pwc-archive/papers-with-abstracts huggingface dataset:
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
Authors: Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, HuaWei Shen, Xueqi Cheng
Date: 2024-01-31 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Recommendation Systems, Sequential Recommendation, World Knowledge

Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item-to-item transitions. However, the inherent openness of sequential recommender systems renders them vulnerable to poisoning attacks, where fraudulent users are injected into the training data to manipulate learned patterns. Traditional defense strategies predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attack types. To solve the above problems, considering the rich open-world knowledge encapsulated in Large Language Models (LLMs), our research initially focuses on the capabilities of LLMs in the detection of unknown fraudulent activities within recommender systems, a strategy we denote as LLM4Dec. Empirical evaluations demonstrate the substantial capability of LLMs in identifying unknown fraudsters, leveraging their expansive, open-world knowledge. Building upon this, we propose the integration of LLMs into defense strategies to extend their effectiveness beyond the confines of known attacks. We propose LoRec, an advanced framework that employs LLM-Enhanced Calibration to strengthen the robustness of sequential recommender systems against poisoning attacks. LoRec integrates an LLM-enhanced CalibraTor (LCT) that refines the training process of sequential recommender systems with knowledge derived from LLMs, applying a user-wise reweighting to diminish the impact of fraudsters injected by attacks. By incorporating LLMs' open-world knowledge, the LCT effectively converts the limited, specific priors or rules into a more general pattern of fraudsters, offering improved defenses against poisoning attacks. Our comprehensive experiments validate that LoRec, as a general framework, significantly strengthens the robustness of sequential recommender systems.
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
Authors: Liang Qu, Wei Yuan, Ruiqi Zheng, Lizhen Cui, Yuhui Shi, Hongzhi Yin
Date: 2024-01-31 00:00:00
Tasks: Contrastive Learning, Graph Neural Network, Recommendation Systems

Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently existing architecture of FedRecs assumes that all users have the same 0-privacy budget, i.e., they do not upload any data to the server, thus overlooking those users who are less concerned about privacy and are willing to upload data to get a better recommendation service. To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server. To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. It trains user-centric ego graphs locally, and high-order graphs based on user-shared data in the server in a collaborative manner via contrastive learning. Furthermore, a graph mending strategy is utilized to predict missing links in the graph on the server, thus leveraging the capabilities of graph neural networks over high-order graphs. Extensive experiments were conducted on two public datasets, and the results demonstrate the effectiveness of the proposed method.
Fréchet Distance for Offline Evaluation of Information Retrieval Systems with Sparse Labels
Authors: Negar Arabzadeh, Charles L. A. Clarke
Date: 2024-01-31 00:00:00
Tasks: Image Generation, Information Retrieval, Retrieval, Text to Image Generation, Text-to-Image Generation

The rapid advancement of natural language processing, information retrieval (IR), computer vision, and other technologies has presented significant challenges in evaluating the performance of these systems. One of the main challenges is the scarcity of human-labeled data, which hinders the fair and accurate assessment of these systems. In this work, we specifically focus on evaluating IR systems with sparse labels, borrowing from recent research on evaluating computer vision tasks. taking inspiration from the success of using Fr\'echet Inception Distance (FID) in assessing text-to-image generation systems. We propose leveraging the Fr\'echet Distance to measure the distance between the distributions of relevant judged items and retrieved results. Our experimental results on MS MARCO V1 dataset and TREC Deep Learning Tracks query sets demonstrate the effectiveness of the Fr\'echet Distance as a metric for evaluating IR systems, particularly in settings where a few labels are available. This approach contributes to the advancement of evaluation methodologies in real-world scenarios such as the assessment of generative IR systems.
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognition
Authors: Siddharth Roheda, Hamid Krim
Date: 2020-04-03 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2020 4
Tasks: Action Recognition, Optical Flow Estimation

The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the complexity of Convolutional Neural Networks, we propose a Volterra filter-inspired Network architecture. This architecture introduces controlled non-linearities in the form of interactions between the delayed input samples of data. We propose a cascaded implementation of Volterra Filtering so as to significantly reduce the number of parameters required to carry out the same classification task as that of a conventional Neural Network. We demonstrate an efficient parallel implementation of this Volterra Neural Network (VNN), along with its remarkable performance while retaining a relatively simpler and potentially more tractable structure. Furthermore, we show a rather sophisticated adaptation of this network to nonlinearly fuse the RGB (spatial) information and the Optical Flow (temporal) information of a video sequence for action recognition. The proposed approach is evaluated on UCF-101 and HMDB-51 datasets for action recognition and is shown to outperform state of the art CNN approaches.
Reputation-Driven Adoption and Avoidance of Algorithmic Decision Aids in Credence Goods Markets
Authors: Alexander Erlei, Lukas Meub
Date: 2024-01-31 00:00:00
Tasks: Diagnostic

In credence goods markets such as health care or repair services, consumers rely on experts with superior information to adequately diagnose and treat them. Experts, however, are constrained in their diagnostic abilities, which hurts market efficiency and consumer welfare. Technological breakthroughs that substitute or complement expert judgments have the potential to alleviate consumer mistreatment. This article studies how competitive experts adopt novel diagnostic technologies when skills are heterogeneously distributed and obfuscated to consumers. We differentiate between novel technologies that increase expert abilities, and algorithmic decision aids that complement expert judgments, but do not affect an expert's personal diagnostic precision. When consumers build up beliefs about an expert's type through repeated interactions, we show that high-ability experts may strategically forego the decision aid in order to escape a pooling equilibrium by differentiating themselves from low-ability experts. Without future visits, signaling concerns cause all experts to randomize their investment choice, leading to under-utilization from low-ability experts and over-utilization from high-ability experts. Results from two online experiments support our hypotheses. High-ability experts are significantly less likely than low-ability experts to invests into an algorithmic decision aid if reputation building is possible. Otherwise, there is no difference, and experts who believe that consumers play a signaling game randomize their investment choice.
Detection of Signals in Colored Noise: Leading Eigenvalue Test for Non-central $F$-matrices
Authors: Prathapasinghe Dharmawansa, Saman Atapattu, Jamie Evans, Kandeepan Sithamparanathan
Date: 2024-01-30 00:00:00

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting an unknown non-random signal by capitalizing on the leading eigenvalue of the whitened sample covariance matrix as the test statistic (a.k.a. Roy's largest root test). Since the unknown signal is non-random, the whitened sample covariance matrix turns out to have a non-central $F$-distribution. This distribution assumes a singular or non-singular form depending on whether the number of observations $p\lessgtr$ the system dimensionality $m$. Therefore, we statistically characterize the leading eigenvalue of the singular and non-singular $F$-matrices by deriving their cumulative distribution functions (c.d.f.). Subsequently, they have been utilized in deriving the corresponding receiver operating characteristic (ROC) profiles. We also extend our analysis into the high dimensional domain. It turns out that, when the signal is sufficiently strong, the maximum eigenvalue can reliably detect it in this regime. Nevertheless, weak signals cannot be detected in the high dimensional regime with the leading eigenvalue.
Assessment of Diagnostic Capabilities of Methods of Recreation of Voltage Fluctuations
Authors: Piotr Kuwałek
Date: 2024-01-31 00:00:00
Tasks: Diagnostic

Voltage fluctuations are one of the most common low-frequency disturbances of power quality. Diagnostics of these disturbances are a complex process because voltage fluctuations affect different loads in different ways. Therefore, there is no measure of power quality that allows for the complementary assessment of severity of this disturbance, allow for the identification of sources of voltage fluctuations, and post-factum investigation of their effects. Among the currently used measures of voltage fluctuations, voltage fluctuation indices have the greatest diagnostic capabilities. Many preliminary studies also show the potential possibility of recreation of voltage fluctuations, including: based on voltage fluctuation indices. This paper presents the results of research on methods of recreation of voltage fluctuations from voltage fluctuation indices. The research carried out included a set of data obtained in a real power grid. Moreover, the impact of the discrimination period on the accuracy of recreation of voltage fluctuations has been assessed. The presented research results show, on the one hand, the usefulness of voltage fluctuation indices in the process of recreation of voltage fluctuations and, on the other hand, further challenges in the recreation of voltage fluctuations.
Detecting gamma-band responses to the speech envelope for the ICASSP 2024 Auditory EEG Decoding Signal Processing Grand Challenge
Authors: Mike Thornton, Jonas Auernheimer, Constantin Jehn, Danilo Mandic, Tobias Reichenbach
Date: 2024-01-30 00:00:00
Tasks: Decoder, EEG, Eeg Decoding

The 2024 ICASSP Auditory EEG Signal Processing Grand Challenge concerns the decoding of electroencephalography (EEG) measurements taken from participants who listened to speech material. This work details our solution to the match-mismatch sub-task: given a short temporal segment of EEG recordings and several candidate speech segments, the task is to classify which of the speech segments was time-aligned with the EEG signals. We show that high-frequency gamma-band responses to the speech envelope can be detected with a high accuracy. By jointly assessing gamma-band responses and low-frequency envelope tracking, we develop a match-mismatch decoder which placed first in this task.
Sigma-lognormal modeling of speech
Authors: C. Carmona-Duarte, M. A. Ferrer, R. Plamondon, A. Gomez-Rodellar, P. Gomez-Vilda
Date: 2024-01-27 00:00:00

Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject's age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics.
Detection of Auditory Brainstem Response Peaks Using Image Processing Techniques in Infants with Normal Hearing Sensitivity
Authors: Amir Majidpour, Samer Kais Jameel, Jafar Majidpour, Houra Bagheri, Tarik A. Rashid, Ahmadreza Nazeri, Mahshid Moheb Aleaba
Date: 2024-01-21 00:00:00
Tasks: Sensitivity

Introduction: The auditory brainstem response (ABR) is measured to find the brainstem-level peripheral auditory nerve system integrity in children having normal hearing. The Auditory Evoked Potential (AEP) is generated using acoustic stimuli. Interpreting these waves requires competence to avoid misdiagnosing hearing problems. Automating ABR test labeling with computer vision may reduce human error. Method: The ABR test results of 26 children aged 1 to 20 months with normal hearing in both ears were used. A new approach is suggested for automatically calculating the peaks of waves of different intensities (in decibels). The procedure entails acquiring wave images from an Audera device using the Color Thresholder method, segmenting each wave as a single wave image using the Image Region Analyzer application, converting all wave images into waves using Image Processing (IP) techniques, and finally calculating the latency of the peaks for each wave to be used by an audiologist for diagnosing the disease. Findings: Image processing techniques were able to detect 1, 3, and 5 waves in the diagnosis field with accuracy (0.82), (0.98), and (0.98), respectively, and its precision for waves 1, 3, and 5, were respectively (0.32), (0.97) and (0.87). This evaluation also worked well in the thresholding part and 82.7 % correctly detected the ABR waves. Conclusion: Our findings indicate that the audiology test battery suite can be made more accurate, quick, and error-free by using technology to automatically detect and label ABR waves.
Reduced-Order Model of Power Converters to Optimize Power Hardware-In-the-Loop Technology in Dc-Distributed Systems
Authors: Marina Sanz, Daniel Santamargarita, Francisco Huerta, Diego Ochoa, Antonio Lazaro, Andres Barrado
Date: 2024-01-31 00:00:00

The Power Hardware-In-the-Loop (PHIL) technology provides a powerful tool for testing scenarios where there is a high-power interchange, in which the performance of field tests can be very complex or expensive. When performing PHIL simulations of systems with a high number of components, such as DC-distributed systems on a ship or aircraft, the use of switched or average models of the converters can require the use of expensive commercial real-time digital simulators (RTDS) reducing the advantages of these technology. This paper is focused on the proposal of a reduced order model of converters to be able to perform PHIL analysis of Dc-distributed systems using low resources of the required real-time digital simulator. The paper validates that the proposed reduced-order model is able to determine the stability on the Dc-distributed system in comparison with more complex converter models. Moreover, a comparison between both models regarding the required resources in the implementation in a commercial RTDS platform is performed to validate the benefits of the proposed model in performing PHIL analysis of large power distribution systems.
Remote Estimation of Markov Processes over Costly Channels: On the Benefits of Implicit Information
Authors: Edoardo David Santi, Touraj Soleymani, Deniz Gunduz
Date: 2024-01-31 00:00:00

In this paper, we study the remote estimation problem of a Markov process over a channel with a cost. We formulate this problem as an infinite horizon optimization problem with two players, i.e., a sensor and a monitor, that have distinct information, and with a reward function that takes into account both the communication cost and the estimation quality. We show that the main challenge in solving this problem is associated with the consideration of implicit information, i.e., information that the monitor can obtain about the source when the sensor is silent. Our main objective is to develop a framework for finding solutions to this problem without neglecting implicit information a priori. To that end, we propose three different algorithms. The first one is an alternating policy algorithm that converges to a Nash equilibrium. The second one is an occupancy-state algorithm that is guaranteed to find a globally optimal solution. The last one is a heuristic algorithm that is able to find a near-optimal solution.
Optimizing Grid Resilience: A Capacity Reserve Market for High Impact Low Probability Events
Authors: Umar T. Salman, Zongjie Wang, Timothy M. Hansen
Date: 2024-01-31 00:00:00

This paper addresses the challenges of high-impact low-probability (HILP) events by proposing a novel capacity reserve event market for mobile generation assets, aimed at supporting the transmission network during such incidents. Despite the usefulness of portable generators and mobile energy units in restoring power, there are drawbacks such as environmental impact, finite operation, and complex cost recovery. The proposed market integrates these resources into a dispatch framework based on pre-established contracts, ensuring fair compensation and considering factors like capacity, pricing, and travel distance. Resource owners receive advanced notifications for potential events, allowing them to adjust their bids for cost recovery. Simulations on an IEEE 30-bus case have been conducted to demonstrate the model effectiveness in increasing grid resiliency.
Moving horizon partition-based state estimation of large-scale systems -- Revised version
Authors: Marcello Farina, Giancarlo Ferrari-Trecate, Riccardo Scattolini
Date: 2024-01-31 00:00:00
Tasks: State Estimation

This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, conditions for the convergence of the estimation error to zero are analyzed.
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances
Authors: Ke Lu, Dongjun Li, Qun Wang, Kaidi Yang, Lin Zhao, Ziyou Song
Date: 2024-01-31 00:00:00
Tasks: Reinforcement Learning (RL), Safe Reinforcement Learning

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during the training process. The lack of safety guarantees is the other concern when deploying a trained policy in real-world application. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which respectively challenge the MPC design and the satisfaction of constraints. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm, compared with RMPC technique, shows an average improvement of 10.88% in holistic energy efficiency, while compared with RL algorithm, it effectively prevents inter-vehicle collisions.
Optimal Dynamic Ancillary Services Provision Based on Local Power Grid Perception
Authors: Verena Häberle, Xiuqiang He, Linbin Huang, Eduardo Prieto-Araujo, Florian Dörfler
Date: 2024-01-31 00:00:00

In this paper, we propose a systematic closed-loop approach to provide optimal dynamic ancillary services with converter-interfaced generation systems based on local power grid perception. In particular, we structurally encode dynamic ancillary services such as fast frequency and voltage regulation in the form of a parametric transfer function matrix, which includes several parameters to define a set of different feasible response behaviors, among which we aim to find the optimal one to be realized by the converter system. Our approach is based on a so-called "perceive-and-optimize" (P&O) strategy: First, we identify a grid dynamic equivalent at the interconnection terminals of the converter system. Second, we consider the closed-loop interconnection of the identified grid equivalent and the parametric transfer function matrix, which we optimize for the set of transfer function parameters, resulting in a stable and optimal closed-loop performance for ancillary services provision. In the process, we ensure that grid-code and device-level requirements are satisfied. Finally, we demonstrate the effectiveness of our approach in different numerical case studies based on a modified Kundur two-area test system.