pwc-archive/papers-with-abstracts huggingface dataset:
Counter-Adversarial Learning with Inverse Unscented Kalman Filter
Authors: Himali Singh, Kumar Vijay Mishra, Arpan Chattopadhyay
Date: 2022-10-01 00:00:00
Tasks: State Space Models

In counter-adversarial systems, to infer the strategy of an intelligent adversarial agent, the defender agent needs to cognitively sense the information that the adversary has gathered about the latter. Prior works on the problem employ linear Gaussian state-space models and solve this inverse cognition problem by designing inverse stochastic filters. However, in practice, counter-adversarial systems are generally highly nonlinear. In this paper, we address this scenario by formulating inverse cognition as a nonlinear Gaussian state-space model, wherein the adversary employs an unscented Kalman filter (UKF) to estimate the defender's state with reduced linearization errors. To estimate the adversary's estimate of the defender, we propose and develop an inverse UKF (IUKF) system. We then derive theoretical guarantees for the stochastic stability of IUKF in the mean-squared boundedness sense. Numerical experiments for multiple practical applications show that the estimation error of IUKF converges and closely follows the recursive Cram\'{e}r-Rao lower bound.
Model-X Sequential Testing for Conditional Independence via Testing by Betting
Authors: Shalev Shaer, Gal Maman, Yaniv Romano
Date: 2022-10-01 00:00:00
Tasks: valid

This paper develops a model-free sequential test for conditional independence. The proposed test allows researchers to analyze an incoming i.i.d. data stream with any arbitrary dependency structure, and safely conclude whether a feature is conditionally associated with the response under study. We allow the processing of data points online, as soon as they arrive, and stop data acquisition once significant results are detected, rigorously controlling the type-I error rate. Our test can work with any sophisticated machine learning algorithm to enhance data efficiency to the extent possible. The developed method is inspired by two statistical frameworks. The first is the model-X conditional randomization test, a test for conditional independence that is valid in offline settings where the sample size is fixed in advance. The second is testing by betting, a ``game-theoretic'' approach for sequential hypothesis testing. We conduct synthetic experiments to demonstrate the advantage of our test over out-of-the-box sequential tests that account for the multiplicity of tests in the time horizon, and demonstrate the practicality of our proposal by applying it to real-world tasks.
Failure-informed adaptive sampling for PINNs
Authors: Zhiwei Gao, Liang Yan, Tao Zhou
Date: 2022-10-01 00:00:00

Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. It is noticed, however, the performance of PINNs can vary dramatically with different sampling procedures. For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially for problems with singularities). To overcome this issue, we present in this work an adaptive strategy, termed the failure-informed PINNs (FI-PINNs), which is inspired by the viewpoint of reliability analysis. The key idea is to define an effective failure probability based on the residual, and then, with the aim of placing more samples in the failure region, the FI-PINNs employs a failure-informed enrichment technique to adaptively add new collocation points to the training set, such that the numerical accuracy is dramatically improved. In short, similar as adaptive finite element methods, the proposed FI-PINNs adopts the failure probability as the posterior error indicator to generate new training points. We prove rigorous error bounds of FI-PINNs and illustrate its performance through several problems.
Primal-dual regression approach for Markov decision processes with general state and action space
Authors: Denis Belomestny, John Schoenmakers
Date: 2022-10-01 00:00:00
Tasks: regression

We develop a regression based primal-dual martingale approach for solving finite time horizon MDPs with general state and action space. As a result, our method allows for the construction of tight upper and lower biased approximations of the value functions, and, provides tight approximations to the optimal policy. In particular, we prove tight error bounds for the estimated duality gap featuring polynomial dependence on the time horizon, and sublinear dependence on the cardinality/dimension of the possibly infinite state and action space.From a computational point of view the proposed method is efficient since, in contrast to usual duality-based methods for optimal control problems in the literature, the Monte Carlo procedures here involved do not require nested simulations.
Factorized Fusion Shrinkage for Dynamic Relational Data
Authors: Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick
Date: 2022-09-30 00:00:00
Tasks: Variational Inference

Modern data science applications often involve complex relational data with dynamic structures. An abrupt change in such dynamic relational data is typically observed in systems that undergo regime changes due to interventions. In such a case, we consider a factorized fusion shrinkage model in which all decomposed factors are dynamically shrunk towards group-wise fusion structures, where the shrinkage is obtained by applying global-local shrinkage priors to the successive differences of the row vectors of the factorized matrices. The proposed priors enjoy many favorable properties in comparison and clustering of the estimated dynamic latent factors. Comparing estimated latent factors involves both adjacent and long-term comparisons, with the time range of comparison considered as a variable. Under certain conditions, we demonstrate that the posterior distribution attains the minimax optimal rate up to logarithmic factors. In terms of computation, we present a structured mean-field variational inference framework that balances optimal posterior inference with computational scalability, exploiting both the dependence among components and across time. The framework can accommodate a wide variety of models, including dynamic matrix factorization, latent space models for networks and low-rank tensors. The effectiveness of our methodology is demonstrated through extensive simulations and real-world data analysis.
CodeDSI: Differentiable Code Search
Authors: Usama Nadeem, Noah Ziems, Shaoen Wu
Date: 2022-10-01 00:00:00
Tasks: Code Generation, Code Search, Natural Language Queries, Text Generation

Reimplementing solutions to previously solved software engineering problems is not only inefficient but also introduces inadequate and error-prone code. Many existing methods achieve impressive performance on this issue by using autoregressive text-generation models trained on code. However, these methods are not without their flaws. The generated code from these models can be buggy, lack documentation, and introduce vulnerabilities that may go unnoticed by developers. An alternative to code generation -- neural code search -- is a field of machine learning where a model takes natural language queries as input and, in turn, relevant code samples from a database are returned. Due to the nature of this pre-existing database, code samples can be documented, tested, licensed, and checked for vulnerabilities before being used by developers in production. In this work, we present CodeDSI, an end-to-end unified approach to code search. CodeDSI is trained to directly map natural language queries to their respective code samples, which can be retrieved later. In an effort to improve the performance of code search, we have investigated docid representation strategies, impact of tokenization on docid structure, and dataset sizes on overall code search performance. Our results demonstrate CodeDSI strong performance, exceeding conventional robust baselines by 2-6% across varying dataset sizes.
A forensic analysis of the Google Home: repairing compressed data without error correction
Authors: Hadrien Barral, Georges-Axel Jaloyan, Fabien Thomas-Brans, Matthieu Regnery, Rémi Géraud-Stewart, Thibaut Heckmann, Thomas Souvignet, David Naccache
Date: 2022-09-30 00:00:00
Tasks: CPU

This paper provides a detailed explanation of the steps taken to extract and repair a Google Home's internal data. Starting with reverse engineering the hardware of a commercial off-the-shelf Google Home, internal data is then extracted by desoldering and dumping the flash memory. As error correction is performed by the CPU using an undisclosed method, a new alternative method is shown to repair a corrupted SquashFS filesystem, under the assumption of a single or double bitflip per gzip-compressed fragment. Finally, a new method to handle multiple possible repairs using three-valued logic is presented.
The current state of single-cell proteomics data analysis
Authors: Christophe Vanderaa, Laurent Gatto
Date: 2022-10-03 00:00:00
Tasks: Benchmarking

Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows, standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap and list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses.
SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks
Authors: Yuna Jeong, Eunhui Kim
Date: 2022-06-08 00:00:00
Proceeding: IEEE Access 2022 6
Tasks: General Knowledge, Joint Entity and Relation Extraction, Named Entity Recognition (NER)

Deep learning-based language models (LMs) have transcended the gold standard (human baseline) of SQuAD 1.1 and GLUE benchmarks in April and July 2019, respectively. As of 2022, the top five LMs on the SuperGLUE benchmark leaderboard have exceeded the gold standard. Even people with good general knowledge will struggle to solve problems in specialized fields such as medicine and artificial intelligence. Just as humans learn specialized knowledge through bachelor’s, master’s, and doctoral courses, LMs also require a process to develop the ability to understand domain specific knowledge. Thus, this study proposes SciDeBERTa and SciDeBERTa(CS) as a pre-trained LM (PLM) specialized in the science technology domain. We further pretrain the DeBERTa, which was trained with a general corpus, with the science technology domain corpus. Experiments verified that SciDeBERTa(CS) continually pre-trained in the computer science domain achieved 3.53% and 2.17% higher accuracies than SciBERT and S2ORC-SciBERT, respectively, which are science technology domain specialized PLMs, in the task of recognizing entity names in SciERC dataset. In the JRE task of the SciERC dataset, SciDeBERTa(CS) demonstrated a 6.7% higher performance than baseline SCIIE. In the Genia dataset, SciDeBERTa achieved the best performance compared to S2ORC-SciBERT, SciBERT, BERT, DeBERTa and SciDeBERTa(CS). Furthermore, re-initialization technology and optimizers after Adam were explored during fine-tuning to verify the language understanding of PLMs.
KGP: An R Package with Metadata from the 1000 Genomes Project
Authors: Stephen D. Turner
Date: 2022-10-02 00:00:00

The 1000 Genomes Project provides sequencing data on 3,202 samples from 26 populations spanning five continental regions with no access or use restrictions. The kgp R package provides consistent and comprehensive metadata about samples and populations in the 1000 Genomes Project and other population sequencing data in the International Genome Sample Resource collection. The kgp package is distributed via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/package=kgp. Source code is available on GitHub at https://github.com/stephenturner/kgp. Further documentation is online at https://stephenturner.github.io/kgp/.
TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification
Authors: Talha Ilyas, Zubaer Ibna Mannan, Abbas Khan, Sami Azam, Hyongsuk Kim, Friso De Boer
Date: 2022-03-09 00:00:00
Proceeding: Elsevier Neural Networks 2022 3
Tasks: Cell Segmentation, Classification, Panoptic Segmentation

Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is a challenging task due to a variety of issues, such as color inconsistency that results from the non-uniform manual staining operations, clustering of nuclei, and blurry and overlapping nuclei boundaries. Existing approaches involve segmenting nuclei by drawing their polygon representations or by measuring the distances between nuclei centroids. In contrast, we leverage the fact that morphological features (appearance, shape, and texture) of nuclei in a tissue vary greatly depending upon the tissue type. We exploit this information by extracting tissue specific (TS) features from raw histopathology images using the proposed tissue specific feature distillation (TSFD) backbone. The bi-directional feature pyramid network (BiFPN) within TSFD-Net generates a robust hierarchical feature pyramid utilizing TS features where the interlinked decoders jointly optimize and fuse these features to generate final predictions. We also propose a novel combinational loss function for joint optimization and faster convergence of our proposed network. Extensive ablation studies are performed to validate the effectiveness of each component of TSFD-Net. The proposed network outperforms state-of-the-art networks such as StarDist, Micro-Net, Mask-RCNN, Hover-Net, and CPP-Net on the PanNuke dataset, which contains 19 different tissue types and 5 clinically important tumor classes, achieving 50.4% and 63.77% mean and binary panoptic quality, respectively.
Isoflat: Flat Provider Network Multiplexing and Firewalling in OpenStack Cloud
Authors: Ruipeng Zhang, Mengjun Xie, Li Yang
Date: 2019-07-15 00:00:00
Proceeding: IEEE International Conference on Communications (ICC) 2019 7
Tasks: Cloud Computing

Networking is one of the key enablers of cloud computing and its security is essential for multi-tenant clouds. As a widely used open source solution to cloud computing, OpenStack allows computing resources to connect to the physical network infrastructure through provider networks for performance and reliability considerations. However, OpenStack users are stuck with either VLAN provider networks that are complex to configure and manage or flat networks that are not isolated and have the limitation on interface multiplexing. To address this problem, in this paper, we propose a new mechanism called Isoflat, which extends OpenStack's ability for creating flat provider networks with both configuration simplicity and flexible isolation capability. Our evaluation results show that a provider network with Isoflat can achieve similar network performance as a flat or VLAN provider network. Our results also show that the Isoflat firewall has much less impact on throughput performance than security group.
Powering Hands-on Cybersecurity Practices with Cloud Computing
Authors: Ruipeng Zhang, Chen Xu, Mengjun Xie
Date: 2019-10-31 00:00:00
Proceeding: IEEE International Conference on Network Protocols (ICNP) 2019 10
Tasks: Cloud Computing

Cybersecurity education and training have gained increasing attention in all sectors due to the prevalence and quick evolution of cyberattacks. A variety of platforms and systems have been proposed and developed to accommodate the growing needs of hands-on cybersecurity practice. However, those systems are either lacking sufficient flexibility (e.g., tied to a specific virtual computing service provider, little customization support) or difficult to scale. In this work, we present a cloud-based platform named EZSetup for hands-on cybersecurity practice at scale and our experience of using it in class. EZSetup is customizable and cloud-agnostic. Users can create labs through an intuitive Web interface and deploy them onto one or multiple clouds. We have used NSF funded Chameleon cloud and our private OpenStack cloud to develop, test and deploy EZSetup. We have developed 14 network and security labs using the tool and included six labs in an undergraduate network security course in spring 2019. Our survey results show that students have very positive feedback on using EZSetup and computing clouds for hands-on cybersecurity practice.
ReLF: Scalable Remote Live Forensics for Android
Authors: Ruipeng Zhang, Mengjun Xie, Jiang Bian
Date: 2021-10-20 00:00:00
Proceeding: IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2021 10

The world has witnessed the proliferation of mobile technologies as well as smartphone-related cybercrimes in recent years. However, due to high mobility of smartphones and tablets and transient nature of those attacks, previous forensic approaches become inadequate to retrieve forensic data and respond to cybersecurity incidents in time, especially when the investigation involves a large number of mobile devices. In this paper, we propose ReLF, a remote live forensics system for Android smartphones and tablets. ReLF enables forensic investigators to effectively triage operating Android devices and acquire a wide range of forensic artifacts at scale. Compared to existing Android forensic tools that are publicly available, ReLF provides a much more comprehensive set of collectible artifacts and better OS compatibility. Our evaluation results demonstrate that the ReLF client only introduces minor energy overhead to Android devices and that the ReLF server can well handle a large number of Android devices with increasing workload. We also showcase how ReLF can be used in real-world forensic investigation through case studies.
SPRAT: A Spatially-Explicit Marine Ecosystem Model Based on Population Balance Equations
Authors: Arne N. Johanson, Andreas Oschlies, Wilhelm Hasselbring, Boris Worm
Date: 2022-09-30 00:00:00

To successfully manage marine fisheries using an ecosystem-based approach, long-term predictions of fish stock development considering changing environmental conditions are necessary. Such predictions can be provided by end-to-end ecosystem models, which couple existing physical and biogeochemical ocean models with newly developed spatially-explicit fish stock models. Typically, Individual-Based Models (IBMs) and models based on Advection-Diffusion-Reaction (ADR) equations are employed for the fish stock models. In this paper, we present a novel fish stock model called SPRAT for end-to\hyp{}end ecosystem modeling based on Population Balance Equations (PBEs) that combines the advantages of IBMs and ADR models while avoiding their main drawbacks. SPRAT accomplishes this by describing the modeled ecosystem processes from the perspective of individuals while still being based on partial differential equations. We apply the SPRAT model to explore a well-documented regime shift observed on the eastern Scotian Shelf in the 1990s from a cod-dominated to a herring-dominated ecosystem. Model simulations are able to reconcile the observed multitrophic dynamics with documented changes in both fishing pressure and water temperature, followed by a predator-prey reversal that may have impeded recovery of depleted cod stocks. We conclude that our model can be used to generate new hypotheses and test ideas about spatially interacting fish populations, and their joint responses to both environmental and fisheries forcing.
Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type
Authors: Julian Barreiro-Gomez, Salah Eddine Choutri, Boualem Djehiche
Date: 2022-09-27 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we present an approach to neural network mean-field-type control and its stochastic stability analysis by means of adversarial inputs (aka adversarial attacks). This is a class of data-driven mean-field-type control where the distribution of the variables such as the system states and control inputs are incorporated into the problem. Besides, we present a methodology to validate the feasibility of the approximations of the solutions via neural networks and evaluate their stability. Moreover, we enhance the stability by enlarging the training set with adversarial inputs to obtain a more robust neural network. Finally, a worked-out example based on the linear-quadratic mean-field type control problem (LQ-MTC) is presented to illustrate our methodology.
Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward
Authors: Awais Khan, Khalid Mahmood Malik, James Ryan, Mikul Saravanan
Date: 2022-10-02 00:00:00
Tasks: Misinformation, Speaker Verification, Speech Synthesis, Voice Conversion

Malicious actors may seek to use different voice-spoofing attacks to fool ASV systems and even use them for spreading misinformation. Various countermeasures have been proposed to detect these spoofing attacks. Due to the extensive work done on spoofing detection in automated speaker verification (ASV) systems in the last 6-7 years, there is a need to classify the research and perform qualitative and quantitative comparisons on state-of-the-art countermeasures. Additionally, no existing survey paper has reviewed integrated solutions to voice spoofing evaluation and speaker verification, adversarial/antiforensics attacks on spoofing countermeasures, and ASV itself, or unified solutions to detect multiple attacks using a single model. Further, no work has been done to provide an apples-to-apples comparison of published countermeasures in order to assess their generalizability by evaluating them across corpora. In this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, end-to-end, and universal spoofing countermeasure solutions to detect speech synthesis (SS), voice conversion (VC), and replay attacks. Additionally, we also review integrated solutions to voice spoofing evaluation and speaker verification, adversarial and anti-forensics attacks on voice countermeasures, and ASV. The limitations and challenges of the existing spoofing countermeasures are also presented. We report the performance of these countermeasures on several datasets and evaluate them across corpora. For the experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of the test bed can be found in our GitHub Repository.