pwc-archive/papers-with-abstracts huggingface dataset:
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations
Authors: Shahed Rezaei, Reza Najian Asl, Shirko Faroughi, Mahdi Asgharzadeh, Ali Harandi, Rasoul Najafi Koopas, Gottfried Laschet, Stefanie Reese, Markus Apel
Date: 2024-03-28 00:00:00
Tasks: Operator learning

To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of the finite element method with physics-informed neural networks and concept of neural operators. This approach generalizes and enhances each method, learning the parametric solution for mechanical problems without relying on data from other resources (e.g. other numerical solvers). We propose directly utilizing the available discretized weak form in finite element packages to construct the loss functions algebraically, thereby demonstrating the ability to find solutions even in the presence of sharp discontinuities. Our focus is on micromechanics as an example, where knowledge of deformation and stress fields for a given heterogeneous microstructure is crucial for further design applications. The primary parameter under investigation is the Young's modulus distribution within the heterogeneous solid system. Our investigations reveal that physics-based training yields higher accuracy compared to purely data-driven approaches for unseen microstructures. Additionally, we offer two methods to directly improve the process of obtaining high-resolution solutions, avoiding the need to use basic interpolation techniques. First is based on an autoencoder approach to enhance the efficiency for calculation on high resolution grid point. Next, Fourier-based parametrization is utilized to address complex 2D and 3D problems in micromechanics. The latter idea aims to represent complex microstructures efficiently using Fourier coefficients. Comparisons with other well-known operator learning algorithms, further emphasize the advantages of the newly proposed method.
A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)
Authors: Dehao Yuan, Cornelia Fermüller, Tahseen Rabbani, Furong Huang, Yiannis Aloimonos
Date: 2024-04-02 00:00:00
Tasks: Descriptive

We propose VecKM, a local point cloud geometry encoder that is descriptive and efficient to compute. VecKM leverages a unique approach by vectorizing a kernel mixture to represent the local point cloud. Such representation's descriptiveness is supported by two theorems that validate its ability to reconstruct and preserve the similarity of the local shape. Unlike existing encoders downsampling the local point cloud, VecKM constructs the local geometry encoding using all neighboring points, producing a more descriptive encoding. Moreover, VecKM is efficient to compute and scalable to large point cloud inputs: VecKM reduces the memory cost from $(n^2+nKd)$ to $(nd+np)$; and reduces the major runtime cost from computing $nK$ MLPs to $n$ MLPs, where $n$ is the size of the point cloud, $K$ is the neighborhood size, $d$ is the encoding dimension, and $p$ is a marginal factor. The efficiency is due to VecKM's unique factorizable property that eliminates the need of explicitly grouping points into neighbors. In the normal estimation task, VecKM demonstrates not only 100x faster inference speed but also highest accuracy and strongest robustness. In classification and segmentation tasks, integrating VecKM as a preprocessing module achieves consistently better performance than the PointNet, PointNet++, and point transformer baselines, and runs consistently faster by up to 10 times.
Extracting Manifold Information from Point Clouds
Authors: Patrick Guidotti
Date: 2024-03-30 00:00:00

A kernel based method is proposed for the construction of signature (defining) functions of subsets of $\mathbb{R}^d$. The subsets can range from full dimensional manifolds (open subsets) to point clouds (a finite number of points) and include bounded smooth manifolds of any codimension. The interpolation and analysis of point clouds are the main application. Two extreme cases in terms of regularity are considered, where the data set is interpolated by an analytic surface, at the one extreme, and by a H\"older continuous surface, at the other. The signature function can be computed as a linear combination of translated kernels, the coefficients of which are the solution of a finite dimensional linear problem. Once it is obtained, it can be used to estimate the dimension as well as the normal and the curvatures of the interpolated surface. The method is global and does not require explicit knowledge of local neighborhoods or any other structure present in the data set. It admits a variational formulation with a natural ``regularized'' counterpart, that proves to be useful in dealing with data sets corrupted by numerical error or noise. The underlying analytical structure of the approach is presented in general before it is applied to the case of point clouds.
Machine Unlearning for Traditional Models and Large Language Models: A Short Survey
Authors: Yi Xu
Date: 2024-04-01 00:00:00
Tasks: Machine Unlearning, Survey

With the implementation of personal data privacy regulations, the field of machine learning (ML) faces the challenge of the "right to be forgotten". Machine unlearning has emerged to address this issue, aiming to delete data and reduce its impact on models according to user requests. Despite the widespread interest in machine unlearning, comprehensive surveys on its latest advancements, especially in the field of Large Language Models (LLMs) is lacking. This survey aims to fill this gap by providing an in-depth exploration of machine unlearning, including the definition, classification and evaluation criteria, as well as challenges in different environments and their solutions. Specifically, this paper categorizes and investigates unlearning on both traditional models and LLMs, and proposes methods for evaluating the effectiveness and efficiency of unlearning, and standards for performance measurement. This paper reveals the limitations of current unlearning techniques and emphasizes the importance of a comprehensive unlearning evaluation to avoid arbitrary forgetting. This survey not only summarizes the key concepts of unlearning technology but also points out its prominent issues and feasible directions for future research, providing valuable guidance for scholars in the field.
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories
Authors: Quanyan Zhu
Date: 2024-04-01 00:00:00
Tasks: Meta-Learning

Cyber resilience is a complementary concept to cybersecurity, focusing on the preparation, response, and recovery from cyber threats that are challenging to prevent. Organizations increasingly face such threats in an evolving cyber threat landscape. Understanding and establishing foundations for cyber resilience provide a quantitative and systematic approach to cyber risk assessment, mitigation policy evaluation, and risk-informed defense design. A systems-scientific view toward cyber risks provides holistic and system-level solutions. This chapter starts with a systemic view toward cyber risks and presents the confluence of game theory, control theory, and learning theories, which are three major pillars for the design of cyber resilience mechanisms to counteract increasingly sophisticated and evolving threats in our networks and organizations. Game and control theoretic methods provide a set of modeling frameworks to capture the strategic and dynamic interactions between defenders and attackers. Control and learning frameworks together provide a feedback-driven mechanism that enables autonomous and adaptive responses to threats. Game and learning frameworks offer a data-driven approach to proactively reason about adversarial behaviors and resilient strategies. The confluence of the three lays the theoretical foundations for the analysis and design of cyber resilience. This chapter presents various theoretical paradigms, including dynamic asymmetric games, moving horizon control, conjectural learning, and meta-learning, as recent advances at the intersection. This chapter concludes with future directions and discussions of the role of neurosymbolic learning and the synergy between foundation models and game models in cyber resilience.
Delay-Induced Watermarking for Detection of Replay Attacks in Linear Systems
Authors: Christoforos Somarakis, Raman Goyal, Erfaun Noorani, Shantanu Rane
Date: 2024-04-01 00:00:00

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Authors: Shanglun Feng, Florian Tramèr
Date: 2024-03-30 00:00:00

Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a new risk of privacy backdoors. By tampering with a pretrained model's weights, an attacker can fully compromise the privacy of the finetuning data. We show how to build privacy backdoors for a variety of models, including transformers, which enable an attacker to reconstruct individual finetuning samples, with a guaranteed success! We further show that backdoored models allow for tight privacy attacks on models trained with differential privacy (DP). The common optimistic practice of training DP models with loose privacy guarantees is thus insecure if the model is not trusted. Overall, our work highlights a crucial and overlooked supply chain attack on machine learning privacy.
Information Security and Privacy in the Digital World: Some Selected Topics
Authors: Jaydip Sen, Joceli Mayer, Subhasis Dasgupta, Subrata Nandi, Srinivasan Krishnaswamy, Pinaki Mitra, Mahendra Pratap Singh, Naga Prasanthi Kundeti, Chandra Sekhara Rao MVP, Sudha Sree Chekuri, Seshu Babu Pallapothu, Preethi Nanjundan, Jossy P. George, Abdelhadi El Allahi, Ilham Morino, Salma AIT Oussous, Siham Beloualid, Ahmed Tamtaoui, Abderrahim Bajit
Date: 2024-03-30 00:00:00

In the era of generative artificial intelligence and the Internet of Things, while there is explosive growth in the volume of data and the associated need for processing, analysis, and storage, several new challenges are faced in identifying spurious and fake information and protecting the privacy of sensitive data. This has led to an increasing demand for more robust and resilient schemes for authentication, integrity protection, encryption, non-repudiation, and privacy-preservation of data. The chapters in this book present some of the state-of-the-art research works in the field of cryptography and security in computing and communications.
Security Risks Concerns of Generative AI in the IoT
Authors: Honghui Xu, Yingshu Li, Olusesi Balogun, Shaoen Wu, Yue Wang, Zhipeng Cai
Date: 2024-03-29 00:00:00

In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration. We explore how generative AI drives innovation in IoT and we analyze the potential for data breaches when using generative AI and the misuse of generative AI technologies in IoT ecosystems. These risks not only threaten the privacy and efficiency of IoT systems but also pose broader implications for trust and safety in AI-driven environments. The discussion in this article extends to strategic approaches for mitigating these risks, including the development of robust security protocols, the multi-layered security approaches, and the adoption of AI technological solutions. Through a comprehensive analysis, this article aims to shed light on the critical balance between embracing AI advancements and ensuring stringent security in IoT, providing insights into the future direction of these intertwined technologies.
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks
Authors: Orson Mengara
Date: 2024-03-29 00:00:00
Tasks: Backdoor Attack, Data Poisoning

Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. This exposes deep neural network (DNN) models trained on such data to potential data poisoning attacks. In this type of assault, attackers can train the DNN model using poisoned data, potentially degrading its performance. Another type of data poisoning attack that is extremely relevant to our investigation is label flipping, in which the attacker manipulates the labels for a subset of data. It has been demonstrated that these assaults may drastically reduce system performance, even for attackers with minimal abilities. In this study, we propose a backdoor attack named 'DirtyFlipping', which uses dirty label techniques, "label-on-label", to input triggers (clapping) in the selected data patterns associated with the target class, thereby enabling a stealthy backdoor.
A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors
Authors: Fatama Tuz Johora, Md Shahedul Islam Khan, Esrath Kanon, Mohammad Abu Tareq Rony, Md Zubair, Iqbal H. Sarker
Date: 2024-03-28 00:00:00
Tasks: Data Augmentation

Cyber risk refers to the risk of defacing reputation, monetary losses, or disruption of an organization or individuals, and this situation usually occurs by the unconscious use of cyber systems. The cyber risk is unhurriedly increasing day by day and it is right now a global threat. Developing countries like Bangladesh face major cyber risk challenges. The growing cyber threat worldwide focuses on the need for effective modeling to predict and manage the associated risk. This paper exhibits a Machine Learning(ML) based model for predicting individuals who may be victims of cyber attacks by analyzing socioeconomic factors. We collected the dataset from victims and non-victims of cyberattacks based on socio-demographic features. The study involved the development of a questionnaire to gather data, which was then used to measure the significance of features. Through data augmentation, the dataset was expanded to encompass 3286 entries, setting the stage for our investigation and modeling. Among several ML models with 19, 20, 21, and 26 features, we proposed a novel Pertinent Features Random Forest (RF) model, which achieved maximum accuracy with 20 features (95.95\%) and also demonstrated the association among the selected features using the Apriori algorithm with Confidence (above 80\%) according to the victim. We generated 10 important association rules and presented the framework that is rigorously evaluated on real-world datasets, demonstrating its potential to predict cyberattacks and associated risk factors effectively. Looking ahead, future efforts will be directed toward refining the predictive model's precision and delving into additional risk factors, to fortify the proposed framework's efficacy in navigating the complex terrain of cybersecurity threats.
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Authors: Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramer, Hamed Hassani, Eric Wong
Date: 2024-03-28 00:00:00

Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address. First, there is no clear standard of practice regarding jailbreaking evaluation. Second, existing works compute costs and success rates in incomparable ways. And third, numerous works are not reproducible, as they withhold adversarial prompts, involve closed-source code, or rely on evolving proprietary APIs. To address these challenges, we introduce JailbreakBench, an open-sourced benchmark with the following components: (1) an evolving repository of state-of-the-art adversarial prompts, which we refer to as jailbreak artifacts; (2) a jailbreaking dataset comprising 100 behaviors -- both original and sourced from prior work (Zou et al., 2023; Mazeika et al., 2023, 2024) -- which align with OpenAI's usage policies; (3) a standardized evaluation framework at https://github.com/JailbreakBench/jailbreakbench that includes a clearly defined threat model, system prompts, chat templates, and scoring functions; and (4) a leaderboard at https://jailbreakbench.github.io/ that tracks the performance of attacks and defenses for various LLMs. We have carefully considered the potential ethical implications of releasing this benchmark, and believe that it will be a net positive for the community.
PerOS: Personalized Self-Adapting Operating Systems in the Cloud
Authors: Hongyu Hè
Date: 2024-03-26 00:00:00

Operating systems (OSes) are foundational to computer systems, managing hardware resources and ensuring secure environments for diverse applications. However, despite their enduring importance, the fundamental design objectives of OSes have seen minimal evolution over decades. Traditionally prioritizing aspects like speed, memory efficiency, security, and scalability, these objectives often overlook the crucial aspect of intelligence as well as personalized user experience. The lack of intelligence becomes increasingly critical amid technological revolutions, such as the remarkable advancements in machine learning (ML). Today's personal devices, evolving into intimate companions for users, pose unique challenges for traditional OSes like Linux and iOS, especially with the emergence of specialized hardware featuring heterogeneous components. Furthermore, the rise of large language models (LLMs) in ML has introduced transformative capabilities, reshaping user interactions and software development paradigms. While existing literature predominantly focuses on leveraging ML methods for system optimization or accelerating ML workloads, there is a significant gap in addressing personalized user experiences at the OS level. To tackle this challenge, this work proposes PerOS, a personalized OS ingrained with LLM capabilities. PerOS aims to provide tailored user experiences while safeguarding privacy and personal data through declarative interfaces, self-adaptive kernels, and secure data management in a scalable cloud-centric architecture; therein lies the main research question of this work: How can we develop intelligent, secure, and scalable OSes that deliver personalized experiences to thousands of users?
Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System
Authors: Mark Roantree, Niamh Murphi, Dinh Viet Cuong, Vuong Minh Ngo
Date: 2024-03-28 00:00:00
Tasks: Community Detection

Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency.
Information Cascade Prediction under Public Emergencies: A Survey
Authors: Qi Zhang, Guang Wang, Li Lin, Kaiwen Xia, Shuai Wang
Date: 2024-03-28 00:00:00
Tasks: Prediction, Survey

With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies. However, the involvement of specialist knowledge from various disciplines has resulted in a primarily application-specific focus (e.g., earthquakes, floods, infectious diseases) for information cascade prediction of public emergencies. The lack of a unified prediction framework poses a challenge for classifying intersectional prediction methods across different application fields. This survey paper offers a systematic classification and summary of information cascade modeling, prediction, and application. We aim to help researchers identify cutting-edge research and comprehend models and methods of information cascade prediction under public emergencies. By summarizing open issues and outlining future directions in this field, this paper has the potential to be a valuable resource for researchers conducting further studies on predicting information cascades.
A drug classification pipeline for Medicaid claims using RxNorm
Authors: Nicholas Williams, Kara E. Rudolph
Date: 2024-04-01 00:00:00
Tasks: Classification

Objective: Freely preprocess drug codes recorded in electronic health records and insurance claims to drug classes that may then be used in biomedical research. Materials and Methods: We developed a drug classification pipeline for linking National Drug Codes to the World Health Organization Anatomical Therapeutic Chemical classification. To implement our solution, we created an R package interface to the National Library of Medicine's RxNorm API. Results: Using the classification pipeline, 59.4% of all unique NDC were linked to an ATC, resulting in 95.5% of all claims being successfully linked to a drug classification. We identified 12,004 unique NDC codes that were classified as being an opioid or non-opioid prescription for treating pain. Discussion: Our proposed pipeline performed similarly well to other NDC classification routines using commercial databases. A check of a small, random sample of non-active NDC found the pipeline to be accurate for classifying these codes. Conclusion: The RxNorm NDC classification pipeline is a practical and reliable tool for categorizing drugs in large-scale administrative claims data.