pwc-archive/papers-with-abstracts huggingface dataset:
Audio Deep Fake Detection System with Neural Stitching for ADD 2022
Authors: Rui Yan, Cheng Wen, Shuran Zhou, Tingwei Guo, Wei Zou, Xiangang Li
Date: 2022-04-19 00:00:00
Tasks: text-to-speech, Text to Speech, Voice Conversion

This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge\cite{Yi2022ADD}. The very same system was used for both two rounds of evaluation in Track 3.2 with a similar training methodology. The first round of Track 3.2 data is generated from Text-to-Speech(TTS) or voice conversion (VC) algorithms, while the second round of data consists of generated fake audio from other participants in Track 3.1, aiming to spoof our systems. Our systems use a standard 34-layer ResNet, with multi-head attention pooling \cite{india2019self} to learn the discriminative embedding for fake audio and spoof detection. We further utilize neural stitching to boost the model's generalization capability in order to perform equally well in different tasks, and more details will be explained in the following sessions. The experiments show that our proposed method outperforms all other systems with a 10.1% equal error rate(EER) in Track 3.2.
Time Domain Adversarial Voice Conversion for ADD 2022
Authors: Cheng Wen, Tingwei Guo, Xingjun Tan, Rui Yan, Shuran Zhou, Chuandong Xie, Wei Zou, Xiangang Li
Date: 2022-04-19 00:00:00
Tasks: Voice Conversion

In this paper, we describe our speech generation system for the first Audio Deep Synthesis Detection Challenge (ADD 2022). Firstly, we build an any-to-many voice conversion (VC) system to convert source speech with arbitrary language content into the target speaker%u2019s fake speech. Then the converted speech generated from VC is post-processed in the time domain to improve the deception ability. The experimental results show that our system has adversarial ability against anti-spoofing detectors with a little compromise in audio quality and speaker similarity. This system ranks top in Track 3.1 in the ADD 2022, showing that our method could also gain good generalization ability against different detectors.
So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale
Authors: Sugandha Doda, Yuanyuan Wang, Matthias Kahl, Eike Jens Hoffmann, Kim Ouan, Hannes Taubenböck, Xiao Xiang Zhu
Date: 2022-04-07 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Management

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation.
Time Difference of Arrival Extraction from Two-Way Ranging
Authors: Patrick Rathje, Olaf Landsiedel
Date: 2022-04-12 00:00:00
Tasks: TAG, Vocal Bursts Valence Prediction

Two-Way Ranging enables the distance estimation between two active parties and allows time of flight measurements despite relative clock offset and drift. Limited by the number of messages, scalable solutions build on Time Difference of Arrival to infer timing information at passive listeners. However, the demand for accurate distance estimates dictates a tight bound on the time synchronization, thus limiting scalability to the localization of passive tags relative to static, synchronized anchors. This work describes the extraction of Time Difference of Arrival information from a Two-Way Ranging process, enabling the extraction of distance information on passive listeners and further allowing scalable tag localization without the need for static or synchronized anchors. The expected error is formally deducted. The extension allows the extraction of the timing difference despite relative clock offset and drift for the Double-Sided Two-Way Ranging and Single-Sided Two-Way Ranging with additional carrier frequency offset estimation.
Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments
Authors: Y. Yu, L. Lu, Y. Zakharov, R. C. de Lamare, B. Chen
Date: 2022-04-09 00:00:00
Tasks: 2D Human Pose Estimation

This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustness and sparsity-aware penalty. Furthermore, by jointly optimizing the forgetting factor and the sparsity penalty parameter, we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only exhibits low misadjustment but also can track well sudden changes of a sparse system. Simulations in impulsive noise scenarios demonstrate that the proposed S-RRLS and JO-S-RRLS algorithms outperform existing techniques.
Consensus of networked double integrator systems under sensor bias
Authors: Pallavi Sinha, Srikant Sukumar, Himani Sinhmar
Date: 2022-04-19 00:00:00
Tasks: Position

A novel distributed control law for consensus of networked double integrator systems with biased measurements is developed in this article. The agents measure relative positions over a time-varying, undirected graph with an unknown and constant sensor bias corrupting the measurements. An adaptive control law is derived using Lyapunov methods to estimate the individual sensor biases accurately. The proposed algorithm ensures that position consensus is achieved exponentially in addition to bias estimation. The results leverage recent advances in collective initial excitation based results in adaptive estimation. Conditions connecting bipartite graphs and collective initial excitation are also developed. The algorithms are illustrated via simulation studies on a network of double integrators with local communication and biased measurements.
Per-clip adaptive Lagrangian multiplier optimisation with low-resolution proxies
Authors: Daniel J. Ringis, François Pitié, Anil Kokaram
Date: 2022-04-19 00:00:00

This work focuses on reducing the computational cost of repeated video encodes by using a lower resolution clip as a proxy. Features extracted from the low resolution clip are used to learn an optimal lagrange multiplier for rate control on the original resolution clip. In addition to reducing the computational cost and encode time by using lower resolution clips, we also investigate the use of older, but faster codecs such as H.264 to create proxies. This work shows that the computational load is reduced by 22 times using 144p proxies. Our tests are based on the YouTube UGC dataset, hence our results are based on a practical instance of the adaptive bitrate encoding problem. Further improvements are possible, by optimising the placement and sparsity of operating points required for the rate distortion curves.
Per Clip Lagrangian Multiplier Optimisation for HEVC
Authors: Daniel J Ringis, François Pitié, Anil Kokaram
Date: 2022-04-19 00:00:00
Tasks: Video Compression

The majority of internet traffic is video content. This drives the demand for video compression in order to deliver high quality video at low target bitrates. This paper investigates the impact of adjusting the rate distortion equation on compression performance. An constant of proportionality, k, is used to modify the Lagrange multiplier used in H.265 (HEVC). Direct optimisation methods are deployed to maximise BD-Rate improvement for a particular clip. This leads to up to 21% BD-Rate improvement for an individual clip. Furthermore we use a more realistic corpus of material provided by YouTube. The results show that direct optimisation using BD-rate as the objective function can lead to further gains in bitrate savings that are not available with previous approaches.
Calculate the Optimum Threshold for Double Energy Detection Technique in Cognitive Radio Networks (CRNs)
Authors: Morteza Alijani, Anas Osman
Date: 2022-04-15 00:00:00

One of the most important technical challenges when designing a Cognitive Radio Networks (CRNs) is spectrum sensing, which has the responsibility of recognizing the presence or absence of the primary users in the frequency bands. A common technique used for spectrum sensing is double energy detection since it can operate without any prior information regarding the characteristics of the primary user signals. A double threshold energy detection algorithm is based on the use of two thresholds, to check the energy of the received signals and decided whether the spectrum is occupied or not. Furthermore, thresholds play a key role in the energy detection algorithm, by considering the stochastic features of noise in this model, as a result calculating the optimal threshold is a crucial task. In this paper, the Bi-Section algorithm was used to detect the optimum energy level in the fuzzy region which is an area between the low and high energy threshold. For this purpose, the decision threshold was determined by the use of the Bisection function for cognitive users. Numerical simulations show that the proposed method achieves better detection performance than the conventional double-threshold energy-sensing schemes. Moreover, the presented technique has advantages such as increasing the probability of detection of primary users and decreasing the probability of Collison between primary and secondary users.
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition
Authors: Niko Moritz, Frank Seide, Duc Le, Jay Mahadeokar, Christian Fuegen
Date: 2022-04-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Hallucination, speech-recognition, Speech Recognition

The two most popular loss functions for streaming end-to-end automatic speech recognition (ASR) are RNN-Transducer (RNN-T) and connectionist temporal classification (CTC). Between these two loss types we can classify the monotonic RNN-T (MonoRNN-T) and the recently proposed CTC-like Transducer (CTC-T). Monotonic transducers have a few advantages. First, RNN-T can suffer from runaway hallucination, where a model keeps emitting non-blank symbols without advancing in time. Secondly, monotonic transducers consume exactly one model score per time step and are therefore more compatible with traditional FST-based ASR decoders. However, the MonoRNN-T so far has been found to have worse accuracy than RNN-T. It does not have to be that way: By regularizing the training via joint LAS training or parameter initialization from RNN-T, both MonoRNN-T and CTC-T perform as well or better than RNN-T. This is demonstrated for LibriSpeech and for a large-scale in-house data set.
Speech Dereverberation with A Reverberation Time Shortening Target
Authors: Rui Zhou, Wenye Zhu, Xiaofei Li
Date: 2022-04-19 00:00:00
Tasks: Denoising, Speech Denoising, Speech Dereverberation

This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training. The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error. Moreover, this work experimentally study to adapt our previously proposed FullSubNet speech denoising network to speech dereverberation. Experiments show that RTS is a more suitable learning target than direct-path speech and early reflections, in terms of better suppressing reverberation and signal distortion. FullSubNet is able to achieve outstanding dereverberation performance.
Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency
Authors: Baichuan Liu, Deqing Yang, Yueyi Wang, Yuchen Shi
Date: 2022-04-07 00:00:00

In the last decade, information diffusion (also known as information cascade) on social networks has been massively investigated due to its application values in many fields. In recent years, many sequential models including those models based on recurrent neural networks have been broadly employed to predict information cascade. However, the user dependencies in a cascade sequence captured by sequential models are generally unidirectional and inconsistent with diffusion trees. For example, the true trigger of a successor may be a non-immediate predecessor rather than the immediate predecessor in the sequence. To capture user dependencies more sufficiently which are crucial to precise cascade modeling, we propose a non-sequential information cascade model named as TAN-DRUD (Topology-aware Attention Networks with Dual Role User Dependency). TAN-DRUD obtains satisfactory performance on information cascade modeling through capturing the dual role user dependencies of information sender and receiver, which is inspired by the classic communication theory. Furthermore, TANDRUD incorporates social topology into two-level attention networks for enhanced information diffusion prediction. Our extensive experiments on three cascade datasets demonstrate that our model is not only superior to the state-of-the-art cascade models, but also capable of exploiting topology information and inferring diffusion trees.
Twitter Dataset on the Russo-Ukrainian War
Authors: Alexander Shevtsov, Christos Tzagkarakis, Despoina Antonakaki, Polyvios Pratikakis, Sotiris Ioannidis
Date: 2022-04-07 00:00:00
Tasks: Sentiment Analysis

On 24 February 2022, Russia invaded Ukraine, also known now as Russo-Ukrainian War. We have initiated an ongoing dataset acquisition from Twitter API. Until the day this paper was written the dataset has reached the amount of 57.3 million tweets, originating from 7.7 million users. We apply an initial volume and sentiment analysis, while the dataset can be used to further exploratory investigation towards topic analysis, hate speech, propaganda recognition, or even show potential malicious entities like botnets.
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Authors: Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, Christos Faloutsos
Date: 2022-04-05 00:00:00
Tasks: Clustering, Community Detection, Deep Clustering, Graph Clustering, Graph Learning, Incremental Learning

Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clustering perspective. Recently, state-of-the-art clustering performance in various domains has been achieved by deep clustering methods. Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework. Despite some differences in modeling choices (e.g., encoder architectures), existing DGC methods are mainly based on autoencoders and use the same clustering objective with relatively minor adaptations. Also, while many real-world graphs are dynamic, previous DGC methods considered only static graphs. In this work, we develop CGC, a novel end-to-end framework for graph clustering, which fundamentally differs from existing methods. CGC learns node embeddings and cluster assignments in a contrastive graph learning framework, where positive and negative samples are carefully selected in a multi-level scheme such that they reflect hierarchical community structures and network homophily. Also, we extend CGC for time-evolving data, where temporal graph clustering is performed in an incremental learning fashion, with the ability to detect change points. Extensive evaluation on real-world graphs demonstrates that the proposed CGC consistently outperforms existing methods.
A Stackelberg game for incentive-based demand response in energy markets
Authors: Marta Fochesato, Carlo Cenedese, John Lygeros
Date: 2022-04-19 00:00:00
Tasks: Bilevel Optimization

In modern buildings renewable energy generators and storage devices are spreading, and consequently the role of the users in the power grid is shifting from passive to active. We design a demand response scheme that exploits the prosumers' flexibility to provide ancillary services to the main grid. We propose a hierarchical scheme to coordinate the interactions between the distribution system operator and a community of smart prosumers. The framework inherits characteristics from price-based and incentive-based schemes and it retains the advantages of both. We cast the problem as a Stackelberg game with the prosumers as followers and the distribution system operator as leader. We solve the resulting bilevel optimization program via a KKT reformulation, proving the existence and the convergence to a local Stackelberg equilibrium. Finally, we provide numerical simulations to corroborate our claims on the benefits of the proposed framework.
Adaptive Conductance Control
Authors: Raphael Schmetterling, Thiago Burghi, Rodolphe Sepulchre
Date: 2022-04-19 00:00:00

Neuromodulation is central to the adaptation and robustness of animal nervous systems. This paper explores the classical paradigm of indirect adaptive control to design neuromodulatory controllers in conductance-based neuronal models. The adaptive control of maximal conductance parameters is shown to provide a methodology aligned with the central concepts of neuromodulation in physiology and of impedance control in robotics.
PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement
Authors: Yin Yu, Xinyuan Jiang, Daning Huang, Yan Li
Date: 2022-04-18 00:00:00
Tasks: Graph Neural Network, Prediction

A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in the presence of disturbances. The graph-based architecture of PIDGeuN provides a natural representation of the microgrid topology. Using only the state information that is practically measurable, PIDGeuN employs a time delay embedding formulation to fully reproduce the system dynamics, avoiding the dependency of conventional methods on internal dynamic states such as controllers. Based on a judiciously designed message passing mechanism, the PIDGeuN incorporates two physics-informed techniques to improve its prediction performance, including a physics-data-infusion approach to determining the inter-dependencies between buses, and a loss term to respect the known physical law of the power system, i.e., the Kirchhoff's law, to ensure the feasibility of the model prediction. Extensive tests show that PIDGeuN can provide accurate and robust prediction of transient dynamics for nonlinear microgrids over a long-term time period. Therefore, the PIDGeuN offers a potent tool for the modeling of large scale networked microgrids (NMs), with potential applications to predictive or preventive control in real time applications for the stable and resilient operations of NMs.
On the Herdability of Linear Time-Invariant Systems with Special Topological Structures
Authors: Giulia De Pasquale, Maria Elena Valcher
Date: 2022-04-18 00:00:00
Tasks: Clustering, State Space Models

In this paper, we investigate the herdability property, namely the capability of a system to be driven towards the (interior of the) positive orthant, for linear time-invariant state-space models. Herdability of certain matrix pairs (A,B), where A is the adjacency matrix of a multi-agent network, and B is a selection matrix that singles out a subset of the agents (the "network leaders"), is explored. The cases when the graph associated with A, G(A), is directed and clustering balanced (in particular, structurally balanced), or it has a tree topology and there is a single leader, are investigated.
Impact of Cyber Failures on Operation and Adequacy of Multi-Microgrid Distribution Systems
Authors: Mostafa Barani, Vijay Venu Vadlamudi, Hossein Farzin
Date: 2022-04-18 00:00:00
Tasks: Scheduling

Large-scale successful integration of microgeneration, together with active loads, energy storage devices, and energy scheduling strategies, requires the extensive adoption of advanced information and communication technologies (ICTs) at the distribution network level; this brings so-called Cyber-Physical Multi-MicroGrid (CPMMG) systems into the picture. However, as such ICTs are not failure-free, their integration affects the system's operation and adequacy. To quantify how cyber failures influence a CPMMG system, this study proposes an adequacy framework based on sequential Monte Carlo Simulation (MCS). In the proposed framework, the typical structure of a CPMMG is exemplified, and the consequences of various cyber failures in this system are explained. Possible operation modes -- normal, island, joint, and shutdown modes -- are then explained and modeled. Finally, the adequacy index Expected Energy Not Served (EENS) is computed based on the proposed methodology, and two new adequacy indices are proposed: Interrupted but Gained Compensation (IbGC) and Supplied by Expensive Resources (SbER). A comprehensive case study is conducted to reveal the salient features of the proposed framework.
Modeling the Cooperative Process of Learning a Task
Authors: Giulia De Pasquale, Maria Elena Valcher
Date: 2022-04-18 00:00:00
Tasks: All

In this paper, we propose a mathematical model for a Transactive Memory System (TMS) involved in the cooperative process of learning a task. The model is based on an intertwined dynamics involving both the individuals level of expertise and the interaction network among the cooperators. The model shows that if all the agents are non-stubborn, then all of them are able to acquire the competence of the most expert members of the group, asymptotically reaching their level of proficiency. Conversely, when dealing with all stubborn agents, the capability to pass on the task depends on the connectedness properties of the interaction graph.