pwc-archive/papers-with-abstracts huggingface dataset:
Automatically Generating Narrative-Style Radiology Reports from Volumetric CT Images; a Proof of Concept
Authors: Marijn Borghouts
Date: 2024-06-18 00:00:00
Tasks: Descriptive

The world faces a shortage of radiologists, leading to longer treatment times and increased stress, negatively impacting patient safety and workforce morale. Integrating artificial intelligence to interpret radiographic images and generate descriptive reports offers a promising solution. However, limited research exists on generating natural language descriptions for volumetric medical images. This study introduces a deep learning-based proof of concept model to accurately identify abnormalities in volumetric CT data and generate narrative-style reports. Various encoder-decoder models were assessed for their efficacy in clinically relevant and surrogate tasks. Clinically relevant tasks involved identifying and describing pulmonary nodules and pleural effusions, while surrogate tasks involved recognizing and describing artificial abnormalities such as mirroring, rotation, and lung lobe occlusion. The results show high accuracy in detecting combinations of artificial abnormalities, with the best model achieving a classification accuracy of 0.97 on an independent dataset with a homogeneously distributed 11-class problem. Furthermore, the best model consistently generated coherent radiology reports in natural language, with a next-word prediction accuracy of 0.84. Additionally, 65% of these reports were factually accurate regarding the identified artificial abnormalities. Unfortunately, these models did not replicate this success for clinically relevant tasks. Overall, this study provides a working proof of concept model for a challenge yet to be fully addressed by the scientific community. Given the success on surrogate tasks, the leap to clinically relevant tasks seems feasible. Acquiring a significantly larger high-quality dataset appears to be the most promising path forward, alongside more computational resources for end-to-end model training.
Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization
Authors: Christoph Schranz, Sebastian Mayr, Severin Bernhart, Christina Halmich
Date: 2024-04-05 00:00:00
Proceeding: EURASIP Journal of Advanced Signal Processing 2024 4
Tasks: Dynamic Time Warping, Time Series

Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.
Quantifying Urban Safety Perception on Street View Images
Authors: Felipe Moreno-Vera, Braham Lavi, Jorge Poco
Date: 2021-12-22 00:00:00
Proceeding: Wi-IAT 2021 12

In the last 40 years, Urban perception has become an important research area covering several fields, such as criminology, psychology, urban planning, Broken windows theory. It aims to analyze and interpret the behavior of the perception in cities. Urban perception focuses on understanding urban environments based on the characteristics of the city. With the rapidly increasing data availability and highly scalable data collection methods powered by modern web services, new techniques from other domains enabled the exploration of solutions to estimate urban perception (i.e., quantify urban perception autonomously). This work presents a methodology to explore the urban perception analysis task. The work relies on the benchmark dataset, Place Pulse. This dataset is used to perform our classification tasks concerning the category of safety in urban perception problems.
Urban Perception: Can we understand why a street is safe?
Authors: Felipe Moreno-Vera, Braham Lavi, Jorge Poco
Date: 2021-10-15 00:00:00
Proceeding: Advances in Computational Intelligence 2021 10
Tasks: Safety Perception Recognition

The importance of urban perception computing is relatively growing in machine learning, particularly in related areas to Urban Planning and Urban Computing. This field of study focuses on developing systems to analyze and map discriminant characteristics that might directly impact the city’s perception. In other words, it seeks to identify and extract discriminant components to define the behavior of a city’s perception. This work will perform a street-level analysis to understand safety perception based on the “visual components”. As our result, we present our experimental evaluation regarding the influence and impact of those visual components on the safety criteria and further discuss how to properly choose confidence on safe or unsafe measures concerning the perceptional scores on the city street levels analysis.
Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns
Authors: Haoren Zhu, Pengfei Zhao, Wilfred Siu Hung NG, Dik Lun Lee
Date: 2024-06-13 00:00:00
Tasks: Prediction, Video Prediction

Financial assets exhibit complex dependency structures, which are crucial for investors to create diversified portfolios to mitigate risk in volatile financial markets. To explore the financial asset dependencies dynamics, we propose a novel approach that models the dependencies of assets as an Asset Dependency Matrix (ADM) and treats the ADM sequences as image sequences. This allows us to leverage deep learning-based video prediction methods to capture the spatiotemporal dependencies among assets. However, unlike images where neighboring pixels exhibit explicit spatiotemporal dependencies due to the natural continuity of object movements, assets in ADM do not have a natural order. This poses challenges to organizing the relational assets to reveal better the spatiotemporal dependencies among neighboring assets for ADM forecasting. To tackle the challenges, we propose the Asset Dependency Neural Network (ADNN), which employs the Convolutional Long Short-Term Memory (ConvLSTM) network, a highly successful method for video prediction. ADNN can employ static and dynamic transformation functions to optimize the representations of the ADM. Through extensive experiments, we demonstrate that our proposed framework consistently outperforms the baselines in the ADM prediction and downstream application tasks. This research contributes to understanding and predicting asset dependencies, offering valuable insights for financial market participants.
Hacking Encrypted Wireless Power: Cyber-Security of Dynamic Charging
Authors: Hui Wang, Nima Tashakor, Wei Jiang, Wei Liu, C. Q. Jiang, Stefan M. Goetz
Date: 2024-06-17 00:00:00

Recently, energy encryption for wireless power transfer has been developed for energy safety, which is important in public places to suppress unauthorized energy extraction. Most techniques vary the frequency so that unauthorized receivers cannot extract energy because of non-resonance. However, this strategy is unreliable. To stimulate the progress of energy encryption technology and point out security holes, this paper proposes a decryption method for the fundamental principle of encrypted frequency-varying wireless power transfer. The paper uses an auxiliary coil to detect the frequency and a switched-capacitor array to adaptively compensate the receiver for a wide frequency range. The switched-capacitor array contains two capacitors and one semi-conductor switch. One capacitor compensates the receiver all the time while the other's active time during one wireless power transfer cycle is regulated by the switch. Thus, the proposed hacking receiver controls the equivalent capacitance of the compensation and steals energy. Finally, a detailed simulation model and experimental results prove the effectiveness of the attack on frequency-hopping energy encryption. Although any nonnegligible energy extracted would be problematic, we achieved to steal 78% to 84% of the energy an authorized receiver could get. When the frequency changes, the interceptor is coarsely tuned very quickly, which can hack fast frequency-varying encrypted system.
Security Decisions for Cyber-Physical Systems based on Solving Critical Node Problems with Vulnerable Nodes
Authors: Jens Otto, Niels Grüttemeier, Felix Specht
Date: 2024-06-12 00:00:00
Tasks: Decision Making

Cyber-physical production systems consist of highly specialized software and hardware components. Most components and communication protocols are not built according to the Secure by Design principle. Therefore, their resilience to cyberattacks is limited. This limitation can be overcome with common operational pictures generated by security monitoring solutions. These pictures provide information about communication relationships of both attacked and non-attacked devices, and serve as a decision-making basis for security officers in the event of cyberattacks. The objective of these decisions is to isolate a limited number of devices rather than shutting down the entire production system. In this work, we propose and evaluate a concept for finding the devices to isolate. Our approach is based on solving the Critical Node Cut Problem with Vulnerable Vertices (CNP-V) - an NP-hard computational problem originally motivated by isolating vulnerable people in case of a pandemic. To the best of our knowledge, this is the first work on applying CNP-V in context of cybersecurity.
Malicious URL Detection using optimized Hist Gradient Boosting Classifier based on grid search method
Authors: Mohammad Maftoun, Nima Shadkam, Seyedeh Somayeh Salehi Komamardakhi, Zulkefli Mansor, Javad Hassannataj Joloudari
Date: 2024-06-12 00:00:00
Tasks: Imputation

Trusting the accuracy of data inputted on online platforms can be difficult due to the possibility of malicious websites gathering information for unlawful reasons. Analyzing each website individually becomes challenging with the presence of such malicious sites, making it hard to efficiently list all Uniform Resource Locators (URLs) on a blacklist. This ongoing challenge emphasizes the crucial need for strong security measures to safeguard against potential threats and unauthorized data collection. To detect the risk posed by malicious websites, it is proposed to utilize Machine Learning (ML)-based techniques. To this, we used several ML techniques such as Hist Gradient Boosting Classifier (HGBC), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM) for detection of the benign and malicious website dataset. The dataset used contains 1781 records of malicious and benign website data with 13 features. First, we investigated missing value imputation on the dataset. Then, we normalized this data by scaling to a range of zero and one. Next, we utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data since the data set was unbalanced. After that, we applied ML algorithms to the balanced training set. Meanwhile, all algorithms were optimized based on grid search. Finally, the models were evaluated based on accuracy, precision, recall, F1 score, and the Area Under the Curve (AUC) metrics. The results demonstrated that the HGBC classifier has the best performance in terms of the mentioned metrics compared to the other classifiers.
I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectors
Authors: Zijin Lin, Yue Zhao, Kai Chen, Jinwen He
Date: 2024-06-12 00:00:00
Tasks: object-detection, Object Detection

Deep neural networks (DNNs) have revolutionized the field of computer vision like object detection with their unparalleled performance. However, existing research has shown that DNNs are vulnerable to adversarial attacks. In the physical world, an adversary could exploit adversarial patches to implement a Hiding Attack (HA) which patches the target object to make it disappear from the detector, and an Appearing Attack (AA) which fools the detector into misclassifying the patch as a specific object. Recently, many defense methods for detectors have been proposed to mitigate the potential threats of adversarial patches. However, such methods still have limitations in generalization, robustness and efficiency. Most defenses are only effective against the HA, leaving the detector vulnerable to the AA. In this paper, we propose \textit{NutNet}, an innovative model for detecting adversarial patches, with high generalization, robustness and efficiency. With experiments for six detectors including YOLOv2-v4, SSD, Faster RCNN and DETR on both digital and physical domains, the results show that our proposed method can effectively defend against both the HA and AA, with only 0.4\% sacrifice of the clean performance. We compare NutNet with four baseline defense methods for detectors, and our method exhibits an average defense performance that is over 2.4 times and 4.7 times higher than existing approaches for HA and AA, respectively. In addition, NutNet only increases the inference time by 8\%, which can meet the real-time requirements of the detection systems. Demos of NutNet are available at: \url{https://sites.google.com/view/nutnet}.
Watermarking Language Models with Error Correcting Codes
Authors: Patrick Chao, Yan Sun, Edgar Dobriban, Hamed Hassani
Date: 2024-06-12 00:00:00

Recent progress in large language models enables the creation of realistic machine-generated content. Watermarking is a promising approach to distinguish machine-generated text from human text, embedding statistical signals in the output that are ideally undetectable to humans. We propose a watermarking framework that encodes such signals through an error correcting code. Our method, termed robust binary code (RBC) watermark, introduces no noticeable degradation in quality. We evaluate our watermark on base and instruction fine-tuned models and find that our watermark is robust to edits, deletions, and translations. We provide an information-theoretic perspective on watermarking, a powerful statistical test for detection and for generating $p$-values, and theoretical guarantees. Our empirical findings suggest our watermark is fast, powerful, and robust, comparing favorably to the state-of-the-art.
Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
Authors: Yu-Hsiang Huang, YuChe Tsai, Hsiang Hsiao, Hong-Yi Lin, Shou-De Lin
Date: 2024-06-12 00:00:00

This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses a surrogate model to mimic the victim model's behavior, allowing the attacker to infer sensitive information from text embeddings without direct access. Our experiments across various embedding models and a clinical dataset demonstrate that our transfer attack significantly outperforms traditional methods, revealing the potential privacy vulnerabilities in embedding technologies and emphasizing the need for enhanced security measures.
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs
Authors: Joseph Spracklen, Raveen Wijewickrama, A H M Nazmus Sakib, Anindya Maiti, Bimal Viswanath, Murtuza Jadliwala
Date: 2024-06-12 00:00:00
Tasks: Code Generation, Hallucination, RAG

The reliance of popular programming languages such as Python and JavaScript on centralized package repositories and open-source software, combined with the emergence of code-generating Large Language Models (LLMs), has created a new type of threat to the software supply chain: package hallucinations. These hallucinations, which arise from fact-conflicting errors when generating code using LLMs, represent a novel form of package confusion attack that poses a critical threat to the integrity of the software supply chain. This paper conducts a rigorous and comprehensive evaluation of package hallucinations across different programming languages, settings, and parameters, exploring how a diverse set of models and configurations affect the likelihood of generating erroneous package recommendations and identifying the root causes of this phenomenon. Using 16 popular LLMs for code generation and two unique prompt datasets, we generate 576,000 code samples in two programming languages that we analyze for package hallucinations. Our findings reveal that that the average percentage of hallucinated packages is at least 5.2% for commercial models and 21.7% for open-source models, including a staggering 205,474 unique examples of hallucinated package names, further underscoring the severity and pervasiveness of this threat. To overcome this problem, we implement several hallucination mitigation strategies and show that they are able to significantly reduce the number of package hallucinations while maintaining code quality. Our experiments and findings highlight package hallucinations as a persistent and systemic phenomenon while using state-of-the-art LLMs for code generation, and a significant challenge which deserves the research community's urgent attention.
Knowledge Return Oriented Prompting (KROP)
Authors: Jason Martin, Kenneth Yeung
Date: 2024-06-11 00:00:00

Many Large Language Models (LLMs) and LLM-powered apps deployed today use some form of prompt filter or alignment to protect their integrity. However, these measures aren't foolproof. This paper introduces KROP, a prompt injection technique capable of obfuscating prompt injection attacks, rendering them virtually undetectable to most of these security measures.
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis
Authors: Matteo Esposito, Francesco Palagiano, Valentina Lenarduzzi, Davide Taibi
Date: 2024-06-11 00:00:00
Tasks: Hallucination, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Context. Risk analysis assesses potential risks in specific scenarios. Risk analysis principles are context-less; the same methodology can be applied to a risk connected to health and information technology security. Risk analysis requires a vast knowledge of national and international regulations and standards and is time and effort-intensive. A large language model can quickly summarize information in less time than a human and can be fine-tuned to specific tasks. Aim. Our empirical study aims to investigate the effectiveness of Retrieval-Augmented Generation and fine-tuned LLM in risk analysis. To our knowledge, no prior study has explored its capabilities in risk analysis. Method. We manually curated 193 unique scenarios leading to 1283 representative samples from over 50 mission-critical analyses archived by the industrial context team in the last five years. We compared the base GPT-3.5 and GPT-4 models versus their Retrieval-Augmented Generation and fine-tuned counterparts. We employ two human experts as competitors of the models and three other human experts to review the models and the former human experts' analysis. The reviewers analyzed 5,000 scenario analyses. Results and Conclusions. Human experts demonstrated higher accuracy, but LLMs are quicker and more actionable. Moreover, our findings show that RAG-assisted LLMs have the lowest hallucination rates, effectively uncovering hidden risks and complementing human expertise. Thus, the choice of model depends on specific needs, with FTMs for accuracy, RAG for hidden risks discovery, and base models for comprehensiveness and actionability. Therefore, experts can leverage LLMs as an effective complementing companion in risk analysis within a condensed timeframe. They can also save costs by averting unnecessary expenses associated with implementing unwarranted countermeasures.
Gender Representation in TV and Radio: Automatic Information Extraction methods versus Manual Analyses
Authors: David Doukhan, Lena Dodson, Manon Conan, Valentin Pelloin, Aurélien Clamouse, Mélina Lepape, Géraldine Van Hille, Cécile Méadel, Marlène Coulomb-Gully
Date: 2024-06-14 00:00:00

This study investigates the relationship between automatic information extraction descriptors and manual analyses to describe gender representation disparities in TV and Radio. Automatic descriptors, including speech time, facial categorization and speech transcriptions are compared with channel reports on a vast 32,000-hour corpus of French broadcasts from 2023. Findings reveal systemic gender imbalances, with women underrepresented compared to men across all descriptors. Notably, manual channel reports show higher women's presence than automatic estimates and references to women are lower than their speech time. Descriptors share common dynamics during high and low audiences, war coverage, or private versus public channels. While women are more visible than audible in French TV, this trend is inverted in news with unseen journalists depicting male protagonists. A statistical test shows 3 main effects influencing references to women: program category, channel and speaker gender.
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning
Authors: Selvia Nafaa, Hafsa Essam, Karim Ashour, Doaa Emad, Rana Mohamed, Mohammed Elhenawy, Huthaifa I. Ashqar, Abdallah A. Hassan, Taqwa I. Alhadidi
Date: 2024-06-11 00:00:00
Tasks: object-detection, Object Detection, Transfer Learning

Roadway signs detection and recognition is an essential element in the Advanced Driving Assistant Systems (ADAS). Several artificial intelligence methods have been used widely among of them YOLOv5 and YOLOv8. In this paper, we used a modified YOLOv5 and YOLOv8 to detect and classify different roadway signs under different illumination conditions. Experimental results indicated that for the YOLOv8 model, varying the number of epochs and batch size yields consistent MAP50 scores, ranging from 94.6% to 97.1% on the testing set. The YOLOv5 model demonstrates competitive performance, with MAP50 scores ranging from 92.4% to 96.9%. These results suggest that both models perform well across different training setups, with YOLOv8 generally achieving slightly higher MAP50 scores. These findings suggest that both models can perform well under different training setups, offering valuable insights for practitioners seeking reliable and adaptable solutions in object detection applications.