pwc-archive/papers-with-abstracts huggingface dataset:
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Authors: Wei Yuan, Shilong Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin
Date: 2023-05-14 00:00:00
Tasks: Collaborative Filtering, Model Poisoning, Privacy Preserving, Recommendation Systems

Federated recommender systems (FedRecs) have been widely explored recently due to their ability to protect user data privacy. In FedRecs, a central server collaboratively learns recommendation models by sharing model public parameters with clients, thereby offering a privacy-preserving solution. Unfortunately, the exposure of model parameters leaves a backdoor for adversaries to manipulate FedRecs. Existing works about FedRec security already reveal that items can easily be promoted by malicious users via model poisoning attacks, but all of them mainly focus on FedRecs with only collaborative information (i.e., user-item interactions). We argue that these attacks are effective because of the data sparsity of collaborative signals. In practice, auxiliary information, such as products' visual descriptions, is used to alleviate collaborative filtering data's sparsity. Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable. In this paper, we conduct extensive experiments to verify that incorporating visual information can beat existing state-of-the-art attacks in reasonable settings. However, since visual information is usually provided by external sources, simply including it will create new security problems. Specifically, we propose a new kind of poisoning attack for visually-aware FedRecs, namely image poisoning attacks, where adversaries can gradually modify the uploaded image to manipulate item ranks during FedRecs' training process. Furthermore, we reveal that the potential collaboration between image poisoning attacks and model poisoning attacks will make visually-aware FedRecs more vulnerable to being manipulated. To safely use visual information, we employ a diffusion model in visually-aware FedRecs to purify each uploaded image and detect the adversarial images.
GPFedRec: Graph-guided Personalization for Federated Recommendation
Authors: Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijjian Zhang, Peng Yan, Bo Yang
Date: 2023-05-13 00:00:00
Tasks: Federated Learning, Privacy Preserving, Relation

The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available to ease reproducibility
aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions
Authors: Enes Altuncu, Jason R. C. Nurse, Meryem Bagriacik, Sophie Kaleba, Haiyue Yuan, Lisa Bonheme, Shujun Li
Date: 2023-05-12 00:00:00
Tasks: Fact Checking, Fake News Detection

In this highly digitised world, fake news is a challenging problem that can cause serious harm to society. Considering how fast fake news can spread, automated methods, tools and services for assisting users to do fact-checking (i.e., fake news detection) become necessary and helpful, for both professionals, such as journalists and researchers, and the general public such as news readers. Experts, especially researchers, play an essential role in informing people about truth and facts, which makes them a good proxy for non-experts to detect fake news by checking relevant expert opinions and comments. Therefore, in this paper, we present aedFaCT, a web browser extension that can help professionals and news readers perform fact-checking via the automatic discovery of expert opinions relevant to the news of concern via shared keywords. Our initial evaluation with three independent testers (who did not participate in the development of the extension) indicated that aedFaCT can provide a faster experience to its users compared with traditional fact-checking practices based on manual online searches, without degrading the quality of retrieved evidence for fact-checking. The source code of aedFaCT is publicly available at https://github.com/altuncu/aedFaCT.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
Authors: Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen
Date: 2023-05-12 00:00:00
Tasks: Recommendation Systems

Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negative engagement metrics while failing to capture its long-term benefits. We here introduce new experiment designs to formally quantify the long-term value of exploration by examining its effects on content corpus, and connecting content corpus growth to the long-term user experience from real-world experiments. Once established the values of exploration, we investigate the Neural Linear Bandit algorithm as a general framework to introduce exploration into any deep learning based ranking systems. We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Research Focused Software Development Kits and Wearable Devices in Physical Activity Research
Authors: Jason Tsang, Harry Prapavessis
Date: 2023-05-12 00:00:00

Introduction: The Canadian Guidelines recommend physical activity for overall health benefits, including cognitive, emotional, functional, and physical health. However, traditional research methods are inefficient and outdated. This paper aims to guide researchers in enhancing their research methods using software development kits and wearable smart devices. Methods: A generic model application was transformed into a research-based mobile application based on the UCLA researchers who collaborated with Apple. First, the research question and goals were identified. Then, three open-source software development kits (SDKs) were used to modify the generic model into the desired application. ResearchKit was used for informed consent, surveys, and active tasks. CareKit was the protocol manager to create participant protocols and track progress. Finally, HealthKit was used to access and share health-related data. The content expert evaluated the application, and the participant experience was optimized for easy use. The collected health-related data were analyzed to identify any significant findings. Results: Wearable health devices offer a convenient and non-invasive way to monitor and track health-related information. Conclusion: Leveraging the data provided by wearable devices, researchers can gain insights into the effectiveness of interventions and inform the development of evidence-based physical activity guidelines. The use of software development kits and wearable devices can enhance research methods and provide valuable insights into overall health benefits.
Interference Cancellation Algorithms for Grant-Free Multiple Access with Massive MIMO
Authors: Lorenzo Valentini, Marco Chiani, Enrico Paolini
Date: 2023-05-15 00:00:00

In next generation Internet-of-Things, the overhead introduced by grant-based multiple access protocols may engulf the access network as a consequence of the unprecedented number of connected devices. Grant-free access protocols are therefore gaining an increasing interest to support massive access from machine-type devices with intermittent activity. In this paper, coded random access (CRA) with massive multiple input multiple output (MIMO) is investigated as a solution to design highly-scalable massive multiple access protocols, taking into account stringent requirements on latency and reliability. With a focus on signal processing aspects at the physical layer and their impact on the overall system performance, critical issues of successive interference cancellation (SIC) over fading channels are first analyzed. Then, SIC algorithms and a scheduler are proposed that can overcome some of the limitations of the current access protocols. The effectiveness of the proposed processing algorithms is validated by Monte Carlo simulation, for different CRA protocols and by comparisons with developed benchmarks.
A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch
Authors: Amir Weiss, Alejandro Lancho, Yuheng Bu, Gregory W. Wornell
Date: 2023-05-14 00:00:00

A bilateral (i.e., upper and lower) bound on the mean-square error under a general model mismatch is developed. The bound, which is derived from the variational representation of the chi-square divergence, is applicable in the Bayesian and nonBayesian frameworks to biased and unbiased estimators. Unlike other classical MSE bounds that depend only on the model, our bound is also estimator-dependent. Thus, it is applicable as a tool for characterizing the MSE of a specific estimator. The proposed bounding technique has a variety of applications, one of which is a tool for proving the consistency of estimators for a class of models. Furthermore, it provides insight as to why certain estimators work well under general model mismatch conditions.
Bifurcation analysis of waning-boosting epidemiological models with repeat infections and varying immunity periods
Authors: Richmond Opoku-Sarkodie, Ferenc A. Bartha, Mónika Polner, Gergely Röst
Date: 2023-05-13 00:00:00

We consider the SIRWJS epidemiological model that includes the waning and boosting of immunity via secondary infections. We carry out combined analytical and numerical investigations of the dynamics. The formulae describing the existence and stability of equilibria are derived. Combining this analysis with numerical continuation techniques, we construct global bifurcation diagrams with respect to several epidemiological parameters. The bifurcation analysis reveals a very rich structure of possible global dynamics. We show that backward bifurcation is possible at the critical value of the basic reproduction number, $\mathcal{R}_0 = 1$. Furthermore, we find stability switches and Hopf bifurcations from steady states forming multiple endemic bubbles, and saddle-node bifurcations of periodic orbits. Regions of bistability are also found, where either two stable steady states, or a stable steady state and a stable periodic orbit coexist. This work provides an insight to the rich and complicated infectious disease dynamics that can emerge from the waning and boosting of immunity.
A Stochastic Compound Failure Model for Testing Resilience of Autonomous Fixed-Wing Aircraft I: Formulation and Simulation
Authors: Thelonious Cooper, Sai Ravela
Date: 2023-05-14 00:00:00

This paper presents a Markov chain model to dynamically emulate the effects of adverse (failure) flight conditions on fixed-wing, autonomous aircraft system actuators. It implements a PX4 Autopilot flight stack module that perturbs the attitude control inputs to the plane's actuator mixer. We apply this approach in simulation on a fixed-wing autonomous aircraft to test the controller response to stochastic compound failures on a range of turning radii. Statistical measures of the differences between target and simulated flight paths demonstrate that a well-tuned PID controller remains competitive with adaptive control in a cascading, compound, transient failure regime.
Vocal Style Factorization for Effective Speaker Recognition in Affective Scenarios
Authors: Morgan Sandler, Arun Ross
Date: 2023-05-13 00:00:00
Tasks: Speaker Recognition

The accuracy of automated speaker recognition is negatively impacted by change in emotions in a person's speech. In this paper, we hypothesize that speaker identity is composed of various vocal style factors that may be learned from unlabeled data and re-combined using a neural network to generate a holistic speaker identity representation for affective scenarios. In this regard, we propose the E-Vector architecture, composed of a 1-D CNN for learning speaker identity features and a vocal style factorization technique for determining vocal styles. Experiments conducted on the MSP-Podcast dataset demonstrate that the proposed architecture improves state-of-the-art speaker recognition accuracy in the affective domain over baseline ECAPA-TDNN speaker recognition models. For instance, the true match rate at a false match rate of 1% improves from 27.6% to 46.2%.
The Whole Is Greater than the Sum of Its Parts: Improving Music Source Separation by Bridging Network
Authors: Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich, Shusuke Takahashi, Yuki Mitsufuji
Date: 2023-05-13 00:00:00
Tasks: Music Source Separation

This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source; hence, we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX), densely connected dilated DenseNet (D3Net) and convolutional time-domain audio separation network (Conv-TasNet) extended with our X-scheme, respectively called X-UMX, X-D3Net and X-Conv-TasNet, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX.
On Assumptions with Respect to Occlusions in Urban Environments for Automated Vehicle Speed Decisions
Authors: Robert Graubohm, Nayel Fabian Salem, Marcus Nolte, Markus Maurer
Date: 2023-05-15 00:00:00

Automated driving systems are subject to various kinds of uncertainty during design, development, and operation. These kinds of uncertainty lead to an inherent risk of the technology that can be mitigated, but never fully eliminated. Situations involving obscured traffic participants have become popular examples in the field to illustrate a subset of these uncertainties that developers must deal with during system design and implementation. In this paper, we describe necessary assumptions for a speed choice in a situation in which an ego-vehicle passes parked vehicles that generate occluded areas where a human intending to cross the road could be obscured. We develop a calculation formula for a dynamic speed limit that mitigates the collision risk in this situation, and investigate the resulting speed profiles in simulation based on example assumptions. This paper has two main results: First, we show that even without worst-case assumptions, dramatically reduced speeds would be driven to avoid collisions. Second, we highlight that design decisions regarding occlusion treatment are directly related to the risk that automated vehicles pose to pedestrians in urban environments. In this respect, we conclude that there needs to be a broader discussion about acceptable assumptions.
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Authors: Lukas Kesper, Sebastian Trimpe, Dominik Baumann
Date: 2023-05-15 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research.
Template-Based Piecewise Affine Regression
Authors: Guillaume O. Berger, Sriram Sankaranarayanan
Date: 2023-05-15 00:00:00
Tasks: regression

We investigate the problem of fitting piecewise affine functions (PWA) to data. Our algorithm divides the input domain into finitely many polyhedral regions whose shapes are specified using a user-defined template such that the data points in each region are fit by an affine function within a desired error bound. We first prove that this problem is NP-hard. Next, we present a top-down algorithm that considers subsets of the overall data set in a systematic manner, trying to fit an affine function for each subset using linear regression. If regression fails on a subset, we extract a minimal set of points that led to a failure in order to split the original index set into smaller subsets. Using a combination of this top-down scheme and a set covering algorithm, we derive an overall approach that is optimal in terms of the number of pieces of the resulting PWA model. We demonstrate our approach on two numerical examples that include PWA approximations of a widely used nonlinear insulin--glucose regulation model and a double inverted pendulum with soft contacts.
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
Authors: Jiadong Yu, Ahmad Alhilal, Tailin Zhou, Pan Hui, Danny H. K. Tsang
Date: 2023-05-15 00:00:00
Tasks: Continual Learning, Edge-computing, Fairness, Reinforcement Learning (RL)

Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user experience. In contrast to online reinforcement learning (RL) algorithms, which can incur substantial communication overheads and longer delays, the majority of existing works employ offline-trained RL algorithms for resource allocation decisions in MEC systems. However, they neglect the impact of desynchronization between the physical and digital worlds on the effectiveness of the allocation strategy. In this paper, we tackle this desynchronization using a continual RL framework that facilitates the resource allocation dynamically for MEC-enabled VR content streaming. We first design a digital twin-empowered edge computing (DTEC) system and formulate a quality of experience (QoE) maximization problem based on attention-based resolution perception. This problem optimizes the allocation of computing and bandwidth resources while adapting the attention-based resolution of the VR content. The continual RL framework in DTEC enables adaptive online execution in a time-varying environment. The reward function is defined based on the QoE and horizon-fairness QoE (hfQoE) constraints. Furthermore, we propose freshness prioritized experience replay - continual deep deterministic policy gradient (FPER-CDDPG) to enhance the performance of continual learning in the presence of time-varying DT updates. We test FPER-CDDPG using extensive experiments and evaluation. FPER-CDDPG outperforms the benchmarks in terms of average latency, QoE, and successful delivery rate as well as meeting the hfQoE requirements and performance over long-term execution while ensuring system scalability with the increasing number of users.
Design and Implementation of Emergency Simulated Lighting System Based on Tello UAV
Authors: Yexin Pan, Yong Xu, Bo Ma, Chuanhuang Li
Date: 2023-05-15 00:00:00

In recent years, with the increasing maturity of UAV technology, the application of UAV in the civilian field has seen explosive growth due to their low cost, high flexibility, and wide adaptability. In order to address the drawbacks of current tethered UAV lighting, which necessitates manual operation and coordination with tethered cables, this paper presents a rapid reaction and autonomous deployment emergency lighting system prototype based on Tello UAV. The system design is divided into three modules. First, the lighting module has designed the protogype lighting extensions for the Tello UAV. By selecting and installing LED light sources reasonably, the stability of the UAV's takeoff, landing, and flight is not affected, while ensuring good lighting effects; Second, the addressing module extracts the optimization objective function based on the optimization deployment requirements of UAV, and uses simulated annealing algorithm to iteratively optimize for different distribution scenarios and user needs, calculating the optimal deployment location of UAV and planning flight missions for UAV. Third, the flight control module has designed a specialized command control framework based on Tello UAV's API, which converts the planned flight path into command statements, forms flight text, and controls the flight of unmanned aerial vehicles accordingly. The test results of the emergency lighting system show that the system can respond quickly to both densely distributed and sparsely distributed user scenarios, deploying UAV to the best service locations, and providing high-quality services to users. This work represents an early attempt of Python-based real-world UAV lighting system prototype design to associate the UAV hardware and Python algorithms.