pwc-archive/papers-with-abstracts huggingface dataset:
Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques
Authors: Adrian Kochsiek, Rainer Gemulla
Date: 2021-11-01 00:00:00
Proceeding: Proceedings of the VLDB Endowment 2021 11
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Link Prediction

Knowledge graph embedding (KGE) models represent the entities and relations of a knowledge graph (KG) using dense continuous representations called embeddings. KGE methods have recently gained traction for tasks such as knowledge graph completion and reasoning as well as to provide suitable entity representations for downstream learning tasks. While a large part of the available literature focuses on small KGs, a number of frameworks that are able to train KGE models for large-scale KGs by parallelization across multiple GPUs or machines have recently been proposed. So far, the benefits and drawbacks of the various parallelization techniques have not been studied comprehensively. In this paper, we report on an experimental study in which we presented, re-implemented in a common computational framework, investigated, and improved the available techniques. We found that the evaluation methodologies used in prior work are often not comparable and can be misleading, and that most of currently implemented training methods tend to have a negative impact on embedding quality. We propose a simple but effective variation of the stratification technique used by PyTorch BigGraph for mitigation. Moreover, basic random partitioning can be an effective or even the best-performing choice when combined with suitable sampling techniques. Ultimately, we found that efficient and effective parallel training of large-scale KGE models is indeed achievable but requires a careful choice of techniques.
Input Reconstruction Attack against Vertical Federated Large Language Models
Authors: Fei Zheng
Date: 2023-11-07 00:00:00
Tasks: Federated Learning, GPU, Reconstruction Attack, Sentence, Text Generation, Vertical Federated Learning

Recently, large language models (LLMs) have drawn extensive attention from academia and the public, due to the advent of the ChatGPT. While LLMs show their astonishing ability in text generation for various tasks, privacy concerns limit their usage in real-life businesses. More specifically, either the user's inputs (the user sends the query to the model-hosting server) or the model (the user downloads the complete model) itself will be revealed during the usage. Vertical federated learning (VFL) is a promising solution to this kind of problem. It protects both the user's input and the knowledge of the model by splitting the model into a bottom part and a top part, which is maintained by the user and the model provider, respectively. However, in this paper, we demonstrate that in LLMs, VFL fails to protect the user input since it is simple and cheap to reconstruct the input from the intermediate embeddings. Experiments show that even with a commercial GPU, the input sentence can be reconstructed in only one second. We also discuss several possible solutions to enhance the privacy of vertical federated LLMs.
Relationship Between Mood, Sleepiness, and EEG Functional Connectivity by 40 Hz Monaural Beats
Authors: Ha-Na Jo, Young-Seok Kweon, Gi-Hwan Shin, Heon-Gyu Kwak, Seong-Whan Lee
Date: 2023-11-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Management

The monaural beat is known that it can modulate brain and personal states. However, which changes in brain waves are related to changes in state is still unclear. Therefore, we aimed to investigate the effects of monaural beats and find the relationship between them. Ten participants took part in five separate random sessions, which included a baseline session and four sessions with monaural beats stimulation: one audible session and three inaudible sessions. Electroencephalogram (EEG) were recorded and participants completed pre- and post-stimulation questionnaires assessing mood and sleepiness. As a result, audible session led to increased arousal and positive mood compared to other conditions. From the neurophysiological analysis, statistical differences in frontal-central, central-central, and central-parietal connectivity were observed only in the audible session. Furthermore, a significant correlation was identified between sleepiness and EEG power in the temporal and occipital regions. These results suggested a more detailed correlation for stimulation to change its personal state. These findings have implications for applications in areas such as cognitive enhancement, mood regulation, and sleep management.
Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications
Authors: Joohyuk Park, Yongjeong Oh, Seonjung Kim, Yo-Seb Jeon
Date: 2023-11-14 00:00:00
Tasks: Decoder, image-classification, Image Classification, Robust Design, Semantic Communication

In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-adaptive digital semantic communications. In semantic communication systems with digital modulation and demodulation, robust design of JSCC encoder and decoder becomes challenging not only due to the unpredictable dynamics of channel conditions but also due to diverse modulation orders. To address this challenge, we first develop a new demodulation method which assesses the uncertainty of the demodulation output to improve the robustness of the digital semantic communication system. We then devise a robust training strategy which enhances the robustness and flexibility of the JSCC encoder and decoder against diverse channel conditions and modulation orders. To this end, we model the relationship between the encoder's output and decoder's input using binary symmetric erasure channels and then sample the parameters of these channels from diverse distributions. We also develop a channel-adaptive modulation technique for an inference phase, in order to reduce the communication latency while maintaining task performance. In this technique, we adaptively determine modulation orders for the latent variables based on channel conditions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for image classification, reconstruction, and retrieval tasks compared to existing JSCC approaches.
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Authors: Wentao Yu, Hengtao He, Xianghao Yu, Shenghui Song, Jun Zhang, Ross D. Murch, Khaled B. Letaief
Date: 2023-11-14 00:00:00

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.
Lateral control for autonomous vehicles: A comparative evaluation
Authors: Antonio Artuñedo, Marcos Moreno-Gonzalez, Jorge Villagra
Date: 2023-11-14 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

The selection of an appropriate control strategy is essential for ensuring safe operation in autonomous driving. While numerous control strategies have been developed for specific driving scenarios, a comprehensive comparative assessment of their performance using the same tuning methodology is lacking in the literature. This paper addresses this gap by presenting a systematic evaluation of state-of-the-art model-free and model-based control strategies. The objective is to evaluate and contrast the performance of these controllers across a wide range of driving scenarios, reflecting the diverse needs of autonomous vehicles. To facilitate the comparative analysis, a comprehensive set of performance metrics is selected, encompassing accuracy, robustness, and comfort. The contributions of this research include the design of a systematic tuning methodology, the use of two novel metrics for stability and comfort comparisons and the evaluation through extensive simulations and real tests in an experimental instrumented vehicle over a wide range of trajectories.
Generative De-Quantization for Neural Speech Codec via Latent Diffusion
Authors: Haici Yang, Inseon Jang, Minje Kim
Date: 2023-11-14 00:00:00
Tasks: Decoder, Quantization, Representation Learning

In low-bitrate speech coding, end-to-end speech coding networks aim to learn compact yet expressive features and a powerful decoder in a single network. A challenging problem as such results in unwelcome complexity increase and inferior speech quality. In this paper, we propose to separate the representation learning and information reconstruction tasks. We leverage an end-to-end codec for learning low-dimensional discrete tokens and employ a latent diffusion model to de-quantize coded features into a high-dimensional continuous space, relieving the decoder's burden of de-quantizing and upsampling. To mitigate the issue of over-smooth generation, we introduce midway-infilling with less noise reduction and stronger conditioning. In ablation studies, we investigate the hyperparameters for midway-infilling and latent diffusion space with different dimensions. Subjective listening tests show that our model outperforms the state-of-the-art at two low bitrates, 1.5 and 3 kbps. Codes and samples of this work are available on our webpage.
Predicting the First Response Latency of Maintainers and Contributors in Pull Requests
Authors: SayedHassan Khatoonabadi, Ahmad Abdellatif, Diego Elias Costa, Emad Shihab
Date: 2023-11-13 00:00:00
Tasks: Feature Importance

The success of a Pull Request (PR) depends on the responsiveness of the maintainers and the contributor during the review process. Being aware of the expected waiting times can lead to better interactions and managed expectations for both the maintainers and the contributor. In this paper, we propose a machine-learning approach to predict the first response latency of the maintainers following the submission of a PR, and the first response latency of the contributor after receiving the first response from the maintainers. We curate a dataset of 20 large and popular open-source projects on GitHub and extract 21 features to characterize projects, contributors, PRs, and review processes. Using these features, we then evaluate seven types of classifiers to identify the best-performing models. We also conduct permutation feature importance and SHAP analyses to understand the importance and the impact of different features on the predicted response latencies. We find that our CatBoost models are the most effective for predicting the first response latencies of both maintainers and contributors. We also observe that PRs submitted earlier in the week, containing an average number of commits, and with concise descriptions are more likely to receive faster first responses from the maintainers. Similarly, PRs with a lower first response latency from maintainers, that received the first response of maintainers earlier in the week, and containing an average number of commits tend to receive faster first responses from the contributors. Additionally, contributors with a higher acceptance rate and a history of timely responses in the project are likely to both obtain and provide faster first responses. Moreover, we show the effectiveness of our approach in a cross-project setting.
Graph GOSPA metric: a metric to measure the discrepancy between graphs of different sizes
Authors: Jinhao Gu, Ángel F. García-Fernández, Robert E. Firth, Lennart Svensson
Date: 2023-11-10 00:00:00
Tasks: Attribute

This paper proposes a metric to measure the dissimilarity between graphs that may have a different number of nodes. The proposed metric extends the generalised optimal subpattern assignment (GOSPA) metric, which is a metric for sets, to graphs. The proposed graph GOSPA metric includes costs associated with node attribute errors for properly assigned nodes, missed and false nodes and edge mismatches between graphs. The computation of this metric is based on finding the optimal assignments between nodes in the two graphs, with the possibility of leaving some of the nodes unassigned. We also propose a lower bound for the metric, which is also a metric for graphs and is computable in polynomial time using linear programming. The metric is first derived for undirected unweighted graphs and it is then extended to directed and weighted graphs. The properties of the metric are demonstrated via simulated and empirical datasets.
Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework
Authors: Amber Srivastava, Alisina Bayati, Srinivasa Salapaka
Date: 2023-11-14 00:00:00
Tasks: regression

This work presents a new approach to solve the sparse linear regression problem, i.e., to determine a k-sparse vector w in R^d that minimizes the cost ||y - Aw||^2_2. In contrast to the existing methods, our proposed approach splits this k-sparse vector into two parts -- (a) a column stochastic binary matrix V, and (b) a vector x in R^k. Here, the binary matrix V encodes the location of the k non-zero entries in w. Equivalently, it encodes the subset of k columns in the matrix A that map w to y. We demonstrate that this enables modeling several non-trivial application-specific structural constraints on w as constraints on V. The vector x comprises of the actual non-zero values in w. We use Maximum Entropy Principle (MEP) to solve the resulting optimization problem. In particular, we ascribe a probability distribution to the set of all feasible binary matrices V, and iteratively determine this distribution and the vector x such that the associated Shannon entropy gets minimized, and the regression cost attains a pre-specified value. The resulting algorithm employs homotopy from the convex entropy function to the non-convex cost function to avoid poor local minimum. We demonstrate the efficacy and flexibility of our proposed approach in incorporating a variety of practical constraints, that are otherwise difficult to model using the existing benchmark methods.
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow
Authors: Rachel Harris, Mohannad Alkhraijah, Daniel K. Molzahn
Date: 2023-11-14 00:00:00
Tasks: Distributed Optimization

The future power grid may rely on distributed optimization to determine the set-points for huge numbers of distributed energy resources. There has been significant work on applying distributed algorithms to optimal power flow (OPF) problems, which require separate computing agents to agree on shared boundary variable values. Looser tolerances for the mismatches in these shared variables generally yield faster convergence at the expense of exacerbating constraint violations, but there is little quantitative understanding of how the convergence tolerance affects solution quality. To address this gap, we first quantify how convergence tolerance impacts constraint violations when the distributed OPF generator dispatch is applied to the power system. Using insights from this analysis, we then develop a bound tightening algorithm which guarantees that operating points from distributed OPF algorithms will not result in violations despite the possibility of shared variable mismatches within the convergence tolerance. We also explore how bounding the cumulative shared variable mismatches can prevent unnecessary conservativeness in the bound tightening. The proposed approach enables control of the trade-off between computational speed, which improves as the convergence tolerance increases, and distributed OPF solution cost, which increases with convergence tolerance due to tightened constraints, while ensuring feasibility.
Data-driven Control Against False Data Injection Attacks
Authors: Wenjie Liu, Lidong Li, Jian Sun, Fang Deng, Gang Wang, Jie Chen
Date: 2023-11-14 00:00:00

The rise of cyber-security concerns has brought significant attention to the analysis and design of cyber-physical systems (CPSs). Among the various types of cyberattacks, denial-of-service (DoS) attacks and false data injection (FDI) attacks can be easily launched and have become prominent threats. While resilient control against DoS attacks has received substantial research efforts, countermeasures developed against FDI attacks have been relatively limited, particularly when explicit system models are not available. To address this gap, the present paper focuses on the design of data-driven controllers for unknown linear systems subject to FDI attacks on the actuators, utilizing input-state data. To this end, a general FDI attack model is presented, which imposes minimally constraints on the switching frequency of attack channels and the magnitude of attack matrices. A dynamic state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving two data-based semi-definite programs (SDPs) on-the-fly to yield a tight approximation of the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack, the proposed SDPs are recursively feasible and controller achieves exponential stability. Numerical examples showcase its effectiveness in mitigating the impact of FDI attacks.
Smart Skin separation control using distributed-input distributed-output, multi-modal actuators, and machine learning
Authors: Songqi Li
Date: 2023-11-14 00:00:00

Efficient flow separation control represents significant economic benefit. This study applies a machine learning algorithm to minimize flow separation in Smart Skin, a flow control device that features distributed-input and distributed-output (DIDO). Smart Skin comprises 30 hybrid actuator units, each integrating a height-adjustable vortex generator and a mini-jet actuator. These units are deployed on a backward-facing ramp to reduce flow separation in a distributed manner. To monitor the flow state, distributed pressure taps are deployed around the multi-modal actuators. Parametric studies indicate that the mapping between control parameters and separation control performance is complex. To optimize separation control, a cutting-edge variant of the particle swarm optimization (PSO-TPME) is used for the control parameters in the Smart Skin. This algorithm is capable of achieving fast optimization in high-dimensional parameter spaces. The results demonstrate the efficiency of PSO-TPME, and the optimized solution significantly outperforms the best result from the parametric study. These findings represent a promising future of machine learning-based flow control using distributed actuators and sensors.
Optimizing Electric Vehicle Efficiency with Real-Time Telemetry using Machine Learning
Authors: Aryaman Rao, Harshit Gupta, Parth Singh, Shivam Mittal, Utkrash Singh, Dinesh Kumar Vishwakarma
Date: 2023-11-14 00:00:00

In the contemporary world with degrading natural resources, the urgency of energy efficiency has become imperative due to the conservation and environmental safeguarding. Therefore, it's crucial to look for advanced technology to minimize energy consumption. This research focuses on the optimization of battery-electric city style vehicles through the use of a real-time in-car telemetry system that communicates between components through the robust Controller Area Network (CAN) protocol. By harnessing real-time data from various sensors embedded within vehicles, our driving assistance system provides the driver with visual and haptic actionable feedback that guides the driver on using the optimum driving style to minimize power consumed by the vehicle. To develop the pace feedback mechanism for the driver, real-time data is collected through a Shell Eco Marathon Urban Concept vehicle platform and after pre-processing, it is analyzed using the novel machine learning algorithm TEMSL, that outperforms the existing baseline approaches across various performance metrics. This innovative method after numerous experimentation has proven effective in enhancing energy efficiency, guiding the driver along the track, and reducing human errors. The driving-assistance system offers a range of utilities, from cost savings and extended vehicle lifespan to significant contributions to environmental conservation and sustainable driving practices.
Nonlinear Stability Boundary Assessment Of Wind Power Plants Based on Reverse-Time Trajectory
Authors: Sujay Ghosh, Mohammad Kazem Bakhshizadeh, Guangya Yang, Lukasz Kocewiak
Date: 2023-11-14 00:00:00

This letter determines the nonlinear stability boundary of a wind power plant (WPP) connected to an AC power grid via a long HVAC cable. The analysis focuses on the slow Phase-Locked Loop (PLL) dynamics, with an assumption that the fast current control dynamics can be neglected. To begin, we propose an aggregated reduced-order wind turbine model. This aggregation can be applied up to a limited frequency, e.g. 400Hz, which aligns with our assumption regarding low-frequency dynamics. The WPP collector and transmission network model is established using impedance/frequency scan approximated around $\pm$5 Hz of the PLL nominal frequency, accounting for the hard saturation limits. The stability boundary of the reduced-order system is determined by reverse time trajectory, offering valuable insights into the WPP's overall stability. The work presents a routine from modelling to nonlinear stability assessment for offshore wind farm applications.