pwc-archive/papers-with-abstracts huggingface dataset:
Enriching a Fashion Knowledge Graph from Product Textual Descriptions
Authors: João Barroca, Abhishek Shivkumar, Beatriz Quintino Ferreira, Evgeny Sherkhonov, João Faria
Date: 2022-06-02 00:00:00
Tasks: Entity Linking, Knowledge Graphs, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

Knowledge Graphs offer a very useful and powerful structure for representing information, consequently, they have been adopted as the backbone for many applications in e-commerce scenarios. In this paper, we describe an application of existing techniques for enriching thelarge-scale Fashion Knowledge Graph (FKG) that we build at Farfetch. In particular, we apply techniques for named entity recognition (NER) and entity linking (EL) in order to extract and link rich metadata from product textual descriptions to entities in the FKG. Having a complete and enriched FKG as an e-commerce backbone can have a highly valuable impact on downstream applications such as search and recommendations. However, enriching a Knowledge Graph in the fashion domain has its own challenges. Data representation is different from a more generic KG, like Wikidata and Yago, as entities (e.g. product attributes) are too specific to the domain, and long textual descriptions are not readily available. Data itself is also scarce, as labelling datasets to train supervised models is a very laborious task. Even more, fashion products display a high variability and require an intricate ontology of attributes to link to. We use a transfer learning based approach to train an NER module on a small amount of manually labeled data, followed by an EL module that links the previously identified named entities to the appropriate entities within the FKG. Experiments using a pre-trained model show that it is possible to achieve 89.75% accuracy in NER even with a small manually labeled dataset. Moreover, the EL module, despite relying on simple rule-based or ML models (due to lack of training data), is able to link relevant attributes to products, thus automatically enriching the FKG.
Bi-convolution matrix factorization algorithm based on improved ConvMF
Authors: Peiyu Liu, Junping Du, Zhe Xue, Ang Li
Date: 2022-06-02 00:00:00

With the rapid development of information technology, "information overload" has become the main theme that plagues people's online life. As an effective tool to help users quickly search for useful information, a personalized recommendation is more and more popular among people. In order to solve the sparsity problem of the traditional matrix factorization algorithm and the problem of low utilization of review document information, this paper proposes a Bicon-vMF algorithm based on improved ConvMF. This algorithm uses two parallel convolutional neural networks to extract deep features from the user review set and item review set respectively and fuses these features into the decomposition of the rating matrix, so as to construct the user latent model and the item latent model more accurately. The experimental results show that compared with traditional recommendation algorithms like PMF, ConvMF, and DeepCoNN, the method proposed in this paper has lower prediction error and can achieve a better recommendation effect. Specifically, compared with the previous three algorithms, the prediction errors of the algorithm proposed in this paper are reduced by 45.8%, 16.6%, and 34.9%, respectively.
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations
Authors: Chang Liu, Zhen-Hua Ling, Ling-Hui Chen
Date: 2022-06-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Synthesis

This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages. In this method, a pretrained wav2vec 2.0 model is adopted to extract UPRs and a language-independent automatic speech recognition (LI-ASR) model is built with a connectionist temporal classification (CTC) loss to extract segment-level SPRs from the audio data of target languages. Then, an acoustic model is designed, which first predicts UPRs and SPRs from texts separately and then combines the predicted UPRs and SPRs to generate mel-spectrograms. The results of our experiments on six languages show that the proposed method outperformed the methods that directly predicted mel-spectrograms from character or phoneme sequences and the ablated models that utilized only UPRs or SPRs.
Analytical SNR Prediction in Long-Haul Optical Transmission using General Dual-Polarization 4D Formats
Authors: Zhiwei Liang, Bin Chen, Yi Lei, Gabriele Liga, Alex Alvarado
Date: 2022-06-02 00:00:00

Nonlinear interference models for dual-polarization 4D (DP-4D) modulation have only been used so far to predict signal-signal nonlinear interference. We show that including the signal-noise term in the prediction of the effective signal-to-noise ratio in long distance DP-4D transmission improves the accuracy by up to 0.2 dB.
Radix-2 Self-Recursive Sparse Factorizations of Delay Vandermonde Matrices for Wideband Multi-Beam Antenna Arrays
Authors: S. M. Perera, A. Madanayake, R. J. Cintra
Date: 2022-06-01 00:00:00

This paper presents a self-contained factorization for the Vandermonde matrices associated with true-time delay based wideband analog multi-beam beamforming using antenna arrays. The proposed factorization contains sparse and orthogonal matrices. Novel self-recursive radix-2 algorithms for Vandermonde matrices associated with true time delay based delay-sum filterbanks are presented to reduce the circuit complexity of multi-beam analog beamforming systems. The proposed algorithms for Vandermonde matrices by a vector attain $\mathcal{O}(N \log N)$ delay-amplifier circuit counts. Error bounds for the Vandermode matrices associated with true-time delay are established and then analyzed for numerical stability. The potential for real-world circuit implementation of the proposed algorithms will be shown through signal flow graphs that are the starting point for high-frequency analog circuit realizations.
Efficient and Self-Recursive Delay Vandermonde Algorithm for Multi-Beam Antenna Arrays
Authors: S. M. Perera, A. Madanayake, R. J. Cintra
Date: 2022-06-01 00:00:00

This paper presents a self-contained factorization for the delay Vandermonde matrix (DVM), which is the super class of the discrete Fourier transform, using sparse and companion matrices. An efficient DVM algorithm is proposed to reduce the complexity of radio-frequency (RF) $N$-beam analog beamforming systems. There exist applications for wideband multi-beam beamformers in wireless communication networks such as 5G/6G systems, system capacity can be improved by exploiting the improvement of the signal to noise ratio (SNR) using coherent summation of propagating waves based on their directions of propagation. The presence of a multitude of RF beams allows multiple independent wireless links to be established at high SNR, or used in conjunction with multiple-input multiple-output (MIMO) wireless systems, with the overall goal of improving system SNR and therefore capacity. To realize such multi-beam beamformers at acceptable analog circuit complexities, we use sparse factorization of the DVM in order to derive a low arithmetic complexity DVM algorithm. The paper also establishes an error bound and stability analysis of the proposed DVM algorithm. The proposed efficient DVM algorithm is aimed at implementation using analog realizations. For purposes of evaluation, the algorithm can be realized using both digital hardware as well as software defined radio platforms.
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
Authors: Shanshan Wang, Archontis Politis, Annamaria Mesaros, Tuomas Virtanen
Date: 2022-06-02 00:00:00
Tasks: Acoustic Scene Classification, Action Recognition, object-detection, Object Detection, Representation Learning, Scene Classification, Self-Supervised Learning, Temporal Action Localization

Learning from audio-visual data offers many possibilities to express correspondence between the audio and visual content, similar to the human perception that relates aural and visual information. In this work, we present a method for self-supervised representation learning based on audio-visual spatial alignment (AVSA), a more sophisticated alignment task than the audio-visual correspondence (AVC). In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content. Based on 360$^\text{o}$ video and Ambisonics audio, we propose selection of visual objects using object detection, and beamforming of the audio signal towards the detected objects, attempting to learn the spatial alignment between objects and the sound they produce. We investigate the use of spatial audio features to represent the audio input, and different audio formats: Ambisonics, mono, and stereo. Experimental results show a 10 $\%$ improvement on AVSA for the first order ambisonics intensity vector (FOA-IV) in comparison with log-mel spectrogram features; the addition of object-oriented crops also brings significant performance increases for the human action recognition downstream task. A number of audio-only downstream tasks are devised for testing the effectiveness of the learnt audio feature representation, obtaining performance comparable to state-of-the-art methods on acoustic scene classification from ambisonic and binaural audio.
Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach
Authors: Jiazhou Wang, Jue Tian, Yang Liu, Xiaohong Guan, Dong Yang, Ting Liu
Date: 2022-06-02 00:00:00

In recent studies, moving target defense (MTD) has been applied to detect false data injection (FDI) attacks using distributed flexible AC transmission system (D-FACTS) devices. However, the inherent conflict between the security goals of MTD (i.e., detecting FDI attacks) and the economic goals of D-FACTS devices (i.e., reducing power losses) would impede the application of MTD in real systems. Moreover, the detection capabilities of existing MTDs are often insufficient. This paper proposes a multi-stage MTD (MMTD) approach to resolve these two issues by adding a group of designed security-oriented schemes before D-FACTS' economic-oriented scheme to detect FDI attacks. We keep these security-oriented schemes for a very short time interval and then revert to the economic-oriented scheme for the remaining time to ensure the economic requirements. We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs. We find the supremum of MMTD's detection capability and study its relationship with system topology and D-FACTS deployment. Meanwhile, a greedy algorithm is proposed to search the MMTD strategy to reach this supremum. Simulation results show that the proposed MMTD can achieve the supremum against FDI attacks while outperforming current MTD strategies on economic indicators.
Decentralized temperature and storage volume control in multi-producer district heating
Authors: Juan E. Machado, Joel Ferguson, Michele Cucuzzella, Jacquelien M. A. Scherpen
Date: 2022-06-02 00:00:00

Modern district heating technologies have a great potential to make the energy sector more flexible and sustainable due to their capabilities to use energy sources of varied nature and to efficiently store energy for subsequent use. Central control tasks within these systems for the efficient and safe distribution of heat refer to the stabilization of overall system temperatures and the regulation of storage units state of charge. These are challenging goals when the networked and nonlinear nature of district heating system models is taken into consideration. In this letter, for district heating systems with multiple, distributed heat producers, we propose a decentralized control scheme to provably meet said tasks stably.
Disturbance Observer Based Frequency & Voltage Regulation for RES Integrated Uncertain Power Systems
Authors: Himanshu Grover, Ashu Verma, T. S. Bhatti
Date: 2022-06-01 00:00:00

This paper proposes a disturbance-observer-based control (DOBC) scheme for frequency and voltage regulation for a renewable energy sources (RES) integrated power systems. The proposed approach acts a feed-forward control which improves the dynamic performance of the conventional proportional-integral-derivative (PID) controller. Robustness of the proposed control scheme has been validated through simulations under worst-case and stochastic uncertainties to mitigate real-time variability in RES output and load. The performance of the proposed control technique is compared to well established technique in presence of communication delay and white noise.
Randomization Inference Tests for Shift-Share Designs
Authors: Luis Alvarez, Bruno Ferman, Raoni Oliveira
Date: 2022-06-02 00:00:00
Tasks: valid

We consider the problem of inference in shift-share research designs. The choice between existing approaches that allow for unrestricted spatial correlation involves tradeoffs, varying in terms of their validity when there are relatively few or concentrated shocks, and in terms of the assumptions on the shock assignment process and treatment effects heterogeneity. We propose alternative randomization inference methods that combine the advantages of different approaches. These methods are valid in finite samples under relatively stronger assumptions, while asymptotically valid under weaker assumptions.
Coarse Wage-Setting and Behavioral Firms
Authors: Germán Reyes
Date: 2022-06-02 00:00:00

This paper shows that the bunching of wages at round numbers is partly driven by firm coarse wage-setting. Using data from over 200 million new hires in Brazil, I first establish that contracted salaries tend to cluster at round numbers. Then, I show that firms that tend to hire workers at round-numbered salaries have worse market outcomes. Next, I develop a wage-posting model in which optimization costs lead to the adoption of coarse rounded wages and provide evidence supporting two model predictions using two research designs. Finally, I examine some consequences of coarse wage-setting for relevant economic outcomes.
Strategic Innovation Through Outsourcing: A Theoretical Review
Authors: Marfri Gambal, Aleksandre Asatiani, Julia Kotlarsky
Date: 2022-06-02 00:00:00
Tasks: Management

Competition in the Information Technology Outsourcing (ITO) and Business Process Outsourcing (BPO) industry is increasingly moving from being motivated by cost savings towards strategic benefits that service providers can offer to their clients. Innovation is one such benefit that is expected nowadays in outsourcing engagements. The rising importance of innovation has been noticed and acknowledged not only in the Information Systems (IS) literature, but also in other management streams such as innovation and strategy. However, to date, these individual strands of research remain largely isolated from each other. Our theoretical review addresses this gap by consolidating and analyzing research on strategic innovation in the ITO and BPO context. The article set includes 95 papers published between 1998 to 2020 in outlets from the IS and related management fields. We craft a four-phase framework that integrates prior insights about (1) the antecedents of the decision to pursue strategic innovation in outsourcing settings; (2) arrangement options that facilitate strategic innovation in outsourcing relationships; (3) the generation of strategic innovations; and (4) realized strategic innovation outcomes, as assessed in the literature. We find that the research landscape to date is skewed, with many studies focusing on the first two phases. The last two phases remain relatively uncharted. We also discuss how innovation-oriented outsourcing insights compare with established research on cost-oriented outsourcing engagements. Finally, we offer directions for future research.
Economic complexity and inequality at the national and regional level
Authors: Dominik Hartmann, Flavio L. Pinheiro
Date: 2022-06-02 00:00:00

Recent studies have found evidence of a negative association between economic complexity and inequality at the country level. Moreover, evidence suggests that sophisticated economies tend to outsource products that are less desirable (e.g. in terms of wage and inequality effects), and instead focus on complex products requiring networks of skilled labor and more inclusive institutions. Yet the negative association between economic complexity and inequality on a coarse scale could hide important dynamics at a fine-grained level. Complex economic activities are difficult to develop and tend to concentrate spatially, leading to 'winner-take-most' effects that spur regional inequality in countries. Large, complex cities tend to attract both high- and low-skills activities and workers, and are also associated with higher levels of hierarchies, competition, and skill premiums. As a result, the association between complexity and inequality reverses at regional scales; in other words, more complex regions tend to be more unequal. Ideas from polarization theories, institutional changes, and urban scaling literature can help to understand this paradox, while new methods from economic complexity and relatedness can help identify inclusive growth constraints and opportunities.
Continuous space core-periphery model with transport costs in differentiated agriculture
Authors: Kensuke Ohtake
Date: 2022-06-02 00:00:00

The core-periphery model with transport costs in differentiated agriculture is extended to continuous space. A homogeneous stationary solution is unstable but exhibits redispersion that it is stabilized by sufficiently low manufacturing transport costs or sufficiently strong preference for manufacturing variety. It is numerically observed that a solution starting from around the unstable homogeneous solution eventually forms a spike-like agglomeration. Furthermore, the redispersion also appears in the sense that the number of the spikes goes from decreasing to increasing as the manufacturing transport costs decrease. It is also observed that lower agricultural transport costs and stronger preference for agricultural variety promote agglomeration.
Revisiting the Wireless Channel from Physical Layer Security Perspective
Authors: Abuu. B. Kihero, Haji. M. Furqan, M. M. Sahin, Huseyin Arslan
Date: 2022-06-02 00:00:00

Security has emerged as one of the critical requirements in future wireless networks. Unlike traditional cryptography-based security, physical layer security (PLS) exploits various features of the random wireless channel to secure not only the information being communicated but the whole communication process from any type of attack. Future wireless networks are envisioned to feature new technologies such as re-configurable intelligent surfaces, massive multiple input multiple output, and sensing, to accommodate the emerging use-cases. Both, the new technologies and the new use-cases have been found to enrich the channel characteristics by unveiling some new channel features which can be readily exploited to facilitate PLS. This article surveys these new channel features to reveal their potential for PLS implementation. In the course of the article, the assessments of important qualities while selecting a certain channel feature for the PLS application are discussed. The importance of the channel control concept and sensing technologies that facilitate the accessibility of certain channel features are highlighted from the PLS perspective. Security attacks aimed at channel characteristics, rather than the communication itself, in order to disrupt PLS implementations are also discussed. Finally, the article summarizes the possible research direction for channel-based PLS.
Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes
Authors: Monira Islam, Tan Lee
Date: 2022-06-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification. MEMD is a data-adaptive analysis approach which is suitable particularly for multi-dimensional non-linear signals like EEG. Applying MEMD results in a set of oscillatory modes called intrinsic mode functions (IMFs). As the decomposition process is data-dependent, the IMFs vary in accordance with signal variation caused by functional brain activity. Among the extracted IMFs, it is found that those corresponding to high-oscillation modes are most useful for detecting different mental states. Non-linear features are computed from the IMFs that contribute most to mental state detection. These MEMD features show a significant performance gain over the conventional tempo-spectral features obtained by Fourier transform and Wavelet transform. The dominance of specific brain region is observed by analysing the MEMD features extracted from associated EEG channels. The frontal region is found to be most significant with a classification accuracy of 98.06%. This multi-dimensional decomposition approach upholds joint channel properties and produces most discriminative features for EEG based mental state detection.
MEMD-HHT based Emotion Detection from EEG using 3D CNN
Authors: Monira Islam, Tan Lee
Date: 2022-06-02 00:00:00
Tasks: Binary Classification, EEG, Electroencephalogram (EEG)

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.
Xavier-Enabled Extreme Reservoir Machine for Millimeter-Wave Beamspace Channel Tracking
Authors: Hosein Zarini, Mohammad Robat Mili, Mehdi Rasti, Pedro H. J. Nardelli, Mehdi Bennis
Date: 2022-06-01 00:00:00
Tasks: Ensemble Learning, Prediction

In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism. Particularly in the first phase, we train an extreme reservoir machine (ERM) for tracking the historical features of the mmWave beamspace channel and predicting them in upcoming time steps. Towards a more accurate prediction, we further fine-tune the ERM by means of Xavier initializer technique, whereby the input weights in ERM are initially derived from a zero mean and finite variance Gaussian distribution, leading to 49% degradation in prediction variance of the conventional ERM. The proposed method numerically improves the achievable spectral efficiency (SE) of the existing counterparts, by 13%, when signal-to-noise-ratio (SNR) is 15dB. We further investigate an ensemble learning technique in the second phase by sequentially incorporating multiple ERMs to form an ensembled model, namely adaptive boosting (AdaBoost), which further reduces the prediction variance in conventional ERM by 56%, and concludes in 21% enhancement of achievable SE upon the existing schemes at SNR=15dB.