pwc-archive/papers-with-abstracts huggingface dataset:
Causal Discovery with Flow-based Conditional Density Estimation
Authors: Shaogang Ren, Haiyan Yin, Mingming Sun, Ping Li
Date: 2021-12-05 00:00:00
Proceeding: ICDM 21 2021 12
Tasks: Causal Discovery, Density Estimation

Cause-effect discovery plays an essential role in many disciplines of science and real-world applications. In this paper, we introduce a new causal discovery method to solve the classic problem of inferring the causal direction under a bivariate setting. In particular, our proposed method first leverages a flow model to estimate the joint probability density of the variables. Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation. By leveraging the flow-based conditional density estimation metric, our causal discovery approach alleviates the restrictive assumptions made by the conventional methods, such as assuming the linearity relationship between the two variables. Therefore, it could potentially be able to better capture the complex causal relationship among data in various problem domains that comes in arbitrary forms. We conduct extensive evaluations to compare our method with decent causal discovery approaches. Empirical results show that our method could promisingly outperform the baseline methods with noticeable margins on both synthetic and real-world datasets.
ANALYSIS OF ECG WITH DB10 WAVELET USING VERILOG HDL
Authors: Shivani Mehrotra
Date: 2015-05-01 00:00:00
Proceeding: International Journal of Technical Research and Applications e-ISSN 2015 5

The abnormal condition of electrical activity of the heart given by ECG (Electrocardiogram) shows the cardiac diseases affecting the human being. The P, QRS, T wave shape, amplitude and time intervals between its various peaks contains
useful information about the nature of disease.This paper presents wavelet technique to analyze ECG signal.
Discrete Wavelet Transform (DWT) is employed as noise removal and feature extraction tool to achieve efficient design.
Daubechies wavelet of order 10 has been designed using Verilog Hardware Description Language (HDL) and ModelSim Altera 
6.4a is used as simulator. MIT-BIH database has been used for the analysis.
Identifying the module structure of swarms using a new framework of network-based time series clustering
Authors: Kongjing Gu, Ziyang Mao, Xiaojun Duan, Guanlin Wu, Liang Yan
Date: 2021-03-01 00:00:00
Proceeding: Engineering Applications of Artificial Intelligence 2021 3
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering, Trajectory Clustering

Swarm is a collective motion phenomenon whose dynamic mechanism and cooperation structure could be identified based on observations. Unmanned Aerial Vehicles (UAV) is a special artificial swarm with unique rules and structures. Therefore, corresponding identification methods need to be developed. One critical identification problem is distinguishing the swarm's cooperation structure, which is usually clustered and grouped to achieve stability of behaviours and low communication cost. This paper proposes a framework of Overlay Network Integrated Time series clustering (ONIT) to identify the UAV swarm structures based on trajectories. The framework consists of Snapshot, Net Growth and Net Split. It can fuse with most distance functions in time series clustering, achieving high accuracy, update ability, and fault tolerance with various data sets. We create point-based and sliding window-based snapshots, allowing the framework compatible with more methods. In particular, the Dynamic Time Wrapping (DTW) correspondence in point-based snapshots shows the high scalability of the framework, and the Euclidean Distance (ED) correspondence shows that the framework can still significantly improve the accuracy while maintaining the simplicity of calculation. The test results show that the fused ONIT-clustering algorithms, especially the point-based ones, outperform original time series clustering methods separately in simulation datasets of UAV swarms and UCR repository by 28% and 27%. In summary, the proposed framework is a flexible and scalable time series clustering method that can solve various time series clustering problems especially the trajectory clustering of the UAV swarm and has great potential for general time series analysis.
AI-generated characters for supporting personalized learning and well-being
Authors: Pat Pataranutaporn, Valdemar Danry, Joanne Leong, Parinya Punpongsanon, Dan Novy, Pattie Maes & Misha Sra
Date: 2021-12-15 00:00:00
Proceeding: Nature Machine Intelligence 2021 12
Tasks: Face Reenactment, Neural Rendering, Talking Head Generation, Text-to-Face Generation

Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspire and intrigue us. AI-generated portrayals of characters can feature synthesized faces, bodies and voices of anyone, from a fictional character to a historical figure, or even a deceased family member. Although negative use cases of this technology have dominated the conversation so far, in this Perspective we highlight emerging positive use cases of AI-generated characters, specifically in supporting learning and well-being. We demonstrate an easy-to-use AI character generation pipeline to enable such outcomes and discuss ethical implications as well as the need for including traceability to help maintain trust in the generated media. As we look towards the future, we foresee generative media as a crucial part of the ever growing landscape of human–AI interaction.
Learning Linear Representations of Nonlinear Dynamics Using Deep Learning
Authors: Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangoz
Date: 2022-04-03 00:00:00
Tasks: Deep Learning

The vast majority of systems of practical interest are characterised by nonlinear dynamics. This renders the control and optimization of such systems a complex task due to their nonlinear behaviour. Additionally, standard methods such as linearizing around a fixed point may not be an effective strategy for many systems, thus requiring an alternative approach. For this reason, we propose a new deep learning framework to discover a transformation of a nonlinear dynamical system to an equivalent higher dimensional linear representation. We demonstrate that the resulting learned linear representation accurately captures the dynamics of the original system for a wider range of conditions than standard linearization. As a result of this, we show that the learned linear model can subsequently be used for the successful control of the original system. We demonstrate this by applying the proposed framework to two examples; one from the literature and a more complex example in the form of a Continuous Stirred Tank Reactor (CSTR).
Calibration window selection based on change-point detection for forecasting electricity prices
Authors: Julia Nasiadka, Weronika Nitka, Rafał Weron
Date: 2022-04-02 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values. Then, contrarily to the traditional time series approach in which the most recent $\tau$ observations are taken as the calibration sample, we estimate autoregressive models only for data in these subperiods. We illustrate our approach using a challenging dataset - day-ahead electricity prices in the German EPEX SPOT market - and observe a significant improvement in forecasting accuracy compared to commonly used approaches, including the Autoregressive Hybrid Nearest Neighbors (ARHNN) method.
Retail Central Bank Digital Currencies (CBDC), Disintermediation and Financial Privacy: The Case of the Bahamian Sand Dollar
Authors: Kilian Wenker
Date: 2022-04-04 00:00:00
Tasks: Sand

The fast-growing, market-driven demand for cryptocurrencies worries central banks, as their monetary policy could be completely undermined. Central bank digital currencies (CBDCs) could offer a solution, yet our understanding of their design and consequences is in its infancy. This non-technical paper examines how The Bahamas has designed the Sand Dollar, the first real-world instance of a retail CBDC. It contrasts the Sand Dollar with definition-based specifications. I then develop a scenario analysis to illustrate commercial bank risks. In this process, the central bank becomes a deposit monopolist, leading to high funding risks, disintermediation risks, and solvency risks for the com-mercial banking sector. I argue that restrictions and caps will be the new specifications of a regulatory framework for CBDCs if disintermediation in the banking sector is to be prevented. I identify the anonymity of CBDCs as a comparative disadvantage that will affect their adoption. These findings provide insight into governance problems facing central banks, and coherently lead to the design of the Sand Dollar. I conclude by suggesting that combating cryptocurrencies is a task that cannot be solved by a CBDC.
Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System
Authors: Fangyu Hong, Guohua Wu, Qizhang Luo, Huan Liu, Xiaoping Fang, Witold Pedrycz
Date: 2022-04-04 00:00:00
Tasks: Scheduling

The application of drones in the last-mile distribution is a research hotspot in recent years. Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices. The proposed mode uses free areas on the top of residential buildings to set automatic devices as delivery and pick-up points of packages, and employs drones to transport packages between buildings and depots. Integrated scheduling problem of package drop-pickup considering m-drone, m-depot, m-customer is crucial for the system. We propose a simulated-annealing-based two-phase optimization approach (SATO) to solve this problem. In the first phase, tasks are allocated to depots for serving, such that the initial problem is decomposed into multiple single depot scheduling problems with m-drone. In the second phase, considering the drone capability constraints and task demand constraints, we generate the route planning scheme for drones in each depot. Concurrently, an improved variable neighborhood descent algorithm (IVND) is designed in the first phase to reallocate tasks, and a local search algorithm (LS) are proposed to search the high-quality solution in the second phase. Finally, extensive experiments and comparative studies are conducted to test the effectiveness of the proposed approach. Experiments indicate that the proposed SATO-IVND can reduce the cost by more than 14% in a reasonable time compared with several other peer algorithms.
Optical modelling of accommodative light field display system and prediction of human eye responses
Authors: Yuta Miyanishi, Erdem Sahin, Atanas Gotchev
Date: 2022-04-02 00:00:00

The spatio-angular resolution of a light field (LF) display is a crucial factor for delivering adequate spatial image quality and eliciting an accommodation response. Previous studies have modelled retinal image formation with an LF display and evaluated whether accommodation would be evoked correctly. The models were mostly based on ray-tracing and a schematic eye model, which pose computational complexity and inaccurately represent the human eye population's behaviour. We propose an efficient wave-optics-based framework to model the human eye and a general LF display. With the model, we simulated the retinal point spread function (PSF) of a point rendered by an LF display at various depths to characterise the retinal image quality. Additionally, accommodation responses to rendered LF images were estimated by computing the visual Strehl ratio based on the optical transfer function (VSOTF) from the PSFs. We assumed an ideal LF display that had an infinite spatial resolution and was free from optical aberrations in the simulation. We tested images rendered at 0--4 dioptres of depths having angular resolutions of up to 4x4 viewpoints within a pupil. The simulation predicted small and constant accommodation errors, which contradict the findings of previous studies. An evaluation of the optical resolution of the rendered retinal image suggested a trade-off between the maximum resolution achievable and the depth range of a rendered image where in-focus resolution is kept high. The proposed framework can be used to evaluate the upper bound of the optical performance of an LF display for realistically aberrated eyes, which may help to find an optimal spatio-angular resolution required to render a high quality 3D scene.
Countering Active Attacks on RAFT-based IoT Blockchain Networks
Authors: Hasan Mujtaba Buttar, Waqas Aman, M. Mahboob Ur Rahman, Qammer H. Abbasi
Date: 2022-04-02 00:00:00

This paper considers an Internet of Thing (IoT) blockchain network consisting of a leader node and various follower nodes which together implement the RAFT consensus protocol to verify a blockchain transaction, as requested by a blockchain client. Further, two kinds of active attacks, i.e., jamming and impersonation, are considered on the IoT blockchain network due to the presence of multiple {\it active} malicious nodes in the close vicinity. When the IoT network is under the jamming attack, we utilize the stochastic geometry tool to derive the closed-form expressions for the coverage probabilities for both uplink and downlink IoT transmissions. On the other hand, when the IoT network is under the impersonation attack, we propose a novel method that enables a receive IoT node to exploit the pathloss of a transmit IoT node as its fingerprint to implement a binary hypothesis test for transmit node identification. To this end, we also provide the closed-form expressions for the probabilities of false alarm, missed detection and miss-classification. Finally, we present detailed simulation results that indicate the following: i) the coverage probability improves as the jammers' locations move away from the IoT network, ii) the three error probabilities decrease as a function of the link quality.
Distributed Anomaly Detection and Estimation over Sensor Networks: Observational-Equivalence and Q-Redundant Observer Design
Authors: Mohammadreza Doostmohammadian, Themistoklis Charalambous
Date: 2022-04-04 00:00:00
Tasks: Anomaly Detection

In this paper, we study stateless and stateful physics-based anomaly detection scenarios via distributed estimation over sensor networks. In the stateful case, the detector keeps track of the sensor residuals (i.e., the difference of estimated and true outputs) and reports an alarm if certain statistics of the recorded residuals deviate over a predefined threshold, e.g., \chi^2 (Chi-square) detector. Instead, only instantaneous deviation of the residuals raises the alarm in the stateless case without considering the history of the sensor outputs and estimation data. Given (approximate) false-alarm rate for both cases, we propose a probabilistic threshold design based on the noise statistics. We show by simulation that increasing the window length in the stateful case may not necessarily reduce the false-alarm rate. On the other hand, it adds unwanted delay to raise the alarm. The distributed aspect of the proposed detection algorithm enables local isolation of the faulty sensors with possible recovery solutions by adding redundant observationally-equivalent sensors. We, then, offer a mechanism to design Q-redundant distributed observers, robust to failure (or removal) of up to Q sensors over the network.
A Sensory Feedback Control Law for Octopus Arm Movements
Authors: Tixian Wang, Udit Halder, Ekaterina Gribkova, Rhanor Gillette, Mattia Gazzola, Prashant G. Mehta
Date: 2022-04-01 00:00:00

The main contribution of this paper is a novel sensory feedback control law for an octopus arm. The control law is inspired by, and helps integrate, several observations made by biologists. The proposed control law is distinct from prior work which has mainly focused on open-loop control strategies. Several analytical results are described including characterization of the equilibrium and its stability analysis. Numerical simulations demonstrate life-like motion of the soft octopus arm, qualitatively matching behavioral experiments. Quantitative comparison with bend propagation experiments helps provide the first explanation of such canonical motion using a sensory feedback control law. Several remarks are included that help draw parallels with natural pursuit strategies such as motion camouflage or classical pursuit.
Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches
Authors: Zifeng Zhao, Dongchao Yang, Rongzhi Gu, Haoran Zhang, Yuexian Zou
Date: 2022-04-04 00:00:00
Tasks: blind source separation, Metric Learning, Speaker Separation, Speech Separation

Recently, end-to-end speaker extraction has attracted increasing attention and shown promising results. However, its performance is often inferior to that of a blind source separation (BSS) counterpart with a similar network architecture, due to the auxiliary speaker encoder may sometimes generate ambiguous speaker embeddings. Such ambiguous guidance information may confuse the separation network and hence lead to wrong extraction results, which deteriorates the overall performance. We refer to this as the target confusion problem. In this paper, we conduct an analysis of such an issue and solve it in two stages. In the training phase, we propose to integrate metric learning methods to improve the distinguishability of embeddings produced by the speaker encoder. While for inference, a novel post-filtering strategy is designed to revise the wrong results. Specifically, we first identify these confusion samples by measuring the similarities between output estimates and enrollment utterances, after which the true target sources are recovered by a subtraction operation. Experiments show that performance improvement of more than 1dB SI-SDRi can be brought, which validates the effectiveness of our methods and emphasizes the impact of the target confusion problem.
tPLCnet: Real-time Deep Packet Loss Concealment in the Time Domain Using a Short Temporal Context
Authors: Nils L. Westhausen, Bernd T. Meyer
Date: 2022-04-04 00:00:00
Tasks: Packet Loss Concealment

This paper introduces a real-time time-domain packet loss concealment (PLC) neural-network (tPLCnet). It efficiently predicts lost frames from a short context buffer in a sequence-to-one (seq2one) fashion. Because of its seq2one structure, a continuous inference of the model is not required since it can be triggered when packet loss is actually detected. It is trained on 64h of open-source speech data and packet-loss traces of real calls provided by the Audio PLC Challenge. The model with the lowest complexity described in this paper reaches a robust PLC performance and consistent improvements over the zero-filling baseline for all metrics. A configuration with higher complexity is submitted to the PLC Challenge and shows a performance increase of 1.07 compared to the zero-filling baseline in terms of PLC-MOS on the blind test set and reaches a competitive 3rd place in the challenge ranking.
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization
Authors: Federico Landini, Alicia Lozano-Diez, Mireia Diez, Lukáš Burget
Date: 2022-04-02 00:00:00
Tasks: speaker-diarization, Speaker Diarization

End-to-end neural diarization (EEND) is nowadays one of the most prominent research topics in speaker diarization. EEND presents an attractive alternative to standard cascaded diarization systems since a single system is trained at once to deal with the whole diarization problem. Several EEND variants and approaches are being proposed, however, all these models require large amounts of annotated data for training but available annotated data are scarce. Thus, EEND works have used mostly simulated mixtures for training. However, simulated mixtures do not resemble real conversations in many aspects. In this work we present an alternative method for creating synthetic conversations that resemble real ones by using statistics about distributions of pauses and overlaps estimated on genuine conversations. Furthermore, we analyze the effect of the source of the statistics, different augmentations and amounts of data. We demonstrate that our approach performs substantially better than the original one, while reducing the dependence on the fine-tuning stage. Experiments are carried out on 2-speaker telephone conversations of Callhome and DIHARD 3. Together with this publication, we release our implementations of EEND and the method for creating simulated conversations.
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature
Authors: Chenpeng Du, Yiwei Guo, Xie Chen, Kai Yu
Date: 2022-04-02 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

The mainstream neural text-to-speech(TTS) pipeline is a cascade system, including an acoustic model(AM) that predicts acoustic feature from the input transcript and a vocoder that generates waveform according to the given acoustic feature. However, the acoustic feature in current TTS systems is typically mel-spectrogram, which is highly correlated along both time and frequency axes in a complicated way, leading to a great difficulty for the AM to predict. Although high-fidelity audio can be generated by recent neural vocoders from ground-truth(GT) mel-spectrogram, the gap between the GT and the predicted mel-spectrogram from AM degrades the performance of the entire TTS system. In this work, we propose VQTTS, consisting of an AM txt2vec and a vocoder vec2wav, which uses self-supervised vector-quantized(VQ) acoustic feature rather than mel-spectrogram. We redesign both the AM and the vocoder accordingly. In particular, txt2vec basically becomes a classification model instead of a traditional regression model while vec2wav uses an additional feature encoder before HifiGAN generator for smoothing the discontinuous quantized feature. Our experiments show that vec2wav achieves better reconstruction performance than HifiGAN when using self-supervised VQ acoustic feature. Moreover, our entire TTS system VQTTS achieves state-of-the-art performance in terms of naturalness among all current publicly available TTS systems.
Multimodal Clustering with Role Induced Constraints for Speaker Diarization
Authors: Nikolaos Flemotomos, Shrikanth Narayanan
Date: 2022-04-01 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

Speaker clustering is an essential step in conventional speaker diarization systems and is typically addressed as an audio-only speech processing task. The language used by the participants in a conversation, however, carries additional information that can help improve the clustering performance. This is especially true in conversational interactions, such as business meetings, interviews, and lectures, where specific roles assumed by interlocutors (manager, client, teacher, etc.) are often associated with distinguishable linguistic patterns. In this paper we propose to employ a supervised text-based model to extract speaker roles and then use this information to guide an audio-based spectral clustering step by imposing must-link and cannot-link constraints between segments. The proposed method is applied on two different domains, namely on medical interactions and on podcast episodes, and is shown to yield improved results when compared to the audio-only approach.