pwc-archive/papers-with-abstracts huggingface dataset:
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings
Authors: Vasilis Efthymiou, Oktie Hassanzadeh, Mariano Rodriguez-Muro, Vassilis Christophides
Date: 2017-10-01 00:00:00
Proceeding: The Semantic Web – ISWC 2017 10
Tasks: Cell Entity Annotation, Entity Embeddings, Ontology Matching, Table annotation

Web tables constitute valuable sources of information for various applications, ranging from Web search to Knowledge Base (KB) augmentation. An underlying common requirement is to annotate the rows of Web tables with semantically rich descriptions of entities published in Web KBs. In this paper, we evaluate three unsupervised annotation methods: (a) a lookup-based method which relies on the minimal entity context provided in Web tables to discover correspondences to the KB, (b) a semantic embeddings method that exploits a vectorial representation of the rich entity context in a KB to identify the most relevant subset of entities in the Web table, and (c) an ontology matching method, which exploits schematic and instance information of entities available both in a KB and a Web table. Our experimental evaluation is conducted using two existing benchmark data sets in addition to a new large-scale benchmark created using Wikipedia tables. Our results show that: (1) our novel lookup-based method outperforms state-of-the-art lookup-based methods, (2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and (3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation. As a result, we propose a hybrid method that significantly outperforms individual methods on all the benchmarks.
Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification
Authors: Kuiliang Gao, Bing Liu, Xuchu Yu, and Anzhu Yu
Date: 2022-05-05 00:00:00
Proceeding: IEEE Transactions on Image Processing 2022 5
Tasks: Classification, domain classification, Meta-Learning, Relation Network

The difficulties of obtaining sufficient labeled samples have always been one of the factors hindering deep learning models from obtaining high accuracy in hyperspectral image (HSI) classification. To reduce the dependence of deep learning models on training samples, meta learning methods have been introduced, effectively improving the classification accuracy in small sample set scenarios. However, the existing methods based on meta learning still need to construct a labeled source data set with several pre-collected HSIs, and must utilize a large number of labeled samples for meta-training, which is actually time-consuming and labor-intensive. To solve this problem, this paper proposes a novel unsupervised meta learning method with multiview constraints for HSI small sample set classification. Specifically, the proposed method first builds an unlabeled source data set using unlabeled HSIs. Then, multiple spatial-spectral multiview features of each unlabeled sample are generated to construct tasks for unsupervised meta learning. Finally, the designed residual relation network is used for meta-training and small sample set classification based on the voting strategy. Compared with existing supervised meta learning methods for HSI classification, our method can only utilize HSIs without any label for unsupervised meta learning, which significantly reduces the number of requisite labeled samples in the whole classification process. To verify the effectiveness of the proposed method, extensive experiments are carried out on 8 public HSIs in the cross-domain and in-domain classification scenarios. The statistical results demonstrate that, compared with existing supervised meta learning methods and other advanced classification models, the proposed method can achieve competitive or better classification performance in small sample set scenarios.
Creating Summaries from User Videos
Authors: Michael Gygli, Helmut Grabner, Hayko Riemenschneider, Luc van Gool
Date: 2014-01-01 00:00:00
Proceeding: ECCV 2014 1
Tasks: Video Summarization

This paper proposes a novel approach and a new benchmark for video summarization. Thereby we focus on user videos, which are raw videos containing a set of interesting events. Our method starts by segmenting the video by using a novel "superframe" segmentation, tailored to raw videos. Then, we estimate visual interestingness per superframe using a set of low-, mid- and high-level features. Based on this scoring, we select an optimal subset of superframes to create an informative and interesting summary. The introduced benchmark comes with multiple human created summaries, which were acquired in a controlled psychological experiment. This data paves the way to evaluate summarization methods objectively and to get new insights in video summarization. When evaluating our method, we find that it generates high-quality results, comparable to manual, human-created summaries.
Matching web tables to DBpedia-A feature utility study
Authors: Dominique Ritze, Christian Bizer
Date: 2017-03-01 00:00:00
Proceeding: EDBT 2017 3
Tasks: Attribute, Columns Property Annotation, Missing Values, Row Annotation, Table annotation, Table Type Detection

Relational HTML tables on the Web contain data describing a multitude of entities and covering a wide range of topics. Thus, web tables are very useful for filling missing values in cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. Before web table data can be used to fill missing values, the tables need to be matched to the knowledge base in question. This involves three matching tasks: table-to-class matching, rowto-instance matching, and attribute-to-property matching. Various matching approaches have been proposed for each of these tasks. Unfortunately, the existing approaches are evaluated using different web table corpora. Each individual approach also only exploits a subset of the web table and knowledge base features that are potentially helpful for the matching tasks. These two shortcomings make it difficult to compare the different matching approaches and to judge the impact of each feature on the overall matching results. This paper contributes to improve the understanding of the utility of different features for web table to knowledge base matching by reimplementing different matching techniques as well as similarity score aggregation methods from literature within a single matching framework and evaluating different combinations of these techniques against a single gold standard. The gold standard consists of class-, instance-, and property correspondences between the DBpedia knowledge base and web tables from the Web Data Commons web table corpus.
Analysis of the AutoML Challenge Series 2015–2018
Authors: Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michèle Sebag, Alexander Statnikov, WeiWei Tu, Evelyne Viegas
Date: 2019-05-18 00:00:00
Proceeding: Springer Cham 2019 5
Tasks: AutoML, BIG-bench Machine Learning, Model Selection

The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed by a one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the
participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have
been made publicly available at http://automl.chalearn.org/.
Low Altitude 3-D Coverage Performance Analysis in Cell-Free Distributed Collaborative Massive MIMO Systems
Authors: Jiamin Li, Qijun Pan, Pengcheng Zhu, Dongming Wang, Xiaohu You
Date: 2022-06-28 00:00:00

To improve the poor performance of distributed operation and non-scalability of centralized operation in traditional cell-free massive MIMO, we propose a cell-free distributed collaborative (CFDC) massive multiple-input multiple-output (MIMO) system based on a novel two-layer model to take advantages of the distributed cloud-edge-end collaborative architecture in beyond 5G (B5G) internet of things (IoT) environment to provide strong flexibility and scalability. We further ultilize the proposed CFDC massive MIMO system to support the low altitude three-dimensional (3-D) coverage scenario with unmanned aerial vehicles (UAVs), while accounting for 3-D Rician channel estimation, user-centric association and different scalable receiving schemes. Since coexisted UAVs and ground users (GUEs) cause greater interference, we ultilize user-centric association strategy and minimum-mean-square error (MMSE) channel state information (CSI) estimation to obtain the estimated CSI of UAVs and GUEs. Under the CFDC scenarios, scalable receiving schemes as maximum ratio combing (MRC), partial zero-forcing (P-ZF) and partial minimum-mean-square error (P-MMSE) can be performed at edge servers and the closed-form expressions for uplink spectral efficiency (SE) are derived. Based on the derived expressions, we propose an efficient power control algorithm by solving a multi-objective optimization problem (MOOP) between maximizing the average SE of UAVs and GUEs simultaneously with Deep Q-Network (DQN). Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the coexisted UAVs and GUEs transmission scheme in CFDC massive MIMO systems. The SE analysis under various system parameters offers numerous flexibilities for system optimization.
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization
Authors: Yifan Chen, Yifan Guo, Qingxuan Li, Gaofeng Cheng, Pengyuan Zhang, Yonghong Yan
Date: 2022-06-28 00:00:00
Tasks: Clustering, Online Clustering, speaker-diarization, Speaker Diarization

For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible. Moreover, in most existing clustering-based methods, the training objective of the embedding extractor is not designed specially for clustering. To improve online speaker diarization performance, we propose a unified online clustering framework, which provides an interactive manner between embedding extractors and clustering algorithms. Specifically, the framework consists of two highly coupled parts: clustering-guided recurrent training (CGRT) and truncated beam searching clustering (TBSC). The CGRT introduces the clustering algorithm into the training process of embedding extractors, which could provide not only cluster-aware information for the embedding extractor, but also crucial parameters for the clustering process afterward. And with these parameters, which contain preliminary information of the metric space, the TBSC penalizes the probability score of each cluster, in order to output more accurate clustering results in online fashion with low latency. With the above innovations, our proposed online clustering system achieves 14.48\% DER with collar 0.25 at 2.5s latency on the AISHELL-4, while the DER of the offline agglomerative hierarchical clustering is 14.57\%.
Expressive, Variable, and Controllable Duration Modelling in TTS
Authors: Ammar Abbas, Thomas Merritt, Alexis Moinet, Sri Karlapati, Ewa Muszynska, Simon Slangen, Elia Gatti, Thomas Drugman
Date: 2022-06-28 00:00:00
Tasks: Normalising Flows, Speech Synthesis, text-to-speech, Text to Speech

Duration modelling has become an important research problem once more with the rise of non-attention neural text-to-speech systems. The current approaches largely fall back to relying on previous statistical parametric speech synthesis technology for duration prediction, which poorly models the expressiveness and variability in speech. In this paper, we propose two alternate approaches to improve duration modelling. First, we propose a duration model conditioned on phrasing that improves the predicted durations and provides better modelling of pauses. We show that the duration model conditioned on phrasing improves the naturalness of speech over our baseline duration model. Second, we also propose a multi-speaker duration model called Cauliflow, that uses normalising flows to predict durations that better match the complex target duration distribution. Cauliflow performs on par with our other proposed duration model in terms of naturalness, whilst providing variable durations for the same prompt and variable levels of expressiveness. Lastly, we propose to condition Cauliflow on parameters that provide an intuitive control of the pacing and pausing in the synthesised speech in a novel way.
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
Authors: Dacheng Yin, Chuanxin Tang, Yanqing Liu, Xiaoqiang Wang, Zhiyuan Zhao, Yucheng Zhao, Zhiwei Xiong, Sheng Zhao, Chong Luo
Date: 2022-06-28 00:00:00
Tasks: Sentence

This paper proposes a new "decompose-and-edit" paradigm for the text-based speech insertion task that facilitates arbitrary-length speech insertion and even full sentence generation. In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody. Specifically, we proposed to represent the global factors by multiple tokens, which are extracted by cross-attention operation and then injected back by link-attention operation. Due to the rich representation of global factors, we manage to achieve high speaker similarity in a zero-shot manner. In addition, we introduce a prosody smoothing task to make the local prosody factor context-aware and therefore achieve satisfactory prosody continuity. We further achieve high voice quality with an adversarial training stage. In the subjective test, our method achieves state-of-the-art performance in both naturalness and similarity. Audio samples can be found at https://ydcustc.github.io/retrieverTTS-demo/.
Speaker Verification in Multi-Speaker Environments Using Temporal Feature Fusion
Authors: Ahmad Aloradi, Wolfgang Mack, Mohamed Elminshawi, Emanuël A. P. Habets
Date: 2022-06-28 00:00:00
Tasks: Speaker Verification

Verifying the identity of a speaker is crucial in modern human-machine interfaces, e.g., to ensure privacy protection or to enable biometric authentication. Classical speaker verification (SV) approaches estimate a fixed-dimensional embedding from a speech utterance that encodes the speaker's voice characteristics. A speaker is verified if his/her voice embedding is sufficiently similar to the embedding of the claimed speaker. However, such approaches assume that only a single speaker exists in the input. The presence of concurrent speakers is likely to have detrimental effects on the performance. To address SV in a multi-speaker environment, we propose an end-to-end deep learning-based SV system that detects whether the target speaker exists within an input or not. First, an embedding is estimated from a reference utterance to represent the target's characteristics. Second, frame-level features are estimated from the input mixture. The reference embedding is then fused frame-wise with the mixture's features to allow distinguishing the target from other speakers on a frame basis. Finally, the fused features are used to predict whether the target speaker is active in the speech segment or not. Experimental evaluation shows that the proposed method outperforms the x-vector in multi-speaker conditions.
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
Authors: Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin
Date: 2022-06-28 00:00:00
Tasks: Speaker Verification

The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade. However, ASV systems can be easily neutralized by spoofing attacks. Therefore, the Spoofing-Aware Speaker Verification (SASV) challenge is designed and held to promote development of systems that can perform ASV considering spoofing attacks by integrating ASV and spoofing countermeasure (CM) systems. In this paper, we propose two back-end systems: multi-layer perceptron score fusion model (MSFM) and integrated embedding projector (IEP). The MSFM, score fusion back-end system, derived SASV score utilizing ASV and CM scores and embeddings. On the other hand,IEP combines ASV and CM embeddings into SASV embedding and calculates final SASV score based on the cosine similarity. We effectively integrated ASV and CM systems through proposed MSFM and IEP and achieved the SASV equal error rates 0.56%, 1.32% on the official evaluation trials of the SASV 2022 challenge.
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
Authors: Ondřej Mokrý, Paul Magron, Thomas Oberlin, Cédric Févotte
Date: 2022-06-28 00:00:00
Tasks: Audio inpainting

Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization (NMF) in a probabilistic framework. First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain. Then, we treat the missing samples as parameters, and we address this novel problem by deriving an alternating minimization scheme. We assess the potential of these algorithms for the task of restoring short- to middle-length gaps in music signals. Experiments reveal great convergence properties of the proposed methods, as well as competitive performance when compared to state-of-the-art audio inpainting techniques.
A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion
Authors: Xu Li, Shansong Liu, Ying Shan
Date: 2022-06-28 00:00:00
Tasks: Speaker Recognition, Voice Conversion

Typically, singing voice conversion (SVC) depends on an embedding vector, extracted from either a speaker lookup table (LUT) or a speaker recognition network (SRN), to model speaker identity. However, singing contains more expressive speaker characteristics than conversational speech. It is suspected that a single embedding vector may only capture averaged and coarse-grained speaker characteristics, which is insufficient for the SVC task. To this end, this work proposes a novel hierarchical speaker representation framework for SVC, which can capture fine-grained speaker characteristics at different granularity. It consists of an up-sampling stream and three down-sampling streams. The up-sampling stream transforms the linguistic features into audio samples, while one down-sampling stream of the three operates in the reverse direction. It is expected that the temporal statistics of each down-sampling block can represent speaker characteristics at different granularity, which will be engaged in the up-sampling blocks to enhance the speaker modeling. Experiment results verify that the proposed method outperforms both the LUT and SRN based SVC systems. Moreover, the proposed system supports the one-shot SVC with only a few seconds of reference audio.
Towards Prescribed Accuracy in Under-tuned Super-Twisting Sliding Mode Control Loops -- Experimental Verification
Authors: Dimitrios Papageorgiou
Date: 2022-06-28 00:00:00

Obtaining prescribed accuracy bounds in super-twisting sliding mode control loops often falls short in terms of the applicability of the controller in high-performance systems. This is due to the fact that the selection of the controller gains that are derived from the conditions for finite-time convergence may be too restrictive in connection to actuator limitations and induced chatter. Previous work has shown that in case of periodic perturbations, there can be a systematic selection of much lower controller gains that guarantees boundedness of the closed-loop solutions within predetermined accuracy bounds. This study presents an experimental validation of these findings carried out on a commercial industrial motor system.
Spatial Positioning Token (SPToken) for Smart Parking
Authors: Roman Overko, Rodrigo Ordóñez-Hurtado, Sergiy Zhuk, Robert Shorten
Date: 2022-06-28 00:00:00
Tasks: Reinforcement Learning (RL)

In this paper, we describe an approach to guide drivers searching for a parking space (PS). The proposed system suggests a sequence of routes that drivers should traverse in order to maximise the expected likelihood of finding a PS and minimise the travel distance. This system is built on our recent architecture SPToken, which combines both Distributed Ledger Technology (DLT) and Reinforcement Learning (RL) to realise a system for the estimation of an unknown distribution without disturbing the environment. For this, we use a number of virtual tokens that are passed from vehicle to vehicle to enable a massively parallelised RL system that estimates the best route for a given origin-destination (OD) pair, using crowdsourced information from participant vehicles. Additionally, a moving window with reward memory mechanism is included to better cope with non-stationary environments. Simulation results are given to illustrate the efficacy of our system.
On the Calculation of the Variance of Algebraic Variables in Power System Dynamic Models with Stochastic Processes
Authors: Muhammad Adeen, Federico Bizzarri, Davide del Giudice, Samuele Grillo, Daniele Linaro, Angelo Brambilla, Federico Milano
Date: 2022-06-28 00:00:00
Tasks: Computational Efficiency

This letter presents a technique to calculate the variance of algebraic variables of power system models represented as a set of stochastic differential-algebraic equations. The technique utilizes the solution of a Lyapunov equation and requires the calculation of the state matrix of the system. The IEEE 14-bus system serves to demonstrate the accuracy of the proposed technique over a wide range of variances of stochastic processes. The accuracy is evaluated by comparing the results with those obtained with Monte Carlo time domain simulations. Finally, a case study based on a 1479-bus dynamic model of the all-island Irish transmission system shows the computational efficiency of the proposed approach compared to the Monte Carlo method.