pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Learning for Wi-Fi AP Load Prediction
Authors: Dariush Salami, Francesc Wilhelmi, Lorenzo Galati-Giordano, Mika Kasslin
Date: 2024-04-22 00:00:00
Tasks: Federated Learning, Knowledge Distillation, Prediction

The increasing cloudification and softwarization of networks foster the interplay among multiple independently managed deployments. An appealing reason for such an interplay lies in distributed Machine Learning (ML), which allows the creation of robust ML models by leveraging collective intelligence and computational power. In this paper, we study the application of the two cornerstones of distributed learning, namely Federated Learning (FL) and Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use case. The analysis conducted in this paper is done on a dataset that contains real measurements from a large Wi-Fi campus network, which we use to train the ML model under study based on different strategies. Performance evaluation includes relevant aspects for the suitability of distributed learning operation in real use cases, including the predictive performance, the associated communication overheads, or the energy consumption. In particular, we prove that distributed learning can improve the predictive accuracy centralized ML solutions by up to 93% while reducing the communication overheads and the energy cost by 80%.
SingIt! Singer Voice Transformation
Authors: Amit Eliav, Aaron Taub, Renana Opochinsky, Sharon Gannot
Date: 2024-05-07 00:00:00
Tasks: Style Transfer, Transfer Learning

In this paper, we propose a model which can generate a singing voice from normal speech utterance by harnessing zero-shot, many-to-many style transfer learning. Our goal is to give anyone the opportunity to sing any song in a timely manner. We present a system comprising several available blocks, as well as a modified auto-encoder, and show how this highly-complex challenge can be achieved by tailoring rather simple solutions together. We demonstrate the applicability of the proposed system using a group of 25 non-expert listeners. Samples of the data generated from our model are provided.
BERP: A Blind Estimator of Room Parameters for Single-Channel Noisy Speech Signals
Authors: Lijun Wang, Yixian Lu, Ziyan Gao, Kai Li, Jianqiang Huang, Yuntao Kong, Shogo Okada
Date: 2024-05-07 00:00:00
Tasks: Room Impulse Response (RIR)

Room acoustical parameters (RAPs), room geometrical parameters (RGPs) and instantaneous occupancy level are essential metrics for parameterizing the room acoustical characteristics (RACs) of a sound field around a listener's local environment, offering comprehensive indications for various applications. Current blind estimation methods either fail to cover a broad range of real-world acoustic environments in the context of real background noise or estimate only a few RAPs and RGPs from noisy single-channel speech signals. In addition, they are limited in their ability to estimate the instantaneous occupancy level. In this paper, we propose a new universal blind estimation framework called the blind estimator of room parameters (BERP) to estimate RAPs, RGPs and occupancy level via a unified methodology. It consists of two modules: a unified room feature encoder that combines attention mechanisms with convolutional layers to learn common features across room parameters, and multiple separate parametric predictors for continuous estimation of each parameter in parallel. The combination of attention and convolutions enables the model to capture acoustic features locally and globally from speech, yielding more robust and multitask generalizable common features. Separate predictors allow the model to independently optimize for each room parameter to reduce task learning conflict and improve per-task performance. This estimation framework enables universal and efficient estimation of room parameters while maintaining satisfactory performance. To evaluate the effectiveness of the proposed framework, we compile a task-specific dataset from several publicly available datasets, including synthetic and real reverberant recordings. The results reveal that BERP achieves state-of-the-art (SOTA) performance and excellent adaptability to real-world scenarios. The code and weights are available on GitHub.
Speaker Characterization by means of Attention Pooling
Authors: Federico Costa, Miquel India, Javier Hernando
Date: 2024-05-07 00:00:00
Tasks: Emotion Recognition, Speaker Recognition, Speaker Verification

State-of-the-art Deep Learning systems for speaker verification are commonly based on speaker embedding extractors. These architectures are usually composed of a feature extractor front-end together with a pooling layer to encode variable-length utterances into fixed-length speaker vectors. The authors have recently proposed the use of a Double Multi-Head Self-Attention pooling for speaker recognition, placed between a CNN-based front-end and a set of fully connected layers. This has shown to be an excellent approach to efficiently select the most relevant features captured by the front-end from the speech signal. In this paper we show excellent experimental results by adapting this architecture to other different speaker characterization tasks, such as emotion recognition, sex classification and COVID-19 detection.
Exploring Speech Pattern Disorders in Autism using Machine Learning
Authors: Chuanbo Hu, Jacob Thrasher, Wenqi Li, Mindi Ruan, Xiangxu Yu, Lynn K Paul, Shuo Wang, Xin Li
Date: 2024-05-03 00:00:00
Tasks: Diagnostic, regression, Rhythm

Diagnosing autism spectrum disorder (ASD) by identifying abnormal speech patterns from examiner-patient dialogues presents significant challenges due to the subtle and diverse manifestations of speech-related symptoms in affected individuals. This study presents a comprehensive approach to identify distinctive speech patterns through the analysis of examiner-patient dialogues. Utilizing a dataset of recorded dialogues, we extracted 40 speech-related features, categorized into frequency, zero-crossing rate, energy, spectral characteristics, Mel Frequency Cepstral Coefficients (MFCCs), and balance. These features encompass various aspects of speech such as intonation, volume, rhythm, and speech rate, reflecting the complex nature of communicative behaviors in ASD. We employed machine learning for both classification and regression tasks to analyze these speech features. The classification model aimed to differentiate between ASD and non-ASD cases, achieving an accuracy of 87.75%. Regression models were developed to predict speech pattern related variables and a composite score from all variables, facilitating a deeper understanding of the speech dynamics associated with ASD. The effectiveness of machine learning in interpreting intricate speech patterns and the high classification accuracy underscore the potential of computational methods in supporting the diagnostic processes for ASD. This approach not only aids in early detection but also contributes to personalized treatment planning by providing insights into the speech and communication profiles of individuals with ASD.
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks
Authors: Mohamad Fazelnia, Viktoria Koscinski, Spencer Herzog, Mehdi Mirakhorli
Date: 2024-04-24 00:00:00
Tasks: Chatbot, Natural Language Inference, Transfer Learning

We investigate the use of Natural Language Inference (NLI) in automating requirements engineering tasks. In particular, we focus on three tasks: requirements classification, identification of requirements specification defects, and detection of conflicts in stakeholders' requirements. While previous research has demonstrated significant benefit in using NLI as a universal method for a broad spectrum of natural language processing tasks, these advantages have not been investigated within the context of software requirements engineering. Therefore, we design experiments to evaluate the use of NLI in requirements analysis. We compare the performance of NLI with a spectrum of approaches, including prompt-based models, conventional transfer learning, Large Language Models (LLMs)-powered chatbot models, and probabilistic models. Through experiments conducted under various learning settings including conventional learning and zero-shot, we demonstrate conclusively that our NLI method surpasses classical NLP methods as well as other LLMs-based and chatbot models in the analysis of requirements specifications. Additionally, we share lessons learned characterizing the learning settings that make NLI a suitable approach for automating requirements engineering tasks.
CodeFort: Robust Training for Code Generation Models
Authors: Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
Date: 2024-04-11 00:00:00
Tasks: Code Generation, Contrastive Learning, Data Augmentation

Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models. Although improving the robustness of code generation models is crucial to enhancing user experience in real-world applications, existing research efforts do not address this issue. To fill this gap, we propose CodeFort, a framework to improve the robustness of code generation models, generalizing a large variety of code perturbations to enrich the training data and enabling various robust training strategies, mixing data augmentation, batch augmentation, adversarial logits pairing, and contrastive learning, all carefully designed to support high-throughput training. Extensive evaluations show that we increase the average robust pass rates of baseline CodeGen models from 14.79 to 21.74. We notably decrease the robustness drop rate from 95.02% to 54.95% against code-syntax perturbations.
GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
Authors: Junxiang Wang, Liang Zhao
Date: 2024-05-06 00:00:00

We introduce GraphSL, a new library for studying the graph source localization problem. graph diffusion and graph source localization are inverse problems in nature: graph diffusion predicts information diffusions from information sources, while graph source localization predicts information sources from information diffusions. GraphSL facilitates the exploration of various graph diffusion models for simulating information diffusions and enables the evaluation of cutting-edge source localization approaches on established benchmark datasets. The source code of GraphSL is made available at Github Repository (https://github.com/xianggebenben/GraphSL). Bug reports and feedback can be directed to the Github issues page (https://github.com/xianggebenben/GraphSL/issues).
SoMeR: Multi-View User Representation Learning for Social Media
Authors: Siyi Guo, Keith Burghardt, Valeria Pantè, Kristina Lerman
Date: 2024-05-02 00:00:00
Tasks: Contrastive Learning, Link Prediction, Recommendation Systems, Representation Learning

Social media user representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations are critical to a range of social problems, including predicting user behaviors and detecting inauthentic accounts. However, existing methods are either designed for commercial applications, or rely on specific features like text contents, activity patterns, or platform metadata, failing to holistically model user behavior across different modalities. To address these limitations, we propose SoMeR, a Social Media user Representation learning framework that incorporates temporal activities, text contents, profile information, and network interactions to learn comprehensive user portraits. SoMeR encodes user post streams as sequences of time-stamped textual features, uses transformers to embed this along with profile data, and jointly trains with link prediction and contrastive learning objectives to capture user similarity. We demonstrate SoMeR's versatility through three applications: 1) Identifying information operation driver accounts, 2) Measuring online polarization after major events, and 3) Predicting future user participation in Reddit hate communities. SoMeR provides new solutions to better understand user behavior in the socio-political domains, enabling more informed decisions and interventions.
Dynamics of a Towed Cable with Sensor-Array for Underwater Target Motion Analysis
Authors: Rohit Kumar Singh, Subrata Kumar, Shovan Bhaumik
Date: 2024-05-09 00:00:00

During a war situation, many times an underwater target motion analysis (TMA) is performed using bearing-only measurements, obtained from a sensor array, which is towed by an own-ship with the help of a connected cable. It is well known that the own-ship is required to perform a manoeuvre in order to make the system observable and localise the target successfully. During the maneuver, it is important to know the location of the sensor array with respect to the own-ship. This paper develops a dynamic model of a cable-sensor array system to localise the sensor array, which is towed behind a sea-surface vessel. We adopt a lumped-mass approach to represent the towed cable. The discretized cable elements are modelled as an interconnected rigid body, kinematically related to one another. The governing equations are derived by balancing the moments acting on each node. The derived dynamics are solved simultaneously for all the nodes to determine the orientation of the cable and sensor array. The position of the sensor array obtained from this proposed model will further be used by TMA algorithms to enhance the accuracy of the tracking system.
Distributed Estimation for a 3-D Moving Target in Quaternion Space with Unknown Correlation
Authors: Yizhi Zhou, Xufan Liu, Xuan Wang
Date: 2024-05-09 00:00:00
Tasks: State Estimation

For distributed estimations in a sensor network, the consistency and accuracy of an estimator are greatly affected by the unknown correlations between individual estimates. An inconsistent or too conservative estimate may degrade the estimation performance and even cause divergence of the estimator. Cooperative estimation methods based on Inverse Covariance Intersection (ICI) can utilize a network of sensors to provide a consistent and tight estimate of a target. In this paper, unlike most existing ICI-based estimators that only consider two-dimensional (2-D) target state estimation in the vector space, we address this problem in a 3-D environment by extending the ICI algorithm to the augmented quaternion space. In addition, the proposed algorithm is fully distributed, as each agent only uses the local information from itself and its communication neighbors, which is also robust to a time-varying communication topology. To evaluate the performance, we test the proposed algorithm in a camera network to track the pose of a target. Extensive Monte Carlo simulations have been performed to show the effectiveness of our approach.
Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management
Authors: George Jones, Angel Garcia-Fernandez, Christian Blackman
Date: 2024-05-09 00:00:00
Tasks: Management

In this paper, we propose an algorithm for non-myopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type minimisation problem, whose cost function is the mean square generalised optimal sub-pattern assignment (GOSPA) error, over a future time window. We also propose an implementation of the sensor management algorithm based on an upper bound of the mean square GOSPA error and a Gaussian single-target posterior. Finally, we develop a Monte Carlo tree search algorithm to find an approximate optimal action within a given computational budget. The benefits of the proposed approach are demonstrated via simulations.
Achieving Precisely-Assigned Performance Requirements for Spacecraft Attitude Control
Authors: Jiakun Lei
Date: 2024-05-09 00:00:00

This paper investigates the attitude control problem of spacecraft, with the objective of achieving precise performance criteria including precise settling time, steady-state error, and overshoot elimination. To tackle this challenge, we propose the Precisely-Assigned Performance (PAP) control scheme. Firstly, we utilize a parameterized function to explicitly characterize a reference for the transient responses, termed the Reference Performance Function (RPF). Subsequently, leveraging the concept of the RPF, we define a performance-satisfied tube region and introduce the concept of control barrier functions to derive a sufficient condition for the state trajectory to converge and remain confined within this tube region. By introducing the concept of Sontag's universal formula for stabilization, a PAP controller, constructed based on the backstepping method, is then designed to guide the system to satisfy these affine constraint conditions, and a disturbance observer is further integrated to handle perturbations. Theoretical proofs are presented to demonstrate the controller's capability to establish the boundedness of the overall system and ensure that each state trajectory will converge into the performance-satisfied region within a finite time duration under any conditions. Finally, numerical simulation results are presented to validate the effectiveness of the proposed method.
2-16 GHz Multifrequency X-Cut Lithium Niobate NEMS Resonators on a Single Chip
Authors: Ryan Tetro, Luca Colombo, Matteo Rinaldi
Date: 2024-05-09 00:00:00

This work presents the design, fabrication, and testing of X-Cut Lithium Niobate (LN) acoustic nanoelectromechanical (NEMS) Laterally Vibrating Resonators (LVRs) and Degenerate LVRs (d-LVRs) operating in the S0 (YZ30) and SH0 (YZ-10) modes between 2 to 16 GHz range, monolithically fabricated on a single chip. The NEMS topology is optimized to extend the aforementioned fundamental modes in the C-, X-, and Ku-bands while preserving performance and mass manufacturability. The devices present acoustic wavelengths ({\lambda}) varying between 1800 and 400 nm and are fabricated on a 100 nm ultra-thin LN film on high resistivity silicon with a 3-mask process. Experimental results highlighted quality factor at resonance (Qs) and mechanical quality factors (Qm) as high as 477 and 1750, respectively, and electromechanical coupling (kt2) as high as 32.7%. Large kt2 (>10%) are recorded over a broad range of frequencies (2 - 8 GHz), while Qm exceeding 100 are measured up to 15 GHz. Further enhancement to performance and range of operation on the same chip can be achieved by decreasing {\lambda}, refining the fabrication process, and optimizing device topology. These additional steps can help pave the way for manufacturing high-performance resonators on a single chip covering the entire 1 - 25 GHz spectrum.
ATLS: Automated Trailer Loading for Surface Vessels
Authors: Amer Abughaida, Meet Gandhi, Jun Heo, Vaishnav Tadiparthi, Yosuke Sakamoto, Joohyun Woo, Sangjae Bae
Date: 2024-05-08 00:00:00

Automated docking technologies of marine boats have been enlightened by an increasing number of literature. This paper contributes to the literature by proposing a mathematical framework that automates "trailer loading" in the presence of wind disturbances, which is unexplored despite its importance to boat owners. The comprehensive pipeline of localization, system identification, and trajectory optimization is structured, followed by several techniques to improve performance reliability. The performance of the proposed method was demonstrated with a commercial pontoon boat in Michigan, in 2023, securing a success rate of 80\% in the presence of perception errors and wind disturbance. This result indicates the strong potential of the proposed pipeline, effectively accommodating the wind effect.
3-way equal filtering power divider using compact folded-arms square open-Loop resonator
Authors: Augustine O. Nwajana, Rose Paul
Date: 2024-05-08 00:00:00
Tasks: Dielectric Constant

Microstrip three-way (that is, 4.8 dB) integrated filtering power divider (FPD) is presented in this paper. The proposed FPD evenly distributes an input power signal into three equal output signals. The design incorporates balanced signal power division, and filtering technology for the removal of unwanted frequency elements and aimed at enhancing signal quality and efficiency in the radiofrequency (RF) front-end of communication systems. Microstrip folded-arms square open-loop resonator (FASOLR) is employed in the design implementation to achieve compact size. The proposed FPD features a 2.6 GHz centre frequency, with a 0.03 fractional bandwidth. The implementation is carried out on Rogers RT/Duroid 6010LM substrate with a dielectric constant of 10.7, a thickness of 1.27 mm and a loss tangent of 0.0023. The good agreement between the theoretical and practical results verifies the effectiveness of the FPD in delivering equal power outputs at the three output ports, and at the same time filtering out unwanted frequencies. The practical results of the prototype FPD indicate a good return loss of better than 15.5 dB and an insertion loss of better than 4.77+0.34 dB. The design prototype achieved compact size of 0.31 {\lambda}g x 0.18 {\lambda}g. {\lambda}g is the guided wavelength for the microstrip line impedance at the centre frequency of the 3-way equal filtering power divider.
Eco-driving Accounting for Interactive Cut-in Vehicles
Authors: Chaozhe R. He, Nan Li
Date: 2024-05-08 00:00:00

Automated vehicles can gather information about surrounding traffic and plan safe and energy-efficient driving behavior, which is known as eco-driving. Conventional eco-driving designs only consider preceding vehicles in the same lane as the ego vehicle. In heavy traffic, however, vehicles in adjacent lanes may cut into the ego vehicle's lane, influencing the ego vehicle's eco-driving behavior and compromising the energy-saving performance. Therefore, in this paper, we propose an eco-driving design that accounts for neighbor vehicles that have cut-in intentions. Specifically, we integrate a leader-follower game to predict the interaction between the ego and the cut-in vehicles and a model-predictive controller for planning energy-efficient behavior for the automated ego vehicle. We show that the leader-follower game model can reasonably represent the interactive motion between the ego vehicle and the cut-in vehicle. More importantly, we show that the proposed design can predict and react to neighbor vehicles' cut-in behaviors properly, leading to improved energy efficiency in cut-in scenarios compared to baseline designs that consider preceding vehicles only.
Dissipativity Conditions for Maximum Dynamic Loadability
Authors: Riley Lawson, Marija Ilic
Date: 2024-05-08 00:00:00

In this paper we consider a possibility of stabilizing very fast electromagnetic interactions between Inverter Based Resources (IBRs), known as the Control Induced System Stability problems. We propose that when these oscillatory interactions are controlled the ability of the grid to deliver power to loads at high rates will be greatly increased. We refer to this grid property as the dynamic grid loadability. The approach is to start by modeling the dynamical behavior of all components. Next, to avoid excessive complexity, interactions between components are captured in terms of unified technology-agnostic aggregate variables, instantaneous power and rate of change of instantaneous reactive power. Sufficient dissipativity conditions in terms of rate of change of energy conversion in components themselves and bounds on their rate of change of interactions are derived in support of achieving the maximum system loadability. These physically intuitive conditions are then used to derive methods to increase loadability using high switching frequency reactive power sources. Numerical simulations confirm the theoretical calculations, and shows dynamic load-side reactive power support increases stable dynamic loadability regions.