pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Mapping of Singing Voices: U-Net-Assisted Vocal Segmentation
Authors: Adam Sorrenti
Date: 2024-05-30 00:00:00
Tasks: Audio Signal Processing, Audio Source Separation

Separating vocal elements from musical tracks is a longstanding challenge in audio signal processing. This study tackles the distinct separation of vocal components from musical spectrograms. We employ the Short Time Fourier Transform (STFT) to extract audio waves into detailed frequency-time spectrograms, utilizing the benchmark MUSDB18 dataset for music separation. Subsequently, we implement a UNet neural network to segment the spectrogram image, aiming to delineate and extract singing voice components accurately. We achieved noteworthy results in audio source separation using of our U-Net-based models. The combination of frequency-axis normalization with Min/Max scaling and the Mean Absolute Error (MAE) loss function achieved the highest Source-to-Distortion Ratio (SDR) of 7.1 dB, indicating a high level of accuracy in preserving the quality of the original signal during separation. This setup also recorded impressive Source-to-Interference Ratio (SIR) and Source-to-Artifact Ratio (SAR) scores of 25.2 dB and 7.2 dB, respectively. These values significantly outperformed other configurations, particularly those using Quantile-based normalization or a Mean Squared Error (MSE) loss function. Our source code, model weights, and demo material can be found at the project's GitHub repository: https://github.com/mbrotos/SoundSeg
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning
Authors: Zhuang Qi, Lei Meng, Weihao He, Ruohan Zhang, Yu Wang, Xin Qi, Xiangxu Meng
Date: 2024-05-30 00:00:00
Tasks: Federated Learning, Representation Learning

Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve the generalization capability. However, the data heterogeneity between sources may lead models to gradually forget previously acquired knowledge when undergoing cross-training to adapt to new tasks or data sources. We argue that integrating personalized and global knowledge to gather information from multiple perspectives could potentially improve performance. To achieve this goal, this paper presents a novel approach that enhances federated learning through a cross-training scheme incorporating multi-view information. Specifically, the proposed method, termed FedCT, includes three main modules, where the consistency-aware knowledge broadcasting module aims to optimize model assignment strategies, which enhances collaborative advantages between clients and achieves an efficient federated learning process. The multi-view knowledge-guided representation learning module leverages fused prototypical knowledge from both global and local views to enhance the preservation of local knowledge before and after model exchange, as well as to ensure consistency between local and global knowledge. The mixup-based feature augmentation module aggregates rich information to further increase the diversity of feature spaces, which enables the model to better discriminate complex samples. Extensive experiments were conducted on four datasets in terms of performance comparison, ablation study, in-depth analysis and case study. The results demonstrated that FedCT alleviates knowledge forgetting from both local and global views, which enables it outperform state-of-the-art methods.
Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion
Authors: Jiangkai Wu, LiMing Liu, Yunpeng Tan, Junlin Hao, Xinggong Zhang
Date: 2024-05-30 00:00:00
Tasks: Semantic Communication, Video Compression, Video Generation

With the exponential growth of video traffic, traditional video streaming systems are approaching their limits in compression efficiency and communication capacity. To further reduce bitrate while maintaining quality, we propose Promptus, a disruptive semantic communication system that streaming prompts instead of video content, which represents real-world video frames with a series of "prompts" for delivery and employs Stable Diffusion to generate videos at the receiver. To ensure that the generated video is pixel-aligned with the original video, a gradient descent-based prompt fitting framework is proposed. Further, a low-rank decomposition-based bitrate control algorithm is introduced to achieve adaptive bitrate. For inter-frame compression, an interpolation-aware fitting algorithm is proposed. Evaluations across various video genres demonstrate that, compared to H.265, Promptus can achieve more than a 4x bandwidth reduction while preserving the same perceptual quality. On the other hand, at extremely low bitrates, Promptus can enhance the perceptual quality by 0.139 and 0.118 (in LPIPS) compared to VAE and H.265, respectively, and decreases the ratio of severely distorted frames by 89.3% and 91.7%. Our work opens up a new paradigm for efficient video communication. Promptus is open-sourced at: https://github.com/JiangkaiWu/Promptus.
Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey
Authors: Thai-Hoc Vu, Senthil Kumar Jagatheesaperumal, Minh-Duong Nguyen, Nguyen Van Huynh, Sunghwan Kim, Quoc-Viet Pham
Date: 2024-05-30 00:00:00
Tasks: Management, Semantic Communication

The success of Artificial Intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet-of-Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, Generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI's applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area.
Efficient Indirect LLM Jailbreak via Multimodal-LLM Jailbreak
Authors: Zhenxing Niu, Yuyao Sun, Haoxuan Ji, Zheng Lin, Haichang Gao, Xinbo Gao, Gang Hua, Rong Jin
Date: 2024-05-30 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, LLM Jailbreak, Multimodal Large Language Model

This paper focuses on jailbreaking attacks against large language models (LLMs), eliciting them to generate objectionable content in response to harmful user queries. Unlike previous LLM-jailbreak methods that directly orient to LLMs, our approach begins by constructing a multimodal large language model (MLLM) built upon the target LLM. Subsequently, we perform an efficient MLLM jailbreak and obtain a jailbreaking embedding. Finally, we convert the embedding into a textual jailbreaking suffix to carry out the jailbreak of target LLM. Compared to the direct LLM-jailbreak methods, our indirect jailbreaking approach is more efficient, as MLLMs are more vulnerable to jailbreak than pure LLM. Additionally, to improve the attack success rate of jailbreak, we propose an image-text semantic matching scheme to identify a suitable initial input. Extensive experiments demonstrate that our approach surpasses current state-of-the-art jailbreak methods in terms of both efficiency and effectiveness. Moreover, our approach exhibits superior cross-class generalization abilities.
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
Authors: Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen
Date: 2024-05-30 00:00:00
Tasks: Text Generation, Uncertainty Quantification

Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. In particular, uncertainty can be used to improve the trustworthiness of LLMs by detecting factually incorrect model responses, commonly called hallucinations. Critically, one should seek to capture the model's semantic uncertainty, i.e., the uncertainty over the meanings of LLM outputs, rather than uncertainty over lexical or syntactic variations that do not affect answer correctness. To address this problem, we propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs. KLE defines positive semidefinite unit trace kernels to encode the semantic similarities of LLM outputs and quantifies uncertainty using the von Neumann entropy. It considers pairwise semantic dependencies between answers (or semantic clusters), providing more fine-grained uncertainty estimates than previous methods based on hard clustering of answers. We theoretically prove that KLE generalizes the previous state-of-the-art method called semantic entropy and empirically demonstrate that it improves uncertainty quantification performance across multiple natural language generation datasets and LLM architectures.
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Authors: Minttu Alakuijala, Reginald McLean, Isaac Woungang, Nariman Farsad, Samuel Kaski, Pekka Marttinen, Kai Yuan
Date: 2024-05-30 00:00:00
Tasks: Binary Classification, Contrastive Learning

Natural language is often the easiest and most convenient modality for humans to specify tasks for robots. However, learning to ground language to behavior typically requires impractical amounts of diverse, language-annotated demonstrations collected on each target robot. In this work, we aim to separate the problem of what to accomplish from how to accomplish it, as the former can benefit from substantial amounts of external observation-only data, and only the latter depends on a specific robot embodiment. To this end, we propose Video-Language Critic, a reward model that can be trained on readily available cross-embodiment data using contrastive learning and a temporal ranking objective, and use it to score behavior traces from a separate actor. When trained on Open X-Embodiment data, our reward model enables 2x more sample-efficient policy training on Meta-World tasks than a sparse reward only, despite a significant domain gap. Using in-domain data but in a challenging task generalization setting on Meta-World, we further demonstrate more sample-efficient training than is possible with prior language-conditioned reward models that are either trained with binary classification, use static images, or do not leverage the temporal information present in video data.
A Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution
Authors: Davoud Sarani, Dr. Parviz Rashidi-Khazaee
Date: 2024-05-30 00:00:00
Tasks: Algorithmic Trading, Deep Reinforcement Learning

In today's forex market traders increasingly turn to algorithmic trading, leveraging computers to seek more profits. Deep learning techniques as cutting-edge advancements in machine learning, capable of identifying patterns in financial data. Traders utilize these patterns to execute more effective trades, adhering to algorithmic trading rules. Deep reinforcement learning methods (DRL), by directly executing trades based on identified patterns and assessing their profitability, offer advantages over traditional DL approaches. This research pioneers the application of a multi-agent (MA) RL framework with the state-of-the-art Asynchronous Advantage Actor-Critic (A3C) algorithm. The proposed method employs parallel learning across multiple asynchronous workers, each specialized in trading across multiple currency pairs to explore the potential for nuanced strategies tailored to different market conditions and currency pairs. Two different A3C with lock and without lock MA model was proposed and trained on single currency and multi-currency. The results indicate that both model outperform on Proximal Policy Optimization model. A3C with lock outperforms other in single currency training scenario and A3C without Lock outperforms other in multi-currency scenario. The findings demonstrate that this approach facilitates broader and faster exploration of different currency pairs, significantly enhancing trading returns. Additionally, the agent can learn a more profitable trading strategy in a shorter time.
Strategies to Counter Artificial Intelligence in Law Enforcement: Cross-Country Comparison of Citizens in Greece, Italy and Spain
Authors: Petra Saskia Bayerl, Babak Akhgar, Ernesto La Mattina, Barbara Pirillo, Ioana Cotoi, Davide Ariu, Matteo Mauri, Jorge Garcia, Dimitris Kavallieros, Antonia Kardara, Konstantina Karagiorgou
Date: 2024-05-30 00:00:00

This paper investigates citizens' counter-strategies to the use of Artificial Intelligence (AI) by law enforcement agencies (LEAs). Based on information from three countries (Greece, Italy and Spain) we demonstrate disparities in the likelihood of ten specific counter-strategies. We further identified factors that increase the propensity for counter-strategies. Our study provides an important new perspective to societal impacts of security-focused AI applications by illustrating the conscious, strategic choices by citizens when confronted with AI capabilities for LEAs.
Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation
Authors: Yi Liu, Xiangyu Liu, Xiangrong Zhu, Wei Hu
Date: 2024-05-30 00:00:00
Tasks: Attribute, counterfactual, Disentanglement, Text Generation

Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., "positive" from sentiment and "sport" from topic). For ease of obtaining training samples, existing works neglect attribute correlations formed by the intertwining of different attributes. Particularly, the stereotype formed by imbalanced attribute correlations significantly affects multi-aspect control. In this paper, we propose MAGIC, a new multi-aspect controllable text generation method with disentangled counterfactual augmentation. We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement. During inference, we enhance attribute correlations by target-guided counterfactual augmentation to further improve multi-aspect control. Experiments show that MAGIC outperforms state-of-the-art baselines in both imbalanced and balanced attribute correlation scenarios. Our source code and data are available at https://github.com/nju-websoft/MAGIC.
PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting
Authors: Qiaowei Miao, JinSheng Quan, Kehan Li, Yawei Luo
Date: 2024-05-30 00:00:00
Tasks: 3D Generation, Contrastive Learning, Text to 3D

Previous text-to-4D methods have leveraged multiple Score Distillation Sampling (SDS) techniques, combining motion priors from video-based diffusion models (DMs) with geometric priors from multiview DMs to implicitly guide 4D renderings. However, differences in these priors result in conflicting gradient directions during optimization, causing trade-offs between motion fidelity and geometry accuracy, and requiring substantial optimization time to reconcile the models. In this paper, we introduce \textbf{P}ixel-\textbf{L}evel \textbf{A}lignment for text-driven \textbf{4D} Gaussian splatting (PLA4D) to resolve this motion-geometry conflict. PLA4D provides an anchor reference, i.e., text-generated video, to align the rendering process conditioned by different DMs in pixel space. For static alignment, our approach introduces a focal alignment method and Gaussian-Mesh contrastive learning to iteratively adjust focal lengths and provide explicit geometric priors at each timestep. At the dynamic level, a motion alignment technique and T-MV refinement method are employed to enforce both pose alignment and motion continuity across unknown viewpoints, ensuring intrinsic geometric consistency across views. With such pixel-level multi-DM alignment, our PLA4D framework is able to generate 4D objects with superior geometric, motion, and semantic consistency. Fully implemented with open-source tools, PLA4D offers an efficient and accessible solution for high-quality 4D digital content creation with significantly reduced generation time.
HOLMES: to Detect Adversarial Examples with Multiple Detectors
Authors: Jing Wen
Date: 2024-05-30 00:00:00

Deep neural networks (DNNs) can easily be cheated by some imperceptible but purposeful noise added to images, and erroneously classify them. Previous defensive work mostly focused on retraining the models or detecting the noise, but has either shown limited success rates or been attacked by new adversarial examples. Instead of focusing on adversarial images or the interior of DNN models, we observed that adversarial examples generated by different algorithms can be identified based on the output of DNNs (logits). Logit can serve as an exterior feature to train detectors. Then, we propose HOLMES (Hierarchically Organized Light-weight Multiple dEtector System) to reinforce DNNs by detecting potential adversarial examples to minimize the threats they may bring in practical. HOLMES is able to distinguish \textit{unseen} adversarial examples from multiple attacks with high accuracy and low false positive rates than single detector systems even in an adaptive model. To ensure the diversity and randomness of detectors in HOLMES, we use two methods: training dedicated detectors for each label and training detectors with top-k logits. Our effective and inexpensive strategies neither modify original DNN models nor require its internal parameters. HOLMES is not only compatible with all kinds of learning models (even only with external APIs), but also complementary to other defenses to achieve higher detection rates (may also fully protect the system against various adversarial examples).
Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine
Authors: Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
Date: 2024-05-30 00:00:00

Learning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities within the same model. Thus, the demand for multimodal machine learning models has sharply risen for modalities that go beyond vision and language, such as sequences, graphs, time series, or tabular data. While there are many available multimodal fusion and alignment approaches, most of them require end-to-end training, scale quadratically with the number of modalities, cannot handle cases of high modality imbalance in the training set, or are highly topology-specific, making them too restrictive for many biomedical learning tasks. This paper presents Multimodal Lego (MM-Lego), a general-purpose fusion framework to turn any set of encoders into a competitive multimodal model with no or minimal fine-tuning. We achieve this by introducing a wrapper for any unimodal encoder that enforces shape consistency between modality representations. It harmonises these representations by learning features in the frequency domain to enable model merging with little signal interference. We show that MM-Lego 1) can be used as a model merging method which achieves competitive performance with end-to-end fusion models without any fine-tuning, 2) can operate on any unimodal encoder, and 3) is a model fusion method that, with minimal fine-tuning, surpasses all benchmarks in five out of seven datasets.
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf
Authors: Xuanfa Jin, Ziyan Wang, Yali Du, Meng Fang, Haifeng Zhang, Jun Wang
Date: 2024-05-30 00:00:00
Tasks: Reinforcement Learning (RL)

Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players' utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework. The project page of our paper: $\href{https://one-night-ultimate-werewolf.github.io}{one-night-ultimate-werewolf.github.io}$.
Learning Latent Graph Structures and their Uncertainty
Authors: Alessandro Manenti, Daniele Zambon, Cesare Alippi
Date: 2024-05-30 00:00:00
Tasks: Graph structure learning, Inductive Bias, Prediction

Within a prediction task, Graph Neural Networks (GNNs) use relational information as an inductive bias to enhance the model's accuracy. As task-relevant relations might be unknown, graph structure learning approaches have been proposed to learn them while solving the downstream prediction task. In this paper, we demonstrate that minimization of a point-prediction loss function, e.g., the mean absolute error, does not guarantee proper learning of the latent relational information and its associated uncertainty. Conversely, we prove that a suitable loss function on the stochastic model outputs simultaneously grants (i) the unknown adjacency matrix latent distribution and (ii) optimal performance on the prediction task. Finally, we propose a sampling-based method that solves this joint learning task. Empirical results validate our theoretical claims and demonstrate the effectiveness of the proposed approach.