pwc-archive/papers-with-abstracts huggingface dataset:
Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks
Authors: Eduardo Fonseca, Andres Ferraro, Xavier Serra
Date: 2021-07-01 00:00:00

Recent studies have put into question the commonly assumed shift invariance property of convolutional networks, showing that small shifts in the input can affect the output predictions substantially. In this paper, we analyze the benefits of addressing lack of shift invariance in CNN-based sound event classification. Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps. These methods are implemented via small architectural modifications inserted into the pooling layers of CNNs. We evaluate the effect of these architectural changes on the FSD50K dataset using models of different capacity and in presence of strong regularization. We show that these modifications consistently improve sound event classification in all cases considered. We also demonstrate empirically that the proposed pooling methods increase shift invariance in the network, making it more robust against time/frequency shifts in input spectrograms. This is achieved by adding a negligible amount of trainable parameters, which makes these methods an appealing alternative to conventional pooling layers. The outcome is a new state-of-the-art mAP of 0.541 on the FSD50K classification benchmark.
Learning-based pose edition for efficient and interactive design
Authors: Léon Victor, Alexandre Meyer, Saïda Bouakaz
Date: 2021-07-01 00:00:00

Authoring an appealing animation for a virtual character is a challenging task. In computer-aided keyframe animation artists define the key poses of a character by manipulating its underlying skeletons. To look plausible, a character pose must respect many ill-defined constraints, and so the resulting realism greatly depends on the animator's skill and knowledge. Animation software provide tools to help in this matter, relying on various algorithms to automatically enforce some of these constraints. The increasing availability of motion capture data has raised interest in data-driven approaches to pose design, with the potential of shifting more of the task of assessing realism from the artist to the computer, and to provide easier access to nonexperts. In this article, we propose such a method, relying on neural networks to automatically learn the constraints from the data. We describe an efficient tool for pose design, allowing na{\"i}ve users to intuitively manipulate a pose to create character animations.
VStegNET: Video Steganography Networkusing Spatio-Temporal features andMicro-Bottleneck
Authors: Aayush Mishra, Suraj Kumar, Aditya Nigam, Saiful Islam
Date: 2019-09-01 00:00:00
Proceeding: ? 2019 9
Tasks: Image Steganography, SSIM, Steganalysis

Steganography is the practice of hiding a secret message in a cover message such thatthe cover stays indiscernible after hiding and only the intended recipients can extract thesecret from it.  Traditional image steganography techniques hide the secret image intohigh-frequency regions of the cover images.  These techniques typically result in lowerembedding  ratios  and  easy  detection.   In  this  paper,  we  propose  VStegNET,  a  videosteganography network that extracts spatio-temporal features using 3D-CNN and micro-bottleneck (Hourglass) which is the first of its kind in the literature of video steganog-raphy.  The proposed network hidesM×N(RGB) secret video frames into same sizedcover video frames.  We have trained our model onUCF 101 action recognitionvideodataset and evaluated its performance using various quantitative metrics (APD, PSNR,and SSIM) and compared it with previous the state-of-the-art. Furthermore, we have alsopresented a detailed analysis, supporting the proposal’s superiority over image steganog-raphy models. Finally, several standard steganalysis tools like StegExpose, SRNET, etc.have been used to justify the steganographic capabilities of VStegNET.
An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography
Authors: AYESHA SAEED, FAWAD, MUHAMMAD JAMIL KHAN, HUMAYUN SHAHID, SYEDA IFFAT NAQVI, MUHAMMAD ALI RIAZ, MANSOOR SHAUKAT KHAN, YASAR AMIN
Date: 2020-01-20 00:00:00
Proceeding: journal IEEE 2020 1
Tasks: Image Segmentation, Image Steganography, Semantic Segmentation, SSIM

Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identi cation, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identi ed based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass lter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.
A New Persian Text Summarization Approach Based on Natural Language Processing and Graph Similarity
Authors: Tayyebeh Hosseinikhah, Abbas Ahmadi, Azadeh Mohebi
Date: 2017-02-01 00:00:00
Proceeding: Iranian Journal of Onformation Processing and Management 2017 2
Tasks: Articles, Extractive Text Summarization, Graph Similarity, Part-Of-Speech Tagging, Sentence, Text Summarization

Abstract: A significant amount of available information is stored in textual
databases which contain a large collection of documents from different
sources (such as news, articles, books, emails and web pages). The
increasing visibility and importance of this class of information motivates
us to work on having better automatic evaluation tools for textual
resources.
The automatic summarization of text is one of the ways to prevent
the waste of users’ time. The extractive text summarization consists
of the extraction of the more important sentences with the purpose of
shortening input text while maintaining the topics covered and the
subjects discussed.
In this paper, we have tried to improve the accuracy of the extracted
summaries by combining natural language processing and text mining
techniques. By modifying the mentioned algorithms and sentence
scoring measures, accuracy is increased as compared to the previously
used techniques.
Part of speech tagging is used for calculating coefficient of words’
importance. Using this approach will in turn help us with picking the more
meaningful words and phrases that will result in better accuracy of the
system.
Graph similarity’s methods are used to select sentences. Changing weight
of the selected sentences in each step leads to solve the redundancy
problem.
A Discrete-time Reputation-based Resilient Consensus Algorithm for Synchronous or Asynchronous Communications
Authors: Guilherme Ramos, Daniel Silvestre, Carlos Silvestre
Date: 2021-07-01 00:00:00

We tackle the problem of a set of agents achieving resilient consensus in the presence of attacked agents. We present a discrete-time reputation-based consensus algorithm for synchronous and asynchronous networks by developing a local strategy where, at each time, each agent assigns a reputation (between zero and one) to each neighbor. The reputation is then used to weigh the neighbors' values in the update of its state. Under mild assumptions, we show that: (i) the proposed method converges exponentially to the consensus of the regular agents; (ii) if a regular agent identifies a neighbor as an attacked node, then it is indeed an attacked node; (iii) if the consensus value of the normal nodes differs from that of any of the attacked nodes' values, then the reputation that a regular agent assigns to the attacked neighbors goes to zero. Further, we extend our method to achieve resilience in the scenarios where there are noisy nodes, dynamic networks and stochastic node selection. Finally, we illustrate our algorithm with several examples, and we delineate some attacking scenarios that can be dealt by the current proposal but not by the state-of-the-art approaches.
Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting
Authors: Justin Luke, Mauro Salazar, Ram Rajagopal, Marco Pavone
Date: 2021-07-01 00:00:00

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.
Prediction of tone detection thresholds in interaurally delayed noise based on interaural phase difference fluctuations
Authors: Mathias Dietz, Jörg Encke, Kristin I. Bracklo, Stephan D. Ewert
Date: 2021-07-01 00:00:00

Differences between the interaural phase of a noise and a target tone improve detection thresholds. The maximum masking release is obtained for detecting an antiphasic tone (S$\pi$) in diotic noise (N0). It has been shown in several studies that this benefit gradually declines as an interaural delay is applied to the N0S$\pi$ complex. This decline has been attributed to the reduced interaural coherence of the noise. Here, we report detection thresholds for a 500 Hz tone in masking noise with up to 8 ms interaural delay and bandwidths from 25 to 1000 Hz. When reducing the noise bandwidth from 100 to 50 and 25 Hz, the masking release at 8 ms delay increases, as expected for increasing temporal coherence with decreasing bandwidth. For bandwidths of 100 to 1000 Hz, no significant difference was observed and detection thresholds with these noises have a delay dependence that is fully described by the temporal coherence imposed by the typical monaurally determined auditory filter bandwidth. A minimalistic binaural model is suggested based on interaural phase difference fluctuations without the assumption of delay lines.
Using Self-Supervised Feature Extractors with Attention for Automatic COVID-19 Detection from Speech
Authors: John Mendonça, Rubén Solera-Ureña, Alberto Abad, Isabel Trancoso
Date: 2021-06-30 00:00:00

The ComParE 2021 COVID-19 Speech Sub-challenge provides a test-bed for the evaluation of automatic detectors of COVID-19 from speech. Such models can be of value by providing test triaging capabilities to health authorities, working alongside traditional testing methods. Herein, we leverage the usage of pre-trained, problem agnostic, speech representations and evaluate their use for this task. We compare the obtained results against a CNN architecture trained from scratch and traditional frequency-domain representations. We also evaluate the usage of Self-Attention Pooling as an utterance-level information aggregation method. Experimental results demonstrate that models trained on features extracted from self-supervised models perform similarly or outperform fully-supervised models and models based on handcrafted features. Our best model improves the Unweighted Average Recall (UAR) from 69.0\% to 72.3\% on a development set comprised of only full-band examples and achieves 64.4\% on the test set. Furthermore, we study where the network is attending, attempting to draw some conclusions regarding its explainability. In this relatively small dataset, we find the network attends especially to vowels and aspirates.
Sequence-level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models
Authors: Amber Afshan, Kshitiz Kumar, Jian Wu
Date: 2021-06-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Scores from traditional confidence classifiers (CCs) in automatic speech recognition (ASR) systems lack universal interpretation and vary with updates to the underlying confidence or acoustic models (AMs). In this work, we build interpretable confidence scores with an objective to closely align with ASR accuracy. We propose a new sequence-level CC with a richer context providing CC scores highly correlated with ASR accuracy and scores stable across CC updates. Hence, expanding CC applications. Recently, AM customization has gained traction with the widespread use of unified models. Conventional adaptation strategies that customize AM expect well-matched data for the target domain with gold-standard transcriptions. We propose a cost-effective method of using CC scores to select an optimal adaptation data set, where we maximize ASR gains from minimal data. We study data in various confidence ranges and optimally choose data for AM adaptation with KL-Divergence regularization. On the Microsoft voice search task, data selection for supervised adaptation using the sequence-level confidence scores achieves word error rate reduction (WERR) of 8.5% for row-convolution LSTM (RC-LSTM) and 5.2% for latency-controlled bidirectional LSTM (LC-BLSTM). In the semi-supervised case, with ASR hypotheses as labels, our method provides WERR of 5.9% and 2.8% for RC-LSTM and LC-BLSTM, respectively.
Computationally efficient spatial rendering of late reverberation in virtual acoustic environments
Authors: Christoph Kirsch, Josef Poppitz, Torben Wendt, Steven van de Par, Stephan D. Ewert
Date: 2021-06-30 00:00:00
Tasks: Computational Efficiency

For 6-DOF (degrees of freedom) interactive virtual acoustic environments (VAEs), the spatial rendering of diffuse late reverberation in addition to early (specular) reflections is important. In the interest of computational efficiency, the acoustic simulation of the late reverberation can be simplified by using a limited number of spatially distributed virtual reverb sources (VRS) each radiating incoherent signals. A sufficient number of VRS is needed to approximate spatially anisotropic late reverberation, e.g., in a room with inhomogeneous distribution of absorption at the boundaries. Here, a highly efficient and perceptually plausible method to generate and spatially render late reverberation is suggested, extending the room acoustics simulator RAZR [Wendt et al., J. Audio Eng. Soc., 62, 11 (2014)]. The room dimensions and frequency-dependent absorption coefficients at the wall boundaries are used to determine the parameters of a physically-based feedback delay network (FDN) to generate the incoherent VRS signals. The VRS are spatially distributed around the listener with weighting factors representing the spatially subsampled distribution of absorption coefficients on the wall boundaries. The minimum number of VRS required to be perceptually distinguishable from the maximum (reference) number of 96 VRS was assessed in a listening test conducted with a spherical loudspeaker array within an anechoic room. For the resulting low numbers of VRS suited for spatial rendering, optimal physically-based parameter choices for the FDN are discussed.
Using Terminal Circuit for Power System Electromagnetic Transient Simulation
Authors: Yijing Liu, Xiang Zhang, Renchang Dai, Guangyi Liu
Date: 2021-07-01 00:00:00

The modern power system is evolving with increasing penetration of power electronics introducing complicated electromagnetic phenomenon. Electromagnetic transient (EMT) simulation is essential to understand power system behavior under disturbance which however is one of the most sophisticated and time-consuming applications in power system. To improve the electromagnetic transient simulation efficiency while keeping the simulation accuracy, this paper proposes to model and simulate power system electromagnetic transients by very large-scale integrated circuit (VLSI) as a preliminary exploration to eventually represent power system by VLSI circuit chip avoiding numerical calculation. To proof the concept, a simple 5 bus system is modeled and simulated to verify the feasibility of the proposed approach.
Multistage Stochastic Model Predictive Control for Urban Automated Driving
Authors: Tommaso Benciolini, Tim Brüdigam, Marion Leibold
Date: 2021-07-01 00:00:00
Tasks: Model Predictive Control, Motion Planning, Trajectory Planning

Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion planning. Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently low. For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately. A high-level layer takes advantage of a long prediction horizon and of an abstract model to plan the optimal maneuver, and a lower level is in charge of executing the selected maneuver by properly planning the vehicle's trajectory. Numerical simulations are included, showing the potential of our proposal.
Safety Embedded Control of Nonlinear Systems via Barrier States
Authors: Hassan Almubarak, Nader Sadegh, Evangelos A. Theodorou
Date: 2021-02-20 00:00:00

In many safety-critical control systems, possibly opposing safety restrictions and control performance objectives arise. To confront such a conflict, this letter proposes a novel methodology that embeds safety into stability of control systems. The development enforces safety by means of barrier functions used in optimization through the construction of barrier states (BaS) which are embedded in the control system's model. As a result, as long as the equilibrium point of interest of the closed loop system is asymptotically stable, the generated trajectories are guaranteed to be safe. Consequently, a conflict between control objectives and safety constraints is substantially avoided. To show the efficacy of the proposed technique, we employ barrier states with the simple pole placement method to design safe linear controls. Nonlinear optimal control is subsequently employed to fulfill safety, stability and performance objectives by solving the associated Hamilton-Jacobi-Bellman (HJB) which minimizes a cost functional that can involve the BaS. Following this further, we exploit optimal control with barrier states on an unstable, constrained second dimensional pendulum on a cart model that is desired to avoid low velocities regions where the system may exhibit some controllability loss and on two mobile robots to safely arrive to opposite targets with an obstacle on the way.
AoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks
Authors: Amir Hossein Zarif, Paeiz Azmi, Nader Mokari, Mohammad Reza Javan, Eduard Jorswieck
Date: 2021-07-01 00:00:00

Spectrum sharing is a method to solve the problem of frequency spectrum deficiency. This paper studies a novel AI based spectrum sharing and energy harvesting system in which the freshness of information (AoI) is guaranteed. The system includes a primary user with access rights to the spectrum and a secondary user. The secondary user is an energy harvesting sensor that intends to use the primary user spectrum opportunistically. The problem is formulated as partially observable Markov decision processes (POMDPs) and solved using two methods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) to achieve the optimal policy. The purpose is to choose the best action adaptively in every time slot based on its situation in both overlay and underlay modes to minimize the average AoI of the secondary user. Finally, simulation experiments are performed to evaluate the effectiveness of the proposed scheme compared to the overlay mode. According to the results, the average AoI in the proposed system is less than that of the existing models, including only overlay mode. The average user access improved from 30% in the overlay mode to 45% in the DQN and 48% in the D3QN.
Learned Global Optimization for Inverse Scattering Problems -- Matching Global Search with Computational Efficiency
Authors: Marco Salucci, Lorenzo Poli, Paolo Rocca, Andrea Massa
Date: 2021-07-01 00:00:00
Tasks: Computational Efficiency, global-optimization

The computationally-efficient solution of fully non-linear microwave inverse scattering problems (ISPs) is addressed. An innovative System-by-Design (SbD) based method is proposed to enable, for the first time to the best of the authors knowledge, an effective, robust, and time-efficient exploitation of an evolutionary algorithm (EA) to perform the global minimization of the data-mismatch cost function. According to the SbD paradigm as suitably applied to ISPs, the proposed approach founds on (i) a smart re-formulation of the ISP based on the definition of a minimum-dimensionality and representative set of degrees-of-freedom (DoFs) and on (ii) the artificial-intelligence (AI)-driven integration of a customized global search technique with a digital twin (DT) predictor based on the Gaussian Process (GP) theory. Representative numerical and experimental results are provided to assess the effectiveness and the efficiency of the proposed approach also in comparison with competitive state-of-the-art inversion techniques.