pwc-archive/papers-with-abstracts huggingface dataset:
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins
Authors: Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos
Date: 2022-07-19 00:00:00
Tasks: Decision Making, Interpretable Machine Learning

Digital Twins (DT) are essentially dynamic data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and reconfiguration aimed at more accurate modelling and analysis in DT. The reconfiguration decisions can be autonomous or interactive, keeping human-in-the-loop. The trustworthiness of these decisions can be hindered by inadequate explainability of the rationale, and utility gained in implementing the decision for the given situation among alternatives. Additionally, different decision-making algorithms and models have varying complexity, quality and can result in different utility gained for the model. The inadequacy of explainability can limit the extent to which humans can evaluate the decisions, often leading to updates which are unfit for the given situation, erroneous, compromising the overall accuracy of the model. The novel contribution of this paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback. The approach utilises interpretable machine learning and goal modelling to explainability, and considers trade-off analysis of utility gained. We use examples from smart warehousing to demonstrate the approach.
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems
Authors: Silvan Mertes, Christina Karle, Tobias Huber, Katharina Weitz, Ruben Schlagowski, Elisabeth André
Date: 2022-07-19 00:00:00
Tasks: counterfactual, Counterfactual Explanation, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, all common approaches from this field are based on communicating information about features or characteristics that are especially important for an AI's decision. We argue that in order to fully understand a decision, not only knowledge about relevant features is needed, but that the awareness of irrelevant information also highly contributes to the creation of a user's mental model of an AI system. Therefore, we introduce a new way of explaining AI systems. Our approach, which we call Alterfactual Explanations, is based on showing an alternative reality where irrelevant features of an AI's input are altered. By doing so, the user directly sees which characteristics of the input data can change arbitrarily without influencing the AI's decision. We evaluate our approach in an extensive user study, revealing that it is able to significantly contribute to the participants' understanding of an AI. We show that alterfactual explanations are suited to convey an understanding of different aspects of the AI's reasoning than established counterfactual explanation methods.
Mimetic Models: Ethical Implications of AI that Acts Like You
Authors: Reid McIlroy-Young, Jon Kleinberg, Siddhartha Sen, Solon Barocas, Ashton Anderson
Date: 2022-07-19 00:00:00
Tasks: Text Generation

An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression. These models go beyond earlier approaches in the way they are tailored to individuals, and the way they are designed for interaction rather than simply the reproduction of fixed, pre-computed behaviors. We refer to these as mimetic models, and in this paper we develop a framework for characterizing the ethical and social issues raised by their growing availability. Our framework includes a number of distinct scenarios for the use of such models, and considers the impacts on a range of different participants, including the target being modeled, the operator who deploys the model, and the entities that interact with it.
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers
Authors: Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh Chawla, Jane Cleland-Huang
Date: 2022-07-18 00:00:00
Tasks: Anomaly Detection, Diagnostic, Time Series, Time Series Analysis

CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate time-series data which they typically produce can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs. Deep learning models, such as Long Short-term memory (LSTM) networks can be used to automate these tasks and to provide clear explanations of diverse anomalies detected in real-time multivariate data-streams. In this paper we present RESAM, a requirements process that integrates knowledge from domain experts, discussion forums, and formal product documentation, to discover and specify requirements and design definitions in the form of time-series attributes that contribute to the construction of effective deep learning anomaly detectors. We present a case-study based on a flight control system for small Uncrewed Aerial Systems and demonstrate that its use guides the construction of effective anomaly detection models whilst also providing underlying support for explainability. RESAM is relevant to domains in which open or closed online forums provide discussion support for log analysis.
Optimizing Indoor Navigation Policies For Spatial Distancing
Authors: Xun Zhang, Mathew Schwartz, Muhammad Usman, Petros Faloutsos, Mubbasir Kapadia
Date: 2022-06-04 00:00:00

In this paper, we focus on the modification of policies that can lead to movement patterns and directional guidance of occupants, which are represented as agents in a 3D simulation engine. We demonstrate an optimization method that improves a spatial distancing metric by modifying the navigation graph by introducing a measure of spatial distancing of agents as a function of agent density (i.e., occupancy). Our optimization framework utilizes such metrics as the target function, using a hybrid approach of combining genetic algorithm and simulated annealing. We show that within our framework, the simulation-optimization process can help to improve spatial distancing between agents by optimizing the navigation policies for a given indoor environment.
A Community-Aware Framework for Social Influence Maximization
Authors: Abhishek K. Umrawal, Christopher J. Quinn, Vaneet Aggarwal
Date: 2022-07-18 00:00:00

We consider the problem of Influence Maximization (IM), the task of selecting $k$ seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on real-world social networks show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence. We also study the effect of the community structure on the performance of the proposed framework. Our experiments show that the community structures with higher modularity lead the proposed framework to perform better in terms of run-time and influence.
An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things
Authors: Liu Yang, Keping Yu, Simon X. Yang, Chinmay Chakraborty, Yinzhi Lu, Tan Guo
Date: 2022-07-19 00:00:00
Tasks: Edge-computing, Management

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while Device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this paper presents an intelligent trust cloud management method. Firstly, an active training mechanism is proposed to construct the standard trust clouds. Secondly, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Thirdly, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices.
GAFX: A General Audio Feature eXtractor
Authors: Zhaoyang Bu, Hanhaodi Zhang, Xiaohu Zhu
Date: 2022-07-19 00:00:00
Tasks: Genre classification, Music Genre Classification

Most machine learning models for audio tasks are dealing with a handcrafted feature, the spectrogram. However, it is still unknown whether the spectrogram could be replaced with deep learning based features. In this paper, we answer this question by comparing the different learnable neural networks extracting features with a successful spectrogram model and proposed a General Audio Feature eXtractor (GAFX) based on a dual U-Net (GAFX-U), ResNet (GAFX-R), and Attention (GAFX-A) modules. We design experiments to evaluate this model on the music genre classification task on the GTZAN dataset and perform a detailed ablation study of different configurations of our framework and our model GAFX-U, following the Audio Spectrogram Transformer (AST) classifier achieves competitive performance.
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
Authors: Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
Date: 2022-07-19 00:00:00
Tasks: Federated Learning, Model Poisoning

Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to learn an accurate global model even if some clients are malicious. However, they can only resist a small number of malicious clients in practice. It is still an open challenge how to defend against model poisoning attacks with a large number of malicious clients. Our FLDetector addresses this challenge via detecting malicious clients. FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust FL method can learn an accurate global model using the remaining clients. Our key observation is that, in model poisoning attacks, the model updates from a client in multiple iterations are inconsistent. Therefore, FLDetector detects malicious clients via checking their model-updates consistency. Roughly speaking, the server predicts a client's model update in each iteration based on its historical model updates using the Cauchy mean value theorem and L-BFGS, and flags a client as malicious if the received model update from the client and the predicted model update are inconsistent in multiple iterations. Our extensive experiments on three benchmark datasets show that FLDetector can accurately detect malicious clients in multiple state-of-the-art model poisoning attacks. After removing the detected malicious clients, existing Byzantine-robust FL methods can learn accurate global models.Our code is available at https://github.com/zaixizhang/FLDetector.
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning
Authors: Houkun Zhu, Dominik Scheinert, Lauritz Thamsen, Kordian Gontarska, Odej Kao
Date: 2022-07-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal. At the same time, tuning configuration parameters is typically challenging and time-consuming. It demands expertise and tuning operations can also be expensive. This is especially the case for static parameters, where changes take effect only after a restart of the system or workloads. We propose a novel approach, Magpie, which utilizes deep reinforcement learning to tune static parameters by strategically exploring and exploiting configuration parameter spaces. To boost the tuning of the static parameters, our method employs both server and client metrics of distributed file systems to understand the relationship between static parameters and performance. Our empirical evaluation results show that Magpie can noticeably improve the performance of the distributed file system Lustre, where our approach on average achieves 91.8% throughput gains against default configuration after tuning towards single performance indicator optimization, while it reaches 39.7% more throughput gains against the baseline.
Few-Shot Teamwork
Authors: Elliot Fosong, Arrasy Rahman, Ignacio Carlucho, Stefano V. Albrecht
Date: 2022-07-19 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and together must learn to adapt to an unseen but related task. We discuss how the FST problem can be seen as addressing two separate problems: one of reducing the experience required to train a team of agents to complete a complex task; and one of collaborating with unfamiliar teammates to complete a new task. Progress towards solving FST could lead to progress in both multi-agent reinforcement learning and ad hoc teamwork.
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios
Authors: Jayprakash S. Nair, Divya D. Kulkarni, Ajitem Joshi, Sruthy Suresh
Date: 2022-07-19 00:00:00
Tasks: Federated Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suffer from inherent problems such as failure of the central node and bottlenecks in channel bandwidth. When FL is used in conjunction with connected robots serving as devices, a failure of the central controlling entity can lead to a chaotic situation. This paper describes a mobile agent based paradigm to decentralize FL in multi-robot scenarios. Using Webots, a popular free open-source robot simulator, and Tartarus, a mobile agent platform, we present a methodology to decentralize federated learning in a set of connected robots. With Webots running on different connected computing systems, we show how mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL). Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables, show the viability of using decentralized FL in the domain of robotics. Since the proposed work can be used in conjunction with other learning algorithms and also real robots, it can act as a vital tool for the study of decentralized FL using heterogeneous learning algorithms concurrently in multi-robot scenarios.
Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs
Authors: Anastasia Borovykh, Dante Kalise, Alexis Laignelet, Panos Parpas
Date: 2022-07-19 00:00:00

A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman partial differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) problem. A state-dependent Riccati equation control law is first used to generate a gradient-augmented synthetic dataset for supervised learning. The resulting model becomes a warm start for the minimization of a loss function based on the residual of the HJB PDE. The combination of supervised learning and residual minimization avoids spurious solutions and mitigate the data inefficiency of a supervised learning-only approach. Numerical tests validate the different advantages of the proposed methodology.
Fast density peaks clustering algorithm in polar coordinate system
Authors: Chao Li, Shifei Ding, Xiao Xu, Shuying Du & Tianhao Shi
Date: 2022-03-08 00:00:00
Proceeding: Applied Intelligence 2022 3
Tasks: Clustering

Density peaks clustering (DPC) algorithm provides an efficient method to quickly find cluster centers with decision graphs. In recent years, due to its unique parameters, no iteration, and good robustness, it has been widely studied and applied. However, it also has some shortcomings, such as no adaptability, inadaptability to high-dimensional data and accuracy is easily affected. For reducing the higher time complexity of DPC, we introduce the polar coordinates to DPC (PC-DPC). Firstly, obtain the distance from every point to third-party point and the cosine value of the angle formed with the third-party vector, and reorder the points by distances and cosine values. Then, select other points in the adjacent sequence number of each point to calculate distances, and build a sparse distance matrix. Finally, the sparse distance matrix is used as the input of DPC to obtain clustering results. Theoretical analysis and experiments show that, compared with DPC and other algorithms, PC-DPC greatly reduces running time of DPC while maintaining clustering precision.
ISAR Imaging Analysis of a Hypersonic Vehicle Covered With Plasma Sheath
Authors: Bian Zheng
Date: 2021-10-09 00:00:00
Proceeding: journal 2021 10
Tasks: Motion Compensation

In this article, a hypersonic target electromagnetic (EM) scattering echo model combined with the inhomogeneous zonal medium model (IZMM) and the classical scattering
center model (SCM) is proposed with a distributed satelliteborne
array radar as the detection platform. A parallel physical
optics (PO) method is used for multiview inverse synthetic aperture radar (ISAR) imaging of a moving hypersonic target covered
with plasma sheath based on the analysis of high-resolution range
profile in the S-X ultrawideband range, reconstructing 2-D EM
scattering echo data (the target) and motion compensation. The
results show that the surface of the inhomogeneous plasma sheath
flow field is an excitation layer with random and irregular
fluctuation characteristics, which increases the false scattering
centroid of the 1-D range profile of the hypersonic target and
can interfere with and disrupt the radar localization of the
target along the radial direction. In addition, shallow scattering of
EM waves occurs in the plasma sheath, and the average signal
intensity of the target can gradually reduce from 0.5 × 10−5
at 60 km and 20 Ma to 0.1 × 10−5 at 30 km and 20 Ma,
with a fivefold weakening of the overall scattered echo signal.
In particular, the faster the hypersonic target travels at 30-km
altitude, the weaker the imaged scattered echo signal becomes,
with the average intensity of the imaged signal weakening by
approximately threefold from 15 to 25 Ma. This study provides
considerable technical support and data assurance to establish
a synthetic aperture radar (SAR) automatic target recognition
(ATR) database, and the findings of this study can be used as a
reference for fine-structure feature analysis of hypersonic targets
for feature extraction and the classification and identification of
targets.
Blacksmith: Scalable Rowhammering in the Frequency Domain
Authors: Patrick Jattke, Victor van der Veen, Pietro Frigo, Stijn Gunter, Kaveh Razavi
Date: 2022-05-01 00:00:00
Proceeding: IEEE Symposium on Security and Privacy 2022 5

We present the new class of non-uniform Rowhammer access patterns that bypass undocumented, proprietary in-DRAM Target Row Refresh (TRR) while operating in a production setting. We show that these patterns trigger bit flips on all 40 DDR4 DRAM devices in our test pool. We make a key observation that all published Rowhammer access patterns always hammer “aggressor” rows uniformly. While uniform accesses maximize the number of aggressor activations, we find that in-DRAM TRR exploits this behavior to catch aggressor rows and refresh neighboring “victims” before they fail. There is no reason, however, to limit Rowhammer attacks to uniform access patterns: smaller technology nodes make underlying DRAM technologies more vulnerable, and significantly fewer accesses are nowadays required to trigger bit flips, making it interesting to investigate less predictable access patterns. The search space for non-uniform access patterns, however, is tremendous. We design experiments to explore this space with respect to the deployed mitigations, highlighting the importance of the order, regularity, and intensity of accessing aggressor rows in non-uniform access patterns. We show how randomizing parameters in the frequency domain captures these aspects and use this insight in the design of Blacksmith, a scalable Rowhammer fuzzer that generates access patterns that hammer aggressor rows with different phases, frequencies, and amplitudes. Blacksmith finds complex patterns that trigger Rowhammer bit flips on all 40 of our recently purchased DDR4 DIMMs, 2.6× more than state of the art, and generating on average 87× more bit flips. We also demonstrate the effectiveness of these patterns on Low Power DDR4X devices. Our extensive analysis using Blacksmith further provides new insights on the properties of currently deployed TRR mitigations. We conclude that after almost a decade of research and deployed in-DRAM mitigations, we are perhaps in a worse situation than when Rowhammer was first discovered.
Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
Authors: Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin Neil Jones
Date: 2021-09-10 00:00:00
Proceeding: CISBAT 2021 9
Tasks: Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them. However, since weeks long experiments are needed to assess the performance of a building controller, people still have to rely on accurate simulation environments to train and tune DRL agents in tractable amounts of time before deploying them, shifting the burden back to the original issue of designing complex models. In this work, we show that it is possible to learn control policies on simple black-box linear room temperature models, thereby alleviating the heavy engineering usually required to build accurate surrogates. We develop a black-box pipeline, where historical data is taken as input to produce room temperature control policies. The trained DRL agents are capable of beating industrial rule-based controllers both in terms of energy consumption and comfort satisfaction, using novel penalties to introduce expert knowledge, i.e. to incentivize agents to follow expected behaviors, in the reward function. Moreover, one of the best agents was deployed on a real building for one week and was able to save energy while maintaining adequate comfort levels, indicating that low-complexity models might be enough to learn control policies that perform well on real buildings.