pwc-archive/papers-with-abstracts huggingface dataset:
From Explicit Rules to Implicit Reasoning in an Interpretable Violence Monitoring System
Authors: Wen-Dong Jiang, Chih-Yung Chang, Ssu-Chi Kuai, Diptendu Sinha Roy
Date: 2024-10-29 00:00:00
Tasks: Binary Classification

Recently, research based on pre-trained models has demonstrated outstanding performance in violence surveillance tasks. However, most of them were black-box systems which faced challenges regarding explainability during training and inference processes. An important question is how to incorporate explicit knowledge into these implicit models, thereby designing expertdriven and interpretable violence surveillance systems. This paper proposes a new paradigm for weakly supervised violence monitoring (WSVM) called Rule base Violence Monitoring (RuleVM). The proposed RuleVM uses a dual-branch structure with different designs for images and text. One of the branches is called the implicit branch, which uses only visual features for coarse-grained binary classification. In this branch, image feature extraction is divided into two channels: one responsible for extracting scene frames and the other focusing on extracting actions. The other branch is called the explicit branch, which utilizes language-image alignment to perform fine-grained classification. For the language channel design in the explicit branch, the proposed RuleVM uses the state-of-the-art YOLOWorld model to detect objects in video frames, and association rules are identified through data mining methods as descriptions of the video. Leveraging the dual-branch architecture, RuleVM achieves interpretable coarse-grained and fine-grained violence surveillance. Extensive experiments were conducted on two commonly used benchmarks, and the results show that RuleVM achieved the best performance in both coarse-grained and finegrained monitoring, significantly outperforming existing state-ofthe-art methods. Moreover, interpretability experiments uncovered some interesting rules, such as the observation that as the number of people increases, the risk level of violent behavior also rises.
Automated Vulnerability Detection Using Deep Learning Technique
Authors: Guan-Yan Yang, Yi-Heng Ko, Farn Wang, Kuo-Hui Yeh, Haw-Shiang Chang, Hsueh-Yi Chen
Date: 2024-10-29 00:00:00
Tasks: Deep Learning, Vulnerability Detection

Our work explores the utilization of deep learning, specifically leveraging the CodeBERT model, to enhance code security testing for Python applications by detecting SQL injection vulnerabilities. Unlike traditional security testing methods that may be slow and error-prone, our approach transforms source code into vector representations and trains a Long Short-Term Memory (LSTM) model to identify vulnerable patterns. When compared with existing static application security testing (SAST) tools, our model displays superior performance, achieving higher precision, recall, and F1-score. The study demonstrates that deep learning techniques, particularly with CodeBERT's advanced contextual understanding, can significantly improve vulnerability detection, presenting a scalable methodology applicable to various programming languages and vulnerability types.
Dual Conditional Diffusion Models for Sequential Recommendation
Authors: Hongtao Huang, Chengkai Huang, Tong Yu, Xiaojun Chang, Wen Hu, Julian McAuley, Lina Yao
Date: 2024-10-29 00:00:00
Tasks: Computational Efficiency, Sequential Recommendation

Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). Existing approaches predominantly rely on implicit conditional diffusion models, which compress user behaviors into a single representation during the forward diffusion process. While effective to some extent, this oversimplification often leads to the loss of sequential and contextual information, which is critical for understanding user behavior. Moreover, explicit information, such as user-item interactions or sequential patterns, remains underutilized, despite its potential to directly guide the recommendation process and improve precision. However, combining implicit and explicit information is non-trivial, as it requires dynamically integrating these complementary signals while avoiding noise and irrelevant patterns within user behaviors. To address these challenges, we propose Dual Conditional Diffusion Models for Sequential Recommendation (DCRec), which effectively integrates implicit and explicit information by embedding dual conditions into both the forward and reverse diffusion processes. This allows the model to retain valuable sequential and contextual information while leveraging explicit user-item interactions to guide the recommendation process. Specifically, we introduce the Dual Conditional Diffusion Transformer (DCDT), which employs a cross-attention mechanism to dynamically integrate explicit signals throughout the diffusion stages, ensuring contextual understanding and minimizing the influence of irrelevant patterns. This design enables precise and contextually relevant recommendations. Extensive experiments on public benchmark datasets demonstrate that DCRec significantly outperforms state-of-the-art methods in both accuracy and computational efficiency.
Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding
Authors: Bohan Li, Hankun Wang, Situo Zhang, Yiwei Guo, Kai Yu
Date: 2024-10-29 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems. However, it incurs substantial inference time, particularly due to the challenges in the next-token prediction posed by lengthy sequences of speech tokens. In this work, we introduce VADUSA, one of the first approaches to accelerate auto-regressive TTS through speculative decoding. Our results show that VADUSA not only significantly improves inference speed but also enhances performance by incorporating draft heads to predict future speech content auto-regressively. Furthermore, the inclusion of a tolerance mechanism during sampling accelerates inference without compromising quality. Our approach demonstrates strong generalization across large datasets and various types of speech tokens.
Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applications
Authors: Monica Riedler, Stefan Langer
Date: 2024-10-29 00:00:00
Tasks: Image Retrieval, RAG, Retrieval, Retrieval-augmented Generation, Text Retrieval

Large Language Models (LLMs) have demonstrated impressive capabilities in answering questions, but they lack domain-specific knowledge and are prone to hallucinations. Retrieval Augmented Generation (RAG) is one approach to address these challenges, while multimodal models are emerging as promising AI assistants for processing both text and images. In this paper we describe a series of experiments aimed at determining how to best integrate multimodal models into RAG systems for the industrial domain. The purpose of the experiments is to determine whether including images alongside text from documents within the industrial domain increases RAG performance and to find the optimal configuration for such a multimodal RAG system. Our experiments include two approaches for image processing and retrieval, as well as two LLMs (GPT4-Vision and LLaVA) for answer synthesis. These image processing strategies involve the use of multimodal embeddings and the generation of textual summaries from images. We evaluate our experiments with an LLM-as-a-Judge approach. Our results reveal that multimodal RAG can outperform single-modality RAG settings, although image retrieval poses a greater challenge than text retrieval. Additionally, leveraging textual summaries from images presents a more promising approach compared to the use of multimodal embeddings, providing more opportunities for future advancements.
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution
Authors: Senne Deproost, Denis Steckelmacher, Ann Nowé
Date: 2024-10-29 00:00:00
Tasks: Deep Reinforcement Learning

With Deep Reinforcement Learning (DRL) being increasingly considered for the control of real-world systems, the lack of transparency of the neural network at the core of RL becomes a concern. Programmatic Reinforcement Learning (PRL) is able to to create representations of this black-box in the form of source code, not only increasing the explainability of the controller but also allowing for user adaptations. However, these methods focus on distilling a black-box policy into a program and do so after learning using the Mean Squared Error between produced and wanted behaviour, discarding other elements of the RL algorithm. The distilled policy may therefore perform significantly worse than the black-box learned policy. In this paper, we propose to directly learn a program as the policy of an RL agent. We build on TD3 and use its critics as the basis of the objective function of a genetic algorithm that syntheses the program. Our approach builds the program during training, as opposed to after the fact. This steers the program to actual high rewards, instead of a simple Mean Squared Error. Also, our approach leverages the TD3 critics to achieve high sample-efficiency, as opposed to pure genetic methods that rely on Monte-Carlo evaluations. Our experiments demonstrate the validity, explainability and sample-efficiency of our approach in a simple gridworld environment.
AI Cyber Risk Benchmark: Automated Exploitation Capabilities
Authors: Dan Ristea, Vasilios Mavroudis, Chris Hicks
Date: 2024-10-29 00:00:00
Tasks: Benchmarking, Vulnerability Detection

We introduce a new benchmark for assessing AI models' capabilities and risks in automated software exploitation, focusing on their ability to detect and exploit vulnerabilities in real-world software systems. Using DARPA's AI Cyber Challenge (AIxCC) framework and the Nginx challenge project, a deliberately modified version of the widely used Nginx web server, we evaluate several leading language models, including OpenAI's o1-preview and o1-mini, Anthropic's Claude-3.5-sonnet-20241022 and Claude-3.5-sonnet-20240620, Google DeepMind's Gemini-1.5-pro, and OpenAI's earlier GPT-4o model. Our findings reveal that these models vary significantly in their success rates and efficiency, with o1-preview achieving the highest success rate of 64.71 percent and o1-mini and Claude-3.5-sonnet-20241022 providing cost-effective but less successful alternatives. This benchmark establishes a foundation for systematically evaluating the AI cyber risk posed by automated exploitation tools.
ReMix: Training Generalized Person Re-identification on a Mixture of Data
Authors: Timur Mamedov, Anton Konushin, Vadim Konushin
Date: 2024-10-29 00:00:00
Tasks: Diversity, Generalizable Person Re-identification, Person Re-Identification

Modern person re-identification (Re-ID) methods have a weak generalization ability and experience a major accuracy drop when capturing environments change. This is because existing multi-camera Re-ID datasets are limited in size and diversity, since such data is difficult to obtain. At the same time, enormous volumes of unlabeled single-camera records are available. Such data can be easily collected, and therefore, it is more diverse. Currently, single-camera data is used only for self-supervised pre-training of Re-ID methods. However, the diversity of single-camera data is suppressed by fine-tuning on limited multi-camera data after pre-training. In this paper, we propose ReMix, a generalized Re-ID method jointly trained on a mixture of limited labeled multi-camera and large unlabeled single-camera data. Effective training of our method is achieved through a novel data sampling strategy and new loss functions that are adapted for joint use with both types of data. Experiments show that ReMix has a high generalization ability and outperforms state-of-the-art methods in generalizable person Re-ID. To the best of our knowledge, this is the first work that explores joint training on a mixture of multi-camera and single-camera data in person Re-ID.
LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysis
Authors: Krishna Chandra Roy, Qian Chen
Date: 2024-10-29 00:00:00
Tasks: Anomaly Detection, Cyber Attack Detection, Graph Embedding, Graph Neural Network

Anomaly-based cyber threat detection using deep learning is on a constant growth in popularity for novel cyber-attack detection and forensics. A robust, efficient, and real-time threat detector in a large-scale operational enterprise network requires high accuracy, high fidelity, and a high throughput model to detect malicious activities. Traditional anomaly-based detection models, however, suffer from high computational overhead and low detection accuracy, making them unsuitable for real-time threat detection. In this work, we propose LogSHIELD, a highly effective graph-based anomaly detection model in host data. We present a real-time threat detection approach using frequency-domain analysis of provenance graphs. To demonstrate the significance of graph-based frequency analysis we proposed two approaches. Approach-I uses a Graph Neural Network (GNN) LogGNN and approach-II performs frequency domain analysis on graph node samples for graph embedding. Both approaches use a statistical clustering algorithm for anomaly detection. The proposed models are evaluated using a large host log dataset consisting of 774M benign logs and 375K malware logs. LogSHIELD explores the provenance graph to extract contextual and causal relationships among logs, exposing abnormal activities. It can detect stealthy and sophisticated attacks with over 98% average AUC and F1 scores. It significantly improves throughput, achieves an average detection latency of 0.13 seconds, and outperforms state-of-the-art models in detection time.
Differentiable Inductive Logic Programming for Fraud Detection
Authors: Boris Wolfson, Erman Acar
Date: 2024-10-29 00:00:00
Tasks: Fraud Detection, Inductive logic programming

Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates the applicability of Differentiable Inductive Logic Programming (DILP) as an explainable AI approach to Fraud Detection. Although the scalability of DILP is a well-known issue, we show that with some data curation such as cleaning and adjusting the tabular and numerical data to the expected format of background facts statements, it becomes much more applicable. While in processing it does not provide any significant advantage on rather more traditional methods such as Decision Trees, or more recent ones like Deep Symbolic Classification, it still gives comparable results. We showcase its limitations and points to improve, as well as potential use cases where it can be much more useful compared to traditional methods, such as recursive rule learning.
Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation
Authors: Halil Utku Unlu, Shuaihang Yuan, Congcong Wen, Hao Huang, Anthony Tzes, Yi Fang
Date: 2024-10-29 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Object

We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. Traditional reliance on labeled data has been a limitation for robotic adaptability, which we address by employing a dual-component framework that integrates a GLIP Vision Language Model for initial detection and an InstructionBLIP model for validation. This combination not only refines object and environmental recognition but also fortifies the semantic interpretation, pivotal for navigational decision-making. Our method, rigorously tested in both simulated and real-world settings, exhibits marked improvements in navigation precision and reliability.
Semi-Supervised Self-Learning Enhanced Music Emotion Recognition
Authors: Yifu Sun, xulong Zhang, Monan Zhou, Wei Li
Date: 2024-10-29 00:00:00
Tasks: Emotion Recognition, Music Emotion Recognition, Self-Learning

Music emotion recognition (MER) aims to identify the emotions conveyed in a given musical piece. However, currently, in the field of MER, the available public datasets have limited sample sizes. Recently, segment-based methods for emotion-related tasks have been proposed, which train backbone networks on shorter segments instead of entire audio clips, thereby naturally augmenting training samples without requiring additional resources. Then, the predicted segment-level results are aggregated to obtain the entire song prediction. The most commonly used method is that the segment inherits the label of the clip containing it, but music emotion is not constant during the whole clip. Doing so will introduce label noise and make the training easy to overfit. To handle the noisy label issue, we propose a semi-supervised self-learning (SSSL) method, which can differentiate between samples with correct and incorrect labels in a self-learning manner, thus effectively utilizing the augmented segment-level data. Experiments on three public emotional datasets demonstrate that the proposed method can achieve better or comparable performance.
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
Authors: Gabriele Onorato
Date: 2024-10-29 00:00:00
Tasks: Bayesian Optimization, Computational Efficiency, global-optimization, image-classification, Image Classification

This study investigates the application of Bayesian Optimization (BO) for the hyperparameter tuning of neural networks, specifically targeting the enhancement of Convolutional Neural Networks (CNN) for image classification tasks. Bayesian Optimization is a derivative-free global optimization method suitable for expensive black-box functions with continuous inputs and limited evaluation budgets. The BO algorithm leverages Gaussian Process regression and acquisition functions like Upper Confidence Bound (UCB) and Expected Improvement (EI) to identify optimal configurations effectively. Using the Ax and BOTorch frameworks, this work demonstrates the efficiency of BO in reducing the number of hyperparameter tuning trials while achieving competitive model performance. Experimental outcomes reveal that BO effectively balances exploration and exploitation, converging rapidly towards optimal settings for CNN architectures. This approach underlines the potential of BO in automating neural network tuning, contributing to improved accuracy and computational efficiency in machine learning pipelines.
Building Altruistic and Moral AI Agent with Brain-inspired Affective Empathy Mechanisms
Authors: Feifei Zhao, Hui Feng, Haibo Tong, Zhengqiang Han, Enmeng Lu, Yinqian Sun, Yi Zeng
Date: 2024-10-29 00:00:00
Tasks: AI Agent, Decision Making

As AI closely interacts with human society, it is crucial to ensure that its decision-making is safe, altruistic, and aligned with human ethical and moral values. However, existing research on embedding ethical and moral considerations into AI remains insufficient, and previous external constraints based on principles and rules are inadequate to provide AI with long-term stability and generalization capabilities. In contrast, the intrinsic altruistic motivation based on empathy is more willing, spontaneous, and robust. Therefore, this paper is dedicated to autonomously driving intelligent agents to acquire morally behaviors through human-like affective empathy mechanisms. We draw inspiration from the neural mechanism of human brain's moral intuitive decision-making, and simulate the mirror neuron system to construct a brain-inspired affective empathy-driven altruistic decision-making model. Here, empathy directly impacts dopamine release to form intrinsic altruistic motivation. Based on the principle of moral utilitarianism, we design the moral reward function that integrates intrinsic empathy and extrinsic self-task goals. A comprehensive experimental scenario incorporating empathetic processes, personal objectives, and altruistic goals is developed. The proposed model enables the agent to make consistent moral decisions (prioritizing altruism) by balancing self-interest with the well-being of others. We further introduce inhibitory neurons to regulate different levels of empathy and verify the positive correlation between empathy levels and altruistic preferences, yielding conclusions consistent with findings from psychological behavioral experiments. This work provides a feasible solution for the development of ethical AI by leveraging the intrinsic human-like empathy mechanisms, and contributes to the harmonious coexistence between humans and AI.
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning
Authors: Yinyi Lai, Anbo Cao, Yuan Gao, Jiaqi Shang, Zongyu Li, Jia Guo
Date: 2024-10-29 00:00:00
Tasks: Brain Tumor Classification, Classification, Mamba, Multi-class Classification, Transfer Learning

Early and accurate diagnosis of brain tumors is crucial for improving patient survival rates. However, the detection and classification of brain tumors are challenging due to their diverse types and complex morphological characteristics. This study investigates the application of pre-trained models for brain tumor classification, with a particular focus on deploying the Mamba model. We fine-tuned several mainstream transfer learning models and applied them to the multi-class classification of brain tumors. By comparing these models to those trained from scratch, we demonstrated the significant advantages of transfer learning, especially in the medical imaging field, where annotated data is often limited. Notably, we introduced the Vision Mamba (Vim), a novel network architecture, and applied it for the first time in brain tumor classification, achieving exceptional classification accuracy. Experimental results indicate that the Vim model achieved 100% classification accuracy on an independent test set, emphasizing its potential for tumor classification tasks. These findings underscore the effectiveness of transfer learning in brain tumor classification and reveal that, compared to existing state-of-the-art models, the Vim model is lightweight, efficient, and highly accurate, offering a new perspective for clinical applications. Furthermore, the framework proposed in this study for brain tumor classification, based on transfer learning and the Vision Mamba model, is broadly applicable to other medical imaging classification problems.