pwc-archive/papers-with-abstracts huggingface dataset:
Generating Piano Practice Policy with a Gaussian Process
Authors: Alexandra Moringen, Elad Vromen, Helge Ritter, Jason Friedman
Date: 2024-06-07 00:00:00
Tasks: Bayesian Optimization

A typical process of learning to play a piece on a piano consists of a progression through a series of practice units that focus on individual dimensions of the skill, the so-called practice modes. Practice modes in learning to play music comprise a particularly large set of possibilities, such as hand coordination, posture, articulation, ability to read a music score, correct timing or pitch, etc. Self-guided practice is known to be suboptimal, and a model that schedules optimal practice to maximize a learner's progress still does not exist. Because we each learn differently and there are many choices for possible piano practice tasks and methods, the set of practice modes should be dynamically adapted to the human learner, a process typically guided by a teacher. However, having a human teacher guide individual practice is not always feasible since it is time-consuming, expensive, and often unavailable. In this work, we present a modeling framework to guide the human learner through the learning process by choosing the practice modes generated by a policy model. To this end, we present a computational architecture building on a Gaussian process that incorporates 1) the learner state, 2) a policy that selects a suitable practice mode, 3) performance evaluation, and 4) expert knowledge. The proposed policy model is trained to approximate the expert-learner interaction during a practice session. In our future work, we will test different Bayesian optimization techniques, e.g., different acquisition functions, and evaluate their effect on the learning progress.
A Survey of Fragile Model Watermarking
Authors: Zhenzhe Gao, Yu Cheng, Zhaoxia Yin
Date: 2024-06-07 00:00:00
Tasks: Autonomous Driving, model, Survey

Model fragile watermarking, inspired by both the field of adversarial attacks on neural networks and traditional multimedia fragile watermarking, has gradually emerged as a potent tool for detecting tampering, and has witnessed rapid development in recent years. Unlike robust watermarks, which are widely used for identifying model copyrights, fragile watermarks for models are designed to identify whether models have been subjected to unexpected alterations such as backdoors, poisoning, compression, among others. These alterations can pose unknown risks to model users, such as misidentifying stop signs as speed limit signs in classic autonomous driving scenarios. This paper provides an overview of the relevant work in the field of model fragile watermarking since its inception, categorizing them and revealing the developmental trajectory of the field, thus offering a comprehensive survey for future endeavors in model fragile watermarking.
Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models
Authors: Sigmund H. Høeg, Yilun Du, Olav Egeland
Date: 2024-06-07 00:00:00
Tasks: Denoising, Image Generation, Imitation Learning, Robot Manipulation

Diffusion models have seen rapid adoption in robotic imitation learning, enabling autonomous execution of complex dexterous tasks. However, action synthesis is often slow, requiring many steps of iterative denoising, limiting the extent to which models can be used in tasks that require fast reactive policies. To sidestep this, recent works have explored how the distillation of the diffusion process can be used to accelerate policy synthesis. However, distillation is computationally expensive and can hurt both the accuracy and diversity of synthesized actions. We propose SDP (Streaming Diffusion Policy), an alternative method to accelerate policy synthesis, leveraging the insight that generating a partially denoised action trajectory is substantially faster than a full output action trajectory. At each observation, our approach outputs a partially denoised action trajectory with variable levels of noise corruption, where the immediate action to execute is noise-free, with subsequent actions having increasing levels of noise and uncertainty. The partially denoised action trajectory for a new observation can then be quickly generated by applying a few steps of denoising to the previously predicted noisy action trajectory (rolled over by one timestep). We illustrate the efficacy of this approach, dramatically speeding up policy synthesis while preserving performance across both simulated and real-world settings.
Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models
Authors: Weizhi Tang, Vaishak Belle
Date: 2024-06-07 00:00:00

Large Language Models (LLMs) have recently shown a promise and emergence of Theory of Mind (ToM) ability and even outperform humans in certain ToM tasks. To evaluate and extend the boundaries of the ToM reasoning ability of LLMs, we propose a novel concept, taxonomy, and framework, the ToM reasoning with Zero, Finite, and Infinite Belief History and develop a multi-round text-based game, called $\textit{Pick the Right Stuff}$, as a benchmark. We have evaluated six LLMs with this game and found their performance on Zero Belief History is consistently better than on Finite Belief History. In addition, we have found two of the models with small parameter sizes outperform all the evaluated models with large parameter sizes. We expect this work to pave the way for future ToM benchmark development and also for the promotion and development of more complex AI agents or systems which are required to be equipped with more complex ToM reasoning ability.
Learning-Augmented Priority Queues
Authors: Ziyad Benomar, Christian Coester
Date: 2024-06-07 00:00:00

Priority queues are one of the most fundamental and widely used data structures in computer science. Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priority element. In this study, we investigate the design of priority queues within the learning-augmented framework, where algorithms use potentially inaccurate predictions to enhance their worst-case performance. We examine three prediction models spanning different use cases, and show how the predictions can be leveraged to enhance the performance of priority queue operations. Moreover, we demonstrate the optimality of our solution and discuss some possible applications.
SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals
Authors: Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang
Date: 2024-06-07 00:00:00
Tasks: Decision Making

Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents often face challenges in achieving high-level goals without detailed instructions and in adapting to environments where feedback is delayed. In this paper, we present SelfGoal, a novel automatic approach designed to enhance agents' capabilities to achieve high-level goals with limited human prior and environmental feedback. The core concept of SelfGoal involves adaptively breaking down a high-level goal into a tree structure of more practical subgoals during the interaction with environments while identifying the most useful subgoals and progressively updating this structure. Experimental results demonstrate that SelfGoal significantly enhances the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments. Project page: https://selfgoal-agent.github.io.
Software Engineering for Collective Cyber-Physical Ecosystems
Authors: Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito, Ferruccio Damiani, Danilo Pianini, Giordano Scarso, Gianluca Torta, Mirko Viroli
Date: 2024-06-07 00:00:00

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.
Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network
Authors: Shirwan Piroti, Ashima Chawla, Tahar Zanouda
Date: 2024-06-07 00:00:00
Tasks: Graph Neural Network

There are vast number of configurable parameters in a Radio Access Telecom Network. A significant amount of these parameters is configured by Radio Node or cell based on their deployment setting. Traditional methods rely on domain knowledge for individual parameter configuration, often leading to sub-optimal results. To improve this, a framework using a Deep Generative Graph Neural Network (GNN) is proposed. It encodes the network into a graph, extracts subgraphs for each RAN node, and employs a Siamese GNN (S-GNN) to learn embeddings. The framework recommends configuration parameters for a multitude of parameters and detects misconfigurations, handling both network expansion and existing cell reconfiguration. Tested on real-world data, the model surpasses baselines, demonstrating accuracy, generalizability, and robustness against concept drift.
OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications
Authors: Tongyang Xu, Shuangyang Li, Jinhong Yuan
Date: 2024-06-07 00:00:00

Traditional communications focus on regular and orthogonal signal waveforms for simplified signal processing and improved spectral efficiency. In contrast, the next-generation communications would aim for irregular and non-orthogonal signal waveforms to introduce new capabilities. This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924, with the aim of enhancing performance in industrial Internet of things (IIoT). In time-critical IIoT applications, low-latency and time-jitter tolerance are two critical factors that significantly impact the performance and reliability. Recognizing the inevitability of latency and jitter in practice, this work aims to propose a waveform technique to mitigate these effects via reducing latency and enhancing the system robustness under time jitter effects. The utilization of irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Integrating the irSinc in a two-stage framework, a single-carrier non-orthogonal frequency shaping (SC-NOFS) waveform is developed, showcasing perfect compatibility with 5G standards, enabling the direct integration of irSinc in existing industrial IoT setups. Through 5G standard signal configuration, our signal achieves faster data transmission within the same spectral bandwidth. Hardware experiments validate an 18% saving in timing resources, leading to either reduced latency or enhanced jitter tolerance.
REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning
Authors: Sungho Jeon, Xinyue Ma, Kwang In Kim, Myeongjae Jeon
Date: 2024-06-07 00:00:00
Tasks: Continual Learning, image-classification, Image Classification, Prompt Learning

Recent rehearsal-free methods, guided by prompts, generally excel in vision-related continual learning (CL) scenarios with continuously drifting data. To be deployable on real-world devices, these methods must contain high resource efficiency during training. In this paper, we introduce Resource-Efficient Prompting (REP), which targets improving the resource efficiency of prompt-based rehearsal-free methods. Our key focus is on avoiding catastrophic trade-offs with accuracy while trimming computational and memory costs during prompt learning. We achieve this by exploiting swift prompt selection that enhances input data using a carefully provisioned model, and by developing adaptive token merging (AToM) and layer dropping (ALD) algorithms for the prompt updating stage. AToM and ALD perform selective skipping across the data and model dimensions without compromising task-specific features while learning new tasks. We validate REP's superior resource efficiency over current state-of-the-art ViT- and CNN-based methods through extensive experiments on three image classification datasets.
WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
Authors: Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, Faeze Brahman, Abhilasha Ravichander, Valentina Pyatkin, Nouha Dziri, Ronan Le Bras, Yejin Choi
Date: 2024-06-07 00:00:00
Tasks: Benchmarking, Chatbot

We introduce WildBench, an automated evaluation framework designed to benchmark large language models (LLMs) using challenging, real-world user queries. WildBench consists of 1,024 tasks carefully selected from over one million human-chatbot conversation logs. For automated evaluation with WildBench, we have developed two metrics, WB-Reward and WB-Score, which are computable using advanced LLMs such as GPT-4-turbo. WildBench evaluation uses task-specific checklists to evaluate model outputs systematically and provides structured explanations that justify the scores and comparisons, resulting in more reliable and interpretable automatic judgments. WB-Reward employs fine-grained pairwise comparisons between model responses, generating five potential outcomes: much better, slightly better, slightly worse, much worse, or a tie. Unlike previous evaluations that employed a single baseline model, we selected three baseline models at varying performance levels to ensure a comprehensive pairwise evaluation. Additionally, we propose a simple method to mitigate length bias, by converting outcomes of ``slightly better/worse'' to ``tie'' if the winner response exceeds the loser one by more than $K$ characters. WB-Score evaluates the quality of model outputs individually, making it a fast and cost-efficient evaluation metric. WildBench results demonstrate a strong correlation with the human-voted Elo ratings from Chatbot Arena on hard tasks. Specifically, WB-Reward achieves a Pearson correlation of 0.98 with top-ranking models. Additionally, WB-Score reaches 0.95, surpassing both ArenaHard's 0.91 and AlpacaEval2.0's 0.89 for length-controlled win rates, as well as the 0.87 for regular win rates.
LLM Whisperer: An Inconspicuous Attack to Bias LLM Responses
Authors: Weiran Lin, Anna Gerchanovsky, Omer Akgul, Lujo Bauer, Matt Fredrikson, Zifan Wang
Date: 2024-06-07 00:00:00
Tasks: Language Modelling, Large Language Model

Writing effective prompts for large language models (LLM) can be unintuitive and burdensome. In response, services that optimize or suggest prompts have emerged. While such services can reduce user effort, they also introduce a risk: the prompt provider can subtly manipulate prompts to produce heavily biased LLM responses. In this work, we show that subtle synonym replacements in prompts can increase the likelihood (by a difference up to 78%) that LLMs mention a target concept (e.g., a brand, political party, nation). We substantiate our observations through a user study, showing that our adversarially perturbed prompts 1) are indistinguishable from unaltered prompts by humans, 2) push LLMs to recommend target concepts more often, and 3) make users more likely to notice target concepts, all without arousing suspicion. The practicality of this attack has the potential to undermine user autonomy. Among other measures, we recommend implementing warnings against using prompts from untrusted parties.
PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction
Authors: Eduard Poesina, Adriana Valentina Costache, Adrian-Gabriel Chifu, Josiane Mothe, Radu Tudor Ionescu
Date: 2024-06-07 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation, Image Retrieval, Information Retrieval, Retrieval, Text to Image Generation, Text-to-Image Generation

Text-to-image generation has recently emerged as a viable alternative to text-to-image retrieval, due to the visually impressive results of generative diffusion models. Although query performance prediction is an active research topic in information retrieval, to the best of our knowledge, there is no prior study that analyzes the difficulty of queries (prompts) in text-to-image generation, based on human judgments. To this end, we introduce the first dataset of prompts which are manually annotated in terms of image generation performance. In order to determine the difficulty of the same prompts in image retrieval, we also collect manual annotations that represent retrieval performance. We thus propose the first benchmark for joint text-to-image prompt and query performance prediction, comprising 10K queries. Our benchmark enables: (i) the comparative assessment of the difficulty of prompts/queries in image generation and image retrieval, and (ii) the evaluation of prompt/query performance predictors addressing both generation and retrieval. We present results with several pre-generation/retrieval and post-generation/retrieval performance predictors, thus providing competitive baselines for future research. Our benchmark and code is publicly available under the CC BY 4.0 license at https://github.com/Eduard6421/PQPP.
Generative AI Models: Opportunities and Risks for Industry and Authorities
Authors: Tobias Alt, Andrea Ibisch, Clemens Meiser, Anna Wilhelm, Raphael Zimmer, Jonas Ditz, Dominique Dresen, Christoph Droste, Jens Karschau, Friederike Laus, Oliver Müller, Matthias Neu, Rainer Plaga, Carola Plesch, Britta Sennewald, Thomas Thaeren, Kristina Unverricht, Steffen Waurick
Date: 2024-06-07 00:00:00

Generative AI models are capable of performing a wide variety of tasks that have traditionally required creativity and human understanding. During training, they learn patterns from existing data and can subsequently generate new content such as texts, images, audio, and videos that align with these patterns. Due to their versatility and generally high-quality results, they represent, on the one hand, an opportunity for digitalisation. On the other hand, the use of generative AI models introduces novel IT security risks that must be considered as part of a comprehensive analysis of the IT security threat landscape. In response to this risk potential, companies or authorities intending to use generative AI should conduct an individual risk analysis before integrating it into their workflows. The same applies to developers and operators, as many risks associated with generative AI must be addressed during development or can only be influenced by the operating organisation. Based on this, existing security measures can be adapted, and additional measures implemented.
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction
Authors: Fanmeng Wang, Wentao Guo, Minjie Cheng, Shen Yuan, Hongteng Xu, Zhifeng Gao
Date: 2024-06-07 00:00:00
Tasks: cross-modal alignment, Prediction, Property Prediction

Polymers are high-molecular-weight compounds constructed by the covalent bonding of numerous identical or similar monomers so that their 3D structures are complex yet exhibit unignorable regularity. Typically, the properties of a polymer, such as plasticity, conductivity, bio-compatibility, and so on, are highly correlated with its 3D structure. However, existing polymer property prediction methods heavily rely on the information learned from polymer SMILES sequences (P-SMILES strings) while ignoring crucial 3D structural information, resulting in sub-optimal performance. In this work, we propose MMPolymer, a novel multimodal multitask pretraining framework incorporating polymer 1D sequential and 3D structural information to encourage downstream polymer property prediction tasks. Besides, considering the scarcity of polymer 3D data, we further introduce the "Star Substitution" strategy to extract 3D structural information effectively. During pretraining, in addition to predicting masked tokens and recovering clear 3D coordinates, MMPolymer achieves the cross-modal alignment of latent representations. Then we further fine-tune the pretrained MMPolymer for downstream polymer property prediction tasks in the supervised learning paradigm. Experiments show that MMPolymer achieves state-of-the-art performance in downstream property prediction tasks. Moreover, given the pretrained MMPolymer, utilizing merely a single modality in the fine-tuning phase can also outperform existing methods, showcasing the exceptional capability of MMPolymer in polymer feature extraction and utilization.
Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning
Authors: Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Date: 2024-06-07 00:00:00
Tasks: counterfactual, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. For instance, a self-driving car could experience catastrophic consequences if its sensory inputs about traffic signs are manipulated by an adversary. The core challenge in such situations is that the true state of the environment becomes only partially observable due to these adversarial manipulations. Two key strategies have so far been employed in the literature; the first set of methods focuses on increasing the likelihood that nearby states--those close to the true state--share the same robust actions. The second set of approaches maximize the value for the worst possible true state within the range of adversarially perturbed observations. Although these approaches provide strong robustness against attacks, they tend to be either overly conservative or not generalizable. We hypothesize that the shortcomings of these approaches stem from their failure to explicitly account for partial observability. By making decisions that directly consider this partial knowledge of the true state, we believe it is possible to achieve a better balance between robustness and performance, particularly in adversarial settings. To achieve this, we introduce a novel objective called Adversarial Counterfactual Error (ACoE), which is defined on the beliefs about the underlying true state and naturally balances value optimization with robustness against adversarial attacks, and a theoretically-grounded, scalable surrogate objective Cumulative-ACoE (C-ACoE). Our empirical evaluations demonstrate that our method significantly outperforms current state-of-the-art approaches for addressing adversarial RL challenges, offering a promising direction for better DRL under adversarial conditions.