pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Rust types for modular specification and verification
Authors: Vytautas Astrauskas, Peter Müller, Federico Poli, Alexander J. Summers
Date: 2019-10-10 00:00:00
Proceeding: OOPSLA 2019 10
Tasks: Formal Logic

Rust's type system ensures memory safety: well-typed Rust programs are guaranteed to not exhibit problems such as dangling pointers, data races, and unexpected side effects through aliased references. Ensuring correctness properties beyond memory safety, for instance, the guaranteed absence of assertion failures or more-general functional correctness, requires static program verification. For traditional system programming languages, formal verification is notoriously difficult and requires complex specifications and logics to reason about pointers, aliasing, and side effects on mutable state. This complexity is a major obstacle to the more-widespread verification of system software.

In this paper, we present a novel verification technique that leverages Rust's type system to greatly simplify the specification and verification of system software written in Rust. We analyse information from the Rust compiler and synthesise a corresponding core proof for the program in a flavour of separation logic tailored to automation. To verify correctness properties beyond memory safety, users can annotate Rust programs with specifications at the abstraction level of Rust expressions; our technique weaves them into the core proof to verify modularly whether these specifications hold. Crucially, our proofs are constructed and checked automatically without exposing the underlying formal logic, allowing users to work exclusively at the level of abstraction of the programming language. As such, our work enables a new kind of verification tool, with the potential to impact a wide audience and allow the Rust community to benefit from state-of-the-art verification techniques. We have implemented our techniques for a subset of Rust; our evaluation on several thousand functions from widely-used Rust crates demonstrates its effectiveness.
How do programmers use unsafe Rust?
Authors: Vytautas Astrauskas, Christoph Matheja, Federico Poli, Peter Müller, Alexander J. Summers
Date: 2020-11-13 00:00:00
Proceeding: OOPSLA 2020 11

Rust’s ownership type system enforces a strict discipline on how memory locations are accessed and shared. This discipline allows the compiler to statically prevent memory errors, data races, inadvertent side effects through aliasing, and other errors that frequently occur in conventional imperative programs. However, the restrictions imposed by Rust’s type system make it difficult or impossible to implement certain designs, such as data structures that require aliasing (e.g. doubly-linked lists and shared caches). To work around this limitation, Rust allows code blocks to be declared as unsafe and thereby exempted from certain restrictions of the type system, for instance, to manipulate C-style raw pointers. Ensuring the safety of unsafe code is the responsibility of the programmer. However, an important assumption of the Rust language, which we dub the Rust hypothesis, is that programmers use Rust by following three main principles: use unsafe code sparingly, make it easy to review, and hide it behind a safe abstraction such that client code can be written in safe Rust.

Understanding how Rust programmers use unsafe code and, in particular, whether the Rust hypothesis holds is essential for Rust developers and testers, language and library designers, as well as tool developers. This paper studies empirically how unsafe code is used in practice by analysing a large corpus of Rust projects to assess the validity of the Rust hypothesis and to classify the purpose of unsafe code. We identify queries that can be answered by automatically inspecting the program’s source code, its intermediate representation MIR, as well as type information provided by the Rust compiler; we complement the results by manual code inspection. Our study supports the Rust hypothesis partially: While most unsafe code is simple and well-encapsulated, unsafe features are used extensively, especially for interoperability with other languages.
The Prusti project: Formal verification for Rust
Authors: Vytautas Astrauskas, Aurel Bílý, Jonáš Fiala, Zachary Grannan, Christoph Matheja, Peter Müller, Federico Poli, Alexander J. Summers
Date: 2022-05-20 00:00:00
Proceeding: NFM 2022 5

Rust is a modern systems programming language designed to offer both performance and static safety. A key distinguishing feature is a strong type system, which enforces by default that memory is either shared or mutable, but never both. This guarantee is used to prevent common pitfalls such as memory errors and data races. It can also be used to greatly simplify formal verification, as we demonstrated by developing the Prusti verifier, which can verify rich correctness properties of Rust programs with a very modest annotation overhead. In this paper, we provide an overview of the Prusti project. We outline its main design goals, illustrate examples of its use, and discuss important outcomes from the perspectives of a user, a verification expert, and a tool developer.
An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular Data
Authors: Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo, Giuseppe Pirlo, Marco Di Ciano
Date: 2024-05-15 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2024 5
Tasks: Classification, feature selection, Multimodal Deep Learning, regression

Deep learning (DL) has been demonstrated to be a valuable tool for analyzing signals such as sounds and images, thanks to its capabilities of automatically extracting relevant patterns as well as its end-to-end training properties. When applied to tabular structured data, DL has exhibited some performance limitations compared to shallow learning techniques. This work presents a novel technique for tabular data called adaptive multiscale attention deep neural network architecture (also named excited attention). By exploiting parallel multilevel feature weighting, the adaptive multiscale attention can successfully learn the feature attention and thus achieve high levels of F1-score on seven different classification tasks (on small, medium, large, and very large datasets) and low mean absolute errors on four regression tasks of different size. In addition, adaptive multiscale attention provides four levels of explainability (i.e., comprehension of its learning process and therefore of its outcomes): 1) calculates attention weights to determine which layers are most important for given classes; 2) shows each feature’s attention across all instances; 3) understands learned feature attention for each class to explore feature attention and behavior for specific classes; and 4) finds nonlinear correlations between co-behaving features to reduce dataset dimensionality and improve interpretability. These interpretability levels, in turn, allow for employing adaptive multiscale attention as a useful tool for feature ranking and feature selection.
Seasonality and susceptibility from measles time series
Authors: Niket Thakkar, Sonia Jindal, Katherine Rosenfeld
Date: 2024-05-15 00:00:00
Tasks: Time Series

This paper develops mathematical tools to estimate seasonal changes in measles transmission rates and corresponding variation in population susceptibility. The tools are designed to leverage times series of cases in the absence of demographic data. In particular, we focus on publicly available suspected case reports from the World Health Organization (WHO), which routinely publishes country-level, monthly aggregated time series. With that as input, we show that measles epidemiologies can be characterized efficiently at global-scale, and we use our estimates to recommend context-specific, future supplementary immunization times. Throughout the paper, comparisons with more data-informed models illustrate that the approach captures the essential dynamics, and broadly speaking, the tools we describe represent a scalable intermediate between conventional empirical approaches and more intricate disease models.
Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines
Authors: Matteo Varotto, Florian Heinrichs, Timo Schuerg, Stefano Tomasin, Stefan Valentin
Date: 2024-05-07 00:00:00
Tasks: Binary Classification, Classification

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.
IoTvulCode: AI-enabled vulnerability detection in software products designed for IoT applications
Authors: Guru Prasad Bhandari, Gebremariam Assres, Nikola Gavric, Andrii Shalaginov, Tor-Morten Grønli
Date: 2024-05-09 00:00:00
Proceeding: International Journal of Information Security 2024 5
Tasks: Vulnerability Detection

The proliferation of the Internet of Things (IoT) paradigm has ushered in a new era of connectivity and convenience. Consequently, rapid IoT expansion has introduced unprecedented security challenges , among which source code vulnerabilities present a significant risk. Recently, machine learning (ML) has been increasingly used to detect source code vulnerabilities. However, there has been a lack of attention to IoT-specific frameworks regarding both tools and datasets. This paper addresses potential source code vulnerabilities in some of the most commonly used IoT frameworks. Hence, we introduce IoTvulCode - a novel framework consisting of a dataset-generating tool and ML-enabled methods for detecting source code vulnerabilities and weaknesses as well as the initial release of an IoT vulnerability dataset. Our framework contributes to improving the existing coding practices, leading to a more secure IoT infrastructure. Additionally, IoTvulCode provides a solid basis for the IoT research community to further explore the topic.
Computer aided diagnosis system for Alzheimers disease using principal component analysis and machine learning based approaches
Authors: Lilia Lazli
Date: 2024-04-15 00:00:00

Alzheimers disease (AD) is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning based approaches are popular and well motivated models for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for accurate diagnosis of AD. In this paper, we investigate the performance of these techniques for AD detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the artificial neural network and support vector machines as classifiers, and principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches.
Neighborhood Homophily-Guided Graph Convolutional Network
Authors: Shengbo Gong, Jiajun Zhou, Chenxuan Xie, Qi Xuan
Date: 2023-10-21 00:00:00
Proceeding: CIKM 2023 10
Tasks: Node Classification

Graph neural networks (GNNs) have been proved powerful in
graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical
GNNs. To solve the universality problem, many studies deepen
networks or concatenate intermediate representations, which does
not inherently change neighbor aggregation and introduces noise.
Recent studies propose new metrics to characterize the homophily,
but rarely consider the correlation of the proposed metrics and
models. In this paper, we first design a new metric, Neighborhood
Homophily (NH), to measure the label complexity or purity in
node neighborhoods. Furthermore, we incorporate the metric into
the classical graph convolutional network (GCN) architecture and
propose Neighborhood Homophily-based Graph Convolutional
Network (NHGCN). In this framework, neighbors are grouped by
estimated NH values and aggregated from different channels, and
the resulting node predictions are then used in turn to estimate
and update NH values. The two processes of metric estimation and
model inference are alternately optimized to achieve better node
classification. NHGCN achieves top overall performance on both
homophilous and heterophilous benchmarks, with an improvement
of up to 7.4% compared to the current SOTA methods
Mask-based Invisible Backdoor Attacks on Object Detection
Authors: Jeongjin Shin
Date: 2024-03-20 00:00:00
Tasks: Autonomous Driving, Backdoor Attack, image-classification, Object Detection

Deep learning models have achieved unprecedented performance in the domain of object detection, resulting in breakthroughs in areas such as autonomous driving and security. However, deep learning models are vulnerable to backdoor attacks. These attacks prompt models to behave similarly to standard models without a trigger; however, they act maliciously upon detecting a predefined trigger. Despite extensive research on backdoor attacks in image classification, their application to object detection remains relatively underexplored. Given the widespread application of object detection in critical real-world scenarios, the sensitivity and potential impact of these vulnerabilities cannot be overstated. In this study, we propose an effective invisible backdoor attack on object detection utilizing a mask-based approach. Three distinct attack scenarios were explored for object detection: object disappearance, object misclassification, and object generation attack. Through extensive experiments, we comprehensively examined the effectiveness of these attacks and tested certain defense methods to determine effective countermeasures. Code will be available at https://github.com/jeongjin0/invisible-backdoor-object-detection
ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection
Authors: Jiayi Wang, Yi-An Mao, Xiaoyu Ma, Sicen Guo, Yuting Shao, Xiao Lv, Wenting Han, Mark Christopher, Linda M. Zangwill, Yanlong Bi, Rui Fan
Date: 2024-04-15 00:00:00
Tasks: Head Detection, Image Segmentation, Semantic Segmentation

Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic segmentation methods using convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks specifically trained for ONH detection. Therefore, in this article, we make contributions from three key aspects: network design, the publication of a dataset, and the establishment of a comprehensive benchmark. Our newly developed ONH detection network, referred to as ODFormer, is based upon the Swin Transformer architecture and incorporates two novel components: a multi-scale context aggregator and a lightweight bidirectional feature recalibrator. Our published large-scale dataset, known as TongjiU-DROD, provides multi-resolution fundus images for each participant, captured using two distinct types of cameras. Our established benchmark involves three datasets: DRIONS-DB, DRISHTI-GS1, and TongjiU-DROD, created by researchers from different countries and containing fundus images captured from participants of diverse races and ages. Extensive experimental results demonstrate that our proposed ODFormer outperforms other state-of-the-art (SoTA) networks in terms of performance and generalizability. Our dataset and source code are publicly available at mias.group/ODFormer.
An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study
Authors: Vittorio Rampa, Federica Fieramosca, Stefano Savazzi, Michele D'Amico
Date: 2024-05-02 00:00:00
Tasks: Integrated sensing and communication

Device-Free Localization (DFL) employs passive radio techniques capable to detect and locate people without imposing them to wear any electronic device. By exploiting the Integrated Sensing and Communication paradigm, DFL networks employ Radio Frequency (RF) nodes to measure the excess attenuation introduced by the subjects (i.e., human bodies) moving inside the monitored area, and to estimate their positions and movements. Physical, statistical, and ElectroMagnetic (EM) models have been proposed in the literature to estimate the body positions according to the RF signals collected by the nodes. These body models usually employ a single-antenna processing for localization purposes. However, the availability of low-cost multi-antenna devices such as those used for WLAN (Wireless Local Area Network) applications and the timely development of array-based body models, allow us to employ array-based processing techniques in DFL networks. By exploiting a suitable array-capable EM body model, this paper proposes an array-based framework to improve people sensing and localization. In particular, some simulations are proposed and discussed to compare the model results in both single- and multi-antenna scenarios. The proposed framework paves the way for a wider use of multi-antenna devices (e.g., those employed in current IEEE 802.11ac/ax/be and forthcoming IEEE 802.11be networks) and novel beamforming algorithms for DFL scenarios.
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey
Authors: Chen Shao, Elias Giacoumidis, Syed Moktacim Billah, Shi Li, Jialei Li, Prashasti Sahu, Andre Richter, Tobias Kaefer, Michael Faerber
Date: 2024-05-02 00:00:00
Tasks: Fault Detection, Survey, Time Series, Traffic Prediction

In recent years, extensive research has been conducted to explore the utilization of machine learning algorithms in various direct-detected and self-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, machine learning demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this extensive survey, we outline the application of machine learning techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. Notably, we introduce a novel taxonomy for time-series methods employed in machine learning signal processing, providing a structured classification framework. Our taxonomy categorizes current time series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of machine learning approaches in short-reach optical communication systems by addressing complexity concerns.
KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
Authors: Christodoulos Kechris, Jonathan Dan, Jose Miranda, David Atienza
Date: 2024-05-02 00:00:00
Tasks: Data Augmentation, Deep Learning, Heart rate estimation, Photoplethysmography (PPG), Photoplethysmography (PPG) heart rate estimation

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.
Using In-Service Train Vibration for Detecting Railway Maintenance Needs
Authors: Irene Alisjahbana
Date: 2024-05-03 00:00:00
Tasks: Binary Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The need for the maintenance of railway track systems have been increasing. Traditional methods that are currently being used are either inaccurate, labor and time intensive, or does not enable continuous monitoring of the system. As a result, in-service train vibrations have been shown to be a cheaper alternative for monitoring of railway track systems. In this paper, a method is proposed to detect different maintenance needs of railway track systems using a single pass of train direction. The DR-Train dataset that is publicly available was used. Results show that by using a simple classifier such as the k-nearest neighbor (k-NN) algorithm, the signal energy features of the acceleration data can achieve 76\% accuracy on two types of maintenance needs, tamping and surfacing. The results show that the transverse direction is able to more accurately detect maintenance needs, and triaxial accelerometer can give further information on the maintenance needs. Furthermore, this paper demonstrates the use of multi-label classification to detect multiple types of maintenance needs simultaneously. The results show multi-label classification performs only slightly worse than the simple binary classification (72\% accuracy) and that this can be a simple method that can easily be deployed in areas that have a history of many maintenance issues.