pwc-archive/papers-with-abstracts huggingface dataset:
Learning the nonlinear dynamics of soft mechanical metamaterials with graph networks
Authors: Tianju Xue, Sigrid Adriaenssens, Sheng Mao
Date: 2022-02-24 00:00:00

The dynamics of soft mechanical metamaterials provides opportunities for many exciting engineering applications. Previous studies often use discrete systems, composed of rigid elements and nonlinear springs, to model the nonlinear dynamic responses of the continuum metamaterials. Yet it remains a challenge to accurately construct such systems based on the geometry of the building blocks of the metamaterial. In this work, we propose a machine learning approach to address this challenge. A metamaterial graph network (MGN) is used to represent the discrete system, where the nodal features contain the positions and orientations the rigid elements, and the edge update functions describe the mechanics of the nonlinear springs. We use Gaussian process regression as the surrogate model to characterize the elastic energy of the nonlinear springs as a function of the relative positions and orientations of the connected rigid elements. The optimal model can be obtained by "learning" from the data generated via finite element calculation over the corresponding building block of the continuum metamaterial. Then, we deploy the optimal model to the network so that the dynamics of the metamaterial at the structural scale can be studied. We verify the accuracy of our machine learning approach against several representative numerical examples. In these examples, the proposed approach can significantly reduce the computational cost when compared to direct numerical simulation while reaching comparable accuracy. Moreover, defects and spatial inhomogeneities can be easily incorporated into our approach, which can be useful for the rational design of soft mechanical metamaterials.
Evaluation of non-pharmaceutical interventions and optimal strategies for containing the COVID-19 pandemic
Authors: Xiao Zhou, Xiaohu Zhang, Paolo Santi, Carlo Ratti
Date: 2022-02-28 00:00:00
Tasks: Decision Making

Given multiple new COVID-19 variants are continuously emerging, non-pharmaceutical interventions are still primary control strategies to curb the further spread of coronavirus. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. With an aim to solve this multi-objective decision-making problem, we investigate the underlying associations between policies, mobility patterns, and virus transmission. We further evaluate the relative performance of existing COVID-19 control measures and explore potential optimal strategies that can strike the right balance between public health and socio-economic recovery for individual states in the US. The results highlight the power of state of emergency declaration and wearing face masks and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to inform policy makers of any country about best practices in pandemic response.
CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion
Authors: Anh Le-Tuan, Manh Nguyen-Duc, Chien-Quang Le, Trung-Kien Tran, Manfred Hauswirth, Thomas Eiter, Danh Le-Phuoc
Date: 2022-02-15 00:00:00

We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neural-symbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. As a platform-agnostic framework, CQELS 2.0 can be implemented for devices with different hardware architectures (from embedded devices to cloud infrastructures). Moreover, this version also includes an adaptive federator that allows CQELS instances on different nodes in a network to coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries
Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes
Authors: Jacques Fleischer, Gregor von Laszewski, Carlos Theran, Yohn Jairo Parra Bautista
Date: 2022-02-19 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's code contains Jupyter notebooks, one of which outputs a timeseries graph of any cryptocurrency price once a CSV file of the historical data is inputted into the program. Another Jupyter notebook trains an LSTM, or a long short-term memory model, to predict a cryptocurrency's closing price. The LSTM is fed the close price, which is the price that the currency has at the end of the day, so it can learn from those values. The notebook creates two sets: a training set and a test set to assess the accuracy of the results. The data is then normalized using manual min-max scaling so that the model does not experience any bias; this also enhances the performance of the model. Then, the model is trained using three layers -- an LSTM, dropout, and dense layer-minimizing the loss through 50 epochs of training; from this training, a recurrent neural network (RNN) is produced and fitted to the training set. Additionally, a graph of the loss over each epoch is produced, with the loss minimizing over time. Finally, the notebook plots a line graph of the actual currency price in red and the predicted price in blue. The process is then repeated for several more cryptocurrencies to compare prediction models. The parameters for the LSTM, such as number of epochs and batch size, are tweaked to try and minimize the root mean square error.
Allocating Duplicate Copies for IoT Data in Cloud Computing Based on Harmony Search Algorithm
Authors: Younes Jahandideh, A. Mirzaei
Date: 2022-02-09 00:00:00
Tasks: Cloud Computing, Management

The Internet of things (IoT) generates a plethora of data nowadays, and cloud computing has been introduced as an efficient solution to IoT data management. A cloud resource administrator usually adopts the replication strategy to guarantee the reliability of IoT data. This mechanism can significantly reduce data access time, and evidently, more replicas of data increase the data storage cost. Furthermore, the process of selecting mini clouds for replica allocation and sorting replicas in mini clouds is considered an NP-hard problem. Therefore, this paper proposes an approach based on the harmony search (HS) algorithm to allocate replicas to the IoT data in the cloud computing environment in order to mitigate the data access cost. The HS algorithm was employed in the proposed approach to determine the best location for data replication in the cloud computing environment. According to the implementation results, the proposed approach outperformed the other methods and managed to significantly decrease data access time and delay as well as energy consumption.
GENOME: A GENeric methodology for Ontological Modelling of Epics
Authors: Udaya Varadarajan, Mayukh Bagchi, Amit Tiwari, M. P. Satija
Date: 2022-02-13 00:00:00

Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often, combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. The paper presents, as a unified solution to the above shortcomings, the design and development of GENOME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GENOME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GENOME is validated via a first brief implementation of ontological modelling of the Indian epic - Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GENOME-produced model being both ontologically thorough and performance-wise competent
Formalizing Oracle Trust Models for blockchain-based business applications. An example from the supply chain sector
Authors: Giulio Caldarelli
Date: 2022-02-28 00:00:00

Blockchain technology truly opened the gate to a wave of unparalleled innovations; however, despite the rapidly growing load of hype, the integration into the business, apart from a few applications, seems to be coming at a slower rate. One reason for that delay may be the need in the real-world applications for the so-called trust model. Trust models are rarely mentioned in blockchain application proposals despite their importance, which creates skepticism about their successful developments. To promote trust model implementation and help practitioners in its redaction, this article provides an outline of what a trust model is, why it is essential, and an example of how it is elaborated. The discussed example comes from a case study of a dairy company that implemented blockchain for the traceability of its products. Despite being tailored on a traceability project, the redaction and elements of the trust model, with few adjustments, could be easily readapted for other applications.
Simulating Network Paths with Recurrent Buffering Units
Authors: Divyam Anshumaan, Sriram Balasubramanian, Shubham Tiwari, Nagarajan Natarajan, Sundararajan Sellamanickam, Venkata N. Padmanabhan
Date: 2022-02-23 00:00:00
Tasks: Decision Making, Sequential Decision Making, Time Series, Time Series Analysis

Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packet delay values in response to the time-varying load offered by a sender, which is typically a function of the previously output delays. The problem setting is unique, and renders the state-of-the-art text and time-series generative models inapplicable or ineffective. We formulate an ML problem at the intersection of dynamical systems, sequential decision making, and time-series modeling. We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style model called RBU, providing the interpretability of standard network simulator tools, the power of neural models, the efficiency of SGD-based techniques for learning, and yielding promising results on synthetic and real-world network traces.
Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
Authors: Wanying Ge, Massimiliano Todisco, Nicholas Evans
Date: 2022-02-28 00:00:00
Tasks: Face Swapping, Speaker Verification

Despite several years of research in deepfake and spoofing detection for automatic speaker verification, little is known about the artefacts that classifiers use to distinguish between bona fide and spoofed utterances. An understanding of these is crucial to the design of trustworthy, explainable solutions. In this paper we report an extension of our previous work to better understand classifier behaviour to the use of SHapley Additive exPlanations (SHAP) to attack analysis. Our goal is to identify the artefacts that characterise utterances generated by different attacks algorithms. Using a pair of classifiers which operate either upon raw waveforms or magnitude spectrograms, we show that visualisations of SHAP results can be used to identify attack-specific artefacts and the differences and consistencies between synthetic speech and converted voice spoofing attacks.
ICASSP 2022 Acoustic Echo Cancellation Challenge
Authors: Ross Cutler, Ando Saabas, Tanel Parnamaa, Marju Purin, Hannes Gamper, Sebastian Braun, Karsten Sørensen, Robert Aichner
Date: 2022-02-27 00:00:00
Tasks: Acoustic echo cancellation, Speech Enhancement, speech-recognition, Speech Recognition

The ICASSP 2022 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and still a top issue in audio communication. This is the third AEC challenge and it is enhanced by including mobile scenarios, adding speech recognition rate in the challenge goal metrics, and making the default sample rate 48 kHz. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We also open source an online subjective test framework and provide an online objective metric service for researchers to quickly test their results. The winners of this challenge are selected based on the average Mean Opinion Score achieved across all different single talk and double talk scenarios, and the speech recognition word acceptance rate.
ICASSP 2022 Deep Noise Suppression Challenge
Authors: Harishchandra Dubey, Vishak Gopal, Ross Cutler, Ashkan Aazami, Sergiy Matusevych, Sebastian Braun, Sefik Emre Eskimez, Manthan Thakker, Takuya Yoshioka, Hannes Gamper, Robert Aichner
Date: 2022-02-27 00:00:00

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. This is the 4th DNS challenge, with the previous editions held at INTERSPEECH 2020, ICASSP 2021, and INTERSPEECH 2021. We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P.835 to rate and rank-order the challenge entries. We provide access to DNSMOS P.835 and word accuracy (WAcc) APIs to challenge participants to help with iterative model improvements. In this challenge, we introduced the following changes: (i) Included mobile device scenarios in the blind test set; (ii) Included a personalized noise suppression track with baseline; (iii) Added WAcc as an objective metric; (iv) Included DNSMOS P.835; (v) Made the training datasets and test sets fullband (48 kHz). We use an average of WAcc and subjective scores P.835 SIG, BAK, and OVRL to get the final score for ranking the DNS models. We believe that as a research community, we still have a long way to go in achieving excellent speech quality in challenging noisy real-world scenarios.
Revisiting Over-Smoothness in Text to Speech
Authors: Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu
Date: 2022-02-26 00:00:00
Proceeding: ACL 2022 5
Tasks: text-to-speech, Text to Speech

Non-autoregressive text to speech (NAR-TTS) models have attracted much attention from both academia and industry due to their fast generation speed. One limitation of NAR-TTS models is that they ignore the correlation in time and frequency domains while generating speech mel-spectrograms, and thus cause blurry and over-smoothed results. In this work, we revisit this over-smoothing problem from a novel perspective: the degree of over-smoothness is determined by the gap between the complexity of data distributions and the capability of modeling methods. Both simplifying data distributions and improving modeling methods can alleviate the problem. Accordingly, we first study methods reducing the complexity of data distributions. Then we conduct a comprehensive study on NAR-TTS models that use some advanced modeling methods. Based on these studies, we find that 1) methods that provide additional condition inputs reduce the complexity of data distributions to model, thus alleviating the over-smoothing problem and achieving better voice quality. 2) Among advanced modeling methods, Laplacian mixture loss performs well at modeling multimodal distributions and enjoys its simplicity, while GAN and Glow achieve the best voice quality while suffering from increased training or model complexity. 3) The two categories of methods can be combined to further alleviate the over-smoothness and improve the voice quality. 4) Our experiments on the multi-speaker dataset lead to similar conclusions as above and providing more variance information can reduce the difficulty of modeling the target data distribution and alleviate the requirements for model capacity.
Deep Neural Network for Automatic Assessment of Dysphonia
Authors: Mario Alejandro García, Ana Lorena Rosset
Date: 2022-02-25 00:00:00

The purpose of this work is to contribute to the understanding and improvement of deep neural networks in the field of vocal quality. A neural network that predicts the perceptual assessment of overall severity of dysphonia in GRBAS scale is obtained. The design focuses on amplitude perturbations, frequency perturbations, and noise. Results are compared with performance of human raters on the same data. Both the precision and the mean absolute error of the neural network are close to human intra-rater performance, exceeding inter-rater performance.
On the relevance of bandwidth extension for speaker identification
Authors: Marcos Faundez-Zanuy, Mattias Nilsson, W. Bastiaan Kleijn
Date: 2022-02-24 00:00:00
Tasks: Bandwidth Extension, Speaker Identification

In this paper we discuss the relevance of bandwidth extension for speaker identification tasks. Mainly we want to study if it is possible to recognize voices that have been bandwith extended. For this purpose, we created two different databases (microphonic and ISDN) of speech signals that were bandwidth extended from telephone bandwidth ([300, 3400] Hz) to full bandwidth ([100, 8000] Hz). We have evaluated different parameterizations, and we have found that the MELCEPST parameterization can take advantage of the bandwidth extension algorithms in several situations.
Efficient Stochastic Simulation of Network Topology Effects on the Peak Number of Infections in Epidemic Outbreaks
Authors: Yulian Kuryliak, Michael Emmerich, Dmytro Dosyn
Date: 2022-02-27 00:00:00

This paper investigates the effect of the structure of the contact network on the dynamics of the epidemic outbreak. In particular, we focus on the peak number of critically infected nodes (PCIN), determining the maximum workload of intensive healthcare units which should be kept low. As a model and simulation method, we develop a continuous-time Markov chain (CTMC) model and an efficient simulation-based on Gillespie's Stochastic Simulation Algorithm (SSA). This methods combine a realistic approximation of the stochastic process not relying on the assumptions of mean-field models and large asymptotically large population sizes as in differential equation models, and at the same time an efficient way to simulate networks of moderate size. The approach is analysed for different scenarios, based on data from the COVID-19 outbreak and demographic data from Ukraine. From these results we extract network topology features that need to be considered to effectively decrease the peak number of infections. The CTMC simulation is implemented in python and integrated in a dashboard that can be used for interactive exploration and it is made openly available.
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control
Authors: Ramij R. Hossain, Ratnesh Kumar
Date: 2022-02-28 00:00:00
Tasks: Anomaly Detection, Distributed Optimization, Model Predictive Control

This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance matrix $\mathbf{Y}$ to let it be time-varying and unknown a priori. The prevalent control designs are either (a) centralized, providing optimal solutions but less scalable and susceptible to single-point failures/attacks, or (b) decentralized or localized, having increased scalability and attack resilience but are suboptimal. The proposed distributed solution offers the attractive features of both methodologies, where neighboring nodes share state information to attain a globally optimal solution. In addition, the presented framework provides a data-driven estimation of Y to circumvent the challenging issue of acquiring accurate knowledge of the line impedance (required to form Y). We first introduce the centralized version of the predictive voltage control problem and then transfer it to a distributed version. The distributed version is solved via the alternating direction method of multipliers (ADMM), using only local measurements and communication leveraging the graph structure of the power system. The proposed framework is resilient to prediction uncertainty, modeling error, and communication link failure, and the in-built redundancy within the proposed framework supports anomaly detection in cyberattacks. We validate the proposed methodology for IEEE-30 bus, IEEE-57 bus transmission systems, and IEEE-123 bus distribution systems.
Systematic Stabilization of Constrained Piecewise Affine Systems
Authors: Reza Lavaei, Leila Bridgeman
Date: 2022-02-28 00:00:00

This paper presents an efficient, offline method to simultaneously synthesize controllers and seek closed-loop Lyapunov functions for constrained piecewise affine systems on triangulated subsets of the admissible states. Triangulation refinements explore a rich class of controllers and Lyapunov functions. Since an explicit Lipschitz Lyapunov function is found, an invariant subset of the closed-loop region of attraction is obtained. Moreover, it is a control Lyapunov function, so minimum-norm controllers can be realized through online quadratic programming. It is formulated as a sequence of semi-definite programs. The method avoids computationally burdensome non-convex optimizations and a-priori design choices that are typical of similar existing methods.
Dynamics of inertial pair coupled via frictional interface
Authors: Michael Ruderman, Andrei Zagvozdkin, Dmitrii Rachinskii
Date: 2022-02-28 00:00:00
Tasks: Friction

Understanding the dynamics of two inertial bodies coupled via a friction interface is essential for a wide range of systems and motion control applications. Coupling terms within the dynamics of an inertial pair connected via a passive frictional contact are non-trivial and have long remained understudied in system communities. This problem is particularly challenging from a point of view of modeling the interaction forces and motion state variables. This paper deals with a generalized motion problem in systems with a free (of additional constraints) friction interface, assuming the classical Coulomb friction with discontinuity at the velocity zero crossing. We formulate the dynamics of motion as the closed-form ordinary differential equations containing the sign operator for mapping both, the Coulomb friction and the switching conditions, and discuss the validity of the model in the generalized force and motion coordinates. The system has one active degree of freedom (the driving body) and one passive degree of freedom (the driven body). We demonstrate the global convergence of trajectories for a free system with no external excitation forces. Then, an illustrative case study is presented for a harmonic oscillator with a frictionally coupled second mass that is not grounded or connected to a fixed frame. This simplified example illustrates a realization and main features of the proposed (general) modeling framework. Some future development and related challenges are discussed at the end of the paper.