. As a result, AttentionHand achieved state-of-the-art among text-to-hand image generation models, and the performance of 3D hand mesh reconstruction was improved by additionally training with hand images generated by AttentionHand.
Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind
Authors: Francesca Bianco, Silvia Rigato, Maria Laura Filippetti, Dimitri Ognibene
Date: 2024-07-25 00:00:00
Tasks: Attribute

Theory of Mind (ToM), the ability to attribute beliefs, intentions, or mental states to others, is a crucial feature of human social interaction. In complex environments, where the human sensory system reaches its limits, behaviour is strongly driven by our beliefs about the state of the world around us. Accessing others' mental states, e.g., beliefs and intentions, allows for more effective social interactions in natural contexts. Yet, these variables are not directly observable, making understanding ToM a challenging quest of interest for different fields, including psychology, machine learning and robotics. In this paper, we contribute to this topic by showing a developmental synergy between learning to predict low-level mental states (e.g., intentions, goals) and attributing high-level ones (i.e., beliefs). Specifically, we assume that learning beliefs attribution can occur by observing one's own decision processes involving beliefs, e.g., in a partially observable environment. Using a simple feed-forward deep learning model, we show that, when learning to predict others' intentions and actions, more accurate predictions can be acquired earlier if beliefs attribution is learnt simultaneously. Furthermore, we show that the learning performance improves even when observed actors have a different embodiment than the observer and the gain is higher when observing beliefs-driven chunks of behaviour. We propose that our computational approach can inform the understanding of human social cognitive development and be relevant for the design of future adaptive social robots able to autonomously understand, assist, and learn from human interaction partners in novel natural environments and tasks.
Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis
Authors: Nicola Franco, Marie Kempkes, Jakob Spiegelberg, Jeanette Miriam Lorenz
Date: 2024-07-25 00:00:00
Tasks: Quantum Machine Learning, Time Series, Time Series Analysis, Time Series Classification

As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research presents an analysis of quantum randomized smoothing, how data encoding and perturbation modeling approaches can be matched to achieve meaningful robustness certificates. By utilizing an innovative approach integrating Grover's algorithm, a quadratic sampling advantage over classical randomized smoothing is achieved. This strategy necessitates a basis state encoding, thus restricting the space of meaningful perturbations. We show how constrained $k$-distant Hamming weight perturbations are a suitable noise distribution here, and elucidate how they can be constructed on a quantum computer. The efficacy of the proposed framework is demonstrated on a time series classification task employing a Bag-of-Words pre-processing solution. The advantage of quadratic sample reduction is recovered especially in the regime with large number of samples. This may allow quantum computers to efficiently scale randomized smoothing to more complex tasks beyond the reach of classical methods.
A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks
Authors: Tom Eivind Glover, Ruben Jahren, Francesco Martinuzzi, Pedro Gonçalves Lind, Stefano Nichele
Date: 2024-07-25 00:00:00
Tasks: Sensitivity

Elementary Cellular Automata (ECA) are a well-studied computational universe that is, despite its simple configurations, capable of impressive computational variety. Harvesting this computation in a useful way has historically shown itself to be difficult, but if combined with reservoir computing (RC), this becomes much more feasible. Furthermore, RC and ECA enable energy-efficient AI, making the combination a promising concept for Edge AI. In this work, we contrast ECA to substrates of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). They are, in comparison, the topological heterogeneous counterparts of ECA. This represents a step from ECA towards more biological-plausible substrates. We analyse these substrates by testing on an RC benchmark (5-bit memory), using Temporal Derrida plots to estimate the sensitivity and assess the defect collapse rate. We find that, counterintuitively, disordered topology does not necessarily mean disordered computation. There are countering computational "forces" of topology imperfections leading to a higher collapse rate (order) and yet, if accounted for, an increased sensitivity to the initial condition. These observations together suggest a shrinking critical range.
Personalized and Context-aware Route Planning for Edge-assisted Vehicles
Authors: Dinesh Cyril Selvaraj, Falko Dressler, Carla Fabiana Chiasserini
Date: 2024-07-25 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning

Conventional route planning services typically offer the same routes to all drivers, focusing primarily on a few standardized factors such as travel distance or time, overlooking individual driver preferences. With the inception of autonomous vehicles expected in the coming years, where vehicles will rely on routes decided by such planners, there arises a need to incorporate the specific preferences of each driver, ensuring personalized navigation experiences. In this work, we propose a novel approach based on graph neural networks (GNNs) and deep reinforcement learning (DRL), aimed at customizing routes to suit individual preferences. By analyzing the historical trajectories of individual drivers, we classify their driving behavior and associate it with relevant road attributes as indicators of driver preferences. The GNN is capable of representing the road network as graph-structured data effectively, while DRL is capable of making decisions utilizing reward mechanisms to optimize route selection with factors such as travel costs, congestion level, and driver satisfaction. We evaluate our proposed GNN-based DRL framework using a real-world road network and demonstrate its ability to accommodate driver preferences, offering a range of route options tailored to individual drivers. The results indicate that our framework can select routes that accommodate driver's preferences with up to a 17% improvement compared to a generic route planner, and reduce the travel time by 33% (afternoon) and 46% (evening) relatively to the shortest distance-based approach.
Relating the Seemingly Unrelated: Principled Understanding of Generalization for Generative Models in Arithmetic Reasoning Tasks
Authors: Xingcheng Xu, Zibo Zhao, Haipeng Zhang, Yanqing Yang
Date: 2024-07-25 00:00:00
Tasks: Arithmetic Reasoning

Large language models (LLMs) have demonstrated impressive versatility across numerous tasks, yet their generalization capabilities remain poorly understood. To investigate these behaviors, arithmetic tasks serve as important venues. In previous studies, seemingly unrelated mysteries still exist -- (1) models with appropriate positional embeddings can correctly perform longer unseen arithmetic operations such as addition, but their effectiveness varies in more complex tasks like multiplication; (2) models perform well for longer unseen cases in modular addition under specific moduli (e.g., modulo 100) but struggle under very close moduli (e.g., modulo 101), regardless of the positional encoding used. We believe previous studies have been treating the symptoms rather than addressing the root cause -- they have paid excessive attention to improving model components, while overlooking the differences in task properties that may be the real drivers. This is confirmed by our unified theoretical framework for different arithmetic scenarios. For example, unlike multiplication, the digital addition task has the property of translation invariance which naturally aligns with the relative positional encoding, and this combination leads to successful generalization of addition to unseen longer domains. The discrepancy in operations modulo 100 and 101 arises from the base. Modulo 100, unlike 101, is compatible with the decimal system (base 10), such that unseen information in digits beyond the units digit and the tens digit is actually not needed for the task. Extensive experiments with GPT-like models validate our theoretical predictions. These findings deepen our understanding of the generalization mechanisms, and facilitate more data-efficient model training and objective-oriented AI alignment.
Pruning Boolean d-DNNF Circuits Through Tseitin-Awareness
Authors: Vincent Derkinderen
Date: 2024-07-25 00:00:00

Boolean circuits in d-DNNF form enable tractable probabilistic inference. However, as a key insight of this work, we show that commonly used d-DNNF compilation approaches introduce irrelevant subcircuits. We call these subcircuits Tseitin artifacts, as they are introduced due to the Tseitin transformation step -- a well-established procedure to transform any circuit into the CNF format required by several d-DNNF knowledge compilers. We discuss how to detect and remove both Tseitin variables and Tseitin artifacts, leading to more succinct circuits. We empirically observe an average size reduction of 77.5% when removing both Tseitin variables and artifacts. The additional pruning of Tseitin artifacts reduces the size by 22.2% on average. This significantly improves downstream tasks that benefit from a more succinct circuit, e.g., probabilistic inference tasks.
Real Time American Sign Language Detection Using Yolo-v9
Authors: Amna Imran, Meghana Shashishekhara Hulikal, Hamza A. A. Gardi
Date: 2024-07-25 00:00:00

This paper focuses on real-time American Sign Language Detection. YOLO is a convolutional neural network (CNN) based model, which was first released in 2015. In recent years, it gained popularity for its real-time detection capabilities. Our study specifically targets YOLO-v9 model, released in 2024. As the model is newly introduced, not much work has been done on it, especially not in Sign Language Detection. Our paper provides deep insight on how YOLO- v9 works and better than previous model.